diff --git a/.github/workflows/docker-publish-mocapnet.yaml b/.github/workflows/docker-publish-mocapnet.yaml index e0c8902..a4222a0 100644 --- a/.github/workflows/docker-publish-mocapnet.yaml +++ b/.github/workflows/docker-publish-mocapnet.yaml @@ -35,7 +35,7 @@ jobs: password: ${{ secrets.GITHUB_TOKEN }} - name: Build image - run: cd animation/MocapNET/ && docker build . --file docker/Dockerfile --tag $IMAGE_NAME --label "runnumber=${GITHUB_RUN_ID}" + run: cd animation/MocapNET-kasisnu/ && docker build . --file docker/Dockerfile --tag $IMAGE_NAME --label "runnumber=${GITHUB_RUN_ID}" - name: Push image to GitHub Container Registry run: | diff --git a/animation/MocapNET-kasisnu/CMakeLists.txt b/animation/MocapNET-kasisnu/CMakeLists.txt new file mode 100644 index 0000000..b995fce --- /dev/null +++ b/animation/MocapNET-kasisnu/CMakeLists.txt @@ -0,0 +1,378 @@ +project( MocapNETProject ) +cmake_minimum_required( VERSION 2.8.13 ) + +#Make fast, lean and platform independent binaries.. +#The -fuse-ld=gold flag solves issue https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/34 +#However it causes https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/38 +set(CMAKE_CXX_FLAGS "-s -O3 -fPIC -march=native -mtune=native") #-fuse-ld=gold +set(CMAKE_C_FLAGS "-s -O3 -fPIC -march=native -mtune=native") #-fuse-ld=gold + +OPTION(ENABLE_OPENGL OFF) +OPTION(INTEL_OPTIMIZATIONS OFF) + + +SET (CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake") + +INCLUDE(FindSSE) +FindSSE () +IF(SSE2_FOUND) + SET(INTEL_OPTIMIZATIONS ON) + MESSAGE("SSE2 detected and will be used..") +ENDIF(SSE2_FOUND) + +if (INTEL_OPTIMIZATIONS) +add_definitions(-DINTEL_OPTIMIZATIONS) +endif(INTEL_OPTIMIZATIONS) + +set_property(GLOBAL PROPERTY USE_FOLDERS ON) + + +#JPEG/PNG libraries.. +set(JPG_Libs jpeg) +set(PNG_Libs png) + + + + +#------------------------------------------------------------------------------------------------------------------------------------------------ +#First search for Tensorflow 2.x +#------------------------------------------------------------------------------------------------------------------------------------------------ +IF(EXISTS "${CMAKE_SOURCE_DIR}/dependencies/libtensorflow/lib/libtensorflow.so.2") + MESSAGE("Using locally found libtensorflow v2 C-API") + set(TENSORFLOW_ROOT "${CMAKE_SOURCE_DIR}/dependencies/libtensorflow/lib/" CACHE PATH "tensorflow root") + set(TENSORFLOW_INCLUDE_ROOT "${CMAKE_SOURCE_DIR}/dependencies/libtensorflow/include/" CACHE PATH "tensorflow include") + set(TENSORFLOW2_FOUND true CACHE BOOL "Tensorflow 2 available") + set(TENSORFLOW_SOURCE_FILES + ${CMAKE_SOURCE_DIR}/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tensorflow.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tf_utils.cpp + ) +ELSEIF(EXISTS "/usr/local/lib/libtensorflow.so.2") + MESSAGE("Using system-wide found libtensorflow v2 C-API") + set(TENSORFLOW_ROOT "/usr/local/lib/" CACHE PATH "tensorflow root") + set(TENSORFLOW_INCLUDE_ROOT "/usr/local/include/" CACHE PATH "tensorflow include") + set(TENSORFLOW2_FOUND true CACHE BOOL "Tensorflow 2 available") + set(TENSORFLOW_SOURCE_FILES + ${CMAKE_SOURCE_DIR}/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tensorflow.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tf_utils.cpp + ) +#------------------------------------------------------------------------------------------------------------------------------------------------ +#If we can't find it then search for Tensorflow 1.x +#------------------------------------------------------------------------------------------------------------------------------------------------ +ELSEIF(EXISTS "${CMAKE_SOURCE_DIR}/dependencies/libtensorflow/lib/libtensorflow.so") + MESSAGE("Using locally found libtensorflow v1 C-API") + set(TENSORFLOW_ROOT "${CMAKE_SOURCE_DIR}/dependencies/libtensorflow/lib/" CACHE PATH "tensorflow root") + set(TENSORFLOW_INCLUDE_ROOT "${CMAKE_SOURCE_DIR}/dependencies/libtensorflow/include/" CACHE PATH "tensorflow include") + set(TENSORFLOW_SOURCE_FILES + ${CMAKE_SOURCE_DIR}/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tensorflow.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tf_utils.cpp + ) +ELSEIF(EXISTS "/usr/local/lib/libtensorflow.so") + MESSAGE("Using system-wide found libtensorflow v1 C-API") + set(TENSORFLOW_ROOT "/usr/local/lib/" CACHE PATH "tensorflow root") + set(TENSORFLOW_INCLUDE_ROOT "/usr/local/include/" CACHE PATH "tensorflow include") + set(TENSORFLOW_SOURCE_FILES + ${CMAKE_SOURCE_DIR}/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tensorflow.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tf_utils.cpp + ) +#------------------------------------------------------------------------------------------------------------------------------------------------ +#If we can't find any Tensorflow just point to the /usr/lib and hope for the best.. +#------------------------------------------------------------------------------------------------------------------------------------------------ +ELSE() + MESSAGE("Did not find a tensorflow version, please consider running the initialize.sh script or reading the README file") + #Set some default tensorflow paths to give some valid error afterwards.. + set(TENSORFLOW_ROOT "/usr/local/lib/" CACHE PATH "tensorflow root") + set(TENSORFLOW_INCLUDE_ROOT "/usr/local/include/" CACHE PATH "tensorflow include") +ENDIF() +#------------------------------------------------------------------------------------------------------------------------------------------------ + + +include_directories(${TENSORFLOW_INCLUDE_ROOT}) +ADD_LIBRARY(TensorflowFramework SHARED IMPORTED) + +IF (TENSORFLOW2_FOUND) + SET_TARGET_PROPERTIES(TensorflowFramework PROPERTIES IMPORTED_LOCATION ${TENSORFLOW_ROOT}/libtensorflow_framework.so.2) +ELSE(TENSORFLOW2_FOUND) + SET_TARGET_PROPERTIES(TensorflowFramework PROPERTIES IMPORTED_LOCATION ${TENSORFLOW_ROOT}/libtensorflow_framework.so) +ENDIF (TENSORFLOW2_FOUND) + +ADD_LIBRARY(Tensorflow SHARED IMPORTED) + +IF (TENSORFLOW2_FOUND) + SET_TARGET_PROPERTIES(Tensorflow PROPERTIES IMPORTED_LOCATION ${TENSORFLOW_ROOT}/libtensorflow.so.2) +ELSE(TENSORFLOW2_FOUND) + SET_TARGET_PROPERTIES(Tensorflow PROPERTIES IMPORTED_LOCATION ${TENSORFLOW_ROOT}/libtensorflow.so) +ENDIF (TENSORFLOW2_FOUND) + + + + +#If our development environment has RGBDAcquisition then we can use BVH capabilities.. +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +set(BVH_SOURCE "") +IF(EXISTS "${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader") +add_definitions(-DUSE_BVH) +set(BVH_SOURCE + #-------------------------------------------------------------------------------- + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/PThreadWorkerPool/pthreadWorkerPool.h + #-------------------------------------------------------------------------------- + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_to_tri_pose.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_transform.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_project.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/import/fromBVH.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_trajectoryParserTRI.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_trajectoryParserPrimitives.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_export.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_c.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_bvh.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_svg.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_csv.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_to_json.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/export/bvh_export.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_randomize.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_filter.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_rename.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_merge.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_cut_paste.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_interpolate.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_remapangles.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/bvh_inverseKinematics.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/hardcodedProblems_inverseKinematics.c + #${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/levmar.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/metrics/bvh_measure.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/tests/test.c + #-------------------------------------------------------------------------------- + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration/calibration.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration/calibration.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration/transform.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration/transform.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration/undistort.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration/undistort.h + #-------------------------------------------------------------------------------- + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/matrixTools.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/matrix3x3Tools.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/matrix4x4Tools.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/matrixOpenGL.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/matrixCalculations.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/solveLinearSystemGJ.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/solveHomography.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/quaternions.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/AmMatrix/simpleRenderer.c + ) + MESSAGE("BVH Code found and will be used..") + add_subdirectory(src/GroundTruthGenerator/) #Ground Truth generator can only be compiled with BVH code.. + add_subdirectory(src/MocapNET2/reshapeCSVFileToMakeClassification/) #Orientation classification needs this tool to be built before working.. + #MESSAGE("${BVH_SOURCE}") +ENDIF() +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ + + + + + +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +set(OPENGL_LIBS "") +set(OPENGL_SOURCE "") +if (ENABLE_OPENGL) +IF(EXISTS "${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library") + +add_subdirectory(${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Codecs) +add_subdirectory(${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/tools/Calibration) +#add_subdirectory(${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library) #OpenGLAcquisition +add_definitions(-DUSE_OPENGL) +#add_definitions(-DUSE_GLEW) +set(OPENGL_LIBS rt m GL GLU GLEW X11 Codecs CalibrationLibrary) +set(OPENGL_SOURCE + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/OpenGLAcquisition.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/OpenGLAcquisition.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/System/glx.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/System/glx.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/System/glx2.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/System/glx2.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/System/glx3.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/System/glx3.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/main.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/OGLRendererSandbox.h + #3D Models and how to load them + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_obj.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_obj.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_tri.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_tri.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_processor.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_processor.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_hardcoded.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_hardcoded.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_transform_joints.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_loader_transform_joints.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_converter.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_converter.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_editor.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/ModelLoader/model_editor.h + #Textures and how to load them + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TextureLoader/texture_loader.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TextureLoader/texture_loader.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TextureLoader/image_proc.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TextureLoader/image_proc.h + #OpenGL Rendering stuff + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/FixedPipeline/ogl_fixed_pipeline_renderer.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/FixedPipeline/ogl_fixed_pipeline_renderer.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/render_buffer.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/render_buffer.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/uploadGeometry.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/uploadGeometry.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/ogl_shader_pipeline_renderer.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/ogl_shader_pipeline_renderer.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/shader_loader.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ShaderPipeline/shader_loader.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ogl_rendering.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/ogl_rendering.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/tiledRenderer.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/tiledRenderer.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/downloadFromRenderer.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Rendering/downloadFromRenderer.h + + + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Tools/tools.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Tools/tools.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Tools/save_to_file.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Tools/save_to_file.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Scene/scene.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Scene/scene.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Scene/control.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Scene/control.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Scene/photoShootingScene.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Scene/photoShootingScene.c + #AmMatrix dependencies + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/quaternions.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/quaternions.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrix3x3Tools.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrix3x3Tools.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrix4x4Tools.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrix4x4Tools.h + #../tools/AmMatrix/matrixProject.c + #../tools/AmMatrix/matrixProject.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrixCalculations.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrixCalculations.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrixOpenGL.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/matrixOpenGL.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/solveLinearSystemGJ.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/AmMatrix/solveLinearSystemGJ.h + #ImageOperations dependencies + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/depthClassifier.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/convolutionFilter.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/imageFilters.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/findSubImage.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/imageOps.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/patchComparison.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/patchComparison.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition//tools/ImageOperations/resize.c + #Rest of the stuff + #${CMAKE_SOURCE_DIR}/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/InputParser_C.c + #${CMAKE_SOURCE_DIR}/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/InputParser_C.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryParser.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryParser.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryParserDataStructures.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryParserDataStructures.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryCalculator.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryCalculator.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryPrimitives.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/TrajectoryPrimitives.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/hashmap.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/hashmap.h + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Interfaces/webInterface.c + ${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/Interfaces/webInterface.h + ) + MESSAGE("OpenGL code found and will be used..") +ELSE() + MESSAGE("OpenGL support enabled, but OpenGL code not found..") +ENDIF() +ELSE (ENABLE_OPENGL) + MESSAGE("OpenGL support will not be compiled in") +ENDIF(ENABLE_OPENGL) + +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ +#------------------------------------------------------------------------------------------------------------------------------------------------ + + + +#Autosearch for the opencv maybe installed by the user..! +find_package(OpenCV PATHS dependencies/opencv-3.2.0/build NO_DEFAULT_PATH) +find_package (OpenCV) +if (OpenCV_FOUND) + add_definitions(-DUSE_OPENCV) + MESSAGE("OpenCV code found and will be used..") + MESSAGE(${OpenCV_DIR}) +ENDIF(OpenCV_FOUND) + + + +#If ammarserver is present then use it.. +set(NETWORK_CLIENT_LIBRARIES "") +IF(EXISTS "${CMAKE_SOURCE_DIR}/dependencies/AmmarServer/src/AmmServerlib") + add_definitions(-DUSE_NETWORKING) + MESSAGE("AmmarServer support will be compiled in") + #add_subdirectory (dependencies/AmmarServer/) #You can force add everything but this adds too much to the build time + set(NETWORK_CLIENT_LIBRARIES "AmmClient") + add_subdirectory (dependencies/AmmarServer/src/AmmClient) + add_subdirectory (dependencies/AmmarServer/src/AmmServerlib) + add_subdirectory (dependencies/AmmarServer/src/Hashmap) + add_subdirectory (dependencies/AmmarServer/src/InputParser) +ENDIF() + + +#This needs Tensorflow C-API installed... +#https://www.tensorflow.org/install/lang_c +#wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-1.13.1.tar.gz +#pip3 show protobuf +#add_subdirectory (MocapNETStandalone/) + + +IF(EXISTS "${CMAKE_SOURCE_DIR}/dependencies/AmmarServer/src/AmmServerlib") + #add_subdirectory (src/MocapNET1/MocapNETServerHTTP/) +ENDIF() + +if (OpenCV_FOUND) +add_subdirectory (src/Webcam/) +#Deactivated to keep build targets sane +add_subdirectory(src/MocapNET2/BVHGUI2) +add_subdirectory(src/MocapNET2/MocapNET2LiveWebcamDemo) +ENDIF(OpenCV_FOUND) + +#add_subdirectory(src/MocapNET2/BVHTemplate/) +add_subdirectory(src/MocapNET2/MocapNETLib2/) +add_subdirectory(src/MocapNET2/MocapNETFromCSV/) +add_subdirectory(src/MocapNET2/Converters/Openpose) +add_subdirectory(src/MocapNET2/Converters/H36M) +add_subdirectory(src/MocapNET2/Converters/convertCSV3D) +add_subdirectory(src/MocapNET2/testCSV/) +add_subdirectory(src/MocapNET2/drawCSV/) +add_subdirectory(src/MocapNET2/CSVClusterPlot) +#------------------------------------------------------------------ +add_subdirectory(src/JointEstimator2D) + + +# TODO FIX INSTALLATION DIRECTORIES + +# install(TARGETS RGBDAcquisitionProject +# LIBRARY DESTINATION lib +# ARCHIVE DESTINATION lib +# RUNTIME DESTINATION bin) + + diff --git a/animation/MocapNET-kasisnu/README.md b/animation/MocapNET-kasisnu/README.md new file mode 100644 index 0000000..b2ad260 --- /dev/null +++ b/animation/MocapNET-kasisnu/README.md @@ -0,0 +1,94 @@ +# MocapNET Project + + +![MocapNET](https://raw.githubusercontent.com/FORTH-ModelBasedTracker/MocapNET/mnet4/doc/method.png) + +Finishing my PhD this will probably be the *final* version of MocapNET! +MocapNET 4 will deal with upperbody / lowerbody / hands / eye tracking and / facial capture +It has a written from scratch python interface, but maintain the same compatible BVH output format. +It will also be compatible with Raspberry Pi 4 and use Tensorflow /Tf-Lite / ONNX backends + +This branch is still under construction, and has been ported to Python to boost usability +so if you want the older C/C++ version of MocapNET you ignore it for now..! + + + + +![MocapNET](https://raw.githubusercontent.com/FORTH-ModelBasedTracker/MocapNET/mnet4/doc/ICCV2023_Presentation_Slide18.png) + + + +## Deploy it now on Google Colab with a single click! +------------------------------------------------------------------ + +Click here for one click setup : [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/mocapnet4.ipynb) + + + + +## Relevant publications! +------------------------------------------------------------------ + + +| Download Paper | Year | Conference | Title | +| ------------- | ------------- | ------------- | ------------- | +| [![A Unified Approach for Occlusion Tolerant 3D Facial Pose Capture and Gaze Estimation using MocapNETs](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/doc/pdf.png?raw=true)](http://users.ics.forth.gr/~argyros/mypapers/2023_10_AMFG_Qammaz.pdf) | 2023 | AMFG@ICCV | A Unified Approach for Occlusion Tolerant 3D Facial Pose Capture and Gaze Estimation using MocapNETs | +| [![Compacting MocapNET-based 3D Human Pose Estimation via Dimensionality Reduction](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/doc/pdf.png?raw=true)](http://users.ics.forth.gr/~argyros/mypapers/2023_07_PETRA_Qammaz.pdf) | 2023 | PeTRA | Compacting MocapNET-based 3D Human Pose Estimation via Dimensionality Reduction | +| [![Towards Holistic Real-time Human 3D Pose Estimation using MocapNETs](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/doc/pdf.png?raw=true)](http://users.ics.forth.gr/~argyros/mypapers/2021_11_BMVC_Qammaz.pdf) | 2021 | BMVC | Towards Holistic Real-time Human 3D Pose Estimation using MocapNETs | +| [![Occlusion-tolerant and personalized 3D human pose estimation in RGB images](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/doc/pdf.png?raw=true)](http://users.ics.forth.gr/~argyros/mypapers/2021_01_ICPR_Qammaz.pdf) | 2021 | ICPR | Occlusion-tolerant and personalized 3D human pose estimation in RGB images | +| [![MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/doc/pdf.png?raw=true)](http://users.ics.forth.gr/~argyros/mypapers/2019_09_BMVC_mocapnet.pdf) | 2019 | BMVC | MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images | + + + + + + + + +## AMFG@ICCV 2023 Poster +------------------------------------------------------------------ + +![Our Poster in the Analysis and Modeling of Faces and Gestures Workshop @ ICCV 2023 ](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet4/doc/ICCV2023_MocapNET4_poster.png?raw=true) + + + + + + + +## Citation +------------------------------------------------------------------ + +Please cite the following papers if this work helps your research : +``` +@inproceedings{Qammaz2023b, + author = {Qammaz, Ammar and Argyros, Antonis}, + title = {A Unified Approach for Occlusion Tolerant 3D Facial Pose Capture and Gaze Estimation using MocapNETs}, + booktitle = {International Conference on Computer Vision Workshops (AMFG 2023 - ICCVW 2023), (to appear)}, + publisher = {IEEE}, + year = {2023}, + month = {October}, + address = {Paris, France}, + projects = {VMWARE,I.C.HUMANS}, + pdflink = {http://users.ics.forth.gr/ argyros/mypapers/2023_10_AMFG_Qammaz.pdf} +} + +@inproceedings{Qammaz2021, + author = {Qammaz, Ammar and Argyros, Antonis A}, + title = {Towards Holistic Real-time Human 3D Pose Estimation using MocapNETs}, + booktitle = {British Machine Vision Conference (BMVC 2021)}, + publisher = {BMVA}, + year = {2021}, + month = {November}, + projects = {I.C.HUMANS}, + videolink = {https://www.youtube.com/watch?v=aaLOSY_p6Zc} +} +``` + + + + +## License +------------------------------------------------------------------ +This library is provided under the [FORTH license](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/license.txt) + diff --git a/animation/MocapNET-kasisnu/citation.bib b/animation/MocapNET-kasisnu/citation.bib new file mode 100644 index 0000000..6e6c6f2 --- /dev/null +++ b/animation/MocapNET-kasisnu/citation.bib @@ -0,0 +1,36 @@ +@inproceedings{Qammaz2021, + author = {Qammaz, Ammar and Argyros, Antonis A}, + title = {Towards Holistic Real-time Human 3D Pose Estimation using MocapNETs}, + booktitle = {British Machine Vision Conference (BMVC 2021)}, + publisher = {BMVA}, + year = {2021}, + month = {November}, + projects = {I.C.HUMANS}, + videolink = {https://www.youtube.com/watch?v=aaLOSY_p6Zc} +} + +@inproceedings{Qammaz2020, + author = {Ammar Qammaz and Antonis A. Argyros}, + title = {Occlusion-tolerant and personalized 3D human pose estimation in RGB images}, + booktitle = {IEEE International Conference on Pattern Recognition (ICPR 2020), (to appear)}, + year = {2021}, + month = {January}, + url = {http://users.ics.forth.gr/argyros/res_mocapnet_II.html}, + projects = {Co4Robots}, + pdflink = {http://users.ics.forth.gr/argyros/mypapers/2021_01_ICPR_Qammaz.pdf}, + videolink = {https://youtu.be/Jgz1MRq-I-k} +} + +@inproceedings{Qammaz2019, + author = {Qammaz, Ammar and Argyros, Antonis A}, + title = {MocapNET: Ensemble of SNN Encoders for 3D Human Pose Estimation in RGB Images}, + booktitle = {British Machine Vision Conference (BMVC 2019)}, + publisher = {BMVA}, + year = {2019}, + month = {September}, + address = {Cardiff, UK}, + url = {http://users.ics.forth.gr/argyros/res_mocapnet.html}, + projects = {CO4ROBOTS,MINGEI}, + pdflink = {http://users.ics.forth.gr/argyros/mypapers/2019_09_BMVC_mocapnet.pdf}, + videolink = {https://youtu.be/fH5e-KMBvM0} +} diff --git a/animation/MocapNET-kasisnu/cmake/FindSSE.cmake b/animation/MocapNET-kasisnu/cmake/FindSSE.cmake new file mode 100644 index 0000000..fb0f61f --- /dev/null +++ b/animation/MocapNET-kasisnu/cmake/FindSSE.cmake @@ -0,0 +1,141 @@ +#https://github.com/hideo55/CMake-FindSSE/blob/master/FindSSE.cmake +# Check if SSE instructions are available on the machine where +# the project is compiled. + +MACRO (FindSSE) + +IF(CMAKE_SYSTEM_NAME MATCHES "Linux") + EXEC_PROGRAM(cat ARGS "/proc/cpuinfo" OUTPUT_VARIABLE CPUINFO) + + STRING(REGEX REPLACE "^.*(sse2).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "sse2" "${SSE_THERE}" SSE2_TRUE) + IF (SSE2_TRUE) + set(SSE2_FOUND true CACHE BOOL "SSE2 available on host") + ELSE (SSE2_TRUE) + set(SSE2_FOUND false CACHE BOOL "SSE2 available on host") + ENDIF (SSE2_TRUE) + + # /proc/cpuinfo apparently omits sse3 :( + STRING(REGEX REPLACE "^.*[^s](sse3).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "sse3" "${SSE_THERE}" SSE3_TRUE) + IF (NOT SSE3_TRUE) + STRING(REGEX REPLACE "^.*(T2300).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "T2300" "${SSE_THERE}" SSE3_TRUE) + ENDIF (NOT SSE3_TRUE) + + STRING(REGEX REPLACE "^.*(ssse3).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "ssse3" "${SSE_THERE}" SSSE3_TRUE) + IF (SSE3_TRUE OR SSSE3_TRUE) + set(SSE3_FOUND true CACHE BOOL "SSE3 available on host") + ELSE (SSE3_TRUE OR SSSE3_TRUE) + set(SSE3_FOUND false CACHE BOOL "SSE3 available on host") + ENDIF (SSE3_TRUE OR SSSE3_TRUE) + IF (SSSE3_TRUE) + set(SSSE3_FOUND true CACHE BOOL "SSSE3 available on host") + ELSE (SSSE3_TRUE) + set(SSSE3_FOUND false CACHE BOOL "SSSE3 available on host") + ENDIF (SSSE3_TRUE) + + STRING(REGEX REPLACE "^.*(sse4_1).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "sse4_1" "${SSE_THERE}" SSE41_TRUE) + IF (SSE41_TRUE) + set(SSE4_1_FOUND true CACHE BOOL "SSE4.1 available on host") + ELSE (SSE41_TRUE) + set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host") + ENDIF (SSE41_TRUE) + + STRING(REGEX REPLACE "^.*(sse4_2).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "sse4_2" "${SSE_THERE}" SSE42_TRUE) + IF (SSE42_TRUE) + set(SSE4_2_FOUND true CACHE BOOL "SSE4.2 available on host") + ELSE (SSE42_TRUE) + set(SSE4_2_FOUND false CACHE BOOL "SSE4.2 available on host") + ENDIF (SSE42_TRUE) + +ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Darwin") + EXEC_PROGRAM("/usr/sbin/sysctl -n machdep.cpu.features" OUTPUT_VARIABLE + CPUINFO) + + STRING(REGEX REPLACE "^.*[^S](SSE2).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "SSE2" "${SSE_THERE}" SSE2_TRUE) + IF (SSE2_TRUE) + set(SSE2_FOUND true CACHE BOOL "SSE2 available on host") + ELSE (SSE2_TRUE) + set(SSE2_FOUND false CACHE BOOL "SSE2 available on host") + ENDIF (SSE2_TRUE) + + STRING(REGEX REPLACE "^.*[^S](SSE3).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "SSE3" "${SSE_THERE}" SSE3_TRUE) + IF (SSE3_TRUE) + set(SSE3_FOUND true CACHE BOOL "SSE3 available on host") + ELSE (SSE3_TRUE) + set(SSE3_FOUND false CACHE BOOL "SSE3 available on host") + ENDIF (SSE3_TRUE) + + STRING(REGEX REPLACE "^.*(SSSE3).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "SSSE3" "${SSE_THERE}" SSSE3_TRUE) + IF (SSSE3_TRUE) + set(SSSE3_FOUND true CACHE BOOL "SSSE3 available on host") + ELSE (SSSE3_TRUE) + set(SSSE3_FOUND false CACHE BOOL "SSSE3 available on host") + ENDIF (SSSE3_TRUE) + + STRING(REGEX REPLACE "^.*(SSE4.1).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "SSE4.1" "${SSE_THERE}" SSE41_TRUE) + IF (SSE41_TRUE) + set(SSE4_1_FOUND true CACHE BOOL "SSE4.1 available on host") + ELSE (SSE41_TRUE) + set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host") + ENDIF (SSE41_TRUE) + + STRING(REGEX REPLACE "^.*(SSE4.2).*$" "\\1" SSE_THERE ${CPUINFO}) + STRING(COMPARE EQUAL "SSE4.2" "${SSE_THERE}" SSE42_TRUE) + IF (SSE42_TRUE) + set(SSE4_2_FOUND true CACHE BOOL "SSE4.2 available on host") + ELSE (SSE42_TRUE) + set(SSE4_2_FOUND false CACHE BOOL "SSE4.2 available on host") + ENDIF (SSE42_TRUE) + +ELSEIF(CMAKE_SYSTEM_NAME MATCHES "Windows") + # TODO + set(SSE2_FOUND true CACHE BOOL "SSE2 available on host") + set(SSE3_FOUND false CACHE BOOL "SSE3 available on host") + set(SSSE3_FOUND false CACHE BOOL "SSSE3 available on host") + set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host") + set(SSE4_2_FOUND false CACHE BOOL "SSE4.2 available on host") +ELSE(CMAKE_SYSTEM_NAME MATCHES "Linux") + set(SSE2_FOUND true CACHE BOOL "SSE2 available on host") + set(SSE3_FOUND false CACHE BOOL "SSE3 available on host") + set(SSSE3_FOUND false CACHE BOOL "SSSE3 available on host") + set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host") + set(SSE4_2_FOUND false CACHE BOOL "SSE4.2 available on host") +ENDIF(CMAKE_SYSTEM_NAME MATCHES "Linux") + +IF(CMAKE_COMPILER_IS_GNUCXX) + EXECUTE_PROCESS(COMMAND ${CMAKE_CXX_COMPILER} -dumpversion OUTPUT_VARIABLE GCC_VERSION) + IF(GCC_VERSION VERSION_LESS 4.2) + set(SSE4_1_FOUND false CACHE BOOL "SSE4.1 available on host" FORCE) + set(SSE4_2_FOUND false CACHE BOOL "SSE4.2 available on host" FORCE) + ENDIF() +ENDIF(CMAKE_COMPILER_IS_GNUCXX) + +if(NOT SSE2_FOUND) + MESSAGE(STATUS "Could not find support for SSE2 on this machine.") +endif(NOT SSE2_FOUND) +if(NOT SSE3_FOUND) + MESSAGE(STATUS "Could not find support for SSE3 on this machine.") +endif(NOT SSE3_FOUND) +if(NOT SSSE3_FOUND) + MESSAGE(STATUS "Could not find support for SSSE3 on this machine.") +endif(NOT SSSE3_FOUND) +if(NOT SSE4_1_FOUND) + MESSAGE(STATUS "Could not find support for SSE4.1 on this machine.") +endif(NOT SSE4_1_FOUND) +if(NOT SSE4_2_FOUND) + MESSAGE(STATUS "Could not find support for SSE4.2 on this machine.") +endif(NOT SSE4_2_FOUND) + +mark_as_advanced(SSE2_FOUND SSE3_FOUND SSSE3_FOUND SSE4_1_FOUND SSE4_2_FOUND) + +ENDMACRO(FindSSE) + diff --git a/animation/MocapNET-kasisnu/dataset/CMU_Sample_05_01.bvh b/animation/MocapNET-kasisnu/dataset/CMU_Sample_05_01.bvh new file mode 100644 index 0000000..1122cd3 --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/CMU_Sample_05_01.bvh @@ -0,0 +1,1616 @@ +HIERARCHY +ROOT hip +{ + OFFSET 0.000000 0.000000 0.000000 + CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation + JOINT abdomen + { + OFFSET 0.000000 20.688101 -0.731520 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT chest + { + OFFSET 0.000000 11.704300 -0.487680 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT neck + { + OFFSET 0.000000 22.189400 -2.194560 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT neck1 + { + OFFSET 0.000000 5.364170 1.574630 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT head + { + OFFSET 0.000000 5.364141 1.574630 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT __jaw + { + OFFSET 0.000000 13.604700 -0.502080 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT jaw + { + OFFSET 0.000000 -13.499860 2.500710 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT special04 + { + OFFSET -0.000000 -6.835370 4.375500 + CHANNELS 3 Zrotation 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b/animation/MocapNET-kasisnu/dataset/README.md @@ -0,0 +1,8 @@ +# MocapNET Project + +## Dataset generation +------------------------------------------------------------------ + +If you are interested in generating the data to train a MocapNET please use the scripts [createRandomizedDataset.sh](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/createRandomizedDataset.sh) and [createTestDataset.sh](https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/createTestDataset.sh) after building the GroundTruthDumper tool. (It will be automatically generated after running the initialization script) + + diff --git a/animation/MocapNET-kasisnu/dataset/axis.bvh b/animation/MocapNET-kasisnu/dataset/axis.bvh new file mode 100644 index 0000000..e643777 --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/axis.bvh @@ -0,0 +1,141 @@ +HIERARCHY +ROOT root +{ + OFFSET 0 0 0 + CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation + JOINT X_Axis + { + OFFSET 10.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT X_Axis_Ornament + { + OFFSET 1.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT X_Ornament_A + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.0 -5.0 -5.0 + } + } + JOINT X_Ornament_B + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.0 5.0 -5.0 + } + } + JOINT X_Ornament_C + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.0 5.0 5.0 + } + } + JOINT X_Ornament_D + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.0 -5.0 5.0 + } + } + } + } + JOINT Y_Axis + { + OFFSET 0.0 10.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Y_Axis_Ornament + { + OFFSET 0.0 1.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Y_Ornament_A + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -5.0 0.0 -5.0 + } + } + JOINT Y_Ornament_B + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 5.0 0.0 -5.0 + } + } + JOINT Y_Ornament_C + { + OFFSET 0.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.0 0.0 5.0 + } + } + } + } + JOINT Z_Axis + { + OFFSET 0.0 0.0 10.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Z_Axis_Ornament + { + OFFSET 0.0 0.0 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Z_Ornament_A0 + { + OFFSET -2.5 -2.5 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Z_Ornament_A1 + { + OFFSET -2.5 -2.5 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Z_Ornament_A2 + { + OFFSET 5.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 5.0 0.0 0.0 + } + } + } + } + JOINT Z_Ornament_B0 + { + OFFSET 2.5 2.5 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Z_Ornament_B1 + { + OFFSET 2.5 2.5 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT Z_Ornament_B2 + { + OFFSET -5.0 0.0 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -5.0 0.0 0.0 + } + } + } + } + } + } +} +MOTION +Frames: 1 +Frame Time: 0.040000 +0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/animation/MocapNET-kasisnu/dataset/gestures/doubleclap.bvh b/animation/MocapNET-kasisnu/dataset/gestures/doubleclap.bvh new file mode 100644 index 0000000..be5916e --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/gestures/doubleclap.bvh @@ -0,0 +1,1039 @@ +HIERARCHY +ROOT hip +{ + OFFSET 0.000000 0.000000 0.000000 + CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation + JOINT abdomen + { + OFFSET 0.000000 20.688101 -0.731520 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT chest + { + OFFSET 0.000000 11.704300 -0.487680 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT neck + { + OFFSET 0.000000 22.189400 -2.194560 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT neck1 + { + OFFSET 0.000000 5.364170 1.574630 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT head + { + OFFSET 0.000000 5.364141 1.574630 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT __jaw + { + OFFSET 0.000000 13.604700 -0.502080 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT jaw + { + OFFSET 0.000000 -13.499860 2.500710 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT special04 + { + OFFSET -0.000000 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 diff --git a/animation/MocapNET-kasisnu/dataset/human3D.conf b/animation/MocapNET-kasisnu/dataset/human3D.conf new file mode 100644 index 0000000..fcc649a --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/human3D.conf @@ -0,0 +1,562 @@ +# This is a rendering configuration for ../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/ +# +# If you want to make a new 3D model to replace the default one install makehuman, install the CMU plus Face rig ( http://www.makehuman.org/content/cmu_plus_face.html ) +# select it as the used rig and make the body pose at a t-pose, finally export it as Collada(dae) with Feet on ground , Y up, face Z , Bone orienation Local=Global and decimeters +# +# compile assimpTester in ../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/submodules/Assimp +# ./assimpTester --convert makehumanexports/newmodel.dae makehuman.tri --paint 123 123 123 +# +# copy the new makehuman.tri to your dataset/makehuman.tri +# +# You can verify that it is properly rendering without running the demo +# cd ../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/ +# mkdir build +# cd build && cmake .. && make +# cd .. +# ./Renderer --from path/to/this/human3D.conf +# + + +BACKGROUND(0,0,0) +#INCLUDE(Scenes/renderLikeMBVRH.conf) +#This is the way to render like the mbv renderer :) +AUTOREFRESH(1500) +NEAR_CLIP(0.1) +FAR_CLIP(1000) +#Bring our world to the MBV coordinate system +SCALE_WORLD(-0.01,-0.01,0.01) +MAP_ROTATIONS(-1,-1,1,zxy) +OFFSET_ROTATIONS(0,0,0) +#EMULATE_PROJECTION_MATRIX(535.423889 , 0.0 , 320.0 , 0.0 , 533.48468, 240.0 , 0 , 1) +EMULATE_PROJECTION_MATRIX(582.18394 , 0.0 , 960.0 , 0.0 , 582.52915, 540.0 , 0 , 1) +SILENT(1) +RATE(120) +INTERPOLATE_TIME(1) +MOVE_VIEW(1) +OBJECT_TYPE(floorType,grid) +#OBJECT(floor,floorType,0,235,255,0 ,0, 50.0,50.0,50.0) + +OBJECT_TYPE(humanMesh,dataset/makehuman.tri,http://ammar.gr/mocapnet/makehuman.tri) +RIGID_OBJECT(human,humanMesh, 255,0,0,0,0 ,1.0,1.0,1.0) + +LIGHT(0,0,-100) + + +#Note of all the bones in file.. +Bone 0 : Scene +Bone 1 : CMU+Face_compliant_skeleton +Bone 2 : Hips +Bone 3 : LHipJoint +Bone 4 : LeftUpLeg +Bone 5 : LeftLeg +Bone 6 : LeftFoot +Bone 7 : LeftToeBase +Bone 8 : LowerBack +Bone 9 : Spine +Bone 10 : Spine1 +Bone 11 : LeftShoulder +Bone 12 : LeftArm +Bone 13 : LeftForeArm +Bone 14 : LeftHand +Bone 15 : metacarpal1_L +Bone 16 : finger2-1_L +Bone 17 : finger2-2_L +Bone 18 : finger2-3_L +Bone 19 : metacarpal2_L +Bone 20 : finger3-1_L +Bone 21 : finger3-2_L +Bone 22 : finger3-3_L +Bone 23 : metacarpal3_L +Bone 24 : finger4-1_L +Bone 25 : finger4-2_L +Bone 26 : finger4-3_L +Bone 27 : metacarpal4_L +Bone 28 : finger5-1_L +Bone 29 : finger5-2_L +Bone 30 : finger5-3_L +Bone 31 : LThumb +Bone 32 : finger1-2_L +Bone 33 : finger1-3_L +Bone 34 : Neck +Bone 35 : Neck1 +Bone 36 : Head +Bone 37 : jaw +Bone 38 : special04 +Bone 39 : oris02 +Bone 40 : oris01 +Bone 41 : oris06_L +Bone 42 : oris07_L +Bone 43 : oris06_R +Bone 44 : oris07_R +Bone 45 : tongue00 +Bone 46 : tongue01 +Bone 47 : tongue02 +Bone 48 : tongue03 +Bone 49 : tongue04 +Bone 50 : tongue07_L +Bone 51 : tongue07_R +Bone 52 : tongue06_L +Bone 53 : tongue06_R +Bone 54 : tongue05_L +Bone 55 : tongue05_R +Bone 56 : levator02_L +Bone 57 : levator03_L +Bone 58 : levator04_L +Bone 59 : levator05_L +Bone 60 : levator02_R +Bone 61 : levator03_R +Bone 62 : levator04_R +Bone 63 : levator05_R +Bone 64 : special01 +Bone 65 : oris04_L +Bone 66 : oris03_L +Bone 67 : oris04_R +Bone 68 : oris03_R +Bone 69 : oris06 +Bone 70 : oris05 +Bone 71 : special03 +Bone 72 : levator06_L +Bone 73 : levator06_R +Bone 74 : special06_L +Bone 75 : special05_L +Bone 76 : eye_L +Bone 77 : orbicularis03_L +Bone 78 : orbicularis04_L +Bone 79 : special06_R +Bone 80 : special05_R +Bone 81 : eye_R +Bone 82 : orbicularis03_R +Bone 83 : orbicularis04_R +Bone 84 : temporalis01_L +Bone 85 : oculi02_L +Bone 86 : oculi01_L +Bone 87 : temporalis01_R +Bone 88 : oculi02_R +Bone 89 : oculi01_R +Bone 90 : temporalis02_L +Bone 91 : risorius02_L +Bone 92 : risorius03_L +Bone 93 : temporalis02_R +Bone 94 : risorius02_R +Bone 95 : risorius03_R +Bone 96 : RightShoulder +Bone 97 : RightArm +Bone 98 : RightForeArm +Bone 99 : RightHand +Bone 100 : metacarpal1_R +Bone 101 : finger2-1_R +Bone 102 : finger2-2_R +Bone 103 : finger2-3_R +Bone 104 : metacarpal2_R +Bone 105 : finger3-1_R +Bone 106 : finger3-2_R +Bone 107 : finger3-3_R +Bone 108 : metacarpal3_R +Bone 109 : finger4-1_R +Bone 110 : finger4-2_R +Bone 111 : finger4-3_R +Bone 112 : metacarpal4_R +Bone 113 : finger5-1_R +Bone 114 : finger5-2_R +Bone 115 : finger5-3_R +Bone 116 : RThumb +Bone 117 : finger1-2_R +Bone 118 : finger1-3_R +Bone 119 : RHipJoint +Bone 120 : RightUpLeg +Bone 121 : RightLeg +Bone 122 : RightFoot +Bone 123 : RightToeBase +Bone 124 : newmodel-baseObject +Bone 125 : newmodel-highpolyeyesObject + + +#cat human3D.conf | grep Bone | cut -d':' -f2 | tr '\n' '~' | tr -d '[:blank:]' | sed -e 's/\~/,ZXY)\nPOSE_ROTATION_ORDER\(human,/g' +OBJECT_ROTATION_ORDER(human,ZYX) +POSE_ROTATION_ORDER(human,Hips,ZYX) +POSE_ROTATION_ORDER(human,LHipJoint,ZXY) +POSE_ROTATION_ORDER(human,LeftUpLeg,ZXY) +POSE_ROTATION_ORDER(human,LeftLeg,ZXY) +POSE_ROTATION_ORDER(human,LeftFoot,ZXY) +POSE_ROTATION_ORDER(human,LeftToeBase,ZXY) +POSE_ROTATION_ORDER(human,LowerBack,ZXY) +POSE_ROTATION_ORDER(human,Spine,ZXY) +POSE_ROTATION_ORDER(human,Spine1,ZXY) +POSE_ROTATION_ORDER(human,LeftShoulder,ZXY) +POSE_ROTATION_ORDER(human,LeftArm,ZXY) +POSE_ROTATION_ORDER(human,LeftForeArm,ZXY) +POSE_ROTATION_ORDER(human,LeftHand,ZXY) +POSE_ROTATION_ORDER(human,metacarpal1_L,ZXY) +POSE_ROTATION_ORDER(human,finger2-1_L,ZXY) +POSE_ROTATION_ORDER(human,finger2-2_L,ZXY) +POSE_ROTATION_ORDER(human,finger2-3_L,ZXY) +POSE_ROTATION_ORDER(human,metacarpal2_L,ZXY) +POSE_ROTATION_ORDER(human,finger3-1_L,ZXY) +POSE_ROTATION_ORDER(human,finger3-2_L,ZXY) +POSE_ROTATION_ORDER(human,finger3-3_L,ZXY) +POSE_ROTATION_ORDER(human,metacarpal3_L,ZXY) +POSE_ROTATION_ORDER(human,finger4-1_L,ZXY) +POSE_ROTATION_ORDER(human,finger4-2_L,ZXY) +POSE_ROTATION_ORDER(human,finger4-3_L,ZXY) +POSE_ROTATION_ORDER(human,metacarpal4_L,ZXY) +POSE_ROTATION_ORDER(human,finger5-1_L,ZXY) +POSE_ROTATION_ORDER(human,finger5-2_L,ZXY) +POSE_ROTATION_ORDER(human,finger5-3_L,ZXY) +POSE_ROTATION_ORDER(human,LThumb,ZXY) +POSE_ROTATION_ORDER(human,finger1-2_L,ZXY) +POSE_ROTATION_ORDER(human,finger1-3_L,ZXY) +POSE_ROTATION_ORDER(human,Neck,ZXY) +POSE_ROTATION_ORDER(human,Neck1,ZXY) +POSE_ROTATION_ORDER(human,Head,ZXY) +POSE_ROTATION_ORDER(human,jaw,ZXY) +POSE_ROTATION_ORDER(human,special04,ZXY) +POSE_ROTATION_ORDER(human,oris02,ZXY) +POSE_ROTATION_ORDER(human,oris01,ZXY) +POSE_ROTATION_ORDER(human,oris06_L,ZXY) +POSE_ROTATION_ORDER(human,oris07_L,ZXY) +POSE_ROTATION_ORDER(human,oris06_R,ZXY) +POSE_ROTATION_ORDER(human,oris07_R,ZXY) +POSE_ROTATION_ORDER(human,tongue00,ZXY) +POSE_ROTATION_ORDER(human,tongue01,ZXY) +POSE_ROTATION_ORDER(human,tongue02,ZXY) +POSE_ROTATION_ORDER(human,tongue03,ZXY) +POSE_ROTATION_ORDER(human,tongue04,ZXY) +POSE_ROTATION_ORDER(human,tongue07_L,ZXY) +POSE_ROTATION_ORDER(human,tongue07_R,ZXY) +POSE_ROTATION_ORDER(human,tongue06_L,ZXY) +POSE_ROTATION_ORDER(human,tongue06_R,ZXY) +POSE_ROTATION_ORDER(human,tongue05_L,ZXY) +POSE_ROTATION_ORDER(human,tongue05_R,ZXY) +POSE_ROTATION_ORDER(human,levator02_L,ZXY) +POSE_ROTATION_ORDER(human,levator03_L,ZXY) +POSE_ROTATION_ORDER(human,levator04_L,ZXY) +POSE_ROTATION_ORDER(human,levator05_L,ZXY) +POSE_ROTATION_ORDER(human,levator02_R,ZXY) +POSE_ROTATION_ORDER(human,levator03_R,ZXY) +POSE_ROTATION_ORDER(human,levator04_R,ZXY) +POSE_ROTATION_ORDER(human,levator05_R,ZXY) +POSE_ROTATION_ORDER(human,special01,ZXY) +POSE_ROTATION_ORDER(human,oris04_L,ZXY) +POSE_ROTATION_ORDER(human,oris03_L,ZXY) +POSE_ROTATION_ORDER(human,oris04_R,ZXY) +POSE_ROTATION_ORDER(human,oris03_R,ZXY) +POSE_ROTATION_ORDER(human,oris06,ZXY) +POSE_ROTATION_ORDER(human,oris05,ZXY) +POSE_ROTATION_ORDER(human,special03,ZXY) +POSE_ROTATION_ORDER(human,levator06_L,ZXY) +POSE_ROTATION_ORDER(human,levator06_R,ZXY) +POSE_ROTATION_ORDER(human,special06_L,ZXY) +POSE_ROTATION_ORDER(human,special05_L,ZXY) +POSE_ROTATION_ORDER(human,eye_L,ZXY) +POSE_ROTATION_ORDER(human,orbicularis03_L,ZXY) +POSE_ROTATION_ORDER(human,orbicularis04_L,ZXY) +POSE_ROTATION_ORDER(human,special06_R,ZXY) +POSE_ROTATION_ORDER(human,special05_R,ZXY) +POSE_ROTATION_ORDER(human,eye_R,ZXY) +POSE_ROTATION_ORDER(human,orbicularis03_R,ZXY) +POSE_ROTATION_ORDER(human,orbicularis04_R,ZXY) +POSE_ROTATION_ORDER(human,temporalis01_L,ZXY) +POSE_ROTATION_ORDER(human,oculi02_L,ZXY) +POSE_ROTATION_ORDER(human,oculi01_L,ZXY) +POSE_ROTATION_ORDER(human,temporalis01_R,ZXY) +POSE_ROTATION_ORDER(human,oculi02_R,ZXY) +POSE_ROTATION_ORDER(human,oculi01_R,ZXY) +POSE_ROTATION_ORDER(human,temporalis02_L,ZXY) +POSE_ROTATION_ORDER(human,risorius02_L,ZXY) +POSE_ROTATION_ORDER(human,risorius03_L,ZXY) +POSE_ROTATION_ORDER(human,temporalis02_R,ZXY) +POSE_ROTATION_ORDER(human,risorius02_R,ZXY) +POSE_ROTATION_ORDER(human,risorius03_R,ZXY) +POSE_ROTATION_ORDER(human,RightShoulder,ZXY) +POSE_ROTATION_ORDER(human,RightArm,ZXY) +POSE_ROTATION_ORDER(human,RightForeArm,ZXY) +POSE_ROTATION_ORDER(human,RightHand,ZXY) +POSE_ROTATION_ORDER(human,metacarpal1_R,ZXY) +POSE_ROTATION_ORDER(human,finger2-1_R,ZXY) +POSE_ROTATION_ORDER(human,finger2-2_R,ZXY) +POSE_ROTATION_ORDER(human,finger2-3_R,ZXY) +POSE_ROTATION_ORDER(human,metacarpal2_R,ZXY) +POSE_ROTATION_ORDER(human,finger3-1_R,ZXY) +POSE_ROTATION_ORDER(human,finger3-2_R,ZXY) +POSE_ROTATION_ORDER(human,finger3-3_R,ZXY) +POSE_ROTATION_ORDER(human,metacarpal3_R,ZXY) +POSE_ROTATION_ORDER(human,finger4-1_R,ZXY) +POSE_ROTATION_ORDER(human,finger4-2_R,ZXY) +POSE_ROTATION_ORDER(human,finger4-3_R,ZXY) +POSE_ROTATION_ORDER(human,metacarpal4_R,ZXY) +POSE_ROTATION_ORDER(human,finger5-1_R,ZXY) +POSE_ROTATION_ORDER(human,finger5-2_R,ZXY) +POSE_ROTATION_ORDER(human,finger5-3_R,ZXY) +POSE_ROTATION_ORDER(human,RThumb,ZXY) +POSE_ROTATION_ORDER(human,finger1-2_R,ZXY) +POSE_ROTATION_ORDER(human,finger1-3_R,ZXY) +POSE_ROTATION_ORDER(human,RHipJoint,ZXY) +POSE_ROTATION_ORDER(human,RightUpLeg,ZXY) +POSE_ROTATION_ORDER(human,RightLeg,ZXY) +POSE_ROTATION_ORDER(human,RightFoot,ZXY) +POSE_ROTATION_ORDER(human,RightToeBase,ZXY) + +#MOVE(floor,0,0.0,2784.976,3699.735,0.0,0.0,0.0,0.0) +#Root joint euler angle order ZYX +MOVE(human,0,0.0,900.0,2000,0.0,180.0,0.0) + + +#cat human3D.conf | grep Bone | cut -d':' -f2 | tr '\n' '~' | tr -d '[:blank:]' | sed -e 's/\~/,0,0,0)\nPOSE(human,0,/g' +POSE(human,0,Hips,0,0,0) +POSE(human,0,LHipJoint,0,0,0) +POSE(human,0,LeftUpLeg,0,0,0) +POSE(human,0,LeftLeg,0,0,0) +POSE(human,0,LeftFoot,0,0,0) +POSE(human,0,LeftToeBase,0,0,0) +POSE(human,0,LowerBack,0,0,0) +POSE(human,0,Spine,0,0,0) +POSE(human,0,Spine1,0,0,0) +POSE(human,0,LeftShoulder,0,0,0) +POSE(human,0,LeftArm,0,0,0) +POSE(human,0,LeftForeArm,0,0,0) +POSE(human,0,LeftHand,0,0,0) +POSE(human,0,metacarpal1_L,0,0,0) +POSE(human,0,finger2-1_L,0,0,0) +POSE(human,0,finger2-2_L,0,0,0) +POSE(human,0,finger2-3_L,0,0,0) +POSE(human,0,metacarpal2_L,0,0,0) +POSE(human,0,finger3-1_L,0,0,0) +POSE(human,0,finger3-2_L,0,0,0) +POSE(human,0,finger3-3_L,0,0,0) +POSE(human,0,metacarpal3_L,0,0,0) +POSE(human,0,finger4-1_L,0,0,0) +POSE(human,0,finger4-2_L,0,0,0) +POSE(human,0,finger4-3_L,0,0,0) +POSE(human,0,metacarpal4_L,0,0,0) +POSE(human,0,finger5-1_L,0,0,0) +POSE(human,0,finger5-2_L,0,0,0) +POSE(human,0,finger5-3_L,0,0,0) +POSE(human,0,LThumb,0,0,0) +POSE(human,0,finger1-2_L,0,0,0) +POSE(human,0,finger1-3_L,0,0,0) +POSE(human,0,Neck,0,0,0) +POSE(human,0,Neck1,0,0,0) +POSE(human,0,Head,0,0,0) +POSE(human,0,jaw,0,0,0) +POSE(human,0,special04,0,0,0) +POSE(human,0,oris02,0,0,0) +POSE(human,0,oris01,0,0,0) +POSE(human,0,oris06_L,0,0,0) +POSE(human,0,oris07_L,0,0,0) +POSE(human,0,oris06_R,0,0,0) +POSE(human,0,oris07_R,0,0,0) +POSE(human,0,tongue00,0,0,0) +POSE(human,0,tongue01,0,0,0) +POSE(human,0,tongue02,0,0,0) +POSE(human,0,tongue03,0,0,0) +POSE(human,0,tongue04,0,0,0) +POSE(human,0,tongue07_L,0,0,0) +POSE(human,0,tongue07_R,0,0,0) +POSE(human,0,tongue06_L,0,0,0) +POSE(human,0,tongue06_R,0,0,0) +POSE(human,0,tongue05_L,0,0,0) +POSE(human,0,tongue05_R,0,0,0) +POSE(human,0,levator02_L,0,0,0) +POSE(human,0,levator03_L,0,0,0) +POSE(human,0,levator04_L,0,0,0) +POSE(human,0,levator05_L,0,0,0) +POSE(human,0,levator02_R,0,0,0) +POSE(human,0,levator03_R,0,0,0) +POSE(human,0,levator04_R,0,0,0) +POSE(human,0,levator05_R,0,0,0) +POSE(human,0,special01,0,0,0) +POSE(human,0,oris04_L,0,0,0) +POSE(human,0,oris03_L,0,0,0) +POSE(human,0,oris04_R,0,0,0) +POSE(human,0,oris03_R,0,0,0) +POSE(human,0,oris06,0,0,0) +POSE(human,0,oris05,0,0,0) +POSE(human,0,special03,0,0,0) +POSE(human,0,levator06_L,0,0,0) +POSE(human,0,levator06_R,0,0,0) +POSE(human,0,special06_L,0,0,0) +POSE(human,0,special05_L,0,0,0) +POSE(human,0,eye_L,0,0,0) +POSE(human,0,orbicularis03_L,0,0,0) +POSE(human,0,orbicularis04_L,0,0,0) +POSE(human,0,special06_R,0,0,0) +POSE(human,0,special05_R,0,0,0) +POSE(human,0,eye_R,0,0,0) +POSE(human,0,orbicularis03_R,0,0,0) +POSE(human,0,orbicularis04_R,0,0,0) +POSE(human,0,temporalis01_L,0,0,0) +POSE(human,0,oculi02_L,0,0,0) +POSE(human,0,oculi01_L,0,0,0) +POSE(human,0,temporalis01_R,0,0,0) +POSE(human,0,oculi02_R,0,0,0) +POSE(human,0,oculi01_R,0,0,0) +POSE(human,0,temporalis02_L,0,0,0) +POSE(human,0,risorius02_L,0,0,0) +POSE(human,0,risorius03_L,0,0,0) +POSE(human,0,temporalis02_R,0,0,0) +POSE(human,0,risorius02_R,0,0,0) +POSE(human,0,risorius03_R,0,0,0) +POSE(human,0,RightShoulder,0,0,0) +POSE(human,0,RightArm,0,0,0) +POSE(human,0,RightForeArm,0,0,0) +POSE(human,0,RightHand,0,0,0) +POSE(human,0,metacarpal1_R,0,0,0) +POSE(human,0,finger2-1_R,0,0,0) +POSE(human,0,finger2-2_R,0,0,0) +POSE(human,0,finger2-3_R,0,0,0) +POSE(human,0,metacarpal2_R,0,0,0) +POSE(human,0,finger3-1_R,0,0,0) +POSE(human,0,finger3-2_R,0,0,0) +POSE(human,0,finger3-3_R,0,0,0) +POSE(human,0,metacarpal3_R,0,0,0) +POSE(human,0,finger4-1_R,0,0,0) +POSE(human,0,finger4-2_R,0,0,0) +POSE(human,0,finger4-3_R,0,0,0) +POSE(human,0,metacarpal4_R,0,0,0) +POSE(human,0,finger5-1_R,0,0,0) +POSE(human,0,finger5-2_R,0,0,0) +POSE(human,0,finger5-3_R,0,0,0) +POSE(human,0,RThumb,0,0,0) +POSE(human,0,finger1-2_R,0,0,0) +POSE(human,0,finger1-3_R,0,0,0) +POSE(human,0,RHipJoint,0,0,0) +POSE(human,0,RightUpLeg,0,0,0) +POSE(human,0,RightLeg,0,0,0) +POSE(human,0,RightFoot,0,0,0) +POSE(human,0,RightToeBase,0,0,0) + + + +#MOVE(floor,1,-19.231,2784.976,3699.735,0.0,0.0,0.0,0.0) + +#Root joint euler angle order ZYX +MOVE(human,1,0.0,900.0,2000,0.0,180.0,0.0) + + +#cat human3D.conf | grep Bone | cut -d':' -f2 | tr '\n' '~' | tr -d '[:blank:]' | sed -e 's/\~/,0,0,0)\nPOSE(human,1,/g' +POSE(human,1,Hips,0,0,0) +POSE(human,1,LHipJoint,0,0,0) +POSE(human,1,LeftUpLeg,0,0,0) +POSE(human,1,LeftLeg,0,0,0) +POSE(human,1,LeftFoot,0,0,0) +POSE(human,1,LeftToeBase,0,0,0) +POSE(human,1,LowerBack,0,0,0) +POSE(human,1,Spine,0,0,0) +POSE(human,1,Spine1,0,0,0) +POSE(human,1,LeftShoulder,0,0,0) +POSE(human,1,LeftArm,0,0,0) +POSE(human,1,LeftForeArm,0,0,0) +POSE(human,1,LeftHand,0,0,0) +POSE(human,1,metacarpal1_L,0,0,0) +POSE(human,1,finger2-1_L,0,0,0) +POSE(human,1,finger2-2_L,0,0,0) +POSE(human,1,finger2-3_L,0,0,0) +POSE(human,1,metacarpal2_L,0,0,0) +POSE(human,1,finger3-1_L,0,0,0) +POSE(human,1,finger3-2_L,0,0,0) +POSE(human,1,finger3-3_L,0,0,0) +POSE(human,1,metacarpal3_L,0,0,0) +POSE(human,1,finger4-1_L,0,0,0) +POSE(human,1,finger4-2_L,0,0,0) +POSE(human,1,finger4-3_L,0,0,0) +POSE(human,1,metacarpal4_L,0,0,0) +POSE(human,1,finger5-1_L,0,0,0) +POSE(human,1,finger5-2_L,0,0,0) +POSE(human,1,finger5-3_L,0,0,0) +POSE(human,1,LThumb,0,0,0) +POSE(human,1,finger1-2_L,0,0,0) +POSE(human,1,finger1-3_L,0,0,0) +POSE(human,1,Neck,0,0,0) +POSE(human,1,Neck1,0,0,0) +POSE(human,1,Head,0,0,0) +POSE(human,1,jaw,0,0,0) +POSE(human,1,special04,0,0,0) +POSE(human,1,oris02,0,0,0) +POSE(human,1,oris01,0,0,0) +POSE(human,1,oris06_L,0,0,0) +POSE(human,1,oris07_L,0,0,0) +POSE(human,1,oris06_R,0,0,0) +POSE(human,1,oris07_R,0,0,0) +POSE(human,1,tongue00,0,0,0) +POSE(human,1,tongue01,0,0,0) +POSE(human,1,tongue02,0,0,0) +POSE(human,1,tongue03,0,0,0) +POSE(human,1,tongue04,0,0,0) +POSE(human,1,tongue07_L,0,0,0) +POSE(human,1,tongue07_R,0,0,0) +POSE(human,1,tongue06_L,0,0,0) +POSE(human,1,tongue06_R,0,0,0) +POSE(human,1,tongue05_L,0,0,0) +POSE(human,1,tongue05_R,0,0,0) +POSE(human,1,levator02_L,0,0,0) +POSE(human,1,levator03_L,0,0,0) +POSE(human,1,levator04_L,0,0,0) +POSE(human,1,levator05_L,0,0,0) +POSE(human,1,levator02_R,0,0,0) +POSE(human,1,levator03_R,0,0,0) +POSE(human,1,levator04_R,0,0,0) +POSE(human,1,levator05_R,0,0,0) +POSE(human,1,special01,0,0,0) +POSE(human,1,oris04_L,0,0,0) +POSE(human,1,oris03_L,0,0,0) +POSE(human,1,oris04_R,0,0,0) +POSE(human,1,oris03_R,0,0,0) +POSE(human,1,oris06,0,0,0) +POSE(human,1,oris05,0,0,0) +POSE(human,1,special03,0,0,0) +POSE(human,1,levator06_L,0,0,0) +POSE(human,1,levator06_R,0,0,0) +POSE(human,1,special06_L,0,0,0) +POSE(human,1,special05_L,0,0,0) +POSE(human,1,eye_L,0,0,0) +POSE(human,1,orbicularis03_L,0,0,0) +POSE(human,1,orbicularis04_L,0,0,0) +POSE(human,1,special06_R,0,0,0) +POSE(human,1,special05_R,0,0,0) +POSE(human,1,eye_R,0,0,0) +POSE(human,1,orbicularis03_R,0,0,0) +POSE(human,1,orbicularis04_R,0,0,0) +POSE(human,1,temporalis01_L,0,0,0) +POSE(human,1,oculi02_L,0,0,0) +POSE(human,1,oculi01_L,0,0,0) +POSE(human,1,temporalis01_R,0,0,0) +POSE(human,1,oculi02_R,0,0,0) +POSE(human,1,oculi01_R,0,0,0) +POSE(human,1,temporalis02_L,0,0,0) +POSE(human,1,risorius02_L,0,0,0) +POSE(human,1,risorius03_L,0,0,0) +POSE(human,1,temporalis02_R,0,0,0) +POSE(human,1,risorius02_R,0,0,0) +POSE(human,1,risorius03_R,0,0,0) +POSE(human,1,RightShoulder,0,0,0) +POSE(human,1,RightArm,0,0,0) +POSE(human,1,RightForeArm,0,0,0) +POSE(human,1,RightHand,0,0,0) +POSE(human,1,metacarpal1_R,0,0,0) +POSE(human,1,finger2-1_R,0,0,0) +POSE(human,1,finger2-2_R,0,0,0) +POSE(human,1,finger2-3_R,0,0,0) +POSE(human,1,metacarpal2_R,0,0,0) +POSE(human,1,finger3-1_R,0,0,0) +POSE(human,1,finger3-2_R,0,0,0) +POSE(human,1,finger3-3_R,0,0,0) +POSE(human,1,metacarpal3_R,0,0,0) +POSE(human,1,finger4-1_R,0,0,0) +POSE(human,1,finger4-2_R,0,0,0) +POSE(human,1,finger4-3_R,0,0,0) +POSE(human,1,metacarpal4_R,0,0,0) +POSE(human,1,finger5-1_R,0,0,0) +POSE(human,1,finger5-2_R,0,0,0) +POSE(human,1,finger5-3_R,0,0,0) +POSE(human,1,RThumb,0,0,0) +POSE(human,1,finger1-2_R,0,0,0) +POSE(human,1,finger1-3_R,0,0,0) +POSE(human,1,RHipJoint,0,0,0) +POSE(human,1,RightUpLeg,0,0,0) +POSE(human,1,RightLeg,0,0,0) +POSE(human,1,RightFoot,0,0,0) +POSE(human,1,RightToeBase,0,0,0) +POSE(human,1,newmodel-baseObject,0,0,0) +POSE(human,1,newmodel-highpolyeyesObject,0,0,0) + + diff --git a/animation/MocapNET-kasisnu/dataset/lhand.bvh b/animation/MocapNET-kasisnu/dataset/lhand.bvh new file mode 100644 index 0000000..f5f3393 --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/lhand.bvh @@ -0,0 +1,140 @@ +HIERARCHY +ROOT lHand +{ + OFFSET 0 0 0 + CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation + JOINT metacarpal1.l + { + OFFSET 4.0 -0.27 2.00 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-1.l + { + OFFSET 4.5 0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-2.l + { + OFFSET 3.5 -0.32 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-3.l + { + OFFSET 2.1 -0.29 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.98 -0.68 0.0 + } + } + } + } + } + JOINT metacarpal2.l + { + OFFSET 4.0 -0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-1.l + { + OFFSET 4.50 0.62 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-2.l + { + OFFSET 3.97 -0.58 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-3.l + { + OFFSET 2.5 -0.42 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.964 -0.73 0.00 + } + } + } + } + } + JOINT __metacarpal3.l + { + OFFSET 2.5 -0.27 -2.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal3.l + { + OFFSET 1.5 -0.16 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-1.l + { + OFFSET 4.5 0.70 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-2.l + { + OFFSET 3.67 -0.48 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-3.l + { + OFFSET 2.27 -0.35 -0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.904 -0.62 0.0 + } + } + } + } + } + } + JOINT __metacarpal4.l + { + OFFSET 2.0 -0.27 -3.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal4.l + { + OFFSET 2.0 -0.16 -1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-1.l + { + OFFSET 4.0 0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-2.l + { + OFFSET 3.2 -0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-3.l + { + OFFSET 1.495 -0.10 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.9 -0.10 0.0 + } + } + } + } + } + } + JOINT lthumbBase + { + OFFSET 2.0 -0.279 4.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT lthumb + { + OFFSET 2.0 -0.142 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-2.l + { + OFFSET 1.9598 -2.10 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-3.l + { + OFFSET 2.76 -0.46 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 2.595 -0.16 0.0 + } + } + } + } + } +} +MOTION +Frames: 1 +Frame Time: 0.040000 +0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/animation/MocapNET-kasisnu/dataset/lhand.qbvh b/animation/MocapNET-kasisnu/dataset/lhand.qbvh new file mode 100644 index 0000000..67b201e --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/lhand.qbvh @@ -0,0 +1,140 @@ +HIERARCHY +ROOT lHand +{ + OFFSET 0 0 0 + CHANNELS 7 Xposition Yposition Zposition Wrotation Xrotation Yrotation Zrotation + JOINT metacarpal1.l + { + OFFSET 4.0 -0.27 2.00 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-1.l + { + OFFSET 4.5 0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-2.l + { + OFFSET 3.5 -0.32 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-3.l + { + OFFSET 2.1 -0.29 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.98 -0.68 0.0 + } + } + } + } + } + JOINT metacarpal2.l + { + OFFSET 4.0 -0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-1.l + { + OFFSET 4.50 0.62 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-2.l + { + OFFSET 3.97 -0.58 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-3.l + { + OFFSET 2.5 -0.42 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.964 -0.73 0.00 + } + } + } + } + } + JOINT __metacarpal3.l + { + OFFSET 2.5 -0.27 -2.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal3.l + { + OFFSET 1.5 -0.16 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-1.l + { + OFFSET 4.5 0.70 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-2.l + { + OFFSET 3.67 -0.48 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-3.l + { + OFFSET 2.27 -0.35 -0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.904 -0.62 0.0 + } + } + } + } + } + } + JOINT __metacarpal4.l + { + OFFSET 2.0 -0.27 -3.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal4.l + { + OFFSET 2.0 -0.16 -1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-1.l + { + OFFSET 4.0 0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-2.l + { + OFFSET 3.2 -0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-3.l + { + OFFSET 1.495 -0.10 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 1.9 -0.10 0.0 + } + } + } + } + } + } + JOINT lthumbBase + { + OFFSET 0.0 -0.279 2.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT lthumb + { + OFFSET 2.0 -0.142 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-2.l + { + OFFSET 1.9598 -2.10 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-3.l + { + OFFSET 2.76 -0.46 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 2.595 -0.16 0.0 + } + } + } + } + } +} +MOTION +Frames: 1 +Frame Time: 0.040000 +0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/animation/MocapNET-kasisnu/dataset/rhand.bvh b/animation/MocapNET-kasisnu/dataset/rhand.bvh new file mode 100644 index 0000000..cdc9635 --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/rhand.bvh @@ -0,0 +1,140 @@ +HIERARCHY +ROOT rHand + { + OFFSET 0 0 0 + CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation + JOINT metacarpal1.r + { + OFFSET -4.0 -0.27 2.00 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-1.r + { + OFFSET -4.5 0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-2.r + { + OFFSET -3.5 -0.32 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-3.r + { + OFFSET -2.1 -0.29 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.98 -0.68 0.0 + } + } + } + } + } + JOINT metacarpal2.r + { + OFFSET -4.0 -0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-1.r + { + OFFSET -4.50 0.62 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-2.r + { + OFFSET -3.97 -0.58 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-3.r + { + OFFSET -2.5 -0.42 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.964 -0.73 0.00 + } + } + } + } + } + JOINT __metacarpal3.r + { + OFFSET -2.5 -0.27 -2.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal3.r + { + OFFSET -1.5 -0.16 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-1.r + { + OFFSET -4.5 0.70 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-2.r + { + OFFSET -3.67 -0.48 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-3.r + { + OFFSET -2.27 -0.35 -0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.904 -0.62 0.0 + } + } + } + } + } + } + JOINT __metacarpal4.r + { + OFFSET -2.0 -0.27 -3.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal4.r + { + OFFSET -2.0 -0.16 -1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-1.r + { + OFFSET -4.0 0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-2.r + { + OFFSET -3.2 -0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-3.r + { + OFFSET -1.495 -0.10 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.9 -0.10 0.0 + } + } + } + } + } + } + JOINT rthumbBase + { + OFFSET -2.0 -0.279 4.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT rthumb + { + OFFSET -2.0 -0.142 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-2.r + { + OFFSET -1.9598 -2.10 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-3.r + { + OFFSET -2.76 -0.46 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -2.595 -0.16 0.0 + } + } + } + } + } +} +MOTION +Frames: 1 +Frame Time: 0.040000 +0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/animation/MocapNET-kasisnu/dataset/rhand.qbvh b/animation/MocapNET-kasisnu/dataset/rhand.qbvh new file mode 100644 index 0000000..215d5e6 --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/rhand.qbvh @@ -0,0 +1,140 @@ +HIERARCHY +ROOT rHand + { + OFFSET 0 0 0 + CHANNELS 7 Xposition Yposition Zposition Wrotation Xrotation Yrotation Zrotation + JOINT metacarpal1.r + { + OFFSET -4.0 -0.27 2.00 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-1.r + { + OFFSET -4.5 0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-2.r + { + OFFSET -3.5 -0.32 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger2-3.r + { + OFFSET -2.1 -0.29 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.98 -0.68 0.0 + } + } + } + } + } + JOINT metacarpal2.r + { + OFFSET -4.0 -0.27 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-1.r + { + OFFSET -4.50 0.62 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-2.r + { + OFFSET -3.97 -0.58 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger3-3.r + { + OFFSET -2.5 -0.42 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.964 -0.73 0.00 + } + } + } + } + } + JOINT __metacarpal3.r + { + OFFSET -2.5 -0.27 -2.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal3.r + { + OFFSET -1.5 -0.16 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-1.r + { + OFFSET -4.5 0.70 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-2.r + { + OFFSET -3.67 -0.48 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger4-3.r + { + OFFSET -2.27 -0.35 -0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.904 -0.62 0.0 + } + } + } + } + } + } + JOINT __metacarpal4.r + { + OFFSET -2.0 -0.27 -3.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT metacarpal4.r + { + OFFSET -2.0 -0.16 -1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-1.r + { + OFFSET -4.0 0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-2.r + { + OFFSET -3.2 -0.17 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger5-3.r + { + OFFSET -1.495 -0.10 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -1.9 -0.10 0.0 + } + } + } + } + } + } + JOINT rthumbBase + { + OFFSET 0.0 -0.279 2.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT rthumb + { + OFFSET -2.0 -0.142 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-2.r + { + OFFSET -1.9598 -2.10 1.0 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT finger1-3.r + { + OFFSET -2.76 -0.46 0.0 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -2.595 -0.16 0.0 + } + } + } + } + } +} +MOTION +Frames: 1 +Frame Time: 0.040000 +0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/animation/MocapNET-kasisnu/dataset/sample.csv b/animation/MocapNET-kasisnu/dataset/sample.csv new file mode 100644 index 0000000..2389bd9 --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/sample.csv @@ -0,0 +1,943 @@ 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diff --git a/animation/MocapNET-kasisnu/dataset/shuffle.csv b/animation/MocapNET-kasisnu/dataset/shuffle.csv new file mode 100644 index 0000000..4311f7d --- /dev/null +++ b/animation/MocapNET-kasisnu/dataset/shuffle.csv @@ -0,0 +1,454 @@ 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diff --git a/animation/MocapNET-kasisnu/dataset/test.jpg b/animation/MocapNET-kasisnu/dataset/test.jpg new file mode 100644 index 0000000..ba52057 Binary files /dev/null and b/animation/MocapNET-kasisnu/dataset/test.jpg differ diff --git a/animation/MocapNET-kasisnu/dependencies/InputParser/InputParser_C.cpp b/animation/MocapNET-kasisnu/dependencies/InputParser/InputParser_C.cpp new file mode 100644 index 0000000..1581b8e --- /dev/null +++ b/animation/MocapNET-kasisnu/dependencies/InputParser/InputParser_C.cpp @@ -0,0 +1,991 @@ +#include "InputParser_C.h" +#include +/* InputParser.. + A small generic library for parsing a string and tokenizing it! + GITHUB Repo : http://github.com/AmmarkoV/InputParser + my URLs: http://ammar.gr + Written by Ammar Qammaz a.k.a. AmmarkoV 2006-2010 + + This program is free software; you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation; either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +*/ + +#define WARN_ABOUT_INCORRECTLY_ALLOCATED_STACK_STRINGS 1 +int warningsAboutIncorrectlyAllocatedStackIssued = 0; + + +char _ipc_ver[]=" 0.362\0"; //17/7/2023 + + +/* + + +TODO + + +void stringToFloatArray(char * str , char delimiter , float * floatArr , int elements) +{ + unsigned int strLength = strlen(str); + char * strPtr=str; + char * strStart=str; + char * strEnd=str+strLength; + + int curElement = 0; + + while ( (curElement0) + { + if (i+skip_chars>=length) + { + inpt[i]=0; + } + else + { + inpt[i]=inpt[i+skip_chars]; + } + } + ++i; + } + return 1; +} + + + +int InputParser_TrimCharactersStart(char * inpt , unsigned int length,char what2trim) +{ + if ( ( inpt==0 ) || (length==0) ) + { + return 0; + } + unsigned int skip_chars=0; + unsigned int i=0; + + + while ((skip_chars0) + { + if (i+skip_chars>=length) + { + inpt[i]=0; + } + else + { + inpt[i]=inpt[i+skip_chars]; + } + } + ++i; + } + + return 1; +} + + +int InputParser_TrimCharactersEnd(char * inpt , unsigned int length,char what2trim) +{ + if ( strlen(inpt)!=length ) + { + length=strlen(inpt); + } + if ( length==0 ) + { + return 1; + } + if ( (length==1) && (inpt[0]==what2trim) ) + { + inpt[0]=0; + return 1; + } + else if ( length==1 ) + { + return 1; + } + + unsigned int i; + i=length-1; + while ((inpt[i]==what2trim)&&(i>0)) + { + --i; + } + if ( i==length-1 ) + { + /*No chars found*/ return 1; + } + + if ((i==0) && (inpt[0]==what2trim) ) + { + inpt[0]=0; + } + else + { + inpt[i+1]=0; + } + return 1; +} + +int InputParser_TrimCharacters(char * inpt , unsigned int length,char what2trim) +{ + int i=0; + i=InputParser_TrimCharactersStart(inpt,length,what2trim); + if (i==0) + { + return 0; + } + + return InputParser_TrimCharactersEnd(inpt,length,what2trim); +} + +static inline signed int Str2Int_internal(char * inpt,unsigned int start_from,unsigned int length) +{ + if ( inpt == 0 ) + { + fprintf(stderr,"Null string to Str2IntInternal!\n"); + return 0; + } + int intresult; + int multiplier; + int curnum; + unsigned char trailing_sign_seek; + unsigned char positive_number; + signed int i; + + intresult=0,multiplier=1,curnum=0; + trailing_sign_seek=1 , positive_number=1; + /*fprintf(stderr,"Converting to int string (%p) begining from %u and ending at %u ",inpt,start_from,start_from+length);*/ + for (i=(signed int) start_from+length-1; i>=(signed int) start_from; i--) + { + if ( i < 0 ) + { + /*fprintf("Gone negative! %u \n",i);*/ break; + } + + curnum=(char) (inpt[i])-'0'; + if ((curnum>=0)&(curnum<=9)) + { + intresult=intresult+(multiplier*curnum); + multiplier=multiplier*10; + } + + if (trailing_sign_seek) + { + /*fprintf(stderr,"Run to %c while searching for sign \n",inpt[i]);*/ + if (inpt[i]=='+') + { + trailing_sign_seek=0; + } + else if (inpt[i]=='-') + { + trailing_sign_seek=0; + positive_number=0; + } + } + } + + if (!positive_number) + { + intresult = intresult * (-1); + } + return intresult; +} + + + +static inline unsigned char CheckIPCOk(struct InputParserC * ipc) +{ + if ( ipc->guardbyte1.checksum != ipc->guardbyte2.checksum ) + { + fprintf(stderr,"Input Parser - Wrong GuardChecksums #1\n"); + return 0; + } + if ( ipc->guardbyte3.checksum != ipc->guardbyte4.checksum ) + { + fprintf(stderr,"Input Parser - Wrong GuardChecksums #2\n"); + return 0; + } + if ( ipc->guardbyte1.checksum != ipc->guardbyte4.checksum ) + { + fprintf(stderr,"Input Parser - Wrong GuardChecksums #3\n"); + return 0; + } + if ( (ipc->tokenlist==0) || (ipc->tokens_maxtokens_count) ) + { + fprintf(stderr,"Input Parser - Tokenlist error\n"); + return 0; + } + if ( (ipc->delimeters==0) || (ipc->max_delimeter_countcur_delimeter_count) ) + { + fprintf(stderr,"Input Parser - Delimeter error\n"); + return 0; + } + + return 1; +} + + +void InputParser_DefaultDelimeters(struct InputParserC * ipc) +{ + if ( CheckIPCOk(ipc) == 0 ) + { + return; + } + if ( ipc->delimeters == 0 ) + { + return; + } + + int i; + /*fprintf(stderr,"Default Delimters ( %u ) ",ipc->max_delimeter_count);*/ + for ( i=0; imax_delimeter_count; i++) + { + switch (i) + { + case 0 : + ipc->delimeters[0]='\n'; + break; + case 1 : + ipc->delimeters[1]=','; + break; + case 2 : + ipc->delimeters[2]='='; + break; + case 3 : + ipc->delimeters[3]='('; + break; + case 4 : + ipc->delimeters[4]=')'; + break; + default : + ipc->delimeters[i]='\n'; + break; + }; + + /*fprintf(stderr," %u - %c ",i,ipc->delimeters[i]);*/ + + } + ipc->cur_delimeter_count = ipc->max_delimeter_count; + /*fprintf(stderr,"\n");*/ + + return; +} + +/* + InitInputParser.. + + Allocates an ipc structure and with memory to it according to options for future use! + for example InitInputParser( 256 , 5 ) will allocate an InputParser Instance capeable of tokenizing up to 256 different words + from 5 delimeters +*/ +struct InputParserC * InputParser_Create(unsigned int max_string_count,unsigned int max_delimiter_count) +{ + struct InputParserC * ipc=0; + + ipc = ( struct InputParserC * ) malloc ( sizeof ( struct InputParserC ) ); + if ( ipc == 0 ) + { + fprintf(stderr,"InputParserC unable to commit memory for a new instance\n"); + return 0; + } + + ipc->tokenlist = (struct tokens *) malloc( sizeof(struct tokens) * (max_string_count+1) ); + if ( ipc->tokenlist == 0 ) + { + fprintf(stderr,"InputParserC unable to commit memory for a new Token List\n"); + free(ipc); + return 0; + } + ipc->tokens_count=0; + ipc->tokens_max = max_string_count; + + + ipc->delimeters = (char *) malloc( sizeof(char) * (max_delimiter_count+1) ); + if ( ipc->delimeters == 0 ) + { + fprintf(stderr,"InputParserC unable to commit memory for a new Delimeter List\n"); + free(ipc->tokenlist); + free(ipc); + return 0; + } + ipc->max_delimeter_count=max_delimiter_count; + ipc->cur_delimeter_count=max_delimiter_count; + + + ipc->container_start=0; + ipc->container_end=0; + + ipc->guardbyte1.checksum=66666; + ipc->guardbyte2.checksum=66666; + ipc->guardbyte3.checksum=66666; + ipc->guardbyte4.checksum=66666; + + ipc->local_allocation = 0; /* No allocation by default! :)*/ + ipc->str_length=0; + ipc->str=0; + + InputParser_DefaultDelimeters(ipc); + + return ipc; +} + +/* + InputParser_Destroy.. + + Safely destroys a non null ipc structure and deallocates all commited memory +*/ +void InputParser_Destroy(struct InputParserC * ipc) +{ + if ( ipc == 0 ) + { + return; + } + + /*fprintf(stderr,"InputParserC freeing delimeters\n");*/ + if ( ipc->delimeters != 0 ) + { + free(ipc->delimeters); + ipc->delimeters=0; + } + ipc->cur_delimeter_count=0; + ipc->max_delimeter_count=0; + + /* + if ( ipc->container_start != 0 ) + { + free(ipc->container_start); + ipc->container_start=0; + } + if ( ipc->container_end != 0 ) + { + free(ipc->container_end); + ipc->container_end=0; + } + */ + ipc->cur_container_count = 0; + ipc->max_container_count = 0; + + /*fprintf(stderr,"InputParserC freeing tokenlist\n");*/ + if ( ipc->tokenlist != 0 ) + { + free(ipc->tokenlist); + ipc->tokenlist=0; + } + ipc->tokens_max=0; + ipc->tokens_count=0; + + /*fprintf(stderr,"InputParserC freeing local allocation\n");*/ + if ( ipc->local_allocation == 1 ) + { + if ( ipc->str!=0 ) free(ipc->str); + } + /*ipc->local_allocation=0;*/ + ipc->str_length=0; + + + + /* RESULT */ + ipc->guardbyte1.checksum = 0; + ipc->guardbyte2.checksum = 0; + ipc->guardbyte3.checksum = 0; + ipc->guardbyte4.checksum = 0; + + /*fprintf(stderr,"InputParserC freeing ipc\n");*/ + free(ipc); + + +} + +/* ........................................................ + Delimeters ........................................................ + ........................................................*/ + +/* + CheckDelimeterNumOk.. + Checks if delimeter with number num has allocated space in memory +*/ +static inline unsigned char CheckDelimeterNumOk(struct InputParserC * ipc,int num) +{ + if ( ipc->max_delimeter_count <= num ) return 0; + return 1; +} + +/* + SetDelimeter.. + Sets Delimeter number num with value tmp +*/ +void InputParser_SetDelimeter(struct InputParserC * ipc,int num,char tmp) +{ + if (CheckDelimeterNumOk(ipc,num)==0) + { + return; + } + ipc->delimeters[num]=tmp; +} + +/* + GetDelimeter.. + Returns value of Delimeter with number num +*/ +char InputParser_GetDelimeter(struct InputParserC * ipc,int num) +{ + if (CheckDelimeterNumOk(ipc,num)==0) + { + return 0; + } + return ipc->delimeters[num]; +} +/* ....................................................................... + ....................................................................... + .......................................................................*/ + + + + +/* + Selfchecks IPC instance and returns 1 if everything is ok , 0 if error.. +*/ +unsigned char InputParser_SelfCheck(struct InputParserC * ipc) +{ + if (CheckIPCOk(ipc)==0) + { + fprintf(stderr,"\n\n!!!!\nThis instance of InputParser is broken\n!!!!\n\n"); + return 0; + } + return 1; +} + +/* + CheckWordNumOk.. + Checks if word with number num has allocated space in memory +*/ +unsigned char CheckWordNumOk(struct InputParserC * ipc,unsigned int num) +{ + if ( CheckIPCOk(ipc)==0) + { + return 0; + } + if ( (ipc->tokenlist==0) || ( ipc->tokens_count <= num ) ) + { + return 0; + } + + + return 1; +} + + +unsigned int InputParser_GetNumberOfArguments(struct InputParserC * ipc) +{ + if ( CheckIPCOk(ipc)==0) + { + return 0; + } + return ipc->tokens_count; +} + +/* + InputParser_GetWord.. + Copies token with number (num) to c string (wheretostore) , variable storagesize contains the total size of wheretostore..! +*/ +unsigned int InputParser_IsEmptyWord(struct InputParserC * ipc,unsigned int num) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 1; + } + if ( ipc->tokenlist[num].length == 0 ) + { + return 1; + } + + unsigned int i=0; + for ( i = ipc->tokenlist[num].token_start; itokenlist[num].token_start+ipc->tokenlist[num].length; i++ ) + { + switch (ipc->str[i]) + { + case 10 : + break; + case 13 : + break; + case ' ' : + break; + default : + return 0; + }; + } + + return 1; +} + + + +/* + InputParser_GetWord.. + Copies token with number (num) to c string (wheretostore) , variable storagesize contains the total size of wheretostore..! +*/ +unsigned int InputParser_GetWord(struct InputParserC * ipc,unsigned int num,char * wheretostore,unsigned int storagesize) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0; + } + if ( storagesize < ipc->tokenlist[num].length+1 ) /* +1 gia to \0 */ return 0; + + + unsigned int i=0; + for ( i = ipc->tokenlist[num].token_start; itokenlist[num].token_start+ipc->tokenlist[num].length; i++ ) + wheretostore[i-ipc->tokenlist[num].token_start] = ipc->str[i]; + + wheretostore[ipc->tokenlist[num].length] = 0; + + return ipc->tokenlist[num].length; +} + + +/* + InputParser_WordCompareNoCase.. + Compares word (word) with token with number (num) , null terminating character is not required , NO CASE SENSITIVITY..! +*/ +unsigned char InputParser_WordCompareNoCase(struct InputParserC * ipc,unsigned int num,const char * word,unsigned int wordsize) +{ + /*fprintf(stderr,"InputParser_WordCompareNoCase( %u , %s , %u )",num,word,wordsize);*/ + if ( wordsize != InputParser_GetWordLength(ipc,num) ) + { + return 0; + } + /*if ( ipc->str_length <= ipc->tokenlist[num].token_start+wordsize ) { fprintf(stderr,"Erroneous input on InputParser_WordCompareNoCase leads out of array \n"); return 0; }*/ + + unsigned int i=0; + for ( i=0; istr[ipc->tokenlist[num].token_start+i])!=toupper(word[i])) + { + /*fprintf(stderr," returning fail ");*/ return 0; + } + } + + /*fprintf(stderr," returning success ");*/ + return 1; +} + +/* + InputParser_WordCompareNoCase.. + Compares word (word) with token with number (num) , null terminating character is required , NO CASE SENSITIVITY..! +*/ +unsigned char InputParser_WordCompareNoCaseAuto(struct InputParserC * ipc,unsigned int num,const char * word) +{ + if (word==0) + { + return 0; + } + unsigned int wordsize=strlen(word); + return InputParser_WordCompareNoCase(ipc,num,word,wordsize); +} + + + +/* + InputParser_WordCompare.. + Compares word (word) with token with number (num) , null terminating character is not required..! +*/ +unsigned char InputParser_WordCompare(struct InputParserC * ipc,unsigned int num,const char * word,unsigned int wordsize) +{ + if ( wordsize != InputParser_GetWordLength(ipc,num) ) + { + return 0; + } + /*if ( ipc->str_length <= ipc->tokenlist[num].token_start+wordsize ) { fprintf(stderr,"Erroneous input on InputParser_WordCompareNoCase leads out of array \n"); return 0; }*/ + + unsigned int i=0; + for ( i=0; istr[ipc->tokenlist[num].token_start+i]!=word[i]) + { + return 0; + } + } + return 1; +} + +/* + InputParser_WordCompareNoCase.. + Compares word (word) with token with number (num) , null terminating character is required , NO CASE SENSITIVITY..! +*/ +unsigned char InputParser_WordCompareAuto(struct InputParserC * ipc,unsigned int num,const char * word) +{ + if (word==0) + { + return 0; + } + unsigned int wordsize=strlen(word); + return InputParser_WordCompare(ipc,num,word,wordsize); +} + + +/* + InputParser_GetUpcaseWord.. + Same with InputParser_GetWord , the result is converted to upcase..! +*/ +unsigned int InputParser_GetUpcaseWord(struct InputParserC * ipc,unsigned int num,char * wheretostore,unsigned int storagesize) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0; + } + if ( storagesize < ipc->tokenlist[num].length+1 ) /* +1 gia to \0 */ return 0; + + unsigned int i=0; + for ( i = ipc->tokenlist[num].token_start; itokenlist[num].token_start+ipc->tokenlist[num].length; i++ ) + wheretostore[i-ipc->tokenlist[num].token_start] = toupper(ipc->str[i]); + + wheretostore[ipc->tokenlist[num].length] = 0; + + return ipc->tokenlist[num].length; +} + +/* + InputParser_GetUpcaseWord.. + Same with InputParser_GetWord , the result is converted to lowercase..! +*/ +unsigned int InputParser_GetLowercaseWord(struct InputParserC * ipc,unsigned int num,char * wheretostore,unsigned int storagesize) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0; + } + if ( storagesize < ipc->tokenlist[num].length+1 ) /* +1 gia to \0 */ return 0; + + unsigned int i=0; + for ( i = ipc->tokenlist[num].token_start; itokenlist[num].token_start+ipc->tokenlist[num].length; i++ ) + wheretostore[i-ipc->tokenlist[num].token_start] = tolower(ipc->str[i]); + + wheretostore[ipc->tokenlist[num].length] = 0; + + return ipc->tokenlist[num].length; +} + + +/* + InputParser_GetChar.. + Returns character (pos) from token (num)..! +*/ +char InputParser_GetWordChar(struct InputParserC * ipc,unsigned int num,unsigned int pos) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0; + } + + if ( pos>=ipc->tokenlist[num].token_start+ipc->tokenlist[num].length ) + { + return 0; + } + + return ipc->str[ipc->tokenlist[num].token_start + pos ]; +} + +/* + InputParser_GetWordInt.. + Same with InputParser_GetWord , if the result can be converted to a number , it returns this number + else 0 is returned +*/ +signed int InputParser_GetWordInt(struct InputParserC * ipc,unsigned int num) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0; + } + return Str2Int_internal(ipc->str,ipc->tokenlist[num].token_start,ipc->tokenlist[num].length); +} + + +/* + InputParser_GetWordFloat.. + Same with InputParser_GetWord , if the result can be converted to a float number , it returns this number + else 0.0 is returned +*/ +float InputParser_GetWordFloat(struct InputParserC * ipc,unsigned int num) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0.0; + } + if (ipc->tokenlist[num].length == 0 ) + { + return 0.0; + } + + char remember = 0; + char * string_segment = 0; + char * last_char_of_string_segment = 0; + unsigned char isLocallyAllocated = ipc->local_allocation; + + unsigned int tokenStart = ipc->tokenlist[num].token_start; + unsigned int tokenLength = ipc->tokenlist[num].length; + unsigned int stringLength = ipc->str_length; + + float return_value=0.0; + //Our string is a "string_segment" , and its last character ( which will be temporary become null ) is last_char_of_string_segment + + + if (!isLocallyAllocated) + { + string_segment = (char*) malloc( (tokenLength+1) * sizeof(char) ); + //memset(string_segment,0,(tokenLength+1) * sizeof(char)); + if (string_segment==0) + { + fprintf(stderr,"InputParser_GetWordFloat could not allocate memory to return float value , returning NaN \n"); + return NAN; + } + + strncpy(string_segment,ipc->str+tokenStart,tokenLength); + last_char_of_string_segment = string_segment + ipc->tokenlist[num].length; + } + else + { + string_segment = ipc->str+tokenStart; + last_char_of_string_segment = string_segment + ipc->tokenlist[num].length; + } + + if (tokenStart + tokenLength < stringLength) + { + remember = *last_char_of_string_segment; + *last_char_of_string_segment = (char) 0; //Temporarily convert the string segment to a null terminated string + } + //else we are on the last part of the string so no reason to do the whole 0 remember thing.. + + +#if USE_SCANF + //fprintf(stderr,"Using sscanf to parse %s \n",string_segment); +#warning "scanf without field width limits can crash with huge input data on libc versions older than 2.13-25. Add a field width specifier to fix this problem" + /* + Sample program that can crash: + #include + int main() + { int a; scanf("%i", &a); return 0; } + + To make it crash: + perl -e 'print "5"x2100000' | ./a.out| + */ + sscanf(string_segment,"%f",&return_value); +#else + //fprintf(stderr,"Using atof to parse `%s` \n",string_segment); + return_value=atof(string_segment); + //fprintf(stderr,"Returns `%0.6f` \n",return_value); +#endif // USE_SCANF + + + if ( tokenStart + tokenLength < stringLength) + { + *last_char_of_string_segment = remember; //Restore string.. + } + + if (!isLocallyAllocated) + { + free(string_segment); + } + + return return_value; +} + +/* + InputParser_GetChar.. + Returns total length of token (num)..! +*/ +unsigned int InputParser_GetWordLength(struct InputParserC * ipc,unsigned int num) +{ + if ( CheckWordNumOk(ipc,num) == 0 ) + { + return 0; + } + return ipc->tokenlist[num].length; +} + +/* + InputParser_SeperateWords.. + Seperates words in c string (inpt) to tokens using delimiters set-up at structure ipc and keeps result at structure ipc + if the c string will be erased before getting back the tokens you can set the keepcopy byte to 1 , this will allocate memory to + keep a copy of the string..! + the number returned is the total of tokens extracted! +*/ +int InputParser_SeperateWords(struct InputParserC * ipc,char * inpt,char keepcopy) +{ + + if (keepcopy==0) + { +#if WARN_ABOUT_INCORRECTLY_ALLOCATED_STACK_STRINGS + if (warningsAboutIncorrectlyAllocatedStackIssued==0) + { + fprintf(stderr,"Please note that feeding input parser with strings allocated on the stack it is generally a good idea to enable keepcopy\n"); + fprintf(stderr,"For example passing here a string allocated as char* hello = \"hello!\"; might lead to a segFault ( i.e. when calling InputParser_GetWordFloat ) \n"); + fprintf(stderr,"The correct way for allocating a string with in place processing is char hello[] = \"hello!\"; \n"); + fprintf(stderr,"Valgrind classifies these errors as \"Bad permissions for mapped region at address\" \n"); + ++warningsAboutIncorrectlyAllocatedStackIssued ; + } +#endif + + + } + + + + + + if (CheckIPCOk(ipc)==0) + { + return 0; + } + if ( inpt == 0 ) return 0; /* NULL INPUT -> NULL OUTPUT*/ + + unsigned int STRING_END = strlen(inpt) ; + int WORDS_SEPERATED = 0 , NEXT_SHOULD_NOT_BE_A_DELIMITER=1 , FOUND_DELIMETER ; /* Ignores starting ,,,,,string,etc*/ + + if ( STRING_END == 0 ) + { + return 0; /* NULL INPUT -> NULL OUTPUT pt 2*/ + } + + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> + COPY STRING ( OR POINTER ) TO IPC STRUCTURE*/ + if ( keepcopy == 1 ) /* IF WE HAVE ALREADY ALLOCATED A STRING TO ipc->str , we should free it to prevent memory leaks!*/ + { + if (ipc->local_allocation == 1) + { + if (ipc->str!=0) + { + /* ipc->str contains a previous value!*/ + free(ipc->str); + ipc->local_allocation = 0; + } + } + ipc->str = (char * ) malloc( sizeof(char) * (STRING_END+1) ); + memset(ipc->str,0,sizeof(char) * (STRING_END+1)); + ipc->local_allocation = 1; + strncpy( ipc->str , inpt , STRING_END ) ; + } + else + { + ipc->str = inpt; + } + + ipc->str_length = STRING_END; + /* COPY STRING ( OR POINTER ) TO IPC STRUCTURE + >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>*/ + + + unsigned int i,z; + ipc->tokens_count = 0 , ipc->tokenlist[0].token_start=0; + for (i=0; icur_delimeter_count; z++) + { + + if ( inpt[i] == ipc->delimeters[z] ) + { + FOUND_DELIMETER = 1; + if (NEXT_SHOULD_NOT_BE_A_DELIMITER==0) + { + ipc->tokenlist[ipc->tokens_count].length = i - ipc->tokenlist[ipc->tokens_count].token_start; + ipc->tokens_count+=1; + ipc->tokenlist[ipc->tokens_count].token_start = i+1; + WORDS_SEPERATED+=1; + break; + } + else + { + ipc->tokenlist[ipc->tokens_count].token_start=i+1; + } + } + } + + if (FOUND_DELIMETER == 0 ) NEXT_SHOULD_NOT_BE_A_DELIMITER=0; + else if (FOUND_DELIMETER == 1 ) NEXT_SHOULD_NOT_BE_A_DELIMITER=1; + + } + + if (NEXT_SHOULD_NOT_BE_A_DELIMITER==0) + { + ipc->tokenlist[ipc->tokens_count].length = i - ipc->tokenlist[ipc->tokens_count].token_start; + ipc->tokens_count+=1; + ipc->tokenlist[ipc->tokens_count].token_start = i+1; + WORDS_SEPERATED+=1; + + } + else + ipc->tokenlist[ipc->tokens_count].length = i - ipc->tokenlist[ipc->tokens_count].token_start; + + return WORDS_SEPERATED; +} + +/* + Same with InputParser_SeperateWords it does the (char *) type cast +*/ +int InputParser_SeperateWordsCC(struct InputParserC * ipc,const char * inpt,char keepcopy) +{ + return InputParser_SeperateWords(ipc,(char * )inpt,keepcopy); +} + +/* + Same with InputParser_SeperateWords it does the (char *) type cast +*/ +int InputParser_SeperateWordsUC(struct InputParserC * ipc,unsigned char * inpt,char keepcopy) +{ + return InputParser_SeperateWords(ipc,(char * )inpt,keepcopy); +} diff --git a/animation/MocapNET-kasisnu/dependencies/InputParser/InputParser_C.h b/animation/MocapNET-kasisnu/dependencies/InputParser/InputParser_C.h new file mode 100644 index 0000000..e900f55 --- /dev/null +++ b/animation/MocapNET-kasisnu/dependencies/InputParser/InputParser_C.h @@ -0,0 +1,392 @@ +/** @file InputParser_C.h + * @brief A very handy string tokenizer for C this basically a swiss army knife kind code segment that can be used wherever we get strings + * and we want to seperate them. This code is used in many of my projects including and not limited to + * https://github.com/AmmarkoV/RoboVision + * https://github.com/AmmarkoV/FlashySlideshows + * https://github.com/AmmarkoV/RGBDAcquisition + * https://github.com/AmmarkoV/AmmarServer + * Basic usage is the following + * + * struct InputParserC * ipc=0; + * ipc = InputParser_Create(512,5); //We want to separte lines of Max 512 different strings seperated using 5 delimiters + * InputParser_SetDelimeter(ipc,0,' '); //We want to seperate spaces + * + * char word[512]; + * int numberOfWords = InputParser_SeperateWords(ipc,"zero,one,2,three(four)",1); + * for (int i=0; i +#include +#include +#include + +/** @brief Controls the use of scanf for getting back floats , + scanf without field width limits can crash with huge input data on libc versions older than 2.13-25. + so this is better turned of and atof is used to do the float conversion*/ +#define USE_SCANF 0 + +/** + * @brief A struct that contains the token list + */ +struct tokens +{ + /* START MEANS THE FIRST CHARACTER TO READ..! + LENGTH MEANS total characters to be read + i.e. a _ S _ A _ M _ P _ L _ E + 0 1 2 3 4 5 6 + this token starts from 0 and has 7 characters length*/ + unsigned int token_start; + unsigned int length; +}; + +/** + * @brief Guard Byte to make sure that there is no overflow + */ +struct guard_byte +{ + unsigned int checksum; +}; + +/** + * @brief The structure that holds all of the Input Parsing Context + */ +struct InputParserC +{ + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + struct guard_byte guardbyte1; + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + + unsigned int str_length; + unsigned char local_allocation; + char * str; /* String to process */ + + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + struct guard_byte guardbyte2; + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + unsigned short cur_container_count; + unsigned short max_container_count; + char *container_start; + char *container_end; + + unsigned short cur_delimeter_count; + unsigned short max_delimeter_count; + char *delimeters; + + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + struct guard_byte guardbyte3; + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + + /* RESULT */ + unsigned int tokens_max; + unsigned int tokens_count; + struct tokens* tokenlist; + + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ + struct guard_byte guardbyte4; + /* >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> */ +}; + + +/** + * @brief Get Back a String containing the version of the InputParser + * @retval Pointer to a C String that contains the version of InputParser + */ +char * InputParserC_Version(); + +/** + * @brief Clear any non-character characters from string + * @ingroup InputParser + * @param String Input/Output + * @param Length of String + * @retval 1 if success , 0 if not + */ +int InputParser_ClearNonCharacters(char * inpt , unsigned int length); + +/** + * @brief Trim Starting Characters preceeding string + * @ingroup InputParser + * @param String Input/Output + * @param Length of String + * @param Character to Trim + * @retval 1 if success , 0 if not + */ +int InputParser_TrimCharactersStart(char * inpt , unsigned int length,char what2trim); + + +/** + * @brief Trim Starting Characters after string + * @ingroup InputParser + * @param String Input/Output + * @param Length of String + * @param Character to Trim + * @retval 1 if success , 0 if not + */ +int InputParser_TrimCharactersEnd(char * inpt , unsigned int length,char what2trim); + + +/** + * @brief This call will remove all characters specified from the string + * @ingroup InputParser + * @param String Input/Output + * @param Length of String + * @param Character to Trim + * @retval 1 if success , 0 if not + */ +int InputParser_TrimCharacters(char * inpt , unsigned int length,char what2trim); + + +/** + * @brief This call will setup the default delimiters that are typically used , these are '\n' ',' '=' '(' and ')' + * these can be also read by InputParser_GetDelimeter or changed at any time using InputParser_SetDelimeter + * @ingroup InputParser + * @param InputParser Context ( needs to be created using InputParser_Create ) + */ +void InputParser_DefaultDelimeters(struct InputParserC * ipc); + + +/** + * @brief This call will change the value of a delimiter so that it will also act as a tokenization split character + * @ingroup InputParser + * @param InputParser Context ( needs to be created using InputParser_Create ) + * @param Number of delimiter we want to set ( has to be less than the max_delimiter_count value we gave in InputParser_Create ) + * @param New Value for the delimiter we want to set + */ +void InputParser_SetDelimeter(struct InputParserC * ipc,int num,char tmp); + +/** + * @brief Get Back the characters that are used as delimiters + * @ingroup InputParser + * @param InputParser Context ( needs to be created using InputParser_Create ) + * @param Number of delimiter we want to set ( has to be less than the max_delimiter_count value we gave in InputParser_Create ) + * @retval Character used as delimiter , or Null if incorrect delimter number requested + */ +char InputParser_GetDelimeter(struct InputParserC * ipc,int num); + + + +/** + * @brief Create an InputParser Structure and Context that can be used to tokenize strings + * @ingroup InputParser + * @param The maximum number of strings/tokens to be returned as output from a SeperateWords command + * @param The maximum number of delimters used typically 5 for the defaults '\n' ',' '=' '(' and ')' + * @retval InputParser Context , or Null if there is an error + */ +struct InputParserC * InputParser_Create(unsigned int max_string_count,unsigned int max_delimiter_count); + + +/** + * @brief Destroy an InputParser Structure and Context and safely free all of the memory it might have allocated + * @ingroup InputParser + * @param InputParser Context ( needs to be created using InputParser_Create ) + */ +void InputParser_Destroy(struct InputParserC * ipc); + + +/** + * @brief Perform an internal integrity check + * @ingroup InputParser + * @param InputParser Context to be checked ( needs to be created using InputParser_Create ) + * @retval 1=Success,0=Error + */ +unsigned char InputParser_SelfCheck(struct InputParserC * ipc); + + + + +/** + * @brief Retrieve the number of arguments seperated from the last InputParser_SeperateWords call + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @retval Number of arguments tokenized , 0=No arguments/Error + */ +unsigned int InputParser_GetNumberOfArguments(struct InputParserC * ipc); + + +/** + * @brief Check if a word ( InputParser_GetWord ) is empty after issuing a InputParser_SeperateWords call + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @retval 1=Success,0=Error + */ +unsigned int InputParser_IsEmptyWord(struct InputParserC * ipc,unsigned int num); + + + +/** + * @brief Checks if word with number num has allocated space in memory ( see InputParser_GetWord ) + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @retval 1=Success,0=Error + */ +unsigned char CheckWordNumOk(struct InputParserC * ipc,unsigned int num); + + +/** + * @brief Get a specific character of a Word ( instead of the whole string that would be returned with InputParser_GetWord ) + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @param Character of word we want to get back + * @retval Character we requested or null if there is an error + */ +char InputParser_GetWordChar(struct InputParserC * ipc,unsigned int num,unsigned int pos); + +/** + * @brief Compare two words without case sensitivity and return wether they match or not..! + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @param String that we want to compare against + * @param Length of String that we want to compare against + * @retval 1=Match,0=Strings are different + */ +unsigned char InputParser_WordCompareNoCase(struct InputParserC * ipc,unsigned int num,const char * word,unsigned int wordsize); + +/** + * @brief Same as InputParser_WordCompareNoCase but supposes word is null terminated and auto-counts its length + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @param String that we want to compare against ( that should be null terminated ) + * @retval 1=Match,0=Strings are different + */ +unsigned char InputParser_WordCompareNoCaseAuto(struct InputParserC * ipc,unsigned int num,const char * word); + +/** + * @brief Compare two words with case sensitivity and return wether they match or not..! + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @param String that we want to compare against ( that should be null terminated ) + * @retval 1=Match,0=Strings are different + */ +unsigned char InputParser_WordCompare(struct InputParserC * ipc,unsigned int num,const char * word,unsigned int wordsize); + +/** + * @brief Same as InputParser_WordCompare but supposes word is null terminated and auto-counts its length + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to check + * @param String that we want to compare against ( that should be null terminated ) + * @retval 1=Match,0=Strings are different + */ +unsigned char InputParser_WordCompareAuto(struct InputParserC * ipc,unsigned int num,const char * word); + +/** + * @brief Get back a string ( token ) after performing InputParser_SeperateWords + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to get + * @param Pointer to a C String that will accomodate the output + * @param Size of Pointer provided , should be big enough for the whole string plus its null terminator + * @retval 1=Sucess,0=Failure + */ +unsigned int InputParser_GetWord(struct InputParserC * ipc,unsigned int num,char * wheretostore,unsigned int storagesize); + +/** + * @brief Get back an uppercase string ( token ) after performing InputParser_SeperateWords + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to get + * @param Pointer to a C String that will accomodate the output + * @param Size of Pointer provided , should be big enough for the whole string plus its null terminator + * @retval 1=Sucess,0=Failure + */ +unsigned int InputParser_GetUpcaseWord(struct InputParserC * ipc,unsigned int num,char * wheretostore,unsigned int storagesize); + +/** + * @brief Get back a lowercase string ( token ) after performing InputParser_SeperateWords + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to get + * @param Pointer to a C String that will accomodate the output + * @param Size of Pointer provided , should be big enough for the whole string plus its null terminator + * @retval 1=Sucess,0=Failure + */ +unsigned int InputParser_GetLowercaseWord(struct InputParserC * ipc,unsigned int num,char * wheretostore,unsigned int storagesize); + + +/** + * @brief Get back a signed integer ( token ) after performing InputParser_SeperateWords + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to get + * @retval The value in the form of a number of token with number num , if the token is not a number returns null + */ +signed int InputParser_GetWordInt(struct InputParserC * ipc,unsigned int num); + + +/** + * @brief Get back a float ( token ) after performing InputParser_SeperateWords + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to get + * @retval The value in the form of a floating precision number of token , if the token is not a number returns NaN + */ +float InputParser_GetWordFloat(struct InputParserC * ipc,unsigned int num); + + +/** + * @brief Get back The Length of a token so that we can allocate a proper buffer and then call InputParser_GetWord + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to get + * @retval The length of the token string size , 0=Failure to access token num + */ +unsigned int InputParser_GetWordLength(struct InputParserC * ipc,unsigned int num); + + +/** + * @brief Split input string in tokens, this is the main functionality of this library..! + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to split to tokens + * @param 1=Keep a copy of the input string, this is safer in case the input string changes from another thread etc. , 0=Zero copy splitting that has more chances of going wrong + * @retval The number of tokens split,0=Failure + */ +int InputParser_SeperateWords(struct InputParserC * ipc,char * inpt,char keepcopy); + +/** + * @brief Same as InputParser_SeperateWords but accepts const char strings as input + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to split to tokens + * @param 1=Keep a copy of the input string, this is safer in case the input string changes from another thread etc. , 0=Zero copy splitting that has more chances of going wrong + * @retval The number of tokens split,0=Failure + */ +int InputParser_SeperateWordsCC(struct InputParserC * ipc,const char * inpt,char keepcopy); + +/** + * @brief Same as InputParser_SeperateWords but accepts unsigned char strings as input + * @ingroup InputParser + * @param InputParser Context to be used ( needs to be created using InputParser_Create ) + * @param Word that we want to split to tokens + * @param 1=Keep a copy of the input string, this is safer in case the input string changes from another thread etc. , 0=Zero copy splitting that has more chances of going wrong + * @retval The number of tokens split,0=Failure + */ +int InputParser_SeperateWordsUC(struct InputParserC * ipc,unsigned char * inpt,char keepcopy); + + +#ifdef __cplusplus +} +#endif + + +#endif + diff --git a/animation/MocapNET-kasisnu/dependencies/InputParser/README b/animation/MocapNET-kasisnu/dependencies/InputParser/README new file mode 100644 index 0000000..4fd6e8a --- /dev/null +++ b/animation/MocapNET-kasisnu/dependencies/InputParser/README @@ -0,0 +1,2 @@ +This code actually has it's own repository ( https://github.com/AmmarkoV/InputParser ) and also included in the AmmarServer dependency ( https://github.com/AmmarkoV/AmmarServer/tree/master/src/InputParser ) +However to simplify build process it is also included as standalone here. diff --git a/animation/MocapNET-kasisnu/dependencies/README.md b/animation/MocapNET-kasisnu/dependencies/README.md new file mode 100644 index 0000000..84a5f0e --- /dev/null +++ b/animation/MocapNET-kasisnu/dependencies/README.md @@ -0,0 +1,3 @@ +This is the dependencies subdirectory + +At minimum here there should be a libtensorflow , RGBDAcquisition and maybe an opencv-3.2.0 diff --git a/animation/MocapNET-kasisnu/docker/Dockerfile b/animation/MocapNET-kasisnu/docker/Dockerfile new file mode 100644 index 0000000..5aafb45 --- /dev/null +++ b/animation/MocapNET-kasisnu/docker/Dockerfile @@ -0,0 +1,54 @@ +FROM nvidia/cuda:11.6.1-devel-ubuntu20.04 + + +ENV DEBIAN_FRONTEND=noninteractive + +RUN mkdir /python_install +RUN mkdir /model_code + +WORKDIR /python_install +RUN \ + echo "**** packages installation ****" \ + && apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub \ + && apt-get update && apt-get install -y \ + vim \ + build-essential \ + cmake \ + libopencv-dev \ + libjpeg-dev \ + libpng-dev \ + libglew-dev \ + libpthread-stubs0-dev \ + git \ + virtualenv \ + time \ + sudo \ + wget \ + nano \ + curl \ + software-properties-common \ + rsync \ + apt-utils \ + ffmpeg \ + unzip + +RUN curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | bash \ + && apt-get install git-lfs \ + && git lfs install + +RUN add-apt-repository ppa:deadsnakes/ppa + +RUN apt-get update +RUN apt-get install -y python3.10 python3.10-venv python3.10-dev python3.10-full python3.10-distutils + +# COPY requirements.txt . +# Install Python dependencies +RUN python3.10 -m venv python \ + && python/bin/pip install --upgrade pip wheel setuptools \ + && . python/bin/activate + +WORKDIR /model_code +COPY . /model_code/MocapNET +RUN cd /model_code/MocapNET && ./initialize.sh +# RUN cd /home/${user_name}/workspace && git clone https://github.com/FORTH-ModelBasedTracker/MocapNET && cd MocapNET && ./initialize.sh --collab + diff --git a/animation/MocapNET-kasisnu/docker/build_and_deploy.sh b/animation/MocapNET-kasisnu/docker/build_and_deploy.sh new file mode 100755 index 0000000..a8c97b1 --- /dev/null +++ b/animation/MocapNET-kasisnu/docker/build_and_deploy.sh @@ -0,0 +1,44 @@ +#!/usr/bin/env bash +# This script builds and runs a docker image for local use. + +#Although I dislike the use of docker for a myriad of reasons, due needing it to deploy on a particular machine +#I am adding a docker container builder for the repository to automate the process + + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" +cd .. +REPOSITORY=`pwd` + +cd "$DIR" + +NAME="mocapnet" +dockerfile_pth="$DIR" +mount_pth="$REPOSITORY" + +# update tensorflow image +docker pull tensorflow/tensorflow:latest-gpu + +# build and run tensorflow +docker build \ + -t $NAME \ + $dockerfile_pth \ + --build-arg user_id=$UID + +docker run -d \ + --gpus all \ + --shm-size 8G \ + -it \ + --name $NAME-container \ + -v $mount_pth:/home/user/workspace \ + $NAME + + +docker ps -a + +OUR_DOCKER_ID=`docker ps -a | grep mocapnet | cut -f1 -d' '` +echo "Our docker ID is : $OUR_DOCKER_ID" +echo "Attaching it using : docker attach $OUD_DOCKER_ID" +docker attach $OUR_DOCKER_ID + +exit 0 diff --git a/animation/MocapNET-kasisnu/initialize.sh b/animation/MocapNET-kasisnu/initialize.sh new file mode 100755 index 0000000..f916aa9 --- /dev/null +++ b/animation/MocapNET-kasisnu/initialize.sh @@ -0,0 +1,119 @@ +#!/bin/bash + +set -ex +DIR=`pwd` +cd "$DIR" + +ORIG_DIR=`pwd` + +#Simple dependency checker that will apt-get stuff if something is missing +# sudo apt-get install build-essential cmake libopencv-dev libjpeg-dev libpng-dev libglew-dev libpthread-stubs0-dev +SYSTEM_DEPENDENCIES="wget git build-essential cmake libopencv-dev libjpeg-dev libpng-dev libglew-dev libpthread-stubs0-dev" +#------------------------------------------------------------------------------ +for REQUIRED_PKG in $SYSTEM_DEPENDENCIES +do +PKG_OK=$(dpkg-query -W --showformat='${Status}\n' $REQUIRED_PKG|grep "install ok installed") +echo "Checking for $REQUIRED_PKG: $PKG_OK" +if [ "" = "$PKG_OK" ]; then + + echo "No $REQUIRED_PKG. Setting up $REQUIRED_PKG." + + #If this is uncommented then only packages that are missing will get prompted.. + #sudo apt-get --yes install $REQUIRED_PKG + + #if this is uncommented then if one package is missing then all missing packages are immediately installed.. + sudo apt-get install $SYSTEM_DEPENDENCIES + break +fi +done +#------------------------------------------------------------------------------ + + + + + +cd "$DIR" +if [ -f dependencies/RGBDAcquisition/README.md ]; then + echo "RGBDAcquisition appears to already exist .." + #cd "$DIR/dependencies/RGBAcquisition" + #echo "We can make sure that it is up to date to avoid issues like https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/48.." + #echo "However someone might not want to update the code.. what to do.." + #git pull + #cd "$DIR" +else + cd "$DIR/dependencies" + git clone https://github.com/AmmarkoV/RGBDAcquisition + cd RGBDAcquisition + # This package has no releases so we just picked a "good" checkout + git checkout b752895 + mkdir build + cd build + cmake .. + #We dont need to make it + #make + cd ../opengl_acquisition_shared_library/opengl_depth_and_color_renderer + mkdir build + cd build + cmake .. + #We dont need to make it + #make + cd "$DIR" + #Also retrieve Renderer + #ln -s dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/Renderer +fi + +if [ -f /usr/local/lib/libtensorflow.so ]; then + echo "Found a system wide tensorflow installation, not altering anything" +elif [ -f dependencies/libtensorflow/lib/libtensorflow.so ]; then + echo "Found a local tensorflow installation, not altering anything" +else + echo "Tensorflow not found, downloading Tensorflow 2.3.1 with GPU support.." + if [ ! -f dependencies/libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz ]; then + cd "$DIR/dependencies" + wget https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz + # mkdir libtensorflow +# tar -C libtensorflow -xzf + #echo "Please give me sudo permissions to install Tensorflow $TENSORFLOW_VERSION C Bindings.." + tar -C /usr/local -xzf libtensorflow-gpu-linux-x86_64-2.3.1.tar.gz + else + echo "Tensorflow 2.3.1 GPU tarball already downloaded." + fi +fi + + +cd "$DIR" +cd src/python/mnet4 +rm -rf BVH/ +ln -s ../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Applications/BVHTester/ BVH +cd BVH +./makeLibrary.sh + +cd .. +#Install rest of python stuff.. +./setup.sh + + + + +#Now that we have everything lets build.. +echo "Now to try and build MocapNET.." +cd "$DIR" + +#if there is an already existing build directory and you called initialize.sh +#it will get initialized as well.. (https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/19) +if [ -d "build" ]; then + echo "Build directory already exists, but since the initialize.sh script was called " + echo "this means the user wants to also initialize the build directory, so doing this" + echo "to prevent problems like https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/19" + rm -rf build/ +fi + +mkdir build +cd build +cmake .. +make +cd "$DIR" + + + +exit 0 diff --git a/animation/MocapNET-kasisnu/license.txt b/animation/MocapNET-kasisnu/license.txt new file mode 100644 index 0000000..6e52ed9 --- /dev/null +++ b/animation/MocapNET-kasisnu/license.txt @@ -0,0 +1,52 @@ +FORTH & END-USER LICENSE AGREEMENT +for the +FORTH-ICS MOCAPNET LIBRARY + +PLEASE READ THE FOLLOWING CAREFULLY BEFORE USING: +By using this hereby licensed to You product you expressly acknowledge and agree, on your own behalf as an individual, and on behalf of your employer or another entity which has not yet done so (collectively "You" or "Your"), that You are entering into a legal binding license agreement with FORTH ("FORTH"), have full authority to bind such employer or other entity with this License Agreement, and have understood and agree to comply with the terms & conditions of this License Agreement below (hereafter "Terms", "License Agreement"). You hereby waive any applicable rights to requirements for an original (non-electronic) signature or delivery or retention of non-electronic records, to the extent permitted under applicable law, customs & codes of practice. + +1. Scope of this License Agreement +Subject to these Terms, FORTH hereby grants to You a limited, worldwide (subject to applicable export restrictions), non-exclusive, personal, non-transferable, revocable, license to use the FORTH proprietary MOCAPNET LIBRARY that was developed at and by FORTH (under the supervision of Prof. A. Argyros, at FORTH-ICS, Computational Vision and Robotics Lab) and is solely owned by FORTH and known as FORTH-ICS MOCAPNET LIBRARY" (referred to hereafter as "LIBRARY") only for "personal" research and/or academic, non-profit, non-commercial use, pursuant to this License Agreement with FORTH. + +2. Restrictions +a. You agree to use this Licensed LIBRARY only as permitted herein; +b. 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If any provision of these Terms shall be held to be invalid, illegal or unenforceable, such holding shall in no way affect or impair the remaining provisions, and the parties shall use their reasonable efforts to substitute valid and enforceable provisions which have the closest meaning and purpose as those so held. +e. Entire Agreement; Variation; Waiver; Counterparts. These Terms set forth the entire understanding of the parties, which supersedes and merges all prior proposals, understandings and all other agreements oral and written between the parties relating to the subject matter herein and may not be modified except in a writing executed by both parties or in new terms posted here or notified to You. Failure of a party to enforce any of the provisions of these Terms will not be construed to be a waiver of the provision, and no waiver of any rights hereunder shall be deemed to be a waiver of the same or other right on any other occasion. + +" Copyright FORTH 2019. All rights reserved. + diff --git a/animation/MocapNET-kasisnu/mocapnet4.ipynb b/animation/MocapNET-kasisnu/mocapnet4.ipynb new file mode 100644 index 0000000..ccb34a9 --- /dev/null +++ b/animation/MocapNET-kasisnu/mocapnet4.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click this play button to setup MocapNET v4!\n", + "\n", + "#This is the Total Capture version that handles body, hands, gaze, face!\n", + "#It also has been rewritten from scratch in Python for your convenience.\n", + "#If you deploy this on your PC, run : jupyter notebook mocapnet4.ipynb\n", + "# and remove --collab from the setup.sh invocation 2 lines below..!\n", + "import os\n", + "if (os.path.isfile(\"MocapNET.py\")):\n", + " !git pull\n", + " exit\n", + "!git clone -b mnet4 https://github.com/FORTH-ModelBasedTracker/MocapNET.git\n", + "!MocapNET/src/python/mnet4/setup.sh --collab\n", + "os.chdir(\"MocapNET/src/python/mnet4\")\n", + "print(\"MocapNET setup is finished, you can run the next cell now..\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click to get a video from the internet and then track it ( e.g. shuffle.webm )\n", + "\n", + "#You can also upload your own by clicking the Files icon and using the menu on the left\n", + "#You need to put files in the /content/MocapNET/src/python/mnet4/ directory\n", + "\n", + "#Make sure we are at the correct directory\n", + "import os\n", + "if (not os.path.isfile(\"mediapipeHolisticWebcamMocapNET.py\")):\n", + " os.chdir(\"MocapNET/src/python/mnet4\")\n", + "\n", + "!wget http://ammar.gr/mocapnet/shuffle.webm -O shuffle.webm\n", + "\n", + "#Analyze the file shuffle.web through MediaPipe 2D + MocapNET 3D Pose Estimation!\n", + "!(python3 -m mediapipeHolisticWebcamMocapNET --from shuffle.webm --ik 0.001 99 99 --smooth 60 10 --all --save --plot --headless 2> /dev/null)\n", + "print(\"MocapNET video input processing finished, you can run the next cells now to see the results..\")\n", + "\n", + "#Outputs are :\n", + "#livelastRun3DHiRes.mp4 | A video showing the regressed output overlayed on RGB\n", + "#livelastPlot3DHiRes.mp4| A video plot of the retrieved BVH degrees of freedom\n", + "#out.bvh | the extracted BVH file \n", + "#2d_out.csv | Input 2D joints used by MocapNET as a CSV file\n", + "#3d_out.csv | Output 3D joints produced by MocapNET as a CSV file\n", + "#bvh_out.csv | Output BVH angles produced by MocapNET as a CSV file\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click to run a sign-language dataset without mediapipe from a dumped Openpose/JSON/CSV source\n", + "\n", + "#Make sure we are at the correct directory\n", + "import os\n", + "if (not os.path.isfile(\"csvNET.py\")):\n", + " os.chdir(\"MocapNET/src/python/mnet4\")\n", + "\n", + "#Download a single sign (con0014) from SIGNUM\n", + "!wget http://ammar.gr/datasets/signumtest.zip -O signumtest.zip\n", + "!unzip -o signumtest.zip\n", + "\n", + "#Analyze the file con0014/2dJoints_v1.4.csv through MediaPipe 2D + MocapNET 3D Pose Estimation!\n", + "!(python3 -m csvNET --from con0014/2dJoints_v1.4.csv --ik 0.001 99 99 --upperbody --reye --mouth --hands --save --plot --headless 2> /dev/null)\n", + "print(\"MocapNET video input processing finished, you can run the next cells now to see the results..\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click to see the generated BVH angle plot of the last MocapNET run!\n", + "from IPython.display import Video\n", + "import os\n", + "if (not os.path.isfile(\"livelastPlot3DHiRes.mp4\")):\n", + " print(\"Please execute the previous cells and wait for them to complete before seeing this visualization!\")\n", + " exit\n", + "\n", + "Video(\"livelastPlot3DHiRes.mp4\",embed=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click to see the generated visualization of the last MocapNET run!\n", + "from IPython.display import Video\n", + "import os\n", + "if (not os.path.isfile(\"livelastRun3DHiRes.mp4\")):\n", + " print(\"Please execute the previous cells and wait for them to complete before seeing this visualization!\")\n", + " exit\n", + "\n", + "Video(\"livelastRun3DHiRes.mp4\",embed=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click to download the generated BVH/3D files of the last MocapNET run!\n", + "import os \n", + "from google.colab import files\n", + "if (not os.path.isfile(\"out.bvh\")) or (not os.path.isfile(\"2d_out.csv\")) or (not os.path.isfile(\"bvh_out.csv\")) or (not os.path.isfile(\"3d_out.csv\")):\n", + " print(\"Please execute the previous cells and wait for them to complete before downloading output!\")\n", + "else:\n", + " print(\"Download Output Files!\")\n", + " files.download(\"out.bvh\")\n", + " files.download(\"2d_out.csv\")\n", + " files.download(\"3d_out.csv\")\n", + " files.download(\"bvh_out.csv\")\n", + "#To render the 3D face get Blender from https://www.blender.org/\n", + "#Download http://ammar.gr/mocapnet/mnet4/face.blend and open it using Blender\n", + "#Download http://ammar.gr/mocapnet/mnet4/headerWithHeadAndOneMotion.bvh and open it using Blender at a 0.01 scale\n", + "#Run The Loaded Python Script\n", + "#Click on the armature and on the menu right click the orange rectangle\n", + "#Select headerWithHeadAndOneMotion as Source BVH\n", + "#Select newgirl as Target Obj\n", + "#Select a directory as a target for generated dataset\n", + "#Select the path to the regressed bvh_out.csv to load pre-generated dataset\n", + "#Click Just Render CSV Dataset\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#<----- Click to attempt MocapNET live demo by streaming your camera to Google Colab\n", + "#EXPERIMENTAL, performance is impacted because of low image throughput!\n", + "\n", + "import cv2\n", + "import os\n", + "from colabStream import init_camera,take_frame,show_frame\n", + "\n", + " \n", + "from mediapipeHolisticWebcamMocapNET import MediaPipeHolistic\n", + "from MocapNET import easyMocapNETConstructor\n", + "from folderStream import resize_with_padding\n", + "\n", + "# Create instances of MediaPipeHolistic and MocapNET\n", + "mp_holistic = MediaPipeHolistic(doMediapipeVisualization=False)\n", + "mnet = easyMocapNETConstructor(\n", + " engine = \"onnx\",\n", + " doProfiling = False,\n", + " multiThreaded = False,\n", + " doHCDPostProcessing = True,\n", + " hcdLearningRate = 0.001,\n", + " hcdEpochs = 99,\n", + " hcdIterations = 99,\n", + " bvhScale = 1.0,\n", + " doBody = True,\n", + " doFace = False,\n", + " doREye = True,\n", + " doMouth = True,\n", + " doHands = True,\n", + " addNoise = 0.0\n", + ")\n", + "\n", + "\n", + "# init JavaScript code\n", + "init_camera()\n", + "\n", + "while True:\n", + " try:\n", + " image = take_frame()\n", + " \n", + " image = resize_with_padding(image,1280,720)\n", + "\n", + " # Perform image processing with MediaPipeHolistic\n", + " mocapNETInput, annotated_image = mp_holistic.convertImageToMocapNETInput(image)\n", + "\n", + " # Perform 3D joint prediction with MocapNET\n", + " mocapNET3DOutput = mnet.predict3DJoints(mocapNETInput)\n", + "\n", + " # Visualize the results on the image\n", + " #image_with_results = Image.fromarray(cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB))\n", + " \n", + " from MocapNETVisualization import visualizeMocapNETEnsemble\n", + " image,plotImage = visualizeMocapNETEnsemble(mnet,annotated_image,plotBVHChannels=False)\n", + " \n", + " show_frame(annotated_image) # it replace previous image\n", + " \n", + " except Exception as err:\n", + " print('Exception:', err)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#D/L and compile a different version of MocapNET and evaluate it against CMUBVH test dataset\n", + "#Running this will take a while, but it is usefult to compare different trained MocapNET Models!\n", + "import os\n", + "\n", + "#Download Test Set!\n", + "if (not os.path.isfile(\"dataset/generated/bvh_body_all_test.csv\")):\n", + " print(\"Downloading test set!\")\n", + " os.system(\"wget http://ammar.gr/datasets/testBody.zip && unzip testBody.zip\")\n", + "\n", + "#Download a different MocapNET build\n", + "!python3 -m getModelFromDatabase --get 319 #<- change number to try a different version\n", + "\n", + "#Do Evaluation\n", + "!python3 -m evaluateMocapNET --config dataset/body_configuration.json --all body --skip 5 --engine onnx > lastEvaluationLog.txt\n", + "!tail -n 30 lastEvaluationLog.txt " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\"\"\"\n", + "\n", + "This library is provided under the FORTH license\n", + "https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/license.txt\n", + "\n", + "If you use this version of MocapNET for your research please consider citing : \n", + "\n", + "@inproceedings{Qammaz2023b,\n", + " author = {Qammaz, Ammar and Argyros, Antonis},\n", + " title = {A Unified Approach for Occlusion Tolerant 3D Facial Pose Capture and Gaze Estimation using MocapNETs},\n", + " booktitle = {International Conference on Computer Vision Workshops (AMFG 2023 - ICCVW 2023), (to appear)},\n", + " publisher = {IEEE},\n", + " year = {2023},\n", + " month = {October},\n", + " address = {Paris, France},\n", + " projects = {VMWARE,I.C.HUMANS},\n", + " pdflink = {http://users.ics.forth.gr/ argyros/mypapers/2023_10_AMFG_Qammaz.pdf}\n", + "}\n", + "\n", + "@inproceedings{Qammaz2021,\n", + " author = {Qammaz, Ammar and Argyros, Antonis A},\n", + " title = {Towards Holistic Real-time Human 3D Pose Estimation using MocapNETs},\n", + " booktitle = {British Machine Vision Conference (BMVC 2021)},\n", + " publisher = {BMVA},\n", + " year = {2021},\n", + " month = {November},\n", + " projects = {I.C.HUMANS},\n", + " videolink = {https://www.youtube.com/watch?v=aaLOSY_p6Zc}\n", + "}\n", + "\"\"\" " + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/animation/MocapNET-kasisnu/revert.sh b/animation/MocapNET-kasisnu/revert.sh new file mode 100755 index 0000000..2d1b896 --- /dev/null +++ b/animation/MocapNET-kasisnu/revert.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +#Reminder , to get a single file out of the repo use "git checkout -- path/to/file.c" + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +#All OpenGL/BVH code relies on RGBDAcquisition code so we need to sync this as well + + +if [ -f dependencies/RGBDAcquisition/README.md ]; then +echo "RGBDAcquisition appears to exist, reverting it .." +cd dependencies/RGBDAcquisition +git reset --hard HEAD +git pull origin master +cd "$DIR" +else +echo "Could not find RGBDAcquisition, please rerun the initialize.sh script .." +fi + + + +if [ -f dependencies/AmmarServer/README.md ]; then +echo "AmmarServer appears to exist, reverting it .." +cd dependencies/AmmarServer +git reset --hard HEAD +git pull origin master +cd "$DIR" +fi + + +#Now sync rest of code +cd "$DIR" +git reset --hard HEAD +git pull origin master + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/createRandomHandDataset.sh b/animation/MocapNET-kasisnu/scripts/createRandomHandDataset.sh new file mode 100755 index 0000000..72c1b5c --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/createRandomHandDataset.sh @@ -0,0 +1,410 @@ +#!/bin/bash + + +# Ansi color code variables +red="\e[0;91m" +blue="\e[0;94m" +expand_bg="\e[K" +blue_bg="\e[0;104m${expand_bg}" +red_bg="\e[0;101m${expand_bg}" +green_bg="\e[0;102m${expand_bg}" +green="\e[0;92m" +white="\e[0;97m" +bold="\e[1m" +uline="\e[4m" +reset="\e[0m" + + +function checkForError +{ +RESULT=$? +if [ $RESULT -eq 0 ]; then + echo "Successfully ran the hand pose estimation" +else + echo -e "${red_bg}TERMINATING SCRIPT${reset}" + echo -e "${red}Failed running $1${reset}" + exit 1 +fi +} + + +#We move to the correct directory.. +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" +cd .. + + +if [ $# -eq 0 ] + then + echo "No arguments supplied, please run with --quaternions or --euler" + exit 0 +fi + + +#Setting this to qbvh will activate +#quaternion code..! +BVH_TYPE="bvh" + +if [ "$1" == "--quaternions" ]; then + BVH_TYPE="qbvh" +elif [ "$1" == "--euler" ]; then + BVH_TYPE="bvh" +else + echo "You gave arguments but where not what we expected.." + exit 0 +fi + + +#FULL SIZE EXPERIMENT +echo "Doing full size experiment" +FULL_RANDOM_POSES="1999999" +ZERO_POSES="199999" +BASE_POSES="599999" +FINGER_POSES="399999" + +#SMALL SIZE (1/2) EXPERIMENT +#echo "Doing small (1/2) size experiment" +#FULL_RANDOM_POSES="1000000" +#ZERO_POSES="100000" +#BASE_POSES="300000" +#FINGER_POSES="200000" + + +#SMALL SIZE (1/10) EXPERIMENT +#echo "Doing small (1/10) size experiment" +#FULL_RANDOM_POSES="199999" +#ZERO_POSES="19999" +#BASE_POSES="59999" +#FINGER_POSES="39999" + + + +#BASE_POSES="99999" +#FINGER_POSES="19999" + +USE_OCCLUSIONS="" # Set to "--occlusions" for occlusions #deactivated @ 2021-03-05 + +USE_END_JOINTS="1" # We need end joints since they are our fingertips.. + +#Where is our output located +outputDir="dataset/" + +# Use --printparams in view commands to view the commands that are given to the utility at stderr +VIEW_COMMANDS=" --printparams " + +HIDEBODY=" $DONT_USE_END_JOINTS 8 abdomen chest eye.r eye.l toe1-2.r toe5-3.r toe1-2.l toe5-3.l" + +SELECT_RHAND="--selectJoints $USE_END_JOINTS 17 rHand finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumbBase rthumb finger1-2.r finger1-3.r --hide2DLocationOfJoints 0 1 rthumbBase" +#Note on --hide2DLocationOfJoints: We want to contain these joints in the BVH file and solve them, but they do not correspond to the BODY25 format so we hide them as 2D points.. + +SELECT_LHAND="--selectJoints $USE_END_JOINTS 17 lHand finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumbBase lthumb finger1-2.l finger1-3.l --hide2DLocationOfJoints 0 1 lthumbBase" +#Note on --hide2DLocationOfJoints: We want to contain these joints in the BVH file and solve them, but they do not correspond to the BODY25 format so we hide them as 2D points.. + +# LEFT RIGHT +MID_THUMBBASE_Y="63" #Thumb Left/Right -56->48 -48->56 +MID_FINGER5_1_Y="54" #Pinky Left/Right -10->22 -22->10 +MID_FINGER4_1_Y="39" #Ring Left/Right -12->12 -12->12 +MID_FINGER3_1_Y="24" #Mid Left/Right -12->12 -12->12 +MID_FINGER2_1_Y="24" #Pointer Left/Right -12->12 -12->12 + +NATURAL_LHAND="--set 12 -5.0 --set 39 5.0 --set 54 10.5 --set 63 -18" + +NATURAL_RHAND="--set 12 5.0 --set 39 -5.0 --set 54 -10.5 --set 63 18" + +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- + + +function generateRightHandDataset +{ +OUTBASE="$1" + +#Make sure file is wiped before +#adding new poses.. +#------------------------- +rm $outputDir/2d_$OUTBASE +rm $outputDir/bvh_$OUTBASE +#------------------------- + +MIN_HAND_DEPTH="$2" +MAX_HAND_DEPTH="$3" #1500 +MIN_RHAND_RZ="$4" +MAX_RHAND_RZ="$7" + +MIN_RHAND_RY="$5" +MAX_RHAND_RY="$8" + +MIN_RHAND_RX="$6" +MAX_RHAND_RX="$9" + +FROM="--haltonerror $VIEW_COMMANDS --from dataset/rhand.$BVH_TYPE" + +RANDOM_POSITION_ROTATION="--randomize2D $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_RHAND_RZ $MIN_RHAND_RY $MIN_RHAND_RX $MAX_RHAND_RZ $MAX_RHAND_RY $MAX_RHAND_RX $USE_OCCLUSIONS" + +OUT="--csv $outputDir $OUTBASE 2d+bvh " + +#RHAND +#./BVHGUI2 --from dataset/rhand.qbvh --set 2 -25 --set 3 0.5 --set 4 -0.5 --set 5 0.5 --set 6 -0.5 --set 12 5.0 --set 39 -5.0 --set 54 -10.5 --set 63 18 + +echo "TEST?" +#New automatic pose generation based on IK Problem.. +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FULL_RANDOM_POSES $SELECT_RHAND --randomizeBasedOnIKConstrains rhand $RANDOM_POSITION_ROTATION $OUT +checkForError "rhand full random" + +#Default BVH poses with everything at zero but hand is naturally open +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $BASE_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $OUT +checkForError "rhand naturally open" + +#Default BVH poses with everything at zero but hand is not naturally open, it is has fingers touching +./GroundTruthDumper $FROM --repeat $ZERO_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $OUT +checkForError "rhand to zero" + +#Default BVH poses with hand naturally open but thumb in the middle +./GroundTruthDumper $FROM $NATURAL_RHAND --set 63 -6.5 --set 64 28.7 --set 65 -8.5 --set 66 -37 --set 67 13 --set 68 -7.6 --set 69 22 --set 70 5 --set 71 -43 --set 72 0 --repeat $ZERO_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $OUT +checkForError "rhand naturally open thumb in the middle" + +#Right hand Thumb Movement on default pose +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION --randomizeMID $MID_THUMBBASE_Y -48.0 56.0 $OUT +checkForError "Right hand Thumb Movement on default pose" + +#All hand open naturally, only finger 5 (pinky) moving +RANDOMIZE_RIGHT_HAND="--/randomizeJointAngles 3 0 65 1 finger5-1.r finger5-2.r finger5-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "All hand open naturally, only finger 5 (pinky) moving" + +#All hand open naturally, only finger 4 (ring) moving +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 3 0 65 1 finger4-1.r finger4-2.r finger4-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "All hand open naturally, only finger 4 (ring) moving" + +#All hand open naturally, only finger 3 (middle) moving +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 3 0 65 1 finger3-1.r finger3-2.r finger3-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "All hand open naturally, only finger 3 (middle) moving" + +#All hand open naturally, only finger 2 (pointer) moving +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 3 0 65 1 finger2-1.r finger2-2.r finger2-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "All hand open naturally, only finger 2 (pointer) moving" + +#All hand open naturally, only finger 1 (thumb) moving +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 2 -75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 0 45 3 rthumb" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "All hand open naturally, only finger 1 (thumb) moving" + +#All fingers closed only thumb moving! +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --randomizeJointAngles 5 90 90 1 finger5-1.r finger4-1.r finger3-1.r finger2-1.r rthumb --randomizeJointAngles 2 -75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 0 45 3 rthumb" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "RHand - All fingers closed only thumb moving!" + +#All fingers closed except Finger 5 ( pinky ) +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --randomizeJointAngles 5 90 90 1 finger5-1.r finger4-1.r finger3-1.r finger2-1.r rthumb --randomizeJointAngles 2 75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 45 45 3 rthumb --randomizeJointAngles 3 0 65 1 finger5-1.r finger5-2.r finger5-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "RHand - All fingers closed except Finger 5 ( pinky )" + +#All fingers closed except Finger 4 ( ring ) +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --randomizeJointAngles 5 90 90 1 finger5-1.r finger4-1.r finger3-1.r finger2-1.r rthumb --randomizeJointAngles 2 75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 45 45 3 rthumb --randomizeJointAngles 3 0 65 1 finger4-1.r finger4-2.r finger4-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "RHand - All fingers closed except Finger 4 ( ring )" + +#All fingers closed except Finger 3 ( middle ) +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --randomizeJointAngles 5 90 90 1 finger5-1.r finger4-1.r finger3-1.r finger2-1.r rthumb --randomizeJointAngles 2 75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 45 45 3 rthumb --randomizeJointAngles 3 0 65 1 finger3-1.r finger3-2.r finger3-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "RHand - All fingers closed except Finger 3 ( middle )" + +#All fingers closed except Finger 2 ( pointer ) +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --randomizeJointAngles 5 90 90 1 finger5-1.r finger4-1.r finger3-1.r finger2-1.r rthumb --randomizeJointAngles 2 75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 45 45 3 rthumb --randomizeJointAngles 3 0 65 1 finger2-1.r finger2-2.r finger2-3.r" +./GroundTruthDumper $FROM $NATURAL_RHAND --repeat $FINGER_POSES $SELECT_RHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_RIGHT_HAND $OUT +checkForError "RHand - All fingers closed except Finger 2 ( pointer )" + +#Done +} + +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- + +function generateLeftHandDataset +{ +OUTBASE="$1" + + +#Make sure file is wiped before +#adding new poses.. +#------------------------- +rm $outputDir/2d_$OUTBASE +rm $outputDir/bvh_$OUTBASE +#------------------------- + +MIN_HAND_DEPTH="$2" +MAX_HAND_DEPTH="$3" #1500 +MIN_LHAND_RZ="$4" +MAX_LHAND_RZ="$7" + +MIN_LHAND_RY="$5" +MAX_LHAND_RY="$8" + +MIN_LHAND_RX="$6" +MAX_LHAND_RX="$9" + +FROM="--haltonerror $VIEW_COMMANDS --from dataset/lhand.$BVH_TYPE" + +RANDOM_POSITION_ROTATION="--randomize2D $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_LHAND_RZ $MIN_LHAND_RY $MIN_LHAND_RX $MAX_LHAND_RZ $MAX_LHAND_RY $MAX_LHAND_RX $USE_OCCLUSIONS" + +OUT="--csv $outputDir $OUTBASE 2d+bvh " + +# ./BVHGUI2 --from dataset/lhand.qbvh --set 2 -25 --set 3 0.5 --set 4 -0.5 --set 5 -0.5 --set 6 0.5 --set 12 -5.0 --set 39 5.0 --set 54 10.5 --set 63 -18 + +#New automatic pose generation based on IK Problem.. +./GroundTruthDumper $FROM --repeat $FULL_RANDOM_POSES $SELECT_LHAND --randomizeBasedOnIKConstrains lhand $RANDOM_POSITION_ROTATION $OUT +checkForError "lhand full random" + +#Default BVH poses with everything at zero but hand is naturally open +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $BASE_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $OUT +checkForError "lhand naturally open" + +#Default BVH poses with everything at zero but hand is not naturally open, it is has fingers touching +./GroundTruthDumper $FROM --repeat $ZERO_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $OUT +checkForError "lhand to zero" + +#Default BVH poses with hand naturally open but thumb in the middle +./GroundTruthDumper $FROM $NATURAL_LHAND --set 63 6.5 --set 64 -28.7 --set 65 8.5 --set 66 37 --set 67 -13 --set 68 7.6 --set 69 -22 --set 70 -5 --set 71 43 --set 72 0 --repeat $ZERO_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $OUT +checkForError "lhand naturally open thumb in the middle" + +#Left hand Thumb Movement on default pose +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION --randomizeMID $MID_THUMBBASE_Y -56.0 48.0 $OUT +checkForError "LHand - Left hand Thumb Movement on default pose" + +#All hand open naturally, only finger 5 (pinky) moving +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 3 0 65 1 finger5-1.l finger5-2.l finger5-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All hand open naturally, only finger 5 (pinky) moving" + +#All hand open naturally, only finger 4 (ring) moving +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 3 0 65 1 finger4-1.l finger4-2.l finger4-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All hand open naturally, only finger 4 (ring) moving" + +#All hand open naturally, only finger 3 (middle) moving +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 3 0 65 1 finger3-1.l finger3-2.l finger3-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All hand open naturally, only finger 3 (middle) moving" + +#All hand open naturally, only finger 2 (pointer) moving +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 3 0 65 1 finger2-1.l finger2-2.l finger2-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All hand open naturally, only finger 2 (pointer) moving" + +#All hand open naturally, only finger 1 (thumb) moving +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 2 -75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 0 45 3 lthumb" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All hand open naturally, only finger 1 (thumb) moving" + +#All fingers closed only thumb Open! +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --randomizeJointAngles 5 90 90 1 finger5-1.l finger4-1.l finger3-1.l finger2-1.l lthumb --randomizeJointAngles 2 -75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 0 45 3 lthumb" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All fingers closed only thumb Open!" + +#All fingers closed except Finger 5 ( pinky ) +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --randomizeJointAngles 5 90 90 1 finger5-1.l finger4-1.l finger3-1.l finger2-1.l lthumb --randomizeJointAngles 2 75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 45 45 3 lthumb --randomizeJointAngles 3 0 65 1 finger5-1.l finger5-2.l finger5-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All fingers closed except Finger 5 ( pinky )" + +#All fingers closed except Finger 4 ( ring ) +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --randomizeJointAngles 5 90 90 1 finger5-1.l finger4-1.l finger3-1.l finger2-1.l lthumb --randomizeJointAngles 2 75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 45 45 3 lthumb --randomizeJointAngles 3 0 65 1 finger4-1.l finger4-2.l finger4-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All fingers closed except Finger 4 ( ring )" + +#All fingers closed except Finger 3 ( middle ) +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --randomizeJointAngles 5 90 90 1 finger5-1.l finger4-1.l finger3-1.l finger2-1.l lthumb --randomizeJointAngles 2 75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 45 45 3 lthumb --randomizeJointAngles 3 0 65 1 finger3-1.l finger3-2.l finger3-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All fingers closed except Finger 3 ( middle )" + +#All fingers closed except Finger 2 ( pointer ) +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 15 65 65 1 finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --randomizeJointAngles 5 90 90 1 finger5-1.l finger4-1.l finger3-1.l finger2-1.l lthumb --randomizeJointAngles 2 75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 45 45 3 lthumb --randomizeJointAngles 3 0 65 1 finger2-1.l finger2-2.l finger2-3.l" +./GroundTruthDumper $FROM $NATURAL_LHAND --repeat $FINGER_POSES $SELECT_LHAND $RANDOM_POSITION_ROTATION $RANDOMIZE_LEFT_HAND $OUT +checkForError "LHand - All fingers closed except Finger 2 ( pointer )" + +#Done + +#Let's count it and store the numbers so we don't have to spam disk reads on every training.. +wc -l $outputDir/2d_$1 > $outputDir/count_$1 +#wc -l $outputDir/3d_$1 > $outputDir/count_$1 +wc -l $outputDir/bvh_$1 > $outputDir/count_$1 +} + + +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- + + +# ./BVHGUI2 --from dataset/lhand.$BVH_TYPE --set 3 -90 --set 4 0 --set 5 -135 + + + +MIN_HAND_DEPTH="200" +MAX_HAND_DEPTH="2000" #1500 + + +MIN_HAND_RZ="-90" +MAX_HAND_RZ="-90" + +MIN_HAND_RY="-50" +MAX_HAND_RY="50" + +#If we have a quaternion we can handle all orientations! +if [ "$1" == "--quaternions" ]; then + MIN_HAND_RZ="-179.9" + MAX_HAND_RZ="179.9" + + MIN_HAND_RY="-179.9" + MAX_HAND_RY="179.9" +fi + + +MIN_HAND_RX="-179.9" +MAX_HAND_RX="179.9" + + +generateLeftHandDataset lhand_all.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +#./ReshapeCSV --from dataset/bvh_lhand_all.csv --faces 20 > dataset/category_lhand_all.csv + +generateRightHandDataset rhand_all.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +#./ReshapeCSV --from dataset/bvh_rhand_all.csv --faces 20 > dataset/category_rhand_all.csv + +echo "Done Generating Hands.." + +exit 0 + +MIN_HAND_RX="-135" +MAX_HAND_RX="-45" +generateRightHandDataset rhand_front.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +generateLeftHandDataset lhand_front.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX + +MIN_HAND_RX="45" +MAX_HAND_RX="135" +generateRightHandDataset rhand_back.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +generateLeftHandDataset lhand_back.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX + +MIN_HAND_RX="-45" +MAX_HAND_RX="45" +generateRightHandDataset rhand_left.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +generateLeftHandDataset lhand_left.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX + +MIN_HAND_RX="135" +MAX_HAND_RX="225" +generateRightHandDataset rhand_right.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +generateLeftHandDataset lhand_right.csv $MIN_HAND_DEPTH $MAX_HAND_DEPTH $MIN_HAND_RZ $MIN_HAND_RY $MIN_HAND_RX $MAX_HAND_RZ $MAX_HAND_RY $MAX_HAND_RX +#--svg tmp/ --bvh randomRHand.$BVH_TYPE +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------------------------- + + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/createRandomizedDataset.sh b/animation/MocapNET-kasisnu/scripts/createRandomizedDataset.sh new file mode 100755 index 0000000..6060d8f --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/createRandomizedDataset.sh @@ -0,0 +1,320 @@ +#!/bin/bash + +#Colors for easier output +red=$(printf "\033[31m") +green=$(printf "\033[32m") +yellow=$(printf "\033[33m") +blue=$(printf "\033[34m") +magenta=$(printf "\033[35m") +cyan=$(printf "\033[36m") +white=$(printf "\033[37m") +normal=$(printf "\033[m") + +#We can use R to gather statistics about the generated ground truth.. +if ! [ -x "$(command -v R)" ]; then + echo 'Warning: R is not installed.' >&2 + sudo apt-get install r-base +fi + +function getStatistics +{ + #use R to generate statistics + #sudo apt-get install r-base + R -q -e "x <- read.csv('$1', header = T); summary(x); sd(x[ , 1])" > $2 + cat $1 | wc -l >> $2 +} + + +#We move to the correct directory.. +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" +cd .. + + + +#Where is our datasets located, compared to the current directory.. +datasetDir="dataset/MotionCapture" +#MocapNET1 -> datasetSubDir="01 02 03 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19" +datasetSubDir="h36 01 02 03 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 45 46 47 49 54 55 56 60 61 62 63 64 69 70 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 93 94 102 103 104 105 106 107 108 111 113 114 115 117 118 120 121 122 123 124 125 126 127 128 131 132 133 134 135 136 137 138 139 140 141 142 143 144" +#datasetSubDir="h36" + +#Where is our output located +outputDir="dataset/" + + +# Use --printparams in view commands to view the commands that are given to the utility at stderr +VIEW_COMMANDS=" " + +RAND_UPPER_BODY="--perturbJointAngles 2 30.0 rshoulder lshoulder --perturbJointAngles 2 16.0 relbow lelbow --perturbJointAngles 2 10.0 abdomen chest" +RAND_LOWER_BODY="--perturbJointAngles 2 30.0 rhip lhip --perturbJointAngles 4 10.0 lknee rknee lfoot rfoot --perturbJointAngles 2 10.0 abdomen chest" + + +RANDOMIZE_LEFT_HAND="--randomizeJointAngles 15 -65 0 1 finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --randomizeJointAngles 5 -90 0 1 finger5-1.l finger4-1.l finger3-1.l finger2-1.l lthumb --randomizeJointAngles 2 -75 75 2 lthumb finger1-2.l --randomizeJointAngles 1 -45 0 3 lthumb" +RANDOMIZE_RIGHT_HAND="--randomizeJointAngles 15 0 65 1 finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --randomizeJointAngles 5 0 90 1 finger5-1.r finger4-1.r finger3-1.r finger2-1.r rthumb --randomizeJointAngles 2 -75 75 2 rthumb finger1-2.r --randomizeJointAngles 1 0 45 3 rthumb" + +FILTER_OUT_POSES_WITH_HANDS_IN_SIDE="--filterout 0 0 -130.0 0 90 0 1920 1080 570.7 570.3 6 rhand lhip 0 120 rhand rhip 0 120 rhand lhand 0 150 lhand rhip 0 120 lhand lhip 0 120 lhand rhand 0 150" +#FILTER_OUT_POSES_WITH_HANDS_IN_SIDE=" " + +DONT_USE_END_JOINTS="0" +USE_END_JOINTS="1" + +HIDEBODY="--hide2DLocationOfJoints $DONT_USE_END_JOINTS 8 abdomen chest eye.r eye.l toe1-2.r toe5-3.r toe1-2.l toe5-3.l" #We want to contain these joints in the BVH file and solve them, but they do not correspond to the BODY25 format so we hide them as 2D points.. +SELECTBODY="--selectJoints $USE_END_JOINTS 23 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand rhip rknee rfoot lhip lknee lfoot toe1-2.r toe5-3.r toe1-2.l toe5-3.l $HIDEBODY" + + +HIDEUPPERBODY="--hide2DLocationOfJoints $DONT_USE_END_JOINTS 4 abdomen chest eye.r eye.l" #We want to contain these joints in the BVH file and solve them, but they do not correspond to the BODY25 format so we hide them as 2D points.. +SELECTUPPERBODY="--selectJoints $USE_END_JOINTS 13 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand $HIDEUPPERBODY" + +HIDELOWERBODY="--hide2DLocationOfJoints $DONT_USE_END_JOINTS 6 abdomen chest toe1-2.r toe5-3.r toe1-2.l toe5-3.l" +SELECTLOWERBODY="--selectJoints $USE_END_JOINTS 14 hip abdomen chest neck rhip rknee rfoot lhip lknee lfoot toe1-2.r toe5-3.r toe1-2.l toe5-3.l $HIDELOWERBODY" + + + +SELECT_RHAND="--selectJoints $USE_END_JOINTS 16 rhand finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r" + +SELECT_LHAND="--selectJoints $USE_END_JOINTS 16 lhand finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l" + +#FAR +MIN_DEPTH="900" #1000 original +MAX_DEPTH="4500" #3000 is too small + + +ITERATIONS="1" #Smallest size.. +#ITERATIONS="2" #float32 targeting 16GB RAM +#ITERATIONS="8" #float16 targeting 16GB RAM + + + +#Get number of bytes in RAM +RAM=`free | grep Mem | tr -s ' '| cut -f2 -d ' '` + +if [ "$RAM" -gt "11286464" ]; then + echo "More than 12GB"; +ITERATIONS="0" +fi +if [ "$RAM" -gt "15297500" ]; then + echo "More than 16GB"; +ITERATIONS="1" +fi +if [ "$RAM" -gt "31861780" ]; then + echo "More than 32GB"; +ITERATIONS="2" +fi + +#Uncomment next line to override settings and make the +#minimum training set for development +#ITERATIONS="0" + + +totaldatasetsToGo=`echo "$datasetSubDir" | awk '{print NF}'` + + +function generateDataset +{ + datasetsProcessed=0 + rm $outputDir/$1 + rm $outputDir/count_$1 + rm $outputDir/2d_$1 + #rm $outputDir/3d_$1 + rm $outputDir/bvh_$1 + for d in $datasetSubDir + do + if [ -d $datasetDir/$d ] + then + echo "Generating $1 - $green Found $d directory $normal" + datasetFile=`ls $datasetDir/$d | grep bvh` + ((datasetsProcessed++)) + #------------------------------------------------------- + + for f in $datasetFile + do + echo "$green Generating $1 - $datasetsProcessed/$totaldatasetsToGo - $datasetDir/$d/$f file $normal" + #|||||||||||||||||||||||||||||||||||||||||||||||||||||||| + BVHFILE="$datasetDir/$d/$f" # --svg $outputDir + ./GroundTruthDumper $VIEW_COMMANDS --haltonerror --from $BVHFILE --filtergimballocks 4 $3 --repeat $ITERATIONS $2 --occlusions --csv $outputDir $1 2d+bvh # --bvh $outputDir/$f-random.bvh + #|||||||||||||||||||||||||||||||||||||||||||||||||||||||| + done + + #------------------------------------------------------- + else + echo "$red Could not find $d directory $normal" + fi + done + + #Let's count it and store the numbers so we don't have to spam disk reads on every training.. + wc -l $outputDir/2d_$1 > $outputDir/count_$1 + #wc -l $outputDir/3d_$1 >> $outputDir/count_$1 + wc -l $outputDir/bvh_$1 >> $outputDir/count_$1 + +} + + + + +#------------------------------------------------------------------------------------------------------------ +# MocapNET 1 +# \ / +# \ BACK / +# \ / +# BACK \ / BACK +# \/ +#-----------/\------------- +# / \ +# FRONT / \ FRONT +# / FRONT \ +# / \ + +#Old way to do that ( works if you don't go very far in depth ) +#------------------------------------------------------------------------------------------------------------ +#MINIMUM_POSITION="-1400 -300 $MIN_DEPTH" +#MINIMUM_ROTATION="-25 -178 -35" +#MAXIMUM_POSITION="1400 300 $MAX_DEPTH" +#MAXIMUM_ROTATION="25 178 35" +#generateDataset dataBack.csv "--flipRandomizationOrientation --randomizeranges $MINIMUM_POSITION -35 -179.999999 -35 $MAXIMUM_POSITION 35 -90 35 $MINIMUM_POSITION -35 90 -35 $MAXIMUM_POSITION 35 180 35" +#generateDataset dataAll.csv "--randomize $MINIMUM_POSITION -35 -179.999999 -35 $MAXIMUM_POSITION 35 180 35" +#generateDataset dataFront.csv "--randomize $MINIMUM_POSITION -35 -90 -35 $MAXIMUM_POSITION 35 90 35" + + + + + + + +#------------------------------------------------------------------------------------------------------------ +#New extra extra fine detail rotated by 45 degrees +# \ / +# \ BACK / +# \ / +# \ / +# \/ +# LEFT /\ RIGHT +# / \ +# / \ +# / FRONT \ +# / \ +#------------------------------------------------------------------------------------------------------------ + + +MIN_LIM="-45" +MAX_LIM="45" + + +#I have added 20 degrees on every limit more than the default +#to have some overlap and redundancy when switching between datasets +#--------------------------- +FRONT_MIN_ORIENTATION="-55" #-45 default , -55 with 10 deg safety +FRONT_MAX_ORIENTATION="55" # 45 default , 55 with 10 deg safety +#--------------------------- +BACK_MIN_ORIENTATION="125" # 135 default , 125 with 10 deg safety +BACK_MAX_ORIENTATION="235" # 225 default , 235 with 10 deg safety +#--------------------------- +LEFT_MIN_ORIENTATION="-145" #-135 default +LEFT_MAX_ORIENTATION="-35" #-45 default +#--------------------------- +RIGHT_MIN_ORIENTATION="35" # 45 default +RIGHT_MAX_ORIENTATION="145" # 135 default + +# $FILTER_OUT_POSES_WITH_HANDS_IN_SIDE +#rm $outputDir/2d_filteredPosesRHand.csv +#rm $outputDir/bvh_filteredPosesRHand.csv +#./GroundTruthDumper --haltonerror --filtergimballocks --from filteredPosesRHand.bvh $SELECTUPPERBODY $RAND_UPPER_BODY --csvOrientation front --randomize2D $MIN_DEPTH $MAX_DEPTH -10 -45 -0 10 45 10 $SELECTUPPERBODY --occlusions --csv $outputDir #filteredPosesRHand.csv 2d+bvh +#exit 0 + +FILTER_OUT="$FILTER_OUT_POSES_WITH_HANDS_IN_SIDE" + +#UPPER BODY +generateDataset upperbody_all.csv "$FILTER_OUT --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM -179.999999 $MIN_LIM $MAX_LIM 180 $MAX_LIM" "$SELECTUPPERBODY" +./ReshapeCSV dataset/bvh_upperbody_all.csv > dataset/category_upperbody_all.csv +echo "Category size = "#cat dataset/category_upperbody_all.csv | wc -c +#----------------------------------------------- +generateDataset upperbody_front.csv "$FILTER_OUT --csvOrientation front --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $FRONT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $FRONT_MAX_ORIENTATION $MAX_LIM" "$SELECTUPPERBODY $RAND_UPPER_BODY" +generateDataset upperbody_back.csv "$FILTER_OUT --csvOrientation back --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $BACK_MIN_ORIENTATION $MIN_LIM $MAX_LIM $BACK_MAX_ORIENTATION $MAX_LIM" "$SELECTUPPERBODY $RAND_UPPER_BODY" +generateDataset upperbody_left.csv "$FILTER_OUT --csvOrientation left --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $LEFT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $LEFT_MAX_ORIENTATION $MAX_LIM" "$SELECTUPPERBODY $RAND_UPPER_BODY" +generateDataset upperbody_right.csv "$FILTER_OUT --csvOrientation right --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $RIGHT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $RIGHT_MAX_ORIENTATION $MAX_LIM" "$SELECTUPPERBODY $RAND_UPPER_BODY" + + +#LOWER BODY +generateDataset lowerbody_all.csv "--randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM -179.999999 $MIN_LIM $MAX_LIM 180 $MAX_LIM" "$SELECTLOWERBODY" +./ReshapeCSV dataset/bvh_lowerbody_all.csv > dataset/category_lowerbody_all.csv +echo "Category size = " +cat dataset/category_lowerbody_all.csv | wc -c +#----------------------------------------------- +generateDataset lowerbody_front.csv "--csvOrientation front --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $FRONT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $FRONT_MAX_ORIENTATION $MAX_LIM" "$SELECTLOWERBODY $RAND_LOWER_BODY" +generateDataset lowerbody_back.csv "--csvOrientation back --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $BACK_MIN_ORIENTATION $MIN_LIM $MAX_LIM $BACK_MAX_ORIENTATION $MAX_LIM" "$SELECTLOWERBODY $RAND_LOWER_BODY" +generateDataset lowerbody_left.csv "--csvOrientation left --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $LEFT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $LEFT_MAX_ORIENTATION $MAX_LIM" "$SELECTLOWERBODY $RAND_LOWER_BODY" +generateDataset lowerbody_right.csv "--csvOrientation right --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $RIGHT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $RIGHT_MAX_ORIENTATION $MAX_LIM" "$SELECTLOWERBODY $RAND_LOWER_BODY" + + +exit 0 # <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> + +#This is a better hand dataset generator that ignores poses +./createRandomHandDataset.sh + +exit 0 # <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> + +#This hand is just based on CMU poses +#LEFT HAND +generateDataset lhand_front.csv "--csvOrientation front --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $FRONT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $FRONT_MAX_ORIENTATION $MAX_LIM" "$SELECT_LHAND $RANDOMIZE_LEFT_HAND" +generateDataset lhand_back.csv "--csvOrientation back --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $BACK_MIN_ORIENTATION $MIN_LIM $MAX_LIM $BACK_MAX_ORIENTATION $MAX_LIM" "$SELECT_LHAND $RANDOMIZE_LEFT_HAND" +generateDataset lhand_left.csv "--csvOrientation left --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $LEFT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $LEFT_MAX_ORIENTATION $MAX_LIM" "$SELECT_LHAND $RANDOMIZE_LEFT_HAND" +generateDataset lhand_right.csv "--csvOrientation right --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $RIGHT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $RIGHT_MAX_ORIENTATION $MAX_LIM" "$SELECT_LHAND $RANDOMIZE_LEFT_HAND" + +#RIGHT HAND +generateDataset rhand_front.csv "--csvOrientation front --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $FRONT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $FRONT_MAX_ORIENTATION $MAX_LIM" "$SELECT_RHAND $RANDOMIZE_RIGHT_HAND" +generateDataset rhand_back.csv "--csvOrientation back --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $BACK_MIN_ORIENTATION $MIN_LIM $MAX_LIM $BACK_MAX_ORIENTATION $MAX_LIM" "$SELECT_RHAND $RANDOMIZE_RIGHT_HAND" +generateDataset rhand_left.csv "--csvOrientation left --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $LEFT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $LEFT_MAX_ORIENTATION $MAX_LIM" "$SELECT_RHAND $RANDOMIZE_RIGHT_HAND" +generateDataset rhand_right.csv "--csvOrientation right --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $RIGHT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $RIGHT_MAX_ORIENTATION $MAX_LIM" "$SELECT_RHAND $RANDOMIZE_RIGHT_HAND" + + +exit 0 # <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> + + +#WHOLE BODY AT ONCE +generateDataset body_all.csv "$FILTER_OUT --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM -179.999999 $MIN_LIM $MAX_LIM 180 $MAX_LIM" "$SELECTBODY" +./ReshapeCSV dataset/bvh_body_all.csv > dataset/category_body_all.csv +echo "Category size = " +cat dataset/category_body_all.csv | wc -c +#----------------------------------------------- +generateDataset body_front.csv "$FILTER_OUT --csvOrientation front --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $FRONT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $FRONT_MAX_ORIENTATION $MAX_LIM" "$SELECTBODY" +generateDataset body_back.csv "$FILTER_OUT --csvOrientation back --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $BACK_MIN_ORIENTATION $MIN_LIM $MAX_LIM $BACK_MAX_ORIENTATION $MAX_LIM" "$SELECTBODY" +generateDataset body_left.csv "$FILTER_OUT --csvOrientation left --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $LEFT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $LEFT_MAX_ORIENTATION $MAX_LIM" "$SELECTBODY" +generateDataset body_right.csv "$FILTER_OUT --csvOrientation right --randomize2D $MIN_DEPTH $MAX_DEPTH $MIN_LIM $RIGHT_MIN_ORIENTATION $MIN_LIM $MAX_LIM $RIGHT_MAX_ORIENTATION $MAX_LIM" "$SELECTBODY" + +exit 0 # <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> <> + + +echo "Getting statistics for front view.." +cat dataset/bvh_body_front.csv | cut -d',' -f 5 > frontOrientations +getStatistics frontOrientations dataset/bvh_body_front_statistics.txt +cat dataset/bvh_body_front_statistics.txt +rm frontOrientations + +echo "Getting statistics for back view.." +cat dataset/bvh_body_back.csv | cut -d',' -f 5 > backOrientations +getStatistics backOrientations dataset/bvh_body_back_statistics.txt +cat dataset/bvh_body_back_statistics.txt +rm backOrientations + +echo "Getting statistics for left view.." +cat dataset/bvh_body_left.csv | cut -d',' -f 5 > leftOrientations +getStatistics leftOrientations dataset/bvh_body_left_statistics.txt +cat dataset/bvh_body_left_statistics.txt +rm leftOrientations + +echo "Getting statistics for right view.." +cat dataset/bvh_body_right.csv | cut -d',' -f 5 > rightOrientations +getStatistics rightOrientations dataset/bvh_body_right_statistics.txt +cat dataset/bvh_body_right_statistics.txt +rm rightOrientations + +echo "Getting statistics for all view.." +cat dataset/bvh_body_all.csv | cut -d',' -f 5 > allOrientations +getStatistics allOrientations dataset/bvh_body_all_statistics.txt +cat dataset/bvh_body_all_statistics.txt +rm allOrientations + + + + +echo "Done.." +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/createTestDataset.sh b/animation/MocapNET-kasisnu/scripts/createTestDataset.sh new file mode 100755 index 0000000..daeeaf1 --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/createTestDataset.sh @@ -0,0 +1,67 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" +cd .. + +red=$(printf "\033[31m") +green=$(printf "\033[32m") +yellow=$(printf "\033[33m") +blue=$(printf "\033[34m") +magenta=$(printf "\033[35m") +cyan=$(printf "\033[36m") +white=$(printf "\033[37m") +normal=$(printf "\033[m") + +SELECTBODY="--selectJoints 21 hip abdomen chest neck head rcollar rshoulder relbow rhand lcollar lshoulder lelbow lhand rhip rknee rfoot lhip lknee lfoot toe3-2.r toe3-2.l" +SELECTRHAND="--selectJoints 11 rhand rthumb1 rthumb2 rindex1 rindex2 rmid1 rmid2 rring1 rring2 rpinky1 rpinky2" +SELECTLHAND="--selectJoints 11 lhand lthumb1 lthumb2 lindex1 lindex2 lmid1 lmid2 lring1 lring2 lpinky1 lpinky2" + +datasetDir="dataset/MotionCapture" +datasetSubDir="01 02 03 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19" +outputDir="dataset/" + +rm $outputDir/2d_body_test.csv +rm $outputDir/3d_body_test.csv +rm $outputDir/bvh_body_test.csv + +#create a small test file with one example +#rm $outputDir/testSMALL.csv +# ./GroundTruthDumper --from MotionCapture/01/01_01.bvh --offsetPositionRotation 0 750 2500 0 0 0 --maxFrames 1 --csv $outputDir testSMALL.csv + +#PRODUCESVG="--svg $outputDir/svg" +PRODUCESVG=" " + + +datasetsProcessed=0 +totaldatasetsToGo=`echo "$datasetSubDir" | awk '{print NF}'` + +rm $outputDir/test.csv + for d in $datasetSubDir + do + if [ -d $datasetDir/$d ] + then + echo "$green Found $d directory $normal" + datasetFile=`ls $datasetDir/$d | grep bvh` + ((datasetsProcessed++)) + #------------------------------------------------------- + + for f in $datasetFile + do + echo "$green Generating Test - $datasetsProcessed/$totaldatasetsToGo - $datasetDir/$d/$f file $normal" + #|||||||||||||||||||||||||||||||||||||||||||||||||||||||| + BVHFILE="$datasetDir/$d/$f" # --svg $outputDir + #./GroundTruthDumper --from $BVHFILE --csv $outputDir test.csv# --bvh $outputDir/$f-random.bvh + ./GroundTruthDumper --from $BVHFILE $SELECTBODY --offsetPositionRotation 0 750 2000 0 0 0 --occlusions --csv $outputDir body_test.csv 2d+bvh $PRODUCESVG #--to ~/Documents/Programming/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/Scenes/testBVH.conf --bvh $outputDir/$f-random.bvh --svg $outputDir/svg + #exit 0 + #./GroundTruthDumper --from $BVHFILE --setPositionRotation 0 400 0 0 0 0 --csv $outputDir test.csv# --bvh $outputDir/$f-random.bvh + #|||||||||||||||||||||||||||||||||||||||||||||||||||||||| + done + + #------------------------------------------------------- + else + echo "$red Could not find $d directory $normal" + fi + done + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/debug.sh b/animation/MocapNET-kasisnu/scripts/debug.sh new file mode 100755 index 0000000..bf88c3e --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/debug.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" +cd .. + + +#Valgrind can also be downloaded.. +#https://www.valgrind.org/downloads/current.html + +#Simple dependency checker that will apt-get stuff if something is missing +SYSTEM_DEPENDENCIES="valgrind" + +for REQUIRED_PKG in $SYSTEM_DEPENDENCIES +do +PKG_OK=$(dpkg-query -W --showformat='${Status}\n' $REQUIRED_PKG|grep "install ok installed") +echo "Checking for $REQUIRED_PKG: $PKG_OK" +if [ "" = "$PKG_OK" ]; then + + echo "No $REQUIRED_PKG. Setting up $REQUIRED_PKG." + + #If this is uncommented then only packages that are missing will get prompted.. + #sudo apt-get --yes install $REQUIRED_PKG + + #if this is uncommented then if one package is missing then all missing packages are immediately installed.. + sudo apt-get install $SYSTEM_DEPENDENCIES + break +fi +done +#------------------------------------------------------------------------------ + + + + +valgrind --tool=memcheck --leak-check=yes --show-reachable=yes --track-origins=yes --num-callers=20 --track-fds=yes ./MocapNETLiveWebcamDemo --from shuffle $@ 2>error.txt + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/dump_and_process_video.sh b/animation/MocapNET-kasisnu/scripts/dump_and_process_video.sh new file mode 100755 index 0000000..99d5d7e --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/dump_and_process_video.sh @@ -0,0 +1,93 @@ +#!/bin/bash + + +DATASET="" +if (( $#<1 )) +then + echo "Please provide arguments first argument is dataset " + exit 1 +else + DATASET=$1-data +fi +# $1 holds the video file we want to process ( it should be something like path/to/videofile.mp4 ) +#DATASET now holds the output directory we will create ( it should be something like path/to/videofile.mp4-data/ ) + + +#Remember the directory where user started +STARTDIR=`pwd` + +#Remember the directory where the script is ( and where MocapNET is :) ) +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" + +#Please give the path to openpose here.. +OPENPOSE_DIR="/home/ammar/Documents/3dParty/openpose/" +OPENPOSE_BINARY_DIR="/home/ammar/Documents/3dParty/openpose/build/examples/openpose/" + +#Dataset dumping using ffmpeg +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#Create the new dataset directory +mkdir $DATASET + +#Dump the video files using our naming scheme +ffmpeg -i $1 -r 30 -q:v 1 $DATASET/colorFrame_0_%05d.jpg + +#Make sure we start at frame 0 +cp $DATASET/colorFrame_0_00001.jpg $DATASET/colorFrame_0_00000.jpg + +#We now want to grab an absolute path to our dataset +cd $DATASET +FULL_PATH_TO_DATASET=`pwd` +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- + + + +#2D pose estimation using OpenPose +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +cd $OPENPOSE_DIR +LD_LIBRARY_PATH=/usr/local/cuda-9.0/lib64 $OPENPOSE_BINARY_DIR/openpose.bin -number_people_max 1 --hand --face --write_json $FULL_PATH_TO_DATASET -image_dir $FULL_PATH_TO_DATASET $@ +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- + + + +#2D pose conversion to CSV using our tool +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +cd "$DIR" +cd .. + +./convertOpenPoseJSONToCSV --from $DATASET +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- + + + +#3D pose estimation using MocapNET2 +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +cd "$DIR" +cd .. + +./MocapNET2CSV --from $FULL_PATH_TO_DATASET/2dJoints_v1.4.csv --mt --show 3 --save +cp out.bvh $FULL_PATH_TO_DATASET/predicted.bvh +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- +#----------------------------------------------------------------------------- + + + + + +cd $STARTDIR +exit 0 + diff --git a/animation/MocapNET-kasisnu/scripts/dump_video.sh b/animation/MocapNET-kasisnu/scripts/dump_video.sh new file mode 100755 index 0000000..32a22ed --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/dump_video.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +#This is a script to dump a video file to JPG files +#it uses ffmpeg so make sure you have sudo apt-get install ffmpeg +#If you do ./dump_video.sh yourvideo.mp4 +#There should be an output folder yourvideo.mp4-data that has all the frames of the video +#you can then point OpenPose to this directory to convert them to JSON 2D detections + +STARTDIR=`pwd` +#Switch to this directory +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +DATASET="" + +if (( $#<1 )) +then + echo "Please provide arguments first argument is dataset " + exit 1 +else + DATASET=$1-data +fi + +THEDATETAG=`date +"%y-%m-%d_%H-%M-%S"` + +mkdir $DATASET + +ffmpeg -i $1 -r 30 -q:v 1 $DATASET/colorFrame_0_%05d.jpg + +cp $DATASET/colorFrame_0_00001.jpg $DATASET/colorFrame_0_00000.jpg + +cd $DATASET +cd .. + +cd $STARTDIR +exit 0 + diff --git a/animation/MocapNET-kasisnu/scripts/experimentWithDifferentIterationNumbers.sh b/animation/MocapNET-kasisnu/scripts/experimentWithDifferentIterationNumbers.sh new file mode 100755 index 0000000..02178b9 --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/experimentWithDifferentIterationNumbers.sh @@ -0,0 +1,48 @@ +#!/bin/bash + + +#Simple dependency checker that will apt-get stuff if something is missing +# sudo apt-get install gnuplot +SYSTEM_DEPENDENCIES="gnuplot" + +for REQUIRED_PKG in $SYSTEM_DEPENDENCIES +do +PKG_OK=$(dpkg-query -W --showformat='${Status}\n' $REQUIRED_PKG|grep "install ok installed") +echo "Checking for $REQUIRED_PKG: $PKG_OK" +if [ "" = "$PKG_OK" ]; then + + echo "No $REQUIRED_PKG. Setting up $REQUIRED_PKG." + + #If this is uncommented then only packages that are missing will get prompted.. + #sudo apt-get --yes install $REQUIRED_PKG + + #if this is uncommented then if one package is missing then all missing packages are immediately installed.. + sudo apt-get install $SYSTEM_DEPENDENCIES + break +fi +done +#------------------------------------------------------------------------------ + + + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +cd .. + + +./GroundTruthDumper --from dataset/CMU_Sample_05_01.bvh --selectJoints 0 23 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand rhip rknee rfoot lhip lknee lfoot toe1-2.r toe5-3.r toe1-2.l toe5-3.l --tuneIterations 130 100 130 0.01 5 30 20 > tuneIterations.dat + + +#set view 45,45; set view map; set yrange [9:90]; set zrange [30:90]; +# set dgrid3d 25,25 qnorm 4; +#splot \"tuneIterations.dat\" using 1:3:2 with points pointsize 3 pointtype 7; +#HCD tuning iteration hyperparameter +GNUPLOT_CMD="set terminal png size 1000,800 font \"Helvetica,34\"; set output \"tuneIterations.png\"; set isosample 160; set pm3d at b; set palette defined (65 \"black\", 70 \"red\", 80 \"yellow\", 90 \"yellow\", 100 \"white\"); set view 30,45; set hidden3d; set xrange [1:25]; set yrange [9:90]; set zrange [30:90]; set style fill solid; set xlabel \"HCD Iterations\" rotate parallel; set ylabel \"Frames per second\" rotate parallel; set zlabel \"Mean average error in mm\" rotate parallel; set ztics 30; set title \"Mean average error in mm\"; set ytics 30; set multiplot; splot \"tuneIterations.dat\" using 1:3:2 with lines palette lw 2 title \" \"; splot \"tuneIterations.dat\" using 1:3:2 with points palette pointsize 1 pointtype 7 title \" \"; " + +gnuplot -e "$GNUPLOT_CMD" + +echo "Result of experiment is now ready @ tuneIterations.png" +timeout 10 gpicview tuneIterations.png + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/getOpenCV.sh b/animation/MocapNET-kasisnu/scripts/getOpenCV.sh new file mode 100755 index 0000000..426d1db --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/getOpenCV.sh @@ -0,0 +1,54 @@ +#/bin/bash + + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR/../dependencies" + +DEPENDENCIES_PATH=`pwd` + +sudo apt-get install build-essential cmake git libgtk2.0-dev pkg-config ffmpeg libavcodec-dev libavformat-dev libavcodec-dev libavformat-dev libswscale-dev libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff5-dev libdc1394-22-dev libeigen3-dev libtheora-dev libvorbis-dev libxvidcore-dev libx264-dev sphinx-common libtbb-dev yasm libfaac-dev libopencore-amrnb-dev libopencore-amrwb-dev libopenexr-dev libgstreamer-plugins-base1.0-dev libavutil-dev libavfilter-dev libavresample-dev libpython3-all-dev python3-numpy python3-dev + +#cd "$1" +echo "Will Install @ `pwd`" + + +#Want a different version? +#Check https://opencv.org/releases/ for versions and +#change the OPENCV_VERSION in the next line.. +OPENCV_VERSION="3.2.0" +echo "Downloading" + +wget http://ammar.gr/programs/opencv-$OPENCV_VERSION.zip +wget http://ammar.gr/programs/opencv_contrib-$OPENCV_VERSION.tar.gz + +#wget https://codeload.github.com/opencv/opencv/zip/$OPENCV_VERSION -O opencv-$OPENCV_VERSION.zip +#wget https://codeload.github.com/opencv/opencv_contrib/zip/$OPENCV_VERSION -O opencv_contrib-$OPENCV_VERSION.tar.gz + +echo "Extracting" + +#tar xvzf opencv_contrib-$OPENCV_VERSION.tar.gz +unzip opencv_contrib-$OPENCV_VERSION.zip +unzip opencv-$OPENCV_VERSION.zip + +echo "Building" + +cd opencv-$OPENCV_VERSION +mkdir build +cd build +cmake -DOPENCV_ENABLE_NONFREE=ON -DOPENCV_EXTRA_MODULES_PATH=$DEPENDENCIES_PATH/opencv_contrib-$OPENCV_VERSION/modules .. +make -j5 + +echo "Do you want to install OpenCV to your system ? " +echo +echo -n " (Y/N)?" +read answer +if test "$answer" != "N" -a "$answer" != "n"; + then + sudo make install +fi + +cd "$DIR" + +echo "Done" + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/processDatasetWithOpenpose.sh b/animation/MocapNET-kasisnu/scripts/processDatasetWithOpenpose.sh new file mode 100755 index 0000000..65f1ee9 --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/processDatasetWithOpenpose.sh @@ -0,0 +1,32 @@ +#!/bin/bash + +#This script should be run after you have dumped a video to a directory using the dump_video.sh utility +#You should give a full path to this utility i.e. ./processDatasetWithOpenpose.sh "~/myDatasets/yourvideo.mp4-data/" +#and also dont forget to change the PATH_TO_OPENPOSE to the directory that has your openpose.bin! +PATH_TO_OPENPOSE="PLEASE/CHANGE/THIS/" + + +STARTDIR=`pwd` +#Switch to this directory +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" + + +DATASET="" + +if (( $#<1 )) +then + echo "Please provide arguments first argument is dataset " + exit 1 +else + DATASET=$1 + +$PATH_TO_OPENPOSE/openpose.bin -number_people_max 1 --hand --face --write_json $DATASET -image_dir $DATASET $@ + + +cd "$DIR" +echo "Went back to $DIR" +echo "Hopefully the path you have given is an absolute path.." +./convertBody25JSONToCSV -i $DATASET + +exit 0 + diff --git a/animation/MocapNET-kasisnu/scripts/splitStereo.sh b/animation/MocapNET-kasisnu/scripts/splitStereo.sh new file mode 100755 index 0000000..cf8ab48 --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/splitStereo.sh @@ -0,0 +1,47 @@ +#!/bin/bash + +FROMDATASET="$1" +X=0 + +# sudo apt-get install imagemagick + +if (( $# != 1 )); then + echo "Please run giving the path to download and build" + echo "$0 \"DatasetNameSource\" " + exit 0 +fi + + +TOTAL_FRAMES=100000 + +cd $FROMDATASET + +echo "Spliting Dataset $FROMDATASET " +echo "Please wait .. " +for (( i=$X; i<=$TOTAL_FRAMES; i++ )) +do + XNUM=`printf %05u $i` + + if [ -f "colorFrame_0_$XNUM.jpg" ] + then + width=$(identify -format "%w" "colorFrame_0_$XNUM.jpg")> /dev/null + height=$(identify -format "%h" "colorFrame_0_$XNUM.jpg")> /dev/null + halfWidth=$((width / 2)) + echo "$width x $height -> 2x $halfWidth x $height" + + + #First crop second half ( before source file gets rewritten ) + convert colorFrame_0_$XNUM.jpg -crop "$halfWidth"x$height+$halfWidth+0 colorFrame_1_$XNUM.jpg + convert colorFrame_0_$XNUM.jpg -crop "$halfWidth"x$height+0+0 colorFrame_0_$XNUM.jpg + else + break + fi + + echo -n "." +done + +echo "Passed TOTAL_FRAMES (!) this is a bug! :S" + +cd .. + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/tensorflow2Build.sh b/animation/MocapNET-kasisnu/scripts/tensorflow2Build.sh new file mode 100755 index 0000000..2f55c42 --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/tensorflow2Build.sh @@ -0,0 +1,135 @@ +#!/bin/bash + +#Script last updated 25-03-2022 + +#Script that seems to be able to build bazel/tf2 on Ubuntu 20.04 +#I really deeply dislike the bazel build system which is bloated and obfuscated for no reason, just Google "NIH syndrome" +#However Tensorflow2 is a great NN framework +# See this video "How To Make Package Managers Cry" -> https://www.youtube.com/watch?v=NSemlYagjIU#t=19m0s + +echo "Check CUDA" +ls /usr/local/cuda/extras/CUPTI/lib64 +ls /usr/local/cuda +ls /usr/local/cuda/lib64/ | grep libcudnn.so +nvcc -V + + +VERSION="2.8" + +#Get number of bytes in RAM +RAM=`free | grep Mem | tr -s ' '| cut -f2 -d ' '` + + +BazelPleaseSlowDown="--local_resources 2048,.5,1.0" + +if [ "$RAM" -gt "11286464" ]; then + echo "More than 12GB"; +BazelPleaseSlowDown=" " +fi +if [ "$RAM" -gt "15297500" ]; then + echo "More than 16GB"; +BazelPleaseSlowDown=" " +fi +if [ "$RAM" -gt "31861780" ]; then + echo "More than 32GB"; +BazelPleaseSlowDown=" " +fi + + +#Tensorflow is a great Neural network library that unfortunately is coupled to the terrible Bazel build system +#This is a download and build script for Ubuntu 20.04, that should work building release 2.8 + +sudo apt-get install python3-dev python3-pip python3-venv python3-tk + +pip3 install -U --user pip numpy wheel packaging +pip3 install -U --user keras_preprocessing --no-deps + +cd ~/Documents +mkdir 3dParty +cd 3dParty + +#wget https://github.com/bazelbuild/bazel/releases/download/4.2.1/bazel-4.2.1-installer-linux-x86_64.sh +#chmod +x bazel-4.2.1-installer-linux-x86_64.sh +#./bazel-4.2.1-installer-linux-x86_64.sh --user + +#r2.8 +mkdir -p "$HOME/.bazel/bin" && cd "$HOME/.bazel/bin" && curl -fLO https://releases.bazel.build/4.2.1/release/bazel-4.2.1-linux-x86_64 && chmod +x bazel-4.2.1-linux-x86_64 + +#Create shared directory +if [ -f ~/.bashrc ] +then + if cat ~/.bashrc | grep -q "BAZEL_CANCER" +then + echo "Bazel includes seem to be set-up.." +else + USER=`whoami` + echo "#BAZEL_CANCER" >> ~/.bashrc + echo "source $HOME/.bazel/bin/bazel-complete.bash" >> ~/.bashrc + echo "export PATH=\"\$PATH:\$HOME/bin\"" >> ~/.bashrc + source ~/.bashrc + fi +fi + +if [ ! -d tensorflow ] +then +git clone https://github.com/tensorflow/tensorflow.git +fi + +cd tensorflow +git pull +git checkout r$VERSION + + +#Make sure to check your target CPU and when asked used the correct -march= / -mtune= +# for example for an old intel i7 -march=nehalem is used.. +#https://gcc.gnu.org/onlinedocs/gcc/x86-Options.html + + +echo "Answers to configure questions :" +echo "/usr/bin/python3" +echo "/usr/lib/python3/dist-packages" +echo "cuda Y" +echo "tensorrt Y" +echo "CUDA 11.2" +echo "CuDNN 8" +echo "TensorRT 8" +echo "/usr/local/cuda/,/usr/local/cuda/include/,/usr/local/cuda/bin/,/usr/local/cuda/lib64/,/usr/local/cuda/lib64/,/usr/local/tensorrt-8.2.3/,/usr/local/tensorrt-8.2.3/include/,/usr/local/tensorrt-8.2.3/lib/," +echo "Compute capability 6.1 ( for GTX 1050 + cards )" + +#Attempt to inform the configure script on how to find the CUDA stuff.. +export CUDNN_INSTALL_PATH=/usr/local/cuda/,/usr/local/cuda/include/,/usr/local/cuda/bin/,/usr/local/cuda/lib64/,/usr/local/cuda/lib64/,/usr/local/tensorrt-8.2.3/,/usr/local/tensorrt-8.2.3/include/,/usr/local/tensorrt-8.2.3/lib/ + + +./configure + +bazel clean --expunge + + + +#You should use CUDA 11.2 and cudnn-11.2-linux-x64-v8.1.1.33 and TensorRT 8.2.3 + +bazel build --config=opt --config=cuda --config=mkl --config=monolithic $BazelPleaseSlowDown //tensorflow/tools/pip_package:build_pip_package +./bazel-bin/tensorflow/tools/pip_package/build_pip_package ~/Documents/3dParty/ +#To install +#pip3 --user install ~/Documents/3dParty/tensorflow-2.4.0-cp36-cp36m-linux_x86_64.whl + + + +bazel build --config opt --config=cuda --config=monolithic //tensorflow/tools/lib_package:libtensorflow +cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz ~/Documents/3dParty/libtensorflow-r$VERSION.tar.gz + + +#Build tensorflow lite +#https://www.tensorflow.org/lite/guide/build_cmake +mkdir tflite_buld +cd tflite_buld/ +cmake ../tensorflow/lite +cmake --build . -j + + +echo "Please visit ~/Documents/3dParty/ to collect your tensorflow python3 wheel, and C Library.." +echo "Will now use : python -c 'import tensorflow as tf;' to test your tensorflow" + +python -c 'import tensorflow as tf;' + +exit 0 diff --git a/animation/MocapNET-kasisnu/scripts/tensorflowBuild.sh b/animation/MocapNET-kasisnu/scripts/tensorflowBuild.sh new file mode 100755 index 0000000..7f3e59d --- /dev/null +++ b/animation/MocapNET-kasisnu/scripts/tensorflowBuild.sh @@ -0,0 +1,93 @@ +#!/bin/bash + +#Script source repository https://github.com/AmmarkoV/MyScripts/blob/master/Tensorflow/tensorflowBuild.sh +#Script last updated 23-06-2020 + +#Script that seems to be able to build bazel/tf2 on Ubuntu 20.04 +#I really deeply dislike the bazel build system which is bloated and obfuscated for no reason, just Google "NIH syndrome" +#However Tensorflow2 is a great NN framework +# See this video "How To Make Package Managers Cry" -> https://www.youtube.com/watch?v=NSemlYagjIU#t=19m0s + + +#Get number of bytes in RAM +RAM=`free | grep Mem | tr -s ' '| cut -f2 -d ' '` + + +BazelPleaseSlowDown="--local_resources 2048,.5,1.0" + +if [ "$RAM" -gt "11286464" ]; then + echo "More than 12GB"; +BazelPleaseSlowDown=" " +fi +if [ "$RAM" -gt "15297500" ]; then + echo "More than 16GB"; +BazelPleaseSlowDown=" " +fi +if [ "$RAM" -gt "31861780" ]; then + echo "More than 32GB"; +BazelPleaseSlowDown=" " +fi + + +#Tensorflow is a great Neural network library that unfortunately is coupled to the terrible Bazel build system +#This is a download and build script for Ubuntu 18.04, that should work building release 1.15 + +sudo apt-get install python3-dev python3-pip python3-venv python3-tk + +pip install -U --user pip six numpy wheel setuptools mock 'future>=0.17.1' +pip install -U --user keras_applications --no-deps +pip install -U --user keras_preprocessing --no-deps + + +cd ~/Documents +mkdir 3dParty +cd 3dParty + +wget http://ammar.gr/mocapnet/bazel-0.24.1-installer-linux-x86_64-for-tensorflow-r1.15.sh +chmod +x bazel-0.24.1-installer-linux-x86_64-for-tensorflow-r1.15.sh +./bazel-0.24.1-installer-linux-x86_64-for-tensorflow-r1.15.sh --user + +#Create shared directory +if [ -f ~/.bashrc ] +then + if cat ~/.bashrc | grep -q "BAZEL_CANCER" +then + echo "Bazel includes seem to be set-up.." +else + USER=`whoami` + echo "#BAZEL_CANCER" >> ~/.bashrc + echo "source ~/.bazel/bin/bazel-complete.bash" >> ~/.bashrc + echo "export PATH=\"\$PATH:\$HOME/bin\"" >> ~/.bashrc + source ~/.bashrc + fi +fi + +if [ ! -d tensorflow ] +then +git clone https://github.com/tensorflow/tensorflow.git +fi + +cd tensorflow +git pull +git checkout r1.15 + + +./configure + +bazel clean --expunge + +#Flags for bazel if you have gcc<5.0 +#--cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" +#--noincompatible_do_not_split_linking_cmdline + +bazel build --config=opt --config=cuda --config=mkl --config=monolithic $BazelPleaseSlowDown //tensorflow/tools/pip_package:build_pip_package +./bazel-bin/tensorflow/tools/pip_package/build_pip_package ~/Documents/3dParty/ + + +bazel build --config opt --config=cuda --config=monolithic //tensorflow/tools/lib_package:libtensorflow +cp bazel-bin/tensorflow/tools/lib_package/libtensorflow.tar.gz ~/Documents/3dParty/libtensorflow-r1.15.tar.gz + +echo "Please visit ~/Documents/3dParty/ to collect your tensorflow python3 wheel, and C Library.." + + +exit 0 diff --git a/animation/MocapNET-kasisnu/src/GroundTruthGenerator/CMakeLists.txt b/animation/MocapNET-kasisnu/src/GroundTruthGenerator/CMakeLists.txt new file mode 100644 index 0000000..cbe3b93 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/GroundTruthGenerator/CMakeLists.txt @@ -0,0 +1,21 @@ +project( GroundTruthDumper ) +cmake_minimum_required( VERSION 2.8.13 ) +set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../cmake/modules ${CMAKE_MODULE_PATH}) + + +add_executable( +GroundTruthDumper +${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Applications/BVHTester/main.c +${CMAKE_SOURCE_DIR}/dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/TrajectoryParser/InputParser_C.c +${BVH_SOURCE} + ) + +target_link_libraries(GroundTruthDumper rt m pthread ) +#add_dependencies(GroundTruthDumper OGLRendererSandbox) + + +set_target_properties(GroundTruthDumper PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) diff --git a/animation/MocapNET-kasisnu/src/GroundTruthGenerator/README.md b/animation/MocapNET-kasisnu/src/GroundTruthGenerator/README.md new file mode 100644 index 0000000..32db86e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/GroundTruthGenerator/README.md @@ -0,0 +1,6 @@ +To simplify package maintenance the GroundTruthGenerator uses the code from the BVHTester of RGBDAcquisition +If you want to browse the code, i.e. to find about different command line options this is the [main.c](https://github.com/AmmarkoV/RGBDAcquisition/blob/master/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Applications/BVHTester/main.c) + +All the implementation files to parse BVH files are part of the MotionCaptureLoader module of the RGDAcquisition OGL Renderer Sandbox. You can [find the bvh code here](https://github.com/AmmarkoV/RGBDAcquisition/tree/master/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader) + +Sorry for having different stuff in different places but it really minimizes the effort needed to relase the code and it also reduces the number of possible bugs from keeping the same code in sync in different machines etc. diff --git a/animation/MocapNET-kasisnu/src/HelloWorld/CMakeLists.txt b/animation/MocapNET-kasisnu/src/HelloWorld/CMakeLists.txt new file mode 100644 index 0000000..6cd1790 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/HelloWorld/CMakeLists.txt @@ -0,0 +1,19 @@ +project( helloWorld ) +cmake_minimum_required(VERSION 3.5) + + + +#----------------------------------------------- +# This is the converter utilities.. +#----------------------------------------------- +project( helloWorld ) +add_executable(helloWorld main.cpp) +target_link_libraries(helloWorld rt dl m ) +set_target_properties(helloWorld PROPERTIES DEBUG_POSTFIX "D") +set_target_properties(helloWorld PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + + diff --git a/animation/MocapNET-kasisnu/src/HelloWorld/main.cpp b/animation/MocapNET-kasisnu/src/HelloWorld/main.cpp new file mode 100644 index 0000000..0249636 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/HelloWorld/main.cpp @@ -0,0 +1,8 @@ + +#include + +int main() { + std::cout << "\n\n\n\n\n\nHello World!\n"; + std::cout << "Congrats, If you can read this you can compile C++ code using CMake..!\n\n\n\n\n"; + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/HelloWorld/testCMake.sh b/animation/MocapNET-kasisnu/src/HelloWorld/testCMake.sh new file mode 100755 index 0000000..4badb65 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/HelloWorld/testCMake.sh @@ -0,0 +1,14 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +mkdir build +cd build +cmake .. +make + +cd "$DIR" +./helloWorld + +exit 0 diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/CMakeLists.txt b/animation/MocapNET-kasisnu/src/JointEstimator2D/CMakeLists.txt new file mode 100644 index 0000000..cc391fb --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/CMakeLists.txt @@ -0,0 +1,45 @@ +project( JointEstimator2D ) +cmake_minimum_required(VERSION 3.5) + + +set_property(GLOBAL PROPERTY USE_FOLDERS ON) +#set(CMAKE_CXX_STANDARD 11) + +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + +add_library( + JointEstimator2D SHARED + jointEstimator2D.cpp + cameraControl.cpp + visualization.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tensorflow.cpp + ${CMAKE_SOURCE_DIR}/src/Tensorflow/tf_utils.cpp + ) + +target_link_libraries(JointEstimator2D rt dl m Tensorflow TensorflowFramework ) +set_target_properties(JointEstimator2D PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(JointEstimator2D PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + + + + +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +add_executable(WebcamAnd2DJoints webcamAnd2DJoints.cpp) +target_link_libraries(WebcamAnd2DJoints rt dl m ${OpenCV_LIBRARIES} JointEstimator2D Tensorflow TensorflowFramework) +set_target_properties(WebcamAnd2DJoints PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(WebcamAnd2DJoints PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/cameraControl.cpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/cameraControl.cpp new file mode 100644 index 0000000..0b22cb6 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/cameraControl.cpp @@ -0,0 +1,433 @@ +#include "cameraControl.hpp" + +#include "../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" + +#include + + +const unsigned int noiseThreshold = 30; //pixels + + +/** + * @brief In order to have the best possible quality we can crop the input frame to only perform detection around the area of the previous skeleton + * This code performs this crop and tries to get the best detection window + * @ingroup demo + * @bug This code is oriented to a single 2D skeleton detected, Multiple skeletons will confuse it and there is no logic to handle them + * @retval 1=Success/0=Failure + */ +int getMaximumCropWindow( + unsigned int * x, + unsigned int * y, + unsigned int * width, + unsigned int * height, + struct boundingBox * bboxExternal, + unsigned int inputWidth2DJointDetector, + unsigned int inputHeight2DJointDetector, + unsigned int fullFrameWidth, + unsigned int fullFrameHeight +) +{ + //Make a local copy in case of failure + struct boundingBox bboxLocal = *bboxExternal; + struct boundingBox * bbox = &bboxLocal; + + + float paddingPercent=0.2; // 0.1 = 10% etc + + if ( (bbox!=0) && (bbox->populated) ) + { + unsigned int previousX = *x; + unsigned int previousY = *y; + unsigned int previousWidth = *width; + unsigned int previousHeight = *height; + + //std::cerr<<"Previous Bounding Box was "<<*x<<","<<*y<<" to "<<*x+*width<<","<<*y+*height<<" \n"; + + // Bring bounding box to normal coordinates + //------------------------------------------------ + bbox->minimumX+= (float) *x; + bbox->maximumX+= (float) *x; + bbox->minimumY+= (float) *y; + bbox->maximumY+= (float) *y; + + //Calculate the dimensions of the bounding box body and its center + //---------------------------------------------------------------------------------------------------------- + float bodyWidth = bbox->maximumX - bbox->minimumX; + float bodyHeight = bbox->maximumY - bbox->minimumY; + float bodyCenterX = bbox->minimumX + (float) bodyWidth/2; + float bodyCenterY = bbox->minimumY + (float) bodyHeight/2; + + + //If the body is smaller than our input size lets get some more detail in so we avoid scaling up + //---------------------------------------------------------------------------------------------------------------------------------------------------- + if (inputWidth2DJointDetector >bodyWidth ) + { + bodyWidth = inputWidth2DJointDetector; + } + if (inputHeight2DJointDetector> bodyHeight ) + { + bodyHeight = inputHeight2DJointDetector; + } + + + //Given that we have our new size and center we can now calculate the new offset from center + //-------------------------------------------------------------------------------------------------------------------------------------------------------- + float bodyOffsetX = (float) bodyWidth/2; + float bodyOffsetY = (float) bodyHeight/2; + + //And using the new offset derive the new bounding box + //-------------------------------------------------------------------------------------------------------------------------------------------------------- + float pixelsPaddedX = paddingPercent * bodyWidth; + float pixelsPaddedY = paddingPercent * bodyHeight; + + //We now have a preliminary bounding box BUT the final bounding box we want is a rectangle so we need to keep the biggest dimension + //------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + if ( bodyOffsetX+pixelsPaddedX> bodyOffsetY+pixelsPaddedY) + { + bodyOffsetX = bodyOffsetX + pixelsPaddedX; + bodyOffsetY = bodyOffsetX + pixelsPaddedX; + } + else + { + bodyOffsetX = bodyOffsetY + pixelsPaddedY; + bodyOffsetY = bodyOffsetY + pixelsPaddedY; + } + + //We now have a rectangle bounding box + //---------------------------------------------------------------- + bbox->minimumX = bodyCenterX - bodyOffsetX; + bbox->maximumX = bodyCenterX + bodyOffsetX; + bbox->minimumY = bodyCenterY - bodyOffsetY; + bbox->maximumY = bodyCenterY + bodyOffsetY; + + //We do a last bounds check + //-------------------------------------------------------------------------------------------------------------------------------------------------------- + signed int limitLX=0,limitLY=0,limitRX=0,limitRY=0; + if (bbox->minimumX < 0 ) + { + limitLX=-1*bbox->minimumX; + bbox->minimumX = 0; + } + if (bbox->minimumY < 0 ) + { + limitLY=-1*bbox->minimumY; + bbox->minimumY = 0; + } + if (bbox->maximumX > fullFrameWidth ) + { + limitRX=bbox->maximumX-fullFrameWidth; + bbox->maximumX = fullFrameWidth; + } + if (bbox->maximumY > fullFrameHeight ) + { + limitRY=bbox->maximumY-fullFrameHeight; + bbox->maximumY = fullFrameHeight; + } + + + //We now have a preliminary bounding box the rectangle might be in a corner and fall out of bounds + //-------------------------------------------------------------------------------------------------------------------------------------------------------- + + //We are maybe constrained left/up add what we lost right/down..! + if (limitLX>0) + { + bbox->maximumX += limitLX; + } + if (limitLY>0) + { + bbox->maximumY += limitLY; + } + + //We are maybe constrained right/down we subtract what we lost left/up ..! + if (limitRX>0) + { + bbox->minimumX -= limitRX; + } + if (limitRY>0) + { + bbox->minimumY -= limitRY; + } + + if ( (limitLX!=0) || (limitLY!=0) || (limitRX!=0) || (limitRY!=0) ) + { + //We have failed to initially resolve the bounding box..! + //std::cerr<<"Initial resolve the bounding box X1:"<minimumX < 0 ) + { + limitLX=1; + bbox->minimumX = 0; + } + if (bbox->minimumY < 0 ) + { + limitLY=1; + bbox->minimumY = 0; + } + if (bbox->maximumX > fullFrameWidth ) + { + limitRX=1; + bbox->maximumX = fullFrameWidth; + } + if (bbox->maximumY > fullFrameHeight ) + { + limitRY=1; + bbox->maximumY = fullFrameHeight; + } + + if ( (limitLX!=0) || (limitLY!=0) || (limitRX!=0) || (limitRY!=0) ) + { + //We have failed to resolve the bounding box..! + //std::cerr<<"We have failed to resolve the bounding box X1:"<minimumX<<",Y1:"<minimumY<<",X2:"<maximumX<<",Y2:"<maximumY<<"..\n"; + return 0; + } + + + //And our output is ready for use..! + //-------------------------------------------------------------------------------------------------------------------------------------------------------- + if ( + ( abs(previousX - (unsigned int) bbox->minimumX ) > noiseThreshold ) || + ( abs(previousY - (unsigned int) bbox->minimumY ) > noiseThreshold ) || + ( abs(previousWidth - (unsigned int) ( bbox->maximumX - bbox->minimumX ) ) > 2*noiseThreshold ) || + ( abs(previousHeight - (unsigned int) ( bbox->maximumY - bbox->minimumY ) ) > 2*noiseThreshold ) + ) + { + *x=(unsigned int) bbox->minimumX ; + *y=(unsigned int) bbox->minimumY ; + *width = (unsigned int) bbox->maximumX - bbox->minimumX; + *height= (unsigned int) bbox->maximumY - bbox->minimumY; + + if (*width == *height +1 ) + { + *width = *height; + } + + //--------------------------------------------------------------------------------------------------------------------------------------------------------------- + //std::cerr<<"New MAX Bounding Box is "<<*x<<","<<*y<<" to "<<*x+*width<<","<<*y+*height<<" Image("<minimumX<<","<< bbox->minimumY<<" to "<< bbox->maximumX<<","<< bbox->maximumY<<" Image("<populated) ) + { + //fprintf(stderr,"This means that the bounding box (%0.2f,%0.2f) -> (%0.2f,%0.2f)\n",bbox->minimumX,bbox->minimumY,bbox->maximumX,bbox->maximumY); + bbox->minimumX+=(float) *x; + bbox->maximumX+=(float) *x; + bbox->minimumY+=(float) *y; + bbox->maximumY+=(float) *y; + //fprintf(stderr,"is actually (%0.2f,%0.2f) -> (%0.2f,%0.2f)\n",bbox->minimumX,bbox->minimumY,bbox->maximumX,bbox->maximumY); + } + + + unsigned int dimension = fullFrameHeight; + float bodyWidth = bbox->maximumX - bbox->minimumX; + float bodyHeight = bbox->maximumY - bbox->minimumY; + //fprintf(stderr,"Body starts at %0.2f and ends at %0.2f for a total of %0.2f pixels\n", bbox->minimumX , bbox->maximumX , bbox->maximumX-bbox->minimumX); + if (fullFrameWidth>=fullFrameHeight) + { + //fprintf(stderr,"Since the whole frame has %u pixels\n",fullFrameWidth); + //fprintf(stderr,"And we will use the dimension of the Y axis aka %u pixels to crop a rectangle\n",fullFrameHeight); + + if ( (bbox!=0) && (bbox->populated) ) + { + dimension = fullFrameHeight; + //TODO: + //if (bodyHeightminimumX + (float) bodyWidth/2; + //fprintf(stderr,"The center X of the body lies at %0.2f\n",bodyCenterX); + + //fprintf(stderr,"We can afford %u pixels left and right of the body center \n",(unsigned int) *width/2); + float cropStartX = bodyCenterX - *width/2; + + if (cropStartX<0) + { + cropStartX=0; //Overflow.. + } + if (cropStartX>fullFrameWidth-fullFrameHeight) + { + cropStartX=fullFrameWidth-fullFrameHeight; + } + + //Neural Networks cause flicker, if we don't have exactly the same + //bounding box it's ok just keep the previous, it will help with flicker a lot.. + if ( abs(*x - (unsigned int) cropStartX) > noiseThreshold ) + { + *x = (unsigned int) cropStartX; + } + //fprintf(stderr,"This means starting at %u and ending at %u\n",(unsigned int) *x, *x+*width); + + /* + //------------------------------------------------------------------------------------------------------- + //------------------------------------------------------------------------------------------------------- + // Center on Y axis.. + //------------------------------------------------------------------------------------------------------- + //------------------------------------------------------------------------------------------------------- + float bodyCenterY = bbox->minimumY+ (float) bodyHeight/2; + //fprintf(stderr,"The center X of the body lies at %0.2f\n",bodyCenterX); + + //fprintf(stderr,"We can afford %u pixels up and down of the body center \n",(unsigned int) *height/2); + float cropStartY = bodyCenterY - *height/2; + + if (cropStartY<0) { cropStartY=0; } //Overflow.. + if (cropStartY>fullFrameHeight) { cropStartY=fullFrameHeight; } + + //Neural Networks cause flicker, if we don't have exactly the same + //bounding box it's ok just keep the previous, it will help with flicker a lot.. + if ( abs(*y - (unsigned int) cropStartY) > 4 ) + { + *y = (unsigned int) cropStartY; + } + */ + + + //std::cerr<<"Normal Bounding Box derived "<<*x<<","<<*y<<" to "<<*x+*width<<","<<*y+*height<<" \n"; + } + else + { + //No skeleton? just crop in the center.. + *x=(fullFrameWidth-fullFrameHeight)/2; + *y=0; + *width=fullFrameHeight; + *height=fullFrameHeight; + } + } + else if (fullFrameWidthpopulated) ) + { + dimension = fullFrameWidth; + //TODO: + //if (bodyHeightminimumY + (float) bodyHeight/2; + //fprintf(stderr,"The center X of the body lies at %0.2f\n",bodyCenterX); + + //fprintf(stderr,"We can afford %u pixels left and right of the body center \n",(unsigned int) *width/2); + float cropStartY = bodyCenterY - *width/2; + + if (cropStartY<0) + { + cropStartY=0; //Overflow.. + } + if (cropStartY>fullFrameHeight-fullFrameWidth) + { + cropStartY=fullFrameHeight-fullFrameWidth; + } + + //Neural Networks cause flicker, if we don't have exactly the same + //bounding box it's ok just keep the previous, it will help with flicker a lot.. + if ( abs(*y - (unsigned int) cropStartY) > noiseThreshold ) + { + *y = (unsigned int) cropStartY; + } + } + else + { + //No skeleton? just crop in the center.. + *x=0; + *y=(fullFrameHeight-fullFrameWidth)/2; + *width=fullFrameWidth; + *height=fullFrameWidth; + } + } + + if ( + ( *x + *width > fullFrameWidth ) || + ( *y + *height > fullFrameHeight ) + ) + { + //We failed..! + *x=0; + *y=0; + *width=fullFrameWidth; + *height=fullFrameWidth; + return 0; + } + + return 1; +} diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/cameraControl.hpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/cameraControl.hpp new file mode 100644 index 0000000..0e05e72 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/cameraControl.hpp @@ -0,0 +1,19 @@ +#pragma once + + +#include "opencv2/opencv.hpp" +using namespace cv; + + +int getBestCropWindow( + int maximumCrop, + unsigned int * x, + unsigned int * y, + unsigned int * width, + unsigned int * height, + struct boundingBox * bbox, + unsigned int inputWidth2DJointDetector, + unsigned int inputHeight2DJointDetector, + unsigned int fullFrameWidth, + unsigned int fullFrameHeight +); diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/jointEstimator2D.cpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/jointEstimator2D.cpp new file mode 100644 index 0000000..508d8a8 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/jointEstimator2D.cpp @@ -0,0 +1,725 @@ +#include "jointEstimator2D.hpp" +#include +#include // std::sort +#include +#include +#include + +unsigned long tickBaseJointEstimator = 0; + +unsigned long GetTickCountMicrosecondsJointEstimator() +{ + struct timespec ts; + if ( clock_gettime(CLOCK_MONOTONIC,&ts) != 0) + { + return 0; + } + + if (tickBaseJointEstimator==0) + { + tickBaseJointEstimator = ts.tv_sec*1000000 + ts.tv_nsec/1000; + return 0; + } + + return ( ts.tv_sec*1000000 + ts.tv_nsec/1000 ) - tickBaseJointEstimator; +} + + +int dumpHeatmapToCSVFile(const char * filename,struct JointEstimator2D * jnet,std::vector heatmap) +{ + unsigned int width = jnet->heatmapWidth2DJointDetector; + unsigned int height = jnet->heatmapHeight2DJointDetector; + + FILE * fp = fopen(filename,"w"); + if (fp!=0) + { + //--------------------------- + // Header + //--------------------------- + for (int x=0; x0.01) + { + fprintf(fp,"%f",heatmap[i]); + } + else if (-0.01>heatmap[i]) + { + fprintf(fp,"%f",heatmap[i]); + } + else + { + fprintf(fp,"0"); + } + ++i; + } + fprintf(fp,"\n"); + } + //--------------------------- + fclose(fp); + return 1; + } + return 0; +} + + +int loadJointEstimator2D(struct JointEstimator2D * jnet,int joint2DEstimatorSelected,int usePAFs,unsigned int forceCPU) +{ + memset(jnet,0,sizeof(struct JointEstimator2D)); + + const char networkPathOpenPoseMiniStatic[]="dataset/combinedModel/openpose_model.pb"; + const char networkPathVnectStatic[]="dataset/combinedModel/vnect_sm_pafs_8.1k.pb"; + const char networkPathFORTHStatic[]="dataset/combinedModel/mobnet2_tiny_vnect_sm_1.9k.pb"; + jnet->networkPath = (char*) networkPathFORTHStatic; + jnet->joint2DSensitivityPercent=30; + + char networkInputLayer[]="input_1"; + char networkOutputLayer[]="k2tfout_0"; + jnet->numberOfOutputTensors = 3; + jnet->heatmapWidth2DJointDetector= 46; + jnet->heatmapHeight2DJointDetector= 46; + + jnet->inputWidth2DJointDetector = 368; + jnet->inputHeight2DJointDetector = 368; + jnet->numberOfHeatmaps = 19; + + //----------------------------------------------------------------------------- + switch (joint2DEstimatorSelected) + { + case JOINT_2D_ESTIMATOR_FORTH : + jnet->networkPath=(char*) networkPathFORTHStatic; + networkOutputLayer[8]='0'; + jnet->joint2DSensitivityPercent=30; + jnet->numberOfOutputTensors = 3; + break; + case JOINT_2D_ESTIMATOR_VNECT : + jnet->networkPath = (char*) networkPathVnectStatic; + networkOutputLayer[8]='1'; + jnet->joint2DSensitivityPercent=20; + jnet->numberOfOutputTensors = 4; + break; + case JOINT_2D_ESTIMATOR_OPENPOSE : + jnet->networkPath=(char*) networkPathOpenPoseMiniStatic; + networkOutputLayer[8]='1'; + jnet->joint2DSensitivityPercent=40; + jnet->numberOfOutputTensors = 4; + break; + default : + fprintf(stderr,"No 2D Joint Estimator Selected..\n"); + break; + }; + + //----------------------------------------------------------------------------- + if ( + loadTensorflowInstance( + &jnet->network, + jnet->networkPath, + networkInputLayer, + networkOutputLayer, + forceCPU + ) + ) + { + return 1; + } + //----------------------------------------------------------------------------- + return 0; +} + + + +int unloadJointEstimator2D(struct JointEstimator2D * jnet) +{ + return unloadTensorflow(&jnet->network); +} + + +int sortBlobsBasedOnMaximumActivation(struct HeatmapBlob blobA,struct HeatmapBlob blobB) +{ + return blobA.peakValue > blobB.peakValue; +} + + + +int updateSubpixelPeak(struct JointEstimator2D * jnet,struct HeatmapBlob * blob,std::vector heatmap,unsigned int width,unsigned int height) +{ + //Uncomment to disable subpixel code.. + //blob->subpixelPeakX = (float) blob->peakX; + //blob->subpixelPeakY = (float) blob->peakY; + //return 0; + +#define DEBUG_SUBPIXEL_GRADIENT 0 + +#if DEBUG_SUBPIXEL_GRADIENT + std::vector gradientX(width*height); + std::vector gradientY(width*height); +#endif + + + if (blob->peakValue==0.0) + { + return 0; + } + float vX=(float) blob->peakX; + float vY=(float) blob->peakY; + float normalizedValue=0.0; + unsigned int location=0; + + for (signed int y=0; yheight; y++) + { + for (signed int x=0; xwidth; x++) + { + //fprintf(stderr,"Blob element (x=%u,y=%u) ",x,y); + location = (width * (y+blob->y)) + (x+blob->x); + //fprintf(stderr,"Location %u",location); + + if (locationpeakValue; + normalizedValue= normalizedValue / ( blob->width * blob->height); + //fprintf(stderr,"NormalValue %0.2f",normalizedValue); + + signed int peakInRelationToBlobX = (signed int) ((signed int) blob->peakX - (signed int) blob->x); + signed int peakInRelationToBlobY = (signed int) ((signed int) blob->peakY - (signed int) blob->y); + + signed int relativePositionToPeakX = (signed int) ( (signed int) x - (signed int) peakInRelationToBlobX); + signed int relativePositionToPeakY = (signed int) ( (signed int) y - (signed int) peakInRelationToBlobY); + //fprintf(stderr,"Ppeak %d,%d",posX,posY); + + + + float nX=(float) relativePositionToPeakX*normalizedValue; + float nY=(float) relativePositionToPeakY*normalizedValue; + //fprintf(stderr,"Add %f,%f\n",nX,nY); + +#if DEBUG_SUBPIXEL_GRADIENT + gradientX[location]=nX; + gradientY[location]=nY; +#endif + + + if ( (nX!=nX) || (nY!=nY) ) + { + //Handle NaN + } + else + { + //fprintf(stderr,"%f,%f ",nX,nY); + vX=vX+nX; + vY=vY+nY; + } + } + else + { + fprintf(stderr,"updateSubpixelPeak overflow..\n"); + return 0; + } + } + } + +#if DEBUG_SUBPIXEL_GRADIENT + char filename[512]; + snprintf(filename,512,"GradientX.csv"); + dumpHeatmapToCSVFile(filename,jnet,gradientX); + snprintf(filename,512,"GradientY.csv"); + dumpHeatmapToCSVFile(filename,jnet,gradientY); + snprintf(filename,512,"Values.csv"); + dumpHeatmapToCSVFile(filename,jnet,heatmap); +#endif + + blob->subpixelPeakX = vX; + blob->subpixelPeakY = vY; + return 1; +} + + +int isNeighborWithBlob(char * blobLabels,int x,int y,int width,int height) +{ + for (int iY=-1; iY<1; iY++) + { + for (int iX=-1; iX<=1; iX++) + { + if ( (iX==0) && (iY==0) ) + { + + } + else if ( + (iX+x>=0) && + (iY+y>=0) && + (iX+x getBlobsFromHeatmap(struct JointEstimator2D * jnet,unsigned int heatmapID,std::vector heatmap) +{ + unsigned int width = jnet->heatmapWidth2DJointDetector; + unsigned int height = jnet->heatmapHeight2DJointDetector; + + struct HeatmapBlob emptyBlob; + emptyBlob.x=width+1; + emptyBlob.y=height+1; + emptyBlob.width=0; + emptyBlob.height=0; + emptyBlob.width=0; + + emptyBlob.peakX=0; + emptyBlob.peakY=0; + emptyBlob.peakValue=0.0; + + emptyBlob.subpixelPeakX=0.0; + emptyBlob.subpixelPeakY=0.0; + + std::vector blobsEncountered; + char blobLabels[width*height]= {0}; + unsigned int currentBlobLabel=1; + int onHorizontalBlob=0; + int labelID=0; + + unsigned int i=0; + float threshold = 0.1; //(float) jnet->joint2DSensitivityPercent/100; + for (int y=0; y threshold ) + { + + if (!onHorizontalBlob) + { + //We were not on a horizontal blob so we need to search..! + labelID=isNeighborWithBlob(blobLabels,x,y,width,height); + if (!labelID) + { + //Our search did not give us a result + //This is a new blob disconnected from previous blobs..! + blobLabels[i]=currentBlobLabel; + onHorizontalBlob=currentBlobLabel; + labelID=currentBlobLabel-1; + ++currentBlobLabel; + blobsEncountered.push_back(emptyBlob); + onHorizontalBlob=1; + } + else + { + //Already Existing Blob found we will add this pixel to it + onHorizontalBlob=labelID; + blobLabels[i]=onHorizontalBlob; + labelID=labelID-1; + } + } + else + { + //If we are already on a horizontal blob dont do expensive search + blobLabels[i]=onHorizontalBlob; + labelID=onHorizontalBlob-1; + } + + + if (labelIDblobsEncountered[labelID].width) + { + blobsEncountered[labelID].width=thisLineWidth; + } + + //Update Blob Height + int thisLineHeight=1+y-blobsEncountered[labelID].y; + if (thisLineHeight>blobsEncountered[labelID].height) + { + blobsEncountered[labelID].height=thisLineHeight; + } + + //Update Peak to use as center later.. + if (blobsEncountered[labelID].peakValue",blobsEncountered[blobID].peakX,blobsEncountered[blobID].peakY); + fprintf(stderr,"%0.2f",blobsEncountered[blobID].peakValue); + + fprintf(stderr," subpixel(%0.2f,%0.2f)",blobsEncountered[blobID].subpixelPeakX,blobsEncountered[blobID].subpixelPeakY); + fprintf(stderr,"\n"); + } + */ + + return blobsEncountered; +} + + + + +int estimate2DSkeletonsFromHeatmaps(struct JointEstimator2D * jnet,struct Skeletons2DDetected * result,std::vector > heatmaps) +{ + //char filename[512]; + //fprintf(stderr,"New Frame:\n"); + + unsigned long startTime = GetTickCountMicrosecondsJointEstimator(); + int blobsProceesed=0; + for (int heatmapID=0; heatmapIDskeletons[0].body.joint2D[body25ID].x = 0; + result->skeletons[0].body.joint2D[body25ID].y = 0; + result->skeletons[0].body.active[body25ID]=0.0; + //-------------------------------------------------------- + std::vector blobs = getBlobsFromHeatmap(jnet,heatmapID,heatmaps[heatmapID]); + if (blobs.size()>0) + { + result->skeletons[0].body.isPopulated=1; + float x=(float) blobs[0].subpixelPeakX/ jnet->heatmapWidth2DJointDetector; + float y=(float) blobs[0].subpixelPeakY/ jnet->heatmapHeight2DJointDetector; + + result->skeletons[0].body.joint2D[body25ID].x = x; + result->skeletons[0].body.joint2D[body25ID].y = y; + if ( (x!=0) && (y!=0) ) { + result->skeletons[0].body.active[body25ID]=1.0; + } + + if (blobsProceesed==0) + { + //Update Bbox Min + result->skeletons[0].body.bbox2D[0].x = x; + result->skeletons[0].body.bbox2D[0].y = y; + //Update Bbox Max + result->skeletons[0].body.bbox2D[1].x = x; + result->skeletons[0].body.bbox2D[1].y = y; + } + else + { + // if Min X > current X + if (result->skeletons[0].body.bbox2D[0].x > x ) + { + result->skeletons[0].body.bbox2D[0].x =x; + } + // if Min Y > current Y + if (result->skeletons[0].body.bbox2D[0].y > y ) + { + result->skeletons[0].body.bbox2D[0].y =y; + } + // if Max X < current X + if (result->skeletons[0].body.bbox2D[1].x < x ) + { + result->skeletons[0].body.bbox2D[1].x =x; + } + // if Max Y < current Y + if (result->skeletons[0].body.bbox2D[1].y < y ) + { + result->skeletons[0].body.bbox2D[1].y =y; + } + } + + ++blobsProceesed; + } + //-------------------------------------------------------- + //snprintf(filename,512,"heatmap_%u.csv",heatmapID); + //dumpHeatmapToCSVFile(filename,jnet,heatmaps[0]); + //-------------------------------------------------------- + } + } + + + /* + The following joints are not covered by the current networks + BODY25_MidHip, + BODY25_LBigToe, + BODY25_LSmallToe, + BODY25_LHeel, + BODY25_RBigToe, + BODY25_RSmallToe, + BODY25_RHeel, + */ + + //Mid hip can be approximated by LHip and RHip + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + if ( + (result->skeletons[0].body.joint2D[BODY25_RHip].x!=0) && (result->skeletons[0].body.joint2D[BODY25_LHip].x!=0) && + (result->skeletons[0].body.joint2D[BODY25_RHip].y!=0) && (result->skeletons[0].body.joint2D[BODY25_LHip].y!=0) + ) + { + result->skeletons[0].body.joint2D[BODY25_MidHip].x = (float) (result->skeletons[0].body.joint2D[BODY25_RHip].x+result->skeletons[0].body.joint2D[BODY25_LHip].x)/2; + result->skeletons[0].body.joint2D[BODY25_MidHip].y = (float) (result->skeletons[0].body.joint2D[BODY25_RHip].y+result->skeletons[0].body.joint2D[BODY25_LHip].y)/2; + result->skeletons[0].body.jointAccuracy[BODY25_MidHip] = (result->skeletons[0].body.jointAccuracy[BODY25_RHip]+result->skeletons[0].body.jointAccuracy[BODY25_LHip])/2; + result->skeletons[0].body.active[BODY25_MidHip] = 1.0; + //fprintf(stderr,"Mid hip populated and resides @ (%0.2f,%0.2f)\n",result->skeletons[0].body.joint2D[BODY25_MidHip].x,result->skeletons[0].body.joint2D[BODY25_MidHip].y); + } else + { + result->skeletons[0].body.joint2D[BODY25_MidHip].x = 0; + result->skeletons[0].body.joint2D[BODY25_MidHip].y = 0; + result->skeletons[0].body.jointAccuracy[BODY25_MidHip] = 0; + result->skeletons[0].body.active[BODY25_MidHip] = 0.0; + //fprintf(stderr,"Cannot populate Mid hip, this will degrade output!\n"); + } + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + result->numberOfSkeletonsDetected=1; + + unsigned long endTime = GetTickCountMicrosecondsJointEstimator(); + +// fprintf(stderr,"Resolving joint subpixel peaks took %lu microseconds\n",endTime-startTime); + + return 1; +} + + + + +int convertNormalized2DJointsToOriginalImageCoordinates( + struct JointEstimator2D * jnet, + float * x, + float * y, + int correctOffset +) +{ + //It is very common for points to be empty, they cannot be normalized so its best if we have + //a quick path for them.. + if ( (*x==0.0) && (*y==0.0) ) { + return 1; + } + + if (*x>1.0) { + *x=1.0; + } + if (*y>1.0) { + *y=1.0; + } + + float normalizedX = *x; + float normalizedY = *y; + + //fprintf(stderr,"Initial point is %0.2f,%0.2f\n",normalizedX,normalizedY); + + float frameOfReferenceOfTensorflowX = normalizedX * jnet->inputWidth2DJointDetector; + float frameOfReferenceOfTensorflowY = normalizedY * jnet->inputHeight2DJointDetector; + + //fprintf(stderr,"Tensorflow point is %0.2f,%0.2f\n",frameOfReferenceOfTensorflowX,frameOfReferenceOfTensorflowY); + + float scaleFromTensorflowToBoundingBoxX = (float) jnet->crop.croppedDimensionWidth / jnet->inputWidth2DJointDetector; + float scaleFromTensorflowToBoundingBoxY = (float) jnet->crop.croppedDimensionHeight / jnet->inputHeight2DJointDetector; + + *x = /*jnet->crop.offsetX +*/ (frameOfReferenceOfTensorflowX * scaleFromTensorflowToBoundingBoxX); + *y = /*jnet->crop.offsetY +*/ (frameOfReferenceOfTensorflowY * scaleFromTensorflowToBoundingBoxY); + + if (correctOffset) + { + *x += jnet->crop.offsetX; + *y += jnet->crop.offsetY; + } + //fprintf(stderr,"Final point is %0.2f,%0.2f\n",*x,*y); + + return 1; +} + + + + +std::vector > getHeatmaps(struct JointEstimator2D * jnet,unsigned char * rgbData,unsigned int width,unsigned int height) +{ +// pass the frame to the Estimator + + unsigned long startTime = GetTickCountMicrosecondsJointEstimator(); + std::vector > joint2DOutput = predictTensorflowOnArrayOfHeatmaps( + &jnet->network, + (unsigned int) width, + (unsigned int) height, + (float*) rgbData, + jnet->heatmapWidth2DJointDetector, + jnet->heatmapHeight2DJointDetector, + jnet->numberOfOutputTensors + ); + unsigned long endTime = GetTickCountMicrosecondsJointEstimator(); + +//fprintf(stderr,"Running 2D joint estimator took %lu microseconds\n",endTime-startTime); + return joint2DOutput; +} + + + +int estimate2DSkeletonsFromImage(struct JointEstimator2D * jnet,struct Skeletons2DDetected * result,unsigned char * rgbData,unsigned int width,unsigned int height) +{ + /* + unsigned long startTime = GetTickCountMicroseconds(); + std::vector > pointsOf2DSkeleton = predictAndReturnSingleSkeletonOf2DCOCOJoints( + &jnet->network, + bgr, + minThreshold, + visualize, + saveVisualization, + frameNumber, + inputWidth2DJointDetector, + inputHeight2DJointDetector, + heatmapWidth2DJointDetector, + heatmapHeight2DJointDetector, + numberOfHeatmaps, + numberOfOutputTensors + ); + unsigned long endTime = GetTickCountMicroseconds(); + *fps = convertStartEndTimeFromMicrosecondsToFPS(startTime,endTime); + */ + + + + // pass the frame to the Estimator + std::vector > joint2DOutput = predictTensorflowOnArrayOfHeatmaps( + &jnet->network, + (unsigned int) width, + (unsigned int) height, + (float*) rgbData, + jnet->heatmapWidth2DJointDetector, + jnet->heatmapHeight2DJointDetector, + jnet->numberOfOutputTensors + ); + + + return 0; +} + + +int restore2DJointsToInputFrameCoordinates(struct JointEstimator2D * jnet,struct Skeletons2DDetected * input) +{ + for (unsigned int skID=0; skIDnumberOfSkeletonsDetected; skID++) + { + for (int i=0; iskeletons[skID].body.joint2D[i].x,input->skeletons[skID].body.joint2D[i].y); + convertNormalized2DJointsToOriginalImageCoordinates(jnet,&input->skeletons[skID].body.joint2D[i].x,&input->skeletons[skID].body.joint2D[i].y,1); + //fprintf(stderr,"regular(%0.2f,%0.2f) ",input->skeletons[skID].body.joint2D[i].x,input->skeletons[skID].body.joint2D[i].y); + } + + /* //Currently dont have hand and head networks..! + for (int i=0; iskeletons[skID].leftHand.joint2D[i].x,&input->skeletons[skID].leftHand.joint2D[i].y,1); + } + + + for (int i=0; iskeletons[skID].rightHand.joint2D[i].x,&input->skeletons[skID].rightHand.joint2D[i].y,1); + } + + for (int i=0; iskeletons[skID].head.joint2D[i].x,&input->skeletons[skID].head.joint2D[i].y,1); + } + */ + } + return 1; +} + + + +float percentOf2DPointsMissing(struct Skeletons2DDetected * input) +{ + if (input->numberOfSkeletonsDetected==0) { + return 100.0; + } + + unsigned int jointsThatExist=0; + unsigned int jointsThatShouldExist=0; + if (input->skeletons[0].body.isPopulated) + { + jointsThatShouldExist=BODY25_PARTS-2; + for (int jID=0; jIDskeletons[0].body.active[jID] ) + { + ++jointsThatExist; + } + } + + //fprintf(stderr,"%u/%u joints observed\n",jointsThatExist,jointsThatShouldExist); + float percentOf2DPointsThatExist = (float) 100 * jointsThatExist / jointsThatShouldExist; + return (float) 100.0 - percentOf2DPointsThatExist; + } + + //fprintf(stderr,"Body is not populated\n"); + return 100.0; +} \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/jointEstimator2D.hpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/jointEstimator2D.hpp new file mode 100644 index 0000000..4ee36fa --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/jointEstimator2D.hpp @@ -0,0 +1,296 @@ +#pragma once +/** @file mocapnet.hpp + * @brief The MocapNET C library + * As seen in https://www.youtube.com/watch?v=fH5e-KMBvM0 , the MocapNET network requires two types of input. + * The first is an uncompressed list of (x,y,v) joints and the second an NSDM array. To add to those the output consists of BVH + * frames that must be accompanied by a header. This library internally handles all of these details. + * @author Ammar Qammaz (AmmarkoV) + */ +#include "../Tensorflow/tensorflow.hpp" +#include "../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" + +#include +#include + + + + + +/** + * @brief 2D Skeletons Retreieved + */ +struct Skeletons2DDetected +{ + unsigned int numberOfSkeletonsDetected; + struct skeletonStructure skeletons[16]; +}; + + + +struct InputImageCrop +{ + unsigned int offsetX; + unsigned int offsetY; + unsigned int croppedDimensionWidth; + unsigned int croppedDimensionHeight; + + //cropBBox, + unsigned int frameWidth; + unsigned int frameHeight; +}; + +/** + * @brief MocapNET consists of separate classes/ensembles that are invoked for particular orientations. + * This structure holds the required tensorflow instances to make MocapNET work. + */ +struct JointEstimator2D +{ + struct TensorflowInstance network; + char * networkPath; + float joint2DSensitivityPercent; + unsigned int numberOfOutputTensors; + unsigned int heatmapWidth2DJointDetector; + unsigned int heatmapHeight2DJointDetector; + unsigned int inputWidth2DJointDetector; + unsigned int inputHeight2DJointDetector; + unsigned int numberOfHeatmaps; + + struct InputImageCrop crop; + int doCrop; +}; + + + + +/** + * @brief Each Blob is a rectangular area.. + * with one peak.. + */ +struct HeatmapBlob +{ + unsigned int x; + unsigned int y; + unsigned int width; + unsigned int height; + + unsigned int peakX; + unsigned int peakY; + float peakValue; + + float subpixelPeakX; + float subpixelPeakY; +}; + + +/** + * @brief This is a list of included Joint 2D Estimators included. + */ +enum JOINT_2D_ESTIMATOR_SELECTED +{ + JOINT_2D_ESTIMATOR_NONE=0, + JOINT_2D_ESTIMATOR_FORTH, + JOINT_2D_ESTIMATOR_VNECT, + JOINT_2D_ESTIMATOR_OPENPOSE, + //----------------------------- + JOINT_2D_ESTIMATOR_NUMBER +}; + + +/** + * @brief This is an array of names for the input Joints expected from the utilities. + */ +static const char * UT_COCOBodyNames[] = +{ + "Nose", //0 + "Neck", //1 + "RShoulder", //2 + "RElbow", //3 + "RWrist", //4 + "LShoulder", //5 + "LElbow", //6 + "LWrist", //7 + "RHip", //8 + "RKnee", //9 + "RAnkle", //10 + "LHip", //11 + "LKnee", //12 + "LAnkle", //13 + "REye", //14 + "LEye", //15 + "REar", //16 + "LEar", //17 + "Bkg", //18 +//================= + "End of Joint Names" +}; + + + +/** + * @brief This is a list of included Joint 2D Estimators included. + */ +enum JOINT_2D_ESTIMATOR_PART_LABEL +{ + JOINT_2D_ESTIMATOR_PART_OF_NOTHING=0, + JOINT_2D_ESTIMATOR_PART_OF_TORSO, + JOINT_2D_ESTIMATOR_PART_OF_HEAD, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, + //----------------------------- + JOINT_2D_ESTIMATOR_NUMBER_OF_PARTS +}; + + +/** + * @brief We want to distinguish limbs with labels ( to color them ) + */ +static const int UT_COCOSkeletonJointsPartLabel[] = +{ + // Part of Joint + JOINT_2D_ESTIMATOR_PART_OF_TORSO, //UT_COCO_Nose, + JOINT_2D_ESTIMATOR_PART_OF_TORSO, //UT_COCO_Neck, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, //UT_COCO_RShoulder, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, //UT_COCO_RElbow, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, //UT_COCO_RWrist, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, //UT_COCO_LShoulder, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, //UT_COCO_LElbow, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, //UT_COCO_LWrist, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, //UT_COCO_RHip, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, //UT_COCO_RKnee, + JOINT_2D_ESTIMATOR_PART_OF_RIGHT, //UT_COCO_RAnkle, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, //UT_COCO_LHip, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, //UT_COCO_LKnee, + JOINT_2D_ESTIMATOR_PART_OF_LEFT, //UT_COCO_LAnkle, + JOINT_2D_ESTIMATOR_PART_OF_HEAD, //UT_COCO_REye, + JOINT_2D_ESTIMATOR_PART_OF_HEAD, //UT_COCO_LEye, + JOINT_2D_ESTIMATOR_PART_OF_HEAD, //UT_COCO_REar, + JOINT_2D_ESTIMATOR_PART_OF_HEAD, //UT_COCO_LEar, + JOINT_2D_ESTIMATOR_PART_OF_NOTHING //UT_COCO_Bkg +}; + + + +/** + * @brief This is a programmer friendly enumerator of joint names expected from the utilities ( hence the prefix UT_ ) . + * Please notice that these are not necessarily the same when converting to different data formats ( COCO / BODY25 / MocapNET etc ) + */ +enum UT_COCOSkeletonJoints +{ + UT_COCO_Nose=0, + UT_COCO_Neck, + UT_COCO_RShoulder, + UT_COCO_RElbow, + UT_COCO_RWrist, + UT_COCO_LShoulder, + UT_COCO_LElbow, + UT_COCO_LWrist, + UT_COCO_RHip, + UT_COCO_RKnee, + UT_COCO_RAnkle, + UT_COCO_LHip, + UT_COCO_LKnee, + UT_COCO_LAnkle, + UT_COCO_REye, + UT_COCO_LEye, + UT_COCO_REar, + UT_COCO_LEar, + UT_COCO_Bkg, + //--------------------- + UT_COCO_PARTS +}; + + +/** + * @brief This is the parent relation map of COCO skeleton joints , we can easily find a joints parent using this enumerator + */ +static const int UT_COCOSkeletonJointsParentRelationMap[] = +{ + // Parent Joint + UT_COCO_Nose, //UT_COCO_Nose, + UT_COCO_Nose, //UT_COCO_Neck, + UT_COCO_Neck, //UT_COCO_RShoulder, + UT_COCO_RShoulder, //UT_COCO_RElbow, + UT_COCO_RElbow, //UT_COCO_RWrist, + UT_COCO_Neck, //UT_COCO_LShoulder, + UT_COCO_LShoulder, //UT_COCO_LElbow, + UT_COCO_LElbow, //UT_COCO_LWrist, + UT_COCO_Neck, //UT_COCO_RHip, + UT_COCO_RHip, //UT_COCO_RKnee, + UT_COCO_RKnee, //UT_COCO_RAnkle, + UT_COCO_Neck, //UT_COCO_LHip, + UT_COCO_LHip, //UT_COCO_LKnee, + UT_COCO_LKnee, //UT_COCO_LAnkle, + UT_COCO_Nose, //UT_COCO_REye, + UT_COCO_Nose, //UT_COCO_LEye, + UT_COCO_REye, //UT_COCO_REar, + UT_COCO_LEye, //UT_COCO_LEar, + UT_COCO_Bkg //UT_COCO_Bkg +}; + + +/** + * @brief An array of indexes to the parents of BODY25 skeleton joints + */ +static const int heatmapCorrespondenceToBODY25[] = +{ + BODY25_Nose, // UT_COCO_Nose=0, + BODY25_Neck, // UT_COCO_Neck, + BODY25_RShoulder, //UT_COCO_RShoulder, + BODY25_RElbow,// UT_COCO_RElbow, + BODY25_RWrist,// UT_COCO_RWrist, + BODY25_LShoulder,//UT_COCO_LShoulder, + BODY25_LElbow,//UT_COCO_LElbow, + BODY25_LWrist,//UT_COCO_LWrist, + BODY25_RHip,//UT_COCO_RHip, + BODY25_RKnee,//UT_COCO_RKnee, + BODY25_RAnkle,//UT_COCO_RAnkle, + BODY25_LHip,//UT_COCO_LHip, + BODY25_LKnee,//UT_COCO_LKnee, + BODY25_LAnkle,//UT_COCO_LAnkle, + BODY25_REye,//UT_COCO_REye, + BODY25_LEye,//UT_COCO_LEye, + BODY25_REar,//UT_COCO_REar, + BODY25_LEar,//UT_COCO_LEar, + BODY25_Bkg,//UT_COCO_Bkg, +}; + + +unsigned long GetTickCountMicrosecondsJointEstimator(); + + + +/** + * @brief Load a 2D Joint Estimator from a .pb file on disk + * @ingroup jointestimator + * @param Pointer to a struct JointEstimator2D that will hold the tensorflow instances on load. + * @param Quality setting, can currently be 1.0 ( highest quality ), 1.5 or 2.0 ( highest performance ) + * @param Flag that controls the use of PAFs + * @param Force the usage of CPU for MocapNET ( should be 1 as MocapNET is designed for CPU while GPU handles 2D ) + * @retval 1 = Success loading the files , 0 = Failure + */ +int loadJointEstimator2D(struct JointEstimator2D * jnet,int joint2DEstimatorSelected,int usePAFs,unsigned int forceCPU); + + +int unloadJointEstimator2D(struct JointEstimator2D * jnet); + +int convertNormalized2DJointsToOriginalImageCoordinates( + struct JointEstimator2D * jnet, + float * x, + float * y, + int correctOffset + ); + +int estimate2DSkeletonsFromImage(struct JointEstimator2D * jnet,struct Skeletons2DDetected * result,unsigned char * rgbData,unsigned int width,unsigned int height); + + + + +int restore2DJointsToInputFrameCoordinates(struct JointEstimator2D * jnet,struct Skeletons2DDetected * input); + + +float percentOf2DPointsMissing(struct Skeletons2DDetected * input); + +int estimate2DSkeletonsFromHeatmaps(struct JointEstimator2D * jnet,struct Skeletons2DDetected * result,std::vector > heatmaps); + + +std::vector > getHeatmaps(struct JointEstimator2D * jnet,unsigned char * rgbData,unsigned int width,unsigned int height); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/visualization.cpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/visualization.cpp new file mode 100644 index 0000000..98cd490 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/visualization.cpp @@ -0,0 +1,217 @@ +#include "visualization.hpp" + +#include "cameraControl.hpp" + +int visualizeHeatmaps(struct JointEstimator2D * jointEstimator,std::vector > heatmapNNOutput,unsigned int frameNumber) +{ + unsigned int cols = jointEstimator->heatmapWidth2DJointDetector; + unsigned int rows = jointEstimator->heatmapHeight2DJointDetector; + unsigned int hm = jointEstimator->numberOfHeatmaps; + std::vector heatmaps; + for(int i=0; i(r,c) = heatmapNNOutput[i][pos]; + } + } + heatmaps.push_back(h); + } + +#define DISPLAY_ALL_HEATMAPS 1 + //This code segment will display every returned heatmap in it's own window.. +#if DISPLAY_ALL_HEATMAPS +int visualize=1; + if (visualize) + { + unsigned int x=0; + unsigned int y=0; + char windowLabel[128]; + snprintf(windowLabel,128,"T"); + + for(int i=0; i<18; ++i) + { + snprintf(windowLabel,128,"Heatmap %u",i); + if (frameNumber==0) + { + cv::namedWindow(windowLabel,1); + cv::moveWindow(windowLabel, x,y); + ++frameNumber; + } + cv::imshow(windowLabel,heatmaps[i]); + y=y+rows+30; + if (y>700) + { + x=x+cols; + y=0; + } + } + } +#endif + + return 1; +} + + + +//Basic Skeleton Visualization with big fonts to be legible on cluttered skeletons +void dj_drawExtractedSkeletons( + cv::Mat img, + struct Skeletons2DDetected * sk, + float factorX, + float factorY + ) +{ + cv::Scalar red = cv::Scalar(0,0,255); + cv::Scalar green = cv::Scalar(0,255,0); + cv::Scalar blue = cv::Scalar(255,0,0); + cv::Scalar color=blue; + +//Just the lines ( background layer) + for (int skID=0; skIDnumberOfSkeletonsDetected; skID++) + { + for (int i=0; iskeletons[skID].body.joint2D[jointID]; + + unsigned int parentID = heatmapCorrespondenceToBODY25[UT_COCOSkeletonJointsParentRelationMap[i%18]]; + if (parentID!=jointID) + { + struct point2D * parentPoint = & sk->skeletons[skID].body.joint2D[parentID]; + + if ( + (jointPoint->x > 0) && + (jointPoint->y > 0) && + (parentPoint->x > 0) && + (parentPoint->y > 0) + ) + { + cv::Point_ jointPointCV; + jointPointCV.x = jointPoint->x * factorX; + jointPointCV.y = jointPoint->y * factorY; + cv::Point_ parentPointCV; + parentPointCV.x = parentPoint->x * factorX; + parentPointCV.y = parentPoint->y * factorY; + + switch (UT_COCOSkeletonJointsPartLabel[i%18]) + { + case JOINT_2D_ESTIMATOR_PART_OF_TORSO: + case JOINT_2D_ESTIMATOR_PART_OF_HEAD: + color=blue; + break; + case JOINT_2D_ESTIMATOR_PART_OF_LEFT: + color=red; + break; + case JOINT_2D_ESTIMATOR_PART_OF_RIGHT: + color=green; + break; + }; + + cv::line(img,jointPointCV,parentPointCV,color,2.0); + } + } + } + } + + +//Just the points and text ( foreground ) + char textInfo[512]; + for (int skID=0; skIDnumberOfSkeletonsDetected; skID++) + { + for (int i=0; iskeletons[skID].body.joint2D[jointID]; + + cv::Point_ jointPointCV; + jointPointCV.x = jointPoint->x * factorX; + jointPointCV.y = jointPoint->y * factorY; + + if ( (jointPointCV.x>0) && (jointPointCV.y>0) ) + { + cv::circle(img,jointPointCV,3,cv::Scalar(255,0,0),3,8,0); + snprintf(textInfo,512,"%s(%u)",UT_COCOBodyNames[i],i); + cv::putText(img, textInfo , jointPointCV, cv::FONT_HERSHEY_DUPLEX, 0.3, cv::Scalar::all(255), 1,8); + } + } + } +} + + + + +int cropAndResizeCVMatToMatchSkeleton( + struct JointEstimator2D * jest, + cv::Mat & frame, + struct Skeletons2DDetected * sk +) +{ + int tryForMaximumCrop=0; + int result=0; + + jest->crop.frameWidth=frame.size().width; + jest->crop.frameHeight=frame.size().height; + + if (sk->numberOfSkeletonsDetected>0) + { + //------------------------------------------------------------------------------------ + sk->skeletons[0].bbox2D.minimumX = sk->skeletons[0].body.bbox2D[0].x; + sk->skeletons[0].bbox2D.minimumY = sk->skeletons[0].body.bbox2D[0].y; + convertNormalized2DJointsToOriginalImageCoordinates( + jest, + &sk->skeletons[0].bbox2D.minimumX, + &sk->skeletons[0].bbox2D.minimumY, + 0 + ); + //------------------------------------------------------------------------------------ + sk->skeletons[0].bbox2D.maximumX = sk->skeletons[0].body.bbox2D[1].x; + sk->skeletons[0].bbox2D.maximumY = sk->skeletons[0].body.bbox2D[1].y; + convertNormalized2DJointsToOriginalImageCoordinates( + jest, + &sk->skeletons[0].bbox2D.maximumX, + &sk->skeletons[0].bbox2D.maximumY, + 0 + ); + //------------------------------------------------------------------------------------ + + sk->skeletons[0].bbox2D.populated=1; + } + + if ( + getBestCropWindow( + tryForMaximumCrop, + &jest->crop.offsetX, + &jest->crop.offsetY, + &jest->crop.croppedDimensionWidth, + &jest->crop.croppedDimensionHeight, + &sk->skeletons[0].bbox2D, + jest->inputWidth2DJointDetector, + jest->inputHeight2DJointDetector, + frame.size().width, + frame.size().height + ) + ) + { + if (jest->crop.croppedDimensionWidth!=jest->crop.croppedDimensionHeight) + { + fprintf(stderr,"Bounding box produced was not a rectangle (%ux%u)..!\n",jest->crop.croppedDimensionWidth,jest->crop.croppedDimensionHeight); + } + cv::Rect rectangleROI( + jest->crop.offsetX, + jest->crop.offsetY, + jest->crop.croppedDimensionWidth, + jest->crop.croppedDimensionHeight + ); + frame = frame(rectangleROI); + sk->skeletons[0].bbox2D.populated=0; + result=1; + } + + cv::resize(frame,frame,cv::Size(jest->inputWidth2DJointDetector,jest->inputHeight2DJointDetector)); + return result; +} diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/visualization.hpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/visualization.hpp new file mode 100644 index 0000000..306283a --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/visualization.hpp @@ -0,0 +1,31 @@ +#pragma once + +#include "opencv2/opencv.hpp" +/** @file webcam.cpp + * @brief This is a simple test file to make sure your camera or video files can be opened using OpenCV + * @author Ammar Qammaz (AmmarkoV) + */ +#include +#include +#include "jointEstimator2D.hpp" + +#include "../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" + +using namespace cv; + +int visualizeHeatmaps(struct JointEstimator2D * jointEstimator,std::vector > heatmapNNOutput,unsigned int frameNumber); + +void dj_drawExtractedSkeletons( + cv::Mat img, + struct Skeletons2DDetected * sk, + float factorX, + float factorY +); + + + +int cropAndResizeCVMatToMatchSkeleton( + struct JointEstimator2D * jest, + cv::Mat & frame, + struct Skeletons2DDetected * sk +); diff --git a/animation/MocapNET-kasisnu/src/JointEstimator2D/webcamAnd2DJoints.cpp b/animation/MocapNET-kasisnu/src/JointEstimator2D/webcamAnd2DJoints.cpp new file mode 100644 index 0000000..1150b4f --- /dev/null +++ b/animation/MocapNET-kasisnu/src/JointEstimator2D/webcamAnd2DJoints.cpp @@ -0,0 +1,157 @@ +#include "opencv2/opencv.hpp" +/** @file webcam.cpp + * @brief This is a simple test file to make sure your camera or video files can be opened using OpenCV + * @author Ammar Qammaz (AmmarkoV) + */ +#include +#include "cameraControl.hpp" +#include "jointEstimator2D.hpp" +#include "visualization.hpp" + +#include "../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" + +using namespace cv; + + +int main(int argc, char *argv[]) +{ + unsigned int width = 640; + unsigned int height = 480; + const char * webcam = 0; + int heatmapDebugVisualizations=0; + + //------------------------------------------------------ + // Parse arguments + //------------------------------------------------------ + for (int i=0; ii+1) + { + webcam = argv[i+1]; + } + } else + if (strcmp(argv[i],"--debug")==0) + { + heatmapDebugVisualizations = 1; + } else + if (strcmp(argv[i],"--size")==0) + { + if (argc>i+2) + { + width = atoi(argv[i+1]); + height = atoi(argv[i+2]); + } + } + + } + //------------------------------------------------------ + + + VideoCapture cap(webcam); // open the default camera + + std::cerr<<"Trying to open webcam ("<> frame; + if ( (frame.size().width>0) && (frame.size().height>0) ) + { + if ( + !cropAndResizeCVMatToMatchSkeleton( + &jointEstimator, + frame, + &result + ) + ) + { + fprintf(stderr,"Failed to crop input video\n"); + } + //imshow("Video Input Feed", frame); + + frame.copyTo(viewMat); + // viewMat.setTo(Scalar(0,0,0)); + + + //This needs to be done to get tensorflow output.. + frame.convertTo(frame,CV_32FC3); + + //Keep time + unsigned long startTime = GetTickCountMicrosecondsJointEstimator(); + + std::vector > heatmaps = getHeatmaps( + &jointEstimator, + frame.data, + jointEstimator.inputWidth2DJointDetector, + jointEstimator.inputHeight2DJointDetector + ); + if (heatmaps.size()>0) + { + if (heatmapDebugVisualizations) + { visualizeHeatmaps(&jointEstimator,heatmaps,frameNumber); } + + estimate2DSkeletonsFromHeatmaps(&jointEstimator,&result,heatmaps); + + //Keep time + unsigned long endTime = GetTickCountMicrosecondsJointEstimator(); + elapsedTime += endTime - startTime; + + dj_drawExtractedSkeletons( + viewMat, + &result, + jointEstimator.inputWidth2DJointDetector, + jointEstimator.inputHeight2DJointDetector + ); + + imshow("Skeletons", viewMat); + + } + + ++frameNumber; + } + else + { + std::cerr<<"Broken frame.. \n"; + ++brokenFrames; + } + waitKey(1); + if (brokenFrames>10) { break; } + } + + if (frameNumber>0) + { + fprintf(stderr,"Elapsed time is %lu microsconds for %u frames\n",elapsedTime,frameNumber); + fprintf(stderr,"Framerate : %0.2f fps\n",(float) elapsedTime/(1000* frameNumber)); + } + } + // the camera will be deinitialized automatically in VideoCapture destructor + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/BVHGUI2/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/BVHGUI2/CMakeLists.txt new file mode 100644 index 0000000..838c38f --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/BVHGUI2/CMakeLists.txt @@ -0,0 +1,24 @@ +project( BVHGUI2 ) +cmake_minimum_required( VERSION 2.8.13 ) +#cmake_minimum_required(VERSION 3.5) +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +#set_property(GLOBAL PROPERTY USE_FOLDERS ON) +set(CMAKE_CXX_STANDARD 11) +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + + +add_executable(BVHGUI2 bvhGUI2.cpp +${BVH_SOURCE} ) + +target_link_libraries(BVHGUI2 rt dl m ${OpenCV_LIBRARIES} ${OPENGL_LIBS} Tensorflow TensorflowFramework MocapNETLib2 ) +set_target_properties(BVHGUI2 PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(BVHGUI2 PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/BVHGUI2/bvhGUI2.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/BVHGUI2/bvhGUI2.cpp new file mode 100644 index 0000000..af340cd --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/BVHGUI2/bvhGUI2.cpp @@ -0,0 +1,554 @@ +#include "opencv2/opencv.hpp" +/** @file bvhgui.cpp + * @brief This is a simple test file to play with the BVH armature + * #Hands up + * // ./BVHGUI --set 241 290.0 --set 242 80 --set 284 117 --set 283 113 + * #Close up hands / face + * // ./BVHGUI --set 1 -70 --set 2 -60 --set 241 290.0 --set 242 80 --set 282 187 --set 283 252 --set 284 112 + * @author Ammar Qammaz (AmmarkoV) + */ +#include +#include "../MocapNETLib2/mocapnet2.hpp" +#include "../MocapNETLib2/IO/bvh.hpp" +#include "../MocapNETLib2/visualization/visualization.hpp" +#include "../MocapNETLib2/visualization/opengl.hpp" +#include + +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_rename.h" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +using namespace cv; + +int visualizeOpenGLEnabled=0; + +struct applicationState +{ + std::vectorbvhConfiguration; + std::vector > bvh2DPoints; + unsigned int numberOfBVHMotionValuesPerFrame; + int previousDistance; + int distance; + int positiveRotation; + int negativeRotation; + int previousPositiveRotation; + int previousNegativeRotation; + int rotation; + int selectedMotionChannel,previousSelection; + int visualizationType,previousVisualizationType; + int play; + int stop; + int save; + int copyClipboard; + int frameID; + int numberOfFrames; + int redraw; +}; + + +int controls(cv::Mat & controlMat,struct applicationState * state) +{ + cv::imshow("3D Control",controlMat); + cv::createTrackbar("Stop Demo", "3D Control", &state->stop, 1); + + cv::createTrackbar("Play", "3D Control", &state->play,1); + cv::createTrackbar("Frame", "3D Control", &state->frameID,state->numberOfFrames); + cv::createTrackbar("Clipboard Pose", "3D Control", &state->copyClipboard, 1); + + if (visualizeOpenGLEnabled) + { + cv::createTrackbar("Visualization Type", "3D Control", &state->visualizationType, 1); + } + + cv::createTrackbar("Save", "3D Control", &state->save, 1); + + cv::createTrackbar("Distance ", "3D Control", &state->distance, 1360); + + cv::createTrackbar("Selected Motion Channel", "3D Control", &state->selectedMotionChannel, state->numberOfBVHMotionValuesPerFrame-1); + cv::createTrackbar("Positive Value ", "3D Control", &state->positiveRotation, 360); + cv::createTrackbar("Negative Value ", "3D Control", &state->negativeRotation, 360); + + return 1; +} + + + +static void onMouse( int event, int x, int y, int, void* stateUncast) +{ + struct applicationState * state = (struct applicationState *) stateUncast; + if( event != EVENT_LBUTTONDOWN ) + return; + + if (visualizeOpenGLEnabled) + { + clickOpenGLView(x,y,event); + } + else + { + fprintf(stderr,"Unhandled click @ %u,%u \n",x,y); + } +} + + + + +int main(int argc, char *argv[]) +{ + unsigned int visWidth=1920; + unsigned int visHeight=1080; + + struct applicationState state; + state.numberOfBVHMotionValuesPerFrame=MOCAPNET_OUTPUT_NUMBER; + state.rotation=0; + state.previousDistance=0; + state.distance=150; + state.numberOfFrames=1; + state.previousSelection=0; + state.selectedMotionChannel=0; + state.visualizationType=0; + state.stop=0; + state.save=0; + state.copyClipboard=0; + state.previousVisualizationType=2;// <- force redraw on first loop + state.redraw=1; + + int useBVH=0; + int forcePosition=1; //By default we want the output to be centered.. + int forceRotation=0; + float forcedRotation=0; + + const char * bvhFilename=0; + + struct BVH_MotionCapture bvhMotion= {0}; + + + for (int i=0; ii+1) + { + state.distance=atoi(argv[i+1]); + } + } + else if (strcmp(argv[i],"--size")==0) + { + if(argc>i+2) + { + visWidth=atoi(argv[i+1]); + visHeight=atoi(argv[i+2]); + } + } + else if (strcmp(argv[i],"--unconstrained")==0) + { + forcePosition=0; + forceRotation=0; + } + else if (strcmp(argv[i],"--rotation")==0) + { + if(argc>i+1) + { + forceRotation=1; + forcedRotation=atof(argv[i+1]); + state.selectedMotionChannel=4; + state.rotation=atoi(argv[i+1]); + } + } + else if (strcmp(argv[i],"--from")==0) + { + if(argc>i+1) + { + if ( bvh_loadBVH(argv[i+1], &bvhMotion, 1.0) ) + { + bvhFilename=argv[i+1]; + bvh_renameJointsForCompatibility(&bvhMotion); + state.frameID=0; + state.numberOfFrames=bvhMotion.numberOfFrames; + state.play=1; + state.numberOfBVHMotionValuesPerFrame=bvhMotion.numberOfValuesPerFrame; + useBVH=1; + } + else + { + fprintf(stderr,"File `%s` does not exist\n",argv[i+1]); + return 1; + } + } + } + else + + if (strcmp(argv[i],"--opengl")==0) + { + visualizeOpenGLEnabled=1; + state.visualizationType=1; + state.previousVisualizationType=state.visualizationType; + state.redraw=3; //Draw 3 consequtive frames to take care of OGL initialization + state.previousSelection=6; + state.selectedMotionChannel=6; + } + } + + + if ( !initializeBVHConverter(bvhFilename,visWidth,visHeight,1) ) + { + fprintf(stderr,"Failed to initialize BVH code from %s ..\n",bvhFilename); + return 0; + } + + if (getBVHNumberOfValuesPerFrame() != state.numberOfBVHMotionValuesPerFrame) + { + fprintf(stderr,"Inconsistency, internal BVH state reports different number of frames compared to external BVH state..\n"); + fprintf(stderr,"Internal(%u) External(%u) ..\n",getBVHNumberOfValuesPerFrame(),state.numberOfBVHMotionValuesPerFrame); + fprintf(stderr,"Cannot continue ..\n"); + return 0; + } + + state.bvhConfiguration.clear(); + for (int i=0; ii+7) + { + changeJointDimensions( + atof(argv[i+1]), + atof(argv[i+2]), + atof(argv[i+3]), + atof(argv[i+4]), + atof(argv[i+5]), + atof(argv[i+6]), + atof(argv[i+7]), + atof(argv[i+8]), + atof(argv[i+9]) + ); + } + else + { + fprintf(stderr,"Incorrect number of parameters given..\n"); + return 0; + } + } + else if (strcmp(argv[i],"--set")==0) + { + // To focus only on head.. + //./BVHGUI2 --set 1 -65 --set 2 -25 + // In OpenGL mode + //./BVHGUI2 --set 1 -14.8 --set 2 2 --opengl + // To focus on RHand + // ./BVHGUI2 --from dataset/rhand.bvh --set 2 -45 --set 3 -90 --set 5 -90 + if(argc>i+2) + { + unsigned int bvhMotionID = atoi(argv[i+1]); + float bvhMotionValue = atof(argv[i+2]); + + unsigned int maximumValidMotionID = getBVHNumberOfValuesPerFrame(); + if (useBVH) + { + maximumValidMotionID = bvhMotion.jointHierarchySize; + } + + if (bvhMotionID > bvhFrames; + bvhFrames.push_back(state.bvhConfiguration); + state.save=0; + controls(controlMat,&state); + writeBVHFile( + "out.bvh", + bvhHeader, + 0,// int prependTPose, + bvhFrames + ); + fprintf(stderr,GREEN "Saved..\n" NORMAL); + } + + + if (state.previousDistance!=state.distance) + { + if (!state.redraw) + { + state.redraw=1; + } + state.previousDistance=state.distance; + state.bvhConfiguration[2]=(float) -1 * state.distance; + } + + //if (!state.play) + //{ + + if (state.visualizationType!=state.previousVisualizationType) + { + if (!state.redraw) + { + state.redraw=1; + } + state.previousVisualizationType=state.visualizationType; + } + + if (state.selectedMotionChannel!=state.previousSelection) + { + if (!state.redraw) + { + state.redraw=1; + } + state.previousSelection=state.selectedMotionChannel; + state.rotation=state.bvhConfiguration[state.selectedMotionChannel]; + if (state.rotation<0) + { + state.negativeRotation=-1*state.rotation; + } + else + { + state.positiveRotation=state.rotation; + } + controls(controlMat,&state); + } + + + if (state.previousPositiveRotation!=state.positiveRotation) + { + state.previousPositiveRotation=state.positiveRotation; + state.rotation=state.positiveRotation; + } + else if (state.previousNegativeRotation!=state.negativeRotation) + { + state.previousNegativeRotation=state.negativeRotation; + state.rotation=-1* state.negativeRotation; + } + + if ( state.bvhConfiguration[state.selectedMotionChannel]!=state.rotation ) + { + if (!state.redraw) + { + state.redraw=1; + } + state.bvhConfiguration[state.selectedMotionChannel]=state.rotation; + if (state.rotation<0) + { + state.negativeRotation=-1*state.rotation; + } + else + { + state.positiveRotation=state.rotation; + } + } + // } + + if (state.redraw) + { + //fprintf(stderr,"redraw..\n"); + memset(viewMat.data,0,visWidth*visHeight*3*sizeof(char)); + + + //cv::Mat * openGLMatForVisualization = 0; + if ( (visualizeOpenGLEnabled) && (state.visualizationType) ) + { + //fprintf(stderr,"updateOpenGLView\n"); + updateOpenGLView(state.bvhConfiguration); + + //fprintf(stderr,"visualizeOpenGL\n"); + unsigned int openGLFrameWidth=visWidth,openGLFrameHeight=visHeight; + char * openGLFrame = visualizeOpenGL(&openGLFrameWidth,&openGLFrameHeight); + //===================================================================== + if (openGLFrame!=0) + { + fprintf(stderr,"Got Back an OpenGL frame..!\n"); + cv::Mat openGLMat(openGLFrameHeight, openGLFrameWidth, CV_8UC3); + unsigned char * initialPointer = openGLMat.data; + openGLMat.data=(unsigned char * ) openGLFrame; + cv::cvtColor(openGLMat,viewMat,COLOR_RGB2BGR); + openGLMat.data=initialPointer; + }//===================================================================== + else + { + fprintf(stderr,"Failed getting an OpenGL frame..!\n"); + } + } + else + { + if (state.bvhConfiguration.size()>0) + { + //fprintf(stderr,"Converting %lu bvh vector\n",state.bvhConfiguration.size()); + state.bvh2DPoints = convertBVHFrameTo2DPoints(state.bvhConfiguration);//visWidth,visHeight); + + drawSkeleton(viewMat,state.bvh2DPoints,0,0,1); + } + } + + + char message[1024]; + snprintf(message,1024,"Frame : %u / %u",state.frameID,state.numberOfFrames); + cv::putText(viewMat,message, cv::Point(10,30), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + + + if (useBVH) + { + char motionChannelName[512]; + getBVHMotionValueName(state.selectedMotionChannel,motionChannelName,512); + snprintf(message,1024,"Selected motion channel #%u : %s ( value %0.2f )",state.selectedMotionChannel,motionChannelName,state.bvhConfiguration[state.selectedMotionChannel]); + } + else + { + snprintf(message,1024,"Selected motion channel #%u : %s ( value %0.2f )",state.selectedMotionChannel,MocapNETOutputArrayNames[state.previousSelection],state.bvhConfiguration[state.selectedMotionChannel]); + } + cv::putText(viewMat,message, cv::Point(10,50), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + + if (visualizeOpenGLEnabled) + { + snprintf(message,1024,"OpenGL Model motion channel name : %s",OpenCOLLADANames[state.previousSelection/3]); + cv::putText(viewMat,message, cv::Point(10,70), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + } + + if (state.redraw>=1) + { + --state.redraw; + } + //state.redraw=0; + } + + + imshow("BVH", viewMat); + + waitKey(15); + + if (useBVH) + { + if (state.play) + { + ++state.frameID; + state.frameID = state.frameID % state.numberOfFrames; + } + } + } + // the camera will be deinitialized automatically in VideoCapture destructor + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/BVHTemplate/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/BVHTemplate/CMakeLists.txt new file mode 100644 index 0000000..6a69281 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/BVHTemplate/CMakeLists.txt @@ -0,0 +1,16 @@ +project( BVHTemplate ) +cmake_minimum_required( VERSION 2.8.7 ) +set(CMAKE_MODULE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/../cmake/modules ${CMAKE_MODULE_PATH}) + + +add_executable(BVHTemplate main.c ${BVH_SOURCE} ) + +target_link_libraries(BVHTemplate rt m pthread ) +#add_dependencies(BVHTemplate OGLRendererSandbox) + + +set_target_properties(BVHTemplate PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/BVHTemplate/main.c b/animation/MocapNET-kasisnu/src/MocapNET2/BVHTemplate/main.c new file mode 100644 index 0000000..e69de29 diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/CMakeLists.txt new file mode 100644 index 0000000..448ebef --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/CMakeLists.txt @@ -0,0 +1,29 @@ +project( CSVClusterPlot ) +cmake_minimum_required( VERSION 2.8.13 ) +#cmake_minimum_required(VERSION 3.5) +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +#set_property(GLOBAL PROPERTY USE_FOLDERS ON) +set(CMAKE_CXX_STANDARD 11) +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + + +add_executable( +CSVClusterPlot +csvClusterPlot.cpp +perform2DClustering.cpp +perform3DClustering.cpp +${BVH_SOURCE} +) + +target_link_libraries(CSVClusterPlot rt dl m ${OpenCV_LIBRARIES} ${OPENGL_LIBS} Tensorflow TensorflowFramework MocapNETLib2 ) +#set_target_properties(CSVClusterPlot PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(CSVClusterPlot PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/csvClusterPlot.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/csvClusterPlot.cpp new file mode 100644 index 0000000..c8997dd --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/csvClusterPlot.cpp @@ -0,0 +1,759 @@ +#include "opencv2/opencv.hpp" +/** @file csvClusterPlot.cpp + * @brief + * @author Ammar Qammaz (AmmarkoV) + */ +#include + +#include //mkdir +#include "../MocapNETLib2/mocapnet2.hpp" +#include "../MocapNETLib2/IO/bvh.hpp" +#include "../MocapNETLib2/visualization/visualization.hpp" +#include "../MocapNETLib2/visualization/opengl.hpp" +#include "../MocapNETLib2/IO/csvRead.hpp" +#include + +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_filter.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_rename.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_randomize.h" + +#include "perform2DClustering.hpp" +#include "perform3DClustering.hpp" + + +//#include + +using namespace cv; + +int visualizeOpenGLEnabled=0; + + +#define BOOST_CONTRAST 2.0 + + + +char MocapNET1List[]="src/MocapNET2/CSVClusterPlot/listOfFilesToClusterMocapNET1"; +char defaultList[]="src/MocapNET2/CSVClusterPlot/listOfFilesToCluster"; + + +const char * perturbList1[] = +{ + " ", + "rshoulder", + "lshoulder", + "rhip", + "lhip" +}; + +const char * perturbList2[] = +{ + " ", + "rhand", + "relbow", + "lelbow", + "lhand", + "lknee", + "rknee", + "lfoot", + "rfoot" +}; + + +const char * perturbList3[] = +{ + " ", + "abdomen", + "chest" +}; + +/* +#define rotationSetting 0" +#define handToCloseHipMin "45" +#define handToCloseHipMax "90" +#define handToFarHipMin "100" +#define handToFarHipMax "160" +#define handToHandMin "150" +#define handToHandMax "250" +*/ + + +#define rotationSetting "90" +#define handToCloseHipMin "0" +#define handToCloseHipMax "120" +#define handToFarHipMin "0" +#define handToFarHipMax "120" +#define handToHandMin "0" +#define handToHandMax "150" + + +const char * removeNeutralPosesRules[] = +{ + "0", // 0 | Move Armature to 0 X Position + "0", // 1 | Move Armature to 0 Y Position + "-130.0", // 2 | Move Armature to -130.0 Z Position + "0", // 3 | Rotate Armature to 0deg X Rotation + rotationSetting, // 4 | Rotate Armature to 0deg Y Rotation + "0", // 5 | Rotate Armature to 0deg Z Rotation + //-------------------------------------------------------------------------- + "1920", // 6 | Virtual 2D Rendering Width + "1080",// 7 | Virtual 2D Rendering Height + "570.7", // 8 | Virtual 2D Rendering Focal Length X + "570.3", // 9 | Virtual 2D Rendering Focal Length Y + //-------------------------------------------------------------------------- + "6", // 10 | Number of joint rules + //-------------------------------------------------------------------------- + "rhand", // 11 | Joint A + "lhip", // 12 | Joint B + handToFarHipMin, // 13 | Minimum Acceptable 2D Distance + handToFarHipMax, // 14 | Maximum Acceptable 2D Distance + "rhand", // 15 | Joint A + "rhip", // 16| Joint B + handToCloseHipMin, // 17 | Minimum Acceptable 2D Distance + handToCloseHipMax, // 18 | Maximum Acceptable 2D Distance + "rhand", // 19 | Joint A + "lhand", // 20 | Joint B + handToHandMin, // 21 | Minimum Acceptable 2D Distance + handToHandMax, // 22 | Maximum Acceptable 2D Distance + //-------------------------------------------------------------------------- + "lhand", // 23 | Joint A + "rhip", // 24| Joint B + handToFarHipMin, // 25 | Minimum Acceptable 2D Distance + handToFarHipMax, // 26 | Maximum Acceptable 2D Distance + "lhand", // 27 | Joint A + "lhip", // 28| Joint B + handToCloseHipMin, // 29 | Minimum Acceptable 2D Distance + handToCloseHipMax, // 30 | Maximum Acceptable 2D Distance + "lhand", // 31 | Joint A + "rhand", // 32 | Joint B + handToHandMin, // 33 | Minimum Acceptable 2D Distance + handToHandMax // 34 | Maximum Acceptable 2D Distance +}; + + +void convertHeatmapValueToRGB(float heatmap,unsigned char * R,unsigned char * G,unsigned char * B) +{ + float r = fminf(1.0f, (fmaxf(0.5f, heatmap) - 0.5f) * 6.0f); + float g = fminf(1.0f, heatmap * 3.0f) - fminf(1.0f, fmaxf(0.0f, heatmap - 0.666f) * 3.0f); + float b = fmaxf(0.0f, 1.0f - fmaxf(0.0f, heatmap - 0.333f) * 6.0f); + + if (heatmap==0.0) + { + r=0.0; + g=0.0; + b=0.0; + } + else if (heatmap<0.1) + { + r=0.0; + g=0.0; + b=0.3+10*heatmap; + if (b>=1.0) + { + b=1.0; + } + } + else if (heatmap>=1.0) //Cap + { + if (heatmap>=2.0) + { + r=1.0; + g=0.0; + b=1.0; //Final Limit + } + else + { + r=1.0 ; + g=0.0; + b=heatmap-1.0; + } + } + + r=r*255.0; + g=g*255.0; + b=b*255.0; + + *R = (unsigned char) r; + *G = (unsigned char) g; + *B = (unsigned char) b; +} + + + +int draw2DClustering(cv::Mat &viewMat,struct BVH_MotionCapture * bvhMotion,std::vector > aPose,struct applicationState * state,const char * line, char * videoPath,unsigned int width,unsigned int height) +{ + char buffer[1024]= {0}; + memset(viewMat.data,0,width*height*3*sizeof(char)); + /* + if ( state->maxAccumulatedSample > state->accumulatedSamples ) + { + state->maxAccumulatedSample=state->accumulatedSamples; + }*/ + + unsigned char R,G,B; + //------------------------------------------------------------------------------ + for (int y=0; yaccumulatedImage[y*width+x] / state->maxAccumulatedSample; + heatmap*=BOOST_CONTRAST; + + convertHeatmapValueToRGB(heatmap,&R,&G,&B); + + unsigned int pos=(y*width*3) + x*3; + viewMat.data[pos++]=B; + viewMat.data[pos++]=G; + viewMat.data[pos]=R; + } + + + //add a sidebar with the colors legend.. + for (int x=width-5; xmaxAccumulatedSample; + snprintf(buffer,1024,"%0.0f",heatmapScaleValue); + cv::putText(viewMat,buffer, cv::Point(width-50,y), cv::FONT_HERSHEY_DUPLEX, 0.3, cv::Scalar::all(255), 0.2, 8 ); + } + } + + drawSkeleton(viewMat,aPose,0,0,1); + + snprintf(buffer,1024,"%lu accumulated samples, max heatmap value = %lu",state->accumulatedSamples,state->maxAccumulatedSample); + cv::putText(viewMat,buffer, cv::Point(10,30), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + + snprintf(buffer,1024,"%s added with %u samples",line,bvhMotion->numberOfFrames); + cv::putText(viewMat,buffer, cv::Point(10,50), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + + if (videoPath!=0) + { + snprintf(buffer,1024,"Label : %s ",videoPath); + cv::putText(viewMat,buffer, cv::Point(10,70), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + } + return 1; +} + + + + +int draw3DClustering(cv::Mat &viewMat,struct clusteringOf3DPoses * state,unsigned int width,unsigned int height,unsigned int depth) +{ + memset(viewMat.data,0,width*height*3*sizeof(char)); + + unsigned char R,G,B; + //------------------------------------------------------------------------------ + for (int y=0; yspace[memoryLocation].element; + } + float heatmap = (float) hits / (depth*255); + heatmap*=10; + + convertHeatmapValueToRGB(heatmap,&R,&G,&B); + + unsigned int pos=(y*width*3) + x*3; + viewMat.data[pos++]=B; + viewMat.data[pos++]=G; + viewMat.data[pos]=R; + } + + } + + return 1; +} + + + +int main(int argc, char *argv[]) +{ + struct clusteringOf3DPoses * state3D = (struct clusteringOf3DPoses *) malloc(sizeof(struct clusteringOf3DPoses) ); + if (state3D==0) + { + fprintf(stderr,"Could not allocate enough memory to start csvClusterPlot..\n"); + return 1; + } + memset(state3D,0,sizeof(struct clusteringOf3DPoses)); + + + struct applicationState * state = (struct applicationState *) malloc(sizeof(struct applicationState) ); + if (state==0) + { + fprintf(stderr,"Could not allocate enough memory to start csvClusterPlot..\n"); + free(state3D); + return 1; + } + state->rotation=0; + state->previousSelection=0; + state->selectedJoint=0; + state->visualizationType=0; + state->stop=0; + state->save=0; + state->previousVisualizationType=2;// <- force redraw on first loop + state->redraw=1; + state->maxAccumulatedSample= 0; + int randomize=0; + int filterPoses=0; + int go3DMode=0; + + unsigned int visualizationFrameNumber=0; + int saveVideo=0; + char * videoPath=0; + char videoFrameFinalFilename[512]= {0}; + + float skeletonDistance=-160; + unsigned int WIDTH = 1000; + unsigned int HEIGHT = 1000; + unsigned int DEPTH = 1000; + char * selectedSource = defaultList; + + for (int i=0; ii+1) + { + videoPath=argv[i+1]; + saveVideo=1; + mkdir(videoPath,0777); + } + } + else if (strcmp(argv[i],"--from")==0) + { + if(argc>i+1) + { + selectedSource=argv[i+1]; + } + } + else if (strcmp(argv[i],"--rotation")==0) + { + if(argc>i+1) + { + state->rotation=atoi(argv[i+1]); + } + } + else if (strcmp(argv[i],"--randomize")==0) + { + randomize=1; + } + else if (strcmp(argv[i],"--filter")==0) + { + filterPoses=1; + } + else if (strcmp(argv[i],"--size")==0) + { + //To produce higher resolution heatmap.. + // ./CSVClusterPlot --rotation 90 --onlyrhand --size 1920 1080 --distance -130 --filter + if(argc>i+2) + { + WIDTH=atoi(argv[i+1]); + HEIGHT=atoi(argv[i+2]); + } + } + else if (strcmp(argv[i],"--depth")==0) + { + if(argc>i+1) + { + DEPTH=atoi(argv[i+1]); + } + } + else if (strcmp(argv[i],"--distance")==0) + { + if(argc>i+1) + { + skeletonDistance=atof(argv[i+1]); + } + } + } + + + state->accumulatedImage = (unsigned long * ) malloc( (WIDTH) * (HEIGHT) * sizeof (unsigned long) ); + if (state->accumulatedImage==0) + { + fprintf(stderr,"Could not allocate enough memory to start csvClusterPlot..\n"); + free(state); + free(state3D); + return 1; + } + for (int y=0; yaccumulatedImage[y*WIDTH+x]=0; + } + } + state->accumulatedSamples=0;; + + + cv::Mat viewMat = Mat(Size(WIDTH,HEIGHT),CV_8UC3, Scalar(0,0,0)); + namedWindow("BVH",1); + //controls(controlMat,&state); + //cv::moveWindow("3D Control",0,0); //y=inputHeight2DJointDetector + cv::moveWindow("BVH",0,0); + + + initializeBVHConverter(0,WIDTH,HEIGHT,1); + + + + //------------------------------------------------------------------------------------------------------------------ + //------------------------------------------------------------------------------------------------------------------ + std::vector activeJoints; + activeJoints.clear(); + for (int i=0; ii+1) + { + activeJoints[ getBVHJointIDFromJointName(argv[i+1])]=1; + } + else + { + fprintf(stderr,"We need a limb to focus on e.g. rHand <- capitalization is imporant..\n"); + } + } + else if (strcmp(argv[i],"--onlylimbs")==0) + { + for (int z=0; zredraw=1; + + srand(time(NULL)); + + //find dataset/MotionCapture/ -type f -name "*.bvh" > src/MocapNET2/CSVClusterPlot/listOfFilesToCluster + struct BVH_MotionCapture bvhMotion= {0}; + + char buffer[1024]= {0}; + + FILE * fp =0; + + fp=fopen(selectedSource,"r"); + + if (fp==0) + { + fprintf(stderr,"Cannot open list of files to cluster\n"); + free(state->accumulatedImage); + free(state); + free(state3D); + return 0; + } + + + char * line = NULL; + size_t len = 0; + ssize_t read; + unsigned int numberOfLinesProcessed=0; + unsigned long numberOfPosesLoaded=0; + + while ((read = getline(&line, &len, fp)) != -1) + { + if (line!=0) + { + ++numberOfLinesProcessed; + //----------------------------------------------------------------- + int lineLength=strlen(line); + if (lineLength>0) + { + if (line[lineLength-1]==10) + { + line[lineLength-1]=0; + } + if (line[lineLength-1]==13) + { + line[lineLength-1]=0; + } + } + if (lineLength>1) + { + if (line[lineLength-2]==10) + { + line[lineLength-2]=0; + } + if (line[lineLength-2]==13) + { + line[lineLength-2]=0; + } + } + //----------------------------------------------------------------- + fprintf(stderr,"Loading `%s`\n",line); + + if ( bvh_loadBVH(line, &bvhMotion, 1.0) ) + { + numberOfPosesLoaded+=bvhMotion.numberOfFrames; + + bvh_renameJointsForCompatibility(&bvhMotion); + + if (filterPoses) + { + filterOutPosesThatAreCloseToRules(&bvhMotion,35,removeNeutralPosesRules); + } + + if (randomize) + { + //--perturbJointAngles 4 38.0 rshoulder lshoulder rhip lhip --perturbJointAngles 8 10.0 rhand relbow lelbow lhand lknee rknee lfoot rfoot + + bvh_PerturbJointAngles( + &bvhMotion, + 4, + 38.0, + perturbList1, + 0 + ); + + bvh_PerturbJointAngles( + &bvhMotion, + 8, + 10.0, + perturbList2, + 0 + ); + + bvh_PerturbJointAngles( + &bvhMotion, + 2, + 10.0, + perturbList3, + 0 + ); + + + } + + + if (go3DMode) + { + //3D Voxel Clustering Mode + std::vector > aPose = collect3DPoses(state3D,activeJoints,&bvhMotion,skeletonDistance,WIDTH,HEIGHT,DEPTH); + + //memset(viewMat.data,0,WIDTH*HEIGHT*3*sizeof(char)); + + if (numberOfLinesProcessed%5==0) + { draw3DClustering(viewMat,state3D,WIDTH,HEIGHT,DEPTH); } + drawSkeleton(viewMat,aPose,0,0,1); + } + else + { + //2D Projection Mode + std::vector > aPose = collectPoses(state,activeJoints,&bvhMotion,skeletonDistance,WIDTH,HEIGHT); + if ( aPose.size()>0 ) + { + state->redraw=1; + } + + if (state->redraw) + { + draw2DClustering(viewMat,&bvhMotion,aPose,state,line,videoPath,WIDTH,HEIGHT); + + state->redraw=1; + } + } + + + + fprintf(stderr,"Done with %s\n",line); + + bvh_free(&bvhMotion); + + fprintf(stderr,"deallocation complete..\n"); + + + imshow("BVH", viewMat); + + if (saveVideo) + { + snprintf(videoFrameFinalFilename,512,"%s/colorFrame_0_%05u.png",videoPath,visualizationFrameNumber); + imwrite(videoFrameFinalFilename,viewMat); + } + + ++visualizationFrameNumber; + + waitKey(1); + } + else + { + fprintf(stderr,"Failed to load %s \n",line); + } + }//Line not empty + } //New line read + + if (line!=0) + { + free(line); + line=0; + len=0; + } + fclose(fp); + // the camera will be deinitialized automatically in VideoCapture destructor + + + + if (go3DMode) + { + fprintf(stderr,"3D clustering accumulated %lu samples \n",state3D->accumulatedSamples); + if (state3D->fp!=0) + { + fclose(state3D->fp); + } + + //------------------------------------------------------------------------ + int i=system("cat dataset/header.bvh > filteredPoses.bvh"); + if (i!=0) { fprintf(stderr,"Could not use header for filterPoses.bvh\n"); } + //------------------------------------------------------------------------ + i=system("echo \"MOTION\" >> filteredPoses.bvh"); + if (i!=0) { fprintf(stderr,"Could not add MOTION to filterPoses.bvh\n"); } + //------------------------------------------------------------------------ + i=system("echo \"Frames: `cat filteredPoses.bvhm | wc -l`\" >> filteredPoses.bvh"); + if (i!=0) { fprintf(stderr,"Could not add number of poses\n"); } + //------------------------------------------------------------------------ + i=system("echo \"Frame Time: 0.04\" >> filteredPoses.bvh"); + if (i!=0) { fprintf(stderr,"Could not add frame time\n"); } + //------------------------------------------------------------------------ + i=system("cat filteredPoses.bvhm >> filteredPoses.bvh"); + if (i!=0) { fprintf(stderr,"Could not add motion frames\n"); } + //------------------------------------------------------------------------ + i=system("rm filteredPoses.bvhm"); + if (i!=0) { fprintf(stderr,"Could not remove intermediate motion frames\n"); } + //------------------------------------------------------------------------ + } + + + free(state); + free(state3D); + + fprintf(stderr,"Done overlaying %lu poses..!\n",numberOfPosesLoaded); + imshow("BVH", viewMat); + imwrite("csvCluster.png",viewMat); + + if (saveVideo) + { + //Low-Res video encoding + //snprintf(videoFrameFinalFilename,512,"ffmpeg -framerate 30 -i %s/colorFrame_0_%%05d.png -y -r 30 -threads 8 -crf 9 -pix_fmt yuv420p %s/video.mp4",videoPath,videoPath); + + //High-Res video encoding + snprintf(videoFrameFinalFilename,512,"ffmpeg -framerate 30 -i %s/colorFrame_0_%%05d.png -s 1200x720 -y -r 30 -pix_fmt yuv420p -threads 8 %s/video.mp4",videoPath,videoPath); + int i=system(videoFrameFinalFilename); + + + if (i==0) + { + fprintf(stderr,"Successfully wrote video file %s/video.mp4.. \n",videoPath); + } + else + { + fprintf(stderr,"Failed to write a video file %s/video.mp4.. \n",videoPath); + } + + fprintf(stderr,"Since we wanted to output video this is probably a scripted run, will not wait for a keystroke..\n"); + waitKey(1); + } + else + { + waitKey(0); + } + + + free(state->accumulatedImage); + free(state); + free(state3D); + + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/listOfFilesToCluster b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/listOfFilesToCluster new file mode 100644 index 0000000..9fbcada --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/listOfFilesToCluster @@ -0,0 +1,2535 @@ +dataset/MotionCapture/01/01_02.bvh +dataset/MotionCapture/01/01_03.bvh +dataset/MotionCapture/01/01_05.bvh +dataset/MotionCapture/01/01_06.bvh +dataset/MotionCapture/01/01_07.bvh +dataset/MotionCapture/01/01_08.bvh +dataset/MotionCapture/01/01_09.bvh +dataset/MotionCapture/01/01_10.bvh +dataset/MotionCapture/01/01_11.bvh +dataset/MotionCapture/01/01_12.bvh +dataset/MotionCapture/01/01_13.bvh +dataset/MotionCapture/01/01_14.bvh +dataset/MotionCapture/02/02_01.bvh +dataset/MotionCapture/02/02_02.bvh +dataset/MotionCapture/02/02_03.bvh +dataset/MotionCapture/02/02_04.bvh +dataset/MotionCapture/02/02_05.bvh +dataset/MotionCapture/02/02_06.bvh +dataset/MotionCapture/02/02_07.bvh +dataset/MotionCapture/02/02_08.bvh +dataset/MotionCapture/02/02_09.bvh 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+dataset/MotionCapture/19/19_06.bvh +dataset/MotionCapture/19/19_07.bvh +dataset/MotionCapture/19/19_08.bvh +dataset/MotionCapture/19/19_09.bvh +dataset/MotionCapture/19/19_10.bvh +dataset/MotionCapture/19/19_11.bvh +dataset/MotionCapture/19/19_12.bvh +dataset/MotionCapture/19/19_13.bvh +dataset/MotionCapture/19/19_14.bvh +dataset/MotionCapture/19/19_15.bvh diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform2DClustering.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform2DClustering.cpp new file mode 100644 index 0000000..e997343 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform2DClustering.cpp @@ -0,0 +1,125 @@ +#include "perform2DClustering.hpp" + + +#include "../MocapNETLib2/IO/bvh.hpp" + +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_filter.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_rename.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_randomize.h" + + + +std::vector > collectPoses( + struct applicationState * state, + std::vector &activeJoints, + struct BVH_MotionCapture * bvhMotion, + float distance, + unsigned int width, + unsigned int height +) +{ + + std::vector > bvh2DPoints; + + if (bvhMotion==0) + { + return bvh2DPoints; + } + + + std::vector bvhConfiguration; + + bvhConfiguration.clear(); + for (int i=0; inumberOfValuesPerFrame; i++) + { + bvhConfiguration.push_back(0.0); + } + bvhConfiguration[2]=(float) -150.0; + + + + if ( bvhConfiguration.size() < bvhMotion->numberOfValuesPerFrame) + { + return bvh2DPoints; + } + + unsigned int mID=0; + + fprintf(stderr,"Collecting %u poses\n",bvhMotion->numberOfFrames); + + // state->maxAccumulatedSample= 0; + + for (int frameID=0; frameIDnumberOfFrames; frameID++) + { + //fprintf(stderr,".%u",frameID); + + for (int i=0; inumberOfValuesPerFrame; i++) + { + //fprintf(stderr,"%u ",i); + int motionValueID = mID % (bvhMotion->numberOfValuesPerFrame * bvhMotion->numberOfFrames); + bvhConfiguration[i]=bvhMotion->motionValues[motionValueID]; + ++mID; + } + //fprintf(stderr,"!"); + + + if (bvhConfiguration.size()>5) + { + bvhConfiguration[0]=0; + bvhConfiguration[1]=0; + bvhConfiguration[2]=distance; + bvhConfiguration[3]=0; + bvhConfiguration[4]=state->rotation; + bvhConfiguration[5]=0; + + bvh2DPoints = convertBVHFrameTo2DPoints(bvhConfiguration); //,width, height + + + + if (bvh2DPoints.size()>0) + { + ++state->accumulatedSamples; + for (int i=0; i0) && (bvh2DPoints[i][1]>0) && (bvh2DPoints[i][0]accumulatedImage[y*width+x] + 1; + + if (newValue<=state->accumulatedSamples) + { + state->accumulatedImage[y*width+x] = newValue; + + if (state->maxAccumulatedSamplemaxAccumulatedSample=newValue; + //fprintf(stderr,"Pixel %u,%u has largest value %lu\n",x,y,newValue); + } + } + } + else + { + fprintf(stderr,"%0.2f,%0.2f wrongly casted to %u,%u",bvh2DPoints[i][0],bvh2DPoints[i][1],x,y); + } + + } + } + } + else + { + fprintf(stderr,"Could not project BVH 2D points\n"); + } + } + } //For every frame loop.. + + return bvh2DPoints; +} \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform2DClustering.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform2DClustering.hpp new file mode 100644 index 0000000..2eb527e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform2DClustering.hpp @@ -0,0 +1,28 @@ +#pragma once + +#include +#include + +struct applicationState +{ + int rotation; + int selectedJoint,previousSelection; + int visualizationType,previousVisualizationType; + int stop; + int save; + int redraw; +//----------------- + unsigned long * accumulatedImage; + unsigned long accumulatedSamples; + unsigned long maxAccumulatedSample; +}; + + +std::vector > collectPoses( + struct applicationState * state, + std::vector &activeJoints, + struct BVH_MotionCapture * bvhMotion, + float distance, + unsigned int width, + unsigned int height +); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform3DClustering.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform3DClustering.cpp new file mode 100644 index 0000000..3359dfb --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform3DClustering.cpp @@ -0,0 +1,223 @@ +#include "perform3DClustering.hpp" +#include +#include +#include + + +#include "../MocapNETLib2/IO/bvh.hpp" + +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_filter.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_rename.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/edit/bvh_randomize.h" + + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +int appendBVHVectorToFile(FILE * fp, std::vector vec) +{ + if ( (fp!=0) && (vec.size()>0) ) + { + for (unsigned int i=0; i > collect3DPoses( + struct clusteringOf3DPoses * state, + std::vector &activeJoints, + struct BVH_MotionCapture * bvhMotion, + float distance, + unsigned int width, + unsigned int height, + unsigned int depth +) +{ + std::vector > bvh2DPointsFront; + std::vector > bvh2DPointsSide; + + if (bvhMotion==0) + { + //Return an empty vector of vectors.. + return bvh2DPointsFront; + } + + if (state->fp==0) + { + state->fp = fopen("filteredPoses.bvhm","w"); + } + + if (state->space==0) + { + state->allocatedSpaceMemorySize = width * height * depth * sizeof(struct voxelElement); + fprintf(stderr,"Will now try to allocate a huge chunk of memory (%lu bytes)\n",state->allocatedSpaceMemorySize); + state->space = (struct voxelElement * ) malloc(state->allocatedSpaceMemorySize); + if (state->space!=0) + { + fprintf(stderr,"Will now try to cleanup the huge chunk of memory (%lu bytes)\n",state->allocatedSpaceMemorySize); + memset(state->space,0,state->allocatedSpaceMemorySize); + fprintf(stderr,"Survived..\n"); + } else + { + state->allocatedSpaceMemorySize=0; + fprintf(stderr,"Error allocating a huge chunk of memory..!\n"); + return bvh2DPointsFront; + } + } + + std::vector bvhConfiguration; + + bvhConfiguration.clear(); + for (int i=0; inumberOfValuesPerFrame; i++) + { + bvhConfiguration.push_back(0.0); + } + bvhConfiguration[2]=(float) -150.0; + + + + + + + if (bvhMotion==0) + { + return bvh2DPointsFront; + } + + if ( bvhConfiguration.size() < bvhMotion->numberOfValuesPerFrame) + { + return bvh2DPointsFront; + } + + unsigned int mID=0; + + fprintf(stderr,"Collecting %u poses\n",bvhMotion->numberOfFrames); + + // state->maxAccumulatedSample= 0; + unsigned int negativesAtThisCall=0; + unsigned int hitsAtThisCall=0; + + for (int frameID=0; frameIDnumberOfFrames; frameID++) + { + //fprintf(stderr,".%u",frameID); + + for (int i=0; inumberOfValuesPerFrame; i++) + { + //fprintf(stderr,"%u ",i); + int motionValueID = mID % (bvhMotion->numberOfValuesPerFrame * bvhMotion->numberOfFrames); + bvhConfiguration[i]=bvhMotion->motionValues[motionValueID]; + ++mID; + } + //fprintf(stderr,"!"); + + + + if (bvhConfiguration.size()>5) + { + bvhConfiguration[0]=0; + bvhConfiguration[1]=0; + bvhConfiguration[2]=distance; + bvhConfiguration[3]=0; + bvhConfiguration[4]=0; + bvhConfiguration[5]=0; + + + bvhConfiguration[4]=0; + bvh2DPointsFront = convertBVHFrameTo2DPoints(bvhConfiguration); //,width,height + bvhConfiguration[4]=90; + bvh2DPointsSide = convertBVHFrameTo2DPoints(bvhConfiguration); //,width,height + bvhConfiguration[4]=0; // So the configuration we save is ok + + if ( (bvh2DPointsFront.size()>0) && (bvh2DPointsSide.size()>0) && (bvh2DPointsFront.size()==bvh2DPointsSide.size()) ) + { + for (int i=0; i0) && (bvh2DPointsFront[i][1]>0) && (bvh2DPointsFront[i][0]0) && (bvh2DPointsSide[i][1]>0) && (bvh2DPointsSide[i][0]allocatedSpaceMemorySize>memoryLocation) + { + if (state->space[memoryLocation].element==0) + { + ++state->accumulatedSamples; + appendBVHVectorToFile(state->fp, bvhConfiguration); + ++hitsAtThisCall; + state->space[memoryLocation].element=1; + } else + { + ++negativesAtThisCall; + if (state->space[memoryLocation].element<254) + { + state->space[memoryLocation].element+=1; + } + } + + + /* + if (newValue<=state->accumulatedSamples) + { + state->space[memoryLocation].element = newValue; + + if (state->maxAccumulatedSamplemaxAccumulatedSample=newValue; + //fprintf(stderr,"Pixel %u,%u has largest value %lu\n",x,y,newValue); + } + } */ + } else + { + fprintf(stderr,"Bad memory location for %u,%u,%u \n",x,y,z); + } + + } + else + { + fprintf(stderr,"%0.2f,%0.2f wrongly casted to %u,%u",bvh2DPointsFront[i][0],bvh2DPointsFront[i][1],x,y); + } + + } + } + } + else + { + fprintf(stderr,"Could not project BVH 2D points\n"); + } + } + } //For every frame loop.. + + if (hitsAtThisCall>0) + { + fprintf(stderr,GREEN "Added %u more poses (%0.2f%% of the dataset is novel)..\n" NORMAL,hitsAtThisCall,(float) (100*hitsAtThisCall)/bvhMotion->numberOfFrames); + } else + { + fprintf(stderr,YELLOW "No new poses observed ( %u/%u negatives )..\n" NORMAL,negativesAtThisCall,bvhMotion->numberOfFrames); + } + + return bvh2DPointsFront; +} \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform3DClustering.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform3DClustering.hpp new file mode 100644 index 0000000..1deb59e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/CSVClusterPlot/perform3DClustering.hpp @@ -0,0 +1,35 @@ +#pragma once + +#include +#include + + +struct voxelElement +{ + char element; +}; + + +struct clusteringOf3DPoses +{ + FILE *fp; + struct voxelElement * space; + unsigned long allocatedSpaceMemorySize; + unsigned int width,height,depth; + unsigned long accumulatedSamples; + unsigned long maxAccumulatedSample; + +}; + + + + +std::vector > collect3DPoses( + struct clusteringOf3DPoses * state, + std::vector &activeJoints, + struct BVH_MotionCapture * bvhMotion, + float distance, + unsigned int width, + unsigned int height, + unsigned int depth +); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/Converters/H36M/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/Converters/H36M/CMakeLists.txt new file mode 100644 index 0000000..8a8e346 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/Converters/H36M/CMakeLists.txt @@ -0,0 +1,36 @@ +project( convertH36GroundTruthToMocapNETInput ) +cmake_minimum_required(VERSION 3.5) + + +set_property(GLOBAL PROPERTY USE_FOLDERS ON) + +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + +#----------------------------------------------- +# This is the converter utilities.. +#----------------------------------------------- +add_executable( + convertH36GroundTruthToMocapNETInput + convertH36GroundTruthToMocapNETInput.cpp + ../../MocapNETLib2/tools.cpp + ../../MocapNETLib2/IO/jsonRead.cpp + ../../MocapNETLib2/IO/jsonMocapNETHelpers.cpp + ../../../../dependencies/InputParser/InputParser_C.cpp + ${BVH_SOURCE} + ${OPENGL_SOURCE} + ../../MocapNETLib2/IO/bvh.cpp + ../../MocapNETLib2/IO/conversions.cpp + ../../MocapNETLib2/IO/csvRead.cpp + ../../MocapNETLib2/IO/csvWrite.cpp + ) + +#----------------------------------------------- + +target_link_libraries(convertH36GroundTruthToMocapNETInput rt dl m pthread MocapNETLib2) +set_target_properties(convertH36GroundTruthToMocapNETInput PROPERTIES DEBUG_POSTFIX "D") +set_target_properties(convertH36GroundTruthToMocapNETInput PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/Converters/H36M/convertH36GroundTruthToMocapNETInput.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/Converters/H36M/convertH36GroundTruthToMocapNETInput.cpp new file mode 100644 index 0000000..b1177c2 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/Converters/H36M/convertH36GroundTruthToMocapNETInput.cpp @@ -0,0 +1,241 @@ +/* + * Export utility from OpenPose BODY 25 JSON format to a more regular CSV file + * Sample call : ./convertBody25JSONToCSV --from frames/GOPR3223.MP4-data/ --label colorFrame_0_ -o . + * */ + +#include +#include +#include +#include + +#include "../../../MocapNET2/MocapNETLib2/tools.hpp" +#include "../../../MocapNET2/MocapNETLib2/mocapnet2.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/jsonRead.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/csvRead.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/csvWrite.hpp" + + +#if USE_BVH +#include "../../../../dependencies/RGBDAcquisition/tools/AmMatrix/matrix4x4Tools.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_project.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_transform.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/bvh_inverseKinematics.h" +#else +#warning "BVH code not included.." +#endif // USE_BVH + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + +int main(int argc, char *argv[]) +{ +#if USE_BVH + + float fX = 582.18394; //570.0 + float fY = 582.52915; //570.0 + unsigned int width = 1920; + unsigned int height = 1080; + + struct simpleRenderer renderer= {0}; + + simpleRendererDefaults( + &renderer, + width,//1920 + height,//1080 + fX, //570.0 + fY //570.0 + ); + + simpleRendererInitialize(&renderer); + + const char * path=0; + const char * label=0; + char outputPathFull[2048]= {"./h36ToMnet.csv"}; + const char * outputPath=outputPathFull; + + float offsetX=0.0,offsetY=0.0,offsetZ=0.0; + + int flipX=0; + int flipY=1; + + for (int i=0; i > result; + std::vector points2DOfASpecificFrame; + + struct CSVFileContext csv= {0}; + struct CSVFloatFileLine csvLine= {0}; + if ( openCSVFile(&csv,path) ) + { + parseCSVHeader(&csv); + while (parseNextCSVFloatLine(&csv,&csvLine)) + { + points2DOfASpecificFrame.clear(); + + float pos3D[4]= {0}; + //pos3D[3]=1.0; + + float position2DX=0,position2DY=0,position2DW=0; + + float center[4]= {0}; + float rotation[3]= {0}; + + float hipX = csvLine.field[9 * 3 + 0]; + float hipY = csvLine.field[9 * 3 + 1]; + float hipZ = csvLine.field[9 * 3 + 2]; + + + for (unsigned int pointID=0; pointID0.0) + { + //fprintf(stderr,"output is %0.2f,%0.2f \n",position2DX,position2DY); + points2DOfASpecificFrame.push_back((float) position2DX); + points2DOfASpecificFrame.push_back((float) position2DY); + } + else + { + //fprintf(stderr,"behind camera(%0.2f).. %0.2f,%0.2f \n",position2DW,position2DX,position2DY); + points2DOfASpecificFrame.push_back(0.0); + points2DOfASpecificFrame.push_back(0.0); + } + } else + { + points2DOfASpecificFrame.push_back(0.0); + points2DOfASpecificFrame.push_back(0.0); + } + } + + result.push_back(points2DOfASpecificFrame); + } + + + if (result.size()>0) + { + FILE * fp = fopen(outputPath,"w"); + if (fp!=0) + { + + fprintf(fp,"frameNumber,skeletonID,totalSkeletons"); + + for (unsigned int pointID=0; pointID https://github.com/AmmarkoV/RGBDAcquisition/blob/master/grabber/frames/dump_video.sh to dump the video to files + * but it is equivalent to running : + * mkdir videoFiles && ffmpeg -i video.mp4 -r 30 -q:v 1 videoFiles/colorFrame_0_%05d.jpg && cp videoFiles/colorFrame_0_00001.jpg videoFiles/colorFrame_0_00000.jpg + * + * Having a well defined serialized version of the dataset ( with serial numbers that start from 0 instead of 1 ) we can be sure on what frame each openpose output will correspond + * + * Running : + * openpose.bin -number_people_max 1 --hand --face --write_json videoFiles/ -image_dir videoFiles/ + * + * The directory will be populated with colorFrame_0_xxxxx_keypoints.json files + * each of the colorFrame_0_xxxxx_keypoints.json will correspond to a colorFrame_0_xxxxx.jpg + * + * This can then be converted to a CSV file using : + * ./convertOpenPoseJSONToCSV --from videoFiles/ + * + * + * If you want to use a custom json labeling scheme and not rely on images as OpenPose input you will need to : + * + * 1) provide the proper image resolution ( since the JSON files alone do not have this information ) + * The image resolution is *CRUCIAL* for the software to correctly normalize input 2D points and respect the training aspect ratio + * if you don't supply the correct size a Full-HD(1920x1080p) input will be assumed. If this does not correspond to the original video resolution output will be adversly affected + * You can do this with the --size WIDTH HEIGHT parameter + * + * 2) Depending on your datasets and filenames supply the proper number of characters for a valid serial frame number length + * i.e. frame_xxxxx_keypoints.json needs --seriallength 5 + * frame_xxxxxxx_keypoints.json needs --seriallength 7 + * etc + * + * 3) Depending on your datasets and filenames supply the correct label for the dataset + * i.e. datasetNameA_xxxxx_keypoints.json needs --label datasetNameA + * AMoreComplexLabel_IsTHIS_0_xxxxxxx_keypoints.json needs --label AMoreComplexLabel_IsTHIS_0 + * etc + * + * 4) Depending on the starting frame and ending frame of your dataset supply the --startAt X , --maxFrames X + * i.e dataset_00001_keypoints.json - dataset_00101_keypoints.json should need --startAt 1 --maxFrames 102 + * i.e dataset_00000_keypoints.json - dataset_0099_keypoints.json should need --startAt 0 --maxFrames 100 + * + * */ + + +#include +#include +#include +#include + +#include "../../../MocapNET2/MocapNETLib2/tools.hpp" +#include "../../../MocapNET2/MocapNETLib2/mocapnet2.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/jsonRead.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/csvRead.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/csvWrite.hpp" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int findFirstJSONFileInDirectory(const char * path,const char * formatString, const char * label, unsigned int * frameIDOutput) +{ + char filePathOfJSONFile[1025]={0}; + const int maximumFilesToSearchFor=1000; + unsigned int frameID=0; + int found=0; + while (!found) + { + snprintf(filePathOfJSONFile,1024,formatString,path,label,frameID); + + if( fileExists(filePathOfJSONFile) ) + { + *frameIDOutput=frameID; + return 1; + } + + ++frameID; + if (frameID==maximumFilesToSearchFor) + { + fprintf(stderr,"Stopping search after %u checks ..\n",maximumFilesToSearchFor); + break; + //return 0; + } + } + fprintf(stderr,"findFirstJSONFileInDirectory: failed to find any JSON files.. :(\n"); + fprintf(stderr,"Path : %s \n",path); + fprintf(stderr,"Format : %s \n",formatString); + fprintf(stderr,"Label : %s \n",label); + return 0; +} + + +int main(int argc, char *argv[]) +{ + unsigned int respectTrainingAspectRatio = 1; + unsigned int width=1920 , height=1080 , frameLimit=100000 , processed = 0 , serialLength = 5 ,forcedSize=0; + const char * path=0; + const char * label=0; + char outputPathFull[2048]={0}; + const char * outputPath=0; + float version=1.4; + int noLabel=0; + float acceptableThreshold = 0.5; + + for (int i=0; i +#include +#include +#include + +#include "../../../MocapNET2/MocapNETLib2/tools.hpp" +#include "../../../MocapNET2/MocapNETLib2/mocapnet2.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/jsonRead.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/csvRead.hpp" +#include "../../../MocapNET2/MocapNETLib2/IO/csvWrite.hpp" + + +#if USE_BVH +#include "../../../../dependencies/RGBDAcquisition/tools/AmMatrix/matrix4x4Tools.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_project.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_transform.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/bvh_inverseKinematics.h" +#else +#warning "BVH code not included.." +#endif // USE_BVH + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + +int main(int argc, char *argv[]) +{ +#if USE_BVH + + float fX = 582.18394; //570.0 + float fY = 582.52915; //570.0 + unsigned int width = 1920; + unsigned int height = 1080; + + struct simpleRenderer renderer= {0}; + + simpleRendererDefaults( + &renderer, + width,//1920 + height,//1080 + fX, //570.0 + fY //570.0 + ); + + simpleRendererInitialize(&renderer); + + const char * path=0; + const char * label=0; + char outputPathFull[2048]= {"./handToMnet.csv"}; + const char * outputPath=outputPathFull; + + float scale2DX=1.0,scale2DY=1.0; + float offset2DX=0,offset2DY=0; + + float scale3DX=1.0,scale3DY=1.0,scale3DZ=-1.0; + float offset3DX=0.0,offset3DY=0.0,offset3DZ=0.0; + + int flipX=0; + int flipY=1; + + unsigned int thereIsALineLimit=0; + unsigned int maxLinesToProcess=0; + + // ./convertCSV3DToMocapNETInput --from /home/ammar/Documents/Datasets/STB/test.csv --to /home/ammar/Documents/Datasets/STB/test2D.csv --meters --flipY --offset3D 0 0 -100 + + + int i=0; + while (i > result; + std::vector points2DOfASpecificFrame; + + struct CSVFileContext csv= {0}; + struct CSVFloatFileLine csvLine= {0}; + unsigned int linesProcessed=0; + + + if ( openCSVFile(&csv,path) ) + { + parseCSVHeader(&csv); + + fprintf(stderr,"%u columns of %u points to be outputted : ",csv.numberOfHeaderFields,(unsigned int) csv.numberOfHeaderFields/3); + for (unsigned int pointID=0; pointID0.0) + { + //fprintf(stderr,"output is %0.2f,%0.2f \n",position2DX,position2DY); + position2DOutputX = (scale2DX*position2DX) + offset2DX; + position2DOutputY = (scale2DY*position2DY) + offset2DY; + } + else + { + //fprintf(stderr,"behind camera(%0.2f).. %0.2f,%0.2f \n",position2DW,position2DX,position2DY); + } + + //We normalize our 2D output..! + position2DOutputX=(float) position2DOutputX/renderer.width; + position2DOutputY=(float) position2DOutputY/renderer.height; + + fprintf(stderr,"3D %0.2f,%0.2f,%0.2f => 2D %0.2f,%0.2f \n",pos3D[0],pos3D[1],pos3D[2],position2DOutputX,position2DOutputY); + points2DOfASpecificFrame.push_back(position2DOutputX); + points2DOfASpecificFrame.push_back(position2DOutputY); + + } + else + { + points2DOfASpecificFrame.push_back(0.0); + points2DOfASpecificFrame.push_back(0.0); + } + } //For every point in this CSV line + + result.push_back(points2DOfASpecificFrame); + ++linesProcessed; + }//While we there are more lines to process + + + if (result.size()>0) + { + FILE * fp = fopen(outputPath,"w"); + if (fp!=0) + { + + fprintf(fp,"frameNumber,skeletonID,totalSkeletons"); + + for (unsigned int pointID=0; pointID1.0): + print("Memory Limit will be interpreted as a raw value..") + numberOfSamplesLimit=int(memPercentage) + #------------------------------------------------------------------------------------------------- + + + thisInput = array.array('f') + #--------------------------------- + + fi = open(filename, "r") + readerIn = csv.reader( fi , delimiter=csvDelimiter, skipinitialspace=True) + for rowIn in readerIn: + #------------------------------------------------------ + if (not receivedHeader): #use header to get labels + #------------------------------------------------------ + inputNumberOfColumns=len(rowIn) + + #Make sure CSV files that end with delimiter are correctly handled.. + if (inputNumberOfColumns>0): + if (rowIn[inputNumberOfColumns-1]==''): + inputNumberOfColumns=inputNumberOfColumns-1 + #----------------------------------------------------- + inputLabels = list(rowIn[i] for i in range(0,inputNumberOfColumns) ) + print("Number of Input elements : ",len(inputLabels)) + #------------------------------------------------------ + + if (memPercentage==0): + print("Will only return labels\n") + return {'labels':inputLabels}; + + + #i=0 + #print("class Input(Enum):") + #for label in inputLabels: + # print(" ",label," = ",i," #",int(i/3)) + # print(" ",label,"=",int(i/3)) + # i=i+1 + + #--------------------------------- + # Allocate Lists + #--------------------------------- + for i in range(inputNumberOfColumns): + thisInput.append(0.0) + #--------------------------------- + + + #--------------------------------- + # Allocate Numpy Arrays + #--------------------------------- + inputSize=0 + startCompressed=0 + + inputSize=inputSize+inputNumberOfColumns + startCompressed=inputNumberOfColumns + + npInputBytesize=0+numberOfSamplesLimit * inputSize * dtypeSelectedByteSize + print(" Input file on disk has a shape of [",numberOfSamples,",",inputSize,"]") + print(" Input we will read has a shape of [",numberOfSamplesLimit,",",inputSize,"]") + print(" Input will occupy ",convert_bytes(npInputBytesize)," of RAM\n") + npInput = np.full([numberOfSamplesLimit,inputSize],fill_value=0,dtype=dtypeSelected,order='C') + #---------------------------------------------------------------------------------------------------------- + receivedHeader=1 + #sys.exit(0) + else: + #------------------------------------------- + # First convert our string INPUT to floats + #------------------------------------------- + for i in range(inputNumberOfColumns): + thisInput[i]=float(rowIn[i]) + #------------------------------------------- + for num in range(0,inputNumberOfColumns): + npInput[sampleNumber,num]=float(thisInput[num]); + #------------------------------------------- + sampleNumber=sampleNumber+1 + + if (numberOfSamples>0): + progress=sampleNumber/numberOfSamplesLimit + + if (sampleNumber%1000==0) : + progressString = "%0.2f"%float(100*progress) + print("\rReading from disk (",sampleNumber,") - ",progressString," % \r", end="", flush=True) + + if (numberOfSamplesLimit<=sampleNumber): + print("\rStopping reading file to obey memory limit given by parameter --mem ",memPercentage,"\n") + break + #------------------------------------------- + fi.close() + del readerIn + gc.collect() + + + print("\n read, Samples: ",sampleNumber,", was expecting ",numberOfSamples," samples\n") + print(npInput.shape) + + totalNumberOfBytes=npInput.nbytes; + totalNumberOfGigaBytes=totalNumberOfBytes/1073741824; + print("Size Occupied by data = ",totalNumberOfGigaBytes," GB \n") + + end = time.time() + print("Time elapsed : ",(end-start)/60," mins") + #--------------------------------------------------------------------- + + if (groupOutput==0): + #New better dictionary + output = dict() + for i in range(0,len(inputLabels)): + lowerCaseName = inputLabels[i].lower() + #print("Joint ",lowerCaseName) + output[lowerCaseName]=list() + for frameID in range(0,len(npInput)): + output[lowerCaseName].append(npInput[frameID][i]) + return output + else: + #This is the old dictionary way (better for tensorflow training) + return {'label':inputLabels, 'body':npInput }; + + + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- + + + +drawPlot=1 + +ground=readCSVFile("test2D.csv",1.0,',',0,1) + + +if (drawPlot): + print("Using matplotlib:",matplotlib.__version__) + # === Plot and animate === + fig = plt.figure() + fig.set_size_inches(19.2, 10.8, forward=True) + + ax = fig.add_subplot(1, 2, 1) + ax2 = fig.add_subplot(1, 2, 2) + #ax3 = fig.add_subplot(2, 2, 3) + #ax4 = fig.add_subplot(2, 2, 4) + fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95) + + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + #ax.set_zlabel('Z Axis') + #ax.view_init(90, 90) + + +print("Ground truth file has %u elements ",len(ground['body'])) + + +for i in range(0,len(ground['body'])): + if (drawPlot): + plt.cla() + ax.cla() + ax2.cla() + + xs, ys = pointListReturnXYListForScatterPlot(ground['body'][i]) + + ax.scatter(xs, ys) + img = plt.imread("images/im%u.png" % i) + ax2.imshow(img) + + #------------------------- + ax.set_xlim(auto=False,left=0,right=width) + ax.set_ylim(auto=False,bottom=height,top=0) + #ax.set_zlim(auto=False,bottom=2000,top=6000) + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + #ax.set_zlabel('Z Axis') + #------------------------- + plt.show(block=False) + #plt.savefig('p%05u.png'%i) + #fig.canvas.draw() + plt.pause(0.102) diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/Converters/convertCSV3D/plot3D.py b/animation/MocapNET-kasisnu/src/MocapNET2/Converters/convertCSV3D/plot3D.py new file mode 100644 index 0000000..0aa9f4b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/Converters/convertCSV3D/plot3D.py @@ -0,0 +1,316 @@ +#!/usr/bin/env python3 + +import numpy as np +import gc +import os +import sys +import csv +import time +import array + +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.animation as animation +from mpl_toolkits.mplot3d import axes3d, Axes3D + +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + +def pointListReturnXYZListForScatterPlot(A): + numberOfPoints=A.shape[0] + xs=list() + ys=list() + zs=list() + for i in range(0,int(numberOfPoints/3)): + xs.append(float(-1000*10*A[i*3+0])) + ys.append(float(1000*10*A[i*3+1])) + zs.append(float(1000*10*A[i*3+2])) + return xs,ys,zs + +def checkIfFileExists(filename): + return os.path.isfile(filename) + +def convert_bytes(num): + """ + this function will convert bytes to MB.... GB... etc + """ + step_unit = 1000.0 #1024 bad the size + + for x in ['bytes', 'KB', 'MB', 'GB', 'TB']: + if num < step_unit: + return "%3.1f %s" % (num, x) + num /= step_unit + +def getNumberOfLines(filename): + print("Counting number of lines in file ",filename) + with open(filename) as f: + return sum(1 for line in f) + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +def readCSVFile(filename,memPercentage,csvDelimiter,useHalfFloats,groupOutput): + print("CSV file :",filename,"..\n") + + if (not checkIfFileExists(filename)): + print( bcolors.WARNING + "Input file "+filename+" does not exist, cannot read ground truth.." + bcolors.ENDC) + print("Current Directory was "+os.getcwd()) + sys.exit(0) + start = time.time() + + dtypeSelected=np.dtype(np.float32) + dtypeSelectedByteSize=int(dtypeSelected.itemsize) + if (useHalfFloats): + dtypeSelected=np.dtype(np.float16) + dtypeSelectedByteSize=int(dtypeSelected.itemsize) + + progress=0.0 + sampleNumber=0 + receivedHeader=0 + inputNumberOfColumns=0 + + inputLabels=list() + + #------------------------------------------------------------------------------------------------- + numberOfSamplesInput=getNumberOfLines(filename)-2 + print(" Input file has ",numberOfSamplesInput," training samples\n") + #------------------------------------------------------------------------------------------------- + + + numberOfSamples = numberOfSamplesInput + numberOfSamplesLimit=int(numberOfSamples*memPercentage) + #------------------------------------------------------------------------------------------------- + if (memPercentage==0.0): + print("readGroundTruthFile was asked to occupy 0 memory so this probably means we just want one record") + numberOfSamplesLimit=2 + if (memPercentage>1.0): + print("Memory Limit will be interpreted as a raw value..") + numberOfSamplesLimit=int(memPercentage) + #------------------------------------------------------------------------------------------------- + + + thisInput = array.array('f') + #--------------------------------- + + fi = open(filename, "r") + readerIn = csv.reader( fi , delimiter=csvDelimiter, skipinitialspace=True) + for rowIn in readerIn: + #------------------------------------------------------ + if (not receivedHeader): #use header to get labels + #------------------------------------------------------ + inputNumberOfColumns=len(rowIn) + + #Make sure CSV files that end with delimiter are correctly handled.. + if (inputNumberOfColumns>0): + if (rowIn[inputNumberOfColumns-1]==''): + inputNumberOfColumns=inputNumberOfColumns-1 + #----------------------------------------------------- + inputLabels = list(rowIn[i] for i in range(0,inputNumberOfColumns) ) + print("Number of Input elements : ",len(inputLabels)) + #------------------------------------------------------ + + if (memPercentage==0): + print("Will only return labels\n") + return {'labels':inputLabels}; + + + #i=0 + #print("class Input(Enum):") + #for label in inputLabels: + # print(" ",label," = ",i," #",int(i/3)) + # print(" ",label,"=",int(i/3)) + # i=i+1 + + #--------------------------------- + # Allocate Lists + #--------------------------------- + for i in range(inputNumberOfColumns): + thisInput.append(0.0) + #--------------------------------- + + + #--------------------------------- + # Allocate Numpy Arrays + #--------------------------------- + inputSize=0 + startCompressed=0 + + inputSize=inputSize+inputNumberOfColumns + startCompressed=inputNumberOfColumns + + npInputBytesize=0+numberOfSamplesLimit * inputSize * dtypeSelectedByteSize + print(" Input file on disk has a shape of [",numberOfSamples,",",inputSize,"]") + print(" Input we will read has a shape of [",numberOfSamplesLimit,",",inputSize,"]") + print(" Input will occupy ",convert_bytes(npInputBytesize)," of RAM\n") + npInput = np.full([numberOfSamplesLimit,inputSize],fill_value=0,dtype=dtypeSelected,order='C') + #---------------------------------------------------------------------------------------------------------- + receivedHeader=1 + #sys.exit(0) + else: + #------------------------------------------- + # First convert our string INPUT to floats + #------------------------------------------- + for i in range(inputNumberOfColumns): + thisInput[i]=float(rowIn[i]) + #------------------------------------------- + for num in range(0,inputNumberOfColumns): + npInput[sampleNumber,num]=float(thisInput[num]); + #------------------------------------------- + sampleNumber=sampleNumber+1 + + if (numberOfSamples>0): + progress=sampleNumber/numberOfSamplesLimit + + if (sampleNumber%1000==0) : + progressString = "%0.2f"%float(100*progress) + print("\rReading from disk (",sampleNumber,") - ",progressString," % \r", end="", flush=True) + + if (numberOfSamplesLimit<=sampleNumber): + print("\rStopping reading file to obey memory limit given by parameter --mem ",memPercentage,"\n") + break + #------------------------------------------- + fi.close() + del readerIn + gc.collect() + + + print("\n read, Samples: ",sampleNumber,", was expecting ",numberOfSamples," samples\n") + print(npInput.shape) + + totalNumberOfBytes=npInput.nbytes; + totalNumberOfGigaBytes=totalNumberOfBytes/1073741824; + print("Size Occupied by data = ",totalNumberOfGigaBytes," GB \n") + + end = time.time() + print("Time elapsed : ",(end-start)/60," mins") + #--------------------------------------------------------------------- + + if (groupOutput==0): + #New better dictionary + output = dict() + for i in range(0,len(inputLabels)): + lowerCaseName = inputLabels[i].lower() + #print("Joint ",lowerCaseName) + output[lowerCaseName]=list() + for frameID in range(0,len(npInput)): + output[lowerCaseName].append(npInput[frameID][i]) + return output + else: + #This is the old dictionary way (better for tensorflow training) + return {'label':inputLabels, 'body':npInput }; + + + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- + + + +drawPlot=1 + +ground=readCSVFile("ground3D.csv",1.0,',',0,1) + + +if (drawPlot): + print("Using matplotlib:",matplotlib.__version__) + # === Plot and animate === + fig = plt.figure() + fig.set_size_inches(19.2, 10.8, forward=True) + + ax = fig.add_subplot(1, 2, 1, projection='3d') + ax2 = fig.add_subplot(1, 2, 2) + #ax3 = fig.add_subplot(2, 2, 3) + #ax4 = fig.add_subplot(2, 2, 4) + fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95) + + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + ax.view_init(90, 90) + + +print("Ground truth file has %u elements ",len(ground['body'])) + + +for i in range(0,len(ground['body'])): + if (drawPlot): + plt.cla() + ax.cla() + ax2.cla() + #ax3.cla() + #ax4.cla() + + + categories = np.array([0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20 + ]) + + color0=(0.0, 1.0, 1.0, 1.0) #Palm + color1=(0.6, 0.0, 0.0, 1.0) #Thumb + color2=(0.8, 0.0, 0.0, 1.0) #Finger 1.2 + color3=(1.0, 0.0, 0.0, 1.0) #Finger 1.3 + color4=(0.0, 1.0, 1.0, 1.0) #BaseOfFourFingers + color5=(0.0, 1.0, 0.0, 1.0) #Finger 2.3 + color6=(0.0, 0.8, 0.0, 1.0) #Finger 2.2 + color7=(0.0, 0.6, 0.0, 1.0) #Finger 2.1 + color8=(0.0, 0.4, 0.0, 1.0) #Metacarpal 1 + color9=(0.0, 0.0, 1.0, 1.0) #Finger 3.3 + color10=(0.0, 0.0, 0.8, 1.0) #Finger 3.2 + color11=(0.0, 0.0, 0.6, 1.0) #Finger 3.1 + color12=(0.0, 0.0, 0.4, 1.0) #Metacarpal 2 + color13=(0.0, 1.0, 1.0, 1.0) #Finger 4.3 + color14=(0.0, 0.8, 0.8, 1.0) #Finger 4.2 + color15=(0.0, 0.6, 0.6, 1.0) #Finger 4.1 + color16=(0.0, 0.4, 0.4, 1.0) #Metacarpal 3 + color17=(1.0, 1.0, 0.0, 1.0) #Finger 5.3 + color18=(0.8, 0.8, 0.0, 1.0) #Finger 5.2 + color19=(0.6, 0.6, 0.0, 1.0) #Finger 5.1 + color20=(0.4, 0.4, 0.0, 1.0) #Metacarpal 4 + + colormap = np.array([color0,color1,color2,color3,color4,color5,color6,color7,color8,color9,color10,color11,color12,color13,color14,color15,color16,color17,color18,color19,color20]) + + xs, ys, zs = pointListReturnXYZListForScatterPlot(ground['body'][i]) + + ax.scatter(xs, ys, zs, c=colormap[categories]) + img = plt.imread("images/im%u.png" % i) + ax2.imshow(img) + + #------------------------- + ax.set_xlim(auto=False,left=-600,right=300) + ax.set_ylim(auto=False,bottom=-1600,top=200) + ax.set_zlim(auto=False,bottom=2000,top=6000) + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + #------------------------- + plt.show(block=False) + #plt.savefig('p%05u.png'%i) + #fig.canvas.draw() + plt.pause(0.001) diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/HandOnlyTest/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/HandOnlyTest/CMakeLists.txt new file mode 100644 index 0000000..4cef56e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/HandOnlyTest/CMakeLists.txt @@ -0,0 +1,18 @@ +project( HandOnlyTest ) +cmake_minimum_required( VERSION 2.8.7 ) +#cmake_minimum_required(VERSION 3.5) + +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +add_executable(HandOnlyTest handTest.cpp) +target_link_libraries(HandOnlyTest rt dl m ${OpenCV_LIBRARIES} Tensorflow TensorflowFramework MocapNETLib2 ) +set_target_properties(HandOnlyTest PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(HandOnlyTest PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/HandOnlyTest/handTest.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/HandOnlyTest/handTest.cpp new file mode 100644 index 0000000..7fe6418 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/HandOnlyTest/handTest.cpp @@ -0,0 +1,1103 @@ +/* + * Utility to extract BVH files straight from OpenPose JSON output + * Sample usage ./MocapNETCSV --from test.csv --visualize + */ + +#include "../MocapNETLib2/mocapnet2.hpp" +#include +#include +#include +#include +#include + +#include "../MocapNETLib2/applicationLogic/parseCommandlineOptions.hpp" + +#include "../MocapNETLib2/tools.hpp" +#include "../MocapNETLib2/visualization/map.hpp" +#include "../MocapNETLib2/visualization/drawHands.hpp" +#include "../MocapNETLib2/visualization/visualization.hpp" +#include "../MocapNETLib2/visualization/camera_ready.hpp" +//------------------------------------------ +#include "../MocapNETLib2/IO/bvh.hpp" +#include "../MocapNETLib2/IO/csvRead.hpp" +#include "../MocapNETLib2/IO/csvWrite.hpp" +#include "../MocapNETLib2/IO/jsonRead.hpp" +#include "../MocapNETLib2/IO/jsonMocapNETHelpers.hpp" +#include "../MocapNETLib2/IO/conversions.hpp" +#include "../MocapNETLib2/IO/skeletonAbstraction.hpp" +//--------------------------------------------------- + +#include "../MocapNETLib2/solutionParts/leftHand.hpp" +#include "../MocapNETLib2/solutionParts/rightHand.hpp" + +//--------------------------------------------------- + +#include "../../../dependencies/RGBDAcquisition/tools/Calibration/calibration.h" +#include "../../../dependencies/RGBDAcquisition/tools/AmMatrix/matrix4x4Tools.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_project.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_transform.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/bvh_inverseKinematics.h" +#include "../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/hardcodedProblems_inverseKinematics.h" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +unsigned int myMin(unsigned int a,unsigned int b) +{ + if (aframeNumber) { finish=frameNumber; } + + fprintf(html,"\n"); + + if (batch>0) { fprintf(html,"Previous",batch-1); } + if (finishNext
",batch+1); } + + fprintf(html,"\n"); + + for (unsigned int i=start; i\n"); + fprintf(html,"",i); + snprintf(filename,1024,"nsrm%u.png",i); + fprintf(html,"",filename); + snprintf(filename,1024,"../images/im%u.png",i); + fprintf(html,"",filename); + snprintf(filename,1024,"hand%u.png",i); + fprintf(html,"",filename); + fprintf(html,"\n"); + } + + fprintf(html,"
%u
\n"); + + if (batch>0) { fprintf(html,"

Previous",batch-1); } + if (finishNext
",batch+1); } + + fprintf(html,"\n"); + fclose(html); + return 1; + } + } + return 0; +} + + + + + + +int writeCSVOutput(const char * filename,struct BVH_MotionCapture * bvhMotion,unsigned int frameNumber,std::vector points3D) +{ + FILE * fp; + + if (frameNumber==0) + { + fp = fopen(filename,"w"); + if (fp!=0) + { + const char* jointName = bvhMotion->jointHierarchy[0].jointName; + fprintf(fp,"3DX_%s,3DY_%s,3DZ_%s",jointName,jointName,jointName); + for (unsigned int jID=1; jIDjointHierarchySize; jID++) + { + jointName = bvhMotion->jointHierarchy[jID].jointName; + fprintf(fp,",3DX_%s,3DY_%s,3DZ_%s",jointName,jointName,jointName); + } + fprintf(fp,"\n"); + fclose(fp); + return 1; + } + } else + { + fp = fopen(filename,"a"); + if (fp!=0) + { + if (points3D.size()>0) + { + fprintf(fp,"%0.2f",points3D[0]); + for (unsigned int i=1; i points2DOriginal,const std::vector > points2DAchieved) +{ + cv::Mat image(1080,1920,CV_8UC3,cv::Scalar(0,0,0)); + + float thickness=1.7; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + cv::Scalar fontColor= cv::Scalar(255,255,255); + char label[129]={0}; + + for (unsigned int pointID=0; pointID3D + cv::Point txtPosition(x+20,y); + cv::putText(image,label,txtPosition,fontUsed,0.3,fontColor,thickness,8); + } + + + for (unsigned int pointID=0; pointID3D + cv::putText(image,label,pointToDraw,fontUsed,0.4,cv::Scalar(0,255,255),thickness,8);*/ + } + + + cv::imshow("Disparity 2D",image); +} + + +enum STB_LOCATIONS +{ +STB_frameNumber, //0 +STB_skeletonID, //1 +STB_totalSkeletons,//2 +STB_2DX_lhand, //3 +STB_2DY_lhand, //4 +STB_visible_lhand, //5 +STB_2DX_endsite_finger1_3_l, //6 +STB_2DY_endsite_finger1_3_l, //7 +STB_visible_endsite_finger1_3_l, //8 +STB_2DX_finger1_3_l, //9 +STB_2DY_finger1_3_l, //10 +STB_visible_finger1_3_l, //11 +STB_2DX_finger1_2_l, //12 +STB_2DY_finger1_2_l, //13 +STB_visible_finger1_2_l, //14 +STB_2DX_lthumb, //15 +STB_2DY_lthumb, //16 +STB_visible_lthumb, //17 +STB_2DX_endsite_finger2_3_l, //18 +STB_2DY_endsite_finger2_3_l, //19 +STB_visible_endsite_finger2_3_l, //20 +STB_2DX_finger2_3_l, // +STB_2DY_finger2_3_l, // +STB_visible_finger2_3_l, // +STB_2DX_finger2_2_l, // +STB_2DY_finger2_2_l, // +STB_visible_finger2_2_l, // +STB_2DX_finger2_1_l, // +STB_2DY_finger2_1_l, // +STB_visible_finger2_1_l, // +STB_2DX_endsite_finger3_3_l, // +STB_2DY_endsite_finger3_3_l, // +STB_visible_endsite_finger3_3_l, // +STB_2DX_finger3_3_l, // +STB_2DY_finger3_3_l, // +STB_visible_finger3_3_l, // +STB_2DX_finger3_2_l, // +STB_2DY_finger3_2_l, // +STB_visible_finger3_2_l, // +STB_2DX_finger3_1_l, // +STB_2DY_finger3_1_l, // +STB_visible_finger3_1_l, // +STB_2DX_endsite_finger4_3_l, // +STB_2DY_endsite_finger4_3_l, // +STB_visible_endsite_finger4_3_l, // +STB_2DX_finger4_3_l, // +STB_2DY_finger4_3_l, // +STB_visible_finger4_3_l, // +STB_2DX_finger4_2_l, // +STB_2DY_finger4_2_l, // +STB_visible_finger4_2_l, // +STB_2DX_finger4_1_l, // +STB_2DY_finger4_1_l, // +STB_visible_finger4_1_l, // +STB_2DX_endsite_finger5_3_l, // +STB_2DY_endsite_finger5_3_l, // +STB_visible_endsite_finger5_3_l, // +STB_2DX_finger5_3_l, // +STB_2DY_finger5_3_l, // +STB_visible_finger5_3_l, // +STB_2DX_finger5_2_l, // +STB_2DY_finger5_2_l, // +STB_visible_finger5_2_l, // +STB_2DX_finger5_1_l, // +STB_2DY_finger5_1_l, // +STB_visible_finger5_1_l // +}; + + + +int extrapolateWristFromPalmAndFingerBases(struct skeletonSerialized * skeleton) +{ + float finger21X = skeleton->skeletonBody[STB_2DX_finger2_1_l].value; + float finger21Y = skeleton->skeletonBody[STB_2DY_finger2_1_l].value; + float finger31X = skeleton->skeletonBody[STB_2DX_finger3_1_l].value; + float finger31Y = skeleton->skeletonBody[STB_2DY_finger3_1_l].value; + float finger51X = skeleton->skeletonBody[STB_2DX_finger5_1_l].value; + float finger51Y = skeleton->skeletonBody[STB_2DY_finger5_1_l].value; + + float palmX = skeleton->skeletonBody[STB_2DX_lhand].value; + float palmY = skeleton->skeletonBody[STB_2DY_lhand].value; + + float fl2X = palmX + (palmX - finger21X); + float fl2Y = palmY + (palmY - finger21Y); + //--------------------------------------- + float fl3X = palmX + (palmX - finger31X); + float fl3Y = palmY + (palmY - finger31Y); + //--------------------------------------- + float fl5X = palmX + (palmX - finger51X); + float fl5Y = palmY + (palmY - finger51Y); + //--------------------------------------- + //x -0.01 Mean :11.8946 + //{-0.015,0.01}; // Mean 11.7829 + //{-0.015,0.015}; Mean :11.5735 + //{-0.015,0.015}; ( 35 iterations ) Mean :11.0499 + //{-0.015,0.009}; ( 35 iterations ) Mean :11.1 + //{-0.012,-0.009}; Mean :10.8625 + //{-0.012,-0.019}; Mean :11.6612 + //{0.012,-0.009}; Mean :13.5712 + //{-0.015,-0.009}; Mean :10.8549 + //{-0.017,-0.009};Mean :11.1785 + //pointTuning[]={-0.017,-0.007}; Mean :11.1785 + //{-0.013,-0.009}; Mean :10.7158 + // Change small finger.. + //={-0.013,-0.009} Mean :10.50822 + //Switch to 128 / 59B + //{-0.014,-0.009}; Mean :10.5741 + //{-0.014,0.009}; Mean :10.727 + //{-0.013,-0.009} Mean :10.8166 + //{-0.010,-0.009}; Mean :10.99447 + //{0.010,-0.009};Mean :13.270 + //{-0.014,-0.009}; Mean :10.5741 + + + float pointTuning[]={-0.014,-0.009}; + skeleton->skeletonBody[STB_2DX_lhand].value=pointTuning[0]+(fl2X+fl3X+fl5X)/3; + skeleton->skeletonBody[STB_2DY_lhand].value=pointTuning[1]+(fl2Y+fl3Y+fl5Y)/3; + return 1; +} + +int doPalmWirstWorkaroundForSTB(struct skeletonSerialized * skeleton) +{ + /* + for (unsigned int i=0; iskeletonHeaderElements; i++) + { + fprintf(stderr,"%u=%s ",i,skeleton->skeletonHeader[i].str); + } + */ + return extrapolateWristFromPalmAndFingerBases(skeleton); +} + + + +int main(int argc, char *argv[]) +{ + //---------------------------------------------- + struct MocapNET2Options options= {0}; + struct MocapNET2 mnet= {0}; + mnet.options = & options; + //---------------------------------------------- + + //---------------------------------------------- + struct ikProblem * leftHandProblem = 0; + struct ikProblem * rightHandProblem = 0; + struct BVH_MotionCapture * bvhMotion; + struct BVH_Transform bvhTransform = {0}; + //Casting our solution/previous solution/penultimate solution in C arrays + + //---------------------------------------------- + + //---------------------------------------------- + struct skeletonSerialized resultAsSkeletonSerialized= {0}; + //---------------------------------------------- + + defaultMocapNET2Options(&options); + options.GPUName[0]=0; //The CSV demo does not use the GPU so don't display it.. + + + std::vector > bvhFrames; + std::vector resultStandaloneBVH; + std::vector penultimateResultStandaloneBVH; + std::vector previousResultStandaloneBVH; + + + unsigned char STBWorkaroundNeeded=0; + + for (unsigned int i=0; i! + options.learningRate=0.009; //<- CAREFUL this can be overriden later.>! + options.iterations=35;//5; //<- CAREFUL this can be overriden later.>! + options.epochs=30.0; //<- CAREFUL this can be overriden later.>! + options.spring=0.0; //Deprecated ? + //============== + options.inputFramerate=60.0; + options.filterCutoff=5.0; + + char enforceDepthLimit=0; + float maximumDepth = -32; + + //640x480 should be a high framerate compatible resolution + //for most webcams, you can change this using --size X Y commandline parameter + options.width = 1920; + options.height = 1080; + + float scaleInput=1.0; + for (int i=0; ii+1) + { + scaleInput=atof(argv[i+1]); + } + } + + } + + loadOptionsFromCommandlineOptions(&options,argc,argv); + + if (options.path==0) + { + fprintf(stderr,RED "No CSV dataset given\n " NORMAL); + return 1; + } + + + if (options.label==0) + { + options. label="colorFrame_0_"; + } + + char filename[1025]={0}; + snprintf(filename,1024,"%s",options.path); + + + + + struct CSVFileContext csv= {0}; + if (!openCSVFile(&csv,filename)) + { + fprintf(stderr,RED "Unable to open CSV file %s \n" NORMAL,filename); + return 0; + } + else + { + fprintf(stderr,GREEN "CSV file %s is now open\n" NORMAL,filename); + options.frameLimit=getBodyLinesOfCSVFIle(&csv,filename); + fprintf(stderr,GREEN "It has %u lines\n" NORMAL,options.frameLimit); + } + + + //------------------------------------------------------- + int havePath=0; + int positionOfSlash=0; + for (int i=strlen(options.path); i>=0; i--) + { + if (options.path[i]=='/') + { + positionOfSlash=i; + havePath=1; + break; + } + } + + if (havePath) + { + options.datasetPath=(char*) malloc(sizeof(char) * (positionOfSlash+2) ); + if (options.datasetPath!=0) + { + for (int i=0; i<=positionOfSlash; i++) + { + options.datasetPath[i]=options.path[i]; + } + options.datasetPath[positionOfSlash+1]=0; //Null termination + } + } + + + //---------------------------------------- + struct CSVFloatFileLine nextHandednessCSVLine; + struct CSVFileContext handedness = {0}; + char handednessFilename[1024]={0}; + snprintf(handednessFilename,1024,"%s/handSide.csv",options.datasetPath); + fprintf(stderr,"hand Side file => %s \n",handednessFilename); + if (!openCSVFile(&handedness,handednessFilename)) + { + fprintf(stderr,RED "Unable to open CSV file %s \n" NORMAL,handednessFilename); + fprintf(stderr,YELLOW " allHandsAreLeft = %u \n" NORMAL,allHandsAreLeft); + fprintf(stderr,YELLOW " allHandsAreRight = %u \n" NORMAL,allHandsAreRight); + //return 0; + } + else + { + fprintf(stderr,GREEN "CSV file %s is now open\n" NORMAL,handednessFilename); + unsigned int numberOfHandednessResults = getBodyLinesOfCSVFIle(&csv,handednessFilename); + if (options.frameLimit!=numberOfHandednessResults) + { + fprintf(stderr,RED "Mismatch of handedness file (%u lines) with 3D position file (%u lines)\n" NORMAL,numberOfHandednessResults,options.frameLimit); + exit(1); + } else + { + allHandsAreLeft=0; + allHandsAreRight=0; + fprintf(stderr,GREEN "It has %u lines\n" NORMAL,numberOfHandednessResults); + } + } + //---------------------------------------- + + + + + + loadCalibration(&options,options.datasetPath,"color.calib"); + + + if(options.visualize) + { + spawnVisualizationWindow("3D Points Output",options.visWidth,options.visHeight); + } + + int useQuaternionHand=1; + const char * handBVHFile = "dataset/lhand.qbvh"; + + if (!initializeBVHConverter(handBVHFile,options.width,options.height,0) ) + { + fprintf(stderr,"Could not open hand using %s \n",handBVHFile); + return 0; + } + + bvhMotion = (struct BVH_MotionCapture *) getBVHMotionHandle(); + + struct simpleRenderer * renderer = (struct simpleRenderer *) getRendererHandle(); + + //Try to replicate convertCSV3DToMocapNETInput + //--------------------------------------------------- + float fX = 582.18394; //570.0 + float fY = 582.52915; //570.0 + unsigned int width = 1920; + unsigned int height = 1080; + + struct simpleRenderer localRenderer= {0}; + + simpleRendererDefaults( + &localRenderer, + width,//1920 + height,//1080 + fX, //570.0 + fY //570.0 + ); + + simpleRendererInitialize(&localRenderer); + //--------------------------------------------------- + renderer = &localRenderer; + + std::vector resultRightHand; + std::vector resultLeftHand; + + + struct MotionBuffer * solution = mallocNewMotionBuffer((struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle()); + struct MotionBuffer * penultimateSolution = mallocNewMotionBuffer((struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle()); + struct MotionBuffer * previousSolution = mallocNewMotionBuffer((struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle()); + + rightHandProblem = allocateEmptyIKProblem(); + leftHandProblem = allocateEmptyIKProblem(); + if ( (leftHandProblem==0) || (rightHandProblem==0) ) + { fprintf(stderr,"Failed to allocate memory for our problems..\n"); exit(0); } + + + int ikFailures=0; + if (!prepareDefaultRightHandProblem( + rightHandProblem, + (struct BVH_MotionCapture *) getBVHRHandQBVHMotionHandle(), + renderer, + previousSolution, + solution, + &bvhTransform, + 1//Standalone mode + ) + ) + { + fprintf(stderr,RED "MocapNET2/BVH: Could not initializeIK() for an IK solution for right hand\n" NORMAL); + ++ikFailures; + } + rightHandProblem->nearCutoffPlaneDeclared=1; + rightHandProblem->nearCutoffPlane=5; // cm + + if (!prepareDefaultLeftHandProblem( + leftHandProblem, + (struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle(), + renderer, + previousSolution, + solution, + &bvhTransform, + 1//Standalone mode + ) + ) + { + fprintf(stderr,RED "MocapNET2/BVH: Could not initializeIK() for an IK solution for left hand\n" NORMAL); + ++ikFailures; + } + leftHandProblem->nearCutoffPlaneDeclared=1; + leftHandProblem->nearCutoffPlane=5; // cm + + if (ikFailures>0) + { + fprintf(stderr,RED "Could not setup IK problems..\n" NORMAL); + exit(0); + } + + viewProblem(leftHandProblem); + + fprintf(stderr,"mocapNET hand initialization\n"); + //int result = mocapnetRightHand_initialize(&mnet," foo ",1,options.mocapNETMode,options.useCPUOnlyForMocapNET); + int result = mocapnetLeftHand_initialize(&mnet," foo ",1,options.mocapNETMode,options.useCPUOnlyForMocapNET); + //Just for the smoothing.. + commonMocapNETInitialization(&mnet); + + struct BVH_Transform bvhSkeletonTransform={0}; + + long totalProcessingTime=0; + unsigned int frameNumber = 0; + if ( (result) && (bvh_allocateTransform(bvhMotion,&bvhSkeletonTransform)) ) + { + struct skeletonSerialized skeleton = {0}; + + while ( parseNextCSVCOCOSkeleton(&csv,&skeleton) ) + { + if (STBWorkaroundNeeded) + { + //Do STB Palm to wrist workaround.. + doPalmWirstWorkaroundForSTB(&skeleton); + } + + if ( + (options.addNormalizedPixelGaussianNoiseX!=0.0) || + (options.addNormalizedPixelGaussianNoiseY!=0.0) + ) + { + fprintf(stderr,RED "Adding Noise to input (%0.2f,%0.2f) assuming (%ux%u) frame..\n" NORMAL,options.addNormalizedPixelGaussianNoiseX,options.addNormalizedPixelGaussianNoiseY,width,height); + perturbSerializedSkeletonUsingGaussianNoise( + &skeleton, + (float) options.addNormalizedPixelGaussianNoiseX/width, + (float) options.addNormalizedPixelGaussianNoiseY/height + ); + } + + + unsigned int thisHandIsLeft=allHandsAreLeft; + if ( (!allHandsAreLeft) && (!allHandsAreRight) ) + { + parseNextCSVFloatLine(&handedness,&nextHandednessCSVLine); + thisHandIsLeft=nextHandednessCSVLine.field[0]; + if (nextHandednessCSVLine.field[0] == nextHandednessCSVLine.field[1]) + { + fprintf(stderr,RED "Error inconsistend handedness..\n" NORMAL); + exit(1); + } + } + + //Make sure associations are initialized on first frame..! + if (frameNumber==0) + { + if ( + (!mocapnetLeftHand_initializeAssociations(&mnet,&skeleton)) || + (!mocapnetRightHand_initializeUsingAssociationsOfLeftHand(&mnet,&skeleton)) + ) + { + fprintf(stderr,RED "\n"); + fprintf(stderr,"___________________________________\n"); + fprintf(stderr,"___________________________________\n"); + fprintf(stderr,"\nFailed initializing associations..\n"); + fprintf(stderr,"CSV: %s\n",filename); + fprintf(stderr,"___________________________________\n"); + fprintf(stderr,"___________________________________\n" NORMAL); + //break; + exit(1); + //fprintf(stderr,RED "CONTINUING \n" NORMAL); + } else + { + //Signal that indexes are populated.. + mnet.indexesPopulated=1; + } + } + //-------------------------------------------------------- + + + long startTimeIK = GetTickCountMicrosecondsMN(); + + mnet.orientation=MOCAPNET_ORIENTATION_FRONT; + + + + int executeNeuralNetworkOnThisFrame = ( + (!mnet.options->skipNeuralNetworkIfItIsNotNeeded) || + (frameNumber<2) || + ( + (mnet.options->skipNeuralNetworkIfItIsNotNeeded) && + (frameNumber % mnet.options->maximumNeuralNetworkSkipFrames==0) + ) + ); + + if (!executeNeuralNetworkOnThisFrame) + { + fprintf(stderr,YELLOW "\n Neural Network execution skipped for frame %u ..\n" NORMAL ,frameNumber); + } else + { + if (mnet.options->skipNeuralNetworkIfItIsNotNeeded) + { + fprintf(stderr,GREEN "\n Neural Network executed for frame %u ..\n" NORMAL ,frameNumber); + } + } + + if (thisHandIsLeft) + { + //std::vector resultLeftHand = mocapnetLeftHand_evaluateInput(&mnet,&skeleton,scaleInput); + if (executeNeuralNetworkOnThisFrame) + { resultLeftHand = mocapnetLeftHand_evaluateInput(&mnet,&skeleton,scaleInput); } + + if (enforceDepthLimit) + { + if (resultLeftHand.size()>3) + { + fprintf(stderr,"LHand Depth %f\n",resultLeftHand[2]); + if(resultLeftHand[2]< maximumDepth ) + { + resultLeftHand[2]= maximumDepth; + fprintf(stderr,"LHand FORCED %f\n",resultLeftHand[2]); + } + } + } + + penultimateResultStandaloneBVH=previousResultStandaloneBVH; + previousResultStandaloneBVH=resultStandaloneBVH; + + mocapnetLeftHand_fillStandaloneResultVector(resultStandaloneBVH,resultLeftHand,useQuaternionHand); + + convertStandaloneLHandSkeletonSerializedToBVHTransform( + (struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle(), + renderer, + &bvhSkeletonTransform, + &skeleton, + 0 //Already normalized.. + ); + } else + { + //std::vector resultRightHand = mocapnetRightHand_evaluateInput(&mnet,&skeleton,scaleInput); + if (executeNeuralNetworkOnThisFrame) + { resultRightHand = mocapnetRightHand_evaluateInput(&mnet,&skeleton,scaleInput); } + + if (enforceDepthLimit) + { + if (resultRightHand.size()>3) + { + fprintf(stderr,"RHand Depth %f\n",resultRightHand[2]); + if(resultRightHand[2]< maximumDepth) + { + resultRightHand[2]= maximumDepth; + fprintf(stderr,"RHand FORCED %f\n",resultRightHand[2]); + } + } + } + + penultimateResultStandaloneBVH=previousResultStandaloneBVH; + previousResultStandaloneBVH=resultStandaloneBVH; + + mocapnetRightHand_fillStandaloneResultVector(resultStandaloneBVH,resultRightHand,useQuaternionHand); + + convertStandaloneRHandSkeletonSerializedToBVHTransform( + (struct BVH_MotionCapture *) getBVHRHandQBVHMotionHandle(), + renderer, + &bvhSkeletonTransform, + &skeleton, + 0 //Already normalized.. + ); + } + + + // ------------------------------------------------------ + transferVectorToMotionBufferArray(solution,resultStandaloneBVH); + // ------------------------------------------------------ + transferVectorToMotionBufferArray(previousSolution,previousResultStandaloneBVH); + // ------------------------------------------------------ + transferVectorToMotionBufferArray(penultimateSolution,penultimateResultStandaloneBVH); + // ------------------------------------------------------ + + + struct ikConfiguration ikConfig = {0}; + ikConfig.epochs=options.epochs; + ikConfig.learningRate=options.learningRate; + ikConfig.iterations=options.iterations; + ikConfig.maximumAcceptableStartingLoss= 30000;//12000; //WARING < - consider setting this to 0 + ikConfig.gradientExplosionThreshold = 10; + ikConfig.spring=options.spring; + ikConfig.dontUseSolutionHistory = 0; // Dont thrash disk + ikConfig.dumpScreenshots = 0; // Dont thrash disk + ikConfig.verbose = 0; //Dont spam console + ikConfig.tryMaintainingLocalOptima=1; //Less Jittery but can be stuck at local optima + ikConfig.dontUseSolutionHistory=(mnet.options->doOutputFiltering==0); + ikConfig.ikVersion = IK_VERSION; + + int multiThreading=0; + float initialMAEInPixels,finalMAEInPixels,initialMAEInMM,finalMAEInMM; + + if (options.useInverseKinematics) + { + //printIkConfiguration(&ikConfig); + unsigned int successfullyPerformedIKStep = 0; + + + if (thisHandIsLeft) + { + + if ( approximateBodyFromMotionBufferUsingInverseKinematics( + (struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle(), + renderer, + leftHandProblem, + &ikConfig, + //---------------- + penultimateSolution, + previousSolution, + solution, + 0, //No ground truth.. + //---------------- + &bvhSkeletonTransform, + //---------------- + multiThreading,// 0=single thread, 1=multi thread + //---------------- + &initialMAEInPixels, + &finalMAEInPixels, + &initialMAEInMM, + &finalMAEInMM + ) + ) + { successfullyPerformedIKStep = 1; } + } else + { + if ( approximateBodyFromMotionBufferUsingInverseKinematics( + (struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle(), + renderer, + rightHandProblem, + &ikConfig, + //---------------- + penultimateSolution, + previousSolution, + solution, + 0, //No ground truth.. + //---------------- + &bvhSkeletonTransform, + //---------------- + multiThreading,// 0=single thread, 1=multi thread + //---------------- + &initialMAEInPixels, + &finalMAEInPixels, + &initialMAEInMM, + &finalMAEInMM + ) + ) + { successfullyPerformedIKStep = 1; } + } + + + if ( successfullyPerformedIKStep ) + { + if ( (solution!=0) && (solution->motion!=0) ) + { + //If we performed inverse kinematics, then copy the output.. + if (resultStandaloneBVH.size()>0) + { + if (resultStandaloneBVH.size()!=solution->bufferSize) + { + fprintf(stderr,RED "Mismatch on IK result (%u) vs standalone BVH result (%lu)..\n" NORMAL,solution->bufferSize,resultStandaloneBVH.size()); + } + else + { + //We have just checked that resultStandaloneBVH and solution->motion have the same size.. + for (unsigned int i=0; imotion[i]; + } + } + } + } else + { + fprintf(stderr,RED "Solution has no space for solution..\n" NORMAL); + } + } else + { + fprintf(stderr,RED "Failed executing IK..\n" NORMAL); + } + + } + + + if (mnet.options->doOutputFiltering) + { + //If we have a good fix we can use the results of the smoothing filter + if (resultStandaloneBVH.size() > points2D = convertStandaloneLHandBVHFrameTo2DPoints(resultStandaloneBVH); + //fprintf(stderr,"Draw hand should be expecting %u motion vectors\n",bvhMotion->numberOfValuesPerFrame); + //draw3DLhand(resultStandaloneBVH); + + int foundImage = 0; + cv::Mat image; + cv::Mat croppedHand; + std::vector > points2D; + + if (thisHandIsLeft) + { + struct BVH_MotionCapture * bvhLHandMotion = (struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle(); + points2D = convertStandaloneLHandBVHFrameTo2DPoints(resultStandaloneBVH); + drawHand("3D Left Hand",bvhLHandMotion,points2D,resultStandaloneBVH,&croppedHand); + + std::vector points3D = convertStandaloneLHandBVHFrameToFlat3DPoints(resultStandaloneBVH); + snprintf(filename,1024,"%s/mocapNETHand3DOutput.csv",options.datasetPath); + writeCSVOutput(filename,(struct BVH_MotionCapture *) getBVHLHandQBVHMotionHandle(),frameNumber,points3D); + } else + { + struct BVH_MotionCapture * bvhRHandMotion = (struct BVH_MotionCapture *) getBVHRHandQBVHMotionHandle(); + points2D = convertStandaloneRHandBVHFrameTo2DPoints(resultStandaloneBVH); + drawHand("3D Right Hand",bvhRHandMotion,points2D,resultStandaloneBVH,&croppedHand); + + std::vector points3D = convertStandaloneRHandBVHFrameToFlat3DPoints(resultStandaloneBVH); + snprintf(filename,1024,"%s/mocapNETHand3DOutput.csv",options.datasetPath); + writeCSVOutput(filename,(struct BVH_MotionCapture *) getBVHRHandQBVHMotionHandle(),frameNumber,points3D); + } + + + snprintf(filename,1024,"%s/images/im%u.png",options.datasetPath,frameNumber); + if (fileExists(filename)) + { + image = imread(filename); + foundImage = 1; + } else + { + snprintf(filename,1024,"%s/colorFrame_0_%05u.jpg",options.datasetPath,frameNumber); + if (fileExists(filename)) + { + image = imread(filename); + foundImage = 1; + } + } + + if (!foundImage) + { + fprintf(stderr,"Could not find image (%s) ..\n",filename); + exit(0); + } + + + cv::Mat NSRMVisualization(200,200,CV_8UC3,cv::Scalar(0,0,0)); + if (thisHandIsLeft) + { + draw2DHandReprojectionError(mnet.leftHand.positionalInput,points2D); + draw2DHand(mnet.leftHand.positionalInput); + visualizeNSDM(NSRMVisualization,"NSRM", mnet.leftHand.NSDM,1,0,0,200,200); + imshow("NSRM",NSRMVisualization); + } else + { + draw2DHandReprojectionError(mnet.rightHand.positionalInput,points2D); + draw2DHand(mnet.rightHand.positionalInput); + visualizeNSDM(NSRMVisualization,"NSRM", mnet.rightHand.NSDM,1,0,0,200,200); + imshow("NSRM",NSRMVisualization); + } + + if (mnet.options->saveVisualization) + { + //Store Images.. + snprintf(filename,1024,"%s/results/hand%u.png",options.datasetPath,frameNumber); + std::vector compression_params; + compression_params.push_back(IMWRITE_PNG_COMPRESSION); + compression_params.push_back(9); + cv::imwrite(filename,croppedHand,compression_params); + + snprintf(filename,1024,"%s/results/nsrm%u.png",options.datasetPath,frameNumber); + cv::imwrite(filename,NSRMVisualization,compression_params); + } + + imshow("Image",image); + cv::waitKey(10); + //fprintf(stderr,"Skeleton #%u received %u elements\n",frameNumber,skeleton.skeletonHeaderElements); + ++frameNumber; + } + + + bvh_freeTransform(&bvhSkeletonTransform); + fprintf(stderr,"Done parsing CSV file..\n"); + } else + { + fprintf(stderr,"Failed initializing MocapNET\n"); + } + + + + //Spit an html file.. + if (mnet.options->saveVisualization) + { + writeHTMLFile(options.datasetPath,frameNumber); + } + + + char targetBVHOutputFile[4096]; + snprintf(targetBVHOutputFile,4096,"%s/out.qbvh",options.datasetPath); + int prependTPose=0; + if ( writeBVHFile(targetBVHOutputFile,0,prependTPose,bvhFrames) ) + { + fprintf(stderr,GREEN "Successfully wrote %lu frames to bvh file.. \n" NORMAL,bvhFrames.size()); + } + else + { + fprintf(stderr,RED "Failed to write %lu frames to bvh file.. \n" NORMAL,bvhFrames.size()); + } + + if (mnet.options->doOutputFiltering) + { + fprintf(stderr,"Output filtering was enabled..\n"); + } else + { + fprintf(stderr,"Output filtering was disabled..\n"); + } + + if (frameNumber>0) + { + float achievedFramerate = 1000000/(totalProcessingTime/frameNumber); + fprintf(stderr,"Achieved a %0.2f fps and %lu microseconds processing time for %u samples\n",achievedFramerate,totalProcessingTime,frameNumber); + writeSingleFloatInFile(options.datasetPath,achievedFramerate); + } else + { + fprintf(stderr,"No frames processed\n"); + return 1; + } + + fprintf(stderr,"Done\n"); + exit(0); + + + //These cause a corrupted size vs. prev_size + if (solution->motion!=0) { free(solution->motion); solution->motion=0; solution->bufferSize = 0; } + if (previousSolution->motion!=0) { free(previousSolution->motion); previousSolution->motion=0; previousSolution->bufferSize = 0; } + if (penultimateSolution->motion!=0) { free(penultimateSolution->motion); penultimateSolution->motion=0; penultimateSolution->bufferSize = 0; } + + // bvh_freeTransform(&bvhTransform); +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNET2LiveWebcamDemo/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNET2LiveWebcamDemo/CMakeLists.txt new file mode 100644 index 0000000..7822eac --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNET2LiveWebcamDemo/CMakeLists.txt @@ -0,0 +1,23 @@ +project( MocapNET2LiveWebcamDemo ) +cmake_minimum_required( VERSION 2.8.13 ) +#cmake_minimum_required(VERSION 3.5) +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +#set_property(GLOBAL PROPERTY USE_FOLDERS ON) +set(CMAKE_CXX_STANDARD 11) +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + + +add_executable(MocapNET2LiveWebcamDemo livedemo.cpp ) + +target_link_libraries(MocapNET2LiveWebcamDemo rt dl m ${OpenCV_LIBRARIES} ${OPENGL_LIBS} JointEstimator2D Tensorflow TensorflowFramework MocapNETLib2 ${NETWORK_CLIENT_LIBRARIES} ${PNG_Libs} ${JPG_Libs} ) +set_target_properties(MocapNET2LiveWebcamDemo PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(MocapNET2LiveWebcamDemo PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNET2LiveWebcamDemo/livedemo.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNET2LiveWebcamDemo/livedemo.cpp new file mode 100644 index 0000000..e95fbbf --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNET2LiveWebcamDemo/livedemo.cpp @@ -0,0 +1,516 @@ +#include "opencv2/opencv.hpp" +/** @file livedemo.cpp + * @brief This is the main "demo" offered in this repository, it will take a stream from a webcam or video file using OpenCV and run +* 2D pose estimation + MocapNET giving you a nice 3D visualization as well as an output .bvh file + * @author Ammar Qammaz (AmmarkoV) + */ +#include +#include +//----------------------------------------------------------------- +#include "../../JointEstimator2D/cameraControl.hpp" +#include "../../JointEstimator2D/jointEstimator2D.hpp" +#include "../../JointEstimator2D/visualization.hpp" +//----------------------------------------------------------------- +#include "../../MocapNET2/MocapNETLib2/mocapnet2.hpp" +#include "../../MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/conversions.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/bvh.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/csvRead.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/csvWrite.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/skeletonAbstraction.hpp" +//----------------------------------------------------------------- +#include "../../MocapNET2/MocapNETLib2/visualization/visualization.hpp" +#include "../../MocapNET2/MocapNETLib2/visualization/map.hpp" +//----------------------------------------------------------------- +#include "../../MocapNET2/MocapNETLib2/tools.hpp" +using namespace cv; + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int main(int argc, char *argv[]) +{ + struct MocapNET2Options options= {0}; + struct MocapNET2 mnet= {0}; + mnet.options = & options; + + struct JointEstimator2D jointEstimator; + + std::vector inputValues; + std::vector > exactMocapNET2DOutput; + std::vector > output3DPositions; + std::vector points3DFlatOutput; + + struct skeletonSerialized resultAsSkeletonSerialized= {0}; + + float frameRateSummary = 0.0; + unsigned int frameSamples=0; + + defaultMocapNET2Options(&options); + + /* + * Force effortless IK configuration on Webcam Demo + */ + //Be unconstrained by default + options.constrainPositionRotation=0; + //Use IK ======== + options.useInverseKinematics=1; + options.learningRate=0.01; + options.iterations=5; + options.epochs=30.0; + options.spring=1.0; + //============== + + //640x480 should be a high framerate compatible resolution + //for most webcams, you can change this using --size X Y commandline parameter + options.width = 640; + options.height = 480; + + + loadOptionsFromCommandlineOptions(&options,argc,argv); + + std::cerr<<"Trying to open source ("<> frame; } + //-------------------------------------------------------------------- + + //We will accept the input resolution and force it + //on visualization.. + options.width = frame.size().width; + options.height = frame.size().height; + options.visWidth = frame.size().width; + options.visHeight = frame.size().height; + //----------------------------------------------------- + + + //We might want to load a special bvh file based on our options..! + loadOptionsAfterBVHLoadFromCommandlineOptions(&options,argc,argv); + + //If the initialization didnt happen inside the previous call lets do it now + if (!options.hasInit) + { + if (initializeBVHConverter(0,options.visWidth,options.visHeight,0)) + { + fprintf(stderr,"BVH code initalization successfull..\n"); + options.hasInit=1; + } + } + //-------------------------------------------------------------------------- + + //Switch to realtime priority before opening tensorflow stuff.. + requestRealtimePriority(); + + if (loadJointEstimator2D( + &jointEstimator, + options. + jointEstimatorUsed, + 1, + options.useCPUOnlyFor2DEstimator + )) + { + if ( loadMocapNET2(&mnet,"Live Demo") ) + { + //------------------------------ + mnet.learningRate=options.learningRate; + mnet.iterations=options.iterations; + mnet.epochs=options.epochs; + mnet.spring=options.spring; + //------------------------------ + + cv::Mat viewMat = Mat(Size(jointEstimator.inputWidth2DJointDetector,jointEstimator.inputHeight2DJointDetector),CV_8UC3, Scalar(0,0,0)); + + struct Skeletons2DDetected skeleton2DEstimations= {0}; + + if (options.visualize) + { + //cv::namedWindow("Video Input Feed",1); + //cv::moveWindow("Video Input Feed",0,368); + cv::namedWindow("3D Points Output",1); + cv::moveWindow("3D Points Output",0,0); + cv::namedWindow("Skeletons",1); + cv::moveWindow("Skeletons",1920-jointEstimator.inputWidth2DJointDetector-50,100); + } + + unsigned int frameID=0; + unsigned int skippedFramesInARow=0; + + + std::vector result; + std::vector previousResult; + + float totalTime=0.0; + unsigned int totalSamples=0; + + std::vector > bvhFrames; + struct skeletonSerialized skeleton= {0}; + + while ( (options.frameLimit==0) || (frameID> frame; } + + //If we are running in a low-end computer and need to keep in sync with a live video feed we can frame-skip + if (options.frameSkip) + { + for (int i=0; i> frame; + } + } + + frame.copyTo(frameCentered); + if ( (frameCentered.size().width>0) && (frameCentered.size().height>0) ) + { + if ( + !cropAndResizeCVMatToMatchSkeleton( + &jointEstimator, + frameCentered, + &skeleton2DEstimations + ) + ) + { + fprintf(stderr,"Failed to crop input video\n"); + } + //imshow("Video Input Feed", frameCentered); + + frameCentered.copyTo(viewMat); + // viewMat.setTo(Scalar(0,0,0)); + + //Tensorflow works with Floating point input so we need to convert our buffer.. + frameCentered.convertTo(frameCentered,CV_32FC3); + + //At this point we are ready to execute the neural network + long startTime2D = GetTickCountMicrosecondsMN(); + + //We count the framerate of our acquisition + options.fpsAcquisition = convertStartEndTimeFromMicrosecondsToFPS(options.loopStartTime,startTime2D); + + + std::vector > heatmaps = getHeatmaps( + &jointEstimator, + frameCentered.data, + jointEstimator.inputWidth2DJointDetector, + jointEstimator.inputHeight2DJointDetector + ); + if (heatmaps.size()>0) + { + //This will spam with small heatmap windows + //visualizeHeatmaps(&jointEstimator,heatmaps,frameID); + + estimate2DSkeletonsFromHeatmaps(&jointEstimator,&skeleton2DEstimations,heatmaps); + + long endTime2D = GetTickCountMicrosecondsMN(); + + options.fps2DEstimator = convertStartEndTimeFromMicrosecondsToFPS(startTime2D,endTime2D); + + if (options.visualize) + { + dj_drawExtractedSkeletons( + viewMat, + &skeleton2DEstimations, + jointEstimator.inputWidth2DJointDetector, + jointEstimator.inputHeight2DJointDetector + ); + } + + float percentageOf2DPointsMissing = percentOf2DPointsMissing(&skeleton2DEstimations); + if ( percentageOf2DPointsMissing < 50.0 ) //only work when less than 50% of information missing.. + { + skippedFramesInARow=0; + //We want to go from the original normalized values of skeleton2DEstimations to the original + //Resolution we grabbed our initial frame @ before cropping.. + restore2DJointsToInputFrameCoordinates(&jointEstimator,&skeleton2DEstimations); + + //Now that our points have their initial size let's perform a conversion to the internal + //serialized skeleton data structure that will prepare them for use in MocapNET + convertSkeletons2DDetectedToSkeletonsSerialized( + &skeleton, + &skeleton2DEstimations, + frameID, + jointEstimator.crop.frameWidth, + jointEstimator.crop.frameHeight + ); + + takeCareOfScalingInputAndAddingNoiseAccordingToOptions(&options,&skeleton); + + unsigned int feetAreMissing=areFeetMissing(&skeleton); + + + long startTime = GetTickCountMicrosecondsMN(); + //-------------------------------------------------------- + previousResult = result; + result = runMocapNET2( + &mnet, + &skeleton, + ( (options.doLowerBody) && (!feetAreMissing) ), + options.doHands, + options.doFace, + options.doGestureDetection, + options.useInverseKinematics, + options.doOutputFiltering + ); + bvhFrames.push_back(result); + //-------------------------------------------------------- + long endTime = GetTickCountMicrosecondsMN(); + options.fpsMocapNET = convertStartEndTimeFromMicrosecondsToFPS(startTime,endTime); + frameRateSummary += options.fpsMocapNET; + ++frameSamples; + //-------------------------------------------------------- + + + options.numberOfMissingJoints = upperbodyCountMissingNSDMElements(mnet.upperBody.NSDM,0 /*Dont spam */); + //Don't spam with missing joints.. + //fprintf(stderr,"Number of missing joints for UpperBody %u\n",options.numberOfMissingJoints); + + //Convert BVH frame to 2D points to show on screen + exactMocapNET2DOutput = convertBVHFrameTo2DPoints(result);//,MocapNETTrainingWidth,MocapNETTrainingHeight); + + if (options.saveCSV3DFile) + { + //Convert BVH frame to 3D points to output on a file + points3DFlatOutput=convertBVHFrameToFlat3DPoints(result);//,MocapNETTrainingWidth,MocapNETTrainingHeight); + output3DPositions.push_back(points3DFlatOutput); //3d Input + } + + resultAsSkeletonSerialized.skeletonHeaderElements = skeleton.skeletonHeaderElements; + resultAsSkeletonSerialized.skeletonBodyElements = skeleton.skeletonBodyElements; + if ( + convertMocapNET2OutputToSkeletonSerialized( + &mnet, + &resultAsSkeletonSerialized, + exactMocapNET2DOutput, + frameID, + MocapNETTrainingWidth, + MocapNETTrainingHeight + ) + ) + { + //TODO : Compare resultAsSkeletonSerialized and skeleton + doReprojectionCheck(&skeleton,&resultAsSkeletonSerialized); + } + + } + else + { + if (skippedFramesInARow%30==0) + { fprintf(stderr,"."); } + ++skippedFramesInARow; + } + + if (options.visualize) + { + visualizationCommander( + &mnet, + &options, + &skeleton, + &frame, + result, + exactMocapNET2DOutput, + frameID, + 1// We will do the waitKey call ourselves + ); + + if ( (viewMat.size().width >0) && (viewMat.size().height>0) ) + { imshow("Skeletons", viewMat); } else + { std::cerr<<"Invalid skeleton visualization frame.. \n"; } + } + } + } + else + { + std::cerr<<"Broken frame.. \n"; + ++options.brokenFrames; + if (options.brokenFrames>10) + { + std::cerr<<"Too many broken frames.. \n"; + break; + } + } + + options.loopEndTime = GetTickCountMicrosecondsMN(); + + options.totalLoopFPS = convertStartEndTimeFromMicrosecondsToFPS(options.loopStartTime,options.loopEndTime); + + + //------------------------------------------------------ + // These final calls add delays to frame processing so + // they are not counted in loop time + //------------------------------------------------------ + + + //Frames should increment even when visualization is off.. + ++frameID; + + + if (options.visualize) + { + char key = 0; + if (options.delay!=0) + { + key = waitKey(options.delay); + } else + { + key = waitKey(1); + } + + if (key==27) + { + fprintf(stderr,GREEN "Received Escape key from UI, terminating the application.." NORMAL); + break; + } + } + + if (options.delay!=0) + { + nsleep(options.delay*1000); + } + //------------------------------------------------------ + } // End of grabber loop + + + if (options.bvhCenter) + { + for (unsigned int i=0; i10) { bvhFrames[i][3]=10; } else + if (bvhFrames[i][3]<-10) { bvhFrames[i][3]=-10; } + } + } + + //fix https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/35 + fixBVHHip(bvhFrames); + + if ( writeBVHFile(options.outputPath,0,options.prependTPose,bvhFrames) ) + { + fprintf(stderr,GREEN "Successfully wrote %lu frames to bvh file.. \n" NORMAL,bvhFrames.size()); + } + else + { + fprintf(stderr,RED "Failed to write %lu frames to bvh file.. \n" NORMAL,bvhFrames.size()); + } + + if (options.saveCSV3DFile) + { + fprintf(stderr,"Will now write 3D output to out3DP.csv.. \n"); + //---------------------------------------------------------------------------------------------------------------------------------- + if ( writeCSVHeaderFromLabelsAndVectorOfVectors("out3DP.csv",MocapNET3DPositionalOutputArrayNames,MOCAPNET_3DPOINT_NUMBER,output3DPositions) ) + { + fprintf(stderr,GREEN "Successfully wrote %lu frames to csv file.. \n" NORMAL,output3DPositions.size()); + } + else + { + fprintf(stderr,RED "Failed to write %lu frames to bvh file.. \n" NORMAL,output3DPositions.size()); + } + } + + + if ( (options.save3DVisualization) || (options.save2DVisualization) ) + { + int highResEncoding=1; + char formatString[256]; + + if (highResEncoding) + { + snprintf(formatString,256,"ffmpeg -framerate %f -i vis%%05d.jpg -s 1200x720 -y -r %f -pix_fmt yuv420p -threads 8 livelastRun3DHiRes.mp4 && rm ./*.jpg",options.inputFramerate,options.inputFramerate); + } else + { + snprintf(formatString,256,"ffmpeg -framerate %f -i vis%%05d.jpg -y -r %f -threads 8 -crf 9 -pix_fmt yuv420p lastRun3D.webm && rm ./*.jpg",options.inputFramerate,options.inputFramerate); + } + int i=system(formatString); + + + if (i==0) + { + fprintf(stderr,"Successfully wrote video file.. \n"); + } + else + { + fprintf(stderr,"Failed to write a video file.. \n"); + } + } + + unloadMocapNET2(&mnet); + + + + } //3D pose estimator ok + } //2D joint estimator ok + + //Offer a summary of system and the achieved framerate..! + if (frameSamples!=0) + { + fprintf(stderr,"\n\nMocapNET v%s execution summary :\n",MocapNETVersion); + fprintf(stderr,"__________________________________________\n"); + //neuralNetworkPrintVersion(); + printBVHCodeVersion(); + fprintf(stderr,"CPU : %s \n",options.CPUName); + fprintf(stderr,"GPU : %s \n",options.GPUName); + fprintf(stderr,"Average framerate for %u samples was %0.2f fps \n",frameSamples,((float) frameRateSummary/frameSamples) ); + + //Offer some info on options of the run executed.. + //------------------------------------------------- + if (options.doMultiThreadedIK) + { fprintf(stderr,"Multi-threading was on\n"); } + //------------------------------------------------- + if (codeOptimizationsForIKEnabled()) + { fprintf(stderr,"Code optimizations where on\n"); } + //------------------------------------------------- + if (options.jointEstimatorUsed==JOINT_2D_ESTIMATOR_FORTH) + { fprintf(stderr,"You can achieve better accuracy by using the homebrewed OpenPose 2D joint estimator using --openpose\n"); } + //------------------------------------------------- + if (options.jointEstimatorUsed==JOINT_2D_ESTIMATOR_OPENPOSE) + { fprintf(stderr,"You can achieve faster framerates with the bundled FORTH 2D joint estimator using --forth\n"); } + //------------------------------------------------- + } + + // the camera will be deinitialized automatically in VideoCapture destructor + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETFromCSV/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETFromCSV/CMakeLists.txt new file mode 100644 index 0000000..a1f1056 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETFromCSV/CMakeLists.txt @@ -0,0 +1,18 @@ +project( MocapNET2CSV ) +cmake_minimum_required( VERSION 2.8.13 ) +#cmake_minimum_required(VERSION 3.5) + +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +add_executable(MocapNET2CSV mocapnet2CSV.cpp ) +target_link_libraries(MocapNET2CSV rt dl m ${OpenCV_LIBRARIES} Tensorflow TensorflowFramework MocapNETLib2 ) +set_target_properties(MocapNET2CSV PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(MocapNET2CSV PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETFromCSV/mocapnet2CSV.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETFromCSV/mocapnet2CSV.cpp new file mode 100644 index 0000000..c609408 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETFromCSV/mocapnet2CSV.cpp @@ -0,0 +1,400 @@ +/* + * Utility to extract BVH files straight from OpenPose JSON output + * Sample usage ./MocapNETCSV --from test.csv --visualize + */ + +#include "../MocapNETLib2/mocapnet2.hpp" +#include +#include +#include +#include +#include + +#include "../MocapNETLib2/applicationLogic/parseCommandlineOptions.hpp" + +#include "../MocapNETLib2/tools.hpp" +#include "../MocapNETLib2/visualization/map.hpp" +#include "../MocapNETLib2/visualization/visualization.hpp" +#include "../MocapNETLib2/visualization/camera_ready.hpp" +//------------------------------------------ +#include "../MocapNETLib2/IO/bvh.hpp" +#include "../MocapNETLib2/IO/csvRead.hpp" +#include "../MocapNETLib2/IO/csvWrite.hpp" +#include "../MocapNETLib2/IO/jsonRead.hpp" +#include "../MocapNETLib2/IO/jsonMocapNETHelpers.hpp" +#include "../MocapNETLib2/IO/conversions.hpp" +#include "../MocapNETLib2/IO/skeletonAbstraction.hpp" +//--------------------------------------------------- + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + +int main(int argc, char *argv[]) +{ + struct MocapNET2Options options= {0}; + struct MocapNET2 mnet= {0}; + mnet.options = & options; + + struct skeletonSerialized resultAsSkeletonSerialized= {0}; + + defaultMocapNET2Options(&options); + options.GPUName[0]=0; //The CSV demo does not use the GPU so don't display it.. + + + /* + * Force effortless IK configuration on CSV demo + */ + //Be unconstrained by default + options.constrainPositionRotation=0; + //Use IK ======== + options.useInverseKinematics=1; + options.learningRate=0.01; + options.iterations=5;//5; + options.epochs=30.0; + options.spring=0.0; //Deprecated ? + //============== + + //640x480 should be a high framerate compatible resolution + //for most webcams, you can change this using --size X Y commandline parameter + options.width = 1920; + options.height = 1080; + + loadOptionsFromCommandlineOptions(&options,argc,argv); + + if (options.path==0) + { + fprintf(stderr,RED "No CSV dataset given\n " NORMAL); + return 1; + } + + + if (options.label==0) + { + options. label="colorFrame_0_"; + } + + + struct CSVFileContext csv= {0}; + if (!options.isCSVFile) + { + fprintf(stderr,RED "The path %s doesn't look like a CSV file.. \n" NORMAL,options.path); + return 0; + } + else if (options.isCSVFile) + { + if (!openCSVFile(&csv,options.path)) + { + fprintf(stderr,RED "Unable to open CSV file %s \n" NORMAL,options.path); + return 0; + } + else + { + fprintf(stderr,GREEN "CSV file %s is now open\n" NORMAL,options.path); + options.frameLimit=getBodyLinesOfCSVFIle(&csv,options.path); + } + + + //------------------------------------------------------- + int havePath=0; + int positionOfSlash=0; + for (int i=strlen(options.path); i>=0; i--) + { + if (options.path[i]=='/') + { + positionOfSlash=i; + havePath=1; + break; + } + } + + if (havePath) + { + options.datasetPath=(char*) malloc(sizeof(char) * (positionOfSlash+2) ); + if (options.datasetPath!=0) + { + for (int i=0; i<=positionOfSlash; i++) + { + options.datasetPath[i]=options.path[i]; + } + options.datasetPath[positionOfSlash+1]=0; //Null termination + } + } + } + + + loadCalibration(&options,options.datasetPath,"color.calib"); + + + if(options.visualize) + { + spawnVisualizationWindow("3D Points Output",options.visWidth,options.visHeight); + } + + + + if ( loadMocapNET2(&mnet,"CSV Demo") ) + { + //------------------------------ + mnet.learningRate=options.learningRate; + mnet.iterations=options.iterations; + mnet.epochs=options.epochs; + mnet.spring=options.spring; + //------------------------------ + + + char filePathOfImageFile[1024]= {0}; + if (options.datasetPath) + { + char formatString[1024]= {0}; + snprintf(formatString,1024,"%%s/%%s%%0%uu.jpg",options.serialLength); + snprintf(filePathOfImageFile,1024,formatString,options.datasetPath,options.label,1/*Frame ID*/); + // snprintf(filePathOfImageFile,1024,"%s/colorFrame_0_00001.jpg",datasetPath); + + if ( getImageWidthHeight(filePathOfImageFile,&options.width,&options.height) ) + { + fprintf(stderr,"Image dimensions changed from default to %ux%u\n",options.width,options.height); + //Force visualization size to the same as image dimensions autodetected.. + options.visWidth = options.width; + options.visHeight = options.height; + } + else + { + fprintf(stderr,"Assuming default image dimensions %ux%u , you can change this using --size x y\n",options.width,options.height); + } + } + + + //We might want to load a special bvh file based on our options..! + loadOptionsAfterBVHLoadFromCommandlineOptions(&options,argc,argv); + + //If the initialization didnt happen inside the previous call lets do it now + if (!options.hasInit) + { + if (initializeBVHConverter(0,options.visWidth,options.visHeight,0)) + { + fprintf(stderr,"BVH code initalization successfull..\n"); + options.hasInit=1; + } + } + //-------------------------------------------------------------------------- + + std::vector result; + std::vector previousResult; + std::vector > output3DPositions; + std::vector points3DFlatOutput; + + float totalTime=0.0; + unsigned int totalSamples=0; + + std::vector > bvhFrames; + struct skeletonSerialized skeleton= {0}; + + char formatString[1024]= {0}; + snprintf(formatString,1024,"%%s/%%s%%0%uu_keypoints.json",options.serialLength); + + unsigned int frameID=0; + while ( (options.frameLimit==0) || (frameID > exactMocapNET2DOutput = convertBVHFrameTo2DPoints(result);//,MocapNETTrainingWidth,MocapNETTrainingHeight); + + + if (options.saveCSV3DFile) + { + //Convert BVH frame to 3D points to output on a file + points3DFlatOutput=convertBVHFrameToFlat3DPoints(result);//,MocapNETTrainingWidth,MocapNETTrainingHeight); + output3DPositions.push_back(points3DFlatOutput); //3d Input + } + + resultAsSkeletonSerialized.skeletonHeaderElements = skeleton.skeletonHeaderElements; + resultAsSkeletonSerialized.skeletonBodyElements = skeleton.skeletonBodyElements; + //exactMocapNET2DOutput = convertBVHFrameTo2DPoints(result,MocapNETTrainingWidth,MocapNETTrainingHeight); + if ( + convertMocapNET2OutputToSkeletonSerialized( + &mnet, + &resultAsSkeletonSerialized, + exactMocapNET2DOutput, + frameID, + MocapNETTrainingWidth, + MocapNETTrainingHeight + ) + ) + { + //TODO : Compare resultAsSkeletonSerialized and skeleton + doReprojectionCheck(&skeleton,&resultAsSkeletonSerialized); + } + + + + + float sampleTime = (float) (endTime-startTime)/1000; + if (sampleTime==0.0) + { + sampleTime=1.0; //Take care of division by null.. + } + + options.totalLoopFPS = (float) 1000/sampleTime; + fprintf(stderr,"Sample %u - %0.4fms - %0.4f fps\n",frameID,sampleTime,options.totalLoopFPS); + + + if (options.visualize) + { + visualizationCommander( + &mnet, + &options, + &skeleton, + 0, + result, + exactMocapNET2DOutput, + frameID, + 0 //Visualization code must handle messages.. + ); + + } + + + totalTime+=sampleTime; + ++totalSamples; + + if (options.delay!=0) + { + fprintf(stderr,"Sleeping for %u milliseconds\n",options.delay); + nsleep(options.delay*1000); + } + + } + else + { + fprintf(stderr,"Done.. \n"); + break; + } + + ++frameID; + } + + fprintf(stderr,"Finished with %u/%u frames\n",frameID,options.frameLimit); + + if (totalSamples>0) + { + char * bvhHeaderToWrite=0; + + if (options.bvhCenter) + { + for (unsigned int i=0; i10) { bvhFrames[i][3]=10; } else + if (bvhFrames[i][3]<-10) { bvhFrames[i][3]=-10; } + } + } + + //fix https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/35 + fixBVHHip(bvhFrames); + + if ( writeBVHFile(options.outputPath,bvhHeaderToWrite,options.prependTPose,bvhFrames) ) + { + fprintf(stderr,"Successfully wrote %lu frames to bvh file %s.. \n",bvhFrames.size(),options.outputPath); + } + else + { + fprintf(stderr,"Failed to write %lu frames to bvh file %s .. \n",bvhFrames.size(),options.outputPath); + } + + if (options.saveCSV3DFile) + { + fprintf(stderr,"Will now write 3D output to out3DP.csv.. \n"); + //---------------------------------------------------------------------------------------------------------------------------------- + if ( writeCSVHeaderFromLabelsAndVectorOfVectors("out3DP.csv",MocapNET3DPositionalOutputArrayNames,MOCAPNET_3DPOINT_NUMBER,output3DPositions) ) + { + fprintf(stderr,GREEN "Successfully wrote %lu frames to csv file.. \n" NORMAL,output3DPositions.size()); + } + else + { + fprintf(stderr,RED "Failed to write %lu frames to bvh file.. \n" NORMAL,output3DPositions.size()); + } + } + + + float averageTime=(float) totalTime/totalSamples; + fprintf(stderr,"\n\nMocapNET v%s execution summary :\n",MocapNETVersion); + fprintf(stderr,"__________________________________________\n"); + //neuralNetworkPrintVersion(); + printBVHCodeVersion(); + fprintf(stderr,"CPU : %s \n",options.CPUName); + fprintf(stderr,"GPU : %s \n",options.GPUName); + fprintf(stderr,"Total %0.2f ms for %u samples - Average %0.2f ms - %0.2f fps\n",totalTime,totalSamples,averageTime,(float) 1000/averageTime); + //----------------------------------------------- + if (options.doMultiThreadedIK) + { fprintf(stderr,"Multi-threading was on\n"); } + if (codeOptimizationsForIKEnabled()) + { fprintf(stderr,"Code optimizations where on\n"); } + } + + if (options.isCSVFile) + { + closeCSVFile(&csv); + } + + + + if (options.saveVisualization) + { + int i; + //Low-Res video encoding + //int i=system("ffmpeg -framerate 25 -i vis%05d.jpg -y -r 30 -threads 8 -crf 9 -pix_fmt yuv420p lastRun3D.webm"); + //High-Res video encoding + snprintf(formatString,1024,"ffmpeg -framerate %f -i vis%%05d.jpg -s 1200x720 -y -r %f -pix_fmt yuv420p -threads 8 %s_lastRun3DHiRes.mp4 && rm ./vis*.jpg",options.inputFramerate,options.inputFramerate,options.path); // + i=system(formatString); + if (i==0) + { + fprintf(stderr,"Successfully wrote video file to %s_lastRun3DHiRes.mp4 .. \n",options.path); + } + else + { + fprintf(stderr,"Failed to write a video file.. \n"); + } + } + + unloadMocapNET2(&mnet); + } + else + { + fprintf(stderr,RED "MocapNET2 failed to load properly and will now exit..\n"); + } +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/CMakeLists.txt new file mode 100644 index 0000000..a0224e7 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/CMakeLists.txt @@ -0,0 +1,72 @@ +project( MocapNETLib2 ) +cmake_minimum_required(VERSION 3.5) + +set_property(GLOBAL PROPERTY USE_FOLDERS ON) + +#Unfortunately needed for tf_utils.cpp +set(CMAKE_CXX_STANDARD 11) + +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + + +#add_executable(MocapNETLib mocapnet.cpp ${CMAKE_SOURCE_DIR}/src/Tensorflow/tf_utils.cpp) + +add_library(MocapNETLib2 SHARED +${BVH_SOURCE} +${OPENGL_SOURCE} +../MocapNETLib2/config.h +../MocapNETLib2/core/core.cpp +../MocapNETLib2/core/core.hpp +../MocapNETLib2/core/singleThreaded.cpp +../MocapNETLib2/core/singleThreaded.hpp +../MocapNETLib2/core/multiThreaded.cpp +../MocapNETLib2/core/multiThreaded.hpp +../MocapNETLib2/mocapnet2.cpp +../MocapNETLib2/tools.cpp +../MocapNETLib2/remoteExecution.cpp +../MocapNETLib2/applicationLogic/parseCommandlineOptions.cpp +../MocapNETLib2/applicationLogic/poseRecognition.cpp +../MocapNETLib2/applicationLogic/gestureRecognition.cpp +../MocapNETLib2/applicationLogic/artifactRecognition.cpp +../MocapNETLib2/qualityControl/qualityControl.cpp +../MocapNETLib2/postProcessing/outputFiltering.hpp +../MocapNETLib2/NSDM/generated_body.hpp +../MocapNETLib2/NSDM/generated_upperbody.hpp +../MocapNETLib2/NSDM/generated_lowerbody.hpp +../MocapNETLib2/IO/bvh.cpp +../MocapNETLib2/IO/commonSkeleton.hpp +../MocapNETLib2/IO/skeletonAbstraction.cpp +../MocapNETLib2/IO/skeletonSerializedToBVHTransform.hpp +../MocapNETLib2/IO/csvRead.cpp +../MocapNETLib2/IO/csvWrite.cpp +../MocapNETLib2/IO/jsonRead.cpp +../MocapNETLib2/IO/jsonMocapNETHelpers.cpp +../MocapNETLib2/IO/conversions.cpp +../MocapNETLib2/visualization/rgb.cpp +../MocapNETLib2/visualization/allInOne.cpp +../MocapNETLib2/visualization/widgets.cpp +../MocapNETLib2/visualization/visualization.cpp +../MocapNETLib2/visualization/drawSkeleton.cpp +../MocapNETLib2/visualization/opengl.cpp +../MocapNETLib2/visualization/camera_ready.cpp +../MocapNETLib2/visualization/map.cpp +../MocapNETLib2/visualization/template.cpp +../MocapNETLib2/solutionParts/body.cpp +../MocapNETLib2/solutionParts/upperBody.cpp +../MocapNETLib2/solutionParts/lowerBody.cpp +${CMAKE_SOURCE_DIR}/dependencies/InputParser/InputParser_C.cpp +#Tensorflow stuff.. +${TENSORFLOW_SOURCE_FILES} +) + + +target_link_libraries(MocapNETLib2 rt dl m pthread ${OpenCV_LIBRARIES} ${OPENGL_LIBS} Tensorflow TensorflowFramework ${NETWORK_CLIENT_LIBRARIES} ${PNG_Libs} ${JPG_Libs} ) +set_target_properties(MocapNETLib2 PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(MocapNETLib2 PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvh.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvh.cpp new file mode 100644 index 0000000..66d9f40 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvh.cpp @@ -0,0 +1,1583 @@ +#include "bvh.hpp" +#include + +#include "../config.h" + +#include "commonSkeleton.hpp" +#include "skeletonSerializedToBVHTransform.hpp" +#include "../mocapnet2.hpp" +#include "../visualization/opengl.hpp" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +#if USE_BVH +#include "../../../../dependencies/RGBDAcquisition/tools/Calibration/calibration.h" +#include "../../../../dependencies/RGBDAcquisition/tools/AmMatrix/matrix4x4Tools.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_project.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_transform.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/bvh_inverseKinematics.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik/hardcodedProblems_inverseKinematics.h" + +struct BVH_MotionCapture bvhMotion = {0}; +struct BVH_Transform bvhTransform = {0}; +//--------------------------------------- +struct ikProblem * bodyProblem = 0; +//--------------------------------------- +struct ikConfiguration ikConfig = {0}; +//--------------------------------------- +int haveBVHInit=0; +#else +#warning "BVH code not included.." +#endif // USE_BVH + + +float fX = 582.18394; +float fY = 582.52915; + + +#if USE_BVH + struct simpleRenderer renderer = {0}; +#endif + + +void overrideBVHSubsystemFocalLength(float newFx,float newFy) +{ + fX = newFx; + fY = newFy; +} + +int codeOptimizationsForIKEnabled() +{ + return codeHasSSE(); +} + +void printBVHCodeVersion() +{ + fprintf(stderr,"BVH subsystem version %s\n",BVH_LOADER_VERSION_STRING); +} + + +int initializeIK() +{ + fprintf(stderr,"Initializing IK code..\n"); + int failures = 0; + + //------------------------------------------------------------------------------------------------------------------------ + bodyProblem = (struct ikProblem * ) malloc(sizeof(struct ikProblem)); + if (bodyProblem!=0) { memset(bodyProblem,0,sizeof(struct ikProblem)); } else + { fprintf(stderr,"Failed to allocate memory for our IK bodyProblem..\n"); ++failures; } + //------------------------------------------------------------------------------------------------------------------------ + + struct MotionBuffer * solution = mallocNewMotionBuffer(&bvhMotion); + failures += (solution==0); + + struct MotionBuffer * previousSolution = mallocNewMotionBuffer(&bvhMotion); + failures += (previousSolution==0); + + //------------------------------------------------------------------------------------------------------------------------ + if (!prepareDefaultBodyProblem( + bodyProblem, + &bvhMotion, + &renderer, + previousSolution, + solution, + &bvhTransform + ) + ) + { + fprintf(stderr,RED "MocapNET2/BVH: Could not initializeIK() for an IK solution\n" NORMAL); + ++failures; + } + + + + if (failures>0) + { + fprintf(stderr,"%u failures encountered..\n",failures); + + //Dump everything on failure..! + if (bodyProblem!=0) { free(bodyProblem); } + + freeMotionBuffer(&solution); + freeMotionBuffer(&previousSolution); + } + + return (failures==0); +} + + + + +int initializeBVHConverter(const char * specificBVHFilename,int width,int height,int noIKNeeded) +{ +#if USE_BVH + fprintf(stderr,"Using BVH codebase version %s\n",BVH_LOADER_VERSION_STRING); + + simpleRendererDefaults( + &renderer, + width,//1920 + height,//1080 + fX, //570.0 + fY //570.0 + ); + simpleRendererInitialize(&renderer); + + const char * selectedBVHFile = specificBVHFilename; + if (specificBVHFilename==0) { selectedBVHFile="dataset/headerWithHeadAndOneMotion.bvh"; } + + //noIKNeeded is not used in this version of the code but added to ensure the function signature is the same with dev snapshot.. + + //if ( bvh_loadBVH("dataset/headerAndOneMotion.bvh",&bvhMotion,1.0) ) //This is the old armature that only has the eyes + if ( bvh_loadBVH(selectedBVHFile,&bvhMotion,1.0) ) // This is the new armature that includes the head + { + fprintf(stderr,"BVH subsystem initialized using %s that contains %u frames ( %u motions each )\n",selectedBVHFile,bvhMotion.numberOfFrames,bvhMotion.numberOfValuesPerFrame); + if (!initializeIK()) + { + fprintf(stderr,RED "Failed initializing IK..\n" NORMAL); + exit(0); + return 0; + } + + //Test joint scaling.. + //changeJointDimensions(&bvhMotion); + haveBVHInit=1; + return 1; + } + else + { + fprintf(stderr,RED "initializeBVHConverter: Failed to bvh_loadBVH(header.bvh)..\n" NORMAL); + } +#else + fprintf(stderr,YELLOW "initializeBVHConverter: BVH code not compiled in..\n" NORMAL); +#endif // USE_BVH + return 0; +} + + + +int loadCalibration(struct MocapNET2Options * options,const char* directory,const char * file) +{ + fprintf(stderr,"loadCalibration %s , %s \n",directory,file); + + char loadPath[512]={0}; + + if (directory!=0) + { snprintf(loadPath,512,"%s/%s",directory,file); } else + { snprintf(loadPath,512,"%s",file); } + //--------------------------- + struct calibration calib={0}; + if (ReadCalibration(loadPath,0,0,&calib) ) + { + //----------------------------------- + fX = calib.intrinsic[CALIB_INTR_FX]; + fY = calib.intrinsic[CALIB_INTR_FY]; + options->width = calib.width; + options->height = calib.height; + options->visWidth= calib.width; + options->visHeight = calib.height; + //----------------------------------- + fprintf(stderr,GREEN "loadCalibration working \n" NORMAL); + fprintf(stderr,"Image Resolution loaded is %u x %u \n",calib.width,calib.height); + fprintf(stderr,"Focal Lengths fx=%0.2f fy=%0.2f \n",fX,fY); + if (!options->hasInit) + { + initializeBVHConverter(0,calib.width,calib.height,0); + options->hasInit=1; + } + + #if USE_OPENGL + //If we are using OpenGL we need to update the renderer settings..! + if (!changeOpenGLCalibration(&calib)) + { + fprintf(stderr,"Failed setting OpenGL calibration\n"); + } + #endif + + return 1; + } + fprintf(stderr,RED "Failed @ loadCalibration %s , %s \n" NORMAL,directory,file); + return 0; +} + + + +int stopIK() +{ + if(bodyProblem!=0) + { + cleanProblem(bodyProblem); + free(bodyProblem); + } + + return 1; +} + + + +//This should fix the wrong Z Y X rotation thing happening +//in https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/35 +int fixBVHHip( + std::vector > &bvhFrames + ) +{ + return 0; + for (unsigned int frameID=0; frameID= 6) + { + bvhFrames[frameID][3]=-bvhFrames[frameID][3]; + bvhFrames[frameID][4]=-bvhFrames[frameID][4]; + bvhFrames[frameID][5]=-bvhFrames[frameID][5]; + } + } + + return 0; +} + + +int writeBVHFile( + const char * filename, + const char * header, + int prependTPose, + std::vector > bvhFrames + ) +{ + FILE * fp = fopen(filename,"w"); + if (fp!=0) + { + if (header==0) + { + header=bvhHeader; + } + fprintf(fp,"%s",header); + fprintf(fp,"\nMOTION\n"); + + unsigned long numberOfFramesToWrite = bvhFrames.size(); + if (prependTPose) + { + numberOfFramesToWrite+=1; + } + + + + fprintf(fp,"Frames: %lu \n",numberOfFramesToWrite); + fprintf(fp,"Frame Time: 0.04\n"); + + unsigned int i=0,j=0; + + if (prependTPose) + { + if (bvhFrames.size()>1) + { + for (j=0; j< bvhFrames[0].size(); j++) + { + fprintf(fp,"0 "); + } + fprintf(fp,"\n"); + } + } + + for (i=0; i< bvhFrames.size(); i++) + { + std::vector frame = bvhFrames[i]; + if (frame.size()>0) + { + //fprintf(fp,"%lu joints",frame.size()); + + //fprintf(fp,"0.0 0.0 0.0 "); + for (j=0; j-0.0001) ) { fprintf(fp,"0 "); } else // Reduce .bvh size.. + { fprintf(fp,"%0.4f ",frame[j]); } + } + fprintf(fp,"\n"); + } + } + fclose(fp); + return 1; + } + return 0; +} + + + + + +void * loadBVHFile(const char * filename) +{ +#if USE_BVH + //struct BVH_MotionCapture tmp={0}; + struct BVH_MotionCapture * newBVHLoadedFile = (struct BVH_MotionCapture *) malloc(sizeof(struct BVH_MotionCapture)); + + if (newBVHLoadedFile!=0) + { + memset(newBVHLoadedFile,0,sizeof(struct BVH_MotionCapture)); + if ( bvh_loadBVH(filename,newBVHLoadedFile,1.0) ) + { + return (void*) newBVHLoadedFile; + } + } +#endif // USE_BVH + return 0; +} + + + + +std::vector > loadBVHFileMotionFrames(const char * filename) +{ + std::vector > result; +#if USE_BVH + struct BVH_MotionCapture bvh={0}; + + if ( bvh_loadBVH(filename,&bvh,1.0) ) + { + unsigned int frameID=0, jointID=0, c=0; + for (frameID=0; frameID currentFrame; + currentFrame.clear(); + for (jointID=0; jointID0) + { + + for (unsigned int jID=0; jID %s \n",jID,bvhMotion.jointHierarchy[jID].jointName); + } + + fprintf(stderr,"Errors (%u) scaling joints..\n",errors); + exit(0); + return 0; + } + + fprintf(stderr,"Feet dimensions have been scaled (hip->knee=>%0.2f) (knee->foot=>%0.2f)\n",hipToKneeLength,kneeToFootLength); + //exit(0); + return 1; + + #endif // USE_BVH + return 0; +} + + +int changeJointDimensions( + float neckLength, + float torsoLength, + float chestWidth, + float shoulderToElbowLength, + float elbowToHandLength, + float waistWidth, + float hipToKneeLength, + float kneeToFootLength, + float shoeLength + ) +{ + #if USE_BVH + int errors = 0; + + if (!bvh_changeJointDimensions(&bvhMotion,"neck",neckLength,1.0,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"neck1",neckLength,1.0,1.0) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"abdomen",torsoLength,1.0,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"chest",torsoLength,1.0,1.0) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"rShldr",1.0,1.0,chestWidth) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"lShldr",1.0,1.0,chestWidth) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"rForeArm",1.0,1.0,shoulderToElbowLength) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"lForeArm",1.0,1.0,shoulderToElbowLength) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"rHand",1.0,1.0,elbowToHandLength) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"lHand",1.0,1.0,elbowToHandLength) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"rButtock",1.0,1.0,waistWidth) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"lButtock",1.0,1.0,waistWidth) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"rShin",hipToKneeLength,1.0,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"lShin",hipToKneeLength,1.0,1.0) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"rFoot",kneeToFootLength,1.0,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"lFoot",kneeToFootLength,1.0,1.0) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"toe1-1.R",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe2-1.R",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe3-1.R",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe4-1.R",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe5-1.R",1.0,shoeLength,1.0) ) { ++errors; } + //-------------------------------------------------------------------------------------- + if (!bvh_changeJointDimensions(&bvhMotion,"toe1-1.L",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe2-1.L",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe3-1.L",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe4-1.L",1.0,shoeLength,1.0) ) { ++errors; } + if (!bvh_changeJointDimensions(&bvhMotion,"toe5-1.L",1.0,shoeLength,1.0) ) { ++errors; } + //-------------------------------------------------------------------------------------- + + + if (errors>0) + { + + for (unsigned int jID=0; jID %s \n",jID,bvhMotion.jointHierarchy[jID].jointName); + } + + fprintf(stderr,"Errors (%u) scaling joints..\n",errors); + exit(0); + return 0; + } + + + return 1; + + #endif // USE_BVH + return 0; +} + + + +float * mallocVector(std::vector bvhFrame) +{ + if (bvhFrame.size()==0) + { + fprintf(stderr,"mallocVector given an empty vector..\n"); + //Empty bvh frame means no vector + return 0; + } + + float * newVector = (float*) malloc(sizeof(float) * bvhFrame.size()); + if (newVector!=0) + { + for (int i=0; icurrentJoint) + { + return bvhMotion.jointHierarchy[currentJoint].jointName; //I could use the lowercase version here + } +#endif + return 0; +} + + +int getBVHMotionValueName(unsigned int currentMotionValue,char * target,unsigned int targetLength) +{ +#if USE_BVH + if (bvhMotion.numberOfValuesPerFrame > currentMotionValue) + { + return bvh_getMotionChannelName(&bvhMotion,currentMotionValue,target,targetLength); + } +#endif + return 0; +} + + + +unsigned int getBVHJointIDFromJointName(const char * jointName) +{ +#if USE_BVH + int i=0; + for (i=0; ijoint!=0) + ) + { + if (jIDnumberOfJointsSpaceAllocated) + { + out->joint[jID].pos2D[0] = (float) in->skeletonBody[skeletonSerializedID_2DX].value * width; + out->joint[jID].pos2D[1] = (float) in->skeletonBody[skeletonSerializedID_2DY].value * height; + out->joint[jID].pos2DCalculated = ( (out->joint[jID].pos2D[0]!=0.0) || (out->joint[jID].pos2D[1]!=0.0) ); + out->joint[jID].isBehindCamera = !out->joint[jID].pos2DCalculated; + } else + { + fprintf(stderr,"bvh2DCopy: Error accessing joint %u/%u\n",jID,out->numberOfJointsSpaceAllocated); + } + } else + { + fprintf(stderr,"bvh2DCopy: Error accessing joint transform\n"); + } +} + + + +void bvh2DBetween2PointsCopy( + struct BVH_Transform * out, + struct skeletonSerialized * in, + BVHJointID jID, + unsigned int skeletonSerializedID_2DX_A, + unsigned int skeletonSerializedID_2DY_A, + unsigned int skeletonSerializedID_2DX_B, + unsigned int skeletonSerializedID_2DY_B, + float width, + float height + ) +{ + if ( + (out!=0) && + (out->joint!=0) + ) + { + if (jIDnumberOfJointsSpaceAllocated) + { + out->joint[jID].pos2D[0] = ((in->skeletonBody[skeletonSerializedID_2DX_A].value + in->skeletonBody[skeletonSerializedID_2DX_B].value)/2) * width; + out->joint[jID].pos2D[1] = ((in->skeletonBody[skeletonSerializedID_2DY_A].value + in->skeletonBody[skeletonSerializedID_2DY_B].value)/2) * height; + out->joint[jID].pos2DCalculated = ( (out->joint[jID].pos2D[0]!=0.0) || (out->joint[jID].pos2D[1]!=0.0) ); + out->joint[jID].isBehindCamera = !out->joint[jID].pos2DCalculated; + } else + { + fprintf(stderr,"bvh2DBetween2PointsCopy: Error accessing joint %u/%u\n",jID,out->numberOfJointsSpaceAllocated); + } + } else + { + fprintf(stderr,"bvh2DBetween2PointsCopy: Error accessing joint transform\n"); + } +} + +void convertFaceSkeletonSerializedToBVHTransform( + struct BVH_MotionCapture * bvhMotion, + struct simpleRenderer * renderer, + struct BVH_Transform * out, + struct skeletonSerialized * in + ) +{ + //unsigned int mID; + unsigned int w = renderer->width; + unsigned int h = renderer->height; + + bvh2DCopy(out,in,BVH_MOTION_LHAND,SKELETON_SERIALIZED_2DX_LHAND,SKELETON_SERIALIZED_2DY_LHAND,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_ORBICULARIS03_R ,SKELETON_SERIALIZED_2DX_HEAD_REYE_2 ,SKELETON_SERIALIZED_2DY_HEAD_REYE_2,w,h); + bvh2DCopy(out,in,BVH_MOTION_EYE_R ,SKELETON_SERIALIZED_2DX_HEAD_REYE ,SKELETON_SERIALIZED_2DY_HEAD_REYE,w,h); + bvh2DCopy(out,in,BVH_MOTION_ORBICULARIS04_R ,SKELETON_SERIALIZED_2DX_HEAD_REYE_5 ,SKELETON_SERIALIZED_2DY_HEAD_REYE_5,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_ORBICULARIS03_L ,SKELETON_SERIALIZED_2DX_HEAD_LEYE_2 ,SKELETON_SERIALIZED_2DY_HEAD_LEYE_2,w,h); + bvh2DCopy(out,in,BVH_MOTION_EYE_L ,SKELETON_SERIALIZED_2DX_HEAD_LEYE ,SKELETON_SERIALIZED_2DY_HEAD_LEYE,w,h); + bvh2DCopy(out,in,BVH_MOTION_ORBICULARIS04_L ,SKELETON_SERIALIZED_2DX_HEAD_LEYE_5 ,SKELETON_SERIALIZED_2DY_HEAD_LEYE_5,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_SPECIAL04 ,SKELETON_SERIALIZED_2DX_HEAD_CHIN ,SKELETON_SERIALIZED_2DY_HEAD_CHIN,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_ORIS03_R ,SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_1 ,SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_1,w,h); + bvh2DCopy(out,in,BVH_MOTION_ORIS05 ,SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_2 ,SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_2,w,h); + bvh2DCopy(out,in,BVH_MOTION_ORIS03_L ,SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_3 ,SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_3,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_ORIS07_R ,SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_7 ,SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_7,w,h); + bvh2DCopy(out,in,BVH_MOTION_ORIS01 ,SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_6 ,SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_6,w,h); + bvh2DCopy(out,in,BVH_MOTION_ORIS07_L ,SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_5 ,SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_5,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ +} + + + +void convertBodySkeletonSerializedToBVHTransform( + struct BVH_MotionCapture * bvhMotion, + struct simpleRenderer * renderer, + struct BVH_Transform * out, + struct skeletonSerialized * in + ) +{ + //unsigned int mID; + unsigned int w = renderer->width; + unsigned int h = renderer->height; + + + //Nose + bvh2DCopy(out,in,BVH_MOTION_SPECIAL03 ,SKELETON_SERIALIZED_2DX_HEAD ,SKELETON_SERIALIZED_2DY_HEAD,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + + //Eyes + bvh2DCopy(out,in,BVH_MOTION_EYE_L ,SKELETON_SERIALIZED_2DX_ENDSITE_EYE_L ,SKELETON_SERIALIZED_2DY_ENDSITE_EYE_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_EYE_R ,SKELETON_SERIALIZED_2DX_ENDSITE_EYE_R ,SKELETON_SERIALIZED_2DY_ENDSITE_EYE_R,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + + //Ears + bvh2DCopy(out,in,BVH_MOTION_TEMPORALIS02_L ,SKELETON_SERIALIZED_2DX_LEAR ,SKELETON_SERIALIZED_2DY_LEAR,w,h); + bvh2DCopy(out,in,BVH_MOTION_TEMPORALIS02_R ,SKELETON_SERIALIZED_2DX_REAR ,SKELETON_SERIALIZED_2DY_REAR,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + + bvh2DCopy(out,in,BVH_MOTION_NECK ,SKELETON_SERIALIZED_2DX_NECK ,SKELETON_SERIALIZED_2DY_NECK,w,h); + bvh2DCopy(out,in,BVH_MOTION_HIP ,SKELETON_SERIALIZED_2DX_HIP ,SKELETON_SERIALIZED_2DY_HIP,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + + // No abdomen in 2D input! -> mID = BVH_MOTION_ABDOMEN; + // No chest in 2D input! -> mID = BVH_MOTION_CHEST; + + //------------------------------------------------------------------------------------------------ + bvh2DBetween2PointsCopy(out,in,BVH_MOTION_RCOLLAR, SKELETON_SERIALIZED_2DX_NECK ,SKELETON_SERIALIZED_2DY_NECK, + SKELETON_SERIALIZED_2DX_RSHOULDER ,SKELETON_SERIALIZED_2DY_RSHOULDER,w,h); + //bvh2DCopy(out,in,BVH_MOTION_RCOLLAR ,SKELETON_SERIALIZED_2DX_RSHOULDER ,SKELETON_SERIALIZED_2DY_RSHOULDER,w,h); //<- This is not needed + bvh2DCopy(out,in,BVH_MOTION_RSHLDR ,SKELETON_SERIALIZED_2DX_RSHOULDER ,SKELETON_SERIALIZED_2DY_RSHOULDER,w,h); + bvh2DCopy(out,in,BVH_MOTION_RFOREARM ,SKELETON_SERIALIZED_2DX_RELBOW ,SKELETON_SERIALIZED_2DY_RELBOW,w,h); + bvh2DCopy(out,in,BVH_MOTION_RHAND ,SKELETON_SERIALIZED_2DX_RHAND ,SKELETON_SERIALIZED_2DY_RHAND,w,h); + //------------------------------------------------------------------------------------------------ + bvh2DBetween2PointsCopy(out,in,BVH_MOTION_LCOLLAR, SKELETON_SERIALIZED_2DX_NECK ,SKELETON_SERIALIZED_2DY_NECK, + SKELETON_SERIALIZED_2DX_LSHOULDER ,SKELETON_SERIALIZED_2DY_LSHOULDER,w,h); + //bvh2DCopy(out,in,BVH_MOTION_LCOLLAR ,SKELETON_SERIALIZED_2DX_LSHOULDER ,SKELETON_SERIALIZED_2DY_LSHOULDER,w,h); //<- This is not needed + bvh2DCopy(out,in,BVH_MOTION_LSHLDR ,SKELETON_SERIALIZED_2DX_LSHOULDER ,SKELETON_SERIALIZED_2DY_LSHOULDER,w,h); + bvh2DCopy(out,in,BVH_MOTION_LFOREARM ,SKELETON_SERIALIZED_2DX_LELBOW ,SKELETON_SERIALIZED_2DY_LELBOW,w,h); + bvh2DCopy(out,in,BVH_MOTION_LHAND ,SKELETON_SERIALIZED_2DX_LHAND ,SKELETON_SERIALIZED_2DY_LHAND,w,h); + //------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_RBUTTOCK ,SKELETON_SERIALIZED_2DX_RHIP ,SKELETON_SERIALIZED_2DY_RHIP,w,h); + bvh2DCopy(out,in,BVH_MOTION_RTHIGH ,SKELETON_SERIALIZED_2DX_RHIP ,SKELETON_SERIALIZED_2DY_RHIP,w,h); + bvh2DCopy(out,in,BVH_MOTION_RSHIN ,SKELETON_SERIALIZED_2DX_RKNEE ,SKELETON_SERIALIZED_2DY_RKNEE,w,h); + bvh2DCopy(out,in,BVH_MOTION_RFOOT ,SKELETON_SERIALIZED_2DX_RFOOT ,SKELETON_SERIALIZED_2DY_RFOOT,w,h); + bvh2DCopy(out,in,BVH_MOTION_TOE1_2_R ,SKELETON_SERIALIZED_2DX_ENDSITE_TOE1_2_R ,SKELETON_SERIALIZED_2DY_ENDSITE_TOE1_2_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_TOE5_3_R ,SKELETON_SERIALIZED_2DX_ENDSITE_TOE5_3_R ,SKELETON_SERIALIZED_2DY_ENDSITE_TOE5_3_R,w,h); + //------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_LBUTTOCK ,SKELETON_SERIALIZED_2DX_LHIP ,SKELETON_SERIALIZED_2DY_LHIP,w,h); + bvh2DCopy(out,in,BVH_MOTION_LTHIGH ,SKELETON_SERIALIZED_2DX_LHIP ,SKELETON_SERIALIZED_2DY_LHIP,w,h); + bvh2DCopy(out,in,BVH_MOTION_LSHIN ,SKELETON_SERIALIZED_2DX_LKNEE ,SKELETON_SERIALIZED_2DY_LKNEE,w,h); + bvh2DCopy(out,in,BVH_MOTION_LFOOT ,SKELETON_SERIALIZED_2DX_LFOOT ,SKELETON_SERIALIZED_2DY_LFOOT,w,h); + bvh2DCopy(out,in,BVH_MOTION_TOE1_2_L ,SKELETON_SERIALIZED_2DX_ENDSITE_TOE1_2_L ,SKELETON_SERIALIZED_2DY_ENDSITE_TOE1_2_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_TOE5_3_L ,SKELETON_SERIALIZED_2DX_ENDSITE_TOE5_3_L ,SKELETON_SERIALIZED_2DY_ENDSITE_TOE5_3_L,w,h); + //------------------------------------------------------------------------------------------------ +} + + + + + + +void convertLHandSkeletonSerializedToBVHTransform( + struct BVH_MotionCapture * bvhMotion, + struct simpleRenderer * renderer, + struct BVH_Transform * out, + struct skeletonSerialized * in + ) +{ + //unsigned int mID; + unsigned int w = renderer->width; + unsigned int h = renderer->height; + + bvh2DCopy(out,in,BVH_MOTION_LHAND,SKELETON_SERIALIZED_2DX_LHAND,SKELETON_SERIALIZED_2DY_LHAND,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL1_L,SKELETON_SERIALIZED_2DX_FINGER2_1_L,SKELETON_SERIALIZED_2DY_FINGER2_1_L,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER2_1_L,SKELETON_SERIALIZED_2DX_FINGER2_1_L,SKELETON_SERIALIZED_2DY_FINGER2_1_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER2_2_L,SKELETON_SERIALIZED_2DX_FINGER2_2_L,SKELETON_SERIALIZED_2DY_FINGER2_2_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER2_3_L,SKELETON_SERIALIZED_2DX_FINGER2_3_L,SKELETON_SERIALIZED_2DY_FINGER2_3_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER2_3_L,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER2_3_L,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER2_3_L,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL2_L,SKELETON_SERIALIZED_2DX_FINGER3_1_L,SKELETON_SERIALIZED_2DY_FINGER3_1_L,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER3_1_L,SKELETON_SERIALIZED_2DX_FINGER3_1_L,SKELETON_SERIALIZED_2DY_FINGER3_1_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER3_2_L,SKELETON_SERIALIZED_2DX_FINGER3_2_L,SKELETON_SERIALIZED_2DY_FINGER3_2_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER3_3_L,SKELETON_SERIALIZED_2DX_FINGER3_3_L,SKELETON_SERIALIZED_2DY_FINGER3_3_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER3_3_L,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER3_3_L,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER3_3_L,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL3_L,SKELETON_SERIALIZED_2DX_FINGER4_1_L,SKELETON_SERIALIZED_2DY_FINGER4_1_L,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER4_1_L,SKELETON_SERIALIZED_2DX_FINGER4_1_L,SKELETON_SERIALIZED_2DY_FINGER4_1_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER4_2_L,SKELETON_SERIALIZED_2DX_FINGER4_2_L,SKELETON_SERIALIZED_2DY_FINGER4_2_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER4_3_L,SKELETON_SERIALIZED_2DX_FINGER4_3_L,SKELETON_SERIALIZED_2DY_FINGER4_3_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER4_3_L,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER4_3_L,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER4_3_L,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL4_L,SKELETON_SERIALIZED_2DX_FINGER5_1_L,SKELETON_SERIALIZED_2DY_FINGER5_1_L,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER5_1_L,SKELETON_SERIALIZED_2DX_FINGER5_1_L,SKELETON_SERIALIZED_2DY_FINGER5_1_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER5_2_L,SKELETON_SERIALIZED_2DX_FINGER5_2_L,SKELETON_SERIALIZED_2DY_FINGER5_2_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER5_3_L,SKELETON_SERIALIZED_2DX_FINGER5_3_L,SKELETON_SERIALIZED_2DY_FINGER5_3_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER5_3_L,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER5_3_L,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER5_3_L,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION___LTHUMB,SKELETON_SERIALIZED_2DX_LTHUMB,SKELETON_SERIALIZED_2DY_LTHUMB,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_LTHUMB,SKELETON_SERIALIZED_2DX_LTHUMB,SKELETON_SERIALIZED_2DY_LTHUMB,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER1_2_L,SKELETON_SERIALIZED_2DX_FINGER1_2_L,SKELETON_SERIALIZED_2DY_FINGER1_2_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER1_3_L,SKELETON_SERIALIZED_2DX_FINGER1_3_L,SKELETON_SERIALIZED_2DY_FINGER1_3_L,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER1_3_L,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER1_3_L,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER1_3_L,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ +} + + + + +void convertRHandSkeletonSerializedToBVHTransform( + struct BVH_MotionCapture * bvhMotion, + struct simpleRenderer * renderer, + struct BVH_Transform * out, + struct skeletonSerialized * in + ) +{ + //unsigned int mID; + unsigned int w = renderer->width; + unsigned int h = renderer->height; + + bvh2DCopy(out,in,BVH_MOTION_RHAND,SKELETON_SERIALIZED_2DX_RHAND,SKELETON_SERIALIZED_2DY_RHAND,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL1_R,SKELETON_SERIALIZED_2DX_FINGER2_1_R,SKELETON_SERIALIZED_2DY_FINGER2_1_R,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER2_1_R,SKELETON_SERIALIZED_2DX_FINGER2_1_R,SKELETON_SERIALIZED_2DY_FINGER2_1_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER2_2_R,SKELETON_SERIALIZED_2DX_FINGER2_2_R,SKELETON_SERIALIZED_2DY_FINGER2_2_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER2_3_R,SKELETON_SERIALIZED_2DX_FINGER2_3_R,SKELETON_SERIALIZED_2DY_FINGER2_3_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER2_3_R,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER2_3_R,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER2_3_R,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL2_R,SKELETON_SERIALIZED_2DX_FINGER3_1_R,SKELETON_SERIALIZED_2DY_FINGER3_1_R,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER3_1_R,SKELETON_SERIALIZED_2DX_FINGER3_1_R,SKELETON_SERIALIZED_2DY_FINGER3_1_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER3_2_R,SKELETON_SERIALIZED_2DX_FINGER3_2_R,SKELETON_SERIALIZED_2DY_FINGER3_2_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER3_3_R,SKELETON_SERIALIZED_2DX_FINGER3_3_R,SKELETON_SERIALIZED_2DY_FINGER3_3_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER3_3_R,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER3_3_R,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER3_3_R,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL3_R,SKELETON_SERIALIZED_2DX_FINGER4_1_R,SKELETON_SERIALIZED_2DY_FINGER4_1_R,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER4_1_R,SKELETON_SERIALIZED_2DX_FINGER4_1_R,SKELETON_SERIALIZED_2DY_FINGER4_1_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER4_2_R,SKELETON_SERIALIZED_2DX_FINGER4_2_R,SKELETON_SERIALIZED_2DY_FINGER4_2_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER4_3_R,SKELETON_SERIALIZED_2DX_FINGER4_3_R,SKELETON_SERIALIZED_2DY_FINGER4_3_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER4_3_R,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER4_3_R,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER4_3_R,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION_METACARPAL4_R,SKELETON_SERIALIZED_2DX_FINGER5_1_R,SKELETON_SERIALIZED_2DY_FINGER5_1_R,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_FINGER5_1_R,SKELETON_SERIALIZED_2DX_FINGER5_1_R,SKELETON_SERIALIZED_2DY_FINGER5_1_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER5_2_R,SKELETON_SERIALIZED_2DX_FINGER5_2_R,SKELETON_SERIALIZED_2DY_FINGER5_2_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER5_3_R,SKELETON_SERIALIZED_2DX_FINGER5_3_R,SKELETON_SERIALIZED_2DY_FINGER5_3_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER5_3_R,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER5_3_R,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER5_3_R,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ + bvh2DCopy(out,in,BVH_MOTION___RTHUMB,SKELETON_SERIALIZED_2DX_RTHUMB,SKELETON_SERIALIZED_2DY_RTHUMB,w,h); //This is not needed + bvh2DCopy(out,in,BVH_MOTION_RTHUMB,SKELETON_SERIALIZED_2DX_RTHUMB,SKELETON_SERIALIZED_2DY_RTHUMB,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER1_2_R,SKELETON_SERIALIZED_2DX_FINGER1_2_R,SKELETON_SERIALIZED_2DY_FINGER1_2_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_FINGER1_3_R,SKELETON_SERIALIZED_2DX_FINGER1_3_R,SKELETON_SERIALIZED_2DY_FINGER1_3_R,w,h); + bvh2DCopy(out,in,BVH_MOTION_ENDSITE_FINGER1_3_R,SKELETON_SERIALIZED_2DX_ENDSITE_FINGER1_3_R,SKELETON_SERIALIZED_2DY_ENDSITE_FINGER1_3_R,w,h); + //------------------------------------------------------------------------------------------------------------------------------------ +} + + + + + +int convertSkeletonSerializedToBVHTransform( + struct BVH_MotionCapture * bvhMotion, + struct simpleRenderer * renderer, + struct BVH_Transform * out, + struct skeletonSerialized * in + ) +{ + // dumpSkeletonSerializedToBVHTransformCode(bvhMotion,out,in); + + if (bvhMotion==0) { return 0; } + if (bvhMotion->jointHierarchySize==0) { return 0; } + if (out==0) { return 0; } + if (in==0) { return 0; } + if (in->skeletonBody==0) { return 0; } + + unsigned int w = renderer->width; + unsigned int h = renderer->height; + + if ( bvh_allocateTransform(bvhMotion,out) ) + { + //Make sure the structure is clean.. + for (BVHJointID jID=0; jIDjointHierarchySize; jID++ ) + { + out->joint[jID].pos2D[0] = 0.0; + out->joint[jID].pos2D[1] = 0.0; + out->joint[jID].pos2DCalculated = 1; + out->joint[jID].isBehindCamera = 0; + } + + //------------------------------------------------------------------------------------------------ + convertBodySkeletonSerializedToBVHTransform( + bvhMotion, + renderer, + out, + in + ); + //------------------------------------------------------------------------------------------------ + convertLHandSkeletonSerializedToBVHTransform( + bvhMotion, + renderer, + out, + in + ); + //------------------------------------------------------------------------------------------------ + convertRHandSkeletonSerializedToBVHTransform( + bvhMotion, + renderer, + out, + in + ); + //------------------------------------------------------------------------------------------------ + convertFaceSkeletonSerializedToBVHTransform( + bvhMotion, + renderer, + out, + in + ); + //------------------------------------------------------------------------------------------------ + + return 1; + } + return 0; +} +#endif + + +int forceLimits(std::vector & bvhFrame) +{ + +//Minima/Maxima : + +float minimumLimits[1024]={0}; +float maximumLimits[1024]={0}; +/* +//Ignore X,Y,Z Pos limits and X,Y,Z Rot limits +minimumLimits[0]=-391.23; +maximumLimits[0]=410.96; +minimumLimits[1]=-119.81; +maximumLimits[1]=209.66; +minimumLimits[2]=-293.22; +maximumLimits[2]=355.36; +minimumLimits[3]=-2893.85; +maximumLimits[3]=3595.49; +minimumLimits[4]=-1345.44; +maximumLimits[4]=1527.27; +minimumLimits[5]=-3095.27; +maximumLimits[5]=6833.06; +minimumLimits[6]=-179.80; +maximumLimits[6]=177.94;*/ +minimumLimits[7]=-85.22; +maximumLimits[7]=89.06; +minimumLimits[8]=-177.62; +maximumLimits[8]=178.93; +minimumLimits[9]=-62.75; +maximumLimits[9]=59.55; +minimumLimits[10]=-59.95; +maximumLimits[10]=38.22; +minimumLimits[11]=-36.58; +maximumLimits[11]=35.48; +minimumLimits[12]=-79.55; +maximumLimits[12]=97.18; +minimumLimits[13]=-63.63; +maximumLimits[13]=81.78; +minimumLimits[14]=-63.31; +maximumLimits[14]=71.73; +minimumLimits[15]=-0.01; +maximumLimits[15]=0.01; +minimumLimits[16]=-0.01; +maximumLimits[16]=0.01; +minimumLimits[17]=-0.01; +maximumLimits[17]=0.01; +minimumLimits[18]=-180.00; +maximumLimits[18]=180.00; +minimumLimits[19]=-89.61; +maximumLimits[19]=86.77; +minimumLimits[20]=-179.98; +maximumLimits[20]=179.74; +minimumLimits[237]=-180.00; +maximumLimits[237]=180.00; +minimumLimits[238]=-89.62; +maximumLimits[238]=89.97; +minimumLimits[239]=-179.98; +maximumLimits[239]=179.97; +minimumLimits[240]=-43.35; +maximumLimits[240]=28.43; +minimumLimits[241]=-60.75; +maximumLimits[241]=16.89; +minimumLimits[242]=-102.92; +maximumLimits[242]=157.57; +//minimumLimits[MOCAPNET_OUTPUT_RHAND_ZROTATION]=-170.93;//243 +//maximumLimits[MOCAPNET_OUTPUT_RHAND_ZROTATION]=7.43;//243 +//minimumLimits[MOCAPNET_OUTPUT_RHAND_XROTATION]=-89.43;//244 +//maximumLimits[MOCAPNET_OUTPUT_RHAND_XROTATION]=86.24;//244 +//minimumLimits[MOCAPNET_OUTPUT_RHAND_YROTATION]=-180.00;//245 +//maximumLimits[MOCAPNET_OUTPUT_RHAND_YROTATION]=180.00;//245 +minimumLimits[315]=-180.00; +maximumLimits[315]=180.00; +minimumLimits[316]=-89.60; +maximumLimits[316]=89.88; +minimumLimits[317]=-180.00; +maximumLimits[317]=180.00; +minimumLimits[318]=-9.30; +maximumLimits[318]=42.50; +minimumLimits[319]=-60.74; +maximumLimits[319]=6.88; +minimumLimits[320]=-157.46; +maximumLimits[320]=8.76; +//minimumLimits[MOCAPNET_OUTPUT_LHAND_ZROTATION]=-7.17;//321 +//maximumLimits[MOCAPNET_OUTPUT_LHAND_ZROTATION]=172.41;//321 +//minimumLimits[MOCAPNET_OUTPUT_LHAND_XROTATION]=-88.13;//322 +//maximumLimits[MOCAPNET_OUTPUT_LHAND_XROTATION]=88.61;//322 +//minimumLimits[MOCAPNET_OUTPUT_LHAND_YROTATION]=-180.00;//323 +//maximumLimits[MOCAPNET_OUTPUT_LHAND_YROTATION]=180.00;//323 +minimumLimits[393]=-180.00; +maximumLimits[393]=179.99; +minimumLimits[394]=-89.97; +maximumLimits[394]=77.29; +minimumLimits[395]=-180.00; +maximumLimits[395]=180.00; +minimumLimits[396]=-180.00; +maximumLimits[396]=179.99; +minimumLimits[397]=-65.47; +maximumLimits[397]=89.86; +minimumLimits[398]=-180.00; +maximumLimits[398]=180.00; +minimumLimits[399]=-180.00; +maximumLimits[399]=180.00; +minimumLimits[400]=-88.69; +maximumLimits[400]=86.79; +minimumLimits[401]=-179.97; +maximumLimits[401]=179.99; +minimumLimits[447]=-180.00; +maximumLimits[447]=180.00; +minimumLimits[448]=-89.92; +maximumLimits[448]=85.35; +minimumLimits[449]=-180.00; +maximumLimits[449]=180.00; +minimumLimits[450]=-180.00; +maximumLimits[450]=180.00; +minimumLimits[451]=-45.83; +maximumLimits[451]=89.88; +minimumLimits[452]=-180.00; +maximumLimits[452]=180.00; +minimumLimits[453]=-180.00; +maximumLimits[453]=180.00; +minimumLimits[454]=-88.95; +maximumLimits[454]=88.66; +minimumLimits[455]=-179.97; +maximumLimits[455]=179.98; + +unsigned int violations=0; +unsigned int mLim = 456; +if (mLim>bvhFrame.size()) {mLim=bvhFrame.size(); } //std::min() :P + +char motionValueLabel[512]={0}; + +for (unsigned int i=0; imaximumLimits[i]) + { + getBVHMotionValueName(i,motionValueLabel,512); + fprintf(stderr,YELLOW "Hit Maximum @ %s(%u) ( limit %0.2f, value %0.2f ) ..\n" NORMAL,motionValueLabel,i,maximumLimits[i],bvhFrame[i]); + bvhFrame[i]=maximumLimits[i]; + ++violations; + } + } +} + + return violations; +} + + +std::vector improveBVHFrameUsingInverseKinematics( + const std::vector bvhPenultimateFrame, + const std::vector bvhPreviousFrame, + const std::vector bvhFrameInput, + //-------------- + unsigned int frameNumber, + //-------------- + int doLeftHand, + int doRightHand, + int doFace, + //-------------- + struct skeletonSerialized * inputPoints2D, + //-------------- + float learningRate, + unsigned int iterations, + unsigned int epochs, + float spring, + unsigned int springIgnoreChanges, + int multiThreading + ) +{ + #if USE_BVH + + std::vector bvhFrame = bvhFrameInput; + + #if APPLY_BVH_FIX_TO_IK_INPUT + //Flip HIP X/Y/Z rotations here as well ? + if (bvhFrame.size()>=6) + { + bvhFrame[3]=-1* bvhFrame[3]; + bvhFrame[4]=-1* bvhFrame[4]; + bvhFrame[5]=-1* bvhFrame[5]; + } + #endif + + if (bvhMotion.numberOfValuesPerFrame!= bvhFrame.size()) + { + fprintf(stderr,"improveBVHFrameUsingInverseKinematics: Mismatch in bvh frame received and bvh frame loaded..\n"); + fprintf(stderr,"BVH frame received had %lu elements..\n",bvhFrame.size()); + fprintf(stderr,"BVH file had %u elements..\n",bvhMotion.numberOfValuesPerFrame); + return bvhFrame; + } + + if (bvhFrame.size()==0) + { + fprintf(stderr,"improveBVHFrameUsingInverseKinematics: Can't improve empty frame using IK \n"); + return bvhFrame; + } + + //Statistics on how well did we do.. + float initialMAEInPixels=0.0; + float finalMAEInPixels=0.0; + float initialMAEInMM=0.0; + float finalMAEInMM=0.0; + + //Casting our solution/previous solution/penultimate solution in C arrays + struct MotionBuffer solution={0}; + struct MotionBuffer previousSolution={0}; + struct MotionBuffer penultimateSolution={0}; + + // ------------------------------------------------------ + // ------------------------------------------------------ + // ------------------------------------------------------ + if (bvhFrame.size()!=0) + { + solution.motion = mallocVector(bvhFrame); + if (solution.motion!=0) + { solution.bufferSize = bvhFrame.size(); } + } + // ------------------------------------------------------ + if (bvhPreviousFrame.size()!=0) + { + previousSolution.motion = mallocVector(bvhPreviousFrame); + if (previousSolution.motion!=0) + { previousSolution.bufferSize = bvhPreviousFrame.size(); } + } + // ------------------------------------------------------ + if (bvhPenultimateFrame.size()!=0) + { + penultimateSolution.motion = mallocVector(bvhPenultimateFrame); + if (penultimateSolution.motion!=0) + { penultimateSolution.bufferSize = bvhPenultimateFrame.size(); } + } + // ------------------------------------------------------ + // ------------------------------------------------------ + // ------------------------------------------------------ + + + if ( (solution.motion!=0) && (previousSolution.motion!=0) && (penultimateSolution.motion!=0) ) + { + std::vector result = bvhFrame; + struct BVH_Transform bvhTargetTransform={0}; //TODO : convert skeletonserialized to this.. + + if ( + convertSkeletonSerializedToBVHTransform( + &bvhMotion, + &renderer, + &bvhTargetTransform, + inputPoints2D + ) + ) + { + //------------------------------------ + ikConfig.learningRate = learningRate; + ikConfig.iterations = iterations; + ikConfig.epochs = epochs; + ikConfig.maximumAcceptableStartingLoss= 30000;//12000; //WARING < - consider setting this to 0 + ikConfig.gradientExplosionThreshold = 50; + ikConfig.spring=spring; + ikConfig.dumpScreenshots = 0; // Dont thrash disk + ikConfig.verbose = 0; //Dont spam console + ikConfig.tryMaintainingLocalOptima=1; //Less Jittery but can be stuck at local optima + ikConfig.ikVersion = IK_VERSION; + //------------------------------------ + + + //ikConfig.dumpScreenshots=1; + + //====================================================================================================== + //====================================================================================================== + //====================================================================================================== + if ( approximateBodyFromMotionBufferUsingInverseKinematics( + &bvhMotion, + &renderer, + bodyProblem, + &ikConfig, + //---------------- + &penultimateSolution, + &previousSolution, + &solution, + 0, //No ground truth.. + //---------------- + &bvhTargetTransform, + //---------------- + multiThreading,// 0=single thread, 1=multi thread + //---------------- + &initialMAEInPixels, + &finalMAEInPixels, + &initialMAEInMM, + &finalMAEInMM + ) + ) + { + //fprintf(stderr,"Finished IK using LR=%0.2f/Epochs=%u/Iterations=%u fx=%0.2f/fy=%0.2f..\n",learningRate,epochs,iterations,fX,fY); + + //If we performed inverse kinematics, then copy the output.. + unsigned int elements=result.size(); + if (elements>solution.bufferSize) + { elements = solution.bufferSize; } //Take care + + for (unsigned int i=0; i 0 ) + { + fprintf(stderr,YELLOW "Solution has %u crossed limits, we corrected it..\n" NORMAL,limitViolations); + } */ + + } else + { + fprintf(stderr,RED "Unable to perform Inverse Kinematics for body..\n" NORMAL); + } + //====================================================================================================== + //====================================================================================================== + //====================================================================================================== + + } else + { + fprintf(stderr,"Unable to convert skeleton serialized to a BVH_Transform\n"); + } + + //fprintf(stderr,"Deallocating..."); + if (solution.motion!=0) { free(solution.motion); solution.motion=0; solution.bufferSize = 0; } + if (previousSolution.motion!=0) { free(previousSolution.motion); previousSolution.motion=0; previousSolution.bufferSize = 0; } + if (penultimateSolution.motion!=0) { free(penultimateSolution.motion); penultimateSolution.motion=0; penultimateSolution.bufferSize = 0; } + //fprintf(stderr,"Survived...\n"); + + + + if ( (ikConfig.dumpScreenshots) && (frameNumber==10) ) + { + fprintf(stderr,RED "DEBUG CODE ON : Stopping.. to dump screenshots\n" NORMAL); + exit(0); + } + return result; + } else + { + fprintf(stderr,YELLOW "Will not perform IK without current,previous and penultimate solutions\n" NORMAL); + } + + #else + fprintf(stderr,"BVH code not compiled in, unable to do inverse kinematics..\n"); + #endif + + return bvhFrame; +} + + + +std::vector > convertBVHFrameTo2DPoints(const std::vector bvhFrame) +{ + unsigned int width = renderer.width; + unsigned int height = renderer.height; + + std::vector > result2DPointVector; + result2DPointVector.clear(); + + + if ( (width>10000) || (height>10000) ) + { + fprintf(stderr,"convertBVHFrameTo2DPoints: Cannot use this crazy resolution %u x %u \n",width,height); + return result2DPointVector; + } + + + + +#if USE_BVH + if (!haveBVHInit) + { + fprintf(stderr,"convertBVHFrameTo2DPoints: Cannot work without a renderer initialization\n"); + return result2DPointVector; + } + + + if (haveBVHInit) + { + if (bvhFrame.size() != bvhMotion.numberOfValuesPerFrame) + { + fprintf(stderr,"convertBVHFrameTo2DPoints was given an inconsistent number of values to convert ( expected %u, got %lu )\n",bvhMotion.numberOfValuesPerFrame,bvhFrame.size()); + return result2DPointVector; + } + + float * motionBuffer= mallocVector(bvhFrame); + + if (motionBuffer!=0) + { + bvh_cleanTransform(&bvhMotion,&bvhTransform); + + if ( (width!=renderer.width) || (height!=renderer.height) ) + { + fprintf(stderr,"Resolution changed from %0.2f x %0.2f to %u x %u.. focal length should also be changed..",renderer.width,renderer.height,width,height); + + // From BVH GroundTruthGenerator settings.. + float renderingConfigurationfX=fX; + float renderingConfigurationfY=fY; + + if ( + (width==1920) && + (height==1080) + ) + { + fprintf(stderr,"Emulating Ground Truth Generator used while training..\n"); + renderingConfigurationfX=582.18394; + renderingConfigurationfY=582.52915; + } + + simpleRendererDefaults( + &renderer, + width,//1920 + height,//1080 + renderingConfigurationfX, //570.0 + renderingConfigurationfY //570.0 + ); + simpleRendererInitialize(&renderer); + } + + if ( + bvh_loadTransformForMotionBuffer( + &bvhMotion, + motionBuffer, + &bvhTransform, + 0//Dont need extra information + ) + ) + { + //----------------- + if ( + bvh_projectTo2D( + &bvhMotion, + &bvhTransform, + &renderer, + 0, + 0 + ) + ) + { //----------------- + std::vector point; + //----------------------- + for (unsigned int jID=0; jID convertBVHFrameToFlat3DPoints(std::vector bvhFrame) +{ +std::vector result; +#if USE_BVH + + if (!haveBVHInit) + { + fprintf(stderr,"convertBVHFrameToFlat3DPoints: Cannot work without a renderer initialization\n"); + return result; + } + + + if (haveBVHInit) + { + if (bvhFrame.size() != bvhMotion.numberOfValuesPerFrame) + { + fprintf(stderr,"convertBVHFrameToFlat3DPoints was given an inconsistent number of values to convert ( expected %u, got %lu )\n",bvhMotion.numberOfValuesPerFrame,bvhFrame.size()); + return result; + } + + float * motionBuffer= mallocVector(bvhFrame); + + if (motionBuffer!=0) + { + if ( + bvh_loadTransformForMotionBuffer( + &bvhMotion, + motionBuffer, + &bvhTransform, + 1//We of course need the extra information for occlusions + ) + ) + { + //----------------- + if ( + bvh_projectTo2D( + &bvhMotion, + &bvhTransform, + &renderer, + 0, + 0 + ) + ) + { + //----------------- + for (unsigned int jID=0; jID > convert3DGridTo2DPoints(float roll,float pitch,float yaw,unsigned int dimensions) +{ + std::vector > result; +#if USE_BVH + + if (!haveBVHInit) + { + fprintf(stderr,"convert3DGridTo2DPoints: Cannot work without a renderer initialization\n"); + return result; + } + + std::vector emptyPoint; + emptyPoint.clear(); + emptyPoint.push_back((float) 0.0); + emptyPoint.push_back((float) 0.0); + + + + float rotation[3]={roll,yaw,pitch}; + float center[4]={0}; + float pos3D[4]={0}; + + signed int x=0,y=0,z=0; + float position2DX=0,position2DY=0,position2DW=0; + + y=-5; + unsigned int halfDimension = (unsigned int) dimensions / 2; + signed int negativeStart = (signed int) -1 * halfDimension; + signed int positiveEnd = (signed int) halfDimension; + + for (z=negativeStart; z0.0) + { + std::vector point; + point.clear(); + point.push_back((float) position2DX); + point.push_back((float) position2DY); + result.push_back(point); + } + else + { + result.push_back(emptyPoint); + } + } + else + { + result.push_back(emptyPoint); + } + } + } + } + + + + +#else + fprintf(stderr,"BVH code is not compiled in this version of MocapNET\n"); +#endif // USE_BVH + return result; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvh.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvh.hpp new file mode 100644 index 0000000..2f8251c --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvh.hpp @@ -0,0 +1,1226 @@ +#pragma once +/** @file bvh.hpp + * @brief This is an interface to the BVH code. The BVH code ( https://github.com/AmmarkoV/RGBDAcquisition/tree/master/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader ) + * might not be available. If this is the case then CMake will not declare the USE_BVH compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + + +/** + * @brief This is a BVH header that can be easily injected in a file ( using just an fprintf call ) + */ +//To do this get replace \n with \\n"\n" in headerWithHead.bvh +static const char * bvhHeader= +"HIERARCHY\n" +"ROOT hip\n" +"{\n" +" OFFSET 0 0 0\n" +" CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation\n" +" JOINT abdomen\n" +" {\n" +" OFFSET 0 20.6881 -0.73152\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT chest\n" +" {\n" +" OFFSET 0 11.7043 -0.48768\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT neck\n" +" {\n" +" OFFSET 0 22.1894 -2.19456\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT neck1\n" +" {\n" +" OFFSET 0.000000 5.364170 1.574630\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT head\n" +" {\n" +" OFFSET 0.000000 5.364141 1.574630\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT __jaw\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT jaw\n" +" {\n" +" OFFSET 0.000000 -13.499860 2.500710\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT special04\n" +" {\n" +" OFFSET -0.000000 -6.835370 4.375500\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris02\n" +" {\n" +" OFFSET 0.000000 1.711150 2.820850\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris01\n" +" {\n" +" OFFSET -0.000000 0.972390 0.845650\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.000000 1.162291 0.607091\n" +" }\n" +" }\n" +" }\n" +" JOINT oris06.l\n" +" {\n" +" OFFSET 0.000000 1.711150 2.820850\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris07.l\n" +" {\n" +" OFFSET 1.168850 0.445180 0.506110\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.450611 1.195178 0.204519\n" +" }\n" +" }\n" +" }\n" +" JOINT oris06.r\n" +" {\n" +" OFFSET 0.000000 1.711150 2.820850\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris07.r\n" +" {\n" +" OFFSET -1.168850 0.445180 0.506110\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.450611 1.195173 0.204519\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT tongue00\n" +" {\n" +" OFFSET -0.000000 -6.835370 4.375500\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT tongue01\n" +" {\n" +" OFFSET 0.000000 3.973650 -3.762340\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT tongue02\n" +" {\n" +" OFFSET 0.000000 0.429760 2.924710\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT tongue03\n" +" {\n" +" OFFSET 0.000000 0.018530 2.059010\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT __tongue04\n" +" {\n" +" OFFSET 0.000000 -0.440240 0.838860\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT tongue04\n" +" {\n" +" OFFSET 0.000000 0.000000 0.000000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.000000 -0.440230 0.838860\n" +" }\n" +" }\n" +" }\n" +" JOINT tongue07.l\n" +" {\n" +" OFFSET 0.000000 -0.440240 0.838860\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 1.160923 -0.331531 0.018227\n" +" }\n" +" }\n" +" JOINT tongue07.r\n" +" {\n" +" OFFSET 0.000000 -0.440240 0.838860\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -1.160922 -0.331531 0.018227\n" +" }\n" +" }\n" +" }\n" +" JOINT tongue06.l\n" +" {\n" +" OFFSET 0.000000 0.018530 2.059010\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 1.644752 -0.526075 -0.203281\n" +" }\n" +" }\n" +" JOINT tongue06.r\n" +" {\n" +" OFFSET 0.000000 0.018530 2.059010\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -1.644752 -0.526075 -0.203282\n" +" }\n" +" }\n" +" }\n" +" JOINT tongue05.l\n" +" {\n" +" OFFSET 0.000000 0.429760 2.924710\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 1.971028 -0.388618 0.239206\n" +" }\n" +" }\n" +" JOINT tongue05.r\n" +" {\n" +" OFFSET 0.000000 0.429760 2.924710\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -1.971028 -0.388618 0.239205\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __levator02.l\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator02.l\n" +" {\n" +" OFFSET 0.313580 -11.321120 11.599360\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator03.l\n" +" {\n" +" OFFSET 1.681690 -1.563730 -1.357570\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator04.l\n" +" {\n" +" OFFSET 0.504730 -1.676760 -0.058160\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator05.l\n" +" {\n" +" OFFSET 0.145440 -1.643170 -0.225470\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.310116 -0.760198 -0.121474\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __levator02.r\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator02.r\n" +" {\n" +" OFFSET -0.313580 -11.321120 11.599360\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator03.r\n" +" {\n" +" OFFSET -1.681690 -1.563740 -1.357570\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator04.r\n" +" {\n" +" OFFSET -0.504730 -1.676750 -0.058160\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator05.r\n" +" {\n" +" OFFSET -0.145440 -1.643170 -0.225470\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.310116 -0.760193 -0.121474\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __special01\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT special01\n" +" {\n" +" OFFSET -0.000000 -14.026930 -5.716970\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris04.l\n" +" {\n" +" OFFSET -0.000000 -0.492640 17.312620\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris03.l\n" +" {\n" +" OFFSET 1.215520 -0.627430 -0.393050\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.288776 -0.560899 -0.149645\n" +" }\n" +" }\n" +" }\n" +" JOINT oris04.r\n" +" {\n" +" OFFSET -0.000000 -0.492640 17.312620\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris03.r\n" +" {\n" +" OFFSET -1.215520 -0.627440 -0.393050\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.288776 -0.560894 -0.149645\n" +" }\n" +" }\n" +" }\n" +" JOINT oris06\n" +" {\n" +" OFFSET -0.000000 -0.492640 17.312620\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oris05\n" +" {\n" +" OFFSET -0.000000 -0.486000 0.000950\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.000000 -0.630493 0.197635\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __special03\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT special03\n" +" {\n" +" OFFSET 0.000000 -13.499860 2.500710\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT __levator06.l\n" +" {\n" +" OFFSET -0.000000 1.035800 10.090229\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator06.l\n" +" {\n" +" OFFSET 0.522240 -0.615720 0.045900\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 1.107505 -0.131898 -2.142227\n" +" }\n" +" }\n" +" }\n" +" JOINT __levator06.r\n" +" {\n" +" OFFSET -0.000000 1.035800 10.090229\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT levator06.r\n" +" {\n" +" OFFSET -0.522240 -0.615730 0.045900\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -1.107506 -0.131893 -2.142227\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT special06.l\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT special05.l\n" +" {\n" +" OFFSET 2.108890 0.153870 5.595070\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT eye.l\n" +" {\n" +" OFFSET 0.857170 -10.254801 2.414670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.111609 0.006490 4.051424\n" +" }\n" +" }\n" +" JOINT orbicularis03.l\n" +" {\n" +" OFFSET 0.857170 -10.254801 2.414670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.097789 1.146431 3.788029\n" +" }\n" +" }\n" +" JOINT orbicularis04.l\n" +" {\n" +" OFFSET 0.857170 -10.254801 2.414670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.113609 -1.130505 3.863064\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT special06.r\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT special05.r\n" +" {\n" +" OFFSET -2.108890 0.153870 5.595070\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT eye.r\n" +" {\n" +" OFFSET -0.857170 -10.254801 2.414670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.111609 0.006490 4.051424\n" +" }\n" +" }\n" +" JOINT orbicularis03.r\n" +" {\n" +" OFFSET -0.857170 -10.254801 2.414670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.097789 1.146431 3.788029\n" +" }\n" +" }\n" +" JOINT orbicularis04.r\n" +" {\n" +" OFFSET -0.857170 -10.254801 2.414670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.113609 -1.130505 3.863064\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __temporalis01.l\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT temporalis01.l\n" +" {\n" +" OFFSET 6.332510 -9.444281 6.595120\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oculi02.l\n" +" {\n" +" OFFSET -0.804920 0.053010 1.621140\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oculi01.l\n" +" {\n" +" OFFSET -2.161570 1.690970 2.142660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -1.740622 0.281157 0.647323\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __temporalis01.r\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT temporalis01.r\n" +" {\n" +" OFFSET -6.332510 -9.444281 6.595120\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oculi02.r\n" +" {\n" +" OFFSET 0.804920 0.053010 1.621140\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT oculi01.r\n" +" {\n" +" OFFSET 2.161570 1.690970 2.142660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 1.740622 0.281157 0.647323\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __temporalis02.l\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT temporalis02.l\n" +" {\n" +" OFFSET 6.377600 -11.680510 6.235180\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT risorius02.l\n" +" {\n" +" OFFSET -0.814250 0.451130 1.721730\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT risorius03.l\n" +" {\n" +" OFFSET -0.649710 -2.514660 0.612550\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 0.479556 -1.760402 -1.642659\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __temporalis02.r\n" +" {\n" +" OFFSET 0.000000 13.604700 -0.502080\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT temporalis02.r\n" +" {\n" +" OFFSET -6.377600 -11.680510 6.235180\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT risorius02.r\n" +" {\n" +" OFFSET 0.814250 0.451130 1.721730\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT risorius03.r\n" +" {\n" +" OFFSET 0.649710 -2.514660 0.612550\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -0.479556 -1.760402 -1.642659\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT rCollar\n" +" {\n" +" OFFSET -2.68224 19.2634 -4.8768\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT rShldr\n" +" {\n" +" OFFSET -8.77824 -1.95073 1.46304\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT rForeArm\n" +" {\n" +" OFFSET -28.1742 -1.7115 0.48768\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT rHand\n" +" {\n" +" OFFSET -21.049400 0.002190 -0.634230\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT metacarpal1.r\n" +" {\n" +" OFFSET -2.815680 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger2-1.r\n" +" {\n" +" OFFSET -6.292930 0.272380 2.520090\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger2-2.r\n" +" {\n" +" OFFSET -2.310530 -0.320530 -0.060510\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger2-3.r\n" +" {\n" +" OFFSET -2.051030 -0.295400 -0.164890\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -2.376838 -0.681367 -0.183877\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT metacarpal2.r\n" +" {\n" +" OFFSET -2.815680 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger3-1.r\n" +" {\n" +" OFFSET -6.313640 0.626130 0.318530\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger3-2.r\n" +" {\n" +" OFFSET -3.015730 -0.589480 -0.088540\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger3-3.r\n" +" {\n" +" OFFSET -2.482120 -0.426280 0.076670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -2.344174 -0.731969 0.003260\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __metacarpal3.r\n" +" {\n" +" OFFSET -2.815680 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT metacarpal3.r\n" +" {\n" +" OFFSET -0.606080 -0.162120 -1.874870\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger4-1.r\n" +" {\n" +" OFFSET -5.355730 0.702040 0.402510\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger4-2.r\n" +" {\n" +" OFFSET -2.643900 -0.485530 -0.117520\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger4-3.r\n" +" {\n" +" OFFSET -2.215850 -0.353160 0.066220\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -2.350273 -0.621223 -0.046375\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __metacarpal4.r\n" +" {\n" +" OFFSET -2.815680 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT metacarpal4.r\n" +" {\n" +" OFFSET -0.606080 -0.162120 -1.874870\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger5-1.r\n" +" {\n" +" OFFSET -4.761700 0.175470 -1.109590\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger5-2.r\n" +" {\n" +" OFFSET -1.916360 -0.173360 -0.146170\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger5-3.r\n" +" {\n" +" OFFSET -1.411290 -0.108670 -0.020110\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -1.799226 -0.102363 -0.078601\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __rthumb\n" +" {\n" +" OFFSET -2.815680 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT rthumb\n" +" {\n" +" OFFSET -0.283040 -0.142720 1.950690\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger1-2.r\n" +" {\n" +" OFFSET -0.915590 -2.152150 1.546760\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger1-3.r\n" +" {\n" +" OFFSET -3.213140 -0.470060 0.247480\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET -2.521224 -0.161543 -0.511272\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT lCollar\n" +" {\n" +" OFFSET 2.68224 19.2634 -4.8768\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT lShldr\n" +" {\n" +" OFFSET 8.77824 -1.95073 1.46304\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT lForeArm\n" +" {\n" +" OFFSET 28.1742 -1.7115 0.48768\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT lHand\n" +" {\n" +" \n" +" OFFSET 21.049408 0.002200 -0.634230\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT metacarpal1.l\n" +" {\n" +" OFFSET 2.815670 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger2-1.l\n" +" {\n" +" OFFSET 6.292930 0.272390 2.520090\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger2-2.l\n" +" {\n" +" OFFSET 2.310530 -0.320520 -0.060510\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger2-3.l\n" +" {\n" +" OFFSET 2.051030 -0.295400 -0.164880\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 2.376823 -0.681367 -0.183876\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT metacarpal2.l\n" +" {\n" +" OFFSET 2.815670 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger3-1.l\n" +" {\n" +" OFFSET 6.313640 0.626120 0.318530\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger3-2.l\n" +" {\n" +" OFFSET 3.015730 -0.589470 -0.088540\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger3-3.l\n" +" {\n" +" OFFSET 2.482120 -0.426270 0.076670\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 2.344170 -0.731978 0.003260\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __metacarpal3.l\n" +" {\n" +" OFFSET 2.815670 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT metacarpal3.l\n" +" {\n" +" OFFSET 0.606080 -0.162120 -1.874870\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger4-1.l\n" +" {\n" +" OFFSET 5.355730 0.702050 0.402510\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger4-2.l\n" +" {\n" +" OFFSET 2.643900 -0.485530 -0.117510\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger4-3.l\n" +" {\n" +" OFFSET 2.215840 -0.353150 0.066210\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 2.350273 -0.621228 -0.046377\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __metacarpal4.l\n" +" {\n" +" OFFSET 2.815670 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT metacarpal4.l\n" +" {\n" +" OFFSET 0.606080 -0.162120 -1.874870\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger5-1.l\n" +" {\n" +" OFFSET 4.761700 0.175480 -1.109600\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger5-2.l\n" +" {\n" +" OFFSET 1.916350 -0.173360 -0.146170\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger5-3.l\n" +" {\n" +" OFFSET 1.411290 -0.108670 -0.020110\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 1.799216 -0.102372 -0.078600\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT __lthumb\n" +" {\n" +" OFFSET 2.815670 -0.279180 0.531660\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT lthumb\n" +" {\n" +" OFFSET 0.283040 -0.142710 1.950690\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger1-2.l\n" +" {\n" +" OFFSET 0.915930 -2.151960 1.546820\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" JOINT finger1-3.l\n" +" {\n" +" OFFSET 3.213210 -0.469680 0.247300\n" +" CHANNELS 3 Zrotation Xrotation Yrotation \n" +" End Site\n" +" {\n" +" OFFSET 2.521210 -0.161290 -0.511422\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT rButtock\n" +" {\n" +" OFFSET -8.77824 4.35084 1.2192\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT rThigh\n" +" {\n" +" OFFSET 0 -1.70687 -2.19456\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT rShin\n" +" {\n" +" OFFSET 0 -36.8199 0.73152\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT rFoot\n" +" {\n" +" \n" +" OFFSET 0.73152 -45.1104 -5.12064\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe1-1.R\n" +" {\n" +" OFFSET 2.454000 -4.050002 13.194999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe1-2.R\n" +" {\n" +" OFFSET -0.214000 -0.646000 2.427000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET -0.401900 -0.827789 2.725930\n" +" }\n" +" }\n" +" }\n" +" JOINT toe2-1.R\n" +" {\n" +" OFFSET 0.177000 -4.299998 13.329000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe2-2.R\n" +" {\n" +" OFFSET -0.177000 -0.323000 2.039000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe2-3.R\n" +" {\n" +" OFFSET -0.067000 -0.440998 1.248000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET -0.042990 -0.647306 1.660872\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT toe3-1.R\n" +" {\n" +" OFFSET -1.396000 -4.461999 13.078999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe3-2.R\n" +" {\n" +" OFFSET -0.161000 -0.247002 1.809000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe3-3.R\n" +" {\n" +" OFFSET -0.033000 -0.441999 1.202000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET 0.032040 -0.433550 1.271800\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT toe4-1.R\n" +" {\n" +" OFFSET -2.888001 -4.480000 12.376999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe4-2.R\n" +" {\n" +" OFFSET -0.160000 -0.331998 1.491001\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe4-3.R\n" +" {\n" +" OFFSET 0.035999 -0.251002 1.138999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET -0.088911 -0.568814 0.969530\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT toe5-1.R\n" +" {\n" +" OFFSET -4.257999 -4.467001 11.711999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe5-2.R\n" +" {\n" +" OFFSET -0.046000 -0.265999 0.982000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe5-3.R\n" +" {\n" +" OFFSET 0.086999 -0.372000 0.791000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET -0.044329 -0.555482 1.085780\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT lButtock\n" +" {\n" +" OFFSET 8.77824 4.35084 1.2192\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT lThigh\n" +" {\n" +" OFFSET 0 -1.70687 -2.19456\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT lShin\n" +" {\n" +" OFFSET 0 -36.8199 0.73152\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT lFoot\n" +" { \n" +"\n" +" OFFSET -0.73152 -45.1104 -5.12064\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe1-1.L\n" +" {\n" +" OFFSET -2.454000 -4.050002 13.194999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe1-2.L\n" +" {\n" +" OFFSET 0.214000 -0.646000 2.427000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET 0.401900 -0.827789 2.725930\n" +" }\n" +" }\n" +" }\n" +" JOINT toe2-1.L\n" +" {\n" +" OFFSET -0.177000 -4.299998 13.329000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe2-2.L\n" +" {\n" +" OFFSET 0.177000 -0.323000 2.039000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe2-3.L\n" +" {\n" +" OFFSET 0.067000 -0.440998 1.248000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET 0.042990 -0.647306 1.660872\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT toe3-1.L\n" +" {\n" +" OFFSET 1.396000 -4.461999 13.078999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe3-2.L\n" +" {\n" +" OFFSET 0.161000 -0.247002 1.809000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe3-3.L\n" +" {\n" +" OFFSET 0.033000 -0.441999 1.202000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET -0.032040 -0.433550 1.271800\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT toe4-1.L\n" +" {\n" +" OFFSET 2.888001 -4.480000 12.376999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe4-2.L\n" +" {\n" +" OFFSET 0.160000 -0.331998 1.491001\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe4-3.L\n" +" {\n" +" OFFSET -0.035999 -0.251002 1.138999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET 0.088911 -0.568814 0.969530\n" +" }\n" +" }\n" +" }\n" +" }\n" +" JOINT toe5-1.L\n" +" {\n" +" OFFSET 4.257999 -4.467001 11.711999\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe5-2.L\n" +" {\n" +" OFFSET 0.046000 -0.265999 0.982000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" JOINT toe5-3.L\n" +" {\n" +" OFFSET -0.086999 -0.372000 0.791000\n" +" CHANNELS 3 Zrotation Xrotation Yrotation\n" +" End Site\n" +" {\n" +" OFFSET 0.044329 -0.555482 1.085780\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +" }\n" +"}\n"; + + + +void printBVHCodeVersion(); + +int loadCalibration(struct MocapNET2Options * options,const char* directory,const char * file); + +void overrideBVHSubsystemFocalLength(float newFx,float newFy); + + +//This should fix the wrong Z Y X rotation thing happening +//in https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/35 +int fixBVHHip(std::vector > &bvhFrames); + + +/** + * @brief After collecting a vector of BVH output vectors this call can write them to disk in BVH format + * to make them accessible by third party 3D animation software like blender etc. + * @param Path to output file i.e. "output.bvh" + * @param Pointer to BVH header string, if set to null it will default to the bvhHeader found in this header file. + * @param Vector of BVH frame vectors. + * @retval 1=Success,0=Failure + */ +int writeBVHFile( + const char * filename, + const char * header, + int prependTPose, + std::vector > bvhFrames + ); + +/** + * @brief This call loads a BVH file into a BVH_MotionCapture* structure that is casted in a void pointer + * to avoid all the include hassle + * @param Path to input file i.e. "input.bvh" + * @retval BVH_MotionCapture pointer that holds all the BVH information + */ +void * loadBVHFile(const char * filename); + + +/** + * @brief This call frees all memory consumed by the BVH file loaded by a loadBVHFile call + * @param Path to input file i.e. "input.bvh" + * @retval 1=Success/0=Failure + */ +int freeBVHFile(void * bvhMemoryHandler); + + +/** + * @brief This call opens a BVH file decodes it and returns a vector of bvh frames where each frame has all the motions in another vector + * @param Path to input file i.e. "input.bvh" + * @retval Vector of Vectors that hold all the BVH motions + */ +std::vector > loadBVHFileMotionFrames(const char * filename); + + +int scaleAllJoints(float scaleRatio); + +int changeFeetDimensions( + float hipToKneeLength, + float kneeToFootLength + ); + +int changeJointDimensions( + float neckLength, + float torsoLength, + float chestWidth, + float shoulderToElbowLength, + float elbowToHandLength, + float waistWidth, + float hipToKneeLength, + float kneeToFootLength, + float shoeLength + ); + + +int codeOptimizationsForIKEnabled(); + +/** + * @brief Initialize BVH code ( if it is present ) , supplying a null first argument uses default bvh armature + * @retval 1=Success/0=Failure + */ +int initializeBVHConverter(const char * specificBVHFilename,int width,int height, int noIKNeeded); + + +unsigned int getBVHNumberOfValuesPerFrame(); + +unsigned int getBVHNumberOfJoints(); + + +int getBVHJointOffset(unsigned int currentJoint,float * x,float *y,float *z); + +/** + * @brief Get the joint ID of the parent of a joint + * @param JointID that we want to get the parent for + * @retval JointID of the parent/ 0 in the case of an error + * @bug 0 can both be a valid joint and signal an error, so it needs special care + */ +unsigned int getBVHParentJoint(unsigned int currentJoint); + + +/** + * @brief Get a string with the name of the joint + * @param JointID that we want to get the name for + * @retval CString with the name, null means no result + */ +const char * getBVHJointName(unsigned int currentJoint); + +/** + * @brief Return the name of a motion channel on the target C-String + * @param Motion channel ID + * @param Output C String that will contain the label + * @param Size of C String + * @retval 1=Success/0=Failure + */ +int getBVHMotionValueName(unsigned int currentMotionValue,char * target,unsigned int targetLength); + + +/** + * @brief Get the jointID based on a string with the name of the joint + * @param CString with the name we want to search for + * @retval JointID that we want to get the name for, 0 means no result or the first joint + */ +unsigned int getBVHJointIDFromJointName(const char * jointName); + + + + + +/** + * @brief This function performs the Inverse Kinematics logic. You give it the previous BVH frame, the current BVH neural network estimation as well as a skeletonSerialized + * structure that holds the observation, it will perform Hierarchical Coordinate Descent and return a BVH vector that is closer to the observation + * https://github.com/AmmarkoV/RGBDAcquisition/tree/master/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/ik + * @param Vector holding the previous BVH frame + * @param Vector holding the current BVH frame + * @param A skeletonSerialized structure holding the current 2D skeleton observed + * @param Toggle Left hand IK + * @param Toggle Right hand IK + * @param The learning rate used for Hierarchical Coordinate Descent + * @param The number of iterations (i.e. total number of passes for each joint) + * @param The number of epochs per joint (i.e. total incremental improvements of each joint) + * @param The magnitude of the spring damping coefficient that keeps solutions from drifting away from previous solutions + * @param A flag that can cause the spring to ignore all incremental changes + * @retval BVH Vector that is derived from bvhFrame argument and that more closely resembles inputPoints2D + */ +std::vector improveBVHFrameUsingInverseKinematics( + const std::vector bvhPenultimateFrame, + const std::vector bvhPreviousFrame, + const std::vector bvhFrame, + //-------------- + unsigned int frameNumber, + //-------------- + int doLeftHand, + int doRightHand, + int doFace, + struct skeletonSerialized * inputPoints2D, + float learningRate, + unsigned int iterations, + unsigned int epochs, + float spring, + unsigned int springIgnoreChanges, + int multiThreading + ); + + +/** + * @brief Initialize BVH code ( if it is present ) + * @param A float corresponding to a BVH frame + * @retval Vector of 2D points + */ +std::vector > convertBVHFrameTo2DPoints(const std::vector bvhFrame); + + + +/** + * @brief Convert a BVH motion frame to 3D points for a given 2D viewport + * @param Input BVH motion frame + * @retval Vector of 3D points + */ +std::vector convertBVHFrameToFlat3DPoints(std::vector bvhFrame); + + + +/** + * @brief Generate 2D points that draws a 3D grid + * @retval Vector of 2D points + */ +std::vector > convert3DGridTo2DPoints( + float roll, + float pitch, + float yaw, + unsigned int dimensions +); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvhJointList b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvhJointList new file mode 100644 index 0000000..19bbfb0 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/bvhJointList @@ -0,0 +1,164 @@ +abdomen +chest +neck +neck1 +head +__jaw +jaw +special04 +oris02 +oris01 +oris06.l +oris07.l +oris06.r +oris07.r +tongue00 +tongue01 +tongue02 +tongue03 +__tongue04 +tongue04 +tongue07.l +tongue07.r +tongue06.l +tongue06.r +tongue05.l +tongue05.r +__levator02.l +levator02.l +levator03.l +levator04.l +levator05.l +__levator02.r +levator02.r +levator03.r +levator04.r +levator05.r +__special01 +special01 +oris04.l +oris03.l +oris04.r +oris03.r +oris06 +oris05 +__special03 +special03 +__levator06.l +levator06.l +__levator06.r +levator06.r +special06.l +special05.l +eye.l +orbicularis03.l +orbicularis04.l +special06.r +special05.r +eye.r +orbicularis03.r +orbicularis04.r +__temporalis01.l +temporalis01.l +oculi02.l +oculi01.l +__temporalis01.r +temporalis01.r +oculi02.r +oculi01.r +__temporalis02.l +temporalis02.l +risorius02.l +risorius03.l +__temporalis02.r +temporalis02.r +risorius02.r +risorius03.r +rCollar +rShldr +rForeArm +rHand +metacarpal1.r +finger2-1.r +finger2-2.r +finger2-3.r +metacarpal2.r +finger3-1.r +finger3-2.r +finger3-3.r +__metacarpal3.r +metacarpal3.r +finger4-1.r +finger4-2.r +finger4-3.r +__metacarpal4.r +metacarpal4.r +finger5-1.r +finger5-2.r +finger5-3.r +__rthumb +rthumb +finger1-2.r +finger1-3.r +lCollar +lShldr +lForeArm +lHand +metacarpal1.l +finger2-1.l +finger2-2.l +finger2-3.l +metacarpal2.l +finger3-1.l +finger3-2.l +finger3-3.l +__metacarpal3.l +metacarpal3.l +finger4-1.l +finger4-2.l +finger4-3.l +__metacarpal4.l +metacarpal4.l +finger5-1.l +finger5-2.l +finger5-3.l +__lthumb +lthumb +finger1-2.l +finger1-3.l +rButtock +rThigh +rShin +rFoot +toe1-1.R +toe1-2.R +toe2-1.R +toe2-2.R +toe2-3.R +toe3-1.R +toe3-2.R +toe3-3.R +toe4-1.R +toe4-2.R +toe4-3.R +toe5-1.R +toe5-2.R +toe5-3.R +lButtock +lThigh +lShin +lFoot +toe1-1.L +toe1-2.L +toe2-1.L +toe2-2.L +toe2-3.L +toe3-1.L +toe3-2.L +toe3-3.L +toe4-1.L +toe4-2.L +toe4-3.L +toe5-1.L +toe5-2.L +toe5-3.L diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp new file mode 100644 index 0000000..6887357 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp @@ -0,0 +1,714 @@ +#pragma once + +#include + +/** + * @brief A C struct to hold a 2D coordinate + */ +struct point2D +{ + float x,y; +}; + +/** + * @brief A C struct to hold a 3D coordinate + */ +struct point3D +{ + float x,y,z; +}; + + +/** + * @brief A structure to hold a bounding box + */ +struct boundingBox +{ + char populated; + float minimumX; + float maximumX; + float minimumY; + float maximumY; +}; + + +/** + * @brief This is an array of names for all the old COCO body parts. + */ +static const char * COCOOldBodyNames[] = +{ + "Nose", //0 + "Neck", //1 + "RShoulder", //2 + "RElbow", //3 + "RWrist", //4 + "LShoulder", //5 + "LElbow", //6 + "LWrist", //7 + "RHip", //8 + "RKnee", //9 + "RAnkle", //10 + "LHip", //11 + "LKnee", //12 + "LAnkle", //13 + "REye", //14 + "LEye", //15 + "REar", //16 + "LEar", //17 + "Bkg", //18 +//================= + "End of Joint Names" +}; + + + +/** + * @brief An enumerator of coco skeleton joints + */ +//https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/media/keypoints_pose_18.png +enum COCOOldSkeletonJoints +{ + COCO_Nose=0, + COCO_Neck, + COCO_RShoulder, + COCO_RElbow, + COCO_RWrist, + COCO_LShoulder, + COCO_LElbow, + COCO_LWrist, + COCO_RHip, + COCO_RKnee, + COCO_RAnkle, + COCO_LHip, + COCO_LKnee, + COCO_LAnkle, + COCO_REye, + COCO_LEye, + COCO_REar, + COCO_LEar, + COCO_Bkg, + //--------------------- + COCO_PARTS +}; + + +/** + * @brief An array of indexes to the parents of coco skeleton joints + */ +static const int COCOSkeletonJointsParentRelationMap[] = +{ + // Parent Joint + COCO_Nose, //COCO_Nose, + COCO_Nose, //COCO_Neck, + COCO_Neck, //COCO_RShoulder, + COCO_RShoulder, //COCO_RElbow, + COCO_RElbow, //COCO_RWrist, + COCO_Neck, //COCO_LShoulder, + COCO_LShoulder, //COCO_LElbow, + COCO_LElbow, //COCO_LWrist, + COCO_Neck, //COCO_RHip, + COCO_RHip, //COCO_RKnee, + COCO_RKnee, //COCO_RAnkle, + COCO_Neck, //COCO_LHip, + COCO_LHip, //COCO_LKnee, + COCO_LKnee, //COCO_LAnkle, + COCO_Nose, //COCO_REye, + COCO_Nose, //COCO_LEye, + COCO_REye, //COCO_REar, + COCO_LEye, //COCO_LEar, + COCO_Bkg //COCO_Bkg +}; + + + +/** + * @brief This is an array of names for all the new BODY25 body parts. + * This is a "friendly" array, meaning it is meant to be easy to understand and does not need to exactly correspond to the BVH file.. + */ +static const char * Body25BodyFriendlyNames[] = +{ + "Nose", //0 + "Neck", //1 + "RShoulder", //2 + "RElbow", //3 + "RWrist", //4 + "LShoulder", //5 + "LElbow", //6 + "LWrist", //7 + "MidHip", //8 + "RHip", //9 + "RKnee", //10 + "RAnkle", //11 + "LHip", //12 + "LKnee", //13 + "LAnkle", //14 + "REye", //15 + "LEye", //16 + "REar", //17 + "LEar", //18 + "LBigToe", //19 + "LSmallToe", //20 + "LHeel", //21 + "RBigToe", //22 + "RSmallToe", //23 + "RHeel", //24 + "Bkg", //25 +//================= + "End of Joint Names" +}; + +/** + * @brief This is an array of names for all the new BODY25 body parts. + * This tries to mirror body_configuration.json and everything used is lowercase exactly for this reason.. + * it also has to be the same with the bvh file headerWithHeadAndOneMotion.bvh + */ +static const char * Body25BodyNames[] = +{ + "head", //0 + "neck", //1 + "rshoulder", //2 + "relbow", //3 + "rhand", //4 + "lshoulder", //5 + "lelbow", //6 + "lhand", //7 + "hip", //8 + "rhip", //9 + "rknee", //10 + "rfoot", //11 + "lhip", //12 + "lknee", //13 + "lfoot", //14 + "endsite_eye.r", //15 + "endsite_eye.l", //16 + "rear", //17 ========= No correspondance + "lear", //18 ========= No correspondance + "endsite_toe1-2.l",//19 + "endsite_toe5-3.l",//20 + "lheel", //21 ========= No correspondance + "endsite_toe1-2.r",//22 + "endsite_toe5-3.r",//23 + "rheel", //24 ========= No correspondance + "bkg", //25 ========= No correspondance + //================== + "End of Joint Names" +}; + +/** + * @brief An enumerator of BODY 25 skeleton joints + */ +enum Body25SkeletonJoints +{ + BODY25_Nose=0, + BODY25_Neck, + BODY25_RShoulder, + BODY25_RElbow, + BODY25_RWrist, + BODY25_LShoulder, + BODY25_LElbow, + BODY25_LWrist, + BODY25_MidHip, + BODY25_RHip, + BODY25_RKnee, + BODY25_RAnkle, + BODY25_LHip, + BODY25_LKnee, + BODY25_LAnkle, + BODY25_REye, + BODY25_LEye, + BODY25_REar, + BODY25_LEar, + BODY25_LBigToe, + BODY25_LSmallToe, + BODY25_LHeel, + BODY25_RBigToe, + BODY25_RSmallToe, + BODY25_RHeel, + BODY25_Bkg, + //--------------------- + BODY25_PARTS +}; + + + +/** + * @brief An array of indexes to the parents of BODY25 skeleton joints + */ +static const int Body25SkeletonJointsParentRelationMap[] = +{ + // Parent Joint + BODY25_Nose, //BODY25_Nose, + BODY25_Nose, //BODY25_Neck, + BODY25_Neck, //BODY25_RShoulder, + BODY25_RShoulder, //BODY25_RElbow, + BODY25_RElbow, //BODY25_RWrist, + BODY25_Neck, //BODY25_LShoulder, + BODY25_LShoulder, //BODY25_LElbow, + BODY25_LElbow, //BODY25_LWrist, + BODY25_Neck, //BODY25_MidHip + BODY25_MidHip, //BODY25_RHip, + BODY25_RHip, //BODY25_RKnee, + BODY25_RKnee, //BODY25_RAnkle, + BODY25_MidHip, //BODY25_LHip, + BODY25_LHip, //BODY25_LKnee, + BODY25_LKnee, //BODY25_LAnkle, + BODY25_Nose, //BODY25_REye, + BODY25_Nose, //BODY25_LEye, + BODY25_REye, //BODY25_REar, + BODY25_LEye, //BODY25_LEar, + BODY25_LHeel, //BODY25_LBigToe, + BODY25_LBigToe, //BODY25_LSmallToe, + BODY25_LAnkle, //BODY25_LHeel, + BODY25_RHeel, //BODY25_RBigToe, + BODY25_RBigToe, //BODY25_RSmallToe, + BODY25_RAnkle, // BODY25_RHeel, + BODY25_Bkg //BODY25_Bkg +}; + + + + + +/** + * @brief An enumerator of COCO Hand joints + */ +//https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/media/keypoints_hand.png +enum COCOHandJoints +{ + COCO_Hand_Wrist=0,//0 + COCO_Hand_Thumb_1,//1 + COCO_Hand_Thumb_2, //2 + COCO_Hand_Thumb_3, //3 + COCO_Hand_Thumb_4,//4 + COCO_Hand_Index_1,//5 + COCO_Hand_Index_2,//6 + COCO_Hand_Index_3,//7 + COCO_Hand_Index_4,//8 + COCO_Hand_Middle_1,//9 + COCO_Hand_Middle_2,//10 + COCO_Hand_Middle_3,//11 + COCO_Hand_Middle_4,//12 + COCO_Hand_Ring_1,//13 + COCO_Hand_Ring_2,//14 + COCO_Hand_Ring_3,//15 + COCO_Hand_Ring_4,//16 + COCO_Hand_Pinky_1,//17 + COCO_Hand_Pinky_2,//18 + COCO_Hand_Pinky_3,//19 + COCO_Hand_Pinky_4,//20 + //--------------------- + COCO_HAND_PARTS +}; + + +/** + * @brief This is an array of names for the left hand. + * This is a "friendly" array, meaning it is meant to be easy to understand and does not need to exactly correspond to the BVH file.. + */ +static const char * COCOLeftHandFriendlyNames[] = +{ + "Left_Hand_Wrist",//0 + "Left_Hand_Thumb_1",//1 + "Left_Hand_Thumb_2", //2 + "Left_Hand_Thumb_3", //3 + "Left_Hand_Thumb_4",//4 + "Left_Hand_Index_1",//5 + "Left_Hand_Index_2",//6 + "Left_Hand_Index_3",//7 + "Left_Hand_Index_4",//8 + "Left_Hand_Middle_1",//9 + "Left_Hand_Middle_2",//10 + "Left_Hand_Middle_3",//11 + "Left_Hand_Middle_4",//12 + "Left_Hand_Ring_1",//13 + "Left_Hand_Ring_2",//14 + "Left_Hand_Ring_3",//15 + "Left_Hand_Ring_4",//16 + "Left_Hand_Pinky_1",//17 + "Left_Hand_Pinky_2",//18 + "Left_Hand_Pinky_3",//19 + "Left_Hand_Pinky_4",//20 + //-------------------- + "End of Left Hand Names" +}; + +/** + * @brief This is an array of names for the left hand. + * This tries to mirror lhand_configuration.json and everything used is lowercase exactly for this reason.. + * it also has to be the same with the bvh file headerWithHeadAndOneMotion.bvh + */ +static const char * COCOLeftHandNames[] = +{ + "lhand",//0 + "lthumb",//1 + "finger1-2.l", //2 + "finger1-3.l", //3 + "endsite_finger1-3.l",//4 + "finger2-1.l",//5 + "finger2-2.l",//6 + "finger2-3.l",//7 + "endsite_finger2-3.l",//8 + "finger3-1.l",//9 + "finger3-2.l",//10 + "finger3-3.l",//11 + "endsite_finger3-3.l",//12 + "finger4-1.l",//13 + "finger4-2.l",//14 + "finger4-3.l",//15 + "endsite_finger4-3.l",//16 + "finger5-1.l",//17 + "finger5-2.l",//18 + "finger5-3.l",//19 + "endsite_finger5-3.l",//20 + //-------------------- + "End of Left Hand Names" +}; + + + +/** + * @brief This is an array of names for the right hand. + * This is a "friendly" array, meaning it is meant to be easy to understand and does not need to exactly correspond to the BVH file.. + */ + +static const char * COCORightHandFriendlyNames[] = +{ + "Right_Hand_Wrist",//0 + "Right_Hand_Thumb_1",//1 + "Right_Hand_Thumb_2", //2 + "Right_Hand_Thumb_3", //3 + "Right_Hand_Thumb_4",//4 + "Right_Hand_Index_1",//5 + "Right_Hand_Index_2",//6 + "Right_Hand_Index_3",//7 + "Right_Hand_Index_4",//8 + "Right_Hand_Middle_1",//9 + "Right_Hand_Middle_2",//10 + "Right_Hand_Middle_3",//11 + "Right_Hand_Middle_4",//12 + "Right_Hand_Ring_1",//13 + "Right_Hand_Ring_2",//14 + "Right_Hand_Ring_3",//15 + "Right_Hand_Ring_4",//16 + "Right_Hand_Pinky_1",//17 + "Right_Hand_Pinky_2",//18 + "Right_Hand_Pinky_3",//19 + "Right_Hand_Pinky_4",//20 + //-------------------- + "End of Right Hand Names" + +}; + + +/** + * @brief This is an array of names for the right hand. + * This tries to mirror rhand_configuration.json and everything used is lowercase exactly for this reason.. + * it also has to be the same with the bvh file headerWithHeadAndOneMotion.bvh + */ +static const char * COCORightHandNames[] = +{ + "rhand",//0 + "rthumb",//1 + "finger1-2.r", //2 + "finger1-3.r", //3 + "endsite_finger1-3.r",//4 + "finger2-1.r",//5 + "finger2-2.r",//6 + "finger2-3.r",//7 + "endsite_finger2-3.r",//8 + "finger3-1.r",//9 + "finger3-2.r",//10 + "finger3-3.r",//11 + "endsite_finger3-3.r",//12 + "finger4-1.r",//13 + "finger4-2.r",//14 + "finger4-3.r",//15 + "endsite_finger4-3.r",//16 + "finger5-1.r",//17 + "finger5-2.r",//18 + "finger5-3.r",//19 + "endsite_finger5-3.r",//20 + //-------------------- + "End of Right Hand Names" +}; + + + +/** + * @brief An enumerator of COCO Hand joints + */ +//https://raw.githubusercontent.com/CMU-Perceptual-Computing-Lab/openpose/master/doc/media/keypoints_face.png +enum OP_HeadJoints +{ + OP_Head_RChin_0=0, //0 + OP_Head_RChin_1, //1 + OP_Head_RChin_2, //2 + OP_Head_RChin_3, //3 + OP_Head_RChin_4, //4 + OP_Head_RChin_5, //5 + OP_Head_RChin_6, //6 + OP_Head_RChin_7, //7 + OP_Head_Chin, //8 + OP_Head_LChin_7 , //9 + OP_Head_LChin_6, //10 + OP_Head_LChin_5, //11 + OP_Head_LChin_4, //12 + OP_Head_LChin_3, //13 + OP_Head_LChin_2, //14 + OP_Head_LChin_1, //15 + OP_Head_LChin_0, //16 + OP_Head_REyebrow_0, //17 + OP_Head_REyebrow_1, //18 + OP_Head_REyebrow_2, //19 + OP_Head_REyebrow_3, //20 + OP_Head_REyebrow_4, //21 + OP_Head_LEyebrow_4, //22 + OP_Head_LEyebrow_3, //23 + OP_Head_LEyebrow_2, //24 + OP_Head_LEyebrow_1, //25 + OP_Head_LEyebrow_0, //26 + OP_Head_NoseBone_0,//27 + OP_Head_NoseBone_1,//28 + OP_Head_NoseBone_2,//29 + OP_Head_NoseBone_3,//30 + OP_Head_Nostrills_0,//31 + OP_Head_Nostrills_1,//32 + OP_Head_Nostrills_2,//33 + OP_Head_Nostrills_3,//34 + OP_Head_Nostrills_4,//35 + OP_Head_REye_0,//36 + OP_Head_REye_1,//37 + OP_Head_REye_2,//38 + OP_Head_REye_3,//39 + OP_Head_REye_4,//40 + OP_Head_REye_5,//41 + OP_Head_LEye_0,//42 + OP_Head_LEye_1,//43 + OP_Head_LEye_2,//44 + OP_Head_LEye_3,//45 + OP_Head_LEye_4,//46 + OP_Head_LEye_5,//47 + OP_Head_OutMouth_0,//48 + OP_Head_OutMouth_1,//49 + OP_Head_OutMouth_2,//50 + OP_Head_OutMouth_3,//51 + OP_Head_OutMouth_4,//52 + OP_Head_OutMouth_5,//53 + OP_Head_OutMouth_6,//54 + OP_Head_OutMouth_7,//55 + OP_Head_OutMouth_8,//56 + OP_Head_OutMouth_9,//57 + OP_Head_OutMouth_10,//58 + OP_Head_OutMouth_11,//59 + OP_Head_InMouth_0,//60 + OP_Head_InMouth_1,//61 + OP_Head_InMouth_2,//62 + OP_Head_InMouth_3,//63 + OP_Head_InMouth_4,//64 + OP_Head_InMouth_5,//65 + OP_Head_InMouth_6,//66 + OP_Head_InMouth_7,//67 + OP_Head_REye,//68 + OP_Head_LEye,//69 + //--------------------- + OP_HEAD_PARTS +}; + + +static const char * HeadNames[] = +{ + "Head_RChin_0", //0 + "Head_RChin_1", //1 + "Head_RChin_2", //2 + "Head_RChin_3", //3 + "Head_RChin_4", //4 + "Head_RChin_5", //5 + "Head_RChin_6", //6 + "Head_RChin_7", //7 + "Head_Chin", //8 + "Head_LChin_7", //9 + "Head_LChin_6", //10 + "Head_LChin_5", //11 + "Head_LChin_4", //12 + "Head_LChin_3", //13 + "Head_LChin_2", //14 + "Head_LChin_1", //15 + "Head_LChin_0", //16 + "Head_REyebrow_0",//17 + "Head_REyebrow_1",//18 + "Head_REyebrow_2",//19 + "Head_REyebrow_3",//20 + "Head_REyebrow_4",//21 + "Head_LEyebrow_4",//22 + "Head_LEyebrow_3",//23 + "Head_LEyebrow_2",//24 + "Head_LEyebrow_1",//25 + "Head_LEyebrow_0",//26 + "Head_NoseBone_0",//27 + "Head_NoseBone_1",//28 + "Head_NoseBone_2",//29 + "Head_NoseBone_3",//30 + "Head_Nostrills_0",//31 + "Head_Nostrills_1",//32 + "Head_Nostrills_2",//33 + "Head_Nostrills_3",//34 + "Head_Nostrills_4",//35 + "Head_REye_0",//36 + "Head_REye_1",//37 + "Head_REye_2",//38 + "Head_REye_3",//39 + "Head_REye_4",//40 + "Head_REye_5",//41 + "Head_LEye_0",//42 + "Head_LEye_1",//43 + "Head_LEye_2",//44 + "Head_LEye_3",//45 + "Head_LEye_4",//46 + "Head_LEye_5",//47 + "Head_OutMouth_0",//48 + "Head_OutMouth_1",//49 + "Head_OutMouth_2",//50 + "Head_OutMouth_3",//51 + "Head_OutMouth_4",//52 + "Head_OutMouth_5",//53 + "Head_OutMouth_6",//54 + "Head_OutMouth_7",//55 + "Head_OutMouth_8",//56 + "Head_OutMouth_9",//57 + "Head_OutMouth_10",//58 + "Head_OutMouth_11",//59 + "Head_InMouth_0",//60 + "Head_InMouth_1",//61 + "Head_InMouth_2",//62 + "Head_InMouth_3",//63 + "Head_InMouth_4",//64 + "Head_InMouth_5",//65 + "Head_InMouth_6",//66 + "Head_InMouth_7",//67 + "Head_REye",//68 + "Head_LEye"//69 +}; + + +/** + * @brief A C struct to hold a hand. It contains its 2D points, its 3D points and some flags that signal + */ +struct handCOCO +{ + int isPopulated; + int isLeft; + int isRight; + + struct point2D joint2D[COCO_HAND_PARTS]; + float jointAccuracy[COCO_HAND_PARTS]; + unsigned int active[COCO_HAND_PARTS]; + struct point3D joint[COCO_HAND_PARTS]; +}; + +/** + * @brief A C struct to hold a hand. It contains its 2D points, its 3D points and some flags that signal + */ +struct headOP +{ + int isPopulated; + struct point2D joint2D[OP_HEAD_PARTS]; + float jointAccuracy[OP_HEAD_PARTS]; + unsigned int active[OP_HEAD_PARTS]; + struct point3D joint[OP_HEAD_PARTS]; +}; + + +/** + * @brief A C struct to hold a body. It contains its 2D points, its 3D points and some flags that signal + */ +struct body25OP +{ + int isPopulated; + struct point2D joint2D[BODY25_PARTS]; + struct point2D bbox2D[2]; + + float jointAccuracy[BODY25_PARTS]; + unsigned int active[BODY25_PARTS]; + struct point3D joint[BODY25_PARTS]; + struct point3D bbox[8]; +}; + + + +/** + * @brief A C struct to hold a skeleton. It contains its 2D points, its 3D points and some flags that signal + */ +struct skeletonStructure +{ + unsigned int observationNumber , observationTotal; + unsigned int userID; + unsigned int totalUsersPresent; + //------------------------ + struct body25OP body; + struct handCOCO leftHand; + struct handCOCO rightHand; + struct headOP head; + //------------------------ + struct boundingBox bbox2D; +}; + + +struct headerField +{ + unsigned int strLength; + char * str; +}; + + +struct bodyField +{ + float value; +}; + + +struct skeletonSerialized +{ + unsigned int skeletonHeaderElements; + struct headerField skeletonHeader[512]; + + unsigned int skeletonBodyElements; + struct bodyField skeletonBody[512]; + + float width; + float height; +}; + + +static void printSkeletonSerialized(const char * msg, struct skeletonSerialized * sk ) +{ + if (msg!=0) + { + fprintf(stderr,"printSkeletonSerialized (%s)\n",msg); + } + + if (sk==0) + { + fprintf(stderr,"struct skeletonSerialized that was given as input is not allocated\n"); + return; + } + + if (sk->skeletonHeaderElements != sk->skeletonBodyElements) + { + fprintf(stderr,"struct skeletonSerialized is corrupted since it has a different number of head and body records..\n"); + return; + } + + fprintf(stderr,"Serialization has %u elements\n",sk->skeletonHeaderElements); + for (unsigned int skID = 0; skIDskeletonHeaderElements; skID++) + { + if (sk->skeletonHeader[skID].str==0) + { + fprintf(stderr," Serial #%u has an empty label => % 0.2f \n" ,skID , sk->skeletonBody[skID].value); + } else + { + fprintf(stderr," %s => % 0.2f \n" ,sk->skeletonHeader[skID].str , sk->skeletonBody[skID].value); + } + } + + +} \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/conversions.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/conversions.cpp new file mode 100644 index 0000000..3599f45 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/conversions.cpp @@ -0,0 +1,827 @@ +#include "conversions.hpp" + +#include +#include +#include "../tools.hpp" +#include "bvh.hpp" + +#include "csvRead.hpp" +#include "../../../../dependencies/RGBDAcquisition/tools/AmMatrix/matrix4x4Tools.h" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int appendVectorToFile(const char * filename, std::vector vec) +{ + FILE * fp = fopen(filename,"a"); + if (fp!=0) + { + for (unsigned int i=0; iskeletonHeaderElements; z++) + { + if (input->skeletonHeader[z].str!=0) + { + if (strcmp(input->skeletonHeader[z].str,targetLabelLowercase)==0) + { + if (targetIndexes[i]==666) + { + targetIndexes[i]=z; + fprintf(stderr,GREEN "%s==%s\n" NORMAL,input->skeletonHeader[z].str,targetLabelLowercase); + } else + { + fprintf(stderr,YELLOW "%s==%s multiple times, only keeping first association..\n" NORMAL,input->skeletonHeader[z].str,targetLabelLowercase); + } + } //We have a matching string.. + } //There is a string to compare + } + } + + unsigned int unassociatedJoints=0; + for (unsigned int i=0; i deriveMocapNET2InputUsingAssociations(struct MocapNET2 * mnet,struct skeletonSerialized * input,unsigned int * targetIndexIsInitializedFlag,unsigned int * targetIndexes,unsigned int targetLength,const char * * targetLabels,int verbose) +{ + std::vector result; + + if (mnet->indexesPopulated) + { + + if (! *targetIndexIsInitializedFlag) + { + fprintf(stderr,RED "deriveMocapNET2InputUsingAssociations called without initialization\n" NORMAL); + + initializeAssociationsForSubsetOfSkeleton( + targetIndexIsInitializedFlag, + targetIndexes, + targetLength, + targetLabels, + input + ); + + } + + for (int i=0; i %s \n" , input->skeletonHeader[targetIndexes[i]].str , input->skeletonBody[targetIndexes[i]].value ,targetLabels[i]); } + result.push_back(input->skeletonBody[targetIndexes[i]].value); + } + } else + { + fprintf(stderr,RED "initializeAssociationsForSubsetOfSkeleton has to be run at initialization before deriveMocapNET2InputUsingAssociations\n"); + } + + return result; +} + + +int convertSkeletons2DDetectedToSkeletonsSerialized( + struct skeletonSerialized * output, + struct Skeletons2DDetected * input, + unsigned int frameNumber, + unsigned int width, + unsigned int height + ) +{ + int check; + if (output->skeletonHeaderElements==0) + { + //Brand new header that needs to be populated..! + int elementsPopulated=3; + output->skeletonHeader[0].strLength = 12; + output->skeletonHeader[0].str=(char *) malloc(sizeof(char) * output->skeletonHeader[0].strLength); + snprintf(output->skeletonHeader[0].str,output->skeletonHeader[0].strLength,"frameNumber"); + + output->skeletonHeader[1].strLength = 11; + output->skeletonHeader[1].str=(char *) malloc(sizeof(char) * output->skeletonHeader[1].strLength); + snprintf(output->skeletonHeader[1].str,output->skeletonHeader[1].strLength,"skeletonID"); + + output->skeletonHeader[2].strLength = 15; + output->skeletonHeader[2].str=(char *) malloc(sizeof(char) * output->skeletonHeader[2].strLength); + snprintf(output->skeletonHeader[2].str,output->skeletonHeader[2].strLength,"totalSkeletons"); + + char lowercaseName[512]={0}; + + for (int i=0; iskeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dx_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dy_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+9; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "visible_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + } + + for (int i=0; iskeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dx_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dy_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+9; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "visible_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + } + + for (int i=0; iskeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dx_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dy_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+9; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "visible_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + } + + for (int i=0; iskeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dx_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+5; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "2dy_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + output->skeletonHeader[elementsPopulated].strLength= strlen(lowercaseName)+9; + output->skeletonHeader[elementsPopulated].str = ( char* ) malloc( sizeof(char) * output->skeletonHeader[elementsPopulated].strLength); + if (output->skeletonHeader[elementsPopulated].str!=0) + { + check = snprintf( + output->skeletonHeader[elementsPopulated].str , + output->skeletonHeader[elementsPopulated].strLength, + "visible_%s", + lowercaseName + ); + if (check<0) { return 0; } + } else { return 0; } + ++elementsPopulated; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + } + output->skeletonHeaderElements = elementsPopulated; + } + // Reduce spam + // else + //{ + // fprintf(stderr,"SkeletonSerialized appears to be already populated, skipping re allocating everything..\n"); + //} + + + //================================================================================= + //================================================================================= + //================================================================================= + //================================================================================= + //================================================================================= + // Now to convert the values.. + //================================================================================= + //================================================================================= + //================================================================================= + //================================================================================= + //================================================================================= + + output->skeletonBodyElements = output->skeletonHeaderElements; + + //Points will be normalized using normalize2DPointWhileAlsoMatchingTrainingAspectRatio + output->width = 1.0; + output->height = 1.0; + + for (unsigned int skID=0; skIDnumberOfSkeletonsDetected; skID++) + { + int bodyElementsPopulated=3; + output->skeletonBody[0].value = frameNumber; + output->skeletonBody[1].value = skID; + output->skeletonBody[2].value = input->numberOfSkeletonsDetected; + + for (int i=0; iskeletons[skID].body.joint2D[i].x, + &input->skeletons[skID].body.joint2D[i].y, + width, + height, + 1,//Respect training aspect ratio + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + output->skeletonBody[bodyElementsPopulated].value = (float) input->skeletons[skID].body.joint2D[i].x; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = (float) input->skeletons[skID].body.joint2D[i].y; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = ( (input->skeletons[skID].body.joint2D[i].x!=0) && (input->skeletons[skID].body.joint2D[i].y!=0) ) ; + /* + if (!output->skeletonBody[bodyElementsPopulated].value) + { + fprintf(stderr,"%s not populated.. \n" ,Body25BodyNames[i]); + }*/ + ++bodyElementsPopulated; + //-------------------------------------------------------------------------------- + } + + + for (int i=0; iskeletons[skID].leftHand.joint2D[i].x, + &input->skeletons[skID].leftHand.joint2D[i].y, + width, + height, + 1,//Respect training aspect ratio + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + output->skeletonBody[bodyElementsPopulated].value = (float) input->skeletons[skID].leftHand.joint2D[i].x; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = (float) input->skeletons[skID].leftHand.joint2D[i].y; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value =( (input->skeletons[skID].leftHand.joint2D[i].x!=0) && (input->skeletons[skID].leftHand.joint2D[i].y!=0) ) ; + ++bodyElementsPopulated; + //-------------------------------------------------------------------------------- + } + + + for (int i=0; iskeletons[skID].rightHand.joint2D[i].x, + &input->skeletons[skID].rightHand.joint2D[i].y, + width, + height, + 1,//Respect training aspect ratio + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + output->skeletonBody[bodyElementsPopulated].value = input->skeletons[skID].rightHand.joint2D[i].x; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = input->skeletons[skID].rightHand.joint2D[i].y; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = ( (input->skeletons[skID].rightHand.joint2D[i].x!=0) && (input->skeletons[skID].rightHand.joint2D[i].y!=0) ); + ++bodyElementsPopulated; + //-------------------------------------------------------------------------------- + } + + for (int i=0; iskeletons[skID].head.joint2D[i].x, + &input->skeletons[skID].head.joint2D[i].y, + width, + height, + 1,//Respect training aspect ratio + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + output->skeletonBody[bodyElementsPopulated].value = (float) input->skeletons[skID].head.joint2D[i].x; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = (float) input->skeletons[skID].head.joint2D[i].y; + ++bodyElementsPopulated; + + output->skeletonBody[bodyElementsPopulated].value = ( (input->skeletons[skID].head.joint2D[i].x!=0) && (input->skeletons[skID].head.joint2D[i].y!=0) ); + ++bodyElementsPopulated; + } + + } + + + return 1; +} + + + + + +int convertMocapNET2OutputToSkeletonSerialized( + struct MocapNET2 * mnet, + struct skeletonSerialized * output, + std::vector > mocapNET2DPointsResult, + unsigned int frameNumber, + unsigned int width, + unsigned int height + ) +{ + if (mocapNET2DPointsResult.size()==0) + { + fprintf(stderr,"convertMocapNET2OutputToSkeletonSerialized cannot work without 3D point results\n"); + return 0; + } + struct Skeletons2DDetected input={0}; + + input.numberOfSkeletonsDetected=1; + input.skeletons[0].body.joint2D[BODY25_Nose].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS05][0]; + input.skeletons[0].body.joint2D[BODY25_Nose].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS05][1]; + + input.skeletons[0].body.joint2D[BODY25_Neck].x = mocapNET2DPointsResult[MOCAPNET_JOINT_NECK][0]; + input.skeletons[0].body.joint2D[BODY25_Neck].y = mocapNET2DPointsResult[MOCAPNET_JOINT_NECK][1]; + + input.skeletons[0].body.joint2D[BODY25_RShoulder].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RSHOULDER][0]; + input.skeletons[0].body.joint2D[BODY25_RShoulder].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RSHOULDER][1]; + + input.skeletons[0].body.joint2D[BODY25_RElbow].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RELBOW][0]; + input.skeletons[0].body.joint2D[BODY25_RElbow].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RELBOW][1]; + + input.skeletons[0].body.joint2D[BODY25_RWrist].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RHAND][0]; + input.skeletons[0].body.joint2D[BODY25_RWrist].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RHAND][1]; + + input.skeletons[0].body.joint2D[BODY25_LShoulder].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LSHOULDER][0]; + input.skeletons[0].body.joint2D[BODY25_LShoulder].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LSHOULDER][1]; + + input.skeletons[0].body.joint2D[BODY25_LElbow].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LELBOW][0]; + input.skeletons[0].body.joint2D[BODY25_LElbow].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LELBOW][1]; + + input.skeletons[0].body.joint2D[BODY25_LWrist].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LHAND][0]; + input.skeletons[0].body.joint2D[BODY25_LWrist].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LHAND][1]; + + input.skeletons[0].body.joint2D[BODY25_MidHip].x = mocapNET2DPointsResult[MOCAPNET_JOINT_HIP][0]; + input.skeletons[0].body.joint2D[BODY25_MidHip].y = mocapNET2DPointsResult[MOCAPNET_JOINT_HIP][1]; + + input.skeletons[0].body.joint2D[BODY25_RHip].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RHIP][0]; + input.skeletons[0].body.joint2D[BODY25_RHip].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RHIP][1]; + + input.skeletons[0].body.joint2D[BODY25_RKnee].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RKNEE][0]; + input.skeletons[0].body.joint2D[BODY25_RKnee].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RKNEE][1]; + + input.skeletons[0].body.joint2D[BODY25_RAnkle].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RFOOT][0]; + input.skeletons[0].body.joint2D[BODY25_RAnkle].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RFOOT][1]; + + input.skeletons[0].body.joint2D[BODY25_LHip].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LHIP][0]; + input.skeletons[0].body.joint2D[BODY25_LHip].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LHIP][1]; + + input.skeletons[0].body.joint2D[BODY25_LKnee].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LKNEE][0]; + input.skeletons[0].body.joint2D[BODY25_LKnee].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LKNEE][1]; + + input.skeletons[0].body.joint2D[BODY25_LAnkle].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LFOOT][0]; + input.skeletons[0].body.joint2D[BODY25_LAnkle].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LFOOT][1]; + + input.skeletons[0].body.joint2D[BODY25_REye].x = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_R][0]; + input.skeletons[0].body.joint2D[BODY25_REye].y = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_R][1]; + + input.skeletons[0].body.joint2D[BODY25_LEye].x = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_L][0]; + input.skeletons[0].body.joint2D[BODY25_LEye].y = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_L][1]; + + input.skeletons[0].body.joint2D[BODY25_REar].x = mocapNET2DPointsResult[MOCAPNET_JOINT_TEMPORALIS02_R][0]; + input.skeletons[0].body.joint2D[BODY25_REar].y = mocapNET2DPointsResult[MOCAPNET_JOINT_TEMPORALIS02_R][1]; + input.skeletons[0].body.joint2D[BODY25_LEar].x = mocapNET2DPointsResult[MOCAPNET_JOINT_TEMPORALIS02_L][0]; + input.skeletons[0].body.joint2D[BODY25_LEar].y = mocapNET2DPointsResult[MOCAPNET_JOINT_TEMPORALIS02_L][1]; + + input.skeletons[0].body.joint2D[BODY25_LBigToe].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE1_2_L][0]; + input.skeletons[0].body.joint2D[BODY25_LBigToe].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE1_2_L][1]; + input.skeletons[0].body.joint2D[BODY25_LSmallToe].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE5_3_L][0]; + input.skeletons[0].body.joint2D[BODY25_LSmallToe].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE5_3_L][1]; + input.skeletons[0].body.joint2D[BODY25_LHeel].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LFOOT][0]; + input.skeletons[0].body.joint2D[BODY25_LHeel].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LFOOT][1]; + + + input.skeletons[0].body.joint2D[BODY25_RBigToe].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE1_2_R][0]; + input.skeletons[0].body.joint2D[BODY25_RBigToe].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE1_2_R][1]; + input.skeletons[0].body.joint2D[BODY25_RSmallToe].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE5_3_R][0]; + input.skeletons[0].body.joint2D[BODY25_RSmallToe].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_TOE5_3_R][1]; + input.skeletons[0].body.joint2D[BODY25_RHeel].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RFOOT][0]; + input.skeletons[0].body.joint2D[BODY25_RHeel].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RFOOT][1]; + //Dont forget to update convertBodySkeletonSerializedToBVHTransform + + + //Populate right hand + input.skeletons[0].rightHand.joint2D[COCO_Hand_Wrist].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RHAND][0]; //MOCAPNET_OUTPUT_JOINT_RHAND + input.skeletons[0].rightHand.joint2D[COCO_Hand_Wrist].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RHAND][1]; //MOCAPNET_OUTPUT_JOINT_RHAND + //--------------------------------------------------------------------------------- + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_RTHUMB][0]; //MOCAPNET_OUTPUT_JOINT_RTHUMB + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_RTHUMB][1]; //MOCAPNET_OUTPUT_JOINT_RTHUMB + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_2_R][0]; //MOCAPNET_OUTPUT_JOINT_RTHUMB + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_2_R][1]; //MOCAPNET_OUTPUT_JOINT_RTHUMB + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER1_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER1_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER1_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER1_3_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Thumb_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER1_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER1_3_R + //--------------------------------------------------------------------------------- + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_1_R][0]; //MOCAPNET_OUTPUT_JOINT_METACARPAL1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_1_R][1]; //MOCAPNET_OUTPUT_JOINT_METACARPAL1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_2_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER2_1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_2_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER2_1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER2_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER2_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER2_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER2_3_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Index_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER2_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER2_3_R + //--------------------------------------------------------------------------------- + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_1_R][0];//MOCAPNET_OUTPUT_JOINT_METACARPAL2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_1_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_2_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER3_1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_2_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER3_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_3_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER3_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER3_3_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Middle_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER3_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER3_3_R + //--------------------------------------------------------------------------------- + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_1_R][0]; //MOCAPNET_OUTPUT_JOINT_METACARPAL3_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_1_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_2_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER4_1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_2_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER4_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_3_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER4_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER4_3_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Ring_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER4_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER4_3_R + //--------------------------------------------------------------------------------- + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_1_R][0]; //MOCAPNET_OUTPUT_JOINT_METACARPAL4_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_1_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_2_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER5_1_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_2_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER5_2_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_3_R][1]; + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER5_3_R][0]; //MOCAPNET_OUTPUT_JOINT_FINGER5_3_R + input.skeletons[0].rightHand.joint2D[COCO_Hand_Pinky_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER5_3_R][1]; //MOCAPNET_OUTPUT_JOINT_FINGER5_3_R + //--------------------------------------------------------------------------------- + //Dont forget convertRHandSkeletonSerializedToBVHTransform needs to also be updated + + + + + + //Populate left hand + input.skeletons[0].leftHand.joint2D[COCO_Hand_Wrist].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LHAND][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Wrist].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LHAND][1]; + //--------------------------------------------------------------------------------- + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_LTHUMB][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_LTHUMB][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_2_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_2_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER1_3_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER1_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Thumb_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER1_3_L][1]; + //--------------------------------------------------------------------------------- + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_1_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_1_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_2_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_2_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER2_3_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER2_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Index_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER2_3_L][1]; //Maybe the end site is prefereable? + //--------------------------------------------------------------------------------- + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_1_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_1_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_2_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_2_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER3_3_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER3_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Middle_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER3_3_L][1]; //Maybe the end site is prefereable? + //--------------------------------------------------------------------------------- + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_1_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_1_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_2_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_2_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER4_3_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER4_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Ring_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER4_3_L][1]; //Maybe the end site is prefereable? + //--------------------------------------------------------------------------------- + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_1_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_1_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_2_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_2_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_FINGER5_3_L][1]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_4].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER5_3_L][0]; + input.skeletons[0].leftHand.joint2D[COCO_Hand_Pinky_4].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ENDSITE_FINGER5_3_L][1]; //Maybe the end site is prefereable? + //--------------------------------------------------------------------------------- + //Dont forget convertLHandSkeletonSerializedToBVHTransform needs to also be updated + + + + + //Populate face + input.skeletons[0].head.joint2D[OP_Head_REye_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS03_R][0]; + input.skeletons[0].head.joint2D[OP_Head_REye_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS03_R][1]; + input.skeletons[0].head.joint2D[OP_Head_REye].x = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_R][0]; + input.skeletons[0].head.joint2D[OP_Head_REye].y = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_R][1]; + input.skeletons[0].head.joint2D[OP_Head_REye_5].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS04_R][0]; + input.skeletons[0].head.joint2D[OP_Head_REye_5].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS04_R][1]; + //--------------------------------------------------------------------------------- + input.skeletons[0].head.joint2D[OP_Head_LEye_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS03_L][0]; + input.skeletons[0].head.joint2D[OP_Head_LEye_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS03_L][1]; + input.skeletons[0].head.joint2D[OP_Head_LEye].x = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_L][0]; + input.skeletons[0].head.joint2D[OP_Head_LEye].y = mocapNET2DPointsResult[MOCAPNET_JOINT_EYE_L][1]; + input.skeletons[0].head.joint2D[OP_Head_LEye_5].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS04_L][0]; + input.skeletons[0].head.joint2D[OP_Head_LEye_5].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORBICULARIS04_L][1]; + //--------------------------------------------------------------------------------- + input.skeletons[0].head.joint2D[OP_Head_Chin].x = mocapNET2DPointsResult[MOCAPNET_JOINT_SPECIAL04][0]; + input.skeletons[0].head.joint2D[OP_Head_Chin].y = mocapNET2DPointsResult[MOCAPNET_JOINT_SPECIAL04][1]; + //--------------------------------------------------------------------------------- + input.skeletons[0].head.joint2D[OP_Head_InMouth_1].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS03_R][0]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_1].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS03_R][1]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_2].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS05][0]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_2].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS05][1]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_3].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS03_L][0]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_3].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS03_L][1]; + //--------------------------------------------------------------------------------- + input.skeletons[0].head.joint2D[OP_Head_InMouth_7].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS07_R][0]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_7].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS07_R][1]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_6].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS01][0]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_6].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS01][1]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_5].x = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS07_L][0]; + input.skeletons[0].head.joint2D[OP_Head_InMouth_5].y = mocapNET2DPointsResult[MOCAPNET_JOINT_ORIS07_L][1]; + //--------------------------------------------------------------------------------- + // Dont forget to update convertFaceSkeletonSerializedToBVHTransform + + /* + for (int jID=0; jIDskeletonBody[hipJoint*3+0].value; + float hipY = input->skeletonBody[hipJoint*3+1].value; + char hipVisibility = (input->skeletonBody[hipJoint*3+2].value > 0.0); + + if (hipVisibility) + { + if (input!=0) + { + for (unsigned int jID = 0; jID< input->skeletonBodyElements/3; jID++) + { + float x=input->skeletonBody[jID*3+0].value; + float y=input->skeletonBody[jID*3+1].value; + char visibility=(char) (input->skeletonBody[jID*3+2].value > 0.0); + + if (visibility) + { + //Subtract HIP from every joint and perform a 2D rotation using the rad variable + float unrotatedX = x-hipX; + //Subtract HIP from every joint and perform a 2D rotation using the rad variable + float unrotatedY = y-hipY; + + input->skeletonBody[jID*3+0].value = hipX + (unrotatedX * cosf(rad)) - (unrotatedY * sinf(rad)); + input->skeletonBody[jID*3+1].value = hipY + (unrotatedX * sinf(rad)) + (unrotatedY * cosf(rad)); + } + } + return 1; + } + } + return 0; +} + + + + + +float rotationRequiredToMakeSkeletonCloserToTrainingDataset(struct skeletonSerialized * input) +{ + //-------------------------------------------------------------------- + unsigned int hipJoint = 1 + BODY25_MidHip; //+1 because the first three parameters are for skeleton ids etc. + float hipX = input->skeletonBody[hipJoint*3+0].value; + float hipY = input->skeletonBody[hipJoint*3+1].value; + char hipVisibility = (input->skeletonBody[hipJoint*3+2].value > 0.0); + //-------------------------------------------------------------------- + unsigned int neckJoint = 1 + BODY25_Neck; //+1 because the first three parameters are for skeleton ids etc. + float neckX = input->skeletonBody[neckJoint*3+0].value; + float neckY = input->skeletonBody[neckJoint*3+1].value; + char neckVisibility = (input->skeletonBody[neckJoint*3+2].value > 0.0); + //-------------------------------------------------------------------- + + if ( (hipVisibility) && (neckVisibility) ) + { + float alignmentAngle=getAngleToAlignToZero_tools(hipX,hipY,neckX,neckY); + return alignmentAngle; + } + return 0.0; +} + + +int makeSkeletonUpright(struct skeletonSerialized * input) +{ + float angleCorrectionNeededInDegrees = 180 + goFromRadToDegrees * rotationRequiredToMakeSkeletonCloserToTrainingDataset(input); + + if (angleCorrectionNeededInDegrees!=180.0) + { + return affineSkeletonRotation(input,angleCorrectionNeededInDegrees); + } + + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/conversions.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/conversions.hpp new file mode 100644 index 0000000..66d1fc4 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/conversions.hpp @@ -0,0 +1,62 @@ +#pragma once +/** @file conversions.hpp + * @brief Unfortunately due to the complexity of the problem and the different libraries used there cannot be a single skeleton representation. + * This module handles conversions between std::vector which is used by Tensorflow/NeuralNetwork layer , skeletonSerialized which is used by MocapNET and Skeletons2DDetected which is used by the 2D estimator + * @author Ammar Qammaz (AmmarkoV) + */ + + +#include +#include "../mocapnet2.hpp" +#include "../../../JointEstimator2D/jointEstimator2D.hpp" + + +int appendVectorToFile(const char * filename, std::vector vec); + +int initializeAssociationsForSubsetOfSkeleton( + unsigned int * targetIndexIsInitializedFlag, + unsigned int * targetIndexes, + unsigned int targetLength, + const char * * targetLabels, + struct skeletonSerialized * input + ); + + + +std::vector deriveMocapNET2InputUsingAssociations( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + unsigned int * targetIndexIsInitializedFlag, + unsigned int * targetIndexes, + unsigned int targetLength, + const char * * targetLabels, + int verbose + ); + + + +int convertSkeletons2DDetectedToSkeletonsSerialized( + struct skeletonSerialized * output, + struct Skeletons2DDetected * input, + unsigned int frameNumber, + unsigned int width, + unsigned int height +); + + +int convertMocapNET2OutputToSkeletonSerialized( + struct MocapNET2 * mnet , + struct skeletonSerialized * output , + std::vector > mocapNET2DPointsResult, + unsigned int frameNumber, + unsigned int width, + unsigned int height + ); + + + +int affineSkeletonRotation(struct skeletonSerialized * input,float degrees); + +float rotationRequiredToMakeSkeletonCloserToTrainingDataset(struct skeletonSerialized * input); + +int makeSkeletonUpright(struct skeletonSerialized * input); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvRead.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvRead.cpp new file mode 100644 index 0000000..111c7bd --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvRead.cpp @@ -0,0 +1,653 @@ + +#include +#include +#include +#include +#include + + +#include "csvRead.hpp" +#include "../../MocapNETLib2/mocapnet2.hpp" +#include "../../MocapNETLib2/IO/jsonMocapNETHelpers.hpp" + +#include "../../../../dependencies/InputParser/InputParser_C.h" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + +unsigned int getBodyLinesOfCSVFIle(struct CSVFileContext * csv,const char * filename) +{ + FILE * fp = fopen(filename,"r"); + { + // Extract characters from file and store in character c + unsigned int count=0; + char c; + for (c = getc(fp); c != EOF; c = getc(fp)) + if (c == '\n') // Increment count if this character is newline + { + count = count + 1; + } + + fclose(fp); + + //The first line of a CSV is its header so we don't count this .. + if (count>0) + { + --count; + } + return count; + } + return 0; +} + + +int openCSVFile(struct CSVFileContext * csv,const char * filename) +{ + csv->fp = fopen(filename,"r"); + if (csv->fp!=0) + { + csv->lineNumber=0; + csv->numberOfHeaderFields=0; + fprintf(stderr,GREEN "Successfully opened CSV file %s\n" NORMAL,filename); + return 1; + } + fprintf(stderr,RED "Failed to open CSV file %s\n" NORMAL,filename); + return 0; +} + +int closeCSVFile(struct CSVFileContext * csv) +{ + if (csv==0) + { + fprintf(stderr,RED "Dont need to close empty CSV struct..\n" NORMAL); + return 1; + } + if (csv->fp==0) + { + fprintf(stderr,RED "Dont need to close CSV header that has already been closed..\n" NORMAL); + return 1; + } + + if (csv->fp!=0) + { + //TODO FREE STUFF HERE.. + int i=0; + for (i=0; inumberOfHeaderFields; i++) + { + if (csv->field[i].str!=0) + { + free(csv->field[i].str); + csv->field[i].str=0; + } + } + + fclose(csv->fp); + } + return 1; +} + + +int parseCSVHeader(struct CSVFileContext * csv) +{ + if (csv==0) + { + fprintf(stderr,RED "Cannot parse empty CSV header..\n" NORMAL); + return 0; + } + if (csv->fp==0) + { + fprintf(stderr,RED "Cannot parse CSV header that hasn't been opened using openCSVFile..\n" NORMAL); + return 0; + } + + fprintf(stderr,"Parsing CSV header..\n"); + char whereToStoreItems[513]= {0}; + char * line = NULL; + size_t len = 0; + ssize_t read; + + if (csv->lineNumber==0) + { + read = getline(&line, &len, csv->fp); + if (line!=0) + { + struct InputParserC * ipc = InputParser_Create(8096,4); + InputParser_SetDelimeter(ipc,0,','); + InputParser_SetDelimeter(ipc,1,0); + InputParser_SetDelimeter(ipc,2,10); + InputParser_SetDelimeter(ipc,3,13); + + int numberOfArguments = InputParser_SeperateWords(ipc,line,1); + + if (numberOfArguments %s\n",i,whereToStoreItems); + csv->field[i].strLength = strlen(whereToStoreItems); + csv->field[i].str = (char*) malloc(sizeof(char) * (csv->field[i].strLength+2)); + memcpy(csv->field[i].str,whereToStoreItems,csv->field[i].strLength); + csv->field[i].str[csv->field[i].strLength]=0; + ++csv->numberOfHeaderFields; + } + } + else + { + fprintf(stderr,"Too many CSV header arguments (encountered %u, max %u)..\n",numberOfArguments,MAX_CSV_HEADER_FIELDS); + InputParser_Destroy(ipc); + if (line!=0) + { + free(line); + line=0; + } + return 0; + } + + + + + InputParser_Destroy(ipc); + csv->lineNumber = csv->lineNumber +1; + if (line!=0) + { + free(line); + line=0; + } + + return 1; + } + } + + if (line!=0) + { + free(line); + line=0; + } + return 0; +} + + + + + +int parseNextCSVFloatLine(struct CSVFileContext * csv,struct CSVFloatFileLine * csvLine) +{ + if (csv==0) + { + return 0; + } + if (csvLine==0) + { + return 0; + } + + char * line = NULL; + size_t len = 0; + ssize_t read; + + if (csv->lineNumber==0) + { + if (!parseCSVHeader(csv)) + { + fprintf(stderr,RED "Could not read CSV header on first loop\n" NORMAL); + return 0; + } + } + + if ((read = getline(&line, &len, csv->fp)) != -1) + { + struct InputParserC * ipc = InputParser_Create(8096,4); + InputParser_SetDelimeter(ipc,0,','); + InputParser_SetDelimeter(ipc,1,0); + InputParser_SetDelimeter(ipc,2,10); + InputParser_SetDelimeter(ipc,3,13); + + csvLine->lineNumber=csv->lineNumber; + csvLine->numberOfFields = InputParser_SeperateWords(ipc,line,1); + + //fprintf(stderr,"Line %u (%s) has %u arguments \n",csvLine->lineNumber,line,csvLine->numberOfFields ); + + if ( csvLine->numberOfFields != csv->numberOfHeaderFields ) + { + fprintf(stderr,RED "Number of header fields %u does not correspond with number of body elements %u \n" NORMAL,csv->numberOfHeaderFields,csvLine->numberOfFields); + } + + for (int i=0; inumberOfFields; i++) + { + csvLine->field[i] = InputParser_GetWordFloat(ipc,i); + //fprintf(stderr,"%s(%u)=%0.2f ",csv->field[i].str,i,csvLine->field[i]); + } + + + InputParser_Destroy(ipc); + + csv->lineNumber = csv->lineNumber+1; + if (line!=0) + { + free(line); + line=0; + } + return 1; + } + if (line!=0) + { + free(line); + line=0; + } + return 0; +} + + + + +int parseNextCSVCOCOSkeleton(struct CSVFileContext * csv, struct skeletonSerialized * skel) +{ + if (csv==0) + { + return 0; + } + if (skel==0) + { + return 0; + } + + char * line = NULL; + size_t len = 0; + ssize_t read; + + if (csv->lineNumber==0) + { + if (!parseCSVHeader(csv)) + { + fprintf(stderr,RED "Could not read CSV header on first loop\n" NORMAL); + return 0; + } + /* + else + { + fprintf(stderr,"Header of skeleton parsed successfully..\n"); + for (unsigned int i=0; inumberOfHeaderFields; i++) + { + fprintf(stderr,"CSV file header element %u => %s \n",i,csv->field[i].str); + } + }*/ + } + + if ((read = getline(&line, &len, csv->fp)) != -1) + { + //CSV files are always stored normalized.. + skel->width = 1.0; + skel->height = 1.0; + + + struct InputParserC * ipc = InputParser_Create(8096,4); + InputParser_SetDelimeter(ipc,0,','); + InputParser_SetDelimeter(ipc,1,0); + InputParser_SetDelimeter(ipc,2,10); + InputParser_SetDelimeter(ipc,3,13); + + skel->skeletonBodyElements = InputParser_SeperateWords(ipc,line,1); + + //fprintf(stderr,"Line %u (%s) has %u arguments \n",csv->lineNumber,line,skel->skeletonBodyElements); + + int typeOfData=0,jointID=0; + float value=0.0; + + if ( skel->skeletonBodyElements != csv->numberOfHeaderFields ) + { + fprintf(stderr,"Number of header fields %u does not correspond with number of body elements %u \n",csv->numberOfHeaderFields,skel->skeletonBodyElements); + } + + //Copy references to header .. + skel->skeletonHeaderElements=csv->numberOfHeaderFields; + for (int i=0; inumberOfHeaderFields; i++) + { + skel->skeletonHeader[i].strLength = csv->field[i].strLength; + skel->skeletonHeader[i].str = csv->field[i].str; + } + + for (int i=0; iskeletonBodyElements; i++) + { + //fprintf(stderr,"CSV Header element %u => %s\n",i,whereToStoreItems); + skel->skeletonBody[i].value = InputParser_GetWordFloat(ipc,i); + //fprintf(stderr,"%s(%u)=%0.2f ",csv->field[i].str,i,skel->skeletonBody[i].value); + } + + + InputParser_Destroy(ipc); + csv->lineNumber = csv->lineNumber+1; + if (line!=0) + { + free(line); + line=0; + } + return 1; + } + if (line!=0) + { + free(line); + line=0; + } + return 0; +} + + + +int uniformlyScaleSerializedSkeleton(struct skeletonSerialized * skel,float factor) +{ + if (skel==0) + { + return 0; + } + + for (int i=0; iskeletonBodyElements; i++) + { + skel->skeletonBody[i].value *= factor; + } + return 1; +} + + + +int scaleSerializedSkeletonX(struct skeletonSerialized * skel,float factorX) +{ + if (skel==0) + { + return 0; + } + if (skel->skeletonHeaderElements!=skel->skeletonBodyElements) + { + fprintf(stderr,"scaleSerializedSkeletonX: Inconsistent headers length\n"); + return 0; + } + + int scaledJoints=0; + for (int i=0; iskeletonHeaderElements; i++) + { + if + ( + ( skel->skeletonHeader[i].strLength >4 ) && + ( skel->skeletonHeader[i].str!=0 ) + ) + { + if ( + (skel->skeletonHeader[i].str[0]=='2') && + ( (skel->skeletonHeader[i].str[1]=='D') || (skel->skeletonHeader[i].str[1]=='d') ) && + ( (skel->skeletonHeader[i].str[2]=='X') || (skel->skeletonHeader[i].str[2]=='x') ) && + (skel->skeletonHeader[i].str[3]=='_') + ) + { + skel->skeletonBody[i].value *= factorX; + ++scaledJoints; + } + } + } + + return scaledJoints; +} + + +int scaleSerializedSkeletonY(struct skeletonSerialized * skel,float factorY) +{ + if (skel==0) + { + return 0; + } + if (skel->skeletonHeaderElements!=skel->skeletonBodyElements) + { + fprintf(stderr,"scaleSerializedSkeletonY: Inconsistent headers length\n"); + return 0; + } + + int scaledJoints=0; + for (int i=0; iskeletonHeaderElements; i++) + { + if + ( + ( skel->skeletonHeader[i].strLength >4 ) && + ( skel->skeletonHeader[i].str!=0 ) + ) + { + if ( + (skel->skeletonHeader[i].str[0]=='2') && + ( (skel->skeletonHeader[i].str[1]=='D') || (skel->skeletonHeader[i].str[1]=='d') ) && + ( (skel->skeletonHeader[i].str[2]=='Y') || (skel->skeletonHeader[i].str[2]=='y') ) && + (skel->skeletonHeader[i].str[3]=='_') + ) + { + skel->skeletonBody[i].value *= factorY; + ++scaledJoints; + } + ///else { fprintf(stderr,"`%s` does not match\n",skel->skeletonHeader[i].str); } + } + } + + return scaledJoints; +} + + + +int scaleSerializedSkeletonFromCenter(struct skeletonSerialized * skel,float factorX,float factorY) +{ + if (skel==0) + { + return 0; + } + +// Assuming we have a 2D point cloud with points 0..1,0..1 +// This is how we model the framebuffer +// 0,0 __________________________________________________________________________________ 1,0 +// | /\ | +// | | 0.5 | +// | | | +// | 0.5 | 0.5 | +// | <----------------------------- * -----------------------------> | +// | | | +// | | | +// | | 0.5 | +// | \/ | +// 0,1------------------------------------------------------------------------------------ 1,1 +// The * is the center of the image.. +// We want to scale X,Y points based on factorX and factorY +//However the magnitued of the scaling will be linear to the distance from the +//Center of the image , Points 0.5 units away from the center will be scaled fully with the factor +//Points close to the center will be not scaled at all.. + if (skel->skeletonHeaderElements!=skel->skeletonBodyElements) + { + fprintf(stderr,"scaleSerializedSkeletonY: Inconsistent headers length\n"); + return 0; + } + + float value,magnitude,maximumFactor,thisFactorY,relativeY,thisFactorX,relativeX; + + int scaledJoints=0; + for (int i=0; iskeletonHeaderElements; i++) + { + if + ( + ( skel->skeletonHeader[i].strLength >4 ) && + ( skel->skeletonHeader[i].str!=0 ) + ) + { + if ( + (skel->skeletonHeader[i].str[0]=='2') && + ( (skel->skeletonHeader[i].str[1]=='D') || (skel->skeletonHeader[i].str[1]=='d') ) && + ( (skel->skeletonHeader[i].str[2]=='X') || (skel->skeletonHeader[i].str[2]=='x') ) && + (skel->skeletonHeader[i].str[3]=='_') + ) + { + if (factorX!=1.0) + { + value = skel->skeletonBody[i].value; + maximumFactor = factorX-1.0; + magnitude = (float) fabs(value-0.5)/0.5; + thisFactorX = (magnitude * maximumFactor) + 1.0; + relativeX = value - 0.5; + relativeX *= thisFactorX; + skel->skeletonBody[i].value = relativeX + 0.5 ; + ++scaledJoints; + } + } + else if ( + (skel->skeletonHeader[i].str[0]=='2') && + ( (skel->skeletonHeader[i].str[1]=='D') || (skel->skeletonHeader[i].str[1]=='d') ) && + ( (skel->skeletonHeader[i].str[2]=='Y') || (skel->skeletonHeader[i].str[2]=='y') ) && + (skel->skeletonHeader[i].str[3]=='_') + ) + { + if (factorY!=1.0) + { + value = skel->skeletonBody[i].value; + maximumFactor = factorY-1.0; + magnitude = (float) fabs(value-0.5)/0.5; + thisFactorY = ( magnitude * maximumFactor ) + 1.0; + relativeY = value - 0.5; + relativeY *= thisFactorY; + skel->skeletonBody[i].value = relativeY + 0.5 ; + ++scaledJoints; + } + } + } //Valid header + } // For each joint + + return scaledJoints; +} + + + +#ifdef __GNUC__ +# if __GNUC_PREREQ(5,5) +// If gcc_version >= RANDOM support +#include //For generating random samples based on a normal distribution +//This function makes the life of the 3D pose estimator more difficult by adding a configurable level of gaussian noise on the 2D pixels estimated +int perturbSerializedSkeletonUsingGaussianNoise(struct skeletonSerialized * skel,float gaussianNoiseInNormalizedPixelsX,float gaussianNoiseInNormalizedPixelsY) +{ + if (skel->skeletonHeaderElements!=skel->skeletonBodyElements) + { + fprintf(stderr,"perturbSerializedSkeletonUsingGaussianNoise: Inconsistent headers length\n"); + return 0; + } + + + std::default_random_engine generator; + std::normal_distribution distributionX(0.0,gaussianNoiseInNormalizedPixelsX); + std::normal_distribution distributionY(0.0,gaussianNoiseInNormalizedPixelsY); + + + float value,magnitude,maximumFactor,thisFactorY,relativeY,thisFactorX,relativeX; + + int perturbedCoordinates=0; + for (int i=0; iskeletonHeaderElements; i++) + { + if + ( + ( skel->skeletonHeader[i].strLength >4 ) && + ( skel->skeletonHeader[i].str!=0 ) + ) + { + if ( + (skel->skeletonHeader[i].str[0]=='2') && + ( (skel->skeletonHeader[i].str[1]=='D') || (skel->skeletonHeader[i].str[1]=='d') ) && + ( (skel->skeletonHeader[i].str[2]=='X') || (skel->skeletonHeader[i].str[2]=='x') ) && + (skel->skeletonHeader[i].str[3]=='_') + ) + { + if (gaussianNoiseInNormalizedPixelsX>0.0) + { + float perturbation = distributionX(generator); + skel->skeletonBody[i].value += perturbation; + + ++perturbedCoordinates; + } + } + else if ( + (skel->skeletonHeader[i].str[0]=='2') && + ( (skel->skeletonHeader[i].str[1]=='D') || (skel->skeletonHeader[i].str[1]=='d') ) && + ( (skel->skeletonHeader[i].str[2]=='Y') || (skel->skeletonHeader[i].str[2]=='y') ) && + (skel->skeletonHeader[i].str[3]=='_') + ) + { + if (gaussianNoiseInNormalizedPixelsY>0.0) + { + float perturbation = distributionY(generator); + skel->skeletonBody[i].value += perturbation; + + ++perturbedCoordinates; + } + } + } //Valid header + } // For each joint + + return perturbedCoordinates; +} +# else +#warning "Your compiler is too old and is missing normal random distributions" +int perturbSerializedSkeletonUsingGaussianNoise(struct skeletonSerialized * skel,float gaussianNoiseInNormalizedPixelsX,float gaussianNoiseInNormalizedPixelsY) +{ + fprintf(stderr,RED "\n\n\n\nYour compiler is too old and is missing normal random distributions\n\n\n\n" NORMAL ); + return 0; +} +// Else +# endif +#else +int perturbSerializedSkeletonUsingGaussianNoise(struct skeletonSerialized * skel,float gaussianNoiseInNormalizedPixelsX,float gaussianNoiseInNormalizedPixelsY) +{ + fprintf(stderr,RED "\n\n\n\nYour compiler is not GCC old and is missing(?) normal random distributions\n\n\n\n" NORMAL ); + return 0; +} +// If not gcc +#endif + + +/* +float rand_FloatRange(float a, float b) +{ + return ((b - a) * ((float)rand() / RAND_MAX)) + a; +}*/ + + + +std::vector > get2DPointsFromSkeleton(struct skeletonSerialized * skel) +{ + fprintf(stderr,"get2DPointsFromSkeleton: "); + std::vector > finalVector; + if (skel==0) + { + fprintf(stderr,"get2DPointsFromSkeleton: No input\n"); + return finalVector; + } + if (skel->skeletonBodyElements!=skel->skeletonHeaderElements) + { + fprintf(stderr,"get2DPointsFromSkeleton: Mismatch of elements of body/header\n"); + return finalVector; + } + + std::vector points2D; + for (int i=0; iskeletonBodyElements; i++) + { + if ( strstr(skel->skeletonHeader[i].str,"2DX_")!=0) + { + points2D.push_back(skel->skeletonBody[i].value); + } + else if ( strstr(skel->skeletonHeader[i].str,"2DY_")!=0) + { + points2D.push_back(skel->skeletonBody[i].value); + } + else if ( strstr(skel->skeletonHeader[i].str,"visible_")!=0) + { + finalVector.push_back(points2D); + points2D.clear(); + } + } + + fprintf(stderr,"survived..\n"); + return finalVector; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvRead.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvRead.hpp new file mode 100644 index 0000000..7774f60 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvRead.hpp @@ -0,0 +1,91 @@ +#pragma once +/** @file csvRead.hpp + * @brief To simplify dataset parsing the very simple CSV ( comma seperated value ) format is used by MocapNET2 + * This module facilitates opening,parsing and performing some trivial processing of CSV input files.. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include + + +#include "commonSkeleton.hpp" +#define MAX_CSV_HEADER_FIELDS 1024 + + +/** + * @brief Each CSV file needs a context to be parsed, this contains the file discriptor the current line number, the number of fields in the header as well as header fields + */ +struct CSVFileContext +{ + FILE * fp; + unsigned int lineNumber; + unsigned int numberOfHeaderFields; + struct headerField field[MAX_CSV_HEADER_FIELDS]; +}; + +/** + * @brief We may choose to parse a CSV file that only consists of floats, for this specific type there is an optimized CSV context + */ + struct CSVFloatFileLine +{ + unsigned int lineNumber; + unsigned int numberOfFields; + float field[MAX_CSV_HEADER_FIELDS]; +}; + + +/** + * @brief Write a skeleton to a CSV. This is the first call that prepares the header + * @param CSV context + * @param filename of CSV file + * @retval 0=Failure/No lines, Otherwise the number of body lines is returned + */ +unsigned int getBodyLinesOfCSVFIle(struct CSVFileContext * csv,const char * filename); + + + +/** + * @brief This is the initial call to open a CSV file, you need to have a preallocated CSVFileContext strcture as well as a filename. Also don't forget to call closeCSVFile after you are done parsing it + * @param CSV context + * @param filename of CSV file + * @retval 0=Failure/1=Success + */ +int openCSVFile(struct CSVFileContext * csv,const char * filename); + +/** + * @brief This call should be executed once after opening a CSV file to parse it's header + * @param CSV context + * @retval 0=Failure/1=Success + */ +int parseCSVHeader(struct CSVFileContext * csv); + + +/** + * @brief This is the final call to close a CSV file and release its file descriptor after you are done parsing it + * @param CSV context + * @retval 0=Failure/1=Success + */ +int closeCSVFile(struct CSVFileContext * csv); + + +int parseNextCSVFloatLine(struct CSVFileContext * csv,struct CSVFloatFileLine * csvLine); + + +int parseNextCSVCOCOSkeleton(struct CSVFileContext * csv, struct skeletonSerialized * skel); + + + +int uniformlyScaleSerializedSkeleton(struct skeletonSerialized * skel,float factor); + + +int scaleSerializedSkeletonX(struct skeletonSerialized * skel,float factorX); +int scaleSerializedSkeletonY(struct skeletonSerialized * skel,float factorY); + +int scaleSerializedSkeletonFromCenter(struct skeletonSerialized * skel,float factorX,float factorY); + + +int perturbSerializedSkeletonUsingGaussianNoise(struct skeletonSerialized * skel,float gaussianNoiseInNormalizedPixelsX,float gaussianNoiseInNormalizedPixelsY); + + + +std::vector > get2DPointsFromSkeleton(struct skeletonSerialized * skel); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvWrite.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvWrite.cpp new file mode 100644 index 0000000..f7410eb --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/csvWrite.cpp @@ -0,0 +1,251 @@ +#include +#include +#include +#include +#include + +#include "../mocapnet2.hpp" +#include "../tools.hpp" +#include "csvWrite.hpp" + + +//---------------------------------------------------------------------------------------------------------------------------------------------------------------------- +//---------------------------------------------------------------------------------------------------------------------------------------------------------------------- +//---------------------------------------------------------------------------------------------------------------------------------------------------------------------- +int writeCSVHeaderFromVector(const char * filename,const char ** labels,unsigned int numberOfLabels) +{ + FILE * fp = fopen(filename,"w"); + if (fp!=0) + { + for (int i=0; i inputValues) +{ + FILE * fp = fopen(filename,"a"); + if (fp!=0) + { + if (inputValues.size()==0) + { + fprintf(stderr,"Failed to read from JSON file..\n"); + } + + for (int i=0; i > inputFrames) +{ + int totalWritesNeeded=0; + int totalWritesSucceeded=0; + + if ( writeCSVHeaderFromVector(filename,labels,numberOfLabels) ) + { + for (int i=0; iobservationNumber); + fprintf(fp,"%u,",skeleton->userID); + fprintf(fp,"%u,",skeleton->totalUsersPresent); + + + + + for (int i=0; ibody.joint2D[i].x, + &skeleton->body.joint2D[i].y, + width, + height, + respectTrainingAspectRatio, + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + fprintf(fp,"%f,",(float) skeleton->body.joint2D[i].x); + fprintf(fp,"%f,",(float) skeleton->body.joint2D[i].y); + fprintf(fp,"%u,", ( (skeleton->body.joint2D[i].x!=0) && (skeleton->body.joint2D[i].y!=0) ) ); + } + + for (int i=0; ileftHand.joint2D[i].x, + &skeleton->leftHand.joint2D[i].y, + width, + height, + respectTrainingAspectRatio, + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + fprintf(fp,"%f,",(float) skeleton->leftHand.joint2D[i].x); + fprintf(fp,"%f,",(float) skeleton->leftHand.joint2D[i].y); + fprintf(fp,"%u,", ( (skeleton->leftHand.joint2D[i].x!=0) && (skeleton->leftHand.joint2D[i].y!=0) ) ); + } + + for (int i=0; irightHand.joint2D[i].x, + &skeleton->rightHand.joint2D[i].y, + width, + height, + respectTrainingAspectRatio, + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + fprintf(fp,"%f,",(float) skeleton->rightHand.joint2D[i].x); + fprintf(fp,"%f,",(float) skeleton->rightHand.joint2D[i].y); + fprintf(fp,"%u,", ( (skeleton->rightHand.joint2D[i].x!=0) && (skeleton->rightHand.joint2D[i].y!=0) ) ); + } + + for (int i=0; ihead.joint2D[i].x, + &skeleton->head.joint2D[i].y, + width, + height, + respectTrainingAspectRatio, + MocapNETTrainingWidth, + MocapNETTrainingHeight + ); + //-------------------------------------------------------------------------------- + fprintf(fp,"%f,",(float) skeleton->head.joint2D[i].x); + fprintf(fp,"%f,",(float) skeleton->head.joint2D[i].y); + fprintf(fp,"%u", ( (skeleton->head.joint2D[i].x!=0) && (skeleton->head.joint2D[i].y!=0) ) ); + if (i inputValues); + + +int writeCSVHeaderFromLabelsAndVectorOfVectors(const char * filename,const char ** labels,unsigned int numberOfLabels,std::vector > inputFrames); + + + +int writeOpenPoseCSVHeaderFromSkeleton(const char * filename,struct skeletonStructure * skeleton,unsigned int width,unsigned int height); + + +int writeOpenPoseCSVBodyFromSkeleton(const char * filename,struct skeletonStructure * skeleton,unsigned int respectTrainingAspectRatio,unsigned int width,unsigned int height); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonMocapNETHelpers.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonMocapNETHelpers.cpp new file mode 100644 index 0000000..812ff53 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonMocapNETHelpers.cpp @@ -0,0 +1,612 @@ +#include "jsonMocapNETHelpers.hpp" +#include +#include + +#include "../../MocapNETLib2/IO/bvh.hpp" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int bothZero(float a,float b) +{ + return (a==0.0)&&(b==0.0); +} + +int jointIsNotZero(struct skeletonStructure * sk,int jointID) +{ + if ( bothZero(sk->body.joint2D[jointID].x,sk->body.joint2D[jointID].y) ) + { + return 0; + } + return 1; +} + +int bothJointAreNotZero(struct skeletonStructure * sk,int jointIDA,int jointIDB) +{ + if ( + ( (sk->body.joint2D[jointIDA].x==0.0)&&(sk->body.joint2D[jointIDA].y==0.0) ) || + ( (sk->body.joint2D[jointIDB].x==0.0)&&(sk->body.joint2D[jointIDB].y==0.0) ) + ) + { + return 0; + } + return 1; +} + + +void addSkeletonJointFromTwoJoints( + struct skeletonStructure * sk, + std::vector &result, + int jointIDA, + int jointIDB +) +{ + float x=(float) (sk->body.joint2D[jointIDA].x + sk->body.joint2D[jointIDB].x)/2; + float y=(float) (sk->body.joint2D[jointIDA].y + sk->body.joint2D[jointIDB].y)/2; + float v=1.0; + if ( + ( (sk->body.joint2D[jointIDA].x==0.0)&&(sk->body.joint2D[jointIDA].y==0.0) ) || + ( (sk->body.joint2D[jointIDB].x==0.0)&&(sk->body.joint2D[jointIDB].y==0.0) ) + ) + { + x=0.0; + y=0.0; + v=0.0; + } + + result.push_back(x); + result.push_back(y); + result.push_back(v); +} + +void addSkeletonJoint( + struct skeletonStructure * sk, + std::vector &result, + int jointID +) + +{ + float x=(float) sk->body.joint2D[jointID].x; + float y=(float) sk->body.joint2D[jointID].y; + float v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); +} + +void addRightFinger( + struct skeletonStructure * sk, + std::vector &result, + int fingerJointA, + int fingerJointB, + int fingerJointC +) + +{ + float x=(float) sk->rightHand.joint2D[fingerJointA].x; + float y=(float) sk->rightHand.joint2D[fingerJointA].y; + float v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //---------------------------------------------- + x=(float) sk->rightHand.joint2D[fingerJointB].x; + y=(float) sk->rightHand.joint2D[fingerJointB].y; + v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //---------------------------------------------- + x=(float) sk->rightHand.joint2D[fingerJointC].x; + y=(float) sk->rightHand.joint2D[fingerJointC].y; + v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); +} + + + +void addLeftFinger( + struct skeletonStructure * sk, + std::vector &result, + int fingerJointA, + int fingerJointB, + int fingerJointC +) + +{ + float x=(float) sk->leftHand.joint2D[fingerJointA].x; + float y=(float) sk->leftHand.joint2D[fingerJointA].y; + float v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //---------------------------------------------- + x=(float) sk->leftHand.joint2D[fingerJointB].x; + y=(float) sk->leftHand.joint2D[fingerJointB].y; + v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //---------------------------------------------- + x=(float) sk->leftHand.joint2D[fingerJointC].x; + y=(float) sk->leftHand.joint2D[fingerJointC].y; + v=1.0; + if ( (x==0.0)&&(y==0.0) ) + { + v=0.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); +} + + + +std::vector normalizeWhileAlsoMatchingTrainingAspectRatio( + std::vector input, + unsigned int currentWidth, + unsigned int currentHeight, + unsigned int trainingWidth, + unsigned int trainingHeight + ) +{ + //fprintf(stderr,YELLOW "normalizeWhileAlsoMatchingTrainingAspectRatio\n" NORMAL); + unsigned int addX=0,addY=0; + unsigned int targetWidth=currentWidth,targetHeight=currentHeight; + float currentAspectRatio = (float) currentWidth/currentHeight; + float trainingAspectRatio = (float) trainingWidth/trainingHeight; + + //fprintf(stderr,"Will try to correct aspect ratio from %0.2f(%ux%u) to %0.2f (%ux%u)\n",currentAspectRatio,currentWidth,currentHeight,trainingAspectRatio,trainingWidth,trainingHeight); + + std::vector fixedAspectRatio=input; + + if (currentHeight=currentHeight) + { + addY=(unsigned int) (targetHeight-currentHeight)/2; + } else + { + //Turns out we will have to enlarge X instead of englarging Y + addY=0; + targetHeight=currentHeight; + targetWidth=(unsigned int)currentHeight*trainingAspectRatio; + addX=(unsigned int) (targetWidth-currentWidth)/2; + } + } else + if (currentWidth<=currentHeight) + { + targetWidth=(unsigned int)currentHeight*trainingAspectRatio; + if (targetWidth>=currentWidth) + { + addX=(unsigned int) (targetWidth-currentWidth)/2; + } else + { + //Turns out we will have to enlarge Y instead of englarging X + addX=0; + targetWidth=currentWidth; + targetHeight=(unsigned int) currentWidth/trainingAspectRatio; + addY=(unsigned int) (targetHeight-currentHeight)/2; + } + } + + //fprintf(stderr,"Target resolution is %ux%u to Y\n",targetWidth,targetHeight); + //fprintf(stderr,"Will add %u to X and %u to Y to achieve it\n",addX,addY); + + float targetAspectRatio=(float) targetWidth/targetHeight; + if ((unsigned int) targetAspectRatio/100!= (unsigned int) trainingAspectRatio/100) + { + fprintf(stderr,RED "Failed to perfectly match training aspect ratio (%0.5f), managed to reach (%0.5f)\n" NORMAL,trainingAspectRatio,targetAspectRatio); + } + + for (int i=0; i flattenskeletonStructureToVector(struct skeletonStructure * sk,unsigned int width ,unsigned int height) +{ + //Extra joints.. + float hipX=0.0; + float hipY=0.0; + int hipExists=0; + + float x,y,v; + std::vector result; + // MOCAPNET_UNCOMPRESSED_JOINT_HIP=0, //0 + x=(float) (sk->body.joint2D[BODY25_LHip].x + sk->body.joint2D[BODY25_RHip].x)/2; + y=(float) (sk->body.joint2D[BODY25_LHip].y + sk->body.joint2D[BODY25_RHip].y)/2; + if ( + ( (sk->body.joint2D[BODY25_LHip].x==0.0)&&(sk->body.joint2D[BODY25_LHip].y==0.0) ) || + ( (sk->body.joint2D[BODY25_RHip].x==0.0)&&(sk->body.joint2D[BODY25_RHip].y==0.0) ) + ) + { + v=0.0; + x=0.0; + y=0.0; + } + else + { + v=1.0; + hipX=x; + hipY=y; + hipExists=1; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //------------------------------------ + + + float chestX= (float) (sk->body.joint2D[BODY25_Neck].x + hipX)/2; + float chestY= (float) (sk->body.joint2D[BODY25_Neck].y + hipY)/2; + int chestNotVisible= (( (sk->body.joint2D[BODY25_Neck].x==0.0)&&(sk->body.joint2D[BODY25_Neck].y==0.0) ) || ( (hipX==0.0)&&(hipY==0.0) ) ); + + + + //MOCAPNET_UNCOMPRESSED_JOINT_ABDOMEN, //1 + x=(float) (sk->body.joint2D[BODY25_Neck].x + chestX)/2; + y=(float) (sk->body.joint2D[BODY25_Neck].y + chestY)/2; + if ( (chestNotVisible) || ((sk->body.joint2D[BODY25_Neck].x==0.0)&&(sk->body.joint2D[BODY25_Neck].y==0.0)) ) + { + v=0.0; + x=0.0; + y=0.0; + } + else + { + v=1.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //TODO: + //------------------------------------ + + //MOCAPNET_UNCOMPRESSED_JOINT_CHEST, //2 + x=chestX; + y=chestY; + if ( chestNotVisible ) + { + v=0.0; + x=0.0; + y=0.0; + } + else + { + v=1.0; + } + result.push_back(x); + result.push_back(y); + result.push_back(v); + //------------------------------------ + + + //MOCAPNET_UNCOMPRESSED_JOINT_NECK, //3 + addSkeletonJoint(sk,result,COCO_Neck); + //------------------------------------ + + //MOCAPNET_UNCOMPRESSED_JOINT_HEAD, //4 + // + int guessNose=1; + if (guessNose==1) + { + //TODO : The nose should only be visible in front + //Eyes the same.. I should also add this to the BVH -> 3D CSV conversions..! + + if (bothJointAreNotZero(sk,BODY25_LEar,BODY25_REar)) + { + addSkeletonJointFromTwoJoints(sk,result,BODY25_LEar,BODY25_REar); + } + else + if (bothJointAreNotZero(sk,BODY25_LEye,BODY25_REye)) + { + addSkeletonJointFromTwoJoints(sk,result,BODY25_LEye,BODY25_REye); + } else + { + addSkeletonJoint(sk,result,BODY25_Nose); + } + } + else + { + addSkeletonJoint(sk,result,BODY25_Nose); + } + //------------------------------------ + + //MOCAPNET_UNCOMPRESSED_JOINT_LEFTEYE, //5 + addSkeletonJoint(sk,result,BODY25_LEye); + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LEFTEYE, //6 + addSkeletonJoint(sk,result,BODY25_LEye); + + //MOCAPNET_UNCOMPRESSED_JOINT_RIGHTEYE, //7 + addSkeletonJoint(sk,result,BODY25_REye); + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RIGHTEYE, //8 + addSkeletonJoint(sk,result,BODY25_REye); + + //MOCAPNET_UNCOMPRESSED_JOINT_RCOLLAR, //9 + addSkeletonJointFromTwoJoints(sk,result,BODY25_Neck,BODY25_RShoulder); + //------------------------------ + + //MOCAPNET_UNCOMPRESSED_JOINT_RSHOULDER, //10 + addSkeletonJoint(sk,result,BODY25_RShoulder); + + //MOCAPNET_UNCOMPRESSED_JOINT_RELBOW, //11 + addSkeletonJoint(sk,result,BODY25_RElbow); + + //MOCAPNET_UNCOMPRESSED_JOINT_RHAND, //12 + addSkeletonJoint(sk,result,BODY25_RWrist); + + //MOCAPNET_UNCOMPRESSED_JOINT_RTHUMB1, //13 + //MOCAPNET_UNCOMPRESSED_JOINT_RTHUMB2, //14 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RTHUMB2, //15 + addRightFinger(sk,result,COCO_Hand_Thumb_1,COCO_Hand_Thumb_2,COCO_Hand_Thumb_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_RINDEX1, //16 + //MOCAPNET_UNCOMPRESSED_JOINT_RINDEX2, //17 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RINDEX2, //18 + addRightFinger(sk,result,COCO_Hand_Index_1,COCO_Hand_Index_2,COCO_Hand_Index_3); + + + //MOCAPNET_UNCOMPRESSED_JOINT_RMID1, //19 + //MOCAPNET_UNCOMPRESSED_JOINT_RMID2, //20 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RMID2, //21 + addRightFinger(sk,result,COCO_Hand_Middle_1,COCO_Hand_Middle_2,COCO_Hand_Middle_3); + + + //MOCAPNET_UNCOMPRESSED_JOINT_RRING1, //22 + //MOCAPNET_UNCOMPRESSED_JOINT_RRING2, //23 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RRING2, //24 + addRightFinger(sk,result,COCO_Hand_Ring_1,COCO_Hand_Ring_2,COCO_Hand_Ring_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_RPINKY1, //25 + //MOCAPNET_UNCOMPRESSED_JOINT_RPINKY2, //26 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RPINKY2, //27 + addRightFinger(sk,result,COCO_Hand_Pinky_1,COCO_Hand_Pinky_2,COCO_Hand_Pinky_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_LCOLLAR, //28 + addSkeletonJointFromTwoJoints(sk,result,BODY25_Neck,BODY25_LShoulder); + + + //MOCAPNET_UNCOMPRESSED_JOINT_LSHOULDER, //29 + addSkeletonJoint(sk,result,BODY25_LShoulder); + + //MOCAPNET_UNCOMPRESSED_JOINT_LELBOW, //30 + addSkeletonJoint(sk,result,BODY25_LElbow); + + //MOCAPNET_UNCOMPRESSED_JOINT_LHAND, //31 + addSkeletonJoint(sk,result,BODY25_LWrist); + + //MOCAPNET_UNCOMPRESSED_JOINT_LTHUMB1, //32 + //MOCAPNET_UNCOMPRESSED_JOINT_LTHUMB2, //33 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LTHUMB2, //34 + addLeftFinger(sk,result,COCO_Hand_Thumb_1,COCO_Hand_Thumb_2,COCO_Hand_Thumb_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_LINDEX1, //35 + //MOCAPNET_UNCOMPRESSED_JOINT_LINDEX2, //36 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LINDEX2, //37 + addLeftFinger(sk,result,COCO_Hand_Index_1,COCO_Hand_Index_2,COCO_Hand_Index_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_LMID1, //38 + //MOCAPNET_UNCOMPRESSED_JOINT_LMID2, //39 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LMID2, //40 + addLeftFinger(sk,result,COCO_Hand_Middle_1,COCO_Hand_Middle_2,COCO_Hand_Middle_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_LRING1, //41 + //MOCAPNET_UNCOMPRESSED_JOINT_LRING2, //42 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LRING2, //43 + addLeftFinger(sk,result,COCO_Hand_Ring_1,COCO_Hand_Ring_2,COCO_Hand_Ring_3); + + //MOCAPNET_UNCOMPRESSED_JOINT_LPINKY1, //44 + //MOCAPNET_UNCOMPRESSED_JOINT_LPINKY2, //45 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LPINKY2, //46 + addLeftFinger(sk,result,COCO_Hand_Pinky_1,COCO_Hand_Pinky_2,COCO_Hand_Pinky_3); + + + //MOCAPNET_UNCOMPRESSED_JOINT_RBUTTOCK, //47 + //MOCAPNET_UNCOMPRESSED_JOINT_RHIP, //48 + addSkeletonJoint(sk,result,BODY25_RHip); + addSkeletonJoint(sk,result,BODY25_RHip); + + //MOCAPNET_UNCOMPRESSED_JOINT_RKNEE, //49 + addSkeletonJoint(sk,result,BODY25_RKnee); + + //MOCAPNET_UNCOMPRESSED_JOINT_RFOOT, //50 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_RFOOT, //51 + addSkeletonJoint(sk,result,BODY25_RAnkle); + addSkeletonJoint(sk,result,BODY25_RAnkle); + + //MOCAPNET_UNCOMPRESSED_JOINT_LBUTTOCK, //52 + //MOCAPNET_UNCOMPRESSED_JOINT_LHIP, //53 + addSkeletonJoint(sk,result,BODY25_LHip); + addSkeletonJoint(sk,result,BODY25_LHip); + + //MOCAPNET_UNCOMPRESSED_JOINT_LKNEE, //54 + addSkeletonJoint(sk,result,BODY25_LKnee); + + //MOCAPNET_UNCOMPRESSED_JOINT_LFOOT, //55 + //MOCAPNET_UNCOMPRESSED_JOINT_ES_LFOOT, //56 + addSkeletonJoint(sk,result,BODY25_LAnkle); + addSkeletonJoint(sk,result,BODY25_LAnkle); + + + + + + + //Last sanity check..! + //----------------------------------------------------------- + for (int i=0; i bvhFrame,unsigned int width ,unsigned int height) +{ + std::vector > bvhFrame2DOutput = convertBVHFrameTo2DPoints(bvhFrame); //,width,height + + const int INVALID_JOINT=6666; + fprintf(stderr,"Converting BVH frame to a SkeletonCOCO assuming a %ux%u frame\n",width,height); + for (unsigned int jointID=0; jointIDbody.joint2D[jointTargetID].x=(float) bvhFrame2DOutput[jointID][0]; + sk->body.joint2D[jointTargetID].y=(float) bvhFrame2DOutput[jointID][1]; + fprintf(stderr,"-> %0.2f,%0.2f \n",sk->body.joint2D[jointTargetID].x,sk->body.joint2D[jointTargetID].y); + } + /* + , //, + , //, + BODY25_Nose, //, + BODY25_Nose, //, + BODY25_REye, //BODY25_REar, + BODY25_LEye, //BODY25_LEar, + BODY25_LHeel, //BODY25_LBigToe, + BODY25_LBigToe, //BODY25_LSmallToe, + BODY25_LAnkle, //BODY25_LHeel, + BODY25_RHeel, //BODY25_RBigToe, + BODY25_RBigToe, //BODY25_RSmallToe, + BODY25_RAnkle, // BODY25_RHeel, + BODY25_Bkg //BODY25_Bkg + */ + + + } + + return 0; +} \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonMocapNETHelpers.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonMocapNETHelpers.hpp new file mode 100644 index 0000000..0f9195b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonMocapNETHelpers.hpp @@ -0,0 +1,46 @@ +#pragma once +/** @file jsonMocapNETHelpers.hpp + * @brief This file contains helpers to facilitate conversion between different skeleton formats. + * @author Ammar Qammaz (AmmarkoV) + */ +#include "jsonRead.hpp" +#include + + +void addSkeletonJointFromTwoJoints( + struct skeletonStructure * sk, + std::vector &result, + int jointIDA, + int jointIDB +); + + +void addSkeletonJoint( + struct skeletonStructure * sk, + std::vector &result, + int jointID +); + +void addRightFinger( + struct skeletonStructure * sk, + std::vector &result, + int fingerJointA, + int fingerJointB, + int fingerJointC +); + + + +void addLeftFinger( + struct skeletonStructure * sk, + std::vector &result, + int fingerJointA, + int fingerJointB, + int fingerJointC +); + +std::vector flattenskeletonCOCOToVector(struct skeletonStructure * sk,unsigned int width ,unsigned int height); + + + +int convertBVHFrameToSkeletonCOCO(struct skeletonStructure * sk,std::vector bvhFrame,unsigned int width ,unsigned int height); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonRead.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonRead.cpp new file mode 100644 index 0000000..591d4a0 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonRead.cpp @@ -0,0 +1,262 @@ +#include +#include +#include +#include +#include + + +#include "jsonRead.hpp" +#include "../../../../dependencies/InputParser/InputParser_C.h" + + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +float checkSkeletonDistance(struct skeletonStructure * PreviousSkelA,struct skeletonStructure * skelB) +{ + float score=0.0f,xSq=0.0f,ySq=0.0f; + for (int i=0; ibody.joint2D[i].x==0) && + (skelB->body.joint2D[i].y==0) + ) + { + score+=1000;// Penalize holes.. + } + else + { + xSq = PreviousSkelA->body.joint2D[i].x - skelB->body.joint2D[i].x ; + xSq = xSq * xSq; + ySq = PreviousSkelA->body.joint2D[i].y - skelB->body.joint2D[i].y; + ySq = ySq * ySq; + + score+=sqrt(xSq+ySq); + } + } + + return score; +} + +int parseJsonCOCOSkeleton(const char * filename , struct skeletonStructure * skel,float acceptableThreshold,unsigned int frameID) +{ + //memset(skel,0,sizeof(struct skeletonStructure)); + + ssize_t read; + + FILE * fp = fopen(filename,"r"); + if (fp!=0) + { + fprintf(stderr,"Parsing 2D skeleton from %s \n",filename); + struct InputParserC * ipc = InputParser_Create(2048,3); + InputParser_SetDelimeter(ipc,0,','); + InputParser_SetDelimeter(ipc,1,','); + + char * line = NULL; + size_t len = 0; + + /* + {"version":1.2,"people":[{"pose_keypoints_2d":[1051.33,389.167,0.90388,1054.25,474.35,0.834839,995.279,477.271,0.840843,977.646,592.078,0.843254,974.836,692.082,0.925783,1119.04,465.536,0.857748,1148.35,574.318,0.82055,1163.13,671.663,0.926104,1065.89,668.604,0.768351,1021.83,668.697,0.789775,998.248,833.448,0.891052,954.023,974.726,0.895817,1104.23,668.623,0.725152,1092.4,833.417,0.892817,1068.87,954.203,0.908514,1042.39,380.222,0.879022,1069.03,380.207,0.905886,1021.77,388.906,0.874647,1092.43,388.924,0.880871,1095.43,1001.27,0.875549,1110.14,995.311,0.873654,1048.35,968.843,0.832852,954.222,1024.87,0.826879,933.585,1021.78,0.818463,951.109,980.601,0.70936],"face_keypoints_2d":[1021.85,379.364,0.312111,1022.56,385.04,0.388846,1022.92,394.265,0.400321,1025.04,404.553,0.559545,1027.88,413.423,0.583821,1031.79,421.938,0.61052,1037.46,429.389,0.609643,1044.2,434.001,0.630441,1051.3,435.775,0.729202,1059.81,435.065,0.657737,1067.97,430.098,0.685271,1074.36,423.003,0.685615,1078.97,415.197,0.731754,1081.81,406.327,0.712712,1082.17,397.103,0.697536,1082.88,388.233,0.6253,1083.94,380.073,0.518587,1022.56,373.332,0.47997,1027.88,370.494,0.637251,1033.91,369.784,0.717338,1041.72,370.139,0.853094,1047.75,372.977,0.90591,1056.27,373.332,0.889469,1062.3,370.494,0.892274,1068.33,370.494,0.831293,1074.36,371.558,0.73095,1078.97,374.751,0.598676,1051.65,380.073,0.765338,1050.94,386.459,0.77752,1050.94,392.491,0.767245,1050.94,398.167,0.696292,1044.56,401.006,0.712432,1047.75,401.715,0.848516,1050.94,403.134,0.929941,1054.85,402.07,0.957662,1058.75,401.36,0.981191,1030.01,378.299,0.420897,1033.91,378.654,0.380504,1037.11,378.654,0.393703,1039.95,379.009,0.464231,1036.75,379.009,0.458442,1033.56,379.009,0.404015,1062.3,381.847,0.749875,1065.49,381.492,0.641693,1069.39,381.137,0.495781,1072.94,381.492,0.501538,1069.75,381.847,0.518428,1065.49,382.202,0.611457,1038.53,408.456,0.72183,1042.78,407.747,0.864191,1048.11,407.392,0.85783,1051.3,408.456,0.80366,1054.85,407.392,0.833569,1060.17,408.456,0.880244,1066.2,410.23,0.902783,1061.94,417.326,0.928561,1056.27,420.519,0.899355,1051.3,420.874,0.923864,1048.11,420.519,0.927453,1042.07,418.035,0.798128,1039.95,409.875,0.841352,1048.11,410.23,0.881547,1051.65,410.23,0.85485,1055.2,410.23,0.805752,1064.43,410.94,0.870964,1055.91,415.907,0.938425,1051.65,416.262,0.891374,1048.11,416.262,0.926013,1034.98,378.654,0.312223,1067.62,381.492,0.513061],"hand_left_keypoints_2d":[1165.94,680.642,0.466609,1156.44,688.082,0.629248,1153.13,701.72,0.761934,1153.13,715.359,0.819062,1151.89,726.518,0.775558,1168.84,711.64,0.671397,1168.84,728.172,0.799059,1163.88,737.264,0.725134,1160.16,742.224,0.736829,1172.97,711.64,0.702249,1171.73,728.172,0.808444,1166.77,738.091,0.642235,1161.4,742.224,0.604293,1173.38,710.813,0.666696,1172.97,724.865,0.61383,1166.77,732.718,0.536511,1163.88,740.157,0.497097,1172.56,708.747,0.597281,1172.14,721.559,0.59746,1167.18,727.345,0.381854,1163.88,729.411,0.33763],"hand_right_keypoints_2d":[976.039,702.5,0.411615,985.84,702.926,0.655772,996.066,713.152,0.692949,1002.03,726.362,0.772721,1009.7,733.605,0.864994,984.561,730.197,0.731554,988.822,745.536,0.812263,990.953,756.615,0.808444,991.805,765.137,0.836125,979.022,737.014,0.686607,982.005,749.798,0.65217,981.579,760.876,0.754628,979.448,767.694,0.727902,976.039,738.719,0.634819,977.317,749.798,0.69985,976.465,758.32,0.664762,975.187,765.137,0.690298,975.187,739.571,0.592209,975.187,748.093,0.492786,975.613,754.059,0.522497,974.761,760.45,0.43477],"pose_keypoints_3d":[],"face_keypoints_3d":[],"hand_left_keypoints_3d":[],"hand_right_keypoints_3d":[]}]} + */ + while ((read = getline(&line, &len, fp)) != -1) + { + skel->observationNumber=frameID; + + //We should have the whole output.. since it is one line + char * poseStart=0; + char * poseEnd=0; + //----------------------------------------------- + poseStart=strstr(line,"\"pose_keypoints_2d\":["); + if(poseStart!=0) + { + poseStart=strstr(poseStart,"[")+1; + poseEnd=strstr(poseStart,"]"); + } + //----------------------------------------------- + char * handLeftStart=0; + char * handLeftEnd=0; + handLeftStart=strstr(line,"\"hand_left_keypoints_2d\":["); + if (handLeftStart!=0) + { + handLeftStart=strstr(handLeftStart,"[")+1; + handLeftEnd=strstr(handLeftStart,"]"); + } + //----------------------------------------------- + char * handRightStart=0; + char * handRightEnd=0; + handRightStart=strstr(line,"\"hand_right_keypoints_2d\":["); + if (handRightStart!=0) + { + handRightStart=strstr(handRightStart,"[")+1; + handRightEnd=strstr(handRightStart,"]"); + } + //----------------------------------------------- + char * headStart=0; + char * headEnd=0; + headStart=strstr(line,"\"face_keypoints_2d\":["); + if (headStart!=0) + { + headStart=strstr(headStart,"[")+1; + headEnd=strstr(headStart,"]"); + } + //----------------------------------------------- + + //Null Terminate strings.. + //---------------------------------------------------------------------------------------------------------------- + if (poseEnd!=0) { *poseEnd=0; } + if (handLeftEnd!=0) { *handLeftEnd=0; } + if (handRightEnd!=0) { *handRightEnd=0; } + if (headEnd!=0) { *headEnd=0; } + //---------------------------------------------------------------------------------------------------------------- + + + //fprintf(stderr,"RHand : %s\n",handLeftStart); + //fprintf(stderr,"LHand : %s\n",handRightStart); + //fprintf(stderr,"Pose : %s\n",poseStart); + //fprintf(stderr,"Head : %s\n",headStart); + + int numberOfJoints; + float value; + + + + //---------------------------------------------------------------------------------------------------------------- + if (poseStart!=0) + { + numberOfJoints = InputParser_SeperateWords(ipc,poseStart,1)/3; + if (numberOfJoints>=BODY25_PARTS) + { + fprintf(stderr,RED "The number of joints found in JSON file (%u) is more than our BODY25 internal structure (%u)\n" NORMAL,numberOfJoints,BODY25_PARTS); + } else + { + if (numberOfJoints>0) { skel->body.isPopulated=1; } + for (int poseNum=0; poseNumbody.joint2D[poseNum].x = InputParser_GetWordFloat(ipc,poseNum*3+0); + //fprintf(stderr,"Pose%u x ( %u ) = %0.2f\n",poseNum,poseNum*3+0, skel->joint2D[poseNum].x ); + + skel->body.joint2D[poseNum].y = InputParser_GetWordFloat(ipc,poseNum*3+1); + //fprintf(stderr,"Pose%u y ( %u ) = %0.2f\n",poseNum,poseNum*3+1,skel->joint2D[poseNum].y); + + value = InputParser_GetWordFloat(ipc,poseNum*3+2); + if (value>1.0) + { + fprintf(stderr,"Warning : Too large value for accuracy\n"); + } + skel->body.jointAccuracy[poseNum] = value; + skel->body.active[poseNum] = (value>acceptableThreshold); + //fprintf(stderr,"Pose%u A ( %u ) = %0.2f\n",poseNum,poseNum*3+2,value); + } + } + } + //---------------------------------------------------------------------------------------------------------------- + + // Left Hand Parser + //---------------------------------------------------------------------------------------------------------------- + if (handLeftStart!=0) + { + numberOfJoints = InputParser_SeperateWords(ipc,handLeftStart,1)/3; + if (numberOfJoints>COCO_HAND_PARTS) + { + fprintf(stderr,RED "The number of left hand joints found in JSON file (%u) is more than our internal structure (%u)\n" NORMAL,numberOfJoints,COCO_HAND_PARTS); + } else + { + skel->leftHand.isRight=0; + skel->leftHand.isLeft=1; + if (numberOfJoints>0) { skel->leftHand.isPopulated=1; } + for (int poseNum=0; poseNumleftHand.joint2D[poseNum].x = value; + + value = InputParser_GetWordFloat(ipc,poseNum*3+1); + skel->leftHand.joint2D[poseNum].y = value; + + value = InputParser_GetWordFloat(ipc,poseNum*3+2); + skel->leftHand.jointAccuracy[poseNum] = value; + skel->leftHand.active[poseNum] = (value>acceptableThreshold); + } + } + } + + + // Right Hand Parser + //---------------------------------------------------------------------------------------------------------------- + if (handRightStart!=0) + { + numberOfJoints = InputParser_SeperateWords(ipc,handRightStart,1)/3; + if (numberOfJoints>COCO_HAND_PARTS) + { + fprintf(stderr,RED "The number of right hand joints found in JSON file (%u) is more than our internal structure (%u)\n" NORMAL,numberOfJoints,COCO_HAND_PARTS); + } else + { + skel->rightHand.isRight=1; + skel->rightHand.isLeft=0; + if (numberOfJoints>0) { skel->rightHand.isPopulated=1; } + for (int poseNum=0; poseNumrightHand.joint2D[poseNum].x = value; + + value = InputParser_GetWordFloat(ipc,poseNum*3+1); + skel->rightHand.joint2D[poseNum].y = value; + + value = InputParser_GetWordFloat(ipc,poseNum*3+2); + skel->rightHand.jointAccuracy[poseNum] = value; + skel->rightHand.active[poseNum] = (value>acceptableThreshold); + } + } + } + //---------------------------------------------------------------------------------------------------------------- + + + // Head Parser + //---------------------------------------------------------------------------------------------------------------- + if (headStart!=0) + { + numberOfJoints = InputParser_SeperateWords(ipc,headStart,1)/3; + if (numberOfJoints>OP_HEAD_PARTS) + { + fprintf(stderr,RED "The number of head joints found in JSON file (%u) is more than our internal structure (%u)\n" NORMAL,numberOfJoints,OP_HEAD_PARTS); + } else + { + if (numberOfJoints>0) { skel->head.isPopulated=1; } + for (int poseNum=0; poseNumhead.joint2D[poseNum].x = value; + + value = InputParser_GetWordFloat(ipc,poseNum*3+1); + skel->head.joint2D[poseNum].y = value; + + value = InputParser_GetWordFloat(ipc,poseNum*3+2); + skel->head.jointAccuracy[poseNum] = value; + skel->head.active[poseNum] = (value>acceptableThreshold); + } + } + } + //---------------------------------------------------------------------------------------------------------------- + + } + + InputParser_Destroy(ipc); + fclose(fp); + return 1; + } + else + { + fprintf(stderr,"Could not open 2D skeleton file %s \n",filename); + return 0; + } + + + fprintf(stderr,"Could not find COCO 2D skeleton in %s \n",filename); + return 0; +} + + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonRead.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonRead.hpp new file mode 100644 index 0000000..b1d66f4 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/jsonRead.hpp @@ -0,0 +1,19 @@ +#pragma once +/** @file jsonCocoSkeleton.hpp + * @brief This is the code needed to parse an OpenPose JSON file to our struct skeletonStructure. This JSON parser barely works for the specific JSON output and so should be treated with caution. + * It is not comformant to the JSON spec nor will it work for an arbitrary JSON file..! + * @author Ammar Qammaz (AmmarkoV) + */ + +#include + +#include "commonSkeleton.hpp" + +/** + * @brief Parse a JSON file and retrieve a skeleton + * @param Path to JSON file + * @param Pointer to a struct skeletonStructure that will hold the information loaded + * @param Threshold to set a joint to active ( 0.4-0.5 is a good value ) + * @retval 1=Success/0=Failure + */ +int parseJsonCOCOSkeleton(const char * filename , struct skeletonStructure * skel,float acceptableThreshold,unsigned int frameID); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonAbstraction.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonAbstraction.cpp new file mode 100644 index 0000000..c8a42e4 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonAbstraction.cpp @@ -0,0 +1,107 @@ +#include "skeletonAbstraction.hpp" + +#include "csvRead.hpp" + + +#include +#include +#include +#include +#include + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +#include //toupper +int strcasecmp_sk(const char * input1,const char * input2) +{ + #if CASE_SENSITIVE_OBJECT_NAMES + return strcmp(input1,input2); + #endif + + if ( (input1==0) || (input2==0) ) + { + fprintf(stderr,"Error , calling strcasecmp_internal with null parameters \n"); + return 1; + } + unsigned int len1 = strlen(input1); + unsigned int len2 = strlen(input2); + if (len1!=len2) + { + //mismatched lengths of strings , they can't be equal..! + return 1; + } + + char A; //<- character buffer for input1 + char B; //<- character buffer for input2 + unsigned int i=0; + while (iskeletonBodyElements; i++) + { + //If the specific label exists in our skeletonSerialized + if (skel->skeletonHeader[i].str!=0) + { + //The strcmp was flipped, thx to yangjituan for noticing this https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/57 + //This is a pretty inefficient function, at some point I need to restructure the skeletonSerialized structure to an enum like + //https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/src/MocapNET2/MocapNETLib2/mocapnet2.hpp#L839 + //it would just be 4 floating point checks instead of this loop going through labels etc. + + //If we are at the correct skeletonSerialized label for the particular r/l foot or knee and check its value and it is non-zero + //we can count it as present in the list of activeLegJoints count the number of active joints.. + if ( (strcasecmp_sk(skel->skeletonHeader[i].str,"visible_rfoot")==0) && (skel->skeletonBody[i].value) ) { ++activeLegJoints; } else + if ( (strcasecmp_sk(skel->skeletonHeader[i].str,"visible_rknee")==0) && (skel->skeletonBody[i].value) ) { ++activeLegJoints; } else + if ( (strcasecmp_sk(skel->skeletonHeader[i].str,"visible_lfoot")==0) && (skel->skeletonBody[i].value) ) { ++activeLegJoints; } else + if ( (strcasecmp_sk(skel->skeletonHeader[i].str,"visible_lknee")==0) && (skel->skeletonBody[i].value) ) { ++activeLegJoints; } + } + } + + if (activeLegJoints<3) + { + fprintf(stderr,YELLOW "Feet are missing, Only %u leg joints detected..\n" NORMAL,activeLegJoints); + return 1; + } + return 0; +} + + + + +int isLeftHardExtended(std::vector result) +{ + //TODO: + return 0; +} + +int isRightHardExtended(std::vector result) +{ + //TODO: + return 0; +} + + +int getPointOrientation(std::vector result,float *x, float *y,float *r) +{ + //TODO: + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonAbstraction.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonAbstraction.hpp new file mode 100644 index 0000000..1b96363 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonAbstraction.hpp @@ -0,0 +1,16 @@ +#pragma once + + +#include "commonSkeleton.hpp" +#include + + + +int areFeetMissing(struct skeletonSerialized * skel); + + +int isLeftHardExtended(std::vector result); +int isRightHardExtended(std::vector result); + + +int getPointOrientation(std::vector result,float *x, float *y,float *r); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonSerializedToBVHTransform.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonSerializedToBVHTransform.hpp new file mode 100644 index 0000000..a454809 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/IO/skeletonSerializedToBVHTransform.hpp @@ -0,0 +1,1401 @@ +#pragma once + + +#if USE_BVH +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/bvh_loader.h" +#include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/src/Library/MotionCaptureLoader/calculate/bvh_transform.h" +#endif // USE_BVH + +#include +#include + + +static const char * skeletonSerializedNames[]= +{ + "frameNumber", + "skeletonID", + "totalSkeletons", + "2dx_head", + "2dy_head", + "visible_head", + "2dx_neck", + "2dy_neck", + "visible_neck", + "2dx_rshoulder", + "2dy_rshoulder", + "visible_rshoulder", + "2dx_relbow", + "2dy_relbow", + "visible_relbow", + "2dx_rhand", + "2dy_rhand", + "visible_rhand", + "2dx_lshoulder", + "2dy_lshoulder", + "visible_lshoulder", + "2dx_lelbow", + "2dy_lelbow", + "visible_lelbow", + "2dx_lhand", + "2dy_lhand", + "visible_lhand", + "2dx_hip", + "2dy_hip", + "visible_hip", + "2dx_rhip", + "2dy_rhip", + "visible_rhip", + "2dx_rknee", + "2dy_rknee", + "visible_rknee", + "2dx_rfoot", + "2dy_rfoot", + "visible_rfoot", + "2dx_lhip", + "2dy_lhip", + "visible_lhip", + "2dx_lknee", + "2dy_lknee", + "visible_lknee", + "2dx_lfoot", + "2dy_lfoot", + "visible_lfoot", + "2dx_endsite_eye.r", + "2dy_endsite_eye.r", + "visible_endsite_eye.r", + "2dx_endsite_eye.l", + "2dy_endsite_eye.l", + "visible_endsite_eye.l", + "2dx_rear", + "2dy_rear", + "visible_rear", + "2dx_lear", + "2dy_lear", + "visible_lear", + "2dx_endsite_toe1-2.l", + "2dy_endsite_toe1-2.l", + "visible_endsite_toe1-2.l", + "2dx_endsite_toe5-3.l", + "2dy_endsite_toe5-3.l", + "visible_endsite_toe5-3.l", + "2dx_lheel", + "2dy_lheel", + "visible_lheel", + "2dx_endsite_toe1-2.r", + "2dy_endsite_toe1-2.r", + "visible_endsite_toe1-2.r", + "2dx_endsite_toe5-3.r", + "2dy_endsite_toe5-3.r", + "visible_endsite_toe5-3.r", + "2dx_rheel", + "2dy_rheel", + "visible_rheel", + "2dx_bkg", + "2dy_bkg", + "visible_bkg", + "2dx_lhand", + "2dy_lhand", + "visible_lhand", + "2dx_lthumb", + "2dy_lthumb", + "visible_lthumb", + "2dx_finger1-2.l", + "2dy_finger1-2.l", + "visible_finger1-2.l", + "2dx_finger1-3.l", + "2dy_finger1-3.l", + "visible_finger1-3.l", + "2dx_endsite_finger1-3.l", + "2dy_endsite_finger1-3.l", + "visible_endsite_finger1-3.l", + "2dx_finger2-1.l", + "2dy_finger2-1.l", + "visible_finger2-1.l", + "2dx_finger2-2.l", + "2dy_finger2-2.l", + "visible_finger2-2.l", + "2dx_finger2-3.l", + "2dy_finger2-3.l", + "visible_finger2-3.l", + "2dx_endsite_finger2-3.l", + "2dy_endsite_finger2-3.l", + "visible_endsite_finger2-3.l", + "2dx_finger3-1.l", + "2dy_finger3-1.l", + "visible_finger3-1.l", + "2dx_finger3-2.l", + "2dy_finger3-2.l", + "visible_finger3-2.l", + "2dx_finger3-3.l", + "2dy_finger3-3.l", + "visible_finger3-3.l", + "2dx_endsite_finger3-3.l", + "2dy_endsite_finger3-3.l", + "visible_endsite_finger3-3.l", + "2dx_finger4-1.l", + "2dy_finger4-1.l", + "visible_finger4-1.l", + "2dx_finger4-2.l", + "2dy_finger4-2.l", + "visible_finger4-2.l", + "2dx_finger4-3.l", + "2dy_finger4-3.l", + "visible_finger4-3.l", + "2dx_endsite_finger4-3.l", + "2dy_endsite_finger4-3.l", + "visible_endsite_finger4-3.l", + "2dx_finger5-1.l", + "2dy_finger5-1.l", + "visible_finger5-1.l", + "2dx_finger5-2.l", + "2dy_finger5-2.l", + "visible_finger5-2.l", + "2dx_finger5-3.l", + "2dy_finger5-3.l", + "visible_finger5-3.l", + "2dx_endsite_finger5-3.l", + "2dy_endsite_finger5-3.l", + "visible_endsite_finger5-3.l", + "2dx_rhand", + "2dy_rhand", + "visible_rhand", + "2dx_rthumb", + "2dy_rthumb", + "visible_rthumb", + "2dx_finger1-2.r", + "2dy_finger1-2.r", + "visible_finger1-2.r", + "2dx_finger1-3.r", + "2dy_finger1-3.r", + "visible_finger1-3.r", + "2dx_endsite_finger1-3.r", + "2dy_endsite_finger1-3.r", + "visible_endsite_finger1-3.r", + "2dx_finger2-1.r", + "2dy_finger2-1.r", + "visible_finger2-1.r", + "2dx_finger2-2.r", + "2dy_finger2-2.r", + "visible_finger2-2.r", + "2dx_finger2-3.r", + "2dy_finger2-3.r", + "visible_finger2-3.r", + "2dx_endsite_finger2-3.r", + "2dy_endsite_finger2-3.r", + "visible_endsite_finger2-3.r", + "2dx_finger3-1.r", + "2dy_finger3-1.r", + "visible_finger3-1.r", + "2dx_finger3-2.r", + "2dy_finger3-2.r", + "visible_finger3-2.r", + "2dx_finger3-3.r", + "2dy_finger3-3.r", + "visible_finger3-3.r", + "2dx_endsite_finger3-3.r", + "2dy_endsite_finger3-3.r", + "visible_endsite_finger3-3.r", + "2dx_finger4-1.r", + "2dy_finger4-1.r", + "visible_finger4-1.r", + "2dx_finger4-2.r", + "2dy_finger4-2.r", + "visible_finger4-2.r", + "2dx_finger4-3.r", + "2dy_finger4-3.r", + "visible_finger4-3.r", + "2dx_endsite_finger4-3.r", + "2dy_endsite_finger4-3.r", + "visible_endsite_finger4-3.r", + "2dx_finger5-1.r", + "2dy_finger5-1.r", + "visible_finger5-1.r", + "2dx_finger5-2.r", + "2dy_finger5-2.r", + "visible_finger5-2.r", + "2dx_finger5-3.r", + "2dy_finger5-3.r", + "visible_finger5-3.r", + "2dx_endsite_finger5-3.r", + "2dy_endsite_finger5-3.r", + "visible_endsite_finger5-3.r", + "2dx_head_rchin_0", + "2dy_head_rchin_0", + "visible_head_rchin_0", + "2dx_head_rchin_1", + "2dy_head_rchin_1", + "visible_head_rchin_1", + "2dx_head_rchin_2", + "2dy_head_rchin_2", + "visible_head_rchin_2", + "2dx_head_rchin_3", + "2dy_head_rchin_3", + "visible_head_rchin_3", + "2dx_head_rchin_4", + "2dy_head_rchin_4", + "visible_head_rchin_4", + "2dx_head_rchin_5", + "2dy_head_rchin_5", + "visible_head_rchin_5", + "2dx_head_rchin_6", + "2dy_head_rchin_6", + "visible_head_rchin_6", + "2dx_head_rchin_7", + "2dy_head_rchin_7", + "visible_head_rchin_7", + "2dx_head_chin", + "2dy_head_chin", + "visible_head_chin", + "2dx_head_lchin_7", + "2dy_head_lchin_7", + "visible_head_lchin_7", + "2dx_head_lchin_6", + "2dy_head_lchin_6", + "visible_head_lchin_6", + "2dx_head_lchin_5", + "2dy_head_lchin_5", + "visible_head_lchin_5", + "2dx_head_lchin_4", + "2dy_head_lchin_4", + "visible_head_lchin_4", + "2dx_head_lchin_3", + "2dy_head_lchin_3", + "visible_head_lchin_3", + "2dx_head_lchin_2", + "2dy_head_lchin_2", + "visible_head_lchin_2", + "2dx_head_lchin_1", + "2dy_head_lchin_1", + "visible_head_lchin_1", + "2dx_head_lchin_0", + "2dy_head_lchin_0", + "visible_head_lchin_0", + "2dx_head_reyebrow_0", + "2dy_head_reyebrow_0", + "visible_head_reyebrow_0", + "2dx_head_reyebrow_1", + "2dy_head_reyebrow_1", + "visible_head_reyebrow_1", + "2dx_head_reyebrow_2", + "2dy_head_reyebrow_2", + "visible_head_reyebrow_2", + "2dx_head_reyebrow_3", + "2dy_head_reyebrow_3", + "visible_head_reyebrow_3", + "2dx_head_reyebrow_4", + "2dy_head_reyebrow_4", + "visible_head_reyebrow_4", + "2dx_head_leyebrow_4", + "2dy_head_leyebrow_4", + "visible_head_leyebrow_4", + "2dx_head_leyebrow_3", + "2dy_head_leyebrow_3", + "visible_head_leyebrow_3", + "2dx_head_leyebrow_2", + "2dy_head_leyebrow_2", + "visible_head_leyebrow_2", + "2dx_head_leyebrow_1", + "2dy_head_leyebrow_1", + "visible_head_leyebrow_1", + "2dx_head_leyebrow_0", + "2dy_head_leyebrow_0", + "visible_head_leyebrow_0", + "2dx_head_nosebone_0", + "2dy_head_nosebone_0", + "visible_head_nosebone_0", + "2dx_head_nosebone_1", + "2dy_head_nosebone_1", + "visible_head_nosebone_1", + "2dx_head_nosebone_2", + "2dy_head_nosebone_2", + "visible_head_nosebone_2", + "2dx_head_nosebone_3", + "2dy_head_nosebone_3", + "visible_head_nosebone_3", + "2dx_head_nostrills_0", + "2dy_head_nostrills_0", + "visible_head_nostrills_0", + "2dx_head_nostrills_1", + "2dy_head_nostrills_1", + "visible_head_nostrills_1", + "2dx_head_nostrills_2", + "2dy_head_nostrills_2", + "visible_head_nostrills_2", + "2dx_head_nostrills_3", + "2dy_head_nostrills_3", + "visible_head_nostrills_3", + "2dx_head_nostrills_4", + "2dy_head_nostrills_4", + "visible_head_nostrills_4", + "2dx_head_reye_0", + "2dy_head_reye_0", + "visible_head_reye_0", + "2dx_head_reye_1", + "2dy_head_reye_1", + "visible_head_reye_1", + "2dx_head_reye_2", + "2dy_head_reye_2", + "visible_head_reye_2", + "2dx_head_reye_3", + "2dy_head_reye_3", + "visible_head_reye_3", + "2dx_head_reye_4", + "2dy_head_reye_4", + "visible_head_reye_4", + "2dx_head_reye_5", + "2dy_head_reye_5", + "visible_head_reye_5", + "2dx_head_leye_0", + "2dy_head_leye_0", + "visible_head_leye_0", + "2dx_head_leye_1", + "2dy_head_leye_1", + "visible_head_leye_1", + "2dx_head_leye_2", + "2dy_head_leye_2", + "visible_head_leye_2", + "2dx_head_leye_3", + "2dy_head_leye_3", + "visible_head_leye_3", + "2dx_head_leye_4", + "2dy_head_leye_4", + "visible_head_leye_4", + "2dx_head_leye_5", + "2dy_head_leye_5", + "visible_head_leye_5", + "2dx_head_outmouth_0", + "2dy_head_outmouth_0", + "visible_head_outmouth_0", + "2dx_head_outmouth_1", + "2dy_head_outmouth_1", + "visible_head_outmouth_1", + "2dx_head_outmouth_2", + "2dy_head_outmouth_2", + "visible_head_outmouth_2", + "2dx_head_outmouth_3", + "2dy_head_outmouth_3", + "visible_head_outmouth_3", + "2dx_head_outmouth_4", + "2dy_head_outmouth_4", + "visible_head_outmouth_4", + "2dx_head_outmouth_5", + "2dy_head_outmouth_5", + "visible_head_outmouth_5", + "2dx_head_outmouth_6", + "2dy_head_outmouth_6", + "visible_head_outmouth_6", + "2dx_head_outmouth_7", + "2dy_head_outmouth_7", + "visible_head_outmouth_7", + "2dx_head_outmouth_8", + "2dy_head_outmouth_8", + "visible_head_outmouth_8", + "2dx_head_outmouth_9", + "2dy_head_outmouth_9", + "visible_head_outmouth_9", + "2dx_head_outmouth_10", + "2dy_head_outmouth_10", + "visible_head_outmouth_10", + "2dx_head_outmouth_11", + "2dy_head_outmouth_11", + "visible_head_outmouth_11", + "2dx_head_inmouth_0", + "2dy_head_inmouth_0", + "visible_head_inmouth_0", + "2dx_head_inmouth_1", + "2dy_head_inmouth_1", + "visible_head_inmouth_1", + "2dx_head_inmouth_2", + "2dy_head_inmouth_2", + "visible_head_inmouth_2", + "2dx_head_inmouth_3", + "2dy_head_inmouth_3", + "visible_head_inmouth_3", + "2dx_head_inmouth_4", + "2dy_head_inmouth_4", + "visible_head_inmouth_4", + "2dx_head_inmouth_5", + "2dy_head_inmouth_5", + "visible_head_inmouth_5", + "2dx_head_inmouth_6", + "2dy_head_inmouth_6", + "visible_head_inmouth_6", + "2dx_head_inmouth_7", + "2dy_head_inmouth_7", + "visible_head_inmouth_7", + "2dx_head_reye", + "2dy_head_reye", + "visible_head_reye", + "2dx_head_leye", + "2dy_head_leye", + "visible_head_leye", +"END" +}; + + +enum skeletonSerializedEnum +{ +SKELETON_SERIALIZED_FRAMENUMBER, +SKELETON_SERIALIZED_SKELETONID, +SKELETON_SERIALIZED_TOTALSKELETONS, +SKELETON_SERIALIZED_2DX_HEAD, +SKELETON_SERIALIZED_2DY_HEAD, +SKELETON_SERIALIZED_VISIBLE_HEAD, +SKELETON_SERIALIZED_2DX_NECK, +SKELETON_SERIALIZED_2DY_NECK, +SKELETON_SERIALIZED_VISIBLE_NECK, +SKELETON_SERIALIZED_2DX_RSHOULDER, +SKELETON_SERIALIZED_2DY_RSHOULDER, +SKELETON_SERIALIZED_VISIBLE_RSHOULDER, +SKELETON_SERIALIZED_2DX_RELBOW, +SKELETON_SERIALIZED_2DY_RELBOW, +SKELETON_SERIALIZED_VISIBLE_RELBOW, +SKELETON_SERIALIZED_2DX_RHAND, +SKELETON_SERIALIZED_2DY_RHAND, +SKELETON_SERIALIZED_VISIBLE_RHAND, +SKELETON_SERIALIZED_2DX_LSHOULDER, +SKELETON_SERIALIZED_2DY_LSHOULDER, +SKELETON_SERIALIZED_VISIBLE_LSHOULDER, +SKELETON_SERIALIZED_2DX_LELBOW, +SKELETON_SERIALIZED_2DY_LELBOW, +SKELETON_SERIALIZED_VISIBLE_LELBOW, +SKELETON_SERIALIZED_2DX_LHAND, +SKELETON_SERIALIZED_2DY_LHAND, +SKELETON_SERIALIZED_VISIBLE_LHAND, +SKELETON_SERIALIZED_2DX_HIP, +SKELETON_SERIALIZED_2DY_HIP, +SKELETON_SERIALIZED_VISIBLE_HIP, +SKELETON_SERIALIZED_2DX_RHIP, +SKELETON_SERIALIZED_2DY_RHIP, +SKELETON_SERIALIZED_VISIBLE_RHIP, +SKELETON_SERIALIZED_2DX_RKNEE, +SKELETON_SERIALIZED_2DY_RKNEE, +SKELETON_SERIALIZED_VISIBLE_RKNEE, +SKELETON_SERIALIZED_2DX_RFOOT, +SKELETON_SERIALIZED_2DY_RFOOT, +SKELETON_SERIALIZED_VISIBLE_RFOOT, +SKELETON_SERIALIZED_2DX_LHIP, +SKELETON_SERIALIZED_2DY_LHIP, +SKELETON_SERIALIZED_VISIBLE_LHIP, +SKELETON_SERIALIZED_2DX_LKNEE, +SKELETON_SERIALIZED_2DY_LKNEE, +SKELETON_SERIALIZED_VISIBLE_LKNEE, +SKELETON_SERIALIZED_2DX_LFOOT, +SKELETON_SERIALIZED_2DY_LFOOT, +SKELETON_SERIALIZED_VISIBLE_LFOOT, +SKELETON_SERIALIZED_2DX_ENDSITE_EYE_R, +SKELETON_SERIALIZED_2DY_ENDSITE_EYE_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_EYE_R, +SKELETON_SERIALIZED_2DX_ENDSITE_EYE_L, +SKELETON_SERIALIZED_2DY_ENDSITE_EYE_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_EYE_L, +SKELETON_SERIALIZED_2DX_REAR, +SKELETON_SERIALIZED_2DY_REAR, +SKELETON_SERIALIZED_VISIBLE_REAR, +SKELETON_SERIALIZED_2DX_LEAR, +SKELETON_SERIALIZED_2DY_LEAR, +SKELETON_SERIALIZED_VISIBLE_LEAR, +SKELETON_SERIALIZED_2DX_ENDSITE_TOE1_2_L, +SKELETON_SERIALIZED_2DY_ENDSITE_TOE1_2_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_TOE1_2_L, +SKELETON_SERIALIZED_2DX_ENDSITE_TOE5_3_L, +SKELETON_SERIALIZED_2DY_ENDSITE_TOE5_3_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_TOE5_3_L, +SKELETON_SERIALIZED_2DX_LHEEL, +SKELETON_SERIALIZED_2DY_LHEEL, +SKELETON_SERIALIZED_VISIBLE_LHEEL, +SKELETON_SERIALIZED_2DX_ENDSITE_TOE1_2_R, +SKELETON_SERIALIZED_2DY_ENDSITE_TOE1_2_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_TOE1_2_R, +SKELETON_SERIALIZED_2DX_ENDSITE_TOE5_3_R, +SKELETON_SERIALIZED_2DY_ENDSITE_TOE5_3_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_TOE5_3_R, +SKELETON_SERIALIZED_2DX_RHEEL, +SKELETON_SERIALIZED_2DY_RHEEL, +SKELETON_SERIALIZED_VISIBLE_RHEEL, +SKELETON_SERIALIZED_2DX_BKG, +SKELETON_SERIALIZED_2DY_BKG, +SKELETON_SERIALIZED_VISIBLE_BKG, +SKELETON_SERIALIZED_2DX_LHAND__, +SKELETON_SERIALIZED_2DY_LHAND__, +SKELETON_SERIALIZED_VISIBLE_LHAND__, +SKELETON_SERIALIZED_2DX_LTHUMB, +SKELETON_SERIALIZED_2DY_LTHUMB, +SKELETON_SERIALIZED_VISIBLE_LTHUMB, +SKELETON_SERIALIZED_2DX_FINGER1_2_L, +SKELETON_SERIALIZED_2DY_FINGER1_2_L, +SKELETON_SERIALIZED_VISIBLE_FINGER1_2_L, +SKELETON_SERIALIZED_2DX_FINGER1_3_L, +SKELETON_SERIALIZED_2DY_FINGER1_3_L, +SKELETON_SERIALIZED_VISIBLE_FINGER1_3_L, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER1_3_L, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER1_3_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER1_3_L, +SKELETON_SERIALIZED_2DX_FINGER2_1_L, +SKELETON_SERIALIZED_2DY_FINGER2_1_L, +SKELETON_SERIALIZED_VISIBLE_FINGER2_1_L, +SKELETON_SERIALIZED_2DX_FINGER2_2_L, +SKELETON_SERIALIZED_2DY_FINGER2_2_L, +SKELETON_SERIALIZED_VISIBLE_FINGER2_2_L, +SKELETON_SERIALIZED_2DX_FINGER2_3_L, +SKELETON_SERIALIZED_2DY_FINGER2_3_L, +SKELETON_SERIALIZED_VISIBLE_FINGER2_3_L, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER2_3_L, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER2_3_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER2_3_L, +SKELETON_SERIALIZED_2DX_FINGER3_1_L, +SKELETON_SERIALIZED_2DY_FINGER3_1_L, +SKELETON_SERIALIZED_VISIBLE_FINGER3_1_L, +SKELETON_SERIALIZED_2DX_FINGER3_2_L, +SKELETON_SERIALIZED_2DY_FINGER3_2_L, +SKELETON_SERIALIZED_VISIBLE_FINGER3_2_L, +SKELETON_SERIALIZED_2DX_FINGER3_3_L, +SKELETON_SERIALIZED_2DY_FINGER3_3_L, +SKELETON_SERIALIZED_VISIBLE_FINGER3_3_L, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER3_3_L, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER3_3_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER3_3_L, +SKELETON_SERIALIZED_2DX_FINGER4_1_L, +SKELETON_SERIALIZED_2DY_FINGER4_1_L, +SKELETON_SERIALIZED_VISIBLE_FINGER4_1_L, +SKELETON_SERIALIZED_2DX_FINGER4_2_L, +SKELETON_SERIALIZED_2DY_FINGER4_2_L, +SKELETON_SERIALIZED_VISIBLE_FINGER4_2_L, +SKELETON_SERIALIZED_2DX_FINGER4_3_L, +SKELETON_SERIALIZED_2DY_FINGER4_3_L, +SKELETON_SERIALIZED_VISIBLE_FINGER4_3_L, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER4_3_L, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER4_3_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER4_3_L, +SKELETON_SERIALIZED_2DX_FINGER5_1_L, +SKELETON_SERIALIZED_2DY_FINGER5_1_L, +SKELETON_SERIALIZED_VISIBLE_FINGER5_1_L, +SKELETON_SERIALIZED_2DX_FINGER5_2_L, +SKELETON_SERIALIZED_2DY_FINGER5_2_L, +SKELETON_SERIALIZED_VISIBLE_FINGER5_2_L, +SKELETON_SERIALIZED_2DX_FINGER5_3_L, +SKELETON_SERIALIZED_2DY_FINGER5_3_L, +SKELETON_SERIALIZED_VISIBLE_FINGER5_3_L, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER5_3_L, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER5_3_L, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER5_3_L, +SKELETON_SERIALIZED_2DX_RHAND__, +SKELETON_SERIALIZED_2DY_RHAND__, +SKELETON_SERIALIZED_VISIBLE_RHAND__, +SKELETON_SERIALIZED_2DX_RTHUMB, +SKELETON_SERIALIZED_2DY_RTHUMB, +SKELETON_SERIALIZED_VISIBLE_RTHUMB, +SKELETON_SERIALIZED_2DX_FINGER1_2_R, +SKELETON_SERIALIZED_2DY_FINGER1_2_R, +SKELETON_SERIALIZED_VISIBLE_FINGER1_2_R, +SKELETON_SERIALIZED_2DX_FINGER1_3_R, +SKELETON_SERIALIZED_2DY_FINGER1_3_R, +SKELETON_SERIALIZED_VISIBLE_FINGER1_3_R, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER1_3_R, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER1_3_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER1_3_R, +SKELETON_SERIALIZED_2DX_FINGER2_1_R, +SKELETON_SERIALIZED_2DY_FINGER2_1_R, +SKELETON_SERIALIZED_VISIBLE_FINGER2_1_R, +SKELETON_SERIALIZED_2DX_FINGER2_2_R, +SKELETON_SERIALIZED_2DY_FINGER2_2_R, +SKELETON_SERIALIZED_VISIBLE_FINGER2_2_R, +SKELETON_SERIALIZED_2DX_FINGER2_3_R, +SKELETON_SERIALIZED_2DY_FINGER2_3_R, +SKELETON_SERIALIZED_VISIBLE_FINGER2_3_R, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER2_3_R, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER2_3_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER2_3_R, +SKELETON_SERIALIZED_2DX_FINGER3_1_R, +SKELETON_SERIALIZED_2DY_FINGER3_1_R, +SKELETON_SERIALIZED_VISIBLE_FINGER3_1_R, +SKELETON_SERIALIZED_2DX_FINGER3_2_R, +SKELETON_SERIALIZED_2DY_FINGER3_2_R, +SKELETON_SERIALIZED_VISIBLE_FINGER3_2_R, +SKELETON_SERIALIZED_2DX_FINGER3_3_R, +SKELETON_SERIALIZED_2DY_FINGER3_3_R, +SKELETON_SERIALIZED_VISIBLE_FINGER3_3_R, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER3_3_R, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER3_3_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER3_3_R, +SKELETON_SERIALIZED_2DX_FINGER4_1_R, +SKELETON_SERIALIZED_2DY_FINGER4_1_R, +SKELETON_SERIALIZED_VISIBLE_FINGER4_1_R, +SKELETON_SERIALIZED_2DX_FINGER4_2_R, +SKELETON_SERIALIZED_2DY_FINGER4_2_R, +SKELETON_SERIALIZED_VISIBLE_FINGER4_2_R, +SKELETON_SERIALIZED_2DX_FINGER4_3_R, +SKELETON_SERIALIZED_2DY_FINGER4_3_R, +SKELETON_SERIALIZED_VISIBLE_FINGER4_3_R, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER4_3_R, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER4_3_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER4_3_R, +SKELETON_SERIALIZED_2DX_FINGER5_1_R, +SKELETON_SERIALIZED_2DY_FINGER5_1_R, +SKELETON_SERIALIZED_VISIBLE_FINGER5_1_R, +SKELETON_SERIALIZED_2DX_FINGER5_2_R, +SKELETON_SERIALIZED_2DY_FINGER5_2_R, +SKELETON_SERIALIZED_VISIBLE_FINGER5_2_R, +SKELETON_SERIALIZED_2DX_FINGER5_3_R, +SKELETON_SERIALIZED_2DY_FINGER5_3_R, +SKELETON_SERIALIZED_VISIBLE_FINGER5_3_R, +SKELETON_SERIALIZED_2DX_ENDSITE_FINGER5_3_R, +SKELETON_SERIALIZED_2DY_ENDSITE_FINGER5_3_R, +SKELETON_SERIALIZED_VISIBLE_ENDSITE_FINGER5_3_R, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_0, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_0, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_1, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_1, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_2, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_2, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_3, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_3, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_4, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_4, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_5, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_5, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_5, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_6, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_6, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_6, +SKELETON_SERIALIZED_2DX_HEAD_RCHIN_7, +SKELETON_SERIALIZED_2DY_HEAD_RCHIN_7, +SKELETON_SERIALIZED_VISIBLE_HEAD_RCHIN_7, +SKELETON_SERIALIZED_2DX_HEAD_CHIN, +SKELETON_SERIALIZED_2DY_HEAD_CHIN, +SKELETON_SERIALIZED_VISIBLE_HEAD_CHIN, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_7, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_7, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_7, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_6, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_6, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_6, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_5, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_5, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_5, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_4, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_4, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_3, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_3, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_2, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_2, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_1, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_1, +SKELETON_SERIALIZED_2DX_HEAD_LCHIN_0, +SKELETON_SERIALIZED_2DY_HEAD_LCHIN_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_LCHIN_0, +SKELETON_SERIALIZED_2DX_HEAD_REYEBROW_0, +SKELETON_SERIALIZED_2DY_HEAD_REYEBROW_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYEBROW_0, +SKELETON_SERIALIZED_2DX_HEAD_REYEBROW_1, +SKELETON_SERIALIZED_2DY_HEAD_REYEBROW_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYEBROW_1, +SKELETON_SERIALIZED_2DX_HEAD_REYEBROW_2, +SKELETON_SERIALIZED_2DY_HEAD_REYEBROW_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYEBROW_2, +SKELETON_SERIALIZED_2DX_HEAD_REYEBROW_3, +SKELETON_SERIALIZED_2DY_HEAD_REYEBROW_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYEBROW_3, +SKELETON_SERIALIZED_2DX_HEAD_REYEBROW_4, +SKELETON_SERIALIZED_2DY_HEAD_REYEBROW_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYEBROW_4, +SKELETON_SERIALIZED_2DX_HEAD_LEYEBROW_4, +SKELETON_SERIALIZED_2DY_HEAD_LEYEBROW_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYEBROW_4, +SKELETON_SERIALIZED_2DX_HEAD_LEYEBROW_3, +SKELETON_SERIALIZED_2DY_HEAD_LEYEBROW_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYEBROW_3, +SKELETON_SERIALIZED_2DX_HEAD_LEYEBROW_2, +SKELETON_SERIALIZED_2DY_HEAD_LEYEBROW_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYEBROW_2, +SKELETON_SERIALIZED_2DX_HEAD_LEYEBROW_1, +SKELETON_SERIALIZED_2DY_HEAD_LEYEBROW_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYEBROW_1, +SKELETON_SERIALIZED_2DX_HEAD_LEYEBROW_0, +SKELETON_SERIALIZED_2DY_HEAD_LEYEBROW_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYEBROW_0, +SKELETON_SERIALIZED_2DX_HEAD_NOSEBONE_0, +SKELETON_SERIALIZED_2DY_HEAD_NOSEBONE_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSEBONE_0, +SKELETON_SERIALIZED_2DX_HEAD_NOSEBONE_1, +SKELETON_SERIALIZED_2DY_HEAD_NOSEBONE_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSEBONE_1, +SKELETON_SERIALIZED_2DX_HEAD_NOSEBONE_2, +SKELETON_SERIALIZED_2DY_HEAD_NOSEBONE_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSEBONE_2, +SKELETON_SERIALIZED_2DX_HEAD_NOSEBONE_3, +SKELETON_SERIALIZED_2DY_HEAD_NOSEBONE_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSEBONE_3, +SKELETON_SERIALIZED_2DX_HEAD_NOSTRILLS_0, +SKELETON_SERIALIZED_2DY_HEAD_NOSTRILLS_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSTRILLS_0, +SKELETON_SERIALIZED_2DX_HEAD_NOSTRILLS_1, +SKELETON_SERIALIZED_2DY_HEAD_NOSTRILLS_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSTRILLS_1, +SKELETON_SERIALIZED_2DX_HEAD_NOSTRILLS_2, +SKELETON_SERIALIZED_2DY_HEAD_NOSTRILLS_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSTRILLS_2, +SKELETON_SERIALIZED_2DX_HEAD_NOSTRILLS_3, +SKELETON_SERIALIZED_2DY_HEAD_NOSTRILLS_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSTRILLS_3, +SKELETON_SERIALIZED_2DX_HEAD_NOSTRILLS_4, +SKELETON_SERIALIZED_2DY_HEAD_NOSTRILLS_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_NOSTRILLS_4, +SKELETON_SERIALIZED_2DX_HEAD_REYE_0, +SKELETON_SERIALIZED_2DY_HEAD_REYE_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE_0, +SKELETON_SERIALIZED_2DX_HEAD_REYE_1, +SKELETON_SERIALIZED_2DY_HEAD_REYE_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE_1, +SKELETON_SERIALIZED_2DX_HEAD_REYE_2, +SKELETON_SERIALIZED_2DY_HEAD_REYE_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE_2, +SKELETON_SERIALIZED_2DX_HEAD_REYE_3, +SKELETON_SERIALIZED_2DY_HEAD_REYE_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE_3, +SKELETON_SERIALIZED_2DX_HEAD_REYE_4, +SKELETON_SERIALIZED_2DY_HEAD_REYE_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE_4, +SKELETON_SERIALIZED_2DX_HEAD_REYE_5, +SKELETON_SERIALIZED_2DY_HEAD_REYE_5, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE_5, +SKELETON_SERIALIZED_2DX_HEAD_LEYE_0, +SKELETON_SERIALIZED_2DY_HEAD_LEYE_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE_0, +SKELETON_SERIALIZED_2DX_HEAD_LEYE_1, +SKELETON_SERIALIZED_2DY_HEAD_LEYE_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE_1, +SKELETON_SERIALIZED_2DX_HEAD_LEYE_2, +SKELETON_SERIALIZED_2DY_HEAD_LEYE_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE_2, +SKELETON_SERIALIZED_2DX_HEAD_LEYE_3, +SKELETON_SERIALIZED_2DY_HEAD_LEYE_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE_3, +SKELETON_SERIALIZED_2DX_HEAD_LEYE_4, +SKELETON_SERIALIZED_2DY_HEAD_LEYE_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE_4, +SKELETON_SERIALIZED_2DX_HEAD_LEYE_5, +SKELETON_SERIALIZED_2DY_HEAD_LEYE_5, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE_5, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_0, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_0, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_1, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_1, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_2, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_2, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_3, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_3, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_4, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_4, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_5, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_5, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_5, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_6, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_6, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_6, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_7, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_7, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_7, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_8, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_8, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_8, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_9, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_9, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_9, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_10, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_10, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_10, +SKELETON_SERIALIZED_2DX_HEAD_OUTMOUTH_11, +SKELETON_SERIALIZED_2DY_HEAD_OUTMOUTH_11, +SKELETON_SERIALIZED_VISIBLE_HEAD_OUTMOUTH_11, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_0, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_0, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_0, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_1, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_1, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_1, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_2, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_2, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_2, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_3, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_3, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_3, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_4, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_4, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_4, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_5, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_5, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_5, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_6, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_6, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_6, +SKELETON_SERIALIZED_2DX_HEAD_INMOUTH_7, +SKELETON_SERIALIZED_2DY_HEAD_INMOUTH_7, +SKELETON_SERIALIZED_VISIBLE_HEAD_INMOUTH_7, +SKELETON_SERIALIZED_2DX_HEAD_REYE, +SKELETON_SERIALIZED_2DY_HEAD_REYE, +SKELETON_SERIALIZED_VISIBLE_HEAD_REYE, +SKELETON_SERIALIZED_2DX_HEAD_LEYE, +SKELETON_SERIALIZED_2DY_HEAD_LEYE, +SKELETON_SERIALIZED_VISIBLE_HEAD_LEYE, +//------------------------------------ +SKELETON_SERIALIZED_LENGTH +}; + + +static const char * bvhMotionNames[]= +{ + "hip", + "abdomen", + "chest", + "neck", + "neck1", + "head", + "__jaw", + "jaw", + "special04", + "oris02", + "oris01", + "EndSite_oris01", + "oris06.l", + "oris07.l", + "EndSite_oris07.l", + "oris06.r", + "oris07.r", + "EndSite_oris07.r", + "tongue00", + "tongue01", + "tongue02", + "tongue03", + "__tongue04", + "tongue04", + "EndSite_tongue04", + "tongue07.l", + "EndSite_tongue07.l", + "tongue07.r", + "EndSite_tongue07.r", + "tongue06.l", + "EndSite_tongue06.l", + "tongue06.r", + "EndSite_tongue06.r", + "tongue05.l", + "EndSite_tongue05.l", + "tongue05.r", + "EndSite_tongue05.r", + "__levator02.l", + "levator02.l", + "levator03.l", + "levator04.l", + "levator05.l", + "EndSite_levator05.l", + "__levator02.r", + "levator02.r", + "levator03.r", + "levator04.r", + "levator05.r", + "EndSite_levator05.r", + "__special01", + "special01", + "oris04.l", + "oris03.l", + "EndSite_oris03.l", + "oris04.r", + "oris03.r", + "EndSite_oris03.r", + "oris06", + "oris05", + "EndSite_oris05", + "__special03", + "special03", + "__levator06.l", + "levator06.l", + "EndSite_levator06.l", + "__levator06.r", + "levator06.r", + "EndSite_levator06.r", + "special06.l", + "special05.l", + "eye.l", + "EndSite_eye.l", + "orbicularis03.l", + "EndSite_orbicularis03.l", + "orbicularis04.l", + "EndSite_orbicularis04.l", + "special06.r", + "special05.r", + "eye.r", + "EndSite_eye.r", + "orbicularis03.r", + "EndSite_orbicularis03.r", + "orbicularis04.r", + "EndSite_orbicularis04.r", + "__temporalis01.l", + "temporalis01.l", + "oculi02.l", + "oculi01.l", + "EndSite_oculi01.l", + "__temporalis01.r", + "temporalis01.r", + "oculi02.r", + "oculi01.r", + "EndSite_oculi01.r", + "__temporalis02.l", + "temporalis02.l", + "risorius02.l", + "risorius03.l", + "EndSite_risorius03.l", + "__temporalis02.r", + "temporalis02.r", + "risorius02.r", + "risorius03.r", + "EndSite_risorius03.r", + "rCollar", + "rShldr", + "rForeArm", + "rHand", + "metacarpal1.r", + "finger2-1.r", + "finger2-2.r", + "finger2-3.r", + "EndSite_finger2-3.r", + "metacarpal2.r", + "finger3-1.r", + "finger3-2.r", + "finger3-3.r", + "EndSite_finger3-3.r", + "__metacarpal3.r", + "metacarpal3.r", + "finger4-1.r", + "finger4-2.r", + "finger4-3.r", + "EndSite_finger4-3.r", + "__metacarpal4.r", + "metacarpal4.r", + "finger5-1.r", + "finger5-2.r", + "finger5-3.r", + "EndSite_finger5-3.r", + "__rthumb", + "rthumb", + "finger1-2.r", + "finger1-3.r", + "EndSite_finger1-3.r", + "lCollar", + "lShldr", + "lForeArm", + "lHand", + "metacarpal1.l", + "finger2-1.l", + "finger2-2.l", + "finger2-3.l", + "EndSite_finger2-3.l", + "metacarpal2.l", + "finger3-1.l", + "finger3-2.l", + "finger3-3.l", + "EndSite_finger3-3.l", + "__metacarpal3.l", + "metacarpal3.l", + "finger4-1.l", + "finger4-2.l", + "finger4-3.l", + "EndSite_finger4-3.l", + "__metacarpal4.l", + "metacarpal4.l", + "finger5-1.l", + "finger5-2.l", + "finger5-3.l", + "EndSite_finger5-3.l", + "__lthumb", + "lthumb", + "finger1-2.l", + "finger1-3.l", + "EndSite_finger1-3.l", + "rButtock", + "rThigh", + "rShin", + "rFoot", + "toe1-1.R", + "toe1-2.R", + "EndSite_toe1-2.R", + "toe2-1.R", + "toe2-2.R", + "toe2-3.R", + "EndSite_toe2-3.R", + "toe3-1.R", + "toe3-2.R", + "toe3-3.R", + "EndSite_toe3-3.R", + "toe4-1.R", + "toe4-2.R", + "toe4-3.R", + "EndSite_toe4-3.R", + "toe5-1.R", + "toe5-2.R", + "toe5-3.R", + "EndSite_toe5-3.R", + "lButtock", + "lThigh", + "lShin", + "lFoot", + "toe1-1.L", + "toe1-2.L", + "EndSite_toe1-2.L", + "toe2-1.L", + "toe2-2.L", + "toe2-3.L", + "EndSite_toe2-3.L", + "toe3-1.L", + "toe3-2.L", + "toe3-3.L", + "EndSite_toe3-3.L", + "toe4-1.L", + "toe4-2.L", + "toe4-3.L", + "EndSite_toe4-3.L", + "toe5-1.L", + "toe5-2.L", + "toe5-3.L", + "EndSite_toe5-3.L", +"END" +}; + + +enum bvhMotionEnum +{ +BVH_MOTION_HIP, //0/212 +BVH_MOTION_ABDOMEN, //1/212 +BVH_MOTION_CHEST, //2/212 +BVH_MOTION_NECK, //3/212 +BVH_MOTION_NECK1, //4/212 +BVH_MOTION_HEAD, //5/212 +BVH_MOTION___JAW, //6/212 +BVH_MOTION_JAW, //7/212 +BVH_MOTION_SPECIAL04, //8/212 +BVH_MOTION_ORIS02, //9/212 +BVH_MOTION_ORIS01, //10/212 +BVH_MOTION_ENDSITE_ORIS01, //11/212 +BVH_MOTION_ORIS06_L, //12/212 +BVH_MOTION_ORIS07_L, //13/212 +BVH_MOTION_ENDSITE_ORIS07_L, //14/212 +BVH_MOTION_ORIS06_R, //15/212 +BVH_MOTION_ORIS07_R, //16/212 +BVH_MOTION_ENDSITE_ORIS07_R, //17/212 +BVH_MOTION_TONGUE00, //18/212 +BVH_MOTION_TONGUE01, //19/212 +BVH_MOTION_TONGUE02, //20/212 +BVH_MOTION_TONGUE03, //21/212 +BVH_MOTION___TONGUE04, //22/212 +BVH_MOTION_TONGUE04, //23/212 +BVH_MOTION_ENDSITE_TONGUE04, //24/212 +BVH_MOTION_TONGUE07_L, //25/212 +BVH_MOTION_ENDSITE_TONGUE07_L, //26/212 +BVH_MOTION_TONGUE07_R, //27/212 +BVH_MOTION_ENDSITE_TONGUE07_R, //28/212 +BVH_MOTION_TONGUE06_L, //29/212 +BVH_MOTION_ENDSITE_TONGUE06_L, //30/212 +BVH_MOTION_TONGUE06_R, //31/212 +BVH_MOTION_ENDSITE_TONGUE06_R, //32/212 +BVH_MOTION_TONGUE05_L, //33/212 +BVH_MOTION_ENDSITE_TONGUE05_L, //34/212 +BVH_MOTION_TONGUE05_R, //35/212 +BVH_MOTION_ENDSITE_TONGUE05_R, //36/212 +BVH_MOTION___LEVATOR02_L, //37/212 +BVH_MOTION_LEVATOR02_L, //38/212 +BVH_MOTION_LEVATOR03_L, //39/212 +BVH_MOTION_LEVATOR04_L, //40/212 +BVH_MOTION_LEVATOR05_L, //41/212 +BVH_MOTION_ENDSITE_LEVATOR05_L, //42/212 +BVH_MOTION___LEVATOR02_R, //43/212 +BVH_MOTION_LEVATOR02_R, //44/212 +BVH_MOTION_LEVATOR03_R, //45/212 +BVH_MOTION_LEVATOR04_R, //46/212 +BVH_MOTION_LEVATOR05_R, //47/212 +BVH_MOTION_ENDSITE_LEVATOR05_R, //48/212 +BVH_MOTION___SPECIAL01, //49/212 +BVH_MOTION_SPECIAL01, //50/212 +BVH_MOTION_ORIS04_L, //51/212 +BVH_MOTION_ORIS03_L, //52/212 +BVH_MOTION_ENDSITE_ORIS03_L, //53/212 +BVH_MOTION_ORIS04_R, //54/212 +BVH_MOTION_ORIS03_R, //55/212 +BVH_MOTION_ENDSITE_ORIS03_R, //56/212 +BVH_MOTION_ORIS06, //57/212 +BVH_MOTION_ORIS05, //58/212 +BVH_MOTION_ENDSITE_ORIS05, //59/212 +BVH_MOTION___SPECIAL03, //60/212 +BVH_MOTION_SPECIAL03, //61/212 +BVH_MOTION___LEVATOR06_L, //62/212 +BVH_MOTION_LEVATOR06_L, //63/212 +BVH_MOTION_ENDSITE_LEVATOR06_L, //64/212 +BVH_MOTION___LEVATOR06_R, //65/212 +BVH_MOTION_LEVATOR06_R, //66/212 +BVH_MOTION_ENDSITE_LEVATOR06_R, //67/212 +BVH_MOTION_SPECIAL06_L, //68/212 +BVH_MOTION_SPECIAL05_L, //69/212 +BVH_MOTION_EYE_L, //70/212 +BVH_MOTION_ENDSITE_EYE_L, //71/212 +BVH_MOTION_ORBICULARIS03_L, //72/212 +BVH_MOTION_ENDSITE_ORBICULARIS03_L, //73/212 +BVH_MOTION_ORBICULARIS04_L, //74/212 +BVH_MOTION_ENDSITE_ORBICULARIS04_L, //75/212 +BVH_MOTION_SPECIAL06_R, //76/212 +BVH_MOTION_SPECIAL05_R, //77/212 +BVH_MOTION_EYE_R, //78/212 +BVH_MOTION_ENDSITE_EYE_R, //79/212 +BVH_MOTION_ORBICULARIS03_R, //80/212 +BVH_MOTION_ENDSITE_ORBICULARIS03_R, //81/212 +BVH_MOTION_ORBICULARIS04_R, //82/212 +BVH_MOTION_ENDSITE_ORBICULARIS04_R, //83/212 +BVH_MOTION___TEMPORALIS01_L, //84/212 +BVH_MOTION_TEMPORALIS01_L, //85/212 +BVH_MOTION_OCULI02_L, //86/212 +BVH_MOTION_OCULI01_L, //87/212 +BVH_MOTION_ENDSITE_OCULI01_L, //88/212 +BVH_MOTION___TEMPORALIS01_R, //89/212 +BVH_MOTION_TEMPORALIS01_R, //90/212 +BVH_MOTION_OCULI02_R, //91/212 +BVH_MOTION_OCULI01_R, //92/212 +BVH_MOTION_ENDSITE_OCULI01_R, //93/212 +BVH_MOTION___TEMPORALIS02_L, //94/212 +BVH_MOTION_TEMPORALIS02_L, //95/212 +BVH_MOTION_RISORIUS02_L, //96/212 +BVH_MOTION_RISORIUS03_L, //97/212 +BVH_MOTION_ENDSITE_RISORIUS03_L, //98/212 +BVH_MOTION___TEMPORALIS02_R, //99/212 +BVH_MOTION_TEMPORALIS02_R, //100/212 +BVH_MOTION_RISORIUS02_R, //101/212 +BVH_MOTION_RISORIUS03_R, //102/212 +BVH_MOTION_ENDSITE_RISORIUS03_R, //103/212 +BVH_MOTION_RCOLLAR, //104/212 +BVH_MOTION_RSHLDR, //105/212 +BVH_MOTION_RFOREARM, //106/212 +BVH_MOTION_RHAND, //107/212 +BVH_MOTION_METACARPAL1_R, //108/212 +BVH_MOTION_FINGER2_1_R, //109/212 +BVH_MOTION_FINGER2_2_R, //110/212 +BVH_MOTION_FINGER2_3_R, //111/212 +BVH_MOTION_ENDSITE_FINGER2_3_R, //112/212 +BVH_MOTION_METACARPAL2_R, //113/212 +BVH_MOTION_FINGER3_1_R, //114/212 +BVH_MOTION_FINGER3_2_R, //115/212 +BVH_MOTION_FINGER3_3_R, //116/212 +BVH_MOTION_ENDSITE_FINGER3_3_R, //117/212 +BVH_MOTION___METACARPAL3_R, //118/212 +BVH_MOTION_METACARPAL3_R, //119/212 +BVH_MOTION_FINGER4_1_R, //120/212 +BVH_MOTION_FINGER4_2_R, //121/212 +BVH_MOTION_FINGER4_3_R, //122/212 +BVH_MOTION_ENDSITE_FINGER4_3_R, //123/212 +BVH_MOTION___METACARPAL4_R, //124/212 +BVH_MOTION_METACARPAL4_R, //125/212 +BVH_MOTION_FINGER5_1_R, //126/212 +BVH_MOTION_FINGER5_2_R, //127/212 +BVH_MOTION_FINGER5_3_R, //128/212 +BVH_MOTION_ENDSITE_FINGER5_3_R, //129/212 +BVH_MOTION___RTHUMB, //130/212 +BVH_MOTION_RTHUMB, //131/212 +BVH_MOTION_FINGER1_2_R, //132/212 +BVH_MOTION_FINGER1_3_R, //133/212 +BVH_MOTION_ENDSITE_FINGER1_3_R, //134/212 +BVH_MOTION_LCOLLAR, //135/212 +BVH_MOTION_LSHLDR, //136/212 +BVH_MOTION_LFOREARM, //137/212 +BVH_MOTION_LHAND, //138/212 +BVH_MOTION_METACARPAL1_L, //139/212 +BVH_MOTION_FINGER2_1_L, //140/212 +BVH_MOTION_FINGER2_2_L, //141/212 +BVH_MOTION_FINGER2_3_L, //142/212 +BVH_MOTION_ENDSITE_FINGER2_3_L, //143/212 +BVH_MOTION_METACARPAL2_L, //144/212 +BVH_MOTION_FINGER3_1_L, //145/212 +BVH_MOTION_FINGER3_2_L, //146/212 +BVH_MOTION_FINGER3_3_L, //147/212 +BVH_MOTION_ENDSITE_FINGER3_3_L, //148/212 +BVH_MOTION___METACARPAL3_L, //149/212 +BVH_MOTION_METACARPAL3_L, //150/212 +BVH_MOTION_FINGER4_1_L, //151/212 +BVH_MOTION_FINGER4_2_L, //152/212 +BVH_MOTION_FINGER4_3_L, //153/212 +BVH_MOTION_ENDSITE_FINGER4_3_L, //154/212 +BVH_MOTION___METACARPAL4_L, //155/212 +BVH_MOTION_METACARPAL4_L, //156/212 +BVH_MOTION_FINGER5_1_L, //157/212 +BVH_MOTION_FINGER5_2_L, //158/212 +BVH_MOTION_FINGER5_3_L, //159/212 +BVH_MOTION_ENDSITE_FINGER5_3_L, //160/212 +BVH_MOTION___LTHUMB, //161/212 +BVH_MOTION_LTHUMB, //162/212 +BVH_MOTION_FINGER1_2_L, //163/212 +BVH_MOTION_FINGER1_3_L, //164/212 +BVH_MOTION_ENDSITE_FINGER1_3_L, //165/212 +BVH_MOTION_RBUTTOCK, //166/212 +BVH_MOTION_RTHIGH, //167/212 +BVH_MOTION_RSHIN, //168/212 +BVH_MOTION_RFOOT, //169/212 +BVH_MOTION_TOE1_1_R, //170/212 +BVH_MOTION_TOE1_2_R, //171/212 +BVH_MOTION_ENDSITE_TOE1_2_R, //172/212 +BVH_MOTION_TOE2_1_R, //173/212 +BVH_MOTION_TOE2_2_R, //174/212 +BVH_MOTION_TOE2_3_R, //175/212 +BVH_MOTION_ENDSITE_TOE2_3_R, //176/212 +BVH_MOTION_TOE3_1_R, //177/212 +BVH_MOTION_TOE3_2_R, //178/212 +BVH_MOTION_TOE3_3_R, //179/212 +BVH_MOTION_ENDSITE_TOE3_3_R, //180/212 +BVH_MOTION_TOE4_1_R, //181/212 +BVH_MOTION_TOE4_2_R, //182/212 +BVH_MOTION_TOE4_3_R, //183/212 +BVH_MOTION_ENDSITE_TOE4_3_R, //184/212 +BVH_MOTION_TOE5_1_R, //185/212 +BVH_MOTION_TOE5_2_R, //186/212 +BVH_MOTION_TOE5_3_R, //187/212 +BVH_MOTION_ENDSITE_TOE5_3_R, //188/212 +BVH_MOTION_LBUTTOCK, //189/212 +BVH_MOTION_LTHIGH, //190/212 +BVH_MOTION_LSHIN, //191/212 +BVH_MOTION_LFOOT, //192/212 +BVH_MOTION_TOE1_1_L, //193/212 +BVH_MOTION_TOE1_2_L, //194/212 +BVH_MOTION_ENDSITE_TOE1_2_L, //195/212 +BVH_MOTION_TOE2_1_L, //196/212 +BVH_MOTION_TOE2_2_L, //197/212 +BVH_MOTION_TOE2_3_L, //198/212 +BVH_MOTION_ENDSITE_TOE2_3_L, //199/212 +BVH_MOTION_TOE3_1_L, //200/212 +BVH_MOTION_TOE3_2_L, //201/212 +BVH_MOTION_TOE3_3_L, //202/212 +BVH_MOTION_ENDSITE_TOE3_3_L, //203/212 +BVH_MOTION_TOE4_1_L, //204/212 +BVH_MOTION_TOE4_2_L, //205/212 +BVH_MOTION_TOE4_3_L, //206/212 +BVH_MOTION_ENDSITE_TOE4_3_L, //207/212 +BVH_MOTION_TOE5_1_L, //208/212 +BVH_MOTION_TOE5_2_L, //209/212 +BVH_MOTION_TOE5_3_L, //210/212 +BVH_MOTION_ENDSITE_TOE5_3_L, //211/212 +BVH_MOTION_LENGTH +}; + + +void convertStringToCFriendly(char * str) +{ + if (str==0) { return;} + while (*str!=0) + { + switch (*str) + { + case '-' : + case '.' : + case '_' : + *str='_'; + break; + + default : + *str=toupper(*str); + }; + ++str; + } + +} + + + +static int dumpSkeletonSerializedToBVHTransformCode( + struct BVH_MotionCapture * bvhMotion, + struct BVH_Transform * outputTransform, + struct skeletonSerialized * inputPoints2D +) +{ + if (bvhMotion==0) { return 0; } + if (bvhMotion->jointHierarchySize==0) { return 0; } + + FILE * fp = fopen("conversionCode.hpp","w"); + + if (fp!=0) + { + + fprintf(fp,"\n\nstatic const char * skeletonSerializedNames[]=\n"); + fprintf(fp,"{\n"); + for (unsigned int jID=0; jIDskeletonHeaderElements; jID++) + { + fprintf(fp," \"%s\",\n",inputPoints2D->skeletonHeader[jID].str); + } + fprintf(fp,"\"END\"\n"); + fprintf(fp,"};\n"); + + + + fprintf(fp,"\n\nenum skeletonSerializedEnum\n"); + fprintf(fp,"{\n"); + + char C_FriendlyLabel[1025]={0}; + for (unsigned int jID=0; jIDskeletonHeaderElements; jID++) + { + snprintf(C_FriendlyLabel,1024,"%s",inputPoints2D->skeletonHeader[jID].str); + convertStringToCFriendly(C_FriendlyLabel); + + fprintf(fp,"SKELETON_SERIALIZED_%s,\n",C_FriendlyLabel); + } + fprintf(fp,"SKELETON_SERIALIZED_LENGTH\n"); + fprintf(fp,"};\n"); + + //------------------------------------------------------------ + //------------------------------------------------------------ + //------------------------------------------------------------ + + fprintf(fp,"\n\nstatic const char * bvhMotionNames[]=\n"); + fprintf(fp,"{\n"); + for (unsigned int jID=0; jIDjointHierarchySize; jID++) + { + fprintf(fp," \"%s\",\n",bvhMotion->jointHierarchy[jID].jointName); + } + fprintf(fp,"\"END\"\n"); + fprintf(fp,"};\n"); + + + fprintf(fp,"\n\nenum bvhMotionEnum\n"); + fprintf(fp,"{\n"); + + for (unsigned int jID=0; jIDjointHierarchySize; jID++) + { + snprintf(C_FriendlyLabel,1024,"%s",bvhMotion->jointHierarchy[jID].jointName); + convertStringToCFriendly(C_FriendlyLabel); + + fprintf(fp,"BVH_MOTION_%s, //%u/%u\n",C_FriendlyLabel,jID,bvhMotion->jointHierarchySize); + } + fprintf(fp,"BVH_MOTION_LENGTH\n"); + fprintf(fp,"};\n"); + + //------------------------------------------------------------ + //------------------------------------------------------------ + //------------------------------------------------------------ + fclose(fp); + + fprintf(stderr,"conversionCode.hpp exported..!!!\n"); + exit(0); + } + + + + + + + + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/NSDM/generated_body.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/NSDM/generated_body.hpp new file mode 100644 index 0000000..988d2d6 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/NSDM/generated_body.hpp @@ -0,0 +1,1895 @@ +/** @file generated_body.hpp + * @brief A Description the body input of a Tensorflow network required for MocapNET + * @author Ammar Qammaz (AmmarkoV) + * Automatically generated using : + * python3 exportCPPCodeFromJSONConfiguration.py --front body --config dataset/body_configuration.json + * please note that since the names of the labels are both affected by the dataset/body_configuration.json configuration + * as well as the ground truth, if you have made any weird additions you might consider running the ./createRandomizedDataset.sh and ./createTestDataset.sh scripts + */ + +#pragma once + +#include +#include +#include +#include "../tools.hpp" + +/** @brief This is an array of names for all uncompressed 2D inputs expected. */ +static const unsigned int mocapNET_InputLength_WithoutNSDM_body = 63; + +/** @brief An array of strings that contains the label for each expected input. */ +static const char * mocapNET_body[] = +{ + "2DX_hip", //0 + "2DY_hip", //1 + "visible_hip", //2 + "2DX_neck", //3 + "2DY_neck", //4 + "visible_neck", //5 + "2DX_head", //6 + "2DY_head", //7 + "visible_head", //8 + "2DX_EndSite_eye.l", //9 + "2DY_EndSite_eye.l", //10 + "visible_EndSite_eye.l", //11 + "2DX_EndSite_eye.r", //12 + "2DY_EndSite_eye.r", //13 + "visible_EndSite_eye.r", //14 + "2DX_rshoulder", //15 + "2DY_rshoulder", //16 + "visible_rshoulder", //17 + "2DX_relbow", //18 + "2DY_relbow", //19 + "visible_relbow", //20 + "2DX_rhand", //21 + "2DY_rhand", //22 + "visible_rhand", //23 + "2DX_lshoulder", //24 + "2DY_lshoulder", //25 + "visible_lshoulder", //26 + "2DX_lelbow", //27 + "2DY_lelbow", //28 + "visible_lelbow", //29 + "2DX_lhand", //30 + "2DY_lhand", //31 + "visible_lhand", //32 + "2DX_rhip", //33 + "2DY_rhip", //34 + "visible_rhip", //35 + "2DX_rknee", //36 + "2DY_rknee", //37 + "visible_rknee", //38 + "2DX_rfoot", //39 + "2DY_rfoot", //40 + "visible_rfoot", //41 + "2DX_EndSite_toe1-2.r", //42 + "2DY_EndSite_toe1-2.r", //43 + "visible_EndSite_toe1-2.r", //44 + "2DX_EndSite_toe5-3.r", //45 + "2DY_EndSite_toe5-3.r", //46 + "visible_EndSite_toe5-3.r", //47 + "2DX_lhip", //48 + "2DY_lhip", //49 + "visible_lhip", //50 + "2DX_lknee", //51 + "2DY_lknee", //52 + "visible_lknee", //53 + "2DX_lfoot", //54 + "2DY_lfoot", //55 + "visible_lfoot", //56 + "2DX_EndSite_toe1-2.l", //57 + "2DY_EndSite_toe1-2.l", //58 + "visible_EndSite_toe1-2.l", //59 + "2DX_EndSite_toe5-3.l", //60 + "2DY_EndSite_toe5-3.l", //61 + "visible_EndSite_toe5-3.l", //62 +//This is where regular input ends and the NSDM data kicks in.. + "hipX-hipX", //63 + "hipY-hipY", //64 + "hipX-EndSite_eye.rX", //65 + "hipY-EndSite_eye.rY", //66 + "hipX-EndSite_eye.lX", //67 + "hipY-EndSite_eye.lY", //68 + "hipX-neckX", //69 + "hipY-neckY", //70 + "hipX-virtual_hip_x_plus0_3_y_0X", //71 + "hipY-virtual_hip_x_plus0_3_y_0Y", //72 + "hipX-rshoulderX", //73 + "hipY-rshoulderY", //74 + "hipX-relbowX", //75 + "hipY-relbowY", //76 + "hipX-rhandX", //77 + "hipY-rhandY", //78 + "hipX-virtual_hip_x_minus_0_3_y_0X", //79 + "hipY-virtual_hip_x_minus_0_3_y_0Y", //80 + "hipX-lshoulderX", //81 + "hipY-lshoulderY", //82 + "hipX-lelbowX", //83 + "hipY-lelbowY", //84 + "hipX-lhandX", //85 + "hipY-lhandY", //86 + "hipX-rhipX", //87 + "hipY-rhipY", //88 + "hipX-rkneeX", //89 + "hipY-rkneeY", //90 + "hipX-rfootX", //91 + "hipY-rfootY", //92 + "hipX-lhipX", //93 + "hipY-lhipY", //94 + "hipX-lkneeX", //95 + "hipY-lkneeY", //96 + "hipX-lfootX", //97 + "hipY-lfootY", //98 + "EndSite_eye.rX-hipX", //99 + "EndSite_eye.rY-hipY", //100 + "EndSite_eye.rX-EndSite_eye.rX", //101 + "EndSite_eye.rY-EndSite_eye.rY", //102 + "EndSite_eye.rX-EndSite_eye.lX", //103 + "EndSite_eye.rY-EndSite_eye.lY", //104 + "EndSite_eye.rX-neckX", //105 + "EndSite_eye.rY-neckY", //106 + "EndSite_eye.rX-virtual_hip_x_plus0_3_y_0X", //107 + "EndSite_eye.rY-virtual_hip_x_plus0_3_y_0Y", //108 + "EndSite_eye.rX-rshoulderX", //109 + "EndSite_eye.rY-rshoulderY", //110 + "EndSite_eye.rX-relbowX", //111 + "EndSite_eye.rY-relbowY", //112 + "EndSite_eye.rX-rhandX", //113 + "EndSite_eye.rY-rhandY", //114 + "EndSite_eye.rX-virtual_hip_x_minus_0_3_y_0X", //115 + "EndSite_eye.rY-virtual_hip_x_minus_0_3_y_0Y", //116 + "EndSite_eye.rX-lshoulderX", //117 + "EndSite_eye.rY-lshoulderY", //118 + "EndSite_eye.rX-lelbowX", //119 + "EndSite_eye.rY-lelbowY", //120 + "EndSite_eye.rX-lhandX", //121 + "EndSite_eye.rY-lhandY", //122 + "EndSite_eye.rX-rhipX", //123 + "EndSite_eye.rY-rhipY", //124 + "EndSite_eye.rX-rkneeX", //125 + "EndSite_eye.rY-rkneeY", //126 + "EndSite_eye.rX-rfootX", //127 + "EndSite_eye.rY-rfootY", //128 + "EndSite_eye.rX-lhipX", //129 + "EndSite_eye.rY-lhipY", //130 + "EndSite_eye.rX-lkneeX", //131 + "EndSite_eye.rY-lkneeY", //132 + "EndSite_eye.rX-lfootX", //133 + "EndSite_eye.rY-lfootY", //134 + "EndSite_eye.lX-hipX", //135 + "EndSite_eye.lY-hipY", //136 + "EndSite_eye.lX-EndSite_eye.rX", //137 + "EndSite_eye.lY-EndSite_eye.rY", //138 + "EndSite_eye.lX-EndSite_eye.lX", //139 + "EndSite_eye.lY-EndSite_eye.lY", //140 + "EndSite_eye.lX-neckX", //141 + "EndSite_eye.lY-neckY", //142 + "EndSite_eye.lX-virtual_hip_x_plus0_3_y_0X", //143 + "EndSite_eye.lY-virtual_hip_x_plus0_3_y_0Y", //144 + "EndSite_eye.lX-rshoulderX", //145 + "EndSite_eye.lY-rshoulderY", //146 + "EndSite_eye.lX-relbowX", //147 + "EndSite_eye.lY-relbowY", //148 + "EndSite_eye.lX-rhandX", //149 + "EndSite_eye.lY-rhandY", //150 + "EndSite_eye.lX-virtual_hip_x_minus_0_3_y_0X", //151 + "EndSite_eye.lY-virtual_hip_x_minus_0_3_y_0Y", //152 + "EndSite_eye.lX-lshoulderX", //153 + "EndSite_eye.lY-lshoulderY", //154 + "EndSite_eye.lX-lelbowX", //155 + "EndSite_eye.lY-lelbowY", //156 + "EndSite_eye.lX-lhandX", //157 + "EndSite_eye.lY-lhandY", //158 + "EndSite_eye.lX-rhipX", //159 + "EndSite_eye.lY-rhipY", //160 + "EndSite_eye.lX-rkneeX", //161 + "EndSite_eye.lY-rkneeY", //162 + "EndSite_eye.lX-rfootX", //163 + "EndSite_eye.lY-rfootY", //164 + "EndSite_eye.lX-lhipX", //165 + "EndSite_eye.lY-lhipY", //166 + "EndSite_eye.lX-lkneeX", //167 + "EndSite_eye.lY-lkneeY", //168 + "EndSite_eye.lX-lfootX", //169 + "EndSite_eye.lY-lfootY", //170 + "neckX-hipX", //171 + "neckY-hipY", //172 + "neckX-EndSite_eye.rX", //173 + "neckY-EndSite_eye.rY", //174 + "neckX-EndSite_eye.lX", //175 + "neckY-EndSite_eye.lY", //176 + "neckX-neckX", //177 + "neckY-neckY", //178 + "neckX-virtual_hip_x_plus0_3_y_0X", //179 + "neckY-virtual_hip_x_plus0_3_y_0Y", //180 + "neckX-rshoulderX", //181 + "neckY-rshoulderY", //182 + "neckX-relbowX", //183 + "neckY-relbowY", //184 + "neckX-rhandX", //185 + "neckY-rhandY", //186 + "neckX-virtual_hip_x_minus_0_3_y_0X", //187 + "neckY-virtual_hip_x_minus_0_3_y_0Y", //188 + "neckX-lshoulderX", //189 + "neckY-lshoulderY", //190 + "neckX-lelbowX", //191 + "neckY-lelbowY", //192 + "neckX-lhandX", //193 + "neckY-lhandY", //194 + "neckX-rhipX", //195 + "neckY-rhipY", //196 + "neckX-rkneeX", //197 + "neckY-rkneeY", //198 + "neckX-rfootX", //199 + "neckY-rfootY", //200 + "neckX-lhipX", //201 + "neckY-lhipY", //202 + "neckX-lkneeX", //203 + "neckY-lkneeY", //204 + "neckX-lfootX", //205 + "neckY-lfootY", //206 + "virtual_hip_x_plus0_3_y_0X-hipX", //207 + "virtual_hip_x_plus0_3_y_0Y-hipY", //208 + "virtual_hip_x_plus0_3_y_0X-EndSite_eye.rX", //209 + "virtual_hip_x_plus0_3_y_0Y-EndSite_eye.rY", //210 + "virtual_hip_x_plus0_3_y_0X-EndSite_eye.lX", //211 + "virtual_hip_x_plus0_3_y_0Y-EndSite_eye.lY", //212 + "virtual_hip_x_plus0_3_y_0X-neckX", //213 + "virtual_hip_x_plus0_3_y_0Y-neckY", //214 + "virtual_hip_x_plus0_3_y_0X-virtual_hip_x_plus0_3_y_0X", //215 + "virtual_hip_x_plus0_3_y_0Y-virtual_hip_x_plus0_3_y_0Y", //216 + "virtual_hip_x_plus0_3_y_0X-rshoulderX", //217 + "virtual_hip_x_plus0_3_y_0Y-rshoulderY", //218 + "virtual_hip_x_plus0_3_y_0X-relbowX", //219 + "virtual_hip_x_plus0_3_y_0Y-relbowY", //220 + "virtual_hip_x_plus0_3_y_0X-rhandX", //221 + "virtual_hip_x_plus0_3_y_0Y-rhandY", //222 + "virtual_hip_x_plus0_3_y_0X-virtual_hip_x_minus_0_3_y_0X", //223 + "virtual_hip_x_plus0_3_y_0Y-virtual_hip_x_minus_0_3_y_0Y", //224 + "virtual_hip_x_plus0_3_y_0X-lshoulderX", //225 + "virtual_hip_x_plus0_3_y_0Y-lshoulderY", //226 + "virtual_hip_x_plus0_3_y_0X-lelbowX", //227 + "virtual_hip_x_plus0_3_y_0Y-lelbowY", //228 + "virtual_hip_x_plus0_3_y_0X-lhandX", //229 + "virtual_hip_x_plus0_3_y_0Y-lhandY", //230 + "virtual_hip_x_plus0_3_y_0X-rhipX", //231 + "virtual_hip_x_plus0_3_y_0Y-rhipY", //232 + "virtual_hip_x_plus0_3_y_0X-rkneeX", //233 + "virtual_hip_x_plus0_3_y_0Y-rkneeY", //234 + "virtual_hip_x_plus0_3_y_0X-rfootX", //235 + "virtual_hip_x_plus0_3_y_0Y-rfootY", //236 + "virtual_hip_x_plus0_3_y_0X-lhipX", //237 + "virtual_hip_x_plus0_3_y_0Y-lhipY", //238 + "virtual_hip_x_plus0_3_y_0X-lkneeX", //239 + "virtual_hip_x_plus0_3_y_0Y-lkneeY", //240 + "virtual_hip_x_plus0_3_y_0X-lfootX", //241 + "virtual_hip_x_plus0_3_y_0Y-lfootY", //242 + "rshoulderX-hipX", //243 + "rshoulderY-hipY", //244 + "rshoulderX-EndSite_eye.rX", //245 + "rshoulderY-EndSite_eye.rY", //246 + "rshoulderX-EndSite_eye.lX", //247 + "rshoulderY-EndSite_eye.lY", //248 + "rshoulderX-neckX", //249 + "rshoulderY-neckY", //250 + "rshoulderX-virtual_hip_x_plus0_3_y_0X", //251 + "rshoulderY-virtual_hip_x_plus0_3_y_0Y", //252 + "rshoulderX-rshoulderX", //253 + "rshoulderY-rshoulderY", //254 + "rshoulderX-relbowX", //255 + "rshoulderY-relbowY", //256 + "rshoulderX-rhandX", //257 + "rshoulderY-rhandY", //258 + "rshoulderX-virtual_hip_x_minus_0_3_y_0X", //259 + "rshoulderY-virtual_hip_x_minus_0_3_y_0Y", //260 + "rshoulderX-lshoulderX", //261 + "rshoulderY-lshoulderY", //262 + "rshoulderX-lelbowX", //263 + "rshoulderY-lelbowY", //264 + "rshoulderX-lhandX", //265 + "rshoulderY-lhandY", //266 + "rshoulderX-rhipX", //267 + "rshoulderY-rhipY", //268 + "rshoulderX-rkneeX", //269 + "rshoulderY-rkneeY", //270 + "rshoulderX-rfootX", //271 + "rshoulderY-rfootY", //272 + "rshoulderX-lhipX", //273 + "rshoulderY-lhipY", //274 + "rshoulderX-lkneeX", //275 + "rshoulderY-lkneeY", //276 + "rshoulderX-lfootX", //277 + "rshoulderY-lfootY", //278 + "relbowX-hipX", //279 + "relbowY-hipY", //280 + "relbowX-EndSite_eye.rX", //281 + "relbowY-EndSite_eye.rY", //282 + "relbowX-EndSite_eye.lX", //283 + "relbowY-EndSite_eye.lY", //284 + "relbowX-neckX", //285 + "relbowY-neckY", //286 + "relbowX-virtual_hip_x_plus0_3_y_0X", //287 + "relbowY-virtual_hip_x_plus0_3_y_0Y", //288 + "relbowX-rshoulderX", //289 + "relbowY-rshoulderY", //290 + "relbowX-relbowX", //291 + "relbowY-relbowY", //292 + "relbowX-rhandX", //293 + "relbowY-rhandY", //294 + "relbowX-virtual_hip_x_minus_0_3_y_0X", //295 + "relbowY-virtual_hip_x_minus_0_3_y_0Y", //296 + "relbowX-lshoulderX", //297 + "relbowY-lshoulderY", //298 + "relbowX-lelbowX", //299 + "relbowY-lelbowY", //300 + "relbowX-lhandX", //301 + "relbowY-lhandY", //302 + "relbowX-rhipX", //303 + "relbowY-rhipY", //304 + "relbowX-rkneeX", //305 + "relbowY-rkneeY", //306 + "relbowX-rfootX", //307 + "relbowY-rfootY", //308 + "relbowX-lhipX", //309 + "relbowY-lhipY", //310 + "relbowX-lkneeX", //311 + "relbowY-lkneeY", //312 + "relbowX-lfootX", //313 + "relbowY-lfootY", //314 + "rhandX-hipX", //315 + "rhandY-hipY", //316 + "rhandX-EndSite_eye.rX", //317 + "rhandY-EndSite_eye.rY", //318 + "rhandX-EndSite_eye.lX", //319 + "rhandY-EndSite_eye.lY", //320 + "rhandX-neckX", //321 + "rhandY-neckY", //322 + "rhandX-virtual_hip_x_plus0_3_y_0X", //323 + "rhandY-virtual_hip_x_plus0_3_y_0Y", //324 + "rhandX-rshoulderX", //325 + "rhandY-rshoulderY", //326 + "rhandX-relbowX", //327 + "rhandY-relbowY", //328 + "rhandX-rhandX", //329 + "rhandY-rhandY", //330 + "rhandX-virtual_hip_x_minus_0_3_y_0X", //331 + "rhandY-virtual_hip_x_minus_0_3_y_0Y", //332 + "rhandX-lshoulderX", //333 + "rhandY-lshoulderY", //334 + "rhandX-lelbowX", //335 + "rhandY-lelbowY", //336 + "rhandX-lhandX", //337 + "rhandY-lhandY", //338 + "rhandX-rhipX", //339 + "rhandY-rhipY", //340 + "rhandX-rkneeX", //341 + "rhandY-rkneeY", //342 + "rhandX-rfootX", //343 + "rhandY-rfootY", //344 + "rhandX-lhipX", //345 + "rhandY-lhipY", //346 + "rhandX-lkneeX", //347 + "rhandY-lkneeY", //348 + "rhandX-lfootX", //349 + "rhandY-lfootY", //350 + "virtual_hip_x_minus_0_3_y_0X-hipX", //351 + "virtual_hip_x_minus_0_3_y_0Y-hipY", //352 + "virtual_hip_x_minus_0_3_y_0X-EndSite_eye.rX", //353 + "virtual_hip_x_minus_0_3_y_0Y-EndSite_eye.rY", //354 + "virtual_hip_x_minus_0_3_y_0X-EndSite_eye.lX", //355 + "virtual_hip_x_minus_0_3_y_0Y-EndSite_eye.lY", //356 + "virtual_hip_x_minus_0_3_y_0X-neckX", //357 + "virtual_hip_x_minus_0_3_y_0Y-neckY", //358 + "virtual_hip_x_minus_0_3_y_0X-virtual_hip_x_plus0_3_y_0X", //359 + "virtual_hip_x_minus_0_3_y_0Y-virtual_hip_x_plus0_3_y_0Y", //360 + "virtual_hip_x_minus_0_3_y_0X-rshoulderX", //361 + "virtual_hip_x_minus_0_3_y_0Y-rshoulderY", //362 + "virtual_hip_x_minus_0_3_y_0X-relbowX", //363 + "virtual_hip_x_minus_0_3_y_0Y-relbowY", //364 + "virtual_hip_x_minus_0_3_y_0X-rhandX", //365 + "virtual_hip_x_minus_0_3_y_0Y-rhandY", //366 + "virtual_hip_x_minus_0_3_y_0X-virtual_hip_x_minus_0_3_y_0X", //367 + "virtual_hip_x_minus_0_3_y_0Y-virtual_hip_x_minus_0_3_y_0Y", //368 + "virtual_hip_x_minus_0_3_y_0X-lshoulderX", //369 + "virtual_hip_x_minus_0_3_y_0Y-lshoulderY", //370 + "virtual_hip_x_minus_0_3_y_0X-lelbowX", //371 + "virtual_hip_x_minus_0_3_y_0Y-lelbowY", //372 + "virtual_hip_x_minus_0_3_y_0X-lhandX", //373 + "virtual_hip_x_minus_0_3_y_0Y-lhandY", //374 + "virtual_hip_x_minus_0_3_y_0X-rhipX", //375 + "virtual_hip_x_minus_0_3_y_0Y-rhipY", //376 + "virtual_hip_x_minus_0_3_y_0X-rkneeX", //377 + "virtual_hip_x_minus_0_3_y_0Y-rkneeY", //378 + "virtual_hip_x_minus_0_3_y_0X-rfootX", //379 + "virtual_hip_x_minus_0_3_y_0Y-rfootY", //380 + "virtual_hip_x_minus_0_3_y_0X-lhipX", //381 + "virtual_hip_x_minus_0_3_y_0Y-lhipY", //382 + "virtual_hip_x_minus_0_3_y_0X-lkneeX", //383 + "virtual_hip_x_minus_0_3_y_0Y-lkneeY", //384 + "virtual_hip_x_minus_0_3_y_0X-lfootX", //385 + "virtual_hip_x_minus_0_3_y_0Y-lfootY", //386 + "lshoulderX-hipX", //387 + "lshoulderY-hipY", //388 + "lshoulderX-EndSite_eye.rX", //389 + "lshoulderY-EndSite_eye.rY", //390 + "lshoulderX-EndSite_eye.lX", //391 + "lshoulderY-EndSite_eye.lY", //392 + "lshoulderX-neckX", //393 + "lshoulderY-neckY", //394 + "lshoulderX-virtual_hip_x_plus0_3_y_0X", //395 + "lshoulderY-virtual_hip_x_plus0_3_y_0Y", //396 + "lshoulderX-rshoulderX", //397 + "lshoulderY-rshoulderY", //398 + "lshoulderX-relbowX", //399 + "lshoulderY-relbowY", //400 + "lshoulderX-rhandX", //401 + "lshoulderY-rhandY", //402 + "lshoulderX-virtual_hip_x_minus_0_3_y_0X", //403 + "lshoulderY-virtual_hip_x_minus_0_3_y_0Y", //404 + "lshoulderX-lshoulderX", //405 + "lshoulderY-lshoulderY", //406 + "lshoulderX-lelbowX", //407 + "lshoulderY-lelbowY", //408 + "lshoulderX-lhandX", //409 + "lshoulderY-lhandY", //410 + "lshoulderX-rhipX", //411 + "lshoulderY-rhipY", //412 + "lshoulderX-rkneeX", //413 + "lshoulderY-rkneeY", //414 + "lshoulderX-rfootX", //415 + "lshoulderY-rfootY", //416 + "lshoulderX-lhipX", //417 + "lshoulderY-lhipY", //418 + "lshoulderX-lkneeX", //419 + "lshoulderY-lkneeY", //420 + "lshoulderX-lfootX", //421 + "lshoulderY-lfootY", //422 + "lelbowX-hipX", //423 + "lelbowY-hipY", //424 + "lelbowX-EndSite_eye.rX", //425 + "lelbowY-EndSite_eye.rY", //426 + "lelbowX-EndSite_eye.lX", //427 + "lelbowY-EndSite_eye.lY", //428 + "lelbowX-neckX", //429 + "lelbowY-neckY", //430 + "lelbowX-virtual_hip_x_plus0_3_y_0X", //431 + "lelbowY-virtual_hip_x_plus0_3_y_0Y", //432 + "lelbowX-rshoulderX", //433 + "lelbowY-rshoulderY", //434 + "lelbowX-relbowX", //435 + "lelbowY-relbowY", //436 + "lelbowX-rhandX", //437 + "lelbowY-rhandY", //438 + "lelbowX-virtual_hip_x_minus_0_3_y_0X", //439 + "lelbowY-virtual_hip_x_minus_0_3_y_0Y", //440 + "lelbowX-lshoulderX", //441 + "lelbowY-lshoulderY", //442 + "lelbowX-lelbowX", //443 + "lelbowY-lelbowY", //444 + "lelbowX-lhandX", //445 + "lelbowY-lhandY", //446 + "lelbowX-rhipX", //447 + "lelbowY-rhipY", //448 + "lelbowX-rkneeX", //449 + "lelbowY-rkneeY", //450 + "lelbowX-rfootX", //451 + "lelbowY-rfootY", //452 + "lelbowX-lhipX", //453 + "lelbowY-lhipY", //454 + "lelbowX-lkneeX", //455 + "lelbowY-lkneeY", //456 + "lelbowX-lfootX", //457 + "lelbowY-lfootY", //458 + "lhandX-hipX", //459 + "lhandY-hipY", //460 + "lhandX-EndSite_eye.rX", //461 + "lhandY-EndSite_eye.rY", //462 + "lhandX-EndSite_eye.lX", //463 + "lhandY-EndSite_eye.lY", //464 + "lhandX-neckX", //465 + "lhandY-neckY", //466 + "lhandX-virtual_hip_x_plus0_3_y_0X", //467 + "lhandY-virtual_hip_x_plus0_3_y_0Y", //468 + "lhandX-rshoulderX", //469 + "lhandY-rshoulderY", //470 + "lhandX-relbowX", //471 + "lhandY-relbowY", //472 + "lhandX-rhandX", //473 + "lhandY-rhandY", //474 + "lhandX-virtual_hip_x_minus_0_3_y_0X", //475 + "lhandY-virtual_hip_x_minus_0_3_y_0Y", //476 + "lhandX-lshoulderX", //477 + "lhandY-lshoulderY", //478 + "lhandX-lelbowX", //479 + "lhandY-lelbowY", //480 + "lhandX-lhandX", //481 + "lhandY-lhandY", //482 + "lhandX-rhipX", //483 + "lhandY-rhipY", //484 + "lhandX-rkneeX", //485 + "lhandY-rkneeY", //486 + "lhandX-rfootX", //487 + "lhandY-rfootY", //488 + "lhandX-lhipX", //489 + "lhandY-lhipY", //490 + "lhandX-lkneeX", //491 + "lhandY-lkneeY", //492 + "lhandX-lfootX", //493 + "lhandY-lfootY", //494 + "rhipX-hipX", //495 + "rhipY-hipY", //496 + "rhipX-EndSite_eye.rX", //497 + "rhipY-EndSite_eye.rY", //498 + "rhipX-EndSite_eye.lX", //499 + "rhipY-EndSite_eye.lY", //500 + "rhipX-neckX", //501 + "rhipY-neckY", //502 + "rhipX-virtual_hip_x_plus0_3_y_0X", //503 + "rhipY-virtual_hip_x_plus0_3_y_0Y", //504 + "rhipX-rshoulderX", //505 + "rhipY-rshoulderY", //506 + "rhipX-relbowX", //507 + "rhipY-relbowY", //508 + "rhipX-rhandX", //509 + "rhipY-rhandY", //510 + "rhipX-virtual_hip_x_minus_0_3_y_0X", //511 + "rhipY-virtual_hip_x_minus_0_3_y_0Y", //512 + "rhipX-lshoulderX", //513 + "rhipY-lshoulderY", //514 + "rhipX-lelbowX", //515 + "rhipY-lelbowY", //516 + "rhipX-lhandX", //517 + "rhipY-lhandY", //518 + "rhipX-rhipX", //519 + "rhipY-rhipY", //520 + "rhipX-rkneeX", //521 + "rhipY-rkneeY", //522 + "rhipX-rfootX", //523 + "rhipY-rfootY", //524 + "rhipX-lhipX", //525 + "rhipY-lhipY", //526 + "rhipX-lkneeX", //527 + "rhipY-lkneeY", //528 + "rhipX-lfootX", //529 + "rhipY-lfootY", //530 + "rkneeX-hipX", //531 + "rkneeY-hipY", //532 + "rkneeX-EndSite_eye.rX", //533 + "rkneeY-EndSite_eye.rY", //534 + "rkneeX-EndSite_eye.lX", //535 + "rkneeY-EndSite_eye.lY", //536 + "rkneeX-neckX", //537 + "rkneeY-neckY", //538 + "rkneeX-virtual_hip_x_plus0_3_y_0X", //539 + "rkneeY-virtual_hip_x_plus0_3_y_0Y", //540 + "rkneeX-rshoulderX", //541 + "rkneeY-rshoulderY", //542 + "rkneeX-relbowX", //543 + "rkneeY-relbowY", //544 + "rkneeX-rhandX", //545 + "rkneeY-rhandY", //546 + "rkneeX-virtual_hip_x_minus_0_3_y_0X", //547 + "rkneeY-virtual_hip_x_minus_0_3_y_0Y", //548 + "rkneeX-lshoulderX", //549 + "rkneeY-lshoulderY", //550 + "rkneeX-lelbowX", //551 + "rkneeY-lelbowY", //552 + "rkneeX-lhandX", //553 + "rkneeY-lhandY", //554 + "rkneeX-rhipX", //555 + "rkneeY-rhipY", //556 + "rkneeX-rkneeX", //557 + "rkneeY-rkneeY", //558 + "rkneeX-rfootX", //559 + "rkneeY-rfootY", //560 + "rkneeX-lhipX", //561 + "rkneeY-lhipY", //562 + "rkneeX-lkneeX", //563 + "rkneeY-lkneeY", //564 + "rkneeX-lfootX", //565 + "rkneeY-lfootY", //566 + "rfootX-hipX", //567 + "rfootY-hipY", //568 + "rfootX-EndSite_eye.rX", //569 + "rfootY-EndSite_eye.rY", //570 + "rfootX-EndSite_eye.lX", //571 + "rfootY-EndSite_eye.lY", //572 + "rfootX-neckX", //573 + "rfootY-neckY", //574 + "rfootX-virtual_hip_x_plus0_3_y_0X", //575 + "rfootY-virtual_hip_x_plus0_3_y_0Y", //576 + "rfootX-rshoulderX", //577 + "rfootY-rshoulderY", //578 + "rfootX-relbowX", //579 + "rfootY-relbowY", //580 + "rfootX-rhandX", //581 + "rfootY-rhandY", //582 + "rfootX-virtual_hip_x_minus_0_3_y_0X", //583 + "rfootY-virtual_hip_x_minus_0_3_y_0Y", //584 + "rfootX-lshoulderX", //585 + "rfootY-lshoulderY", //586 + "rfootX-lelbowX", //587 + "rfootY-lelbowY", //588 + "rfootX-lhandX", //589 + "rfootY-lhandY", //590 + "rfootX-rhipX", //591 + "rfootY-rhipY", //592 + "rfootX-rkneeX", //593 + "rfootY-rkneeY", //594 + "rfootX-rfootX", //595 + "rfootY-rfootY", //596 + "rfootX-lhipX", //597 + "rfootY-lhipY", //598 + "rfootX-lkneeX", //599 + "rfootY-lkneeY", //600 + "rfootX-lfootX", //601 + "rfootY-lfootY", //602 + "lhipX-hipX", //603 + "lhipY-hipY", //604 + "lhipX-EndSite_eye.rX", //605 + "lhipY-EndSite_eye.rY", //606 + "lhipX-EndSite_eye.lX", //607 + "lhipY-EndSite_eye.lY", //608 + "lhipX-neckX", //609 + "lhipY-neckY", //610 + "lhipX-virtual_hip_x_plus0_3_y_0X", //611 + "lhipY-virtual_hip_x_plus0_3_y_0Y", //612 + "lhipX-rshoulderX", //613 + "lhipY-rshoulderY", //614 + "lhipX-relbowX", //615 + "lhipY-relbowY", //616 + "lhipX-rhandX", //617 + "lhipY-rhandY", //618 + "lhipX-virtual_hip_x_minus_0_3_y_0X", //619 + "lhipY-virtual_hip_x_minus_0_3_y_0Y", //620 + "lhipX-lshoulderX", //621 + "lhipY-lshoulderY", //622 + "lhipX-lelbowX", //623 + "lhipY-lelbowY", //624 + "lhipX-lhandX", //625 + "lhipY-lhandY", //626 + "lhipX-rhipX", //627 + "lhipY-rhipY", //628 + "lhipX-rkneeX", //629 + "lhipY-rkneeY", //630 + "lhipX-rfootX", //631 + "lhipY-rfootY", //632 + "lhipX-lhipX", //633 + "lhipY-lhipY", //634 + "lhipX-lkneeX", //635 + "lhipY-lkneeY", //636 + "lhipX-lfootX", //637 + "lhipY-lfootY", //638 + "lkneeX-hipX", //639 + "lkneeY-hipY", //640 + "lkneeX-EndSite_eye.rX", //641 + "lkneeY-EndSite_eye.rY", //642 + "lkneeX-EndSite_eye.lX", //643 + "lkneeY-EndSite_eye.lY", //644 + "lkneeX-neckX", //645 + "lkneeY-neckY", //646 + "lkneeX-virtual_hip_x_plus0_3_y_0X", //647 + "lkneeY-virtual_hip_x_plus0_3_y_0Y", //648 + "lkneeX-rshoulderX", //649 + "lkneeY-rshoulderY", //650 + "lkneeX-relbowX", //651 + "lkneeY-relbowY", //652 + "lkneeX-rhandX", //653 + "lkneeY-rhandY", //654 + "lkneeX-virtual_hip_x_minus_0_3_y_0X", //655 + "lkneeY-virtual_hip_x_minus_0_3_y_0Y", //656 + "lkneeX-lshoulderX", //657 + "lkneeY-lshoulderY", //658 + "lkneeX-lelbowX", //659 + "lkneeY-lelbowY", //660 + "lkneeX-lhandX", //661 + "lkneeY-lhandY", //662 + "lkneeX-rhipX", //663 + "lkneeY-rhipY", //664 + "lkneeX-rkneeX", //665 + "lkneeY-rkneeY", //666 + "lkneeX-rfootX", //667 + "lkneeY-rfootY", //668 + "lkneeX-lhipX", //669 + "lkneeY-lhipY", //670 + "lkneeX-lkneeX", //671 + "lkneeY-lkneeY", //672 + "lkneeX-lfootX", //673 + "lkneeY-lfootY", //674 + "lfootX-hipX", //675 + "lfootY-hipY", //676 + "lfootX-EndSite_eye.rX", //677 + "lfootY-EndSite_eye.rY", //678 + "lfootX-EndSite_eye.lX", //679 + "lfootY-EndSite_eye.lY", //680 + "lfootX-neckX", //681 + "lfootY-neckY", //682 + "lfootX-virtual_hip_x_plus0_3_y_0X", //683 + "lfootY-virtual_hip_x_plus0_3_y_0Y", //684 + "lfootX-rshoulderX", //685 + "lfootY-rshoulderY", //686 + "lfootX-relbowX", //687 + "lfootY-relbowY", //688 + "lfootX-rhandX", //689 + "lfootY-rhandY", //690 + "lfootX-virtual_hip_x_minus_0_3_y_0X", //691 + "lfootY-virtual_hip_x_minus_0_3_y_0Y", //692 + "lfootX-lshoulderX", //693 + "lfootY-lshoulderY", //694 + "lfootX-lelbowX", //695 + "lfootY-lelbowY", //696 + "lfootX-lhandX", //697 + "lfootY-lhandY", //698 + "lfootX-rhipX", //699 + "lfootY-rhipY", //700 + "lfootX-rkneeX", //701 + "lfootY-rkneeY", //702 + "lfootX-rfootX", //703 + "lfootY-rfootY", //704 + "lfootX-lhipX", //705 + "lfootY-lhipY", //706 + "lfootX-lkneeX", //707 + "lfootY-lkneeY", //708 + "lfootX-lfootX", //709 + "lfootY-lfootY", //710 + "end" +}; +/** @brief Programmer friendly enumerator of expected inputs*/ +enum mocapNET_body_enum +{ + MNET_BODY_IN_2DX_HIP = 0, //0 + MNET_BODY_IN_2DY_HIP, //1 + MNET_BODY_IN_VISIBLE_HIP, //2 + MNET_BODY_IN_2DX_NECK, //3 + MNET_BODY_IN_2DY_NECK, //4 + MNET_BODY_IN_VISIBLE_NECK, //5 + MNET_BODY_IN_2DX_HEAD, //6 + MNET_BODY_IN_2DY_HEAD, //7 + MNET_BODY_IN_VISIBLE_HEAD, //8 + MNET_BODY_IN_2DX_ENDSITE_EYE_L, //9 + MNET_BODY_IN_2DY_ENDSITE_EYE_L, //10 + MNET_BODY_IN_VISIBLE_ENDSITE_EYE_L, //11 + MNET_BODY_IN_2DX_ENDSITE_EYE_R, //12 + MNET_BODY_IN_2DY_ENDSITE_EYE_R, //13 + MNET_BODY_IN_VISIBLE_ENDSITE_EYE_R, //14 + MNET_BODY_IN_2DX_RSHOULDER, //15 + MNET_BODY_IN_2DY_RSHOULDER, //16 + MNET_BODY_IN_VISIBLE_RSHOULDER, //17 + MNET_BODY_IN_2DX_RELBOW, //18 + MNET_BODY_IN_2DY_RELBOW, //19 + MNET_BODY_IN_VISIBLE_RELBOW, //20 + MNET_BODY_IN_2DX_RHAND, //21 + MNET_BODY_IN_2DY_RHAND, //22 + MNET_BODY_IN_VISIBLE_RHAND, //23 + MNET_BODY_IN_2DX_LSHOULDER, //24 + MNET_BODY_IN_2DY_LSHOULDER, //25 + MNET_BODY_IN_VISIBLE_LSHOULDER, //26 + MNET_BODY_IN_2DX_LELBOW, //27 + MNET_BODY_IN_2DY_LELBOW, //28 + MNET_BODY_IN_VISIBLE_LELBOW, //29 + MNET_BODY_IN_2DX_LHAND, //30 + MNET_BODY_IN_2DY_LHAND, //31 + MNET_BODY_IN_VISIBLE_LHAND, //32 + MNET_BODY_IN_2DX_RHIP, //33 + MNET_BODY_IN_2DY_RHIP, //34 + MNET_BODY_IN_VISIBLE_RHIP, //35 + MNET_BODY_IN_2DX_RKNEE, //36 + MNET_BODY_IN_2DY_RKNEE, //37 + MNET_BODY_IN_VISIBLE_RKNEE, //38 + MNET_BODY_IN_2DX_RFOOT, //39 + MNET_BODY_IN_2DY_RFOOT, //40 + MNET_BODY_IN_VISIBLE_RFOOT, //41 + MNET_BODY_IN_2DX_ENDSITE_TOE1_2_R, //42 + MNET_BODY_IN_2DY_ENDSITE_TOE1_2_R, //43 + MNET_BODY_IN_VISIBLE_ENDSITE_TOE1_2_R, //44 + MNET_BODY_IN_2DX_ENDSITE_TOE5_3_R, //45 + MNET_BODY_IN_2DY_ENDSITE_TOE5_3_R, //46 + MNET_BODY_IN_VISIBLE_ENDSITE_TOE5_3_R, //47 + MNET_BODY_IN_2DX_LHIP, //48 + MNET_BODY_IN_2DY_LHIP, //49 + MNET_BODY_IN_VISIBLE_LHIP, //50 + MNET_BODY_IN_2DX_LKNEE, //51 + MNET_BODY_IN_2DY_LKNEE, //52 + MNET_BODY_IN_VISIBLE_LKNEE, //53 + MNET_BODY_IN_2DX_LFOOT, //54 + MNET_BODY_IN_2DY_LFOOT, //55 + MNET_BODY_IN_VISIBLE_LFOOT, //56 + MNET_BODY_IN_2DX_ENDSITE_TOE1_2_L, //57 + MNET_BODY_IN_2DY_ENDSITE_TOE1_2_L, //58 + MNET_BODY_IN_VISIBLE_ENDSITE_TOE1_2_L, //59 + MNET_BODY_IN_2DX_ENDSITE_TOE5_3_L, //60 + MNET_BODY_IN_2DY_ENDSITE_TOE5_3_L, //61 + MNET_BODY_IN_VISIBLE_ENDSITE_TOE5_3_L, //62 + MNET_BODY_IN_HIPX_HIPX, //63 + MNET_BODY_IN_HIPY_HIPY, //64 + MNET_BODY_IN_HIPX_ENDSITE_EYE_RX, //65 + MNET_BODY_IN_HIPY_ENDSITE_EYE_RY, //66 + MNET_BODY_IN_HIPX_ENDSITE_EYE_LX, //67 + MNET_BODY_IN_HIPY_ENDSITE_EYE_LY, //68 + MNET_BODY_IN_HIPX_NECKX, //69 + MNET_BODY_IN_HIPY_NECKY, //70 + MNET_BODY_IN_HIPX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //71 + MNET_BODY_IN_HIPY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //72 + MNET_BODY_IN_HIPX_RSHOULDERX, //73 + MNET_BODY_IN_HIPY_RSHOULDERY, //74 + MNET_BODY_IN_HIPX_RELBOWX, //75 + MNET_BODY_IN_HIPY_RELBOWY, //76 + MNET_BODY_IN_HIPX_RHANDX, //77 + MNET_BODY_IN_HIPY_RHANDY, //78 + MNET_BODY_IN_HIPX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //79 + MNET_BODY_IN_HIPY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //80 + MNET_BODY_IN_HIPX_LSHOULDERX, //81 + MNET_BODY_IN_HIPY_LSHOULDERY, //82 + MNET_BODY_IN_HIPX_LELBOWX, //83 + MNET_BODY_IN_HIPY_LELBOWY, //84 + MNET_BODY_IN_HIPX_LHANDX, //85 + MNET_BODY_IN_HIPY_LHANDY, //86 + MNET_BODY_IN_HIPX_RHIPX, //87 + MNET_BODY_IN_HIPY_RHIPY, //88 + MNET_BODY_IN_HIPX_RKNEEX, //89 + MNET_BODY_IN_HIPY_RKNEEY, //90 + MNET_BODY_IN_HIPX_RFOOTX, //91 + MNET_BODY_IN_HIPY_RFOOTY, //92 + MNET_BODY_IN_HIPX_LHIPX, //93 + MNET_BODY_IN_HIPY_LHIPY, //94 + MNET_BODY_IN_HIPX_LKNEEX, //95 + MNET_BODY_IN_HIPY_LKNEEY, //96 + MNET_BODY_IN_HIPX_LFOOTX, //97 + MNET_BODY_IN_HIPY_LFOOTY, //98 + MNET_BODY_IN_ENDSITE_EYE_RX_HIPX, //99 + MNET_BODY_IN_ENDSITE_EYE_RY_HIPY, //100 + MNET_BODY_IN_ENDSITE_EYE_RX_ENDSITE_EYE_RX, //101 + MNET_BODY_IN_ENDSITE_EYE_RY_ENDSITE_EYE_RY, //102 + MNET_BODY_IN_ENDSITE_EYE_RX_ENDSITE_EYE_LX, //103 + MNET_BODY_IN_ENDSITE_EYE_RY_ENDSITE_EYE_LY, //104 + MNET_BODY_IN_ENDSITE_EYE_RX_NECKX, //105 + MNET_BODY_IN_ENDSITE_EYE_RY_NECKY, //106 + MNET_BODY_IN_ENDSITE_EYE_RX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //107 + MNET_BODY_IN_ENDSITE_EYE_RY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //108 + MNET_BODY_IN_ENDSITE_EYE_RX_RSHOULDERX, //109 + MNET_BODY_IN_ENDSITE_EYE_RY_RSHOULDERY, //110 + MNET_BODY_IN_ENDSITE_EYE_RX_RELBOWX, //111 + MNET_BODY_IN_ENDSITE_EYE_RY_RELBOWY, //112 + MNET_BODY_IN_ENDSITE_EYE_RX_RHANDX, //113 + MNET_BODY_IN_ENDSITE_EYE_RY_RHANDY, //114 + MNET_BODY_IN_ENDSITE_EYE_RX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //115 + MNET_BODY_IN_ENDSITE_EYE_RY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //116 + MNET_BODY_IN_ENDSITE_EYE_RX_LSHOULDERX, //117 + MNET_BODY_IN_ENDSITE_EYE_RY_LSHOULDERY, //118 + MNET_BODY_IN_ENDSITE_EYE_RX_LELBOWX, //119 + MNET_BODY_IN_ENDSITE_EYE_RY_LELBOWY, //120 + MNET_BODY_IN_ENDSITE_EYE_RX_LHANDX, //121 + MNET_BODY_IN_ENDSITE_EYE_RY_LHANDY, //122 + MNET_BODY_IN_ENDSITE_EYE_RX_RHIPX, //123 + MNET_BODY_IN_ENDSITE_EYE_RY_RHIPY, //124 + MNET_BODY_IN_ENDSITE_EYE_RX_RKNEEX, //125 + MNET_BODY_IN_ENDSITE_EYE_RY_RKNEEY, //126 + MNET_BODY_IN_ENDSITE_EYE_RX_RFOOTX, //127 + MNET_BODY_IN_ENDSITE_EYE_RY_RFOOTY, //128 + MNET_BODY_IN_ENDSITE_EYE_RX_LHIPX, //129 + MNET_BODY_IN_ENDSITE_EYE_RY_LHIPY, //130 + MNET_BODY_IN_ENDSITE_EYE_RX_LKNEEX, //131 + MNET_BODY_IN_ENDSITE_EYE_RY_LKNEEY, //132 + MNET_BODY_IN_ENDSITE_EYE_RX_LFOOTX, //133 + MNET_BODY_IN_ENDSITE_EYE_RY_LFOOTY, //134 + MNET_BODY_IN_ENDSITE_EYE_LX_HIPX, //135 + MNET_BODY_IN_ENDSITE_EYE_LY_HIPY, //136 + MNET_BODY_IN_ENDSITE_EYE_LX_ENDSITE_EYE_RX, //137 + MNET_BODY_IN_ENDSITE_EYE_LY_ENDSITE_EYE_RY, //138 + MNET_BODY_IN_ENDSITE_EYE_LX_ENDSITE_EYE_LX, //139 + MNET_BODY_IN_ENDSITE_EYE_LY_ENDSITE_EYE_LY, //140 + MNET_BODY_IN_ENDSITE_EYE_LX_NECKX, //141 + MNET_BODY_IN_ENDSITE_EYE_LY_NECKY, //142 + MNET_BODY_IN_ENDSITE_EYE_LX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //143 + MNET_BODY_IN_ENDSITE_EYE_LY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //144 + MNET_BODY_IN_ENDSITE_EYE_LX_RSHOULDERX, //145 + MNET_BODY_IN_ENDSITE_EYE_LY_RSHOULDERY, //146 + MNET_BODY_IN_ENDSITE_EYE_LX_RELBOWX, //147 + MNET_BODY_IN_ENDSITE_EYE_LY_RELBOWY, //148 + MNET_BODY_IN_ENDSITE_EYE_LX_RHANDX, //149 + MNET_BODY_IN_ENDSITE_EYE_LY_RHANDY, //150 + MNET_BODY_IN_ENDSITE_EYE_LX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //151 + MNET_BODY_IN_ENDSITE_EYE_LY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //152 + MNET_BODY_IN_ENDSITE_EYE_LX_LSHOULDERX, //153 + MNET_BODY_IN_ENDSITE_EYE_LY_LSHOULDERY, //154 + MNET_BODY_IN_ENDSITE_EYE_LX_LELBOWX, //155 + MNET_BODY_IN_ENDSITE_EYE_LY_LELBOWY, //156 + MNET_BODY_IN_ENDSITE_EYE_LX_LHANDX, //157 + MNET_BODY_IN_ENDSITE_EYE_LY_LHANDY, //158 + MNET_BODY_IN_ENDSITE_EYE_LX_RHIPX, //159 + MNET_BODY_IN_ENDSITE_EYE_LY_RHIPY, //160 + MNET_BODY_IN_ENDSITE_EYE_LX_RKNEEX, //161 + MNET_BODY_IN_ENDSITE_EYE_LY_RKNEEY, //162 + MNET_BODY_IN_ENDSITE_EYE_LX_RFOOTX, //163 + MNET_BODY_IN_ENDSITE_EYE_LY_RFOOTY, //164 + MNET_BODY_IN_ENDSITE_EYE_LX_LHIPX, //165 + MNET_BODY_IN_ENDSITE_EYE_LY_LHIPY, //166 + MNET_BODY_IN_ENDSITE_EYE_LX_LKNEEX, //167 + MNET_BODY_IN_ENDSITE_EYE_LY_LKNEEY, //168 + MNET_BODY_IN_ENDSITE_EYE_LX_LFOOTX, //169 + MNET_BODY_IN_ENDSITE_EYE_LY_LFOOTY, //170 + MNET_BODY_IN_NECKX_HIPX, //171 + MNET_BODY_IN_NECKY_HIPY, //172 + MNET_BODY_IN_NECKX_ENDSITE_EYE_RX, //173 + MNET_BODY_IN_NECKY_ENDSITE_EYE_RY, //174 + MNET_BODY_IN_NECKX_ENDSITE_EYE_LX, //175 + MNET_BODY_IN_NECKY_ENDSITE_EYE_LY, //176 + MNET_BODY_IN_NECKX_NECKX, //177 + MNET_BODY_IN_NECKY_NECKY, //178 + MNET_BODY_IN_NECKX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //179 + MNET_BODY_IN_NECKY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //180 + MNET_BODY_IN_NECKX_RSHOULDERX, //181 + MNET_BODY_IN_NECKY_RSHOULDERY, //182 + MNET_BODY_IN_NECKX_RELBOWX, //183 + MNET_BODY_IN_NECKY_RELBOWY, //184 + MNET_BODY_IN_NECKX_RHANDX, //185 + MNET_BODY_IN_NECKY_RHANDY, //186 + MNET_BODY_IN_NECKX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //187 + MNET_BODY_IN_NECKY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //188 + MNET_BODY_IN_NECKX_LSHOULDERX, //189 + MNET_BODY_IN_NECKY_LSHOULDERY, //190 + MNET_BODY_IN_NECKX_LELBOWX, //191 + MNET_BODY_IN_NECKY_LELBOWY, //192 + MNET_BODY_IN_NECKX_LHANDX, //193 + MNET_BODY_IN_NECKY_LHANDY, //194 + MNET_BODY_IN_NECKX_RHIPX, //195 + MNET_BODY_IN_NECKY_RHIPY, //196 + MNET_BODY_IN_NECKX_RKNEEX, //197 + MNET_BODY_IN_NECKY_RKNEEY, //198 + MNET_BODY_IN_NECKX_RFOOTX, //199 + MNET_BODY_IN_NECKY_RFOOTY, //200 + MNET_BODY_IN_NECKX_LHIPX, //201 + MNET_BODY_IN_NECKY_LHIPY, //202 + MNET_BODY_IN_NECKX_LKNEEX, //203 + MNET_BODY_IN_NECKY_LKNEEY, //204 + MNET_BODY_IN_NECKX_LFOOTX, //205 + MNET_BODY_IN_NECKY_LFOOTY, //206 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_HIPX, //207 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_HIPY, //208 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_ENDSITE_EYE_RX, //209 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ENDSITE_EYE_RY, //210 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_ENDSITE_EYE_LX, //211 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ENDSITE_EYE_LY, //212 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_NECKX, //213 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_NECKY, //214 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //215 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //216 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_RSHOULDERX, //217 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RSHOULDERY, //218 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_RELBOWX, //219 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RELBOWY, //220 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_RHANDX, //221 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RHANDY, //222 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //223 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //224 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_LSHOULDERX, //225 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LSHOULDERY, //226 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_LELBOWX, //227 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LELBOWY, //228 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_LHANDX, //229 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LHANDY, //230 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_RHIPX, //231 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RHIPY, //232 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_RKNEEX, //233 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RKNEEY, //234 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_RFOOTX, //235 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RFOOTY, //236 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_LHIPX, //237 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LHIPY, //238 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_LKNEEX, //239 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LKNEEY, //240 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0X_LFOOTX, //241 + MNET_BODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LFOOTY, //242 + MNET_BODY_IN_RSHOULDERX_HIPX, //243 + MNET_BODY_IN_RSHOULDERY_HIPY, //244 + MNET_BODY_IN_RSHOULDERX_ENDSITE_EYE_RX, //245 + MNET_BODY_IN_RSHOULDERY_ENDSITE_EYE_RY, //246 + MNET_BODY_IN_RSHOULDERX_ENDSITE_EYE_LX, //247 + MNET_BODY_IN_RSHOULDERY_ENDSITE_EYE_LY, //248 + MNET_BODY_IN_RSHOULDERX_NECKX, //249 + MNET_BODY_IN_RSHOULDERY_NECKY, //250 + MNET_BODY_IN_RSHOULDERX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //251 + MNET_BODY_IN_RSHOULDERY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //252 + MNET_BODY_IN_RSHOULDERX_RSHOULDERX, //253 + MNET_BODY_IN_RSHOULDERY_RSHOULDERY, //254 + MNET_BODY_IN_RSHOULDERX_RELBOWX, //255 + MNET_BODY_IN_RSHOULDERY_RELBOWY, //256 + MNET_BODY_IN_RSHOULDERX_RHANDX, //257 + MNET_BODY_IN_RSHOULDERY_RHANDY, //258 + MNET_BODY_IN_RSHOULDERX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //259 + MNET_BODY_IN_RSHOULDERY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //260 + MNET_BODY_IN_RSHOULDERX_LSHOULDERX, //261 + MNET_BODY_IN_RSHOULDERY_LSHOULDERY, //262 + MNET_BODY_IN_RSHOULDERX_LELBOWX, //263 + MNET_BODY_IN_RSHOULDERY_LELBOWY, //264 + MNET_BODY_IN_RSHOULDERX_LHANDX, //265 + MNET_BODY_IN_RSHOULDERY_LHANDY, //266 + MNET_BODY_IN_RSHOULDERX_RHIPX, //267 + MNET_BODY_IN_RSHOULDERY_RHIPY, //268 + MNET_BODY_IN_RSHOULDERX_RKNEEX, //269 + MNET_BODY_IN_RSHOULDERY_RKNEEY, //270 + MNET_BODY_IN_RSHOULDERX_RFOOTX, //271 + MNET_BODY_IN_RSHOULDERY_RFOOTY, //272 + MNET_BODY_IN_RSHOULDERX_LHIPX, //273 + MNET_BODY_IN_RSHOULDERY_LHIPY, //274 + MNET_BODY_IN_RSHOULDERX_LKNEEX, //275 + MNET_BODY_IN_RSHOULDERY_LKNEEY, //276 + MNET_BODY_IN_RSHOULDERX_LFOOTX, //277 + MNET_BODY_IN_RSHOULDERY_LFOOTY, //278 + MNET_BODY_IN_RELBOWX_HIPX, //279 + MNET_BODY_IN_RELBOWY_HIPY, //280 + MNET_BODY_IN_RELBOWX_ENDSITE_EYE_RX, //281 + MNET_BODY_IN_RELBOWY_ENDSITE_EYE_RY, //282 + MNET_BODY_IN_RELBOWX_ENDSITE_EYE_LX, //283 + MNET_BODY_IN_RELBOWY_ENDSITE_EYE_LY, //284 + MNET_BODY_IN_RELBOWX_NECKX, //285 + MNET_BODY_IN_RELBOWY_NECKY, //286 + MNET_BODY_IN_RELBOWX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //287 + MNET_BODY_IN_RELBOWY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //288 + MNET_BODY_IN_RELBOWX_RSHOULDERX, //289 + MNET_BODY_IN_RELBOWY_RSHOULDERY, //290 + MNET_BODY_IN_RELBOWX_RELBOWX, //291 + MNET_BODY_IN_RELBOWY_RELBOWY, //292 + MNET_BODY_IN_RELBOWX_RHANDX, //293 + MNET_BODY_IN_RELBOWY_RHANDY, //294 + MNET_BODY_IN_RELBOWX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //295 + MNET_BODY_IN_RELBOWY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //296 + MNET_BODY_IN_RELBOWX_LSHOULDERX, //297 + MNET_BODY_IN_RELBOWY_LSHOULDERY, //298 + MNET_BODY_IN_RELBOWX_LELBOWX, //299 + MNET_BODY_IN_RELBOWY_LELBOWY, //300 + MNET_BODY_IN_RELBOWX_LHANDX, //301 + MNET_BODY_IN_RELBOWY_LHANDY, //302 + MNET_BODY_IN_RELBOWX_RHIPX, //303 + MNET_BODY_IN_RELBOWY_RHIPY, //304 + MNET_BODY_IN_RELBOWX_RKNEEX, //305 + MNET_BODY_IN_RELBOWY_RKNEEY, //306 + MNET_BODY_IN_RELBOWX_RFOOTX, //307 + MNET_BODY_IN_RELBOWY_RFOOTY, //308 + MNET_BODY_IN_RELBOWX_LHIPX, //309 + MNET_BODY_IN_RELBOWY_LHIPY, //310 + MNET_BODY_IN_RELBOWX_LKNEEX, //311 + MNET_BODY_IN_RELBOWY_LKNEEY, //312 + MNET_BODY_IN_RELBOWX_LFOOTX, //313 + MNET_BODY_IN_RELBOWY_LFOOTY, //314 + MNET_BODY_IN_RHANDX_HIPX, //315 + MNET_BODY_IN_RHANDY_HIPY, //316 + MNET_BODY_IN_RHANDX_ENDSITE_EYE_RX, //317 + MNET_BODY_IN_RHANDY_ENDSITE_EYE_RY, //318 + MNET_BODY_IN_RHANDX_ENDSITE_EYE_LX, //319 + MNET_BODY_IN_RHANDY_ENDSITE_EYE_LY, //320 + MNET_BODY_IN_RHANDX_NECKX, //321 + MNET_BODY_IN_RHANDY_NECKY, //322 + MNET_BODY_IN_RHANDX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //323 + MNET_BODY_IN_RHANDY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //324 + MNET_BODY_IN_RHANDX_RSHOULDERX, //325 + MNET_BODY_IN_RHANDY_RSHOULDERY, //326 + MNET_BODY_IN_RHANDX_RELBOWX, //327 + MNET_BODY_IN_RHANDY_RELBOWY, //328 + MNET_BODY_IN_RHANDX_RHANDX, //329 + MNET_BODY_IN_RHANDY_RHANDY, //330 + MNET_BODY_IN_RHANDX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //331 + MNET_BODY_IN_RHANDY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //332 + MNET_BODY_IN_RHANDX_LSHOULDERX, //333 + MNET_BODY_IN_RHANDY_LSHOULDERY, //334 + MNET_BODY_IN_RHANDX_LELBOWX, //335 + MNET_BODY_IN_RHANDY_LELBOWY, //336 + MNET_BODY_IN_RHANDX_LHANDX, //337 + MNET_BODY_IN_RHANDY_LHANDY, //338 + MNET_BODY_IN_RHANDX_RHIPX, //339 + MNET_BODY_IN_RHANDY_RHIPY, //340 + MNET_BODY_IN_RHANDX_RKNEEX, //341 + MNET_BODY_IN_RHANDY_RKNEEY, //342 + MNET_BODY_IN_RHANDX_RFOOTX, //343 + MNET_BODY_IN_RHANDY_RFOOTY, //344 + MNET_BODY_IN_RHANDX_LHIPX, //345 + MNET_BODY_IN_RHANDY_LHIPY, //346 + MNET_BODY_IN_RHANDX_LKNEEX, //347 + MNET_BODY_IN_RHANDY_LKNEEY, //348 + MNET_BODY_IN_RHANDX_LFOOTX, //349 + MNET_BODY_IN_RHANDY_LFOOTY, //350 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_HIPX, //351 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_HIPY, //352 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_ENDSITE_EYE_RX, //353 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ENDSITE_EYE_RY, //354 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_ENDSITE_EYE_LX, //355 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ENDSITE_EYE_LY, //356 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_NECKX, //357 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_NECKY, //358 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //359 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //360 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_RSHOULDERX, //361 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RSHOULDERY, //362 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_RELBOWX, //363 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RELBOWY, //364 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_RHANDX, //365 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RHANDY, //366 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //367 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //368 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_LSHOULDERX, //369 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LSHOULDERY, //370 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_LELBOWX, //371 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LELBOWY, //372 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_LHANDX, //373 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LHANDY, //374 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_RHIPX, //375 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RHIPY, //376 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_RKNEEX, //377 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RKNEEY, //378 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_RFOOTX, //379 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RFOOTY, //380 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_LHIPX, //381 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LHIPY, //382 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_LKNEEX, //383 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LKNEEY, //384 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0X_LFOOTX, //385 + MNET_BODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LFOOTY, //386 + MNET_BODY_IN_LSHOULDERX_HIPX, //387 + MNET_BODY_IN_LSHOULDERY_HIPY, //388 + MNET_BODY_IN_LSHOULDERX_ENDSITE_EYE_RX, //389 + MNET_BODY_IN_LSHOULDERY_ENDSITE_EYE_RY, //390 + MNET_BODY_IN_LSHOULDERX_ENDSITE_EYE_LX, //391 + MNET_BODY_IN_LSHOULDERY_ENDSITE_EYE_LY, //392 + MNET_BODY_IN_LSHOULDERX_NECKX, //393 + MNET_BODY_IN_LSHOULDERY_NECKY, //394 + MNET_BODY_IN_LSHOULDERX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //395 + MNET_BODY_IN_LSHOULDERY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //396 + MNET_BODY_IN_LSHOULDERX_RSHOULDERX, //397 + MNET_BODY_IN_LSHOULDERY_RSHOULDERY, //398 + MNET_BODY_IN_LSHOULDERX_RELBOWX, //399 + MNET_BODY_IN_LSHOULDERY_RELBOWY, //400 + MNET_BODY_IN_LSHOULDERX_RHANDX, //401 + MNET_BODY_IN_LSHOULDERY_RHANDY, //402 + MNET_BODY_IN_LSHOULDERX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //403 + MNET_BODY_IN_LSHOULDERY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //404 + MNET_BODY_IN_LSHOULDERX_LSHOULDERX, //405 + MNET_BODY_IN_LSHOULDERY_LSHOULDERY, //406 + MNET_BODY_IN_LSHOULDERX_LELBOWX, //407 + MNET_BODY_IN_LSHOULDERY_LELBOWY, //408 + MNET_BODY_IN_LSHOULDERX_LHANDX, //409 + MNET_BODY_IN_LSHOULDERY_LHANDY, //410 + MNET_BODY_IN_LSHOULDERX_RHIPX, //411 + MNET_BODY_IN_LSHOULDERY_RHIPY, //412 + MNET_BODY_IN_LSHOULDERX_RKNEEX, //413 + MNET_BODY_IN_LSHOULDERY_RKNEEY, //414 + MNET_BODY_IN_LSHOULDERX_RFOOTX, //415 + MNET_BODY_IN_LSHOULDERY_RFOOTY, //416 + MNET_BODY_IN_LSHOULDERX_LHIPX, //417 + MNET_BODY_IN_LSHOULDERY_LHIPY, //418 + MNET_BODY_IN_LSHOULDERX_LKNEEX, //419 + MNET_BODY_IN_LSHOULDERY_LKNEEY, //420 + MNET_BODY_IN_LSHOULDERX_LFOOTX, //421 + MNET_BODY_IN_LSHOULDERY_LFOOTY, //422 + MNET_BODY_IN_LELBOWX_HIPX, //423 + MNET_BODY_IN_LELBOWY_HIPY, //424 + MNET_BODY_IN_LELBOWX_ENDSITE_EYE_RX, //425 + MNET_BODY_IN_LELBOWY_ENDSITE_EYE_RY, //426 + MNET_BODY_IN_LELBOWX_ENDSITE_EYE_LX, //427 + MNET_BODY_IN_LELBOWY_ENDSITE_EYE_LY, //428 + MNET_BODY_IN_LELBOWX_NECKX, //429 + MNET_BODY_IN_LELBOWY_NECKY, //430 + MNET_BODY_IN_LELBOWX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //431 + MNET_BODY_IN_LELBOWY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //432 + MNET_BODY_IN_LELBOWX_RSHOULDERX, //433 + MNET_BODY_IN_LELBOWY_RSHOULDERY, //434 + MNET_BODY_IN_LELBOWX_RELBOWX, //435 + MNET_BODY_IN_LELBOWY_RELBOWY, //436 + MNET_BODY_IN_LELBOWX_RHANDX, //437 + MNET_BODY_IN_LELBOWY_RHANDY, //438 + MNET_BODY_IN_LELBOWX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //439 + MNET_BODY_IN_LELBOWY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //440 + MNET_BODY_IN_LELBOWX_LSHOULDERX, //441 + MNET_BODY_IN_LELBOWY_LSHOULDERY, //442 + MNET_BODY_IN_LELBOWX_LELBOWX, //443 + MNET_BODY_IN_LELBOWY_LELBOWY, //444 + MNET_BODY_IN_LELBOWX_LHANDX, //445 + MNET_BODY_IN_LELBOWY_LHANDY, //446 + MNET_BODY_IN_LELBOWX_RHIPX, //447 + MNET_BODY_IN_LELBOWY_RHIPY, //448 + MNET_BODY_IN_LELBOWX_RKNEEX, //449 + MNET_BODY_IN_LELBOWY_RKNEEY, //450 + MNET_BODY_IN_LELBOWX_RFOOTX, //451 + MNET_BODY_IN_LELBOWY_RFOOTY, //452 + MNET_BODY_IN_LELBOWX_LHIPX, //453 + MNET_BODY_IN_LELBOWY_LHIPY, //454 + MNET_BODY_IN_LELBOWX_LKNEEX, //455 + MNET_BODY_IN_LELBOWY_LKNEEY, //456 + MNET_BODY_IN_LELBOWX_LFOOTX, //457 + MNET_BODY_IN_LELBOWY_LFOOTY, //458 + MNET_BODY_IN_LHANDX_HIPX, //459 + MNET_BODY_IN_LHANDY_HIPY, //460 + MNET_BODY_IN_LHANDX_ENDSITE_EYE_RX, //461 + MNET_BODY_IN_LHANDY_ENDSITE_EYE_RY, //462 + MNET_BODY_IN_LHANDX_ENDSITE_EYE_LX, //463 + MNET_BODY_IN_LHANDY_ENDSITE_EYE_LY, //464 + MNET_BODY_IN_LHANDX_NECKX, //465 + MNET_BODY_IN_LHANDY_NECKY, //466 + MNET_BODY_IN_LHANDX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //467 + MNET_BODY_IN_LHANDY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //468 + MNET_BODY_IN_LHANDX_RSHOULDERX, //469 + MNET_BODY_IN_LHANDY_RSHOULDERY, //470 + MNET_BODY_IN_LHANDX_RELBOWX, //471 + MNET_BODY_IN_LHANDY_RELBOWY, //472 + MNET_BODY_IN_LHANDX_RHANDX, //473 + MNET_BODY_IN_LHANDY_RHANDY, //474 + MNET_BODY_IN_LHANDX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //475 + MNET_BODY_IN_LHANDY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //476 + MNET_BODY_IN_LHANDX_LSHOULDERX, //477 + MNET_BODY_IN_LHANDY_LSHOULDERY, //478 + MNET_BODY_IN_LHANDX_LELBOWX, //479 + MNET_BODY_IN_LHANDY_LELBOWY, //480 + MNET_BODY_IN_LHANDX_LHANDX, //481 + MNET_BODY_IN_LHANDY_LHANDY, //482 + MNET_BODY_IN_LHANDX_RHIPX, //483 + MNET_BODY_IN_LHANDY_RHIPY, //484 + MNET_BODY_IN_LHANDX_RKNEEX, //485 + MNET_BODY_IN_LHANDY_RKNEEY, //486 + MNET_BODY_IN_LHANDX_RFOOTX, //487 + MNET_BODY_IN_LHANDY_RFOOTY, //488 + MNET_BODY_IN_LHANDX_LHIPX, //489 + MNET_BODY_IN_LHANDY_LHIPY, //490 + MNET_BODY_IN_LHANDX_LKNEEX, //491 + MNET_BODY_IN_LHANDY_LKNEEY, //492 + MNET_BODY_IN_LHANDX_LFOOTX, //493 + MNET_BODY_IN_LHANDY_LFOOTY, //494 + MNET_BODY_IN_RHIPX_HIPX, //495 + MNET_BODY_IN_RHIPY_HIPY, //496 + MNET_BODY_IN_RHIPX_ENDSITE_EYE_RX, //497 + MNET_BODY_IN_RHIPY_ENDSITE_EYE_RY, //498 + MNET_BODY_IN_RHIPX_ENDSITE_EYE_LX, //499 + MNET_BODY_IN_RHIPY_ENDSITE_EYE_LY, //500 + MNET_BODY_IN_RHIPX_NECKX, //501 + MNET_BODY_IN_RHIPY_NECKY, //502 + MNET_BODY_IN_RHIPX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //503 + MNET_BODY_IN_RHIPY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //504 + MNET_BODY_IN_RHIPX_RSHOULDERX, //505 + MNET_BODY_IN_RHIPY_RSHOULDERY, //506 + MNET_BODY_IN_RHIPX_RELBOWX, //507 + MNET_BODY_IN_RHIPY_RELBOWY, //508 + MNET_BODY_IN_RHIPX_RHANDX, //509 + MNET_BODY_IN_RHIPY_RHANDY, //510 + MNET_BODY_IN_RHIPX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //511 + MNET_BODY_IN_RHIPY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //512 + MNET_BODY_IN_RHIPX_LSHOULDERX, //513 + MNET_BODY_IN_RHIPY_LSHOULDERY, //514 + MNET_BODY_IN_RHIPX_LELBOWX, //515 + MNET_BODY_IN_RHIPY_LELBOWY, //516 + MNET_BODY_IN_RHIPX_LHANDX, //517 + MNET_BODY_IN_RHIPY_LHANDY, //518 + MNET_BODY_IN_RHIPX_RHIPX, //519 + MNET_BODY_IN_RHIPY_RHIPY, //520 + MNET_BODY_IN_RHIPX_RKNEEX, //521 + MNET_BODY_IN_RHIPY_RKNEEY, //522 + MNET_BODY_IN_RHIPX_RFOOTX, //523 + MNET_BODY_IN_RHIPY_RFOOTY, //524 + MNET_BODY_IN_RHIPX_LHIPX, //525 + MNET_BODY_IN_RHIPY_LHIPY, //526 + MNET_BODY_IN_RHIPX_LKNEEX, //527 + MNET_BODY_IN_RHIPY_LKNEEY, //528 + MNET_BODY_IN_RHIPX_LFOOTX, //529 + MNET_BODY_IN_RHIPY_LFOOTY, //530 + MNET_BODY_IN_RKNEEX_HIPX, //531 + MNET_BODY_IN_RKNEEY_HIPY, //532 + MNET_BODY_IN_RKNEEX_ENDSITE_EYE_RX, //533 + MNET_BODY_IN_RKNEEY_ENDSITE_EYE_RY, //534 + MNET_BODY_IN_RKNEEX_ENDSITE_EYE_LX, //535 + MNET_BODY_IN_RKNEEY_ENDSITE_EYE_LY, //536 + MNET_BODY_IN_RKNEEX_NECKX, //537 + MNET_BODY_IN_RKNEEY_NECKY, //538 + MNET_BODY_IN_RKNEEX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //539 + MNET_BODY_IN_RKNEEY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //540 + MNET_BODY_IN_RKNEEX_RSHOULDERX, //541 + MNET_BODY_IN_RKNEEY_RSHOULDERY, //542 + MNET_BODY_IN_RKNEEX_RELBOWX, //543 + MNET_BODY_IN_RKNEEY_RELBOWY, //544 + MNET_BODY_IN_RKNEEX_RHANDX, //545 + MNET_BODY_IN_RKNEEY_RHANDY, //546 + MNET_BODY_IN_RKNEEX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //547 + MNET_BODY_IN_RKNEEY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //548 + MNET_BODY_IN_RKNEEX_LSHOULDERX, //549 + MNET_BODY_IN_RKNEEY_LSHOULDERY, //550 + MNET_BODY_IN_RKNEEX_LELBOWX, //551 + MNET_BODY_IN_RKNEEY_LELBOWY, //552 + MNET_BODY_IN_RKNEEX_LHANDX, //553 + MNET_BODY_IN_RKNEEY_LHANDY, //554 + MNET_BODY_IN_RKNEEX_RHIPX, //555 + MNET_BODY_IN_RKNEEY_RHIPY, //556 + MNET_BODY_IN_RKNEEX_RKNEEX, //557 + MNET_BODY_IN_RKNEEY_RKNEEY, //558 + MNET_BODY_IN_RKNEEX_RFOOTX, //559 + MNET_BODY_IN_RKNEEY_RFOOTY, //560 + MNET_BODY_IN_RKNEEX_LHIPX, //561 + MNET_BODY_IN_RKNEEY_LHIPY, //562 + MNET_BODY_IN_RKNEEX_LKNEEX, //563 + MNET_BODY_IN_RKNEEY_LKNEEY, //564 + MNET_BODY_IN_RKNEEX_LFOOTX, //565 + MNET_BODY_IN_RKNEEY_LFOOTY, //566 + MNET_BODY_IN_RFOOTX_HIPX, //567 + MNET_BODY_IN_RFOOTY_HIPY, //568 + MNET_BODY_IN_RFOOTX_ENDSITE_EYE_RX, //569 + MNET_BODY_IN_RFOOTY_ENDSITE_EYE_RY, //570 + MNET_BODY_IN_RFOOTX_ENDSITE_EYE_LX, //571 + MNET_BODY_IN_RFOOTY_ENDSITE_EYE_LY, //572 + MNET_BODY_IN_RFOOTX_NECKX, //573 + MNET_BODY_IN_RFOOTY_NECKY, //574 + MNET_BODY_IN_RFOOTX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //575 + MNET_BODY_IN_RFOOTY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //576 + MNET_BODY_IN_RFOOTX_RSHOULDERX, //577 + MNET_BODY_IN_RFOOTY_RSHOULDERY, //578 + MNET_BODY_IN_RFOOTX_RELBOWX, //579 + MNET_BODY_IN_RFOOTY_RELBOWY, //580 + MNET_BODY_IN_RFOOTX_RHANDX, //581 + MNET_BODY_IN_RFOOTY_RHANDY, //582 + MNET_BODY_IN_RFOOTX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //583 + MNET_BODY_IN_RFOOTY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //584 + MNET_BODY_IN_RFOOTX_LSHOULDERX, //585 + MNET_BODY_IN_RFOOTY_LSHOULDERY, //586 + MNET_BODY_IN_RFOOTX_LELBOWX, //587 + MNET_BODY_IN_RFOOTY_LELBOWY, //588 + MNET_BODY_IN_RFOOTX_LHANDX, //589 + MNET_BODY_IN_RFOOTY_LHANDY, //590 + MNET_BODY_IN_RFOOTX_RHIPX, //591 + MNET_BODY_IN_RFOOTY_RHIPY, //592 + MNET_BODY_IN_RFOOTX_RKNEEX, //593 + MNET_BODY_IN_RFOOTY_RKNEEY, //594 + MNET_BODY_IN_RFOOTX_RFOOTX, //595 + MNET_BODY_IN_RFOOTY_RFOOTY, //596 + MNET_BODY_IN_RFOOTX_LHIPX, //597 + MNET_BODY_IN_RFOOTY_LHIPY, //598 + MNET_BODY_IN_RFOOTX_LKNEEX, //599 + MNET_BODY_IN_RFOOTY_LKNEEY, //600 + MNET_BODY_IN_RFOOTX_LFOOTX, //601 + MNET_BODY_IN_RFOOTY_LFOOTY, //602 + MNET_BODY_IN_LHIPX_HIPX, //603 + MNET_BODY_IN_LHIPY_HIPY, //604 + MNET_BODY_IN_LHIPX_ENDSITE_EYE_RX, //605 + MNET_BODY_IN_LHIPY_ENDSITE_EYE_RY, //606 + MNET_BODY_IN_LHIPX_ENDSITE_EYE_LX, //607 + MNET_BODY_IN_LHIPY_ENDSITE_EYE_LY, //608 + MNET_BODY_IN_LHIPX_NECKX, //609 + MNET_BODY_IN_LHIPY_NECKY, //610 + MNET_BODY_IN_LHIPX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //611 + MNET_BODY_IN_LHIPY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //612 + MNET_BODY_IN_LHIPX_RSHOULDERX, //613 + MNET_BODY_IN_LHIPY_RSHOULDERY, //614 + MNET_BODY_IN_LHIPX_RELBOWX, //615 + MNET_BODY_IN_LHIPY_RELBOWY, //616 + MNET_BODY_IN_LHIPX_RHANDX, //617 + MNET_BODY_IN_LHIPY_RHANDY, //618 + MNET_BODY_IN_LHIPX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //619 + MNET_BODY_IN_LHIPY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //620 + MNET_BODY_IN_LHIPX_LSHOULDERX, //621 + MNET_BODY_IN_LHIPY_LSHOULDERY, //622 + MNET_BODY_IN_LHIPX_LELBOWX, //623 + MNET_BODY_IN_LHIPY_LELBOWY, //624 + MNET_BODY_IN_LHIPX_LHANDX, //625 + MNET_BODY_IN_LHIPY_LHANDY, //626 + MNET_BODY_IN_LHIPX_RHIPX, //627 + MNET_BODY_IN_LHIPY_RHIPY, //628 + MNET_BODY_IN_LHIPX_RKNEEX, //629 + MNET_BODY_IN_LHIPY_RKNEEY, //630 + MNET_BODY_IN_LHIPX_RFOOTX, //631 + MNET_BODY_IN_LHIPY_RFOOTY, //632 + MNET_BODY_IN_LHIPX_LHIPX, //633 + MNET_BODY_IN_LHIPY_LHIPY, //634 + MNET_BODY_IN_LHIPX_LKNEEX, //635 + MNET_BODY_IN_LHIPY_LKNEEY, //636 + MNET_BODY_IN_LHIPX_LFOOTX, //637 + MNET_BODY_IN_LHIPY_LFOOTY, //638 + MNET_BODY_IN_LKNEEX_HIPX, //639 + MNET_BODY_IN_LKNEEY_HIPY, //640 + MNET_BODY_IN_LKNEEX_ENDSITE_EYE_RX, //641 + MNET_BODY_IN_LKNEEY_ENDSITE_EYE_RY, //642 + MNET_BODY_IN_LKNEEX_ENDSITE_EYE_LX, //643 + MNET_BODY_IN_LKNEEY_ENDSITE_EYE_LY, //644 + MNET_BODY_IN_LKNEEX_NECKX, //645 + MNET_BODY_IN_LKNEEY_NECKY, //646 + MNET_BODY_IN_LKNEEX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //647 + MNET_BODY_IN_LKNEEY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //648 + MNET_BODY_IN_LKNEEX_RSHOULDERX, //649 + MNET_BODY_IN_LKNEEY_RSHOULDERY, //650 + MNET_BODY_IN_LKNEEX_RELBOWX, //651 + MNET_BODY_IN_LKNEEY_RELBOWY, //652 + MNET_BODY_IN_LKNEEX_RHANDX, //653 + MNET_BODY_IN_LKNEEY_RHANDY, //654 + MNET_BODY_IN_LKNEEX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //655 + MNET_BODY_IN_LKNEEY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //656 + MNET_BODY_IN_LKNEEX_LSHOULDERX, //657 + MNET_BODY_IN_LKNEEY_LSHOULDERY, //658 + MNET_BODY_IN_LKNEEX_LELBOWX, //659 + MNET_BODY_IN_LKNEEY_LELBOWY, //660 + MNET_BODY_IN_LKNEEX_LHANDX, //661 + MNET_BODY_IN_LKNEEY_LHANDY, //662 + MNET_BODY_IN_LKNEEX_RHIPX, //663 + MNET_BODY_IN_LKNEEY_RHIPY, //664 + MNET_BODY_IN_LKNEEX_RKNEEX, //665 + MNET_BODY_IN_LKNEEY_RKNEEY, //666 + MNET_BODY_IN_LKNEEX_RFOOTX, //667 + MNET_BODY_IN_LKNEEY_RFOOTY, //668 + MNET_BODY_IN_LKNEEX_LHIPX, //669 + MNET_BODY_IN_LKNEEY_LHIPY, //670 + MNET_BODY_IN_LKNEEX_LKNEEX, //671 + MNET_BODY_IN_LKNEEY_LKNEEY, //672 + MNET_BODY_IN_LKNEEX_LFOOTX, //673 + MNET_BODY_IN_LKNEEY_LFOOTY, //674 + MNET_BODY_IN_LFOOTX_HIPX, //675 + MNET_BODY_IN_LFOOTY_HIPY, //676 + MNET_BODY_IN_LFOOTX_ENDSITE_EYE_RX, //677 + MNET_BODY_IN_LFOOTY_ENDSITE_EYE_RY, //678 + MNET_BODY_IN_LFOOTX_ENDSITE_EYE_LX, //679 + MNET_BODY_IN_LFOOTY_ENDSITE_EYE_LY, //680 + MNET_BODY_IN_LFOOTX_NECKX, //681 + MNET_BODY_IN_LFOOTY_NECKY, //682 + MNET_BODY_IN_LFOOTX_VIRTUAL_HIP_X_PLUS0_3_Y_0X, //683 + MNET_BODY_IN_LFOOTY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y, //684 + MNET_BODY_IN_LFOOTX_RSHOULDERX, //685 + MNET_BODY_IN_LFOOTY_RSHOULDERY, //686 + MNET_BODY_IN_LFOOTX_RELBOWX, //687 + MNET_BODY_IN_LFOOTY_RELBOWY, //688 + MNET_BODY_IN_LFOOTX_RHANDX, //689 + MNET_BODY_IN_LFOOTY_RHANDY, //690 + MNET_BODY_IN_LFOOTX_VIRTUAL_HIP_X_MINUS_0_3_Y_0X, //691 + MNET_BODY_IN_LFOOTY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y, //692 + MNET_BODY_IN_LFOOTX_LSHOULDERX, //693 + MNET_BODY_IN_LFOOTY_LSHOULDERY, //694 + MNET_BODY_IN_LFOOTX_LELBOWX, //695 + MNET_BODY_IN_LFOOTY_LELBOWY, //696 + MNET_BODY_IN_LFOOTX_LHANDX, //697 + MNET_BODY_IN_LFOOTY_LHANDY, //698 + MNET_BODY_IN_LFOOTX_RHIPX, //699 + MNET_BODY_IN_LFOOTY_RHIPY, //700 + MNET_BODY_IN_LFOOTX_RKNEEX, //701 + MNET_BODY_IN_LFOOTY_RKNEEY, //702 + MNET_BODY_IN_LFOOTX_RFOOTX, //703 + MNET_BODY_IN_LFOOTY_RFOOTY, //704 + MNET_BODY_IN_LFOOTX_LHIPX, //705 + MNET_BODY_IN_LFOOTY_LHIPY, //706 + MNET_BODY_IN_LFOOTX_LKNEEX, //707 + MNET_BODY_IN_LFOOTY_LKNEEY, //708 + MNET_BODY_IN_LFOOTX_LFOOTX, //709 + MNET_BODY_IN_LFOOTY_LFOOTY, //710 + MNET_BODY_IN_NUMBER +}; + +/** @brief Programmer friendly enumerator of expected outputs*/ +enum mocapNET_Output_body_enum +{ + MOCAPNET_BODY_OUTPUT_HIP_XPOSITION = 0, //0 + MOCAPNET_BODY_OUTPUT_HIP_YPOSITION, //1 + MOCAPNET_BODY_OUTPUT_HIP_ZPOSITION, //2 + MOCAPNET_BODY_OUTPUT_HIP_ZROTATION, //3 + MOCAPNET_BODY_OUTPUT_HIP_YROTATION, //4 + MOCAPNET_BODY_OUTPUT_HIP_XROTATION, //5 + MOCAPNET_BODY_OUTPUT_ABDOMEN_ZROTATION, //6 + MOCAPNET_BODY_OUTPUT_ABDOMEN_XROTATION, //7 + MOCAPNET_BODY_OUTPUT_ABDOMEN_YROTATION, //8 + MOCAPNET_BODY_OUTPUT_CHEST_ZROTATION, //9 + MOCAPNET_BODY_OUTPUT_CHEST_XROTATION, //10 + MOCAPNET_BODY_OUTPUT_CHEST_YROTATION, //11 + MOCAPNET_BODY_OUTPUT_NECK_ZROTATION, //12 + MOCAPNET_BODY_OUTPUT_NECK_XROTATION, //13 + MOCAPNET_BODY_OUTPUT_NECK_YROTATION, //14 + MOCAPNET_BODY_OUTPUT_HEAD_ZROTATION, //15 + MOCAPNET_BODY_OUTPUT_HEAD_XROTATION, //16 + MOCAPNET_BODY_OUTPUT_HEAD_YROTATION, //17 + MOCAPNET_BODY_OUTPUT_EYE_L_ZROTATION, //18 + MOCAPNET_BODY_OUTPUT_EYE_L_XROTATION, //19 + MOCAPNET_BODY_OUTPUT_EYE_L_YROTATION, //20 + MOCAPNET_BODY_OUTPUT_EYE_R_ZROTATION, //21 + MOCAPNET_BODY_OUTPUT_EYE_R_XROTATION, //22 + MOCAPNET_BODY_OUTPUT_EYE_R_YROTATION, //23 + MOCAPNET_BODY_OUTPUT_RSHOULDER_ZROTATION, //24 + MOCAPNET_BODY_OUTPUT_RSHOULDER_XROTATION, //25 + MOCAPNET_BODY_OUTPUT_RSHOULDER_YROTATION, //26 + MOCAPNET_BODY_OUTPUT_RELBOW_ZROTATION, //27 + MOCAPNET_BODY_OUTPUT_RELBOW_XROTATION, //28 + MOCAPNET_BODY_OUTPUT_RELBOW_YROTATION, //29 + MOCAPNET_BODY_OUTPUT_RHAND_ZROTATION, //30 + MOCAPNET_BODY_OUTPUT_RHAND_XROTATION, //31 + MOCAPNET_BODY_OUTPUT_RHAND_YROTATION, //32 + MOCAPNET_BODY_OUTPUT_LSHOULDER_ZROTATION, //33 + MOCAPNET_BODY_OUTPUT_LSHOULDER_XROTATION, //34 + MOCAPNET_BODY_OUTPUT_LSHOULDER_YROTATION, //35 + MOCAPNET_BODY_OUTPUT_LELBOW_ZROTATION, //36 + MOCAPNET_BODY_OUTPUT_LELBOW_XROTATION, //37 + MOCAPNET_BODY_OUTPUT_LELBOW_YROTATION, //38 + MOCAPNET_BODY_OUTPUT_LHAND_ZROTATION, //39 + MOCAPNET_BODY_OUTPUT_LHAND_XROTATION, //40 + MOCAPNET_BODY_OUTPUT_LHAND_YROTATION, //41 + MOCAPNET_BODY_OUTPUT_RHIP_ZROTATION, //42 + MOCAPNET_BODY_OUTPUT_RHIP_XROTATION, //43 + MOCAPNET_BODY_OUTPUT_RHIP_YROTATION, //44 + MOCAPNET_BODY_OUTPUT_RKNEE_ZROTATION, //45 + MOCAPNET_BODY_OUTPUT_RKNEE_XROTATION, //46 + MOCAPNET_BODY_OUTPUT_RKNEE_YROTATION, //47 + MOCAPNET_BODY_OUTPUT_RFOOT_ZROTATION, //48 + MOCAPNET_BODY_OUTPUT_RFOOT_XROTATION, //49 + MOCAPNET_BODY_OUTPUT_RFOOT_YROTATION, //50 + MOCAPNET_BODY_OUTPUT_TOE1_2_R_ZROTATION, //51 + MOCAPNET_BODY_OUTPUT_TOE1_2_R_XROTATION, //52 + MOCAPNET_BODY_OUTPUT_TOE1_2_R_YROTATION, //53 + MOCAPNET_BODY_OUTPUT_TOE5_3_R_ZROTATION, //54 + MOCAPNET_BODY_OUTPUT_TOE5_3_R_XROTATION, //55 + MOCAPNET_BODY_OUTPUT_TOE5_3_R_YROTATION, //56 + MOCAPNET_BODY_OUTPUT_LHIP_ZROTATION, //57 + MOCAPNET_BODY_OUTPUT_LHIP_XROTATION, //58 + MOCAPNET_BODY_OUTPUT_LHIP_YROTATION, //59 + MOCAPNET_BODY_OUTPUT_LKNEE_ZROTATION, //60 + MOCAPNET_BODY_OUTPUT_LKNEE_XROTATION, //61 + MOCAPNET_BODY_OUTPUT_LKNEE_YROTATION, //62 + MOCAPNET_BODY_OUTPUT_LFOOT_ZROTATION, //63 + MOCAPNET_BODY_OUTPUT_LFOOT_XROTATION, //64 + MOCAPNET_BODY_OUTPUT_LFOOT_YROTATION, //65 + MOCAPNET_BODY_OUTPUT_TOE1_2_L_ZROTATION, //66 + MOCAPNET_BODY_OUTPUT_TOE1_2_L_XROTATION, //67 + MOCAPNET_BODY_OUTPUT_TOE1_2_L_YROTATION, //68 + MOCAPNET_BODY_OUTPUT_TOE5_3_L_ZROTATION, //69 + MOCAPNET_BODY_OUTPUT_TOE5_3_L_XROTATION, //70 + MOCAPNET_BODY_OUTPUT_TOE5_3_L_YROTATION, //71 + MOCAPNET_BODY_OUTPUT_NUMBER +}; + +/** @brief Programmer friendly enumerator of NSDM elments*/ +enum mocapNET_NSDM_body_enum +{ + MNET_NSDM_BODY_HIP = 0, //0 + MNET_NSDM_BODY_ENDSITE_EYE_R, //1 + MNET_NSDM_BODY_ENDSITE_EYE_L, //2 + MNET_NSDM_BODY_NECK, //3 + MNET_NSDM_BODY_VIRTUAL_HIP_X_PLUS0_3_Y_0, //4 + MNET_NSDM_BODY_RSHOULDER, //5 + MNET_NSDM_BODY_RELBOW, //6 + MNET_NSDM_BODY_RHAND, //7 + MNET_NSDM_BODY_VIRTUAL_HIP_X_MINUS_0_3_Y_0, //8 + MNET_NSDM_BODY_LSHOULDER, //9 + MNET_NSDM_BODY_LELBOW, //10 + MNET_NSDM_BODY_LHAND, //11 + MNET_NSDM_BODY_RHIP, //12 + MNET_NSDM_BODY_RKNEE, //13 + MNET_NSDM_BODY_RFOOT, //14 + MNET_NSDM_BODY_LHIP, //15 + MNET_NSDM_BODY_LKNEE, //16 + MNET_NSDM_BODY_LFOOT, //17 + MNET_NSDM_BODY_NUMBER +}; + +/** @brief This is a lookup table to immediately resolve referred Joints*/ +static const int mocapNET_ResolveJoint_body[] = +{ + 0, //0 + 4, //1 + 3, //2 + 1, //3 + 0, //4 + 5, //5 + 6, //6 + 7, //7 + 0, //8 + 8, //9 + 9, //10 + 10, //11 + 11, //12 + 12, //13 + 13, //14 + 16, //15 + 17, //16 + 18, //17 + 0//end of array +}; + +/** @brief This is a lookup table to immediately resolve referred Joints of second targets*/ +static const int mocapNET_ResolveSecondTargetJoint_body[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + 0, //9 + 0, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0, //17 + 0//end of array +}; + +/** @brief This is the configuration of NSDM elements : + * A value of 0 is a normal 2D point + * A value of 1 is a 2D point plus some offset + * A value of 2 is a virtual point between two 2D points */ +static const int mocapNET_ArtificialJoint_body[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 1, //4 + 0, //5 + 0, //6 + 0, //7 + 1, //8 + 0, //9 + 0, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0, //17 + 0//end of array +}; + +/** @brief These are X offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_body )*/ +static const float mocapNET_ArtificialJointXOffset_body[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0.3, //4 + 0, //5 + 0, //6 + 0, //7 + -0.3, //8 + 0, //9 + 0, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0, //17 + 0//end of array +}; + +/** @brief These are Y offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_body )*/ +static const float mocapNET_ArtificialJointYOffset_body[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + 0, //9 + 0, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0, //17 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as starting points for scaling vectors*/ +static const int mocapNET_ScalingStart_body[] = +{ + 0, //0 + 0, //1 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as ending points for scaling vectors*/ +static const int mocapNET_ScalingEnd_body[] = +{ + 5, //0 + 8, //1 + 0//end of array +}; + +/** @brief This function can be used to debug NSDM input and find in a user friendly what is missing..!*/ +static int bodyCountMissingNSDMElements(std::vector mocapNETInput,int verbose) +{ + unsigned int numberOfZeros=0; + for (int i=0; i skeletonSerialized %s\n ",mocapNET_body[i],labels[i]); + } +} + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistance_BODY(std::vector in,int jointA,int jointB) +{ + float aX=in[jointA*3+0]; + float aY=in[jointA*3+1]; + float bX=in[jointB*3+0]; + float bY=in[jointB*3+1]; + if ( ((aX==0) && (aY==0)) || ((bX==0) && (bY==0)) ) { + return 0.0; + } + + + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + +/** @brief This function returns a vector of NSDM values ready for use with the MocapNET body network */ +static std::vector bodyCreateNDSM(std::vector in,int havePositionalElements,int haveAngularElements,int doScaleCompensation) +{ + std::vector result; + for (int i=0; i0) + { + unsigned int numberOfDistanceSamples=0; + float sumOfDistanceSamples=0.0; + for ( int i=0; i0.0) + { + numberOfDistanceSamples=numberOfDistanceSamples+1; + sumOfDistanceSamples=sumOfDistanceSamples+distance; + } + } +//------------------------------------------------------------------------------------------------- + float scaleDistance=1.0; +//------------------------------------------------------------------------------------------------- + if (numberOfDistanceSamples>0) + { + scaleDistance=(float) sumOfDistanceSamples/numberOfDistanceSamples; + } +//------------------------------------------------------------------------------------------------- + if (scaleDistance!=1.0) + { + for (int i=0; i +#include +#include +#include "../tools.hpp" + +/** @brief This is an array of names for all uncompressed 2D inputs expected. */ +static const unsigned int mocapNET_InputLength_WithoutNSDM_lowerbody = 36; + +/** @brief An array of strings that contains the label for each expected input. */ +static const char * mocapNET_lowerbody[] = +{ + "2DX_hip", //0 + "2DY_hip", //1 + "visible_hip", //2 + "2DX_neck", //3 + "2DY_neck", //4 + "visible_neck", //5 + "2DX_rhip", //6 + "2DY_rhip", //7 + "visible_rhip", //8 + "2DX_rknee", //9 + "2DY_rknee", //10 + "visible_rknee", //11 + "2DX_rfoot", //12 + "2DY_rfoot", //13 + "visible_rfoot", //14 + "2DX_EndSite_toe1-2.r", //15 + "2DY_EndSite_toe1-2.r", //16 + "visible_EndSite_toe1-2.r", //17 + "2DX_EndSite_toe5-3.r", //18 + "2DY_EndSite_toe5-3.r", //19 + "visible_EndSite_toe5-3.r", //20 + "2DX_lhip", //21 + "2DY_lhip", //22 + "visible_lhip", //23 + "2DX_lknee", //24 + "2DY_lknee", //25 + "visible_lknee", //26 + "2DX_lfoot", //27 + "2DY_lfoot", //28 + "visible_lfoot", //29 + "2DX_EndSite_toe1-2.l", //30 + "2DY_EndSite_toe1-2.l", //31 + "visible_EndSite_toe1-2.l", //32 + "2DX_EndSite_toe5-3.l", //33 + "2DY_EndSite_toe5-3.l", //34 + "visible_EndSite_toe5-3.l", //35 +//This is where regular input ends and the NSDM data kicks in.. + "hipY-hipY-Angle", //36 + "hipY-rhipY-Angle", //37 + "hipY-halfway_rhip_and_rkneeY-Angle", //38 + "hipY-rkneeY-Angle", //39 + "hipY-halfway_rknee_and_rfootY-Angle", //40 + "hipY-rfootY-Angle", //41 + "hipY-EndSite_toe1-2.rY-Angle", //42 + "hipY-virtual_hip_x_plus0_3_y_0Y-Angle", //43 + "hipY-virtual_hip_x_minus_0_3_y_0Y-Angle", //44 + "hipY-lhipY-Angle", //45 + "hipY-halfway_lhip_and_lkneeY-Angle", //46 + "hipY-lkneeY-Angle", //47 + "hipY-halfway_lknee_and_lfootY-Angle", //48 + "hipY-lfootY-Angle", //49 + "hipY-EndSite_toe1-2.lY-Angle", //50 + "hipY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //51 + "hipY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //52 + "rhipY-hipY-Angle", //53 + "rhipY-rhipY-Angle", //54 + "rhipY-halfway_rhip_and_rkneeY-Angle", //55 + "rhipY-rkneeY-Angle", //56 + "rhipY-halfway_rknee_and_rfootY-Angle", //57 + "rhipY-rfootY-Angle", //58 + "rhipY-EndSite_toe1-2.rY-Angle", //59 + "rhipY-virtual_hip_x_plus0_3_y_0Y-Angle", //60 + "rhipY-virtual_hip_x_minus_0_3_y_0Y-Angle", //61 + "rhipY-lhipY-Angle", //62 + "rhipY-halfway_lhip_and_lkneeY-Angle", //63 + "rhipY-lkneeY-Angle", //64 + "rhipY-halfway_lknee_and_lfootY-Angle", //65 + "rhipY-lfootY-Angle", //66 + "rhipY-EndSite_toe1-2.lY-Angle", //67 + "rhipY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //68 + "rhipY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //69 + "halfway_rhip_and_rkneeY-hipY-Angle", //70 + "halfway_rhip_and_rkneeY-rhipY-Angle", //71 + "halfway_rhip_and_rkneeY-halfway_rhip_and_rkneeY-Angle", //72 + "halfway_rhip_and_rkneeY-rkneeY-Angle", //73 + "halfway_rhip_and_rkneeY-halfway_rknee_and_rfootY-Angle", //74 + "halfway_rhip_and_rkneeY-rfootY-Angle", //75 + "halfway_rhip_and_rkneeY-EndSite_toe1-2.rY-Angle", //76 + "halfway_rhip_and_rkneeY-virtual_hip_x_plus0_3_y_0Y-Angle", //77 + "halfway_rhip_and_rkneeY-virtual_hip_x_minus_0_3_y_0Y-Angle", //78 + "halfway_rhip_and_rkneeY-lhipY-Angle", //79 + "halfway_rhip_and_rkneeY-halfway_lhip_and_lkneeY-Angle", //80 + "halfway_rhip_and_rkneeY-lkneeY-Angle", //81 + "halfway_rhip_and_rkneeY-halfway_lknee_and_lfootY-Angle", //82 + "halfway_rhip_and_rkneeY-lfootY-Angle", //83 + "halfway_rhip_and_rkneeY-EndSite_toe1-2.lY-Angle", //84 + "halfway_rhip_and_rkneeY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //85 + "halfway_rhip_and_rkneeY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //86 + "rkneeY-hipY-Angle", //87 + "rkneeY-rhipY-Angle", //88 + "rkneeY-halfway_rhip_and_rkneeY-Angle", //89 + "rkneeY-rkneeY-Angle", //90 + "rkneeY-halfway_rknee_and_rfootY-Angle", //91 + "rkneeY-rfootY-Angle", //92 + "rkneeY-EndSite_toe1-2.rY-Angle", //93 + "rkneeY-virtual_hip_x_plus0_3_y_0Y-Angle", //94 + "rkneeY-virtual_hip_x_minus_0_3_y_0Y-Angle", //95 + "rkneeY-lhipY-Angle", //96 + "rkneeY-halfway_lhip_and_lkneeY-Angle", //97 + "rkneeY-lkneeY-Angle", //98 + "rkneeY-halfway_lknee_and_lfootY-Angle", //99 + "rkneeY-lfootY-Angle", //100 + "rkneeY-EndSite_toe1-2.lY-Angle", //101 + "rkneeY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //102 + "rkneeY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //103 + "halfway_rknee_and_rfootY-hipY-Angle", //104 + "halfway_rknee_and_rfootY-rhipY-Angle", //105 + "halfway_rknee_and_rfootY-halfway_rhip_and_rkneeY-Angle", //106 + "halfway_rknee_and_rfootY-rkneeY-Angle", //107 + "halfway_rknee_and_rfootY-halfway_rknee_and_rfootY-Angle", //108 + "halfway_rknee_and_rfootY-rfootY-Angle", //109 + "halfway_rknee_and_rfootY-EndSite_toe1-2.rY-Angle", //110 + "halfway_rknee_and_rfootY-virtual_hip_x_plus0_3_y_0Y-Angle", //111 + "halfway_rknee_and_rfootY-virtual_hip_x_minus_0_3_y_0Y-Angle", //112 + "halfway_rknee_and_rfootY-lhipY-Angle", //113 + "halfway_rknee_and_rfootY-halfway_lhip_and_lkneeY-Angle", //114 + "halfway_rknee_and_rfootY-lkneeY-Angle", //115 + "halfway_rknee_and_rfootY-halfway_lknee_and_lfootY-Angle", //116 + "halfway_rknee_and_rfootY-lfootY-Angle", //117 + "halfway_rknee_and_rfootY-EndSite_toe1-2.lY-Angle", //118 + "halfway_rknee_and_rfootY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //119 + "halfway_rknee_and_rfootY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //120 + "rfootY-hipY-Angle", //121 + "rfootY-rhipY-Angle", //122 + "rfootY-halfway_rhip_and_rkneeY-Angle", //123 + "rfootY-rkneeY-Angle", //124 + "rfootY-halfway_rknee_and_rfootY-Angle", //125 + "rfootY-rfootY-Angle", //126 + "rfootY-EndSite_toe1-2.rY-Angle", //127 + "rfootY-virtual_hip_x_plus0_3_y_0Y-Angle", //128 + "rfootY-virtual_hip_x_minus_0_3_y_0Y-Angle", //129 + "rfootY-lhipY-Angle", //130 + "rfootY-halfway_lhip_and_lkneeY-Angle", //131 + "rfootY-lkneeY-Angle", //132 + "rfootY-halfway_lknee_and_lfootY-Angle", //133 + "rfootY-lfootY-Angle", //134 + "rfootY-EndSite_toe1-2.lY-Angle", //135 + "rfootY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //136 + "rfootY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //137 + "EndSite_toe1-2.rY-hipY-Angle", //138 + "EndSite_toe1-2.rY-rhipY-Angle", //139 + "EndSite_toe1-2.rY-halfway_rhip_and_rkneeY-Angle", //140 + "EndSite_toe1-2.rY-rkneeY-Angle", //141 + "EndSite_toe1-2.rY-halfway_rknee_and_rfootY-Angle", //142 + "EndSite_toe1-2.rY-rfootY-Angle", //143 + "EndSite_toe1-2.rY-EndSite_toe1-2.rY-Angle", //144 + "EndSite_toe1-2.rY-virtual_hip_x_plus0_3_y_0Y-Angle", //145 + "EndSite_toe1-2.rY-virtual_hip_x_minus_0_3_y_0Y-Angle", //146 + "EndSite_toe1-2.rY-lhipY-Angle", //147 + "EndSite_toe1-2.rY-halfway_lhip_and_lkneeY-Angle", //148 + "EndSite_toe1-2.rY-lkneeY-Angle", //149 + "EndSite_toe1-2.rY-halfway_lknee_and_lfootY-Angle", //150 + "EndSite_toe1-2.rY-lfootY-Angle", //151 + "EndSite_toe1-2.rY-EndSite_toe1-2.lY-Angle", //152 + "EndSite_toe1-2.rY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //153 + "EndSite_toe1-2.rY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //154 + "virtual_hip_x_plus0_3_y_0Y-hipY-Angle", //155 + "virtual_hip_x_plus0_3_y_0Y-rhipY-Angle", //156 + "virtual_hip_x_plus0_3_y_0Y-halfway_rhip_and_rkneeY-Angle", //157 + "virtual_hip_x_plus0_3_y_0Y-rkneeY-Angle", //158 + "virtual_hip_x_plus0_3_y_0Y-halfway_rknee_and_rfootY-Angle", //159 + "virtual_hip_x_plus0_3_y_0Y-rfootY-Angle", //160 + "virtual_hip_x_plus0_3_y_0Y-EndSite_toe1-2.rY-Angle", //161 + "virtual_hip_x_plus0_3_y_0Y-virtual_hip_x_plus0_3_y_0Y-Angle", //162 + "virtual_hip_x_plus0_3_y_0Y-virtual_hip_x_minus_0_3_y_0Y-Angle", //163 + "virtual_hip_x_plus0_3_y_0Y-lhipY-Angle", //164 + "virtual_hip_x_plus0_3_y_0Y-halfway_lhip_and_lkneeY-Angle", //165 + "virtual_hip_x_plus0_3_y_0Y-lkneeY-Angle", //166 + "virtual_hip_x_plus0_3_y_0Y-halfway_lknee_and_lfootY-Angle", //167 + "virtual_hip_x_plus0_3_y_0Y-lfootY-Angle", //168 + "virtual_hip_x_plus0_3_y_0Y-EndSite_toe1-2.lY-Angle", //169 + "virtual_hip_x_plus0_3_y_0Y-virtual_hip_x_0_0_y_plus0_15Y-Angle", //170 + "virtual_hip_x_plus0_3_y_0Y-virtual_hip_x_0_0_y_plus0_3Y-Angle", //171 + "virtual_hip_x_minus_0_3_y_0Y-hipY-Angle", //172 + "virtual_hip_x_minus_0_3_y_0Y-rhipY-Angle", //173 + "virtual_hip_x_minus_0_3_y_0Y-halfway_rhip_and_rkneeY-Angle", //174 + "virtual_hip_x_minus_0_3_y_0Y-rkneeY-Angle", //175 + "virtual_hip_x_minus_0_3_y_0Y-halfway_rknee_and_rfootY-Angle", //176 + "virtual_hip_x_minus_0_3_y_0Y-rfootY-Angle", //177 + "virtual_hip_x_minus_0_3_y_0Y-EndSite_toe1-2.rY-Angle", //178 + "virtual_hip_x_minus_0_3_y_0Y-virtual_hip_x_plus0_3_y_0Y-Angle", //179 + "virtual_hip_x_minus_0_3_y_0Y-virtual_hip_x_minus_0_3_y_0Y-Angle", //180 + "virtual_hip_x_minus_0_3_y_0Y-lhipY-Angle", //181 + "virtual_hip_x_minus_0_3_y_0Y-halfway_lhip_and_lkneeY-Angle", //182 + "virtual_hip_x_minus_0_3_y_0Y-lkneeY-Angle", //183 + "virtual_hip_x_minus_0_3_y_0Y-halfway_lknee_and_lfootY-Angle", //184 + "virtual_hip_x_minus_0_3_y_0Y-lfootY-Angle", //185 + "virtual_hip_x_minus_0_3_y_0Y-EndSite_toe1-2.lY-Angle", //186 + "virtual_hip_x_minus_0_3_y_0Y-virtual_hip_x_0_0_y_plus0_15Y-Angle", //187 + "virtual_hip_x_minus_0_3_y_0Y-virtual_hip_x_0_0_y_plus0_3Y-Angle", //188 + "lhipY-hipY-Angle", //189 + "lhipY-rhipY-Angle", //190 + "lhipY-halfway_rhip_and_rkneeY-Angle", //191 + "lhipY-rkneeY-Angle", //192 + "lhipY-halfway_rknee_and_rfootY-Angle", //193 + "lhipY-rfootY-Angle", //194 + "lhipY-EndSite_toe1-2.rY-Angle", //195 + "lhipY-virtual_hip_x_plus0_3_y_0Y-Angle", //196 + "lhipY-virtual_hip_x_minus_0_3_y_0Y-Angle", //197 + "lhipY-lhipY-Angle", //198 + "lhipY-halfway_lhip_and_lkneeY-Angle", //199 + "lhipY-lkneeY-Angle", //200 + "lhipY-halfway_lknee_and_lfootY-Angle", //201 + "lhipY-lfootY-Angle", //202 + "lhipY-EndSite_toe1-2.lY-Angle", //203 + "lhipY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //204 + "lhipY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //205 + "halfway_lhip_and_lkneeY-hipY-Angle", //206 + "halfway_lhip_and_lkneeY-rhipY-Angle", //207 + "halfway_lhip_and_lkneeY-halfway_rhip_and_rkneeY-Angle", //208 + "halfway_lhip_and_lkneeY-rkneeY-Angle", //209 + "halfway_lhip_and_lkneeY-halfway_rknee_and_rfootY-Angle", //210 + "halfway_lhip_and_lkneeY-rfootY-Angle", //211 + "halfway_lhip_and_lkneeY-EndSite_toe1-2.rY-Angle", //212 + "halfway_lhip_and_lkneeY-virtual_hip_x_plus0_3_y_0Y-Angle", //213 + "halfway_lhip_and_lkneeY-virtual_hip_x_minus_0_3_y_0Y-Angle", //214 + "halfway_lhip_and_lkneeY-lhipY-Angle", //215 + "halfway_lhip_and_lkneeY-halfway_lhip_and_lkneeY-Angle", //216 + "halfway_lhip_and_lkneeY-lkneeY-Angle", //217 + "halfway_lhip_and_lkneeY-halfway_lknee_and_lfootY-Angle", //218 + "halfway_lhip_and_lkneeY-lfootY-Angle", //219 + "halfway_lhip_and_lkneeY-EndSite_toe1-2.lY-Angle", //220 + "halfway_lhip_and_lkneeY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //221 + "halfway_lhip_and_lkneeY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //222 + "lkneeY-hipY-Angle", //223 + "lkneeY-rhipY-Angle", //224 + "lkneeY-halfway_rhip_and_rkneeY-Angle", //225 + "lkneeY-rkneeY-Angle", //226 + "lkneeY-halfway_rknee_and_rfootY-Angle", //227 + "lkneeY-rfootY-Angle", //228 + "lkneeY-EndSite_toe1-2.rY-Angle", //229 + "lkneeY-virtual_hip_x_plus0_3_y_0Y-Angle", //230 + "lkneeY-virtual_hip_x_minus_0_3_y_0Y-Angle", //231 + "lkneeY-lhipY-Angle", //232 + "lkneeY-halfway_lhip_and_lkneeY-Angle", //233 + "lkneeY-lkneeY-Angle", //234 + "lkneeY-halfway_lknee_and_lfootY-Angle", //235 + "lkneeY-lfootY-Angle", //236 + "lkneeY-EndSite_toe1-2.lY-Angle", //237 + "lkneeY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //238 + "lkneeY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //239 + "halfway_lknee_and_lfootY-hipY-Angle", //240 + "halfway_lknee_and_lfootY-rhipY-Angle", //241 + "halfway_lknee_and_lfootY-halfway_rhip_and_rkneeY-Angle", //242 + "halfway_lknee_and_lfootY-rkneeY-Angle", //243 + "halfway_lknee_and_lfootY-halfway_rknee_and_rfootY-Angle", //244 + "halfway_lknee_and_lfootY-rfootY-Angle", //245 + "halfway_lknee_and_lfootY-EndSite_toe1-2.rY-Angle", //246 + "halfway_lknee_and_lfootY-virtual_hip_x_plus0_3_y_0Y-Angle", //247 + "halfway_lknee_and_lfootY-virtual_hip_x_minus_0_3_y_0Y-Angle", //248 + "halfway_lknee_and_lfootY-lhipY-Angle", //249 + "halfway_lknee_and_lfootY-halfway_lhip_and_lkneeY-Angle", //250 + "halfway_lknee_and_lfootY-lkneeY-Angle", //251 + "halfway_lknee_and_lfootY-halfway_lknee_and_lfootY-Angle", //252 + "halfway_lknee_and_lfootY-lfootY-Angle", //253 + "halfway_lknee_and_lfootY-EndSite_toe1-2.lY-Angle", //254 + "halfway_lknee_and_lfootY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //255 + "halfway_lknee_and_lfootY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //256 + "lfootY-hipY-Angle", //257 + "lfootY-rhipY-Angle", //258 + "lfootY-halfway_rhip_and_rkneeY-Angle", //259 + "lfootY-rkneeY-Angle", //260 + "lfootY-halfway_rknee_and_rfootY-Angle", //261 + "lfootY-rfootY-Angle", //262 + "lfootY-EndSite_toe1-2.rY-Angle", //263 + "lfootY-virtual_hip_x_plus0_3_y_0Y-Angle", //264 + "lfootY-virtual_hip_x_minus_0_3_y_0Y-Angle", //265 + "lfootY-lhipY-Angle", //266 + "lfootY-halfway_lhip_and_lkneeY-Angle", //267 + "lfootY-lkneeY-Angle", //268 + "lfootY-halfway_lknee_and_lfootY-Angle", //269 + "lfootY-lfootY-Angle", //270 + "lfootY-EndSite_toe1-2.lY-Angle", //271 + "lfootY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //272 + "lfootY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //273 + "EndSite_toe1-2.lY-hipY-Angle", //274 + "EndSite_toe1-2.lY-rhipY-Angle", //275 + "EndSite_toe1-2.lY-halfway_rhip_and_rkneeY-Angle", //276 + "EndSite_toe1-2.lY-rkneeY-Angle", //277 + "EndSite_toe1-2.lY-halfway_rknee_and_rfootY-Angle", //278 + "EndSite_toe1-2.lY-rfootY-Angle", //279 + "EndSite_toe1-2.lY-EndSite_toe1-2.rY-Angle", //280 + "EndSite_toe1-2.lY-virtual_hip_x_plus0_3_y_0Y-Angle", //281 + "EndSite_toe1-2.lY-virtual_hip_x_minus_0_3_y_0Y-Angle", //282 + "EndSite_toe1-2.lY-lhipY-Angle", //283 + "EndSite_toe1-2.lY-halfway_lhip_and_lkneeY-Angle", //284 + "EndSite_toe1-2.lY-lkneeY-Angle", //285 + "EndSite_toe1-2.lY-halfway_lknee_and_lfootY-Angle", //286 + "EndSite_toe1-2.lY-lfootY-Angle", //287 + "EndSite_toe1-2.lY-EndSite_toe1-2.lY-Angle", //288 + "EndSite_toe1-2.lY-virtual_hip_x_0_0_y_plus0_15Y-Angle", //289 + "EndSite_toe1-2.lY-virtual_hip_x_0_0_y_plus0_3Y-Angle", //290 + "virtual_hip_x_0_0_y_plus0_15Y-hipY-Angle", //291 + "virtual_hip_x_0_0_y_plus0_15Y-rhipY-Angle", //292 + "virtual_hip_x_0_0_y_plus0_15Y-halfway_rhip_and_rkneeY-Angle", //293 + "virtual_hip_x_0_0_y_plus0_15Y-rkneeY-Angle", //294 + "virtual_hip_x_0_0_y_plus0_15Y-halfway_rknee_and_rfootY-Angle", //295 + "virtual_hip_x_0_0_y_plus0_15Y-rfootY-Angle", //296 + "virtual_hip_x_0_0_y_plus0_15Y-EndSite_toe1-2.rY-Angle", //297 + "virtual_hip_x_0_0_y_plus0_15Y-virtual_hip_x_plus0_3_y_0Y-Angle", //298 + "virtual_hip_x_0_0_y_plus0_15Y-virtual_hip_x_minus_0_3_y_0Y-Angle", //299 + "virtual_hip_x_0_0_y_plus0_15Y-lhipY-Angle", //300 + "virtual_hip_x_0_0_y_plus0_15Y-halfway_lhip_and_lkneeY-Angle", //301 + "virtual_hip_x_0_0_y_plus0_15Y-lkneeY-Angle", //302 + "virtual_hip_x_0_0_y_plus0_15Y-halfway_lknee_and_lfootY-Angle", //303 + "virtual_hip_x_0_0_y_plus0_15Y-lfootY-Angle", //304 + "virtual_hip_x_0_0_y_plus0_15Y-EndSite_toe1-2.lY-Angle", //305 + "virtual_hip_x_0_0_y_plus0_15Y-virtual_hip_x_0_0_y_plus0_15Y-Angle", //306 + "virtual_hip_x_0_0_y_plus0_15Y-virtual_hip_x_0_0_y_plus0_3Y-Angle", //307 + "virtual_hip_x_0_0_y_plus0_3Y-hipY-Angle", //308 + "virtual_hip_x_0_0_y_plus0_3Y-rhipY-Angle", //309 + "virtual_hip_x_0_0_y_plus0_3Y-halfway_rhip_and_rkneeY-Angle", //310 + "virtual_hip_x_0_0_y_plus0_3Y-rkneeY-Angle", //311 + "virtual_hip_x_0_0_y_plus0_3Y-halfway_rknee_and_rfootY-Angle", //312 + "virtual_hip_x_0_0_y_plus0_3Y-rfootY-Angle", //313 + "virtual_hip_x_0_0_y_plus0_3Y-EndSite_toe1-2.rY-Angle", //314 + "virtual_hip_x_0_0_y_plus0_3Y-virtual_hip_x_plus0_3_y_0Y-Angle", //315 + "virtual_hip_x_0_0_y_plus0_3Y-virtual_hip_x_minus_0_3_y_0Y-Angle", //316 + "virtual_hip_x_0_0_y_plus0_3Y-lhipY-Angle", //317 + "virtual_hip_x_0_0_y_plus0_3Y-halfway_lhip_and_lkneeY-Angle", //318 + "virtual_hip_x_0_0_y_plus0_3Y-lkneeY-Angle", //319 + "virtual_hip_x_0_0_y_plus0_3Y-halfway_lknee_and_lfootY-Angle", //320 + "virtual_hip_x_0_0_y_plus0_3Y-lfootY-Angle", //321 + "virtual_hip_x_0_0_y_plus0_3Y-EndSite_toe1-2.lY-Angle", //322 + "virtual_hip_x_0_0_y_plus0_3Y-virtual_hip_x_0_0_y_plus0_15Y-Angle", //323 + "virtual_hip_x_0_0_y_plus0_3Y-virtual_hip_x_0_0_y_plus0_3Y-Angle", //324 + "end" +}; +/** @brief Programmer friendly enumerator of expected inputs*/ +enum mocapNET_lowerbody_enum +{ + MNET_LOWERBODY_IN_2DX_HIP = 0, //0 + MNET_LOWERBODY_IN_2DY_HIP, //1 + MNET_LOWERBODY_IN_VISIBLE_HIP, //2 + MNET_LOWERBODY_IN_2DX_NECK, //3 + MNET_LOWERBODY_IN_2DY_NECK, //4 + MNET_LOWERBODY_IN_VISIBLE_NECK, //5 + MNET_LOWERBODY_IN_2DX_RHIP, //6 + MNET_LOWERBODY_IN_2DY_RHIP, //7 + MNET_LOWERBODY_IN_VISIBLE_RHIP, //8 + MNET_LOWERBODY_IN_2DX_RKNEE, //9 + MNET_LOWERBODY_IN_2DY_RKNEE, //10 + MNET_LOWERBODY_IN_VISIBLE_RKNEE, //11 + MNET_LOWERBODY_IN_2DX_RFOOT, //12 + MNET_LOWERBODY_IN_2DY_RFOOT, //13 + MNET_LOWERBODY_IN_VISIBLE_RFOOT, //14 + MNET_LOWERBODY_IN_2DX_ENDSITE_TOE1_2_R, //15 + MNET_LOWERBODY_IN_2DY_ENDSITE_TOE1_2_R, //16 + MNET_LOWERBODY_IN_VISIBLE_ENDSITE_TOE1_2_R, //17 + MNET_LOWERBODY_IN_2DX_ENDSITE_TOE5_3_R, //18 + MNET_LOWERBODY_IN_2DY_ENDSITE_TOE5_3_R, //19 + MNET_LOWERBODY_IN_VISIBLE_ENDSITE_TOE5_3_R, //20 + MNET_LOWERBODY_IN_2DX_LHIP, //21 + MNET_LOWERBODY_IN_2DY_LHIP, //22 + MNET_LOWERBODY_IN_VISIBLE_LHIP, //23 + MNET_LOWERBODY_IN_2DX_LKNEE, //24 + MNET_LOWERBODY_IN_2DY_LKNEE, //25 + MNET_LOWERBODY_IN_VISIBLE_LKNEE, //26 + MNET_LOWERBODY_IN_2DX_LFOOT, //27 + MNET_LOWERBODY_IN_2DY_LFOOT, //28 + MNET_LOWERBODY_IN_VISIBLE_LFOOT, //29 + MNET_LOWERBODY_IN_2DX_ENDSITE_TOE1_2_L, //30 + MNET_LOWERBODY_IN_2DY_ENDSITE_TOE1_2_L, //31 + MNET_LOWERBODY_IN_VISIBLE_ENDSITE_TOE1_2_L, //32 + MNET_LOWERBODY_IN_2DX_ENDSITE_TOE5_3_L, //33 + MNET_LOWERBODY_IN_2DY_ENDSITE_TOE5_3_L, //34 + MNET_LOWERBODY_IN_VISIBLE_ENDSITE_TOE5_3_L, //35 + MNET_LOWERBODY_IN_HIPY_HIPY_ANGLE, //36 + MNET_LOWERBODY_IN_HIPY_RHIPY_ANGLE, //37 + MNET_LOWERBODY_IN_HIPY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //38 + MNET_LOWERBODY_IN_HIPY_RKNEEY_ANGLE, //39 + MNET_LOWERBODY_IN_HIPY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //40 + MNET_LOWERBODY_IN_HIPY_RFOOTY_ANGLE, //41 + MNET_LOWERBODY_IN_HIPY_ENDSITE_TOE1_2_RY_ANGLE, //42 + MNET_LOWERBODY_IN_HIPY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //43 + MNET_LOWERBODY_IN_HIPY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //44 + MNET_LOWERBODY_IN_HIPY_LHIPY_ANGLE, //45 + MNET_LOWERBODY_IN_HIPY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //46 + MNET_LOWERBODY_IN_HIPY_LKNEEY_ANGLE, //47 + MNET_LOWERBODY_IN_HIPY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //48 + MNET_LOWERBODY_IN_HIPY_LFOOTY_ANGLE, //49 + MNET_LOWERBODY_IN_HIPY_ENDSITE_TOE1_2_LY_ANGLE, //50 + MNET_LOWERBODY_IN_HIPY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //51 + MNET_LOWERBODY_IN_HIPY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //52 + MNET_LOWERBODY_IN_RHIPY_HIPY_ANGLE, //53 + MNET_LOWERBODY_IN_RHIPY_RHIPY_ANGLE, //54 + MNET_LOWERBODY_IN_RHIPY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //55 + MNET_LOWERBODY_IN_RHIPY_RKNEEY_ANGLE, //56 + MNET_LOWERBODY_IN_RHIPY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //57 + MNET_LOWERBODY_IN_RHIPY_RFOOTY_ANGLE, //58 + MNET_LOWERBODY_IN_RHIPY_ENDSITE_TOE1_2_RY_ANGLE, //59 + MNET_LOWERBODY_IN_RHIPY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //60 + MNET_LOWERBODY_IN_RHIPY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //61 + MNET_LOWERBODY_IN_RHIPY_LHIPY_ANGLE, //62 + MNET_LOWERBODY_IN_RHIPY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //63 + MNET_LOWERBODY_IN_RHIPY_LKNEEY_ANGLE, //64 + MNET_LOWERBODY_IN_RHIPY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //65 + MNET_LOWERBODY_IN_RHIPY_LFOOTY_ANGLE, //66 + MNET_LOWERBODY_IN_RHIPY_ENDSITE_TOE1_2_LY_ANGLE, //67 + MNET_LOWERBODY_IN_RHIPY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //68 + MNET_LOWERBODY_IN_RHIPY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //69 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_HIPY_ANGLE, //70 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_RHIPY_ANGLE, //71 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //72 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_RKNEEY_ANGLE, //73 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //74 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_RFOOTY_ANGLE, //75 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_ENDSITE_TOE1_2_RY_ANGLE, //76 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //77 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //78 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_LHIPY_ANGLE, //79 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //80 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_LKNEEY_ANGLE, //81 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //82 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_LFOOTY_ANGLE, //83 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_ENDSITE_TOE1_2_LY_ANGLE, //84 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //85 + MNET_LOWERBODY_IN_HALFWAY_RHIP_AND_RKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //86 + MNET_LOWERBODY_IN_RKNEEY_HIPY_ANGLE, //87 + MNET_LOWERBODY_IN_RKNEEY_RHIPY_ANGLE, //88 + MNET_LOWERBODY_IN_RKNEEY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //89 + MNET_LOWERBODY_IN_RKNEEY_RKNEEY_ANGLE, //90 + MNET_LOWERBODY_IN_RKNEEY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //91 + MNET_LOWERBODY_IN_RKNEEY_RFOOTY_ANGLE, //92 + MNET_LOWERBODY_IN_RKNEEY_ENDSITE_TOE1_2_RY_ANGLE, //93 + MNET_LOWERBODY_IN_RKNEEY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //94 + MNET_LOWERBODY_IN_RKNEEY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //95 + MNET_LOWERBODY_IN_RKNEEY_LHIPY_ANGLE, //96 + MNET_LOWERBODY_IN_RKNEEY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //97 + MNET_LOWERBODY_IN_RKNEEY_LKNEEY_ANGLE, //98 + MNET_LOWERBODY_IN_RKNEEY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //99 + MNET_LOWERBODY_IN_RKNEEY_LFOOTY_ANGLE, //100 + MNET_LOWERBODY_IN_RKNEEY_ENDSITE_TOE1_2_LY_ANGLE, //101 + MNET_LOWERBODY_IN_RKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //102 + MNET_LOWERBODY_IN_RKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //103 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_HIPY_ANGLE, //104 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_RHIPY_ANGLE, //105 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //106 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_RKNEEY_ANGLE, //107 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //108 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_RFOOTY_ANGLE, //109 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_ENDSITE_TOE1_2_RY_ANGLE, //110 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //111 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //112 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_LHIPY_ANGLE, //113 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //114 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_LKNEEY_ANGLE, //115 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //116 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_LFOOTY_ANGLE, //117 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_ENDSITE_TOE1_2_LY_ANGLE, //118 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //119 + MNET_LOWERBODY_IN_HALFWAY_RKNEE_AND_RFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //120 + MNET_LOWERBODY_IN_RFOOTY_HIPY_ANGLE, //121 + MNET_LOWERBODY_IN_RFOOTY_RHIPY_ANGLE, //122 + MNET_LOWERBODY_IN_RFOOTY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //123 + MNET_LOWERBODY_IN_RFOOTY_RKNEEY_ANGLE, //124 + MNET_LOWERBODY_IN_RFOOTY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //125 + MNET_LOWERBODY_IN_RFOOTY_RFOOTY_ANGLE, //126 + MNET_LOWERBODY_IN_RFOOTY_ENDSITE_TOE1_2_RY_ANGLE, //127 + MNET_LOWERBODY_IN_RFOOTY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //128 + MNET_LOWERBODY_IN_RFOOTY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //129 + MNET_LOWERBODY_IN_RFOOTY_LHIPY_ANGLE, //130 + MNET_LOWERBODY_IN_RFOOTY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //131 + MNET_LOWERBODY_IN_RFOOTY_LKNEEY_ANGLE, //132 + MNET_LOWERBODY_IN_RFOOTY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //133 + MNET_LOWERBODY_IN_RFOOTY_LFOOTY_ANGLE, //134 + MNET_LOWERBODY_IN_RFOOTY_ENDSITE_TOE1_2_LY_ANGLE, //135 + MNET_LOWERBODY_IN_RFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //136 + MNET_LOWERBODY_IN_RFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //137 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_HIPY_ANGLE, //138 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_RHIPY_ANGLE, //139 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //140 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_RKNEEY_ANGLE, //141 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //142 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_RFOOTY_ANGLE, //143 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_ENDSITE_TOE1_2_RY_ANGLE, //144 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //145 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //146 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_LHIPY_ANGLE, //147 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //148 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_LKNEEY_ANGLE, //149 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //150 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_LFOOTY_ANGLE, //151 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_ENDSITE_TOE1_2_LY_ANGLE, //152 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //153 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_RY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //154 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_HIPY_ANGLE, //155 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RHIPY_ANGLE, //156 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //157 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RKNEEY_ANGLE, //158 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //159 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_RFOOTY_ANGLE, //160 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ENDSITE_TOE1_2_RY_ANGLE, //161 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //162 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //163 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LHIPY_ANGLE, //164 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //165 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LKNEEY_ANGLE, //166 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //167 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_LFOOTY_ANGLE, //168 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ENDSITE_TOE1_2_LY_ANGLE, //169 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //170 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //171 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_HIPY_ANGLE, //172 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RHIPY_ANGLE, //173 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //174 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RKNEEY_ANGLE, //175 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //176 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_RFOOTY_ANGLE, //177 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ENDSITE_TOE1_2_RY_ANGLE, //178 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //179 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //180 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LHIPY_ANGLE, //181 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //182 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LKNEEY_ANGLE, //183 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //184 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_LFOOTY_ANGLE, //185 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ENDSITE_TOE1_2_LY_ANGLE, //186 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //187 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //188 + MNET_LOWERBODY_IN_LHIPY_HIPY_ANGLE, //189 + MNET_LOWERBODY_IN_LHIPY_RHIPY_ANGLE, //190 + MNET_LOWERBODY_IN_LHIPY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //191 + MNET_LOWERBODY_IN_LHIPY_RKNEEY_ANGLE, //192 + MNET_LOWERBODY_IN_LHIPY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //193 + MNET_LOWERBODY_IN_LHIPY_RFOOTY_ANGLE, //194 + MNET_LOWERBODY_IN_LHIPY_ENDSITE_TOE1_2_RY_ANGLE, //195 + MNET_LOWERBODY_IN_LHIPY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //196 + MNET_LOWERBODY_IN_LHIPY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //197 + MNET_LOWERBODY_IN_LHIPY_LHIPY_ANGLE, //198 + MNET_LOWERBODY_IN_LHIPY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //199 + MNET_LOWERBODY_IN_LHIPY_LKNEEY_ANGLE, //200 + MNET_LOWERBODY_IN_LHIPY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //201 + MNET_LOWERBODY_IN_LHIPY_LFOOTY_ANGLE, //202 + MNET_LOWERBODY_IN_LHIPY_ENDSITE_TOE1_2_LY_ANGLE, //203 + MNET_LOWERBODY_IN_LHIPY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //204 + MNET_LOWERBODY_IN_LHIPY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //205 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_HIPY_ANGLE, //206 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_RHIPY_ANGLE, //207 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //208 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_RKNEEY_ANGLE, //209 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //210 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_RFOOTY_ANGLE, //211 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_ENDSITE_TOE1_2_RY_ANGLE, //212 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //213 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //214 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_LHIPY_ANGLE, //215 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //216 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_LKNEEY_ANGLE, //217 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //218 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_LFOOTY_ANGLE, //219 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_ENDSITE_TOE1_2_LY_ANGLE, //220 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //221 + MNET_LOWERBODY_IN_HALFWAY_LHIP_AND_LKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //222 + MNET_LOWERBODY_IN_LKNEEY_HIPY_ANGLE, //223 + MNET_LOWERBODY_IN_LKNEEY_RHIPY_ANGLE, //224 + MNET_LOWERBODY_IN_LKNEEY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //225 + MNET_LOWERBODY_IN_LKNEEY_RKNEEY_ANGLE, //226 + MNET_LOWERBODY_IN_LKNEEY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //227 + MNET_LOWERBODY_IN_LKNEEY_RFOOTY_ANGLE, //228 + MNET_LOWERBODY_IN_LKNEEY_ENDSITE_TOE1_2_RY_ANGLE, //229 + MNET_LOWERBODY_IN_LKNEEY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //230 + MNET_LOWERBODY_IN_LKNEEY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //231 + MNET_LOWERBODY_IN_LKNEEY_LHIPY_ANGLE, //232 + MNET_LOWERBODY_IN_LKNEEY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //233 + MNET_LOWERBODY_IN_LKNEEY_LKNEEY_ANGLE, //234 + MNET_LOWERBODY_IN_LKNEEY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //235 + MNET_LOWERBODY_IN_LKNEEY_LFOOTY_ANGLE, //236 + MNET_LOWERBODY_IN_LKNEEY_ENDSITE_TOE1_2_LY_ANGLE, //237 + MNET_LOWERBODY_IN_LKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //238 + MNET_LOWERBODY_IN_LKNEEY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //239 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_HIPY_ANGLE, //240 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_RHIPY_ANGLE, //241 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //242 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_RKNEEY_ANGLE, //243 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //244 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_RFOOTY_ANGLE, //245 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_ENDSITE_TOE1_2_RY_ANGLE, //246 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //247 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //248 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_LHIPY_ANGLE, //249 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //250 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_LKNEEY_ANGLE, //251 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //252 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_LFOOTY_ANGLE, //253 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_ENDSITE_TOE1_2_LY_ANGLE, //254 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //255 + MNET_LOWERBODY_IN_HALFWAY_LKNEE_AND_LFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //256 + MNET_LOWERBODY_IN_LFOOTY_HIPY_ANGLE, //257 + MNET_LOWERBODY_IN_LFOOTY_RHIPY_ANGLE, //258 + MNET_LOWERBODY_IN_LFOOTY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //259 + MNET_LOWERBODY_IN_LFOOTY_RKNEEY_ANGLE, //260 + MNET_LOWERBODY_IN_LFOOTY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //261 + MNET_LOWERBODY_IN_LFOOTY_RFOOTY_ANGLE, //262 + MNET_LOWERBODY_IN_LFOOTY_ENDSITE_TOE1_2_RY_ANGLE, //263 + MNET_LOWERBODY_IN_LFOOTY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //264 + MNET_LOWERBODY_IN_LFOOTY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //265 + MNET_LOWERBODY_IN_LFOOTY_LHIPY_ANGLE, //266 + MNET_LOWERBODY_IN_LFOOTY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //267 + MNET_LOWERBODY_IN_LFOOTY_LKNEEY_ANGLE, //268 + MNET_LOWERBODY_IN_LFOOTY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //269 + MNET_LOWERBODY_IN_LFOOTY_LFOOTY_ANGLE, //270 + MNET_LOWERBODY_IN_LFOOTY_ENDSITE_TOE1_2_LY_ANGLE, //271 + MNET_LOWERBODY_IN_LFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //272 + MNET_LOWERBODY_IN_LFOOTY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //273 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_HIPY_ANGLE, //274 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_RHIPY_ANGLE, //275 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //276 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_RKNEEY_ANGLE, //277 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //278 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_RFOOTY_ANGLE, //279 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_ENDSITE_TOE1_2_RY_ANGLE, //280 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //281 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //282 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_LHIPY_ANGLE, //283 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //284 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_LKNEEY_ANGLE, //285 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //286 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_LFOOTY_ANGLE, //287 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_ENDSITE_TOE1_2_LY_ANGLE, //288 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //289 + MNET_LOWERBODY_IN_ENDSITE_TOE1_2_LY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //290 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_HIPY_ANGLE, //291 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_RHIPY_ANGLE, //292 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //293 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_RKNEEY_ANGLE, //294 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //295 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_RFOOTY_ANGLE, //296 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ENDSITE_TOE1_2_RY_ANGLE, //297 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //298 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //299 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_LHIPY_ANGLE, //300 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //301 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_LKNEEY_ANGLE, //302 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //303 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_LFOOTY_ANGLE, //304 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ENDSITE_TOE1_2_LY_ANGLE, //305 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //306 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //307 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_HIPY_ANGLE, //308 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_RHIPY_ANGLE, //309 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_HALFWAY_RHIP_AND_RKNEEY_ANGLE, //310 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_RKNEEY_ANGLE, //311 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_HALFWAY_RKNEE_AND_RFOOTY_ANGLE, //312 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_RFOOTY_ANGLE, //313 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ENDSITE_TOE1_2_RY_ANGLE, //314 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_VIRTUAL_HIP_X_PLUS0_3_Y_0Y_ANGLE, //315 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_VIRTUAL_HIP_X_MINUS_0_3_Y_0Y_ANGLE, //316 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_LHIPY_ANGLE, //317 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_HALFWAY_LHIP_AND_LKNEEY_ANGLE, //318 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_LKNEEY_ANGLE, //319 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_HALFWAY_LKNEE_AND_LFOOTY_ANGLE, //320 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_LFOOTY_ANGLE, //321 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ENDSITE_TOE1_2_LY_ANGLE, //322 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_15Y_ANGLE, //323 + MNET_LOWERBODY_IN_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_VIRTUAL_HIP_X_0_0_Y_PLUS0_3Y_ANGLE, //324 + MNET_LOWERBODY_IN_NUMBER +}; + +/** @brief Programmer friendly enumerator of expected outputs*/ +enum mocapNET_Output_lowerbody_enum +{ + MOCAPNET_LOWERBODY_OUTPUT_HIP_XPOSITION = 0, //0 + MOCAPNET_LOWERBODY_OUTPUT_HIP_YPOSITION, //1 + MOCAPNET_LOWERBODY_OUTPUT_HIP_ZPOSITION, //2 + MOCAPNET_LOWERBODY_OUTPUT_HIP_ZROTATION, //3 + MOCAPNET_LOWERBODY_OUTPUT_HIP_YROTATION, //4 + MOCAPNET_LOWERBODY_OUTPUT_HIP_XROTATION, //5 + MOCAPNET_LOWERBODY_OUTPUT_ABDOMEN_ZROTATION, //6 + MOCAPNET_LOWERBODY_OUTPUT_ABDOMEN_XROTATION, //7 + MOCAPNET_LOWERBODY_OUTPUT_ABDOMEN_YROTATION, //8 + MOCAPNET_LOWERBODY_OUTPUT_CHEST_ZROTATION, //9 + MOCAPNET_LOWERBODY_OUTPUT_CHEST_XROTATION, //10 + MOCAPNET_LOWERBODY_OUTPUT_CHEST_YROTATION, //11 + MOCAPNET_LOWERBODY_OUTPUT_NECK_ZROTATION, //12 + MOCAPNET_LOWERBODY_OUTPUT_NECK_XROTATION, //13 + MOCAPNET_LOWERBODY_OUTPUT_NECK_YROTATION, //14 + MOCAPNET_LOWERBODY_OUTPUT_RHIP_ZROTATION, //15 + MOCAPNET_LOWERBODY_OUTPUT_RHIP_XROTATION, //16 + MOCAPNET_LOWERBODY_OUTPUT_RHIP_YROTATION, //17 + MOCAPNET_LOWERBODY_OUTPUT_RKNEE_ZROTATION, //18 + MOCAPNET_LOWERBODY_OUTPUT_RKNEE_XROTATION, //19 + MOCAPNET_LOWERBODY_OUTPUT_RKNEE_YROTATION, //20 + MOCAPNET_LOWERBODY_OUTPUT_RFOOT_ZROTATION, //21 + MOCAPNET_LOWERBODY_OUTPUT_RFOOT_XROTATION, //22 + MOCAPNET_LOWERBODY_OUTPUT_RFOOT_YROTATION, //23 + MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_R_ZROTATION, //24 + MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_R_XROTATION, //25 + MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_R_YROTATION, //26 + MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_R_ZROTATION, //27 + MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_R_XROTATION, //28 + MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_R_YROTATION, //29 + MOCAPNET_LOWERBODY_OUTPUT_LHIP_ZROTATION, //30 + MOCAPNET_LOWERBODY_OUTPUT_LHIP_XROTATION, //31 + MOCAPNET_LOWERBODY_OUTPUT_LHIP_YROTATION, //32 + MOCAPNET_LOWERBODY_OUTPUT_LKNEE_ZROTATION, //33 + MOCAPNET_LOWERBODY_OUTPUT_LKNEE_XROTATION, //34 + MOCAPNET_LOWERBODY_OUTPUT_LKNEE_YROTATION, //35 + MOCAPNET_LOWERBODY_OUTPUT_LFOOT_ZROTATION, //36 + MOCAPNET_LOWERBODY_OUTPUT_LFOOT_XROTATION, //37 + MOCAPNET_LOWERBODY_OUTPUT_LFOOT_YROTATION, //38 + MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_L_ZROTATION, //39 + MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_L_XROTATION, //40 + MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_L_YROTATION, //41 + MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_L_ZROTATION, //42 + MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_L_XROTATION, //43 + MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_L_YROTATION, //44 + MOCAPNET_LOWERBODY_OUTPUT_NUMBER +}; + +/** @brief Programmer friendly enumerator of NSDM elments*/ +enum mocapNET_NSDM_lowerbody_enum +{ + MNET_NSDM_LOWERBODY_HIP = 0, //0 + MNET_NSDM_LOWERBODY_RHIP, //1 + MNET_NSDM_LOWERBODY_VIRTUAL_HALFWAY_BETWEEN_RHIP_AND_RKNEE, //2 + MNET_NSDM_LOWERBODY_RKNEE, //3 + MNET_NSDM_LOWERBODY_VIRTUAL_HALFWAY_BETWEEN_RKNEE_AND_RFOOT, //4 + MNET_NSDM_LOWERBODY_RFOOT, //5 + MNET_NSDM_LOWERBODY_ENDSITE_TOE1_2_R, //6 + MNET_NSDM_LOWERBODY_VIRTUAL_HIP_X_PLUS0_3_Y_0, //7 + MNET_NSDM_LOWERBODY_VIRTUAL_HIP_X_MINUS_0_3_Y_0, //8 + MNET_NSDM_LOWERBODY_LHIP, //9 + MNET_NSDM_LOWERBODY_VIRTUAL_HALFWAY_BETWEEN_LHIP_AND_LKNEE, //10 + MNET_NSDM_LOWERBODY_LKNEE, //11 + MNET_NSDM_LOWERBODY_VIRTUAL_HALFWAY_BETWEEN_LKNEE_AND_LFOOT, //12 + MNET_NSDM_LOWERBODY_LFOOT, //13 + MNET_NSDM_LOWERBODY_ENDSITE_TOE1_2_L, //14 + MNET_NSDM_LOWERBODY_VIRTUAL_HIP_X_0_0_Y_PLUS0_15, //15 + MNET_NSDM_LOWERBODY_VIRTUAL_HIP_X_0_0_Y_PLUS0_3, //16 + MNET_NSDM_LOWERBODY_NUMBER +}; + +/** @brief This is a lookup table to immediately resolve referred Joints*/ +static const int mocapNET_ResolveJoint_lowerbody[] = +{ + 0, //0 + 2, //1 + 2, //2 + 3, //3 + 3, //4 + 4, //5 + 5, //6 + 0, //7 + 0, //8 + 7, //9 + 7, //10 + 8, //11 + 8, //12 + 9, //13 + 10, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief This is a lookup table to immediately resolve referred Joints of second targets*/ +static const int mocapNET_ResolveSecondTargetJoint_lowerbody[] = +{ + 0, //0 + 0, //1 + 3, //2 + 0, //3 + 4, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + 0, //9 + 8, //10 + 0, //11 + 9, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief This is the configuration of NSDM elements : + * A value of 0 is a normal 2D point + * A value of 1 is a 2D point plus some offset + * A value of 2 is a virtual point between two 2D points */ +static const int mocapNET_ArtificialJoint_lowerbody[] = +{ + 0, //0 + 0, //1 + 2, //2 + 0, //3 + 2, //4 + 0, //5 + 0, //6 + 1, //7 + 1, //8 + 0, //9 + 2, //10 + 0, //11 + 2, //12 + 0, //13 + 0, //14 + 1, //15 + 1, //16 + 0//end of array +}; + +/** @brief These are X offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_lowerbody )*/ +static const float mocapNET_ArtificialJointXOffset_lowerbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0.3, //7 + -0.3, //8 + 0, //9 + 0, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0.0, //15 + 0.0, //16 + 0//end of array +}; + +/** @brief These are Y offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_lowerbody )*/ +static const float mocapNET_ArtificialJointYOffset_lowerbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + 0, //9 + 0, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0.15, //15 + 0.3, //16 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as starting points for scaling vectors*/ +static const int mocapNET_ScalingStart_lowerbody[] = +{ + 0, //0 + 0, //1 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as ending points for scaling vectors*/ +static const int mocapNET_ScalingEnd_lowerbody[] = +{ + 2, //0 + 7, //1 + 0//end of array +}; + +/** @brief These is a 2D Joints that is used as alignment for the skeleton*/ +static const int mocapNET_AlignmentStart_lowerbody[] = +{ + 3, //0 + 0//end of array +}; + +/** @brief These is a 2D Joints that is used as alignment for the skeleton*/ +static const int mocapNET_AlignmentEnd_lowerbody[] = +{ + 2, //0 + 0//end of array +}; + +/** @brief This function can be used to debug NSDM input and find in a user friendly what is missing..!*/ +static int lowerbodyCountMissingNSDMElements(std::vector mocapNETInput,int verbose) +{ + unsigned int numberOfZeros=0; + for (int i=0; i skeletonSerialized %s\n ",mocapNET_lowerbody[i],labels[i]); + } +} + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistance_LOWERBODY(std::vector in,int jointA,int jointB) +{ + float aX=in[jointA*3+0]; + float aY=in[jointA*3+1]; + float bX=in[jointB*3+0]; + float bY=in[jointB*3+1]; + if ( ((aX==0) && (aY==0)) || ((bX==0) && (bY==0)) ) { + return 0.0; + } + + + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + +/** @brief This function returns a vector of NSDM values ready for use with the MocapNET lowerbody network */ +static std::vector lowerbodyCreateNDSM(std::vector in,int havePositionalElements,int haveAngularElements,int doNormalization) +{ + std::vector result; + int secondTargetJointID; + float sIX,sIY,sJX,sJY; + for (int i=0; i0) + { + unsigned int numberOfDistanceSamples=0; + float sumOfDistanceSamples=0.0; + for ( int i=0; i0.0) + { + numberOfDistanceSamples=numberOfDistanceSamples+1; + sumOfDistanceSamples=sumOfDistanceSamples+distance; + } + } +//------------------------------------------------------------------------------------------------- + float scaleDistance=1.0; +//------------------------------------------------------------------------------------------------- + if (numberOfDistanceSamples>0) + { + scaleDistance=(float) sumOfDistanceSamples/numberOfDistanceSamples; + } +//------------------------------------------------------------------------------------------------- + if (scaleDistance!=1.0) + { + for (int i=0; imaxValue) { + maxValue=result[i]; + } + } + fprintf(stderr,"Original Min Value %0.2f, Max Value %0.2f \n",minValue,maxValue); + + + unsigned int iJointID=mocapNET_AlignmentStart_lowerbody[0]; + unsigned int jJointID=mocapNET_AlignmentEnd_lowerbody[0]; + float aX=in[iJointID*3+0]; + float aY=in[iJointID*3+1]; + float bX=in[jJointID*3+0]; + float bY=in[jJointID*3+1]; + float alignmentAngle=getAngleToAlignToZero_tools(aX,aY,bX,bY); + for (int i=0; imaxValue) { + maxValue=result[i]; + } + } + fprintf(stderr,"Aligned Min Value %0.2f, Max Value %0.2f \n",minValue,maxValue); + + + } +//------------------------------------------------------------------------------------------------- + + + } //If normalization is enabled.. + return result; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/NSDM/generated_upperbody.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/NSDM/generated_upperbody.hpp new file mode 100644 index 0000000..57d8866 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/NSDM/generated_upperbody.hpp @@ -0,0 +1,1175 @@ +/** @file generated_upperbody.hpp + * @brief A Description the upperbody input of a Tensorflow network required for MocapNET + * @author Ammar Qammaz (AmmarkoV) + * Automatically generated using : + * python3 exportCPPCodeFromJSONConfiguration.py --front upperbody --config dataset/upperbody_configuration.json + * please note that since the names of the labels are both affected by the dataset/upperbody_configuration.json configuration + * as well as the ground truth, if you have made any weird additions you might consider running the ./createRandomizedDataset.sh and ./createTestDataset.sh scripts + */ + +#pragma once + +#include +#include +#include +#include "../tools.hpp" + +/** @brief This is an array of names for all uncompressed 2D inputs expected. */ +static const unsigned int mocapNET_InputLength_WithoutNSDM_upperbody = 33; + +/** @brief An array of strings that contains the label for each expected input. */ +static const char * mocapNET_upperbody[] = +{ + "2DX_hip", //0 + "2DY_hip", //1 + "visible_hip", //2 + "2DX_neck", //3 + "2DY_neck", //4 + "visible_neck", //5 + "2DX_head", //6 + "2DY_head", //7 + "visible_head", //8 + "2DX_EndSite_eye.l", //9 + "2DY_EndSite_eye.l", //10 + "visible_EndSite_eye.l", //11 + "2DX_EndSite_eye.r", //12 + "2DY_EndSite_eye.r", //13 + "visible_EndSite_eye.r", //14 + "2DX_rshoulder", //15 + "2DY_rshoulder", //16 + "visible_rshoulder", //17 + "2DX_relbow", //18 + "2DY_relbow", //19 + "visible_relbow", //20 + "2DX_rhand", //21 + "2DY_rhand", //22 + "visible_rhand", //23 + "2DX_lshoulder", //24 + "2DY_lshoulder", //25 + "visible_lshoulder", //26 + "2DX_lelbow", //27 + "2DY_lelbow", //28 + "visible_lelbow", //29 + "2DX_lhand", //30 + "2DY_lhand", //31 + "visible_lhand", //32 +//This is where regular input ends and the NSDM data kicks in.. + "hipY-hipY-Angle", //33 + "hipY-EndSite_eye.rY-Angle", //34 + "hipY-EndSite_eye.lY-Angle", //35 + "hipY-neckY-Angle", //36 + "hipY-rshoulderY-Angle", //37 + "hipY-halfway_rshoulder_and_relbowY-Angle", //38 + "hipY-relbowY-Angle", //39 + "hipY-halfway_relbow_and_rhandY-Angle", //40 + "hipY-rhandY-Angle", //41 + "hipY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //42 + "hipY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //43 + "hipY-lshoulderY-Angle", //44 + "hipY-halfway_lshoulder_and_lelbowY-Angle", //45 + "hipY-lelbowY-Angle", //46 + "hipY-halfway_lelbow_and_lhandY-Angle", //47 + "hipY-lhandY-Angle", //48 + "hipY-halfway_neck_and_hipY-Angle", //49 + "EndSite_eye.rY-hipY-Angle", //50 + "EndSite_eye.rY-EndSite_eye.rY-Angle", //51 + "EndSite_eye.rY-EndSite_eye.lY-Angle", //52 + "EndSite_eye.rY-neckY-Angle", //53 + "EndSite_eye.rY-rshoulderY-Angle", //54 + "EndSite_eye.rY-halfway_rshoulder_and_relbowY-Angle", //55 + "EndSite_eye.rY-relbowY-Angle", //56 + "EndSite_eye.rY-halfway_relbow_and_rhandY-Angle", //57 + "EndSite_eye.rY-rhandY-Angle", //58 + "EndSite_eye.rY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //59 + "EndSite_eye.rY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //60 + "EndSite_eye.rY-lshoulderY-Angle", //61 + "EndSite_eye.rY-halfway_lshoulder_and_lelbowY-Angle", //62 + "EndSite_eye.rY-lelbowY-Angle", //63 + "EndSite_eye.rY-halfway_lelbow_and_lhandY-Angle", //64 + "EndSite_eye.rY-lhandY-Angle", //65 + "EndSite_eye.rY-halfway_neck_and_hipY-Angle", //66 + "EndSite_eye.lY-hipY-Angle", //67 + "EndSite_eye.lY-EndSite_eye.rY-Angle", //68 + "EndSite_eye.lY-EndSite_eye.lY-Angle", //69 + "EndSite_eye.lY-neckY-Angle", //70 + "EndSite_eye.lY-rshoulderY-Angle", //71 + "EndSite_eye.lY-halfway_rshoulder_and_relbowY-Angle", //72 + "EndSite_eye.lY-relbowY-Angle", //73 + "EndSite_eye.lY-halfway_relbow_and_rhandY-Angle", //74 + "EndSite_eye.lY-rhandY-Angle", //75 + "EndSite_eye.lY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //76 + "EndSite_eye.lY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //77 + "EndSite_eye.lY-lshoulderY-Angle", //78 + "EndSite_eye.lY-halfway_lshoulder_and_lelbowY-Angle", //79 + "EndSite_eye.lY-lelbowY-Angle", //80 + "EndSite_eye.lY-halfway_lelbow_and_lhandY-Angle", //81 + "EndSite_eye.lY-lhandY-Angle", //82 + "EndSite_eye.lY-halfway_neck_and_hipY-Angle", //83 + "neckY-hipY-Angle", //84 + "neckY-EndSite_eye.rY-Angle", //85 + "neckY-EndSite_eye.lY-Angle", //86 + "neckY-neckY-Angle", //87 + "neckY-rshoulderY-Angle", //88 + "neckY-halfway_rshoulder_and_relbowY-Angle", //89 + "neckY-relbowY-Angle", //90 + "neckY-halfway_relbow_and_rhandY-Angle", //91 + "neckY-rhandY-Angle", //92 + "neckY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //93 + "neckY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //94 + "neckY-lshoulderY-Angle", //95 + "neckY-halfway_lshoulder_and_lelbowY-Angle", //96 + "neckY-lelbowY-Angle", //97 + "neckY-halfway_lelbow_and_lhandY-Angle", //98 + "neckY-lhandY-Angle", //99 + "neckY-halfway_neck_and_hipY-Angle", //100 + "rshoulderY-hipY-Angle", //101 + "rshoulderY-EndSite_eye.rY-Angle", //102 + "rshoulderY-EndSite_eye.lY-Angle", //103 + "rshoulderY-neckY-Angle", //104 + "rshoulderY-rshoulderY-Angle", //105 + "rshoulderY-halfway_rshoulder_and_relbowY-Angle", //106 + "rshoulderY-relbowY-Angle", //107 + "rshoulderY-halfway_relbow_and_rhandY-Angle", //108 + "rshoulderY-rhandY-Angle", //109 + "rshoulderY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //110 + "rshoulderY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //111 + "rshoulderY-lshoulderY-Angle", //112 + "rshoulderY-halfway_lshoulder_and_lelbowY-Angle", //113 + "rshoulderY-lelbowY-Angle", //114 + "rshoulderY-halfway_lelbow_and_lhandY-Angle", //115 + "rshoulderY-lhandY-Angle", //116 + "rshoulderY-halfway_neck_and_hipY-Angle", //117 + "halfway_rshoulder_and_relbowY-hipY-Angle", //118 + "halfway_rshoulder_and_relbowY-EndSite_eye.rY-Angle", //119 + "halfway_rshoulder_and_relbowY-EndSite_eye.lY-Angle", //120 + "halfway_rshoulder_and_relbowY-neckY-Angle", //121 + "halfway_rshoulder_and_relbowY-rshoulderY-Angle", //122 + "halfway_rshoulder_and_relbowY-halfway_rshoulder_and_relbowY-Angle", //123 + "halfway_rshoulder_and_relbowY-relbowY-Angle", //124 + "halfway_rshoulder_and_relbowY-halfway_relbow_and_rhandY-Angle", //125 + "halfway_rshoulder_and_relbowY-rhandY-Angle", //126 + "halfway_rshoulder_and_relbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //127 + "halfway_rshoulder_and_relbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //128 + "halfway_rshoulder_and_relbowY-lshoulderY-Angle", //129 + "halfway_rshoulder_and_relbowY-halfway_lshoulder_and_lelbowY-Angle", //130 + "halfway_rshoulder_and_relbowY-lelbowY-Angle", //131 + "halfway_rshoulder_and_relbowY-halfway_lelbow_and_lhandY-Angle", //132 + "halfway_rshoulder_and_relbowY-lhandY-Angle", //133 + "halfway_rshoulder_and_relbowY-halfway_neck_and_hipY-Angle", //134 + "relbowY-hipY-Angle", //135 + "relbowY-EndSite_eye.rY-Angle", //136 + "relbowY-EndSite_eye.lY-Angle", //137 + "relbowY-neckY-Angle", //138 + "relbowY-rshoulderY-Angle", //139 + "relbowY-halfway_rshoulder_and_relbowY-Angle", //140 + "relbowY-relbowY-Angle", //141 + "relbowY-halfway_relbow_and_rhandY-Angle", //142 + "relbowY-rhandY-Angle", //143 + "relbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //144 + "relbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //145 + "relbowY-lshoulderY-Angle", //146 + "relbowY-halfway_lshoulder_and_lelbowY-Angle", //147 + "relbowY-lelbowY-Angle", //148 + "relbowY-halfway_lelbow_and_lhandY-Angle", //149 + "relbowY-lhandY-Angle", //150 + "relbowY-halfway_neck_and_hipY-Angle", //151 + "halfway_relbow_and_rhandY-hipY-Angle", //152 + "halfway_relbow_and_rhandY-EndSite_eye.rY-Angle", //153 + "halfway_relbow_and_rhandY-EndSite_eye.lY-Angle", //154 + "halfway_relbow_and_rhandY-neckY-Angle", //155 + "halfway_relbow_and_rhandY-rshoulderY-Angle", //156 + "halfway_relbow_and_rhandY-halfway_rshoulder_and_relbowY-Angle", //157 + "halfway_relbow_and_rhandY-relbowY-Angle", //158 + "halfway_relbow_and_rhandY-halfway_relbow_and_rhandY-Angle", //159 + "halfway_relbow_and_rhandY-rhandY-Angle", //160 + "halfway_relbow_and_rhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //161 + "halfway_relbow_and_rhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //162 + "halfway_relbow_and_rhandY-lshoulderY-Angle", //163 + "halfway_relbow_and_rhandY-halfway_lshoulder_and_lelbowY-Angle", //164 + "halfway_relbow_and_rhandY-lelbowY-Angle", //165 + "halfway_relbow_and_rhandY-halfway_lelbow_and_lhandY-Angle", //166 + "halfway_relbow_and_rhandY-lhandY-Angle", //167 + "halfway_relbow_and_rhandY-halfway_neck_and_hipY-Angle", //168 + "rhandY-hipY-Angle", //169 + "rhandY-EndSite_eye.rY-Angle", //170 + "rhandY-EndSite_eye.lY-Angle", //171 + "rhandY-neckY-Angle", //172 + "rhandY-rshoulderY-Angle", //173 + "rhandY-halfway_rshoulder_and_relbowY-Angle", //174 + "rhandY-relbowY-Angle", //175 + "rhandY-halfway_relbow_and_rhandY-Angle", //176 + "rhandY-rhandY-Angle", //177 + "rhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //178 + "rhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //179 + "rhandY-lshoulderY-Angle", //180 + "rhandY-halfway_lshoulder_and_lelbowY-Angle", //181 + "rhandY-lelbowY-Angle", //182 + "rhandY-halfway_lelbow_and_lhandY-Angle", //183 + "rhandY-lhandY-Angle", //184 + "rhandY-halfway_neck_and_hipY-Angle", //185 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-hipY-Angle", //186 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-EndSite_eye.rY-Angle", //187 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-EndSite_eye.lY-Angle", //188 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-neckY-Angle", //189 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-rshoulderY-Angle", //190 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_rshoulder_and_relbowY-Angle", //191 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-relbowY-Angle", //192 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_relbow_and_rhandY-Angle", //193 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-rhandY-Angle", //194 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //195 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //196 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-lshoulderY-Angle", //197 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_lshoulder_and_lelbowY-Angle", //198 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-lelbowY-Angle", //199 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_lelbow_and_lhandY-Angle", //200 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-lhandY-Angle", //201 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_neck_and_hipY-Angle", //202 + "virtual_hip_x_plus0_15_y_minus_0_15Y-hipY-Angle", //203 + "virtual_hip_x_plus0_15_y_minus_0_15Y-EndSite_eye.rY-Angle", //204 + "virtual_hip_x_plus0_15_y_minus_0_15Y-EndSite_eye.lY-Angle", //205 + "virtual_hip_x_plus0_15_y_minus_0_15Y-neckY-Angle", //206 + "virtual_hip_x_plus0_15_y_minus_0_15Y-rshoulderY-Angle", //207 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_rshoulder_and_relbowY-Angle", //208 + "virtual_hip_x_plus0_15_y_minus_0_15Y-relbowY-Angle", //209 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_relbow_and_rhandY-Angle", //210 + "virtual_hip_x_plus0_15_y_minus_0_15Y-rhandY-Angle", //211 + "virtual_hip_x_plus0_15_y_minus_0_15Y-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //212 + "virtual_hip_x_plus0_15_y_minus_0_15Y-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //213 + "virtual_hip_x_plus0_15_y_minus_0_15Y-lshoulderY-Angle", //214 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_lshoulder_and_lelbowY-Angle", //215 + "virtual_hip_x_plus0_15_y_minus_0_15Y-lelbowY-Angle", //216 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_lelbow_and_lhandY-Angle", //217 + "virtual_hip_x_plus0_15_y_minus_0_15Y-lhandY-Angle", //218 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_neck_and_hipY-Angle", //219 + "lshoulderY-hipY-Angle", //220 + "lshoulderY-EndSite_eye.rY-Angle", //221 + "lshoulderY-EndSite_eye.lY-Angle", //222 + "lshoulderY-neckY-Angle", //223 + "lshoulderY-rshoulderY-Angle", //224 + "lshoulderY-halfway_rshoulder_and_relbowY-Angle", //225 + "lshoulderY-relbowY-Angle", //226 + "lshoulderY-halfway_relbow_and_rhandY-Angle", //227 + "lshoulderY-rhandY-Angle", //228 + "lshoulderY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //229 + "lshoulderY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //230 + "lshoulderY-lshoulderY-Angle", //231 + "lshoulderY-halfway_lshoulder_and_lelbowY-Angle", //232 + "lshoulderY-lelbowY-Angle", //233 + "lshoulderY-halfway_lelbow_and_lhandY-Angle", //234 + "lshoulderY-lhandY-Angle", //235 + "lshoulderY-halfway_neck_and_hipY-Angle", //236 + "halfway_lshoulder_and_lelbowY-hipY-Angle", //237 + "halfway_lshoulder_and_lelbowY-EndSite_eye.rY-Angle", //238 + "halfway_lshoulder_and_lelbowY-EndSite_eye.lY-Angle", //239 + "halfway_lshoulder_and_lelbowY-neckY-Angle", //240 + "halfway_lshoulder_and_lelbowY-rshoulderY-Angle", //241 + "halfway_lshoulder_and_lelbowY-halfway_rshoulder_and_relbowY-Angle", //242 + "halfway_lshoulder_and_lelbowY-relbowY-Angle", //243 + "halfway_lshoulder_and_lelbowY-halfway_relbow_and_rhandY-Angle", //244 + "halfway_lshoulder_and_lelbowY-rhandY-Angle", //245 + "halfway_lshoulder_and_lelbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //246 + "halfway_lshoulder_and_lelbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //247 + "halfway_lshoulder_and_lelbowY-lshoulderY-Angle", //248 + "halfway_lshoulder_and_lelbowY-halfway_lshoulder_and_lelbowY-Angle", //249 + "halfway_lshoulder_and_lelbowY-lelbowY-Angle", //250 + "halfway_lshoulder_and_lelbowY-halfway_lelbow_and_lhandY-Angle", //251 + "halfway_lshoulder_and_lelbowY-lhandY-Angle", //252 + "halfway_lshoulder_and_lelbowY-halfway_neck_and_hipY-Angle", //253 + "lelbowY-hipY-Angle", //254 + "lelbowY-EndSite_eye.rY-Angle", //255 + "lelbowY-EndSite_eye.lY-Angle", //256 + "lelbowY-neckY-Angle", //257 + "lelbowY-rshoulderY-Angle", //258 + "lelbowY-halfway_rshoulder_and_relbowY-Angle", //259 + "lelbowY-relbowY-Angle", //260 + "lelbowY-halfway_relbow_and_rhandY-Angle", //261 + "lelbowY-rhandY-Angle", //262 + "lelbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //263 + "lelbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //264 + "lelbowY-lshoulderY-Angle", //265 + "lelbowY-halfway_lshoulder_and_lelbowY-Angle", //266 + "lelbowY-lelbowY-Angle", //267 + "lelbowY-halfway_lelbow_and_lhandY-Angle", //268 + "lelbowY-lhandY-Angle", //269 + "lelbowY-halfway_neck_and_hipY-Angle", //270 + "halfway_lelbow_and_lhandY-hipY-Angle", //271 + "halfway_lelbow_and_lhandY-EndSite_eye.rY-Angle", //272 + "halfway_lelbow_and_lhandY-EndSite_eye.lY-Angle", //273 + "halfway_lelbow_and_lhandY-neckY-Angle", //274 + "halfway_lelbow_and_lhandY-rshoulderY-Angle", //275 + "halfway_lelbow_and_lhandY-halfway_rshoulder_and_relbowY-Angle", //276 + "halfway_lelbow_and_lhandY-relbowY-Angle", //277 + "halfway_lelbow_and_lhandY-halfway_relbow_and_rhandY-Angle", //278 + "halfway_lelbow_and_lhandY-rhandY-Angle", //279 + "halfway_lelbow_and_lhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //280 + "halfway_lelbow_and_lhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //281 + "halfway_lelbow_and_lhandY-lshoulderY-Angle", //282 + "halfway_lelbow_and_lhandY-halfway_lshoulder_and_lelbowY-Angle", //283 + "halfway_lelbow_and_lhandY-lelbowY-Angle", //284 + "halfway_lelbow_and_lhandY-halfway_lelbow_and_lhandY-Angle", //285 + "halfway_lelbow_and_lhandY-lhandY-Angle", //286 + "halfway_lelbow_and_lhandY-halfway_neck_and_hipY-Angle", //287 + "lhandY-hipY-Angle", //288 + "lhandY-EndSite_eye.rY-Angle", //289 + "lhandY-EndSite_eye.lY-Angle", //290 + "lhandY-neckY-Angle", //291 + "lhandY-rshoulderY-Angle", //292 + "lhandY-halfway_rshoulder_and_relbowY-Angle", //293 + "lhandY-relbowY-Angle", //294 + "lhandY-halfway_relbow_and_rhandY-Angle", //295 + "lhandY-rhandY-Angle", //296 + "lhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //297 + "lhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //298 + "lhandY-lshoulderY-Angle", //299 + "lhandY-halfway_lshoulder_and_lelbowY-Angle", //300 + "lhandY-lelbowY-Angle", //301 + "lhandY-halfway_lelbow_and_lhandY-Angle", //302 + "lhandY-lhandY-Angle", //303 + "lhandY-halfway_neck_and_hipY-Angle", //304 + "halfway_neck_and_hipY-hipY-Angle", //305 + "halfway_neck_and_hipY-EndSite_eye.rY-Angle", //306 + "halfway_neck_and_hipY-EndSite_eye.lY-Angle", //307 + "halfway_neck_and_hipY-neckY-Angle", //308 + "halfway_neck_and_hipY-rshoulderY-Angle", //309 + "halfway_neck_and_hipY-halfway_rshoulder_and_relbowY-Angle", //310 + "halfway_neck_and_hipY-relbowY-Angle", //311 + "halfway_neck_and_hipY-halfway_relbow_and_rhandY-Angle", //312 + "halfway_neck_and_hipY-rhandY-Angle", //313 + "halfway_neck_and_hipY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //314 + "halfway_neck_and_hipY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //315 + "halfway_neck_and_hipY-lshoulderY-Angle", //316 + "halfway_neck_and_hipY-halfway_lshoulder_and_lelbowY-Angle", //317 + "halfway_neck_and_hipY-lelbowY-Angle", //318 + "halfway_neck_and_hipY-halfway_lelbow_and_lhandY-Angle", //319 + "halfway_neck_and_hipY-lhandY-Angle", //320 + "halfway_neck_and_hipY-halfway_neck_and_hipY-Angle", //321 + "end" +}; +/** @brief Programmer friendly enumerator of expected inputs*/ +enum mocapNET_upperbody_enum +{ + MNET_UPPERBODY_IN_2DX_HIP = 0, //0 + MNET_UPPERBODY_IN_2DY_HIP, //1 + MNET_UPPERBODY_IN_VISIBLE_HIP, //2 + MNET_UPPERBODY_IN_2DX_NECK, //3 + MNET_UPPERBODY_IN_2DY_NECK, //4 + MNET_UPPERBODY_IN_VISIBLE_NECK, //5 + MNET_UPPERBODY_IN_2DX_HEAD, //6 + MNET_UPPERBODY_IN_2DY_HEAD, //7 + MNET_UPPERBODY_IN_VISIBLE_HEAD, //8 + MNET_UPPERBODY_IN_2DX_ENDSITE_EYE_L, //9 + MNET_UPPERBODY_IN_2DY_ENDSITE_EYE_L, //10 + MNET_UPPERBODY_IN_VISIBLE_ENDSITE_EYE_L, //11 + MNET_UPPERBODY_IN_2DX_ENDSITE_EYE_R, //12 + MNET_UPPERBODY_IN_2DY_ENDSITE_EYE_R, //13 + MNET_UPPERBODY_IN_VISIBLE_ENDSITE_EYE_R, //14 + MNET_UPPERBODY_IN_2DX_RSHOULDER, //15 + MNET_UPPERBODY_IN_2DY_RSHOULDER, //16 + MNET_UPPERBODY_IN_VISIBLE_RSHOULDER, //17 + MNET_UPPERBODY_IN_2DX_RELBOW, //18 + MNET_UPPERBODY_IN_2DY_RELBOW, //19 + MNET_UPPERBODY_IN_VISIBLE_RELBOW, //20 + MNET_UPPERBODY_IN_2DX_RHAND, //21 + MNET_UPPERBODY_IN_2DY_RHAND, //22 + MNET_UPPERBODY_IN_VISIBLE_RHAND, //23 + MNET_UPPERBODY_IN_2DX_LSHOULDER, //24 + MNET_UPPERBODY_IN_2DY_LSHOULDER, //25 + MNET_UPPERBODY_IN_VISIBLE_LSHOULDER, //26 + MNET_UPPERBODY_IN_2DX_LELBOW, //27 + MNET_UPPERBODY_IN_2DY_LELBOW, //28 + MNET_UPPERBODY_IN_VISIBLE_LELBOW, //29 + MNET_UPPERBODY_IN_2DX_LHAND, //30 + MNET_UPPERBODY_IN_2DY_LHAND, //31 + MNET_UPPERBODY_IN_VISIBLE_LHAND, //32 + MNET_UPPERBODY_IN_HIPY_HIPY_ANGLE, //33 + MNET_UPPERBODY_IN_HIPY_ENDSITE_EYE_RY_ANGLE, //34 + MNET_UPPERBODY_IN_HIPY_ENDSITE_EYE_LY_ANGLE, //35 + MNET_UPPERBODY_IN_HIPY_NECKY_ANGLE, //36 + MNET_UPPERBODY_IN_HIPY_RSHOULDERY_ANGLE, //37 + MNET_UPPERBODY_IN_HIPY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //38 + MNET_UPPERBODY_IN_HIPY_RELBOWY_ANGLE, //39 + MNET_UPPERBODY_IN_HIPY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //40 + MNET_UPPERBODY_IN_HIPY_RHANDY_ANGLE, //41 + MNET_UPPERBODY_IN_HIPY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //42 + MNET_UPPERBODY_IN_HIPY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //43 + MNET_UPPERBODY_IN_HIPY_LSHOULDERY_ANGLE, //44 + MNET_UPPERBODY_IN_HIPY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //45 + MNET_UPPERBODY_IN_HIPY_LELBOWY_ANGLE, //46 + MNET_UPPERBODY_IN_HIPY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //47 + MNET_UPPERBODY_IN_HIPY_LHANDY_ANGLE, //48 + MNET_UPPERBODY_IN_HIPY_HALFWAY_NECK_AND_HIPY_ANGLE, //49 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HIPY_ANGLE, //50 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_ENDSITE_EYE_RY_ANGLE, //51 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_ENDSITE_EYE_LY_ANGLE, //52 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_NECKY_ANGLE, //53 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_RSHOULDERY_ANGLE, //54 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //55 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_RELBOWY_ANGLE, //56 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //57 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_RHANDY_ANGLE, //58 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //59 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //60 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_LSHOULDERY_ANGLE, //61 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //62 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_LELBOWY_ANGLE, //63 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //64 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_LHANDY_ANGLE, //65 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_NECK_AND_HIPY_ANGLE, //66 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HIPY_ANGLE, //67 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_ENDSITE_EYE_RY_ANGLE, //68 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_ENDSITE_EYE_LY_ANGLE, //69 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_NECKY_ANGLE, //70 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_RSHOULDERY_ANGLE, //71 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //72 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_RELBOWY_ANGLE, //73 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //74 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_RHANDY_ANGLE, //75 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //76 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //77 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_LSHOULDERY_ANGLE, //78 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //79 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_LELBOWY_ANGLE, //80 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //81 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_LHANDY_ANGLE, //82 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_NECK_AND_HIPY_ANGLE, //83 + MNET_UPPERBODY_IN_NECKY_HIPY_ANGLE, //84 + MNET_UPPERBODY_IN_NECKY_ENDSITE_EYE_RY_ANGLE, //85 + MNET_UPPERBODY_IN_NECKY_ENDSITE_EYE_LY_ANGLE, //86 + MNET_UPPERBODY_IN_NECKY_NECKY_ANGLE, //87 + MNET_UPPERBODY_IN_NECKY_RSHOULDERY_ANGLE, //88 + MNET_UPPERBODY_IN_NECKY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //89 + MNET_UPPERBODY_IN_NECKY_RELBOWY_ANGLE, //90 + MNET_UPPERBODY_IN_NECKY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //91 + MNET_UPPERBODY_IN_NECKY_RHANDY_ANGLE, //92 + MNET_UPPERBODY_IN_NECKY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //93 + MNET_UPPERBODY_IN_NECKY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //94 + MNET_UPPERBODY_IN_NECKY_LSHOULDERY_ANGLE, //95 + MNET_UPPERBODY_IN_NECKY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //96 + MNET_UPPERBODY_IN_NECKY_LELBOWY_ANGLE, //97 + MNET_UPPERBODY_IN_NECKY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //98 + MNET_UPPERBODY_IN_NECKY_LHANDY_ANGLE, //99 + MNET_UPPERBODY_IN_NECKY_HALFWAY_NECK_AND_HIPY_ANGLE, //100 + MNET_UPPERBODY_IN_RSHOULDERY_HIPY_ANGLE, //101 + MNET_UPPERBODY_IN_RSHOULDERY_ENDSITE_EYE_RY_ANGLE, //102 + MNET_UPPERBODY_IN_RSHOULDERY_ENDSITE_EYE_LY_ANGLE, //103 + MNET_UPPERBODY_IN_RSHOULDERY_NECKY_ANGLE, //104 + MNET_UPPERBODY_IN_RSHOULDERY_RSHOULDERY_ANGLE, //105 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //106 + MNET_UPPERBODY_IN_RSHOULDERY_RELBOWY_ANGLE, //107 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //108 + MNET_UPPERBODY_IN_RSHOULDERY_RHANDY_ANGLE, //109 + MNET_UPPERBODY_IN_RSHOULDERY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //110 + MNET_UPPERBODY_IN_RSHOULDERY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //111 + MNET_UPPERBODY_IN_RSHOULDERY_LSHOULDERY_ANGLE, //112 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //113 + MNET_UPPERBODY_IN_RSHOULDERY_LELBOWY_ANGLE, //114 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //115 + MNET_UPPERBODY_IN_RSHOULDERY_LHANDY_ANGLE, //116 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_NECK_AND_HIPY_ANGLE, //117 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HIPY_ANGLE, //118 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_ENDSITE_EYE_RY_ANGLE, //119 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_ENDSITE_EYE_LY_ANGLE, //120 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_NECKY_ANGLE, //121 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_RSHOULDERY_ANGLE, //122 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //123 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_RELBOWY_ANGLE, //124 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //125 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_RHANDY_ANGLE, //126 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //127 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //128 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_LSHOULDERY_ANGLE, //129 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //130 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_LELBOWY_ANGLE, //131 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //132 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_LHANDY_ANGLE, //133 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //134 + MNET_UPPERBODY_IN_RELBOWY_HIPY_ANGLE, //135 + MNET_UPPERBODY_IN_RELBOWY_ENDSITE_EYE_RY_ANGLE, //136 + MNET_UPPERBODY_IN_RELBOWY_ENDSITE_EYE_LY_ANGLE, //137 + MNET_UPPERBODY_IN_RELBOWY_NECKY_ANGLE, //138 + MNET_UPPERBODY_IN_RELBOWY_RSHOULDERY_ANGLE, //139 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //140 + MNET_UPPERBODY_IN_RELBOWY_RELBOWY_ANGLE, //141 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //142 + MNET_UPPERBODY_IN_RELBOWY_RHANDY_ANGLE, //143 + MNET_UPPERBODY_IN_RELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //144 + MNET_UPPERBODY_IN_RELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //145 + MNET_UPPERBODY_IN_RELBOWY_LSHOULDERY_ANGLE, //146 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //147 + MNET_UPPERBODY_IN_RELBOWY_LELBOWY_ANGLE, //148 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //149 + MNET_UPPERBODY_IN_RELBOWY_LHANDY_ANGLE, //150 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //151 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HIPY_ANGLE, //152 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_ENDSITE_EYE_RY_ANGLE, //153 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_ENDSITE_EYE_LY_ANGLE, //154 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_NECKY_ANGLE, //155 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_RSHOULDERY_ANGLE, //156 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //157 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_RELBOWY_ANGLE, //158 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //159 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_RHANDY_ANGLE, //160 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //161 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //162 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_LSHOULDERY_ANGLE, //163 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //164 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_LELBOWY_ANGLE, //165 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //166 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_LHANDY_ANGLE, //167 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //168 + MNET_UPPERBODY_IN_RHANDY_HIPY_ANGLE, //169 + MNET_UPPERBODY_IN_RHANDY_ENDSITE_EYE_RY_ANGLE, //170 + MNET_UPPERBODY_IN_RHANDY_ENDSITE_EYE_LY_ANGLE, //171 + MNET_UPPERBODY_IN_RHANDY_NECKY_ANGLE, //172 + MNET_UPPERBODY_IN_RHANDY_RSHOULDERY_ANGLE, //173 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //174 + MNET_UPPERBODY_IN_RHANDY_RELBOWY_ANGLE, //175 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //176 + MNET_UPPERBODY_IN_RHANDY_RHANDY_ANGLE, //177 + MNET_UPPERBODY_IN_RHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //178 + MNET_UPPERBODY_IN_RHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //179 + MNET_UPPERBODY_IN_RHANDY_LSHOULDERY_ANGLE, //180 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //181 + MNET_UPPERBODY_IN_RHANDY_LELBOWY_ANGLE, //182 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //183 + MNET_UPPERBODY_IN_RHANDY_LHANDY_ANGLE, //184 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //185 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HIPY_ANGLE, //186 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ENDSITE_EYE_RY_ANGLE, //187 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ENDSITE_EYE_LY_ANGLE, //188 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_NECKY_ANGLE, //189 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_RSHOULDERY_ANGLE, //190 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //191 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_RELBOWY_ANGLE, //192 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //193 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_RHANDY_ANGLE, //194 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //195 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //196 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_LSHOULDERY_ANGLE, //197 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //198 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_LELBOWY_ANGLE, //199 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //200 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_LHANDY_ANGLE, //201 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_NECK_AND_HIPY_ANGLE, //202 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HIPY_ANGLE, //203 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ENDSITE_EYE_RY_ANGLE, //204 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ENDSITE_EYE_LY_ANGLE, //205 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_NECKY_ANGLE, //206 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_RSHOULDERY_ANGLE, //207 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //208 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_RELBOWY_ANGLE, //209 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //210 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_RHANDY_ANGLE, //211 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //212 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //213 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_LSHOULDERY_ANGLE, //214 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //215 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_LELBOWY_ANGLE, //216 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //217 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_LHANDY_ANGLE, //218 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_NECK_AND_HIPY_ANGLE, //219 + MNET_UPPERBODY_IN_LSHOULDERY_HIPY_ANGLE, //220 + MNET_UPPERBODY_IN_LSHOULDERY_ENDSITE_EYE_RY_ANGLE, //221 + MNET_UPPERBODY_IN_LSHOULDERY_ENDSITE_EYE_LY_ANGLE, //222 + MNET_UPPERBODY_IN_LSHOULDERY_NECKY_ANGLE, //223 + MNET_UPPERBODY_IN_LSHOULDERY_RSHOULDERY_ANGLE, //224 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //225 + MNET_UPPERBODY_IN_LSHOULDERY_RELBOWY_ANGLE, //226 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //227 + MNET_UPPERBODY_IN_LSHOULDERY_RHANDY_ANGLE, //228 + MNET_UPPERBODY_IN_LSHOULDERY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //229 + MNET_UPPERBODY_IN_LSHOULDERY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //230 + MNET_UPPERBODY_IN_LSHOULDERY_LSHOULDERY_ANGLE, //231 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //232 + MNET_UPPERBODY_IN_LSHOULDERY_LELBOWY_ANGLE, //233 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //234 + MNET_UPPERBODY_IN_LSHOULDERY_LHANDY_ANGLE, //235 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_NECK_AND_HIPY_ANGLE, //236 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HIPY_ANGLE, //237 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_ENDSITE_EYE_RY_ANGLE, //238 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_ENDSITE_EYE_LY_ANGLE, //239 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_NECKY_ANGLE, //240 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_RSHOULDERY_ANGLE, //241 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //242 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_RELBOWY_ANGLE, //243 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //244 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_RHANDY_ANGLE, //245 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //246 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //247 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_LSHOULDERY_ANGLE, //248 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //249 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_LELBOWY_ANGLE, //250 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //251 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_LHANDY_ANGLE, //252 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //253 + MNET_UPPERBODY_IN_LELBOWY_HIPY_ANGLE, //254 + MNET_UPPERBODY_IN_LELBOWY_ENDSITE_EYE_RY_ANGLE, //255 + MNET_UPPERBODY_IN_LELBOWY_ENDSITE_EYE_LY_ANGLE, //256 + MNET_UPPERBODY_IN_LELBOWY_NECKY_ANGLE, //257 + MNET_UPPERBODY_IN_LELBOWY_RSHOULDERY_ANGLE, //258 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //259 + MNET_UPPERBODY_IN_LELBOWY_RELBOWY_ANGLE, //260 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //261 + MNET_UPPERBODY_IN_LELBOWY_RHANDY_ANGLE, //262 + MNET_UPPERBODY_IN_LELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //263 + MNET_UPPERBODY_IN_LELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //264 + MNET_UPPERBODY_IN_LELBOWY_LSHOULDERY_ANGLE, //265 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //266 + MNET_UPPERBODY_IN_LELBOWY_LELBOWY_ANGLE, //267 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //268 + MNET_UPPERBODY_IN_LELBOWY_LHANDY_ANGLE, //269 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //270 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HIPY_ANGLE, //271 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_ENDSITE_EYE_RY_ANGLE, //272 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_ENDSITE_EYE_LY_ANGLE, //273 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_NECKY_ANGLE, //274 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_RSHOULDERY_ANGLE, //275 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //276 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_RELBOWY_ANGLE, //277 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //278 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_RHANDY_ANGLE, //279 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //280 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //281 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_LSHOULDERY_ANGLE, //282 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //283 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_LELBOWY_ANGLE, //284 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //285 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_LHANDY_ANGLE, //286 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //287 + MNET_UPPERBODY_IN_LHANDY_HIPY_ANGLE, //288 + MNET_UPPERBODY_IN_LHANDY_ENDSITE_EYE_RY_ANGLE, //289 + MNET_UPPERBODY_IN_LHANDY_ENDSITE_EYE_LY_ANGLE, //290 + MNET_UPPERBODY_IN_LHANDY_NECKY_ANGLE, //291 + MNET_UPPERBODY_IN_LHANDY_RSHOULDERY_ANGLE, //292 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //293 + MNET_UPPERBODY_IN_LHANDY_RELBOWY_ANGLE, //294 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //295 + MNET_UPPERBODY_IN_LHANDY_RHANDY_ANGLE, //296 + MNET_UPPERBODY_IN_LHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //297 + MNET_UPPERBODY_IN_LHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //298 + MNET_UPPERBODY_IN_LHANDY_LSHOULDERY_ANGLE, //299 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //300 + MNET_UPPERBODY_IN_LHANDY_LELBOWY_ANGLE, //301 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //302 + MNET_UPPERBODY_IN_LHANDY_LHANDY_ANGLE, //303 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //304 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HIPY_ANGLE, //305 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_ENDSITE_EYE_RY_ANGLE, //306 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_ENDSITE_EYE_LY_ANGLE, //307 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_NECKY_ANGLE, //308 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_RSHOULDERY_ANGLE, //309 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //310 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_RELBOWY_ANGLE, //311 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //312 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_RHANDY_ANGLE, //313 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //314 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //315 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_LSHOULDERY_ANGLE, //316 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //317 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_LELBOWY_ANGLE, //318 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //319 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_LHANDY_ANGLE, //320 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_NECK_AND_HIPY_ANGLE, //321 + MNET_UPPERBODY_IN_NUMBER +}; + +/** @brief Programmer friendly enumerator of expected outputs*/ +enum mocapNET_Output_upperbody_enum +{ + MOCAPNET_UPPERBODY_OUTPUT_HIP_XPOSITION = 0, //0 + MOCAPNET_UPPERBODY_OUTPUT_HIP_YPOSITION, //1 + MOCAPNET_UPPERBODY_OUTPUT_HIP_ZPOSITION, //2 + MOCAPNET_UPPERBODY_OUTPUT_HIP_ZROTATION, //3 + MOCAPNET_UPPERBODY_OUTPUT_HIP_YROTATION, //4 + MOCAPNET_UPPERBODY_OUTPUT_HIP_XROTATION, //5 + MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_ZROTATION, //6 + MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_XROTATION, //7 + MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_YROTATION, //8 + MOCAPNET_UPPERBODY_OUTPUT_CHEST_ZROTATION, //9 + MOCAPNET_UPPERBODY_OUTPUT_CHEST_XROTATION, //10 + MOCAPNET_UPPERBODY_OUTPUT_CHEST_YROTATION, //11 + MOCAPNET_UPPERBODY_OUTPUT_NECK_ZROTATION, //12 + MOCAPNET_UPPERBODY_OUTPUT_NECK_XROTATION, //13 + MOCAPNET_UPPERBODY_OUTPUT_NECK_YROTATION, //14 + MOCAPNET_UPPERBODY_OUTPUT_HEAD_ZROTATION, //15 + MOCAPNET_UPPERBODY_OUTPUT_HEAD_XROTATION, //16 + MOCAPNET_UPPERBODY_OUTPUT_HEAD_YROTATION, //17 + MOCAPNET_UPPERBODY_OUTPUT_EYE_L_ZROTATION, //18 + MOCAPNET_UPPERBODY_OUTPUT_EYE_L_XROTATION, //19 + MOCAPNET_UPPERBODY_OUTPUT_EYE_L_YROTATION, //20 + MOCAPNET_UPPERBODY_OUTPUT_EYE_R_ZROTATION, //21 + MOCAPNET_UPPERBODY_OUTPUT_EYE_R_XROTATION, //22 + MOCAPNET_UPPERBODY_OUTPUT_EYE_R_YROTATION, //23 + MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_ZROTATION, //24 + MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_XROTATION, //25 + MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_YROTATION, //26 + MOCAPNET_UPPERBODY_OUTPUT_RELBOW_ZROTATION, //27 + MOCAPNET_UPPERBODY_OUTPUT_RELBOW_XROTATION, //28 + MOCAPNET_UPPERBODY_OUTPUT_RELBOW_YROTATION, //29 + MOCAPNET_UPPERBODY_OUTPUT_RHAND_ZROTATION, //30 + MOCAPNET_UPPERBODY_OUTPUT_RHAND_XROTATION, //31 + MOCAPNET_UPPERBODY_OUTPUT_RHAND_YROTATION, //32 + MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_ZROTATION, //33 + MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_XROTATION, //34 + MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_YROTATION, //35 + MOCAPNET_UPPERBODY_OUTPUT_LELBOW_ZROTATION, //36 + MOCAPNET_UPPERBODY_OUTPUT_LELBOW_XROTATION, //37 + MOCAPNET_UPPERBODY_OUTPUT_LELBOW_YROTATION, //38 + MOCAPNET_UPPERBODY_OUTPUT_LHAND_ZROTATION, //39 + MOCAPNET_UPPERBODY_OUTPUT_LHAND_XROTATION, //40 + MOCAPNET_UPPERBODY_OUTPUT_LHAND_YROTATION, //41 + MOCAPNET_UPPERBODY_OUTPUT_NUMBER +}; + +/** @brief Programmer friendly enumerator of NSDM elments*/ +enum mocapNET_NSDM_upperbody_enum +{ + MNET_NSDM_UPPERBODY_HIP = 0, //0 + MNET_NSDM_UPPERBODY_ENDSITE_EYE_R, //1 + MNET_NSDM_UPPERBODY_ENDSITE_EYE_L, //2 + MNET_NSDM_UPPERBODY_NECK, //3 + MNET_NSDM_UPPERBODY_RSHOULDER, //4 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_RSHOULDER_AND_RELBOW, //5 + MNET_NSDM_UPPERBODY_RELBOW, //6 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_RELBOW_AND_RHAND, //7 + MNET_NSDM_UPPERBODY_RHAND, //8 + MNET_NSDM_UPPERBODY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15, //9 + MNET_NSDM_UPPERBODY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15, //10 + MNET_NSDM_UPPERBODY_LSHOULDER, //11 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_LSHOULDER_AND_LELBOW, //12 + MNET_NSDM_UPPERBODY_LELBOW, //13 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_LELBOW_AND_LHAND, //14 + MNET_NSDM_UPPERBODY_LHAND, //15 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_NECK_AND_HIP, //16 + MNET_NSDM_UPPERBODY_NUMBER +}; + +/** @brief This is a lookup table to immediately resolve referred Joints*/ +static const int mocapNET_ResolveJoint_upperbody[] = +{ + 0, //0 + 4, //1 + 3, //2 + 1, //3 + 5, //4 + 5, //5 + 6, //6 + 6, //7 + 7, //8 + 0, //9 + 0, //10 + 8, //11 + 8, //12 + 9, //13 + 9, //14 + 10, //15 + 1, //16 + 0//end of array +}; + +/** @brief This is a lookup table to immediately resolve referred Joints of second targets*/ +static const int mocapNET_ResolveSecondTargetJoint_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 6, //5 + 0, //6 + 7, //7 + 0, //8 + 0, //9 + 0, //10 + 0, //11 + 9, //12 + 0, //13 + 10, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief This is the configuration of NSDM elements : + * A value of 0 is a normal 2D point + * A value of 1 is a 2D point plus some offset + * A value of 2 is a virtual point between two 2D points */ +static const int mocapNET_ArtificialJoint_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 2, //5 + 0, //6 + 2, //7 + 0, //8 + 1, //9 + 1, //10 + 0, //11 + 2, //12 + 0, //13 + 2, //14 + 0, //15 + 2, //16 + 0//end of array +}; + +/** @brief These are X offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_upperbody )*/ +static const float mocapNET_ArtificialJointXOffset_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + -0.15, //9 + 0.15, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief These are Y offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_upperbody )*/ +static const float mocapNET_ArtificialJointYOffset_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + -0.15, //9 + -0.15, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as starting points for scaling vectors*/ +static const int mocapNET_ScalingStart_upperbody[] = +{ + 0, //0 + 0, //1 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as ending points for scaling vectors*/ +static const int mocapNET_ScalingEnd_upperbody[] = +{ + 5, //0 + 8, //1 + 0//end of array +}; + +/** @brief These is a 2D Joints that is used as alignment for the skeleton*/ +static const int mocapNET_AlignmentStart_upperbody[] = +{ + 0, //0 + 0//end of array +}; + +/** @brief These is a 2D Joints that is used as alignment for the skeleton*/ +static const int mocapNET_AlignmentEnd_upperbody[] = +{ + 1, //0 + 0//end of array +}; + +/** @brief This function can be used to debug NSDM input and find in a user friendly what is missing..!*/ +static int upperbodyCountMissingNSDMElements(std::vector mocapNETInput,int verbose) +{ + unsigned int numberOfZeros=0; + for (int i=0; i skeletonSerialized %s\n ",mocapNET_upperbody[i],labels[i]); + } +} + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistance_UPPERBODY(std::vector in,int jointA,int jointB) +{ + float aX=in[jointA*3+0]; + float aY=in[jointA*3+1]; + float bX=in[jointB*3+0]; + float bY=in[jointB*3+1]; + if ( ((aX==0) && (aY==0)) || ((bX==0) && (bY==0)) ) { + return 0.0; + } + + + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + +/** @brief This function returns a vector of NSDM values ready for use with the MocapNET upperbody network */ +static std::vector upperbodyCreateNDSM(std::vector in,int havePositionalElements,int haveAngularElements,int doNormalization) +{ + std::vector result; + int secondTargetJointID; + float sIX,sIY,sJX,sJY; + for (int i=0; i0) + { + unsigned int numberOfDistanceSamples=0; + float sumOfDistanceSamples=0.0; + for ( int i=0; i0.0) + { + numberOfDistanceSamples=numberOfDistanceSamples+1; + sumOfDistanceSamples=sumOfDistanceSamples+distance; + } + } +//------------------------------------------------------------------------------------------------- + float scaleDistance=1.0; +//------------------------------------------------------------------------------------------------- + if (numberOfDistanceSamples>0) + { + scaleDistance=(float) sumOfDistanceSamples/numberOfDistanceSamples; + } +//------------------------------------------------------------------------------------------------- + if (scaleDistance!=1.0) + { + for (int i=0; imaxValue) { + maxValue=result[i]; + } + } + fprintf(stderr,"Original Min Value %0.2f, Max Value %0.2f \n",minValue,maxValue); + + + unsigned int iJointID=mocapNET_AlignmentStart_upperbody[0]; + unsigned int jJointID=mocapNET_AlignmentEnd_upperbody[0]; + float aX=in[iJointID*3+0]; + float aY=in[iJointID*3+1]; + float bX=in[jJointID*3+0]; + float bY=in[jJointID*3+1]; + float alignmentAngle=getAngleToAlignToZero_tools(aX,aY,bX,bY); + for (int i=0; imaxValue) { + maxValue=result[i]; + } + } + fprintf(stderr,"Aligned Min Value %0.2f, Max Value %0.2f \n",minValue,maxValue); + + + } + }//Normalization enabled.. +//------------------------------------------------------------------------------------------------- + + + return result; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/artifactRecognition.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/artifactRecognition.cpp new file mode 100644 index 0000000..0dfd1a1 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/artifactRecognition.cpp @@ -0,0 +1,276 @@ +#include "artifactRecognition.hpp" + +#include "../../../../dependencies/InputParser/InputParser_C.h" +#include "../../../../dependencies/RGBDAcquisition/tools/AmMatrix/matrixCalculations.h" + + + #include + + +#include + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + int initializeArtifactsFromFile(struct sceneArtifacts * scene,const char * filename) + { + fprintf(stderr,"Initializing from %s .. \n",filename); + + FILE * fp = fopen(filename,"r"); + if (fp!=0) + { + fprintf(stderr,GREEN "Successfully opened map file %s\n" NORMAL,filename); + + char * line = NULL; + size_t len = 0; + ssize_t read; + + scene->numberOfArtifacts=0; + unsigned int id=0; + + struct InputParserC * ipc = InputParser_Create(8096,5); + InputParser_SetDelimeter(ipc,0,','); + InputParser_SetDelimeter(ipc,1,'('); + InputParser_SetDelimeter(ipc,2,')'); + InputParser_SetDelimeter(ipc,3,10); + InputParser_SetDelimeter(ipc,4,13); + + do { + read = getline(&line, &len, fp); + if (line!=0) + { + int numberOfArguments = InputParser_SeperateWords(ipc,line,1); + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"AREA2D") ) + { + if (scene->numberOfArtifacts+1numberOfArtifacts; + if (scene->numberOfArtifacts>1) + { + ++id; + } + + InputParser_GetWord(ipc,1,scene->artifact[id].label,512); + fprintf(stderr,"New 2D Area declared ( %s )..!\n",scene->artifact[id].label); + scene->artifact[id].y1 = InputParser_GetWordFloat(ipc,2); + scene->artifact[id].x1 = InputParser_GetWordFloat(ipc,3); + scene->artifact[id].y2 = InputParser_GetWordFloat(ipc,4); + scene->artifact[id].x2 = InputParser_GetWordFloat(ipc,5); + scene->artifact[id].is3D=0; + scene->artifact[id].active=0; + scene->artifact[id].hasAction=1; + } + } else + + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"OBJECT3D") ) + { + if (scene->numberOfArtifacts+1numberOfArtifacts; + if (scene->numberOfArtifacts>1) + { + ++id; + } + + InputParser_GetWord(ipc,1,scene->artifact[id].label,512); + fprintf(stderr,"New 3D Object declared ( %s )..!\n",scene->artifact[id].label); + scene->artifact[id].y1 = InputParser_GetWordFloat(ipc,2); + scene->artifact[id].x1 = InputParser_GetWordFloat(ipc,3); + scene->artifact[id].z1 = InputParser_GetWordFloat(ipc,4); + scene->artifact[id].y2 = InputParser_GetWordFloat(ipc,5); + scene->artifact[id].x2 = InputParser_GetWordFloat(ipc,6); + scene->artifact[id].z2 = InputParser_GetWordFloat(ipc,7); + scene->artifact[id].is3D=1; + scene->artifact[id].active=0; + scene->artifact[id].hasAction=1; + } + } else + + + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"ACTIVATE_ON_POSITION") ) + { + fprintf(stderr,"%s will activate on position\n",scene->artifact[id].label); + scene->artifact[id].activatesOnPosition=1; + } else + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"ACTIVATE_ON_LOOK") ) + { + fprintf(stderr,"%s will activate on look\n",scene->artifact[id].label); + scene->artifact[id].activatesOnLook=1; + } else + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"ACTIVATE_ON_GESTURE") ) + { + fprintf(stderr,"%s will activate on gesture\n",scene->artifact[id].label); + scene->artifact[id].activateOnGesture=1; + } else + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"ACTIVATE_ACTION") ) + { + InputParser_GetWord(ipc,1,scene->artifact[id].actionToExecuteOnActivation,512); + fprintf(stderr,"New Command for %s (%s) !\n",scene->artifact[id].label,scene->artifact[id].actionToExecuteOnActivation); + scene->artifact[id].hasAction=1; + } else + if ( InputParser_WordCompareNoCaseAuto(ipc,0,"DEACTIVATE_ACTION") ) + { + InputParser_GetWord(ipc,1,scene->artifact[id].actionToExecuteOnDeactivation,512); + fprintf(stderr,"New Command for %s (%s) !\n",scene->artifact[id].label,scene->artifact[id].actionToExecuteOnDeactivation); + scene->artifact[id].hasAction=1; + } + + } + } + while( (line!=0) && (read>0) ); + + InputParser_Destroy(ipc); + fclose(fp); + return 1; + } + + return 0; + } + + +int check3DArtifactCollision(struct artifactData * artifact,float x1, float y1, float z1,float x2, float y2, float z2) +{ + if (artifact->is3D) + { + float initial[4]={x1,y1,z1,1.0}; + float target[4]={x2,y2,z2,1.0}; + //-------------------------------------------------------------- + float v0[4] = { + std::min(artifact->x1,artifact->x2), + std::min(artifact->y1,artifact->y2), + (float) (artifact->z1+artifact->z2)/2, + 1.0 + }; + //-------------------------------------------------------------- + float v1[4] = { + std::min(artifact->x1,artifact->x2), + std::max(artifact->y1,artifact->y2), + (float) (artifact->z1+artifact->z2)/2, + 1.0 + }; + //-------------------------------------------------------------- + float v2[4] = { + std::max(artifact->x1,artifact->x2), + std::max(artifact->y1,artifact->y2), + (float) (artifact->z1+artifact->z2)/2, + 1.0 + }; + //-------------------------------------------------------------- + float v3[4] = { + std::max(artifact->x1,artifact->x2), + std::min(artifact->y1,artifact->y2), + (float) (artifact->z1+artifact->z2)/2, + 1.0 + }; + //-------------------------------------------------------------- + if (rayIntersectsRectangle(initial,target,v0,v1,v2,v3)) + { + return 1; + } + } + return 0; +} + + +int checkArtifactCollision(struct artifactData * artifact,float x, float y, float r) + { + if ( + (artifact->x1y1x2) && (yy2) + ) + { + /* + fprintf(stderr,"Artifact collision %0.2f %0.2f %0.2f %0.2f -> %0.2f %0.2f \n", + artifact->x1,artifact->y1, + artifact->x2,artifact->y2, + x,y); + */ + + return 1; + } + /* + fprintf(stderr,"NO collision %0.2f %0.2f %0.2f %0.2f -> %0.2f %0.2f \n", + artifact->x1,artifact->y1, + artifact->x2,artifact->y2, + x,y); */ + return 0; + } + + + +// Given three colinear points p, q, r, the function checks if +// point q lies on line segment 'pr' +int onSegment(float pX,float pY, float qX,float qY, float rX, float rY) +{ + if (qX <= std::max(pX, rX) && qX >= std::min(pX, rX) && qY <= std::max(pY, rY) && qY >= std::min(pY, rY)) { return 1; } + + return 0; +} + +// To find orientation of ordered triplet (p, q, r). +// The function returns following values +// 0 --> p, q and r are colinear +// 1 --> Clockwise +// 2 --> Counterclockwise +int orientation(float pX,float pY, float qX,float qY, float rX, float rY) +{ + // See https://www.geeksforgeeks.org/orientation-3-ordered-points/ + // for details of below formula. + int val = (qY - pY) * (rX - qX) - (qX - pX) * (rY - qY); + + if (val == 0) return 0; // colinear + + return (val > 0)? 1: 2; // clock or counterclock wise +} + +// The main function that returns true if line segment 'p1q1' +// and 'p2q2' intersect. +int doIntersect(float p1X,float p1Y, float q1X, float q1Y,float p2X,float p2Y,float q2X, float q2Y) +{ + // Find the four orientations needed for general and + // special cases + int o1 = orientation(p1X,p1Y, q1X,q1Y, p2X,p2Y); + int o2 = orientation(p1X,p1Y, q1X,q1Y, q2X,q2Y); + int o3 = orientation(p2X,p2Y, q2X,q2Y, p1X,p1Y); + int o4 = orientation(p2X,p2Y, q2X,q2Y, q1X,q1Y); + + // General case + if (o1 != o2 && o3 != o4) + return 1; + + // Special Cases + // p1, q1 and p2 are colinear and p2 lies on segment p1q1 + if (o1 == 0 && onSegment(p1X,p1Y, p2X,p2Y, q1X,q1Y)) return 1; + + // p1, q1 and q2 are colinear and q2 lies on segment p1q1 + if (o2 == 0 && onSegment(p1X,p1Y, q2X,q2Y, q1X,q1Y)) return 1; + + // p2, q2 and p1 are colinear and p1 lies on segment p2q2 + if (o3 == 0 && onSegment(p2X,p2Y, p1X,p1Y, q2X,q2Y)) return 1; + + // p2, q2 and q1 are colinear and q1 lies on segment p2q2 + if (o4 == 0 && onSegment(p2X,p2Y, q1X,q1Y, q2X,q2Y)) return 1; + + return 0; // Doesn't fall in any of the above cases +} + + + + +int checkArtifactDirection(struct artifactData * artifact,float x1, float y1, float x2,float y2) + { + unsigned int check=0; + + check+=doIntersect(x1,y1,x2,y2,artifact->x1,artifact->y1,artifact->x2,artifact->y1) ; + if (check==0) { check+=doIntersect(x1,y1,x2,y2,artifact->x1,artifact->y2,artifact->x2,artifact->y2) ; } + if (check==0) { check+=doIntersect(x1,y1,x2,y2,artifact->x1,artifact->y1,artifact->x1,artifact->y2) ; } + if (check==0) { check+=doIntersect(x1,y1,x2,y2,artifact->x2,artifact->y1,artifact->x2,artifact->y2) ; } + + + return check; + } + + \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/artifactRecognition.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/artifactRecognition.hpp new file mode 100644 index 0000000..676ba99 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/artifactRecognition.hpp @@ -0,0 +1,58 @@ +#pragma once +/** @file artifactRecognition.hpp + * @brief MocapNET Artifact recognition is implemented here. Artifacts are places in 3D space that can trigger specific events for applications that need human computer interaction + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + +#define NUMBER_OF_ARTIFACTS 100 + +struct artifactData +{ + float x1; + float y1; + float z1; + float x2; + float y2; + float z2; + char is3D; + char activatesOnOrientation; + char activatesOnLocation; + char activatesOnGestures; + + char hasAction; + char active; + char activatesOnLook; + char activatesOnPosition; + char activateOnGesture; + char label[512]; + char actionToExecuteOnActivation[512]; + char actionToExecuteOnDeactivation[512]; +}; + + + +/** + * @brief recorded gestures that can be used + */ +struct sceneArtifacts +{ + int numberOfArtifacts; + struct artifactData artifact[NUMBER_OF_ARTIFACTS]; +}; + + +//int initializeArtifacts(struct sceneArtifacts * scene); + +int initializeArtifactsFromFile(struct sceneArtifacts * scene,const char * filename); + + +int check3DArtifactCollision(struct artifactData * artifact,float x1, float y1, float z1,float x2, float y2, float z2); + +int checkArtifactCollision(struct artifactData * artifact,float x, float y, float r); + +int checkArtifactDirection(struct artifactData * artifact,float x1, float y1, float x2,float y2); + +int doIntersect(float p1X,float p1Y, float q1X, float q1Y,float p2X,float p2Y,float q2X, float q2Y) ; \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/gestureRecognition.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/gestureRecognition.cpp new file mode 100644 index 0000000..d031491 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/gestureRecognition.cpp @@ -0,0 +1,252 @@ +#include "gestureRecognition.hpp" +#include "../mocapnet2.hpp" +#include "../tools.hpp" +#include "../IO/bvh.hpp" +#include "../IO/csvRead.hpp" +#include "poseRecognition.hpp" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +int addToMotionHistory(struct PoseHistory * poseHistoryStorage,std::vector pose) +{ + poseHistoryStorage->history.push_back(pose); + if (poseHistoryStorage->history.size() > poseHistoryStorage->maxPoseHistory) + { + poseHistoryStorage->history.erase(poseHistoryStorage->history.begin()); + } + return 1; +} + +int dumpMotionHistory(const char * filename,struct PoseHistory * poseHistoryStorage) +{ + return writeBVHFile(filename,0,0,poseHistoryStorage->history); +} + +int updateGestureActivity(std::vector vecA,std::vector vecB,std::vector &active,float threshold) +{ + if (vecA.size()!=vecB.size()) + { + fprintf(stderr,"updateGestureActivity cannot compare different sized vectors..\n"); + return 0; + } + + if (vecA.size()!=active.size()) + { + fprintf(stderr,"updateGestureActivity cannot compare without allocated active vector..\n"); + return 0; + } + + + //Always skip X pos, Y pos, Z pos , and rotation + for (int i=6; ithreshold) + { + active[i]=1; + } + } + return 1; +} + + +int automaticallyObserveActiveJointsInGesture(struct RecordedGesture * gesture) +{ + //fprintf(stderr,"automaticallyObserveActiveJointsInGesture:"); + if (gesture->gesture.size()==0) + { + fprintf(stderr,"Failed to automatically observe active joints\n"); + return 0; + } + + std::vector initialPose = gesture->gesture[0]; + + gesture->usedJoints.clear(); + for (int jointID=0; jointIDusedJoints.push_back(0); + } + + for (int gestureID=0; gestureIDgesture.size(); gestureID++) + { + updateGestureActivity( + initialPose, + gesture->gesture[gestureID], + gesture->usedJoints, + 5 + ); + } + + //fprintf(stderr,"done\n"); + return 1; +} + + +int loadGestures(struct GestureDatabase * gestureDB) +{ + unsigned int gestureID; + char gesturePath[512]= {0}; + + gestureDB->gestureChecksPerformed=0; + + for (gestureID=0; gestureID %s : ",gestureID,gesturePath); + + gestureDB->gesture[gestureID].gesture=loadBVHFileMotionFrames(gesturePath); + //gestureDB->gesture[gestureID].bvhMotionGesture=loadBVHFile(gesturePath); + + //if (gestureDB->gesture[gestureID].bvhMotionGesture!=0) + if (gestureDB->gesture[gestureID].gesture.size()>0) + { + fprintf(stderr,"Gesture %s has %lu frames with %lu fields each\n",gesturePath,gestureDB->gesture[gestureID].gesture.size(),gestureDB->gesture[gestureID].gesture[0].size()); + gestureDB->gesture[gestureID].loaded=1; + gestureDB->gesture[gestureID].gestureCallback=0; + snprintf(gestureDB->gesture[gestureID].label,128,"%s",hardcodedGestureName[gestureID]); + gestureDB->numberOfLoadedGestures +=1; + + //Populate + //gestureDB->gesture[gestureID].gesture; + if (automaticallyObserveActiveJointsInGesture(&gestureDB->gesture[gestureID])) + { + for (int jointID=0; jointIDgesture[gestureID].usedJoints.size(); jointID++) + { + if (gestureDB->gesture[gestureID].usedJoints[jointID]) + { + fprintf(stderr,YELLOW "Joint %u/%s is going to be used for gesture #%u\n" NORMAL,jointID,MocapNETOutputArrayNames[jointID],gestureID); + } + } + } + else + { + fprintf(stderr,YELLOW "Failure observing active joints\n" NORMAL); + } + + fprintf(stderr,GREEN "Success , loaded %lu frames\n" NORMAL,gestureDB->gesture[gestureID].gesture.size()); + } + else + { + fprintf(stderr,RED "Failure\n" NORMAL); + } + + } + + return (gestureDB->numberOfLoadedGestures>0); +} + + + + + + +int compareHistoryWithGesture( + struct RecordedGesture * gesture, + struct PoseHistory * poseHistoryStorage, + unsigned int checkSerialNumber, + float currentFramerate, + float targetFramerate, + float percentageForDetection, + float threshold +) +{ + unsigned int matchingFrames=0; + if (poseHistoryStorage->history.size()==0) + { + fprintf(stderr,"Requested comparison with empty history..\n"); + return 0; + } + + if (gesture->lastActivation>checkSerialNumber) + { + fprintf(stderr,RED "compareHistoryWithGesture: inverted timestmaps, something is terribly wrong..\n" NORMAL); + } + else + { + if (checkSerialNumber-gesture->lastActivationlabel); + return 0; + } + } + + + //TODO: Compensate for different targetFramerate / currentFramerate here..! + //In case of the currentFramerate being larger than the targetFramerate there should be more frames checked to increase accuracy + //In case of the currentFramerate being smaller than the targetFramerate there should be skipped frames + + if (poseHistoryStorage->history.size()>=gesture->gesture.size()) + { + unsigned int frameID=0; + unsigned int historyStart=poseHistoryStorage->history.size() - gesture->gesture.size(); + //unsigned int jointNumber=poseHistoryStorage->history[0].size(); + //unsigned int jointID=0; + for (frameID=0; frameIDgesture.size(); frameID++) + { + matchingFrames+=areTwoBVHFramesCloseEnough(gesture->gesture[frameID],poseHistoryStorage->history[frameID+historyStart],gesture->usedJoints,threshold); + } + + gesture->percentageComplete = (float) matchingFrames/gesture->gesture.size(); + float percentComplete = 100*gesture->percentageComplete; + + if (percentComplete > 100 ) + { + fprintf(stderr,RED); + fprintf(stderr,"There is something wrong in the gesture files ( size = %lu )\n ",gesture->gesture.size()); + } + else if (percentComplete >= percentageForDetection ) + { + fprintf(stderr,GREEN); + gesture->lastActivation=checkSerialNumber; + } + else if (percentComplete >= 50.0 ) + { + fprintf(stderr,YELLOW); + } + fprintf(stderr,"Gesture %s - %0.2f %% %u/%lu\n" NORMAL,gesture->label,percentComplete,matchingFrames,gesture->gesture.size()); + + //If we have more than 75% or whatever is the target percentage correct trigger..! + return (percentComplete >= percentageForDetection ); + } + + return 0; +} + + + +int compareHistoryWithKnownGestures(struct GestureDatabase * gestureDB,struct PoseHistory * poseHistoryStorage,float percentageForDetection,float threshold) +{ + unsigned long now=GetTickCountMicrosecondsMN(); + unsigned long previousRun=gestureDB->previousGestureCheckTimestamp; + gestureDB->previousGestureCheckTimestamp = now; + + float currentFramerate = convertStartEndTimeFromMicrosecondsToFPS(previousRun,now); + float targetFramerate=16.0; + + unsigned int gestureID=0; + gestureDB->gestureChecksPerformed+=1; + + for (gestureID=0; gestureIDnumberOfLoadedGestures; gestureID++) + { + if ( + compareHistoryWithGesture( + &gestureDB->gesture[gestureID], + poseHistoryStorage, + gestureDB->gestureChecksPerformed, + currentFramerate, + targetFramerate, + percentageForDetection, + threshold + ) + ) + { + return 1+gestureID; + } + } + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/gestureRecognition.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/gestureRecognition.hpp new file mode 100644 index 0000000..2367d5b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/gestureRecognition.hpp @@ -0,0 +1,93 @@ +#pragma once +/** @file gestureRecognition.hpp + * @brief MocapNET Gesture recognition is implemented here + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + +/** + * @brief This is an array of names for the hardcoded gestures included in the dataset/gestures/ subdirectory + * Please remember to update hardcodedPoseNumber, its value should be the number of arguments in this array + * If you have arguments 0 - 10 you should set hardcodedGestureNumber to 11 + */ +static const char * hardcodedGestureName[] = +{ + "help.bvh", //0 + "push.bvh", //1 + "lefthandcircle.bvh", //2 + "righthandcircle.bvh", //3 + "waveleft.bvh", //4 + "doubleclap.bvh", //5 + "waveright.bvh", //6 + "leftkick.bvh", //8 + "rightkick.bvh", //9 + "tpose.bvh", //10 + "handsup.bvh", //7 + "", // + "" //13 + //hardcodedGestureNumber should be kept in sync +}; + + +/** + * @brief This needs to be kept in sync with hardcodedGestureName */ +const unsigned int hardcodedGestureNumber=10; + + +/** + * @brief Gesture tuning controls for the brave.. + **/ +const unsigned int GESTURE_ACTIVATION_COOLDOWN_IN_FRAMES=50; +const float GESTURE_COMPLETION_PERCENT=80.0; +const float GESTURE_ANGLE_SENSITIVITY=25.0; + + +/** + * @brief history of poses + */ +struct PoseHistory +{ + unsigned int maxPoseHistory; + std::vector > history; +}; + + +/** + * @brief recorded gestures that can be used + */ +struct RecordedGesture +{ + unsigned int lastActivation; + float percentageComplete; + char loaded; + char label[128]; + std::vector > gesture; + std::vector usedJoints; + void * gestureCallback; +}; + + +/** + * @brief gesture detection context, to facilitate gestures + */ +struct GestureDatabase +{ + unsigned int gestureChecksPerformed; + unsigned int numberOfLoadedGestures; + struct RecordedGesture gesture[hardcodedGestureNumber]; + unsigned long previousGestureCheckTimestamp; +}; + + +int loadGestures(struct GestureDatabase * gestureDB); + +int addToMotionHistory(struct PoseHistory * poseHistoryStorage,std::vector pose); + + +int dumpMotionHistory(const char * filename,struct PoseHistory * poseHistoryStorage); + + +int compareHistoryWithKnownGestures(struct GestureDatabase * gestureDB,struct PoseHistory * poseHistoryStorage,float percentageForDetection,float threshold); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.cpp new file mode 100644 index 0000000..d1fc47e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.cpp @@ -0,0 +1,663 @@ +#include +#include +#include +#include + +#include "../mocapnet2.hpp" +#include "parseCommandlineOptions.hpp" +#include "../IO/bvh.hpp" +#include "../tools.hpp" + +#include "../../../JointEstimator2D/jointEstimator2D.hpp" +#include "../IO/jsonMocapNETHelpers.hpp" +#include "../IO/csvRead.hpp" + + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +const char outputPathStatic[]="out.bvh"; + +void defaultMocapNET2Options(struct MocapNET2Options * options) +{ + memset(options,0,sizeof(struct MocapNET2Options)); + + options->isJSONFile=0; + options->isCSVFile=0; + options->webcamSource = 0; + options->path=0; + options->datasetPath=0; + options->jointEstimatorUsed = JOINT_2D_ESTIMATOR_OPENPOSE; // JOINT_2D_ESTIMATOR_FORTH; + + options->doUpperBody=1; + options->doLowerBody=1; + options->doFace=0; + options->doHands=0; + options->forceFront=0; + options->forceLeft=0; + options->forceRight=0; + options->forceBack=0; + options->visualizationType=0; + + //Default IK options + options->useInverseKinematics=0; + options->learningRate=0.01; + options->spring =20.0; + options->iterations=5; + options->epochs=30; + + //Test under Simulated Gaussian Noise + options->addNormalizedPixelGaussianNoiseX=0.0; + options->addNormalizedPixelGaussianNoiseY=0.0; + + options->outputPath = (char*) outputPathStatic; + + options->visualize=1; + options->doMultiThreadedIK=0; + options->useOpenGLVisualization=0; + options->save3DVisualization=0; + options->save2DVisualization=0; + options->saveVisualization=0; + options->saveCSV3DFile=0; + options->constrainPositionRotation=1; + + options->delay=0; + options->prependTPose=0; + options->serialLength=5; + options->bvhCenter=0; + + if (getCPUName(options->CPUName,512)) + { + fprintf(stderr,"CPU : %s\n",options->CPUName); + } + + if (getGPUName(options->GPUName,512)) + { + fprintf(stderr,"GPU : %s\n",options->GPUName); + } + + options->inputFramerate = 30.0; + + options->quality=1.0; + options->mocapNETMode=5; + options->doGestureDetection=0; + options->doOutputFiltering=1; + options->useCPUOnlyForMocapNET=1; //Use CPU for MocapNET + options->useCPUOnlyFor2DEstimator=0; // Use GPU for 2D estimator + options->brokenFrames=0; + options->numberOfMissingJoints=0; + + options->visWidth=1920; + options->visHeight=1080; + options->width=1920; + options->height=1080; + + + options->scale=1.0; + options->scaleX=1.0; + options->scaleY=1.0; + options->fScaleX=1.0; + options->fScaleY=1.0; + + options->loopStartTime=0; + options->loopEndTime=1000; + options->totalLoopFPS=0.0; + options->fpsMocapNET=0.0; + options->fps2DEstimator=0.0; + + options->frameSkip=0; + options->frameLimit=0; +} + + +void checkVersion() +{ + char hostname[1024]; + gethostname(hostname, 1024); + //========================== + //char username[1024]; + //getlogin_r(username,1024); + //========================== + + char command[2048]; + snprintf(command,2048,"wget -qO- \"http://ammar.gr/mocapnet/version/index.php?h=%s&v=%s\"&",hostname,MocapNETVersion); + int i=0; + //i = system(command); ammar.gr is not working :( +} + + +void argumentError(int currentlyAt,int extraAt,int argc, char *argv[]) +{ + fprintf(stderr,RED "Incorrect number of arguments, %u required ( @ %s )..\n" NORMAL,extraAt,argv[currentlyAt]); + exit(1); +} + + +int loadOptionsFromCommandlineOptions(struct MocapNET2Options * options,int argc, char *argv[]) +{ + checkVersion(); +//(struct BVH_MotionCapture * bvhMotion,float shoulderToElbowLength,float elbowToHandLength,float hipToKneeLength,float kneeToFootLength) + //------------------------------------------------------ + // Parse arguments + //------------------------------------------------------ + for (int i=0; ii+1) + { + options->forceOutputPositionRotation=1; + options->outputPosRot[0]=atof(argv[i+1]); + options->outputPosRot[1]=atof(argv[i+2]); + options->outputPosRot[2]=atof(argv[i+3]); + options->outputPosRot[3]=atof(argv[i+4]); + options->outputPosRot[4]=atof(argv[i+5]); + options->outputPosRot[5]=atof(argv[i+6]); + } + } + else + if (strcmp(argv[i],"--rotateSkeleton")==0) + { + if (argc>i+1) + { + if (strcmp(argv[i+1],"auto")==0) + { + options->skeletonRotation=360.0; + } else + { + options->skeletonRotation=atof(argv[i+1]); + } + } + } + else + + if (strcmp(argv[i],"--inputFramerate")==0) + { + if (argc>i+1) + { + options->inputFramerate = atof(argv[i+1]); + fprintf(stderr,"Input Framerate set to %0.2f \n", options->inputFramerate); + } + } + else + + if (strcmp(argv[i],"--msg")==0) + { + if (argc>i+1) + { + fprintf(stderr,"Message set to %s \n",argv[i+1]); + snprintf(options->message,512,"%s",argv[i+1]); + } + } + else + if (strcmp(argv[i],"--map")==0) + { + if (argc>i+1) + { + fprintf(stderr,"Configuring Map to use file %s \n",argv[i+1]); + snprintf(options->mapFilePath,512,"%s",argv[i+1]); + options->visualizationType=2; + } + } + else + if (strcmp(argv[i],"--unconstrained")==0) + { + options->constrainPositionRotation=0; + } + else if ( (strcmp(argv[i],"-o")==0) || (strcmp(argv[i],"--output")==0) ) + { + if (argc>i+1) + { + options->outputPath=argv[i+1]; + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--dontbend")==0) + { + options->dontBend=1; + } + else if (strcmp(argv[i],"--opengl")==0) + { + options->useOpenGLVisualization=1; + } + else if (strcmp(argv[i],"--save")==0) + { + options->saveVisualization=1; + } + else if ( (strcmp(argv[i],"--save2D")==0) || (strcmp(argv[i],"--save2d")==0) ) + { + options->save2DVisualization=1; + } + else if ( (strcmp(argv[i],"--save3D")==0) || (strcmp(argv[i],"--save3d")==0) ) + { + options->save3DVisualization=1; + } + else if ( (strcmp(argv[i],"--saveCSV3D")==0) || (strcmp(argv[i],"--savecsv3d")==0) ) + { + options->saveCSV3DFile=1; + } + else if (strcmp(argv[i],"--noik")==0) + { + options->useInverseKinematics=0; + } + else if (strcmp(argv[i],"--ik")==0) + { + if (argc>i+3) + { + options->useInverseKinematics=1; + options->learningRate=atof(argv[i+1]); + options->iterations=atoi(argv[i+2]); + options->epochs=atoi(argv[i+3]); + //options->spring=atof(argv[i+4]); + } + else + { argumentError(i,3,argc,argv); } + } + else if (strcmp(argv[i],"--nv")==0) + { + fprintf(stderr,"Visualization disabled\n"); + options->visualize=0; + } + else if (strcmp(argv[i],"--novisualization")==0) + { + fprintf(stderr,"Visualization disabled\n"); + options->visualize=0; + } + else if (strcmp(argv[i],"-v")==0) + { + fprintf(stderr,"Visualization enabled\n"); + options->visualize=1; + } + else if (strcmp(argv[i],"--visualize")==0) + { + fprintf(stderr,"Visualization enabled\n"); + options->visualize=1; + } + else if (strcmp(argv[i],"--frameskip")==0) + { + if (argc>i+1) + { + options->frameSkip=atoi(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--nofilter")==0) + { + fprintf(stderr,"Filtering Disabled\n"); + options->doOutputFiltering=0; + } + else if (strcmp(argv[i],"--scale")==0) + { + if (argc>i+1) + { + options->scale=atof(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--scaleX")==0) + { + if (argc>i+1) + { + options->scaleX=atof(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--scaleY")==0) + { + if (argc>i+1) + { + options->scaleY=atof(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--focalLength")==0) + { + overrideBVHSubsystemFocalLength(atof(argv[i+1]),atof(argv[i+2])); + } + else if (strcmp(argv[i],"--fscale")==0) + { + if (argc>i+2) + { + options->fScaleX=atof(argv[i+1]); + options->fScaleY=atof(argv[i+2]); + } else + { argumentError(i,2,argc,argv); } + } + else if (strcmp(argv[i],"--noise")==0) + { + if (argc>i+2) + { + options->addNormalizedPixelGaussianNoiseX=atof(argv[i+1]); + options->addNormalizedPixelGaussianNoiseY=atof(argv[i+2]); + } else + { argumentError(i,2,argc,argv); } + } + + else if (strcmp(argv[i],"--front")==0) + { + options->forceFront=1; + } + else if (strcmp(argv[i],"--back")==0) + { + options->forceBack=1; + } + else if (strcmp(argv[i],"--left")==0) + { + options->forceLeft=1; + } + else if (strcmp(argv[i],"--right")==0) + { + options->forceRight=1; + } + else if (strcmp(argv[i],"--bvhcenter")==0) + { + options->bvhCenter=1; + } + else if (strcmp(argv[i],"--forth")==0) + { + options->jointEstimatorUsed = JOINT_2D_ESTIMATOR_FORTH; + } + else if (strcmp(argv[i],"--openpose")==0) + { + options->jointEstimatorUsed = JOINT_2D_ESTIMATOR_OPENPOSE; + } + else if (strcmp(argv[i],"--vnect")==0) + { + options->jointEstimatorUsed = JOINT_2D_ESTIMATOR_VNECT; + } + else if (strcmp(argv[i],"--frames")==0) + { + if (argc>i+1) + { + options->frameLimit=atoi(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--maxFrames")==0) + { + if (argc>i+1) + { + options->frameLimit=atoi(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--from")==0) + { + if (argc>i+1) + { + options->webcamSource = argv[i+1]; + options->path = argv[i+1]; + if (strstr(options->path,".json")!=0) + { + fprintf(stderr,"JSON files no longer directly supported, please use the converter tool to convert them to .CSV"); + options->isJSONFile=1; + options->isCSVFile=0; + } + if (strstr(options->path,".csv")!=0) + { + options->isJSONFile=0; + options->isCSVFile=1; + } + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--quality")==0) + { + if (argc>i+1) + { + options->quality=atof(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else + if (strcmp(argv[i],"--mt")==0) + { + options->doMultiThreadedIK=1; + } + else + if (strcmp(argv[i],"--cpu")==0) + { + options->useCPUOnlyForMocapNET=1; + options->useCPUOnlyFor2DEstimator=1; + } + else + //if (strcmp(argv[i],"--cpu")==0) { setenv("CUDA_VISIBLE_DEVICES", "", 1); } else + if (strcmp(argv[i],"--gpu")==0) + { + options->useCPUOnlyForMocapNET=0; + options->useCPUOnlyFor2DEstimator=0; + } + else if (strcmp(argv[i],"--delay")==0) + { + //If you want to take some time to check the results that + //might otherwise pass by very fast + if (argc>i+1) + { + options->delay=atoi(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--skip")==0) + { + //Allow skipping of frames in the neural network and using only the very fast IK module.. + options->maximumNeuralNetworkSkipFrames=atoi(argv[i+1]); + + if (options->maximumNeuralNetworkSkipFrames>0) + { + options->skipNeuralNetworkIfItIsNotNeeded=1; + if (options->maximumNeuralNetworkSkipFrames==1) + { + fprintf(stderr,"Skipping 1 frame every one frame makes no sense, assuming user wants to skip 1 frame every 2 frames\n"); + options->maximumNeuralNetworkSkipFrames=2; + } + } + } + else if (strcmp(argv[i],"--tpose")==0) + { + options-> prependTPose=1; + } + else if (strcmp(argv[i],"--nolowerbody")==0) + { + options->doLowerBody=0; + } + else if (strcmp(argv[i],"--show")==0) + { + options->visualizationType=atoi(argv[i+1]); + } + else if (strcmp(argv[i],"--nohands")==0) + { + options->doHands=0; + } + else if (strcmp(argv[i],"--hands")==0) + { + options->doHands=1; + } + else if (strcmp(argv[i],"--face")==0) + { + options->doFace=1; + } + else if (strcmp(argv[i],"--noface")==0) + { + options->doFace=0; + } + else if (strcmp(argv[i],"--label")==0) + { + if (argc>i+1) + { + options->label = argv[i+1]; + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--seriallength")==0) + { + if (argc>i+1) + { + options->serialLength = atoi(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if ( (strcmp(argv[i],"-o")==0) || (strcmp(argv[i],"--output")==0) ) + { + if (argc>i+1) + { + options->outputPath=argv[i+1]; + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--mode")==0) + { + if (argc>i+1) + { + options->mocapNETMode=atoi(argv[i+1]); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--size")==0) + { + if (argc>i+2) + { + options->width = atoi(argv[i+1]); + options->height = atoi(argv[i+2]); + } else + { argumentError(i,2,argc,argv); } + } + else if (strcmp(argv[i],"--visualizationSize")==0) + { + if (argc>i+2) + { + options->visWidth = atoi(argv[i+1]); + options->visHeight = atoi(argv[i+2]); + } else + { argumentError(i,2,argc,argv); } + } + else if (strcmp(argv[i],"--gestures")==0) + { + options->doGestureDetection=1; + } + } + +return 1; +} + + +int loadOptionsAfterBVHLoadFromCommandlineOptions(struct MocapNET2Options * options,int argc, char *argv[]) +{ + options->hasInit=0; + for (int i=0; ii+2) + { + if (!options->hasInit) + { + initializeBVHConverter(0,options->visWidth,options->visHeight,0); + options->hasInit=1; + } + + changeFeetDimensions( + atof(argv[i+1]), + atof(argv[i+2]) + ); + } else + { argumentError(i,2,argc,argv); } + } + else + if (strcmp(argv[i],"--scaleAllJointDimensions")==0) + { + if(argc>i+1) + { + if (!options->hasInit) + { + initializeBVHConverter(0,options->visWidth,options->visHeight,0); + options->hasInit=1; + } + scaleAllJoints(atof(argv[i+1])); + } else + { argumentError(i,1,argc,argv); } + } + else if (strcmp(argv[i],"--changeJointDimensions")==0) + { + if(argc>i+9) + { + if (!options->hasInit) + { + initializeBVHConverter(0,options->visWidth,options->visHeight,0); + options->hasInit=1; + } + changeJointDimensions( + atof(argv[i+1]), + atof(argv[i+2]), + atof(argv[i+3]), + atof(argv[i+4]), + atof(argv[i+5]), + atof(argv[i+6]), + atof(argv[i+7]), + atof(argv[i+8]), + atof(argv[i+9]) + ); + } + else + { + argumentError(i,9,argc,argv); + fprintf(stderr,"Incorrect number of parameters given..\n"); + return 0; + } + } + } + + return 1; +} + + + + +int takeCareOfScalingInputAndAddingNoiseAccordingToOptions(struct MocapNET2Options * options,struct skeletonSerialized * skeleton) +{ + //--------------------------------------------------------------------------------------------------------------------- + // Changing Input 2D Point Cloud by Scaling it + //--------------------------------------------------------------------------------------------------------------------- + if (options->scale!=1.0) + { + uniformlyScaleSerializedSkeleton(skeleton,options->scale); + } + //--------------------------------------------------------------------------------------------------------------------- + if (options->scaleX!=1.0) + { + if (!scaleSerializedSkeletonX(skeleton,options->scaleX)) + { + fprintf(stderr,RED "Failed to scale skeleton in X axis\n" NORMAL); + } + } + //--------------------------------------------------------------------------------------------------------------------- + if (options->scaleY!=1.0) + { + if (!scaleSerializedSkeletonY(skeleton,options->scaleY)) + { + fprintf(stderr,RED "Failed to scale skeleton in Y axis\n" NORMAL); + } + } + //--------------------------------------------------------------------------------------------------------------------- + if ( (options->fScaleX!=1.0) || (options->fScaleY!=1.0) ) + { + if (! scaleSerializedSkeletonFromCenter(skeleton,options->fScaleX,options->fScaleY) ) + { + fprintf(stderr,RED "Failed to scale skeleton to approximate focal length change\n" NORMAL); + } + } + //--------------------------------------------------------------------------------------------------------------------- + + if ( (options->addNormalizedPixelGaussianNoiseX>0.0) || (options->addNormalizedPixelGaussianNoiseY>0.0) ) + { + fprintf(stderr,RED "Note: You are running adding gaussian noise of std deviation %0.2f,%0.2f pixels on 2D joints\n" NORMAL, + options->width * options->addNormalizedPixelGaussianNoiseX, + options->height * options->addNormalizedPixelGaussianNoiseY + ); + perturbSerializedSkeletonUsingGaussianNoise(skeleton,options->addNormalizedPixelGaussianNoiseX,options->addNormalizedPixelGaussianNoiseY); + } + return 1; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.hpp new file mode 100644 index 0000000..c724bd6 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/parseCommandlineOptions.hpp @@ -0,0 +1,99 @@ +#pragma once +/** @file parseCommandlineOptions.hpp + * @brief MocapNET applications handle a large number of parameters. In order to simplify development and not having to manually sync all accepted parameters through all end-user applications this is the + * central module that parses commandline parameters and populates the MocapNET2Options structure that holds them + * @author Ammar Qammaz (AmmarkoV) + */ + + +/** + * @brief MocapNET has many options depending on datasets etc, instead of storing them in each application + * this is a central strcture to make it easier to parse them.. + */ +struct MocapNET2Options +{ + const char * webcamSource; + const char * path; + char * datasetPath; + unsigned int inputIsSingleImage; + + unsigned int isJSONFile; + unsigned int isCSVFile; + unsigned int jointEstimatorUsed; + + unsigned int doUpperBody,doLowerBody,doFace,doHands; + unsigned int forceFront,forceLeft,forceRight,forceBack; + unsigned int useInverseKinematics; + unsigned int visualizationType; + + unsigned int skipNeuralNetworkIfItIsNotNeeded; + unsigned int maximumNeuralNetworkSkipFrames; + + float inputFramerate; + + float learningRate; + float spring; + unsigned int iterations; + unsigned int epochs; + + float addNormalizedPixelGaussianNoiseX,addNormalizedPixelGaussianNoiseY; + + char * outputPath; + + unsigned int visualize,useOpenGLVisualization,save3DVisualization,save2DVisualization,saveVisualization,saveCSV3DFile,constrainPositionRotation; + + char CPUName[512]; + char GPUName[512]; + char message[512]; + + unsigned int delay; + unsigned int prependTPose; + unsigned int serialLength; + const char * label; + unsigned int bvhCenter; + + float quality; + unsigned int mocapNETMode; + int doGestureDetection; + int doOutputFiltering; + int doMultiThreadedIK; + + unsigned int useCPUOnlyForMocapNET; + unsigned int useCPUOnlyFor2DEstimator; + unsigned int brokenFrames; + unsigned int numberOfMissingJoints; + + unsigned int visWidth,visHeight; + unsigned int width,height; + + long loopStartTime,loopEndTime; + float totalLoopFPS; + float fpsAcquisition,fpsMocapNET,fps2DEstimator,fpsIK; + unsigned int frameLimit; + unsigned int frameSkip; + + float scale,scaleX,scaleY,fScaleX,fScaleY; + + + float skeletonRotation; + + int dontBend; + char forceOutputPositionRotation; + float outputPosRot[6]; + + + char mapFilePath[512]; + + int hasInit; +}; + + + +void defaultMocapNET2Options(struct MocapNET2Options * options); + +int loadOptionsFromCommandlineOptions(struct MocapNET2Options * options,int argc, char *argv[]); + +int loadOptionsAfterBVHLoadFromCommandlineOptions(struct MocapNET2Options * options,int argc, char *argv[]); + + +int takeCareOfScalingInputAndAddingNoiseAccordingToOptions(struct MocapNET2Options * options,struct skeletonSerialized * skeleton); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/poseRecognition.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/poseRecognition.cpp new file mode 100644 index 0000000..128ef36 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/poseRecognition.cpp @@ -0,0 +1,180 @@ +#include "poseRecognition.hpp" +#include "../mocapnet2.hpp" +#include "../tools.hpp" +#include "../IO/bvh.hpp" +#include "../IO/csvRead.hpp" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + + +int loadPoses(struct PoseDatabase * poseDB) +{ + unsigned int poseID; + char posePath[512]= {0}; + + poseDB->poseChecksPerformed=0; + + for (poseID=0; poseID %s : ",poseID,posePath); + + std::vector > bvhFileLoaded = loadBVHFileMotionFrames(posePath); + + if (bvhFileLoaded.size()==0) + { + fprintf(stderr,RED "Pose %u -> %s is corrupted\n" NORMAL,poseID,posePath); + } else + { + poseDB->pose[poseID].pose=bvhFileLoaded[0]; + if (bvhFileLoaded.size()>1) + { + fprintf(stderr,YELLOW "Pose %u -> %s has more than one frames\n" NORMAL,poseID,posePath); + } + } + + + if (poseDB->pose[poseID].pose.size()>0) + { + fprintf(stderr,"Pose %s has %lu frames with %lu fields each\n",posePath,poseDB->pose[poseID].pose.size(),poseDB->pose[poseID].pose.size()); + poseDB->pose[poseID].loaded=1; + poseDB->pose[poseID].poseCallback=0; + snprintf(poseDB->pose[poseID].label,128,"%s",hardcodedPoseName[poseID]); + poseDB->numberOfLoadedPoses +=1; + + fprintf(stderr,GREEN "Success , loaded a pose with %lu values\n" NORMAL,poseDB->pose[poseID].pose.size()); + } + else + { + fprintf(stderr,RED "Failure\n" NORMAL); + } + + } + + return (poseDB->numberOfLoadedPoses>0); +} + + +int areTwoBVHFramesCloseEnough(std::vector vecA,std::vector vecB,std::vector active,float threshold) +{ + if (vecA.size()!=vecB.size()) + { + fprintf(stderr,"areTwoBVHFramesCloseEnough cannot compare different sized vectors..\n"); + return 0; + } + + + //Always skip X pos, Y pos, Z pos , and rotation + for (int i=6; ithreshold) + { + return 0; + } + } + else + { + if (i>=active.size()) + { + fprintf(stderr,RED "Failure comparing bvh frames due to short activeJoint vector\n" NORMAL); + return 0; + } + else + { + if ( (active[i]) && (difference>threshold) ) + { + return 0; + } + } + } + } + + + return 1; +} + + + +int getBVHFramesMSE(std::vector vecA,std::vector vecB,std::vector active) +{ + if (vecA.size()!=vecB.size()) + { + fprintf(stderr,"areTwoBVHFramesCloseEnough cannot compare different sized vectors..\n"); + return 0; + } + + unsigned int samples=0; + float total=0.0; + + //Always skip X pos, Y pos, Z pos , and rotation + for (int i=6; i=active.size()) + { + fprintf(stderr,RED "Failure comparing bvh frames due to short activeJoint vector\n" NORMAL); + return 0; + } + else + if ( (active[i]) ) + { + ++samples; + total += difference * difference; + } + } + } + + return total/samples; +} + + + +int isThisPoseFamiliar( + struct PoseDatabase * poseDB, + std::vector currentPose, + float percentageForDetection, + float threshold +) +{ + std::vector active; + + if (poseDB!=0) + { + for (unsigned int poseID=0; poseIDnumberOfLoadedPoses; poseID++) + { + + float thisD = getBVHFramesMSE(currentPose,poseDB->pose[poseID].pose,active); + fprintf(stderr,GREEN "Difference from pose %u ( %s = %0.2f ) \n" NORMAL,poseID,hardcodedPoseName[poseID],thisD); + + if ( areTwoBVHFramesCloseEnough(currentPose,poseDB->pose[poseID].pose,active,threshold)) + { + fprintf(stderr,GREEN "Detected pose %u ( %s ) \n" NORMAL,poseID,hardcodedPoseName[poseID]); + return poseID; + } + } + } + + fprintf(stderr,YELLOW "Not a familiar pose \n" NORMAL); + return 0; +} \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/poseRecognition.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/poseRecognition.hpp new file mode 100644 index 0000000..7322de6 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/applicationLogic/poseRecognition.hpp @@ -0,0 +1,76 @@ +#pragma once +/** @file poseRecognition.hpp + * @brief MocapNET Pose recognition is implemented here + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + +/** + * @brief This is an array of names for the hardcoded poses included in the dataset/poses/ subdirectory + * Please remember to update hardcodedPoseNumber, its value should be the number of arguments in this array + * If you have arguments 0 - 10 you should set hardcodedPoseNumber to 11 + */ +static const char * hardcodedPoseName[] = +{ + "neutral.bvh", + "tpose.bvh", //0 + "x.bvh", + "handsup.bvh", + "leftwave.bvh", + "rightright.bvh", + "leftleft.bvh", + "push.bvh", + "rightwave.bvh", + "rightkick.bvh", + "leftkick.bvh", + "" + //hardcodedPoseName should be kept in sync +}; + + +/** + * @brief This needs to be kept in sync with hardcodedPoseName */ +const unsigned int hardcodedPoseNumber=9; + + +/** + * @brief recorded gestures that can be used + */ +struct RecordedPose +{ + unsigned int lastActivation; + float percentageComplete; + char loaded; + char label[128]; + std::vector pose; + std::vector usedJoints; + void * poseCallback; +}; + + +/** + * @brief gesture detection context, to facilitate gestures + */ +struct PoseDatabase +{ + unsigned int poseChecksPerformed; + unsigned int numberOfLoadedPoses; + struct RecordedPose pose[hardcodedPoseNumber]; + unsigned long previousPoseCheckTimestamp; +}; + + +int loadPoses(struct PoseDatabase * poseDB); + +int areTwoBVHFramesCloseEnough(std::vector vecA,std::vector vecB,std::vector active,float threshold); + + +int isThisPoseFamiliar( + struct PoseDatabase * poseDB, + std::vector currentPose, + float percentageForDetection, + float threshold +); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/config.h b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/config.h new file mode 100644 index 0000000..ba7a6e5 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/config.h @@ -0,0 +1,40 @@ +#ifndef MOCAPNET_CONFIGURATION_H_INCLUDED +#define MOCAPNET_CONFIGURATION_H_INCLUDED + +#ifdef __cplusplus +extern "C" +{ +#endif + + +//Neural network orientations centered around 0 +#define NN_ORIENTATIONS_TRAINED_AROUND_ZERO_AND_REQUIRE_TRICK 1 + +//Also swap bvh rotations before IK step +#define APPLY_BVH_FIX_TO_IK_INPUT 0 + +//Test swapped +#define SWAP_LEFT_RIGHT_ENSEMBLES 0 + +//Limits synced to scripts/createRandomizedDatset.sh +const float FRONT_MIN_ORIENTATION = -45.0; +const float FRONT_MAX_ORIENTATION = 45.0; +//-------------------------------- +const float BACK_MIN_ORIENTATION = 135.0; +const float BACK_MAX_ORIENTATION = 225.0; +const float BACK_ALT_MIN_ORIENTATION = -225; +const float BACK_ALT_MAX_ORIENTATION = -135; +//-------------------------------- +const float LEFT_MIN_ORIENTATION = -135.0; +const float LEFT_MAX_ORIENTATION = -45.0; +//-------------------------------- +const float RIGHT_MIN_ORIENTATION = 45.0; +const float RIGHT_MAX_ORIENTATION = 135.0; +//-------------------------------- + + +#ifdef __cplusplus +} +#endif + +#endif // MOCAPNET_CONFIGURATION_H_INCLUDED diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/core.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/core.cpp new file mode 100644 index 0000000..531fa44 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/core.cpp @@ -0,0 +1,360 @@ +#include "core.hpp" + +#include "../../../Tensorflow/tf_utils.hpp" + +#include "../config.h" + +#include + +//MOCAPNET2 ------------------------------------ +#include "../../MocapNETLib2/mocapnet2.hpp" +#include "../../MocapNETLib2/tools.hpp" +#include "../../MocapNETLib2/IO/bvh.hpp" +#include "../../MocapNETLib2/IO/jsonRead.hpp" +#include "../../MocapNETLib2/IO/conversions.hpp" +//---------------------------------------------- +#include "../../MocapNETLib2/remoteExecution.hpp" +//---------------------------------------------- +#include "../../MocapNETLib2/solutionParts/body.hpp" +#include "../../MocapNETLib2/solutionParts/upperBody.hpp" +#include "../../MocapNETLib2/solutionParts/lowerBody.hpp" +//---------------------------------------------- +#include "../../MocapNETLib2/core/singleThreaded.hpp" +#include "../../MocapNETLib2/core/multiThreaded.hpp" +//---------------------------------------------- + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +float undoOrientationTrickForBackOrientation(float orientation) +{ + if (orientation==180) + { + orientation=0; + } + else if (orientation<0) + { + orientation=180-orientation; + } + else + { + orientation=180-orientation; + } + return orientation; +} + + +int getMocapNETOrientationFromAngle(float direction) +{ + float orientation = direction; + + if ( (FRONT_MIN_ORIENTATION<=orientation) && (orientation<=FRONT_MAX_ORIENTATION) ) + { + return MOCAPNET_ORIENTATION_FRONT; + } + else if ( (RIGHT_MIN_ORIENTATION<=orientation) && (orientation<=RIGHT_MAX_ORIENTATION) ) + { + // To visualy inspect these orienteations use : + // ./BVHGUI2 --from dataset/headerWithHeadAndOneMotion.bvh --set 4 135 + // ./BVHGUI2 --from dataset/headerWithHeadAndOneMotion.bvh --set 4 45 + #if SWAP_LEFT_RIGHT_ENSEMBLES + fprintf(stderr,YELLOW "Swapped 45<=range<=135 to left ensemble\n" NORMAL); + return MOCAPNET_ORIENTATION_LEFT; + #else + return MOCAPNET_ORIENTATION_RIGHT; + #endif + } + else if ( (LEFT_MIN_ORIENTATION<=orientation) && (orientation<=LEFT_MAX_ORIENTATION) ) + { + // To visualy inspect these orienteations use : + // ./BVHGUI2 --from dataset/headerWithHeadAndOneMotion.bvh --set 4 -135 + // ./BVHGUI2 --from dataset/headerWithHeadAndOneMotion.bvh --set 4 -45 + #if SWAP_LEFT_RIGHT_ENSEMBLES + fprintf(stderr,YELLOW "Swapped -135<=range<=-45 to right ensemble\n" NORMAL); + return MOCAPNET_ORIENTATION_RIGHT; + #else + return MOCAPNET_ORIENTATION_LEFT; + #endif + } + else if ( (BACK_ALT_MIN_ORIENTATION<=orientation) && (orientation<=BACK_ALT_MAX_ORIENTATION) ) + { + return MOCAPNET_ORIENTATION_BACK; + } + else if ( (BACK_MIN_ORIENTATION<=orientation) && (orientation<=BACK_MAX_ORIENTATION) ) + { + return MOCAPNET_ORIENTATION_BACK; + } + else + { + fprintf(stderr,RED "[Unhandled orientation]\n" NORMAL); + fprintf(stderr,"This probably means difficult input\n"); + return MOCAPNET_ORIENTATION_BACK; + } + fprintf(stderr,RED "Empty Direction Vector\n" NORMAL); + return MOCAPNET_ORIENTATION_NONE; +} + + + +int getMocapNETOrientationFromOutputVector(std::vector direction) +{ + if (direction.size()>0) + { + return getMocapNETOrientationFromAngle(direction[0]); + } + fprintf(stderr,RED "Empty Direction Vector\n" NORMAL); + return MOCAPNET_ORIENTATION_NONE; +} + + +int getMocapNETOrientationFromProbabilities(float frontProb,float backProb,float leftProb,float rightProb) +{ + if ((frontProb>backProb) && (frontProb>leftProb) && (frontProb>rightProb)) + { + return MOCAPNET_ORIENTATION_FRONT; + } + else if ((backProb>frontProb) && (backProb>leftProb) && (backProb>rightProb)) + { + return MOCAPNET_ORIENTATION_BACK; + } + else if ((leftProb>frontProb) && (leftProb>backProb) && (leftProb>rightProb)) + { + return MOCAPNET_ORIENTATION_LEFT; + } + else if ((rightProb>frontProb) && (rightProb>backProb) && (rightProb>leftProb)) + { + return MOCAPNET_ORIENTATION_RIGHT; + } + + fprintf(stderr,RED "Inconclusive Direction Classification\n" NORMAL); + return MOCAPNET_ORIENTATION_NONE; +} + + +std::vector MNET3Classes(struct MocapNET2SolutionPart * mnet,std::vector mnetInput,int orientation) +{ + std::vector result; + + if (orientation!=MOCAPNET_ORIENTATION_NONE) + { + fprintf(stderr,NORMAL "Orientation is : %d " NORMAL,orientation ); + //=========================================================== + if ( orientation==MOCAPNET_ORIENTATION_BACK ) + { + //Back ----------------------------------------------= + fprintf(stderr,"Back\n"); + result = predictTensorflow(&mnet->models[2],mnetInput); + if (result.size()>4) + { + result[4]=undoOrientationTrickForBackOrientation(result[4]); + } + } + else + //=========================================================== + { + //Front ---------------------------------------------- + fprintf(stderr,"Front\n"); + result = predictTensorflow(&mnet->models[1],mnetInput); + } + //=========================================================== + } + return result; +} + +std::vector MNET5Classes(struct MocapNET2SolutionPart * mnet,std::vector mnetInput,int orientation,int targetHasOrientationTrick) +{ + std::vector result; + int targetModel=0; + + if (orientation!=MOCAPNET_ORIENTATION_NONE) + { + fprintf(stderr, "%s ",mnet->partName); + switch (orientation) + { + //=========================================================== + case MOCAPNET_ORIENTATION_FRONT: + fprintf(stderr,GREEN "Front " NORMAL); + targetModel=1; + if (mnet->loadedModels==4) + { + targetModel-=1; + } + result = predictTensorflow(&mnet->models[targetModel],mnetInput); + + /* + if ( (targetHasOrientationTrick) && (result.size()>4) ) + { + fprintf(stderr,"Orientation changed from %0.2f ",result[4]); + result[4]=-1.0 * result[4]; + fprintf(stderr,"to %0.2f",result[4]); + } + */ + break; + //=========================================================== + case MOCAPNET_ORIENTATION_BACK: + fprintf(stderr,GREEN "Back " NORMAL); + targetModel=2; + if (mnet->loadedModels==4) + { + targetModel-=1; + } + result = predictTensorflow(&mnet->models[targetModel],mnetInput); + + if ( (targetHasOrientationTrick) && (result.size()>4) ) + { + fprintf(stderr,"Orientation changed from %0.2f ",result[4]); + result[4]=undoOrientationTrickForBackOrientation(result[4]); + //result[4]-=180.0; + fprintf(stderr,"to %0.2f",result[4]); + } + break; + //=========================================================== + case MOCAPNET_ORIENTATION_LEFT: + fprintf(stderr,GREEN "Left " NORMAL); + targetModel=3; + if (mnet->loadedModels==4) + { + targetModel-=1; + } + result = predictTensorflow(&mnet->models[targetModel],mnetInput); + + if ( (targetHasOrientationTrick) && (result.size()>4) ) + { + fprintf(stderr,"Orientation changed from %0.2f ",result[4]); + result[4]+=90.0; + fprintf(stderr,"to %0.2f",result[4]); + } + break; + //=========================================================== + case MOCAPNET_ORIENTATION_RIGHT: + fprintf(stderr,GREEN "Right " NORMAL); + targetModel=4; + if (mnet->loadedModels==4) + { + targetModel-=1; + } + result = predictTensorflow(&mnet->models[targetModel],mnetInput); + + if ( (targetHasOrientationTrick) && (result.size()>4) ) + { + fprintf(stderr,"Orientation changed from %0.2f ",result[4]); + result[4]+=-90.0; + fprintf(stderr,"to %0.2f",result[4]); + } + break; + //=========================================================== + default : + fprintf(stderr,RED "Unhandled orientation, using front as a last resort \n" NORMAL); + result = predictTensorflow(&mnet->models[1],mnetInput); + break; + //=========================================================== + }; + } + else + { + fprintf(stderr,NORMAL "5Class Direction is not defined\n" NORMAL ); + } + + fprintf(stderr,"\n"); + return result; +} + + + +int localOrientationExtraction(struct MocapNET2SolutionPart * mnet,std::vector mnetInput) +{ + if (mnet->loadedModels!=5) + { + fprintf(stderr,YELLOW "%s does not have an orientation classifier..\n" NORMAL, mnet->partName); + return MOCAPNET_ORIENTATION_NONE; + } + else + { + fprintf(stderr,GREEN "%s has an orientation classifier..\n" NORMAL, mnet->partName); + } + + std::vector direction = predictTensorflow(&mnet->models[0],mnetInput); + + if (direction.size()>=4) + { + fprintf(stderr,NORMAL "Orientation : Front(%0.2f)/Back(%0.2f)/Left(%0.2f)/Right(%0.2f)\n" NORMAL,direction[0],direction[1],direction[2],direction[3]); + //Output of each Neural Network is -45.0 to 0.0 to 45.0 + //We need to correct it .. + + //Apply smoothing to probabilities ! + direction[0] = filter(&mnet->directionSignals[0],direction[0]); + direction[1] = filter(&mnet->directionSignals[1],direction[1]); + direction[2] = filter(&mnet->directionSignals[2],direction[2]); + direction[3] = filter(&mnet->directionSignals[3],direction[3]); + + //Cut negative values.. + if (direction[0]<0.0) { direction[0]=0.0; } + if (direction[1]<0.0) { direction[1]=0.0; } + if (direction[2]<0.0) { direction[2]=0.0; } + if (direction[3]<0.0) { direction[3]=0.0; } + + mnet->hasOrientationScan=1; + mnet->orientationClassifications[0]=direction[0]; + mnet->orientationClassifications[1]=direction[1]; + mnet->orientationClassifications[2]=direction[2]; + mnet->orientationClassifications[3]=direction[3]; + + //int orientation = getMocapNETOrientationFromOutputVector(direction); + return getMocapNETOrientationFromProbabilities(direction[0],direction[1],direction[2],direction[3]); + } + else + { + fprintf(stderr,RED "No Orientation Received ( direction vector -> %lu elements ) ..\n" NORMAL,direction.size()); + } + return MOCAPNET_ORIENTATION_NONE; +} + + +std::vector localExecution(struct MocapNET2SolutionPart * mnet,std::vector mnetInput,int orientation,int targetHasOrientationTrick) +{ + std::vector result; + + + + //Don't run empty data------------------------------------------------- + unsigned int emptyInputElements=0; + for (unsigned int i=0; imode) + { + case 3: + result=MNET3Classes(mnet,mnetInput,orientation); + break; + case 5: + result=MNET5Classes(mnet,mnetInput,orientation,targetHasOrientationTrick); + break; + //----------------------------------------------------------- + default: + fprintf(stderr,RED "MocapNET: Incorrect Mode %u for part %s ..\n" NORMAL,mnet->mode,mnet->partName); + break; + }; + } + else + { + fprintf(stderr,RED "Unable to predict pose orientation..\n" NORMAL); + } + return result; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/core.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/core.hpp new file mode 100644 index 0000000..9b190f8 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/core.hpp @@ -0,0 +1,35 @@ +#pragma once + + +#include "../../MocapNETLib2/mocapnet2.hpp" + + +/** + * @brief Given an orientation angle this function can decide on which orientation class it belong to corresponding to the MOCAPNET_Orientation enum + * @param Angle in degrees + * @retval MOCAPNET_Orientation enumeration value + */ +int getMocapNETOrientationFromAngle(float direction); + +int getMocapNETOrientationFromOutputVector(std::vector direction); + + +int localOrientationExtraction(struct MocapNET2SolutionPart * mnet,std::vector mnetInput); + + + +/** + * @brief An internal function that handles local execution of a part of the final solution + * @param Pointer to a valid and populated MocapNET2SolutionPart instance + * @param The input to this MocapNET solution part + * @param Orientation extracted from the localOrientationExtraction call + * @param Some ensembles require an orientation change + * @retval 1=Success,0=Failure + */ +std::vector localExecution( + struct MocapNET2SolutionPart * mnet, + std::vector mnetInput, + int orientation, + int targetHasOrientationTrick + ); + \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/multiThreaded.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/multiThreaded.cpp new file mode 100644 index 0000000..9e936a8 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/multiThreaded.cpp @@ -0,0 +1,149 @@ +#include "multiThreaded.hpp" +#include "singleThreaded.hpp" +#include "core.hpp" + +void * mocapNETWorkerThread(void * arg) +{ + //We are a thread so lets retrieve our variables.. + struct threadContext * ptr = (struct threadContext *) arg; + fprintf(stderr,"MNET Thread-%u: Started..!\n",ptr->threadID); + struct mocapNETContext * contextArray = (struct mocapNETContext *) ptr->argumentToPass; + struct mocapNETContext * ctx = &contextArray[ptr->threadID]; + + struct MocapNET2 * mnet = ctx->mnet; + struct skeletonSerialized * input = ctx->input; + int doLowerbody = ctx->doLowerbody; + int doHands = ctx->doHands; + int doFace = ctx->doFace; + int doGestureDetection = ctx->doGestureDetection; + unsigned int useInverseKinematics = ctx->useInverseKinematics; + int doOutputFiltering = ctx->doOutputFiltering; + + std::vector result; + + threadpoolWorkerInitialWait(ptr); + + while (threadpoolWorkerLoopCondition(ptr)) + { + switch (ptr->threadID) + { + case 0: + result = mocapnetUpperBody_evaluateInput(mnet,input); + break; + //---------------------------------------------------------------- + case 1: + if ( (doLowerbody) && (mnet->lowerBody.loadedModels>0) ) + { + result = mocapnetLowerBody_evaluateInput(mnet,input); + } + break; + //---------------------------------------------------------------- + case 2: + if ( (doHands) && (mnet->leftHand.loadedModels>0) ) + { + //TODO add hands + //result = mocapnetLeftHand_evaluateInput(mnet,input); + } + break; + //---------------------------------------------------------------- + case 3: + if ( (doHands) && (mnet->rightHand.loadedModels>0) ) + { + //TODO add hands + //result = mocapnetRightHand_evaluateInput(mnet,input); + } + break; + }; + + //-------------------------------- + threadpoolWorkerLoopEnd(ptr); + } + + return 0; +} + + + +std::vector multiThreadedMocapNET( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ) +{ + #if USE_BVH + if (mnet->options->doMultiThreadedIK) + { + struct mocapNETContext ctx[4]; + + for (int i=0; i<4; i++) + { + ctx[i].mnet=mnet; + ctx[i].input=input; + ctx[i].doLowerbody=doLowerbody; + ctx[i].doHands=doHands; + ctx[i].doFace=doFace; + ctx[i].doGestureDetection=doGestureDetection; + ctx[i].useInverseKinematics=useInverseKinematics; + ctx[i].doOutputFiltering=doOutputFiltering; + } + + int okToRunMTCode=0; + if (!mnet->threadPool.initialized) + { + if ( + threadpoolCreate( + &mnet->threadPool, + 4, + (void*) mocapNETWorkerThread, + ctx + ) + ) + { + fprintf(stderr,"MNET2: Survived threadpool creation \n"); + nanoSleepT(1000*1000); + okToRunMTCode=1; + } + } else + { + okToRunMTCode=1; + } + + + if (okToRunMTCode) + { + threadpoolMainThreadPrepareWorkForWorkers(&mnet->threadPool); + mocapnetUpperBody_getOrientation(mnet,input); + threadpoolMainThreadWaitForWorkersToFinish(&mnet->threadPool); + + std::vector result = gatherResults( + mnet, + mnet->body.result, + mnet->upperBody.result, + mnet->lowerBody.result, + mnet->leftHand.result, + mnet->rightHand.result, + mnet->face.result + ); + return result; + } + } + #endif + + //If we have reached this point it means that the multi-threaded code has failed..! + //Fallback on single-threaded code + return singleThreadedMocapNET( + mnet, + input, + doLowerbody, + doHands, + doFace, + doGestureDetection, + useInverseKinematics, + doOutputFiltering + ); +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/multiThreaded.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/multiThreaded.hpp new file mode 100644 index 0000000..37adcac --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/multiThreaded.hpp @@ -0,0 +1,47 @@ +#pragma once + +#include "../../../Tensorflow/tf_utils.hpp" + +#include + +//MOCAPNET2 ------------------------------------ +#include "../../MocapNETLib2/mocapnet2.hpp" +#include "../../MocapNETLib2/tools.hpp" +#include "../../MocapNETLib2/IO/bvh.hpp" +#include "../../MocapNETLib2/IO/jsonRead.hpp" +#include "../../MocapNETLib2/IO/conversions.hpp" + +#include "../../MocapNETLib2/remoteExecution.hpp" +//---------------------------------------------- +#include "../../MocapNETLib2/solutionParts/body.hpp" +#include "../../MocapNETLib2/solutionParts/upperBody.hpp" +#include "../../MocapNETLib2/solutionParts/lowerBody.hpp" + +#if USE_BVH +#include "../../../../dependencies/RGBDAcquisition/tools/PThreadWorkerPool/pthreadWorkerPool.h" +#endif + + +struct mocapNETContext +{ + struct MocapNET2 * mnet; + struct skeletonSerialized * input; + int doLowerbody; + int doHands; + int doFace; + int doGestureDetection; + unsigned int useInverseKinematics; + int doOutputFiltering; + int forceFront; +}; + +std::vector multiThreadedMocapNET( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/singleThreaded.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/singleThreaded.cpp new file mode 100644 index 0000000..6b08307 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/singleThreaded.cpp @@ -0,0 +1,132 @@ +#include "singleThreaded.hpp" +#include "core.hpp" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + +std::vector gatherResults( + struct MocapNET2 * mnet, + std::vector resultBody, + std::vector resultUpperBody, + std::vector resultLowerBody, + std::vector resultLeftHand, + std::vector resultRightHand, + std::vector resultFace + ) +{ + std::vector result; + //---------------------------------------------------------------------------------------------- + for (int i=0; ibody.loadedModels>0) + { + mocapnetBody_fillResultVector(result,resultBody); + } + + if (mnet->upperBody.loadedModels>0) + { + mocapnetUpperBody_fillResultVector(result,resultUpperBody); + } + + if (mnet->lowerBody.loadedModels>0) + { + mocapnetLowerBody_fillResultVector(result,resultLowerBody); + } + //---------------------------------------------------------------------------------------------- + + + return result; +} + + + + +std::vector singleThreadedMocapNET( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ) +{ + std::vector resultBody,resultLowerBody,resultUpperBody,resultLeftHand,resultRightHand,resultFace; + //--------------------------------------------------------------------------------------------------------------------------------------------- + //resultBody = mocapnetBody_evaluateInput(mnet,input); + //- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + mocapnetUpperBody_getOrientation(mnet,input); + resultUpperBody = mocapnetUpperBody_evaluateInput(mnet,input); + //- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + if ( (doLowerbody) && (mnet->lowerBody.loadedModels>0) ) + { + resultLowerBody = mocapnetLowerBody_evaluateInput(mnet,input); + } + else + { + mnet->lowerBody.NSDM.clear(); + } + //- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + if ( (doHands) && (mnet->leftHand.loadedModels>0) ) + { + //Not implemented.. + //resultLeftHand = mocapnetLeftHand_evaluateInput(mnet,input); + } + else + { + mnet->rightHand.NSDM.clear(); + } + //- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + if ( (doHands) && (mnet->rightHand.loadedModels>0) ) + { + //Not implemented.. + //resultRightHand = mocapnetRightHand_evaluateInput(mnet,input); + } + else + { + mnet->leftHand.NSDM.clear(); + } + + if ( (doFace) && (mnet->face.loadedModels>0) ) + { + //Not implemented.. + //resultFace = mocapnetFace_evaluateInput(mnet,input); + } + else + { + mnet->face.NSDM.clear(); + } + + + //--------------------------------------------------------------------------------------------------------------------------------------------- + std::vector result = gatherResults( + mnet, + resultBody, + resultUpperBody, + resultLowerBody, + resultLeftHand, + resultRightHand, + resultFace + ); + //--------------------------------------------------------------------------------------------------------------------------------------------- + + + return result; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/singleThreaded.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/singleThreaded.hpp new file mode 100644 index 0000000..6bd8e1e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/core/singleThreaded.hpp @@ -0,0 +1,39 @@ +#pragma once + +#include "../../../Tensorflow/tf_utils.hpp" + +#include + +//MOCAPNET2 ------------------------------------ +#include "../../MocapNETLib2/mocapnet2.hpp" +#include "../../MocapNETLib2/tools.hpp" +#include "../../MocapNETLib2/IO/bvh.hpp" +#include "../../MocapNETLib2/IO/jsonRead.hpp" +#include "../../MocapNETLib2/IO/conversions.hpp" + +#include "../../MocapNETLib2/remoteExecution.hpp" +//---------------------------------------------- +#include "../../MocapNETLib2/solutionParts/body.hpp" +#include "../../MocapNETLib2/solutionParts/upperBody.hpp" +#include "../../MocapNETLib2/solutionParts/lowerBody.hpp" + +std::vector gatherResults( + struct MocapNET2 * mnet, + std::vector resultBody, + std::vector resultUpperBody, + std::vector resultLowerBody, + std::vector resultLeftHand, + std::vector resultRightHand, + std::vector resultFace +); + +std::vector singleThreadedMocapNET( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/mocapnet2.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/mocapnet2.cpp new file mode 100644 index 0000000..5f18f45 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/mocapnet2.cpp @@ -0,0 +1,512 @@ +//MOCAPNET2 ------------------------------------ +#include "../MocapNETLib2/mocapnet2.hpp" +//---------------------------------------------- +#include "../MocapNETLib2/tools.hpp" +#include "../MocapNETLib2/IO/bvh.hpp" +#include "../MocapNETLib2/IO/jsonRead.hpp" +#include "../MocapNETLib2/IO/conversions.hpp" +//---------------------------------------------- +#include "../MocapNETLib2/remoteExecution.hpp" +//---------------------------------------------- +#include "../MocapNETLib2/core/multiThreaded.hpp" +//---------------------------------------------- + +#include + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int registerGestureEventCallbackWithMocapNET(struct MocapNET2 * mnet,void * callback) +{ + fprintf(stderr,GREEN "New Gesture Event Callback registered\n" NORMAL); + mnet->newGestureEventCallback=callback; + return 1; +} + +void requestRealtimePriority() +{ + set_realtime_priority(); +} + + +void commonInitialization(struct MocapNET2 * mnet) +{ + mnet->newGestureEventCallback=0; + + + fprintf(stderr,"MocapNET2 Initializing..\n"); + + //The following line will attempt to escalate the priority + //to realtime, you probably need to run with sudo otherwise + //it will silently fail. + set_realtime_priority(); + + //Artifacts logic --------------- + if (mnet->options!=0) + { + fprintf(stderr,"Map will be initialized from file %s \n",mnet->options->mapFilePath); + initializeArtifactsFromFile(&mnet->artifacts,mnet->options->mapFilePath); + } + fprintf(stderr," artifacts ok\n"); + //--------------------------------- + + //Reset pose history.. + mnet->gesturesMasterSwitch=mnet->options->doGestureDetection; + mnet->poseHistoryStorage.maxPoseHistory=150; + mnet->poseHistoryStorage.history.clear(); + mnet->framesReceived=0; + + //Inverse Kinematics defaults + mnet->learningRate=0.01; + mnet->iterations=5; + mnet->epochs=30; + mnet->spring = 20; + + mnet->upperBody.perform2DAlignmentBeforeEvaluation=0; + mnet->lowerBody.perform2DAlignmentBeforeEvaluation=0; + //-------------------------------------------------- + mnet->leftHand.perform2DAlignmentBeforeEvaluation=1; + mnet->rightHand.perform2DAlignmentBeforeEvaluation=1; + + snprintf(mnet->body.partName,64,"Whole Body"); + snprintf(mnet->upperBody.partName,64,"Upper Body"); + snprintf(mnet->lowerBody.partName,64,"Lower Body"); + snprintf(mnet->leftHand.partName,64,"Left Hand"); + snprintf(mnet->rightHand.partName,64,"Right Hand"); + + if (mnet->gesturesMasterSwitch) + { + if (!loadGestures(&mnet->recognizedGestures)) + { + fprintf(stderr,RED "Failed to read recognized Gestures\n" NORMAL); + fprintf(stderr,RED "This is not fatal, but gestures/poses will be deactivated..\n" NORMAL); + mnet->gesturesMasterSwitch=0; + } + + if (!loadPoses(&mnet->recognizedPoses)) + { + fprintf(stderr,RED "Failed to read recognized Poses\n" NORMAL); + fprintf(stderr,RED "This is not fatal, but poses/gestures will be deactivated..\n" NORMAL); + mnet->gesturesMasterSwitch=0; + } + } + + fprintf(stderr,"Initializing output filters : "); + float filterCutoff = 5.0; + float approximateFramerate = mnet->options->inputFramerate; + //--------------------------------------------------- + //initButterWorth(&mnet->directionSignal,approximateFramerate,filterCutoff); + + for (int i=0; iupperBody.directionSignals[i],approximateFramerate,filterCutoff); + } + + //XYZ are smoother + for (int i=0; i<3; i++ ) + { + fprintf(stderr,"."); + initButterWorth(&mnet->outputSignals[i],approximateFramerate*2,filterCutoff); + } + //hip rotations are large and smoother + for (int i=3; i<6; i++ ) + { + fprintf(stderr,"."); + initButterWorth(&mnet->outputSignals[i],approximateFramerate*2,filterCutoff); + } + + for (int i=3; ioutputSignals[i],approximateFramerate,filterCutoff); + } + + fprintf(stderr,"\n"); +} + + +int loadMocapNET2(struct MocapNET2 * mnet, const char * description) +{ + commonInitialization(mnet); + + int result = 0; + int target = 0; + + float qualitySetting = mnet->options->quality; + int mode = mnet->options->mocapNETMode; + unsigned int doUpperBody = mnet->options->doUpperBody; + unsigned int doLowerBody = mnet->options->doLowerBody; + unsigned int doFace = mnet->options->doFace; + unsigned int doHands = mnet->options->doHands; + unsigned int forceCPU = mnet->options->useCPUOnlyForMocapNET; + + if (doUpperBody) + { + result += mocapnetUpperBody_initialize(mnet,description,qualitySetting,mode,forceCPU); + ++target; + } + + if (doLowerBody) + { + result += mocapnetLowerBody_initialize(mnet,description,qualitySetting,mode,forceCPU); + ++target; + } + + if (doHands) + { + //TODO add hands + //result += mocapnetRightHand_initialize(mnet,description,qualitySetting,mode,forceCPU); + //result += mocapnetLeftHand_initialize(mnet,description,qualitySetting,mode,forceCPU); + //target += 2; + } + + //doFace + + if (target==0) + { + fprintf(stderr,RED "loadMocapNET2: No body pose estimation functionality was selected..\n" NORMAL); + return 0; + } + + return (result==target); +} + + + + +int initializeMocapNET2InputAssociation(struct MocapNET2 * mnet,struct skeletonSerialized * input,int doLowerbody,int doHands,int doFace) +{ + int results=0; + int attempts=0; + + //============================================== + + fprintf(stderr,"Doing body associations..\n"); + results+=mocapnetBody_initializeAssociations(mnet,input); + ++attempts; + + fprintf(stderr,"Doing upper body associations..\n"); + results+=mocapnetUpperBody_initializeAssociations(mnet,input); + ++attempts; + + //if (doLowerbody) // always do lowerbody.. + { + fprintf(stderr,"Doing lower body associations..\n"); + results+=mocapnetLowerBody_initializeAssociations(mnet,input); + ++attempts; + } + + if (doHands) + { + //TODO add hands + //fprintf(stderr,"Doing lhand associations..\n"); + //results+= mocapnetLeftHand_initializeAssociations(mnet,input); + //++attempts; + + //fprintf(stderr,"Doing rhand associations..\n"); + //results+= mocapnetRightHand_initializeAssociations(mnet,input); + //++attempts; + } + + //This message is confusing and removed - https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/31 + //fprintf(stderr,"TODO: do face associations..\n"); + + //============================================== + if (results==attempts) + { + mnet->indexesPopulated=1; + } + + return (results==attempts); +} + + + + + + +std::vector runMocapNET2( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ) +{ + + std::vector emptyResult; + + if (mnet==0) + { + fprintf(stderr,RED "MocapNET: Cannot work without initialization\n" NORMAL); + return emptyResult; + } + if (input==0) + { + fprintf(stderr,RED "MocapNET: Cannot work without input\n" NORMAL); + return emptyResult; + } + + //We need indexes to be populated in order to be able to convert input to a usable vector + if (!mnet->indexesPopulated) + { + if (initializeMocapNET2InputAssociation(mnet,input,doLowerbody,doHands,doFace)) + { + fprintf(stderr,GREEN "MocapNET adapted to source of input\n" NORMAL); + } + else + { + fprintf(stderr,RED "MocapNET: Could not adapt to particular source of input\n" NORMAL); + exit(1); + return emptyResult; + } + } + + ++mnet->framesReceived; + + if (input->skeletonHeaderElements != input->skeletonBodyElements) + { + fprintf(stderr,RED "MocapNET: Mismatch of input Header/Body element number\n" NORMAL); + fprintf(stderr,RED "Check your input source(!)\n" NORMAL); + fprintf(stderr,RED "Header suggested %u elements, body has %u elements\n" NORMAL,input->skeletonHeaderElements,input->skeletonBodyElements); + + return emptyResult; + } + + fprintf(stderr,GREEN "MocapNET: Received %u elements\n" NORMAL,input->skeletonHeaderElements); + + int useNeuralNetworkToExtractResult = ( (!mnet->options->skipNeuralNetworkIfItIsNotNeeded) || (mnet->framesReceived % mnet->options->maximumNeuralNetworkSkipFrames == 1) ); + + std::vector result; + + //We might not want to run the neural network based on our options + //If we dont use the neural network we will rely on the inverse kinematics module..! + //If we dont want to use the inverse kinematics module then we are forced to use the neural network.. + if ( (useNeuralNetworkToExtractResult) || (!useInverseKinematics) ) + { + //If user has enabled skeleton Rotation turned on on auto ( auto = 360 ) then the skeleton will be automatically rotated..! + //------------------------------------------------------------- + char inputHasBeenAltered=0; + struct skeletonSerialized pureInput; + memcpy(&pureInput,input,sizeof(struct skeletonSerialized)); + //----------------------------------- + if (mnet->options->skeletonRotation==360.0) + { + inputHasBeenAltered=1; + makeSkeletonUpright(input); //Give MocapNET a hand with aligning the skeleton to an easier orientation + } else + { + inputHasBeenAltered=1; + affineSkeletonRotation(input,mnet->options->skeletonRotation); + } + //------------------------------------------------------------- + + + //Run multithreaded mocapNET ( it will fallback to single threaded if multi threading is not enabled through options ) + long startTimeNeuralNetwork = GetTickCountMicrosecondsMN(); + //----------------------------------------------------- + result = multiThreadedMocapNET( + mnet, + input, + doLowerbody, + doHands, + doFace, + doGestureDetection, + useInverseKinematics, + doOutputFiltering + ); + //----------------------------------------------------- + long endTimeNeuralNetwork = GetTickCountMicrosecondsMN(); + mnet->neuralNetworkFramerate = convertStartEndTimeFromMicrosecondsToFPS(startTimeNeuralNetwork,endTimeNeuralNetwork); + + if (inputHasBeenAltered) + { + //If we have altered the input to help the neural network lets restore it for the IK module..! + memcpy(input,&pureInput,sizeof(struct skeletonSerialized)); + } + } else + { + result = mnet->previousSolution; + } + + if (result.size()>0) + { + + if (useInverseKinematics) + { + long startTimeIK = GetTickCountMicrosecondsMN(); + unsigned int springIgnoreChanges=0; + result = improveBVHFrameUsingInverseKinematics( + mnet->penultimateSolution, + mnet->previousSolution, + result, + mnet->framesReceived, + ( (doHands) && (mnet->leftHand.loadedModels>0) ), + ( (doHands) && (mnet->rightHand.loadedModels>0)), + doFace, + input, + mnet->learningRate, + mnet->iterations, + mnet->epochs, + mnet->spring, + springIgnoreChanges, + mnet->options->doMultiThreadedIK + ); + long endTimeIK = GetTickCountMicrosecondsMN(); + + mnet->inverseKinematicsFramerate = convertStartEndTimeFromMicrosecondsToFPS(startTimeIK,endTimeIK); + } + else + { + //If we suddenly turn of IK we need to signal this in the framerate.. + mnet->inverseKinematicsFramerate = 0.0; + } + + + //User wants to force a specific position and rotation + //on BVH output + if (mnet->options->forceOutputPositionRotation) + { + fprintf(stderr,YELLOW "forcing output position rotation \n" NORMAL); + result[0]=mnet->options->outputPosRot[0]; + result[1]=mnet->options->outputPosRot[1]; + result[2]=mnet->options->outputPosRot[2]; + result[3]=mnet->options->outputPosRot[3]; + result[4]=mnet->options->outputPosRot[4]; + result[5]=mnet->options->outputPosRot[5]; + } + + + if (doOutputFiltering) + { + if (result.size()==MOCAPNET_OUTPUT_NUMBER) + { + for (int i=0; ioutputSignals[i],result[i]); + } + } + else + { + fprintf(stderr,RED "MocapNET: Incorrect number of output elements/Cannot filter output as a result..!\n" NORMAL); + fprintf(stderr,RED "Result size = %lu , MocapNET output = %u \n" NORMAL,result.size(),MOCAPNET_OUTPUT_NUMBER); + } + } + + addToMotionHistory(&mnet->poseHistoryStorage,result); + //---------------------------------------------------------------------------------------------- + if (doGestureDetection) + { + if (mnet->gesturesMasterSwitch) + { + + int gestureDetected=compareHistoryWithKnownGestures( + &mnet->recognizedGestures, + &mnet->poseHistoryStorage, + GESTURE_COMPLETION_PERCENT,//Percentage complete.. + GESTURE_ANGLE_SENSITIVITY //Angle threshold + ); + if (gestureDetected!=0) + { + mnet->lastActivatedGesture=gestureDetected; + mnet->gestureTimestamp=mnet->recognizedGestures.gestureChecksPerformed; + fprintf(stderr,GREEN "Gesture Detection : %u\n" NORMAL,gestureDetected); + + if (mnet->newGestureEventCallback!=0) + { //We have a callback associated..! + void ( *DoCallback) (struct MocapNET2 * ,unsigned int)=0 ; + DoCallback = (void(*) (struct MocapNET2 * ,unsigned int) ) mnet->newGestureEventCallback; + DoCallback(mnet ,gestureDetected-1); + } + } + + + //Check if the pose we see is familiar, based on the ones we know.. + int poseDetected = isThisPoseFamiliar( + &mnet->recognizedPoses, + result, + GESTURE_COMPLETION_PERCENT,//Percentage complete.. + 34.0 //Angle threshold + ); + if (poseDetected!=0) + { + if (mnet->lastActivatedPose==poseDetected) + { + fprintf(stderr,YELLOW "Pose is still on : %u\n" NORMAL,poseDetected); + mnet->activePose=0; + } + else if (mnet->activePose!=poseDetected) + { + mnet->activePose=poseDetected; + mnet->lastActivatedPose=poseDetected; + fprintf(stderr,GREEN "Pose Detection : %u\n" NORMAL,poseDetected); + if (mnet->newPoseEventCallback!=0) + { + //We have a callback associated..! + void ( *DoCallback) (struct MocapNET2 *,unsigned int)=0 ; + DoCallback = (void(*) (struct MocapNET2 *,unsigned int) ) mnet->newPoseEventCallback; + DoCallback(mnet,poseDetected-1); + } + } + else + { + fprintf(stderr,YELLOW "Pose Detection but filtered : %u\n" NORMAL,poseDetected); + mnet->activePose=0; + } + } + else + { + mnet->activePose=0; + mnet->lastActivatedPose=0; + } + + + } + else + { + fprintf(stderr,RED "Gesture Detection is enabled but it failed to initialize..\n" NORMAL); + } + } + //---------------------------------------------------------------------------------------------- + + mnet->penultimateSolution = mnet->previousSolution; + mnet->previousSolution = mnet->currentSolution; + mnet->currentSolution = result; + + return result; + } + + fprintf(stderr,RED "MocapNET: failed to retrieve a result..\n" NORMAL); + //----------------- + return emptyResult; +} + + + + +int unloadMocapNET2(struct MocapNET2 * mnet) +{ + if (mnet->threadPool.initialized) + { + if (!threadpoolDestroy(&mnet->threadPool)) + { + fprintf(stderr,"Failed deleting IK thread pool\n"); + } + } + + return ( + mocapnetBody_unload(mnet) && + mocapnetUpperBody_unload(mnet) && + mocapnetLowerBody_unload(mnet) + //&& mocapnetRightHand_unload(mnet) + //&& mocapnetLeftHand_unload(mnet) + ); +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/mocapnet2.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/mocapnet2.hpp new file mode 100644 index 0000000..5a862fb --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/mocapnet2.hpp @@ -0,0 +1,3614 @@ +#pragma once +/** @file mocapnet2.hpp + * @brief The MocapNET C library + * As seen in https://www.youtube.com/watch?v=fH5e-KMBvM0 , the MocapNET network requires two types of input. + * The first is an uncompressed list of (x,y,v) joints and the second an NSDM array. To add to those the output consists of BVH + * frames that must be accompanied by a header. This library internally handles all of these details. + * @author Ammar Qammaz (AmmarkoV) + */ +#include "../../Tensorflow/tensorflow.hpp" + + +#include "applicationLogic/poseRecognition.hpp" +#include "applicationLogic/gestureRecognition.hpp" +#include "applicationLogic/artifactRecognition.hpp" +#include "applicationLogic/parseCommandlineOptions.hpp" +#include "postProcessing/outputFiltering.hpp" +#include "IO/commonSkeleton.hpp" + + +#include "NSDM/generated_upperbody.hpp" +#include "NSDM/generated_lowerbody.hpp" +#include "NSDM/generated_body.hpp" + +#include +#include + + +/** + * @brief MocapNET version + */ +static const char MocapNETVersion[] = { "2.1" }; + +/** + * @brief MocapNET has been trained on 1920x1080 frames, so all the received coordinates are normalized in the +* 0..1 range based on that. This means that the NN learns the X and Y variations. If a joint lies at pixel 500,500 +* it will be represented as 500/1920 , 500/1080. +* Now if a user uses another configuration, let's say a vertical (portrait) feed where the resolution is 1080x1920 +* the 2D points will get normalized at 500/1080 , 500/1920 and the resulting 2D joint cloud won't work as well +* This is why it is better to change the aspect ratio while normalizing + */ +static const unsigned int MocapNETTrainingWidth=1920, MocapNETTrainingHeight=1080; + + + +/** + * @brief MocapNET output joint names that correspond to the BVH file + * These should correspond to `cat dataset/headerWithHeadAndOneMotion.bvh | grep JOINT` +*/ +static const char * MocapNETOutputJointNames[] = +{ +"hip", +"abdomen", +"chest", +"neck", +"neck1", +"head", +"__jaw", +"jaw", +"special04", +"oris02", +"oris01", +"oris06.l", +"oris07.l", +"oris06.r", +"oris07.r", +"tongue00", +"tongue01", +"tongue02", +"tongue03", +"__tongue04", +"tongue04", +"tongue07.l", +"tongue07.r", +"tongue06.l", +"tongue06.r", +"tongue05.l", +"tongue05.r", +"__levator02.l", +"levator02.l", +"levator03.l", +"levator04.l", +"levator05.l", +"__levator02.r", +"levator02.r", +"levator03.r", +"levator04.r", +"levator05.r", +"__special01", +"special01", +"oris04.l", +"oris03.l", +"oris04.r", +"oris03.r", +"oris06", +"oris05", +"__special03", +"special03", +"__levator06.l", +"levator06.l", +"__levator06.r", +"levator06.r", +"special06.l", +"special05.l", +"eye.l", +"orbicularis03.l", +"orbicularis04.l", +"special06.r", +"special05.r", +"eye.r", +"orbicularis03.r", +"orbicularis04.r", +"__temporalis01.l", +"temporalis01.l", +"oculi02.l", +"oculi01.l", +"__temporalis01.r", +"temporalis01.r", +"oculi02.r", +"oculi01.r", +"__temporalis02.l", +"temporalis02.l", +"risorius02.l", +"risorius03.l", +"__temporalis02.r", +"temporalis02.r", +"risorius02.r", +"risorius03.r", +"rCollar", +"rShldr", +"rForeArm", +"rHand", +"metacarpal1.r", +"finger2-1.r", +"finger2-2.r", +"finger2-3.r", +"metacarpal2.r", +"finger3-1.r", +"finger3-2.r", +"finger3-3.r", +"__metacarpal3.r", +"metacarpal3.r", +"finger4-1.r", +"finger4-2.r", +"finger4-3.r", +"__metacarpal4.r", +"metacarpal4.r", +"finger5-1.r", +"finger5-2.r", +"finger5-3.r", +"__rthumb", +"rthumb", +"finger1-2.r", +"finger1-3.r", +"lCollar", +"lShldr", +"lForeArm", +"lHand", +"metacarpal1.l", +"finger2-1.l", +"finger2-2.l", +"finger2-3.l", +"metacarpal2.l", +"finger3-1.l", +"finger3-2.l", +"finger3-3.l", +"__metacarpal3.l", +"metacarpal3.l", +"finger4-1.l", +"finger4-2.l", +"finger4-3.l", +"__metacarpal4.l", +"metacarpal4.l", +"finger5-1.l", +"finger5-2.l", +"finger5-3.l", +"__lthumb", +"lthumb", +"finger1-2.l", +"finger1-3.l", +"rButtock", +"rThigh", +"rShin", +"rFoot", +"toe1-1.R", +"toe1-2.R", +"toe2-1.R", +"toe2-2.R", +"toe2-3.R", +"toe3-1.R", +"toe3-2.R", +"toe3-3.R", +"toe4-1.R", +"toe4-2.R", +"toe4-3.R", +"toe5-1.R", +"toe5-2.R", +"toe5-3.R", +"lButtock", +"lThigh", +"lShin", +"lFoot", +"toe1-1.L", +"toe1-2.L", +"toe2-1.L", +"toe2-2.L", +"toe2-3.L", +"toe3-1.L", +"toe3-2.L", +"toe3-3.L", +"toe4-1.L", +"toe4-2.L", +"toe4-3.L", +"toe5-1.L", +"toe5-2.L", +"toe5-3.L" +}; + + + + + + +/** + * @brief This is a programmer friendly enumerator of joint output extracted from MocapNET. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +enum MOCAPNET_Output_Joint_Name_ENUM +{ +MOCAPNET_OUTPUT_JOINT_HIP, +MOCAPNET_OUTPUT_JOINT_ABDOMEN, +MOCAPNET_OUTPUT_JOINT_CHEST, +MOCAPNET_OUTPUT_JOINT_NECK, +MOCAPNET_OUTPUT_JOINT_NECK1, +MOCAPNET_OUTPUT_JOINT_HEAD, +MOCAPNET_OUTPUT_JOINT___JAW, +MOCAPNET_OUTPUT_JOINT_JAW, +MOCAPNET_OUTPUT_JOINT_SPECIAL04, +MOCAPNET_OUTPUT_JOINT_ORIS02, +MOCAPNET_OUTPUT_JOINT_ORIS01, +MOCAPNET_OUTPUT_JOINT_ORIS06_L, +MOCAPNET_OUTPUT_JOINT_ORIS07_L, +MOCAPNET_OUTPUT_JOINT_ORIS06_R, +MOCAPNET_OUTPUT_JOINT_ORIS07_R, +MOCAPNET_OUTPUT_JOINT_TONGUE00, +MOCAPNET_OUTPUT_JOINT_TONGUE01, +MOCAPNET_OUTPUT_JOINT_TONGUE02, +MOCAPNET_OUTPUT_JOINT_TONGUE03, +MOCAPNET_OUTPUT_JOINT___TONGUE04, +MOCAPNET_OUTPUT_JOINT_TONGUE04, +MOCAPNET_OUTPUT_JOINT_TONGUE07_L, +MOCAPNET_OUTPUT_JOINT_TONGUE07_R, +MOCAPNET_OUTPUT_JOINT_TONGUE06_L, +MOCAPNET_OUTPUT_JOINT_TONGUE06_R, +MOCAPNET_OUTPUT_JOINT_TONGUE05_L, +MOCAPNET_OUTPUT_JOINT_TONGUE05_R, +MOCAPNET_OUTPUT_JOINT___LEVATOR02_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR02_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR03_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR04_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR05_L, +MOCAPNET_OUTPUT_JOINT___LEVATOR02_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR02_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR03_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR04_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR05_R, +MOCAPNET_OUTPUT_JOINT___SPECIAL01, +MOCAPNET_OUTPUT_JOINT_SPECIAL01, +MOCAPNET_OUTPUT_JOINT_ORIS04_L, +MOCAPNET_OUTPUT_JOINT_ORIS03_L, +MOCAPNET_OUTPUT_JOINT_ORIS04_R, +MOCAPNET_OUTPUT_JOINT_ORIS03_R, +MOCAPNET_OUTPUT_JOINT_ORIS06, +MOCAPNET_OUTPUT_JOINT_ORIS05, +MOCAPNET_OUTPUT_JOINT___SPECIAL03, +MOCAPNET_OUTPUT_JOINT_SPECIAL03, +MOCAPNET_OUTPUT_JOINT___LEVATOR06_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR06_L, +MOCAPNET_OUTPUT_JOINT___LEVATOR06_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR06_R, +MOCAPNET_OUTPUT_JOINT_SPECIAL06_L, +MOCAPNET_OUTPUT_JOINT_SPECIAL05_L, +MOCAPNET_OUTPUT_JOINT_EYE_L, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS03_L, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS04_L, +MOCAPNET_OUTPUT_JOINT_SPECIAL06_R, +MOCAPNET_OUTPUT_JOINT_SPECIAL05_R, +MOCAPNET_OUTPUT_JOINT_EYE_R, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS03_R, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS04_R, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS01_L, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS01_L, +MOCAPNET_OUTPUT_JOINT_OCULI02_L, +MOCAPNET_OUTPUT_JOINT_OCULI01_L, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS01_R, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS01_R, +MOCAPNET_OUTPUT_JOINT_OCULI02_R, +MOCAPNET_OUTPUT_JOINT_OCULI01_R, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS02_L, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS02_L, +MOCAPNET_OUTPUT_JOINT_RISORIUS02_L, +MOCAPNET_OUTPUT_JOINT_RISORIUS03_L, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS02_R, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS02_R, +MOCAPNET_OUTPUT_JOINT_RISORIUS02_R, +MOCAPNET_OUTPUT_JOINT_RISORIUS03_R, +MOCAPNET_OUTPUT_JOINT_RCOLLAR, +MOCAPNET_OUTPUT_JOINT_RSHLDR, +MOCAPNET_OUTPUT_JOINT_RFOREARM, +MOCAPNET_OUTPUT_JOINT_RHAND, +MOCAPNET_OUTPUT_JOINT_METACARPAL1_R, +MOCAPNET_OUTPUT_JOINT_FINGER2_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER2_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER2_3_R, +MOCAPNET_OUTPUT_JOINT_METACARPAL2_R, +MOCAPNET_OUTPUT_JOINT_FINGER3_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER3_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER3_3_R, +MOCAPNET_OUTPUT_JOINT___METACARPAL3_R, +MOCAPNET_OUTPUT_JOINT_METACARPAL3_R, +MOCAPNET_OUTPUT_JOINT_FINGER4_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER4_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER4_3_R, +MOCAPNET_OUTPUT_JOINT___METACARPAL4_R, +MOCAPNET_OUTPUT_JOINT_METACARPAL4_R, +MOCAPNET_OUTPUT_JOINT_FINGER5_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER5_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER5_3_R, +MOCAPNET_OUTPUT_JOINT___RTHUMB, +MOCAPNET_OUTPUT_JOINT_RTHUMB, +MOCAPNET_OUTPUT_JOINT_FINGER1_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER1_3_R, +MOCAPNET_OUTPUT_JOINT_LCOLLAR, +MOCAPNET_OUTPUT_JOINT_LSHLDR, +MOCAPNET_OUTPUT_JOINT_LFOREARM, +MOCAPNET_OUTPUT_JOINT_LHAND, +MOCAPNET_OUTPUT_JOINT_METACARPAL1_L, +MOCAPNET_OUTPUT_JOINT_FINGER2_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER2_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER2_3_L, +MOCAPNET_OUTPUT_JOINT_METACARPAL2_L, +MOCAPNET_OUTPUT_JOINT_FINGER3_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER3_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER3_3_L, +MOCAPNET_OUTPUT_JOINT___METACARPAL3_L, +MOCAPNET_OUTPUT_JOINT_METACARPAL3_L, +MOCAPNET_OUTPUT_JOINT_FINGER4_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER4_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER4_3_L, +MOCAPNET_OUTPUT_JOINT___METACARPAL4_L, +MOCAPNET_OUTPUT_JOINT_METACARPAL4_L, +MOCAPNET_OUTPUT_JOINT_FINGER5_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER5_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER5_3_L, +MOCAPNET_OUTPUT_JOINT___LTHUMB, +MOCAPNET_OUTPUT_JOINT_LTHUMB, +MOCAPNET_OUTPUT_JOINT_FINGER1_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER1_3_L, +MOCAPNET_OUTPUT_JOINT_RBUTTOCK, +MOCAPNET_OUTPUT_JOINT_RTHIGH, +MOCAPNET_OUTPUT_JOINT_RSHIN, +MOCAPNET_OUTPUT_JOINT_RFOOT, +MOCAPNET_OUTPUT_JOINT_TOE1_1_R, +MOCAPNET_OUTPUT_JOINT_TOE1_2_R, +MOCAPNET_OUTPUT_JOINT_TOE2_1_R, +MOCAPNET_OUTPUT_JOINT_TOE2_2_R, +MOCAPNET_OUTPUT_JOINT_TOE2_3_R, +MOCAPNET_OUTPUT_JOINT_TOE3_1_R, +MOCAPNET_OUTPUT_JOINT_TOE3_2_R, +MOCAPNET_OUTPUT_JOINT_TOE3_3_R, +MOCAPNET_OUTPUT_JOINT_TOE4_1_R, +MOCAPNET_OUTPUT_JOINT_TOE4_2_R, +MOCAPNET_OUTPUT_JOINT_TOE4_3_R, +MOCAPNET_OUTPUT_JOINT_TOE5_1_R, +MOCAPNET_OUTPUT_JOINT_TOE5_2_R, +MOCAPNET_OUTPUT_JOINT_TOE5_3_R, +MOCAPNET_OUTPUT_JOINT_LBUTTOCK, +MOCAPNET_OUTPUT_JOINT_LTHIGH, +MOCAPNET_OUTPUT_JOINT_LSHIN, +MOCAPNET_OUTPUT_JOINT_LFOOT, +MOCAPNET_OUTPUT_JOINT_TOE1_1_L, +MOCAPNET_OUTPUT_JOINT_TOE1_2_L, +MOCAPNET_OUTPUT_JOINT_TOE2_1_L, +MOCAPNET_OUTPUT_JOINT_TOE2_2_L, +MOCAPNET_OUTPUT_JOINT_TOE2_3_L, +MOCAPNET_OUTPUT_JOINT_TOE3_1_L, +MOCAPNET_OUTPUT_JOINT_TOE3_2_L, +MOCAPNET_OUTPUT_JOINT_TOE3_3_L, +MOCAPNET_OUTPUT_JOINT_TOE4_1_L, +MOCAPNET_OUTPUT_JOINT_TOE4_2_L, +MOCAPNET_OUTPUT_JOINT_TOE4_3_L, +MOCAPNET_OUTPUT_JOINT_TOE5_1_L, +MOCAPNET_OUTPUT_JOINT_TOE5_2_L, +MOCAPNET_OUTPUT_JOINT_TOE5_3_L +}; + + + + +/** + * @brief This is a programmer friendly enumerator to access 3D output extracted from the BVH file_ + * Use _/GroundTruthDumper __from dataset/headerWithHeadAndOneMotion_bvh __printc to extract this automatically + */ +enum MOCAPNET_2D_Output_Joints +{ +MOCAPNET_2DPOINT_HIPX,//0 +MOCAPNET_2DPOINT_HIPY,//1 +MOCAPNET_2DPOINT_ABDOMENX,//2 +MOCAPNET_2DPOINT_ABDOMENY,//3 +MOCAPNET_2DPOINT_CHESTX,//4 +MOCAPNET_2DPOINT_CHESTY,//5 +MOCAPNET_2DPOINT_NECKX,//6 +MOCAPNET_2DPOINT_NECKY,//7 +MOCAPNET_2DPOINT_NECK1X,//8 +MOCAPNET_2DPOINT_NECK1Y,//9 +MOCAPNET_2DPOINT_HEADX,//10 +MOCAPNET_2DPOINT_HEADY,//11 +MOCAPNET_2DPOINT___JAWX,//12 +MOCAPNET_2DPOINT___JAWY,//13 +MOCAPNET_2DPOINT_JAWX,//14 +MOCAPNET_2DPOINT_JAWY,//15 +MOCAPNET_2DPOINT_SPECIAL04X,//16 +MOCAPNET_2DPOINT_SPECIAL04Y,//17 +MOCAPNET_2DPOINT_ORIS02X,//18 +MOCAPNET_2DPOINT_ORIS02Y,//19 +MOCAPNET_2DPOINT_ORIS01X,//20 +MOCAPNET_2DPOINT_ORIS01Y,//21 +MOCAPNET_2DPOINT_ENDSITE_ORIS01X,//22 +MOCAPNET_2DPOINT_ENDSITE_ORIS01Y,//23 +MOCAPNET_2DPOINT_ORIS06_LX,//24 +MOCAPNET_2DPOINT_ORIS06_LY,//25 +MOCAPNET_2DPOINT_ORIS07_LX,//26 +MOCAPNET_2DPOINT_ORIS07_LY,//27 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_LX,//28 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_LY,//29 +MOCAPNET_2DPOINT_ORIS06_RX,//30 +MOCAPNET_2DPOINT_ORIS06_RY,//31 +MOCAPNET_2DPOINT_ORIS07_RX,//32 +MOCAPNET_2DPOINT_ORIS07_RY,//33 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_RX,//34 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_RY,//35 +MOCAPNET_2DPOINT_TONGUE00X,//36 +MOCAPNET_2DPOINT_TONGUE00Y,//37 +MOCAPNET_2DPOINT_TONGUE01X,//38 +MOCAPNET_2DPOINT_TONGUE01Y,//39 +MOCAPNET_2DPOINT_TONGUE02X,//40 +MOCAPNET_2DPOINT_TONGUE02Y,//41 +MOCAPNET_2DPOINT_TONGUE03X,//42 +MOCAPNET_2DPOINT_TONGUE03Y,//43 +MOCAPNET_2DPOINT___TONGUE04X,//44 +MOCAPNET_2DPOINT___TONGUE04Y,//45 +MOCAPNET_2DPOINT_TONGUE04X,//46 +MOCAPNET_2DPOINT_TONGUE04Y,//47 +MOCAPNET_2DPOINT_ENDSITE_TONGUE04X,//48 +MOCAPNET_2DPOINT_ENDSITE_TONGUE04Y,//49 +MOCAPNET_2DPOINT_TONGUE07_LX,//50 +MOCAPNET_2DPOINT_TONGUE07_LY,//51 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_LX,//52 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_LY,//53 +MOCAPNET_2DPOINT_TONGUE07_RX,//54 +MOCAPNET_2DPOINT_TONGUE07_RY,//55 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_RX,//56 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_RY,//57 +MOCAPNET_2DPOINT_TONGUE06_LX,//58 +MOCAPNET_2DPOINT_TONGUE06_LY,//59 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_LX,//60 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_LY,//61 +MOCAPNET_2DPOINT_TONGUE06_RX,//62 +MOCAPNET_2DPOINT_TONGUE06_RY,//63 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_RX,//64 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_RY,//65 +MOCAPNET_2DPOINT_TONGUE05_LX,//66 +MOCAPNET_2DPOINT_TONGUE05_LY,//67 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_LX,//68 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_LY,//69 +MOCAPNET_2DPOINT_TONGUE05_RX,//70 +MOCAPNET_2DPOINT_TONGUE05_RY,//71 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_RX,//72 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_RY,//73 +MOCAPNET_2DPOINT___LEVATOR02_LX,//74 +MOCAPNET_2DPOINT___LEVATOR02_LY,//75 +MOCAPNET_2DPOINT_LEVATOR02_LX,//76 +MOCAPNET_2DPOINT_LEVATOR02_LY,//77 +MOCAPNET_2DPOINT_LEVATOR03_LX,//78 +MOCAPNET_2DPOINT_LEVATOR03_LY,//79 +MOCAPNET_2DPOINT_LEVATOR04_LX,//80 +MOCAPNET_2DPOINT_LEVATOR04_LY,//81 +MOCAPNET_2DPOINT_LEVATOR05_LX,//82 +MOCAPNET_2DPOINT_LEVATOR05_LY,//83 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_LX,//84 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_LY,//85 +MOCAPNET_2DPOINT___LEVATOR02_RX,//86 +MOCAPNET_2DPOINT___LEVATOR02_RY,//87 +MOCAPNET_2DPOINT_LEVATOR02_RX,//88 +MOCAPNET_2DPOINT_LEVATOR02_RY,//89 +MOCAPNET_2DPOINT_LEVATOR03_RX,//90 +MOCAPNET_2DPOINT_LEVATOR03_RY,//91 +MOCAPNET_2DPOINT_LEVATOR04_RX,//92 +MOCAPNET_2DPOINT_LEVATOR04_RY,//93 +MOCAPNET_2DPOINT_LEVATOR05_RX,//94 +MOCAPNET_2DPOINT_LEVATOR05_RY,//95 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_RX,//96 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_RY,//97 +MOCAPNET_2DPOINT___SPECIAL01X,//98 +MOCAPNET_2DPOINT___SPECIAL01Y,//99 +MOCAPNET_2DPOINT_SPECIAL01X,//100 +MOCAPNET_2DPOINT_SPECIAL01Y,//101 +MOCAPNET_2DPOINT_ORIS04_LX,//102 +MOCAPNET_2DPOINT_ORIS04_LY,//103 +MOCAPNET_2DPOINT_ORIS03_LX,//104 +MOCAPNET_2DPOINT_ORIS03_LY,//105 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_LX,//106 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_LY,//107 +MOCAPNET_2DPOINT_ORIS04_RX,//108 +MOCAPNET_2DPOINT_ORIS04_RY,//109 +MOCAPNET_2DPOINT_ORIS03_RX,//110 +MOCAPNET_2DPOINT_ORIS03_RY,//111 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_RX,//112 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_RY,//113 +MOCAPNET_2DPOINT_ORIS06X,//114 +MOCAPNET_2DPOINT_ORIS06Y,//115 +MOCAPNET_2DPOINT_ORIS05X,//116 +MOCAPNET_2DPOINT_ORIS05Y,//117 +MOCAPNET_2DPOINT_ENDSITE_ORIS05X,//118 +MOCAPNET_2DPOINT_ENDSITE_ORIS05Y,//119 +MOCAPNET_2DPOINT___SPECIAL03X,//120 +MOCAPNET_2DPOINT___SPECIAL03Y,//121 +MOCAPNET_2DPOINT_SPECIAL03X,//122 +MOCAPNET_2DPOINT_SPECIAL03Y,//123 +MOCAPNET_2DPOINT___LEVATOR06_LX,//124 +MOCAPNET_2DPOINT___LEVATOR06_LY,//125 +MOCAPNET_2DPOINT_LEVATOR06_LX,//126 +MOCAPNET_2DPOINT_LEVATOR06_LY,//127 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_LX,//128 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_LY,//129 +MOCAPNET_2DPOINT___LEVATOR06_RX,//130 +MOCAPNET_2DPOINT___LEVATOR06_RY,//131 +MOCAPNET_2DPOINT_LEVATOR06_RX,//132 +MOCAPNET_2DPOINT_LEVATOR06_RY,//133 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_RX,//134 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_RY,//135 +MOCAPNET_2DPOINT_SPECIAL06_LX,//136 +MOCAPNET_2DPOINT_SPECIAL06_LY,//137 +MOCAPNET_2DPOINT_SPECIAL05_LX,//138 +MOCAPNET_2DPOINT_SPECIAL05_LY,//139 +MOCAPNET_2DPOINT_EYE_LX,//140 +MOCAPNET_2DPOINT_EYE_LY,//141 +MOCAPNET_2DPOINT_ENDSITE_EYE_LX,//142 +MOCAPNET_2DPOINT_ENDSITE_EYE_LY,//143 +MOCAPNET_2DPOINT_ORBICULARIS03_LX,//144 +MOCAPNET_2DPOINT_ORBICULARIS03_LY,//145 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_LX,//146 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_LY,//147 +MOCAPNET_2DPOINT_ORBICULARIS04_LX,//148 +MOCAPNET_2DPOINT_ORBICULARIS04_LY,//149 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_LX,//150 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_LY,//151 +MOCAPNET_2DPOINT_SPECIAL06_RX,//152 +MOCAPNET_2DPOINT_SPECIAL06_RY,//153 +MOCAPNET_2DPOINT_SPECIAL05_RX,//154 +MOCAPNET_2DPOINT_SPECIAL05_RY,//155 +MOCAPNET_2DPOINT_EYE_RX,//156 +MOCAPNET_2DPOINT_EYE_RY,//157 +MOCAPNET_2DPOINT_ENDSITE_EYE_RX,//158 +MOCAPNET_2DPOINT_ENDSITE_EYE_RY,//159 +MOCAPNET_2DPOINT_ORBICULARIS03_RX,//160 +MOCAPNET_2DPOINT_ORBICULARIS03_RY,//161 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_RX,//162 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_RY,//163 +MOCAPNET_2DPOINT_ORBICULARIS04_RX,//164 +MOCAPNET_2DPOINT_ORBICULARIS04_RY,//165 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_RX,//166 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_RY,//167 +MOCAPNET_2DPOINT___TEMPORALIS01_LX,//168 +MOCAPNET_2DPOINT___TEMPORALIS01_LY,//169 +MOCAPNET_2DPOINT_TEMPORALIS01_LX,//170 +MOCAPNET_2DPOINT_TEMPORALIS01_LY,//171 +MOCAPNET_2DPOINT_OCULI02_LX,//172 +MOCAPNET_2DPOINT_OCULI02_LY,//173 +MOCAPNET_2DPOINT_OCULI01_LX,//174 +MOCAPNET_2DPOINT_OCULI01_LY,//175 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_LX,//176 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_LY,//177 +MOCAPNET_2DPOINT___TEMPORALIS01_RX,//178 +MOCAPNET_2DPOINT___TEMPORALIS01_RY,//179 +MOCAPNET_2DPOINT_TEMPORALIS01_RX,//180 +MOCAPNET_2DPOINT_TEMPORALIS01_RY,//181 +MOCAPNET_2DPOINT_OCULI02_RX,//182 +MOCAPNET_2DPOINT_OCULI02_RY,//183 +MOCAPNET_2DPOINT_OCULI01_RX,//184 +MOCAPNET_2DPOINT_OCULI01_RY,//185 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_RX,//186 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_RY,//187 +MOCAPNET_2DPOINT___TEMPORALIS02_LX,//188 +MOCAPNET_2DPOINT___TEMPORALIS02_LY,//189 +MOCAPNET_2DPOINT_TEMPORALIS02_LX,//190 +MOCAPNET_2DPOINT_TEMPORALIS02_LY,//191 +MOCAPNET_2DPOINT_RISORIUS02_LX,//192 +MOCAPNET_2DPOINT_RISORIUS02_LY,//193 +MOCAPNET_2DPOINT_RISORIUS03_LX,//194 +MOCAPNET_2DPOINT_RISORIUS03_LY,//195 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_LX,//196 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_LY,//197 +MOCAPNET_2DPOINT___TEMPORALIS02_RX,//198 +MOCAPNET_2DPOINT___TEMPORALIS02_RY,//199 +MOCAPNET_2DPOINT_TEMPORALIS02_RX,//200 +MOCAPNET_2DPOINT_TEMPORALIS02_RY,//201 +MOCAPNET_2DPOINT_RISORIUS02_RX,//202 +MOCAPNET_2DPOINT_RISORIUS02_RY,//203 +MOCAPNET_2DPOINT_RISORIUS03_RX,//204 +MOCAPNET_2DPOINT_RISORIUS03_RY,//205 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_RX,//206 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_RY,//207 +MOCAPNET_2DPOINT_RCOLLARX,//208 +MOCAPNET_2DPOINT_RCOLLARY,//209 +MOCAPNET_2DPOINT_RSHOULDERX,//210 +MOCAPNET_2DPOINT_RSHOULDERY,//211 +MOCAPNET_2DPOINT_RELBOWX,//212 +MOCAPNET_2DPOINT_RELBOWY,//213 +MOCAPNET_2DPOINT_RHANDX,//214 +MOCAPNET_2DPOINT_RHANDY,//215 +MOCAPNET_2DPOINT_METACARPAL1_RX,//216 +MOCAPNET_2DPOINT_METACARPAL1_RY,//217 +MOCAPNET_2DPOINT_FINGER2_1_RX,//218 +MOCAPNET_2DPOINT_FINGER2_1_RY,//219 +MOCAPNET_2DPOINT_FINGER2_2_RX,//220 +MOCAPNET_2DPOINT_FINGER2_2_RY,//221 +MOCAPNET_2DPOINT_FINGER2_3_RX,//222 +MOCAPNET_2DPOINT_FINGER2_3_RY,//223 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_RX,//224 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_RY,//225 +MOCAPNET_2DPOINT_METACARPAL2_RX,//226 +MOCAPNET_2DPOINT_METACARPAL2_RY,//227 +MOCAPNET_2DPOINT_FINGER3_1_RX,//228 +MOCAPNET_2DPOINT_FINGER3_1_RY,//229 +MOCAPNET_2DPOINT_FINGER3_2_RX,//230 +MOCAPNET_2DPOINT_FINGER3_2_RY,//231 +MOCAPNET_2DPOINT_FINGER3_3_RX,//232 +MOCAPNET_2DPOINT_FINGER3_3_RY,//233 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_RX,//234 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_RY,//235 +MOCAPNET_2DPOINT___METACARPAL3_RX,//236 +MOCAPNET_2DPOINT___METACARPAL3_RY,//237 +MOCAPNET_2DPOINT_METACARPAL3_RX,//238 +MOCAPNET_2DPOINT_METACARPAL3_RY,//239 +MOCAPNET_2DPOINT_FINGER4_1_RX,//240 +MOCAPNET_2DPOINT_FINGER4_1_RY,//241 +MOCAPNET_2DPOINT_FINGER4_2_RX,//242 +MOCAPNET_2DPOINT_FINGER4_2_RY,//243 +MOCAPNET_2DPOINT_FINGER4_3_RX,//244 +MOCAPNET_2DPOINT_FINGER4_3_RY,//245 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_RX,//246 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_RY,//247 +MOCAPNET_2DPOINT___METACARPAL4_RX,//248 +MOCAPNET_2DPOINT___METACARPAL4_RY,//249 +MOCAPNET_2DPOINT_METACARPAL4_RX,//250 +MOCAPNET_2DPOINT_METACARPAL4_RY,//251 +MOCAPNET_2DPOINT_FINGER5_1_RX,//252 +MOCAPNET_2DPOINT_FINGER5_1_RY,//253 +MOCAPNET_2DPOINT_FINGER5_2_RX,//254 +MOCAPNET_2DPOINT_FINGER5_2_RY,//255 +MOCAPNET_2DPOINT_FINGER5_3_RX,//256 +MOCAPNET_2DPOINT_FINGER5_3_RY,//257 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_RX,//258 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_RY,//259 +MOCAPNET_2DPOINT___RTHUMBX,//260 +MOCAPNET_2DPOINT___RTHUMBY,//261 +MOCAPNET_2DPOINT_RTHUMBX,//262 +MOCAPNET_2DPOINT_RTHUMBY,//263 +MOCAPNET_2DPOINT_FINGER1_2_RX,//264 +MOCAPNET_2DPOINT_FINGER1_2_RY,//265 +MOCAPNET_2DPOINT_FINGER1_3_RX,//266 +MOCAPNET_2DPOINT_FINGER1_3_RY,//267 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_RX,//268 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_RY,//269 +MOCAPNET_2DPOINT_LCOLLARX,//270 +MOCAPNET_2DPOINT_LCOLLARY,//271 +MOCAPNET_2DPOINT_LSHOULDERX,//272 +MOCAPNET_2DPOINT_LSHOULDERY,//273 +MOCAPNET_2DPOINT_LELBOWX,//274 +MOCAPNET_2DPOINT_LELBOWY,//275 +MOCAPNET_2DPOINT_LHANDX,//276 +MOCAPNET_2DPOINT_LHANDY,//277 +MOCAPNET_2DPOINT_METACARPAL1_LX,//278 +MOCAPNET_2DPOINT_METACARPAL1_LY,//279 +MOCAPNET_2DPOINT_FINGER2_1_LX,//280 +MOCAPNET_2DPOINT_FINGER2_1_LY,//281 +MOCAPNET_2DPOINT_FINGER2_2_LX,//282 +MOCAPNET_2DPOINT_FINGER2_2_LY,//283 +MOCAPNET_2DPOINT_FINGER2_3_LX,//284 +MOCAPNET_2DPOINT_FINGER2_3_LY,//285 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_LX,//286 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_LY,//287 +MOCAPNET_2DPOINT_METACARPAL2_LX,//288 +MOCAPNET_2DPOINT_METACARPAL2_LY,//289 +MOCAPNET_2DPOINT_FINGER3_1_LX,//290 +MOCAPNET_2DPOINT_FINGER3_1_LY,//291 +MOCAPNET_2DPOINT_FINGER3_2_LX,//292 +MOCAPNET_2DPOINT_FINGER3_2_LY,//293 +MOCAPNET_2DPOINT_FINGER3_3_LX,//294 +MOCAPNET_2DPOINT_FINGER3_3_LY,//295 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_LX,//296 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_LY,//297 +MOCAPNET_2DPOINT___METACARPAL3_LX,//298 +MOCAPNET_2DPOINT___METACARPAL3_LY,//299 +MOCAPNET_2DPOINT_METACARPAL3_LX,//300 +MOCAPNET_2DPOINT_METACARPAL3_LY,//301 +MOCAPNET_2DPOINT_FINGER4_1_LX,//302 +MOCAPNET_2DPOINT_FINGER4_1_LY,//303 +MOCAPNET_2DPOINT_FINGER4_2_LX,//304 +MOCAPNET_2DPOINT_FINGER4_2_LY,//305 +MOCAPNET_2DPOINT_FINGER4_3_LX,//306 +MOCAPNET_2DPOINT_FINGER4_3_LY,//307 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_LX,//308 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_LY,//309 +MOCAPNET_2DPOINT___METACARPAL4_LX,//310 +MOCAPNET_2DPOINT___METACARPAL4_LY,//311 +MOCAPNET_2DPOINT_METACARPAL4_LX,//312 +MOCAPNET_2DPOINT_METACARPAL4_LY,//313 +MOCAPNET_2DPOINT_FINGER5_1_LX,//314 +MOCAPNET_2DPOINT_FINGER5_1_LY,//315 +MOCAPNET_2DPOINT_FINGER5_2_LX,//316 +MOCAPNET_2DPOINT_FINGER5_2_LY,//317 +MOCAPNET_2DPOINT_FINGER5_3_LX,//318 +MOCAPNET_2DPOINT_FINGER5_3_LY,//319 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_LX,//320 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_LY,//321 +MOCAPNET_2DPOINT___LTHUMBX,//322 +MOCAPNET_2DPOINT___LTHUMBY,//323 +MOCAPNET_2DPOINT_LTHUMBX,//324 +MOCAPNET_2DPOINT_LTHUMBY,//325 +MOCAPNET_2DPOINT_FINGER1_2_LX,//326 +MOCAPNET_2DPOINT_FINGER1_2_LY,//327 +MOCAPNET_2DPOINT_FINGER1_3_LX,//328 +MOCAPNET_2DPOINT_FINGER1_3_LY,//329 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_LX,//330 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_LY,//331 +MOCAPNET_2DPOINT_RBUTTOCKX,//332 +MOCAPNET_2DPOINT_RBUTTOCKY,//333 +MOCAPNET_2DPOINT_RHIPX,//334 +MOCAPNET_2DPOINT_RHIPY,//335 +MOCAPNET_2DPOINT_RKNEEX,//336 +MOCAPNET_2DPOINT_RKNEEY,//337 +MOCAPNET_2DPOINT_RFOOTX,//338 +MOCAPNET_2DPOINT_RFOOTY,//339 +MOCAPNET_2DPOINT_TOE1_1_RX,//340 +MOCAPNET_2DPOINT_TOE1_1_RY,//341 +MOCAPNET_2DPOINT_TOE1_2_RX,//342 +MOCAPNET_2DPOINT_TOE1_2_RY,//343 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_RX,//344 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_RY,//345 +MOCAPNET_2DPOINT_TOE2_1_RX,//346 +MOCAPNET_2DPOINT_TOE2_1_RY,//347 +MOCAPNET_2DPOINT_TOE2_2_RX,//348 +MOCAPNET_2DPOINT_TOE2_2_RY,//349 +MOCAPNET_2DPOINT_TOE2_3_RX,//350 +MOCAPNET_2DPOINT_TOE2_3_RY,//351 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_RX,//352 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_RY,//353 +MOCAPNET_2DPOINT_TOE3_1_RX,//354 +MOCAPNET_2DPOINT_TOE3_1_RY,//355 +MOCAPNET_2DPOINT_TOE3_2_RX,//356 +MOCAPNET_2DPOINT_TOE3_2_RY,//357 +MOCAPNET_2DPOINT_TOE3_3_RX,//358 +MOCAPNET_2DPOINT_TOE3_3_RY,//359 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_RX,//360 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_RY,//361 +MOCAPNET_2DPOINT_TOE4_1_RX,//362 +MOCAPNET_2DPOINT_TOE4_1_RY,//363 +MOCAPNET_2DPOINT_TOE4_2_RX,//364 +MOCAPNET_2DPOINT_TOE4_2_RY,//365 +MOCAPNET_2DPOINT_TOE4_3_RX,//366 +MOCAPNET_2DPOINT_TOE4_3_RY,//367 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_RX,//368 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_RY,//369 +MOCAPNET_2DPOINT_TOE5_1_RX,//370 +MOCAPNET_2DPOINT_TOE5_1_RY,//371 +MOCAPNET_2DPOINT_TOE5_2_RX,//372 +MOCAPNET_2DPOINT_TOE5_2_RY,//373 +MOCAPNET_2DPOINT_TOE5_3_RX,//374 +MOCAPNET_2DPOINT_TOE5_3_RY,//375 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_RX,//376 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_RY,//377 +MOCAPNET_2DPOINT_LBUTTOCKX,//378 +MOCAPNET_2DPOINT_LBUTTOCKY,//379 +MOCAPNET_2DPOINT_LHIPX,//380 +MOCAPNET_2DPOINT_LHIPY,//381 +MOCAPNET_2DPOINT_LKNEEX,//382 +MOCAPNET_2DPOINT_LKNEEY,//383 +MOCAPNET_2DPOINT_LFOOTX,//384 +MOCAPNET_2DPOINT_LFOOTY,//385 +MOCAPNET_2DPOINT_TOE1_1_LX,//386 +MOCAPNET_2DPOINT_TOE1_1_LY,//387 +MOCAPNET_2DPOINT_TOE1_2_LX,//388 +MOCAPNET_2DPOINT_TOE1_2_LY,//389 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_LX,//390 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_LY,//391 +MOCAPNET_2DPOINT_TOE2_1_LX,//392 +MOCAPNET_2DPOINT_TOE2_1_LY,//393 +MOCAPNET_2DPOINT_TOE2_2_LX,//394 +MOCAPNET_2DPOINT_TOE2_2_LY,//395 +MOCAPNET_2DPOINT_TOE2_3_LX,//396 +MOCAPNET_2DPOINT_TOE2_3_LY,//397 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_LX,//398 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_LY,//399 +MOCAPNET_2DPOINT_TOE3_1_LX,//400 +MOCAPNET_2DPOINT_TOE3_1_LY,//401 +MOCAPNET_2DPOINT_TOE3_2_LX,//402 +MOCAPNET_2DPOINT_TOE3_2_LY,//403 +MOCAPNET_2DPOINT_TOE3_3_LX,//404 +MOCAPNET_2DPOINT_TOE3_3_LY,//405 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_LX,//406 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_LY,//407 +MOCAPNET_2DPOINT_TOE4_1_LX,//408 +MOCAPNET_2DPOINT_TOE4_1_LY,//409 +MOCAPNET_2DPOINT_TOE4_2_LX,//410 +MOCAPNET_2DPOINT_TOE4_2_LY,//411 +MOCAPNET_2DPOINT_TOE4_3_LX,//412 +MOCAPNET_2DPOINT_TOE4_3_LY,//413 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_LX,//414 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_LY,//415 +MOCAPNET_2DPOINT_TOE5_1_LX,//416 +MOCAPNET_2DPOINT_TOE5_1_LY,//417 +MOCAPNET_2DPOINT_TOE5_2_LX,//418 +MOCAPNET_2DPOINT_TOE5_2_LY,//419 +MOCAPNET_2DPOINT_TOE5_3_LX,//420 +MOCAPNET_2DPOINT_TOE5_3_LY,//421 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_LX,//422 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_LY//423 +}; + + + + +/** + * @brief This is a programmer friendly enumerator to access 3D output extracted from the BVH file_ + * Use _/GroundTruthDumper __from dataset/headerWithHeadAndOneMotion_bvh __printc to extract this automatically + */ +enum MOCAPNET_JointHierarchy_Joints +{ +MOCAPNET_JOINT_HIP,//0 +MOCAPNET_JOINT_ABDOMEN,//1 +MOCAPNET_JOINT_CHEST,//2 +MOCAPNET_JOINT_NECK,//3 +MOCAPNET_JOINT_NECK1,//4 +MOCAPNET_JOINT_HEAD,//5 +MOCAPNET_JOINT___JAW,//6 +MOCAPNET_JOINT_JAW,//7 +MOCAPNET_JOINT_SPECIAL04,//8 +MOCAPNET_JOINT_ORIS02,//9 +MOCAPNET_JOINT_ORIS01,//10 +MOCAPNET_JOINT_ENDSITE_ORIS01,//11 +MOCAPNET_JOINT_ORIS06_L,//12 +MOCAPNET_JOINT_ORIS07_L,//13 +MOCAPNET_JOINT_ENDSITE_ORIS07_L,//14 +MOCAPNET_JOINT_ORIS06_R,//15 +MOCAPNET_JOINT_ORIS07_R,//16 +MOCAPNET_JOINT_ENDSITE_ORIS07_R,//17 +MOCAPNET_JOINT_TONGUE00,//18 +MOCAPNET_JOINT_TONGUE01,//19 +MOCAPNET_JOINT_TONGUE02,//20 +MOCAPNET_JOINT_TONGUE03,//21 +MOCAPNET_JOINT___TONGUE04,//22 +MOCAPNET_JOINT_TONGUE04,//23 +MOCAPNET_JOINT_ENDSITE_TONGUE04,//24 +MOCAPNET_JOINT_TONGUE07_L,//25 +MOCAPNET_JOINT_ENDSITE_TONGUE07_L,//26 +MOCAPNET_JOINT_TONGUE07_R,//27 +MOCAPNET_JOINT_ENDSITE_TONGUE07_R,//28 +MOCAPNET_JOINT_TONGUE06_L,//29 +MOCAPNET_JOINT_ENDSITE_TONGUE06_L,//30 +MOCAPNET_JOINT_TONGUE06_R,//31 +MOCAPNET_JOINT_ENDSITE_TONGUE06_R,//32 +MOCAPNET_JOINT_TONGUE05_L,//33 +MOCAPNET_JOINT_ENDSITE_TONGUE05_L,//34 +MOCAPNET_JOINT_TONGUE05_R,//35 +MOCAPNET_JOINT_ENDSITE_TONGUE05_R,//36 +MOCAPNET_JOINT___LEVATOR02_L,//37 +MOCAPNET_JOINT_LEVATOR02_L,//38 +MOCAPNET_JOINT_LEVATOR03_L,//39 +MOCAPNET_JOINT_LEVATOR04_L,//40 +MOCAPNET_JOINT_LEVATOR05_L,//41 +MOCAPNET_JOINT_ENDSITE_LEVATOR05_L,//42 +MOCAPNET_JOINT___LEVATOR02_R,//43 +MOCAPNET_JOINT_LEVATOR02_R,//44 +MOCAPNET_JOINT_LEVATOR03_R,//45 +MOCAPNET_JOINT_LEVATOR04_R,//46 +MOCAPNET_JOINT_LEVATOR05_R,//47 +MOCAPNET_JOINT_ENDSITE_LEVATOR05_R,//48 +MOCAPNET_JOINT___SPECIAL01,//49 +MOCAPNET_JOINT_SPECIAL01,//50 +MOCAPNET_JOINT_ORIS04_L,//51 +MOCAPNET_JOINT_ORIS03_L,//52 +MOCAPNET_JOINT_ENDSITE_ORIS03_L,//53 +MOCAPNET_JOINT_ORIS04_R,//54 +MOCAPNET_JOINT_ORIS03_R,//55 +MOCAPNET_JOINT_ENDSITE_ORIS03_R,//56 +MOCAPNET_JOINT_ORIS06,//57 +MOCAPNET_JOINT_ORIS05,//58 +MOCAPNET_JOINT_ENDSITE_ORIS05,//59 +MOCAPNET_JOINT___SPECIAL03,//60 +MOCAPNET_JOINT_SPECIAL03,//61 +MOCAPNET_JOINT___LEVATOR06_L,//62 +MOCAPNET_JOINT_LEVATOR06_L,//63 +MOCAPNET_JOINT_ENDSITE_LEVATOR06_L,//64 +MOCAPNET_JOINT___LEVATOR06_R,//65 +MOCAPNET_JOINT_LEVATOR06_R,//66 +MOCAPNET_JOINT_ENDSITE_LEVATOR06_R,//67 +MOCAPNET_JOINT_SPECIAL06_L,//68 +MOCAPNET_JOINT_SPECIAL05_L,//69 +MOCAPNET_JOINT_EYE_L,//70 +MOCAPNET_JOINT_ENDSITE_EYE_L,//71 +MOCAPNET_JOINT_ORBICULARIS03_L,//72 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS03_L,//73 +MOCAPNET_JOINT_ORBICULARIS04_L,//74 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS04_L,//75 +MOCAPNET_JOINT_SPECIAL06_R,//76 +MOCAPNET_JOINT_SPECIAL05_R,//77 +MOCAPNET_JOINT_EYE_R,//78 +MOCAPNET_JOINT_ENDSITE_EYE_R,//79 +MOCAPNET_JOINT_ORBICULARIS03_R,//80 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS03_R,//81 +MOCAPNET_JOINT_ORBICULARIS04_R,//82 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS04_R,//83 +MOCAPNET_JOINT___TEMPORALIS01_L,//84 +MOCAPNET_JOINT_TEMPORALIS01_L,//85 +MOCAPNET_JOINT_OCULI02_L,//86 +MOCAPNET_JOINT_OCULI01_L,//87 +MOCAPNET_JOINT_ENDSITE_OCULI01_L,//88 +MOCAPNET_JOINT___TEMPORALIS01_R,//89 +MOCAPNET_JOINT_TEMPORALIS01_R,//90 +MOCAPNET_JOINT_OCULI02_R,//91 +MOCAPNET_JOINT_OCULI01_R,//92 +MOCAPNET_JOINT_ENDSITE_OCULI01_R,//93 +MOCAPNET_JOINT___TEMPORALIS02_L,//94 +MOCAPNET_JOINT_TEMPORALIS02_L,//95 +MOCAPNET_JOINT_RISORIUS02_L,//96 +MOCAPNET_JOINT_RISORIUS03_L,//97 +MOCAPNET_JOINT_ENDSITE_RISORIUS03_L,//98 +MOCAPNET_JOINT___TEMPORALIS02_R,//99 +MOCAPNET_JOINT_TEMPORALIS02_R,//100 +MOCAPNET_JOINT_RISORIUS02_R,//101 +MOCAPNET_JOINT_RISORIUS03_R,//102 +MOCAPNET_JOINT_ENDSITE_RISORIUS03_R,//103 +MOCAPNET_JOINT_RCOLLAR,//104 +MOCAPNET_JOINT_RSHOULDER,//105 +MOCAPNET_JOINT_RELBOW,//106 +MOCAPNET_JOINT_RHAND,//107 +MOCAPNET_JOINT_METACARPAL1_R,//108 +MOCAPNET_JOINT_FINGER2_1_R,//109 +MOCAPNET_JOINT_FINGER2_2_R,//110 +MOCAPNET_JOINT_FINGER2_3_R,//111 +MOCAPNET_JOINT_ENDSITE_FINGER2_3_R,//112 +MOCAPNET_JOINT_METACARPAL2_R,//113 +MOCAPNET_JOINT_FINGER3_1_R,//114 +MOCAPNET_JOINT_FINGER3_2_R,//115 +MOCAPNET_JOINT_FINGER3_3_R,//116 +MOCAPNET_JOINT_ENDSITE_FINGER3_3_R,//117 +MOCAPNET_JOINT___METACARPAL3_R,//118 +MOCAPNET_JOINT_METACARPAL3_R,//119 +MOCAPNET_JOINT_FINGER4_1_R,//120 +MOCAPNET_JOINT_FINGER4_2_R,//121 +MOCAPNET_JOINT_FINGER4_3_R,//122 +MOCAPNET_JOINT_ENDSITE_FINGER4_3_R,//123 +MOCAPNET_JOINT___METACARPAL4_R,//124 +MOCAPNET_JOINT_METACARPAL4_R,//125 +MOCAPNET_JOINT_FINGER5_1_R,//126 +MOCAPNET_JOINT_FINGER5_2_R,//127 +MOCAPNET_JOINT_FINGER5_3_R,//128 +MOCAPNET_JOINT_ENDSITE_FINGER5_3_R,//129 +MOCAPNET_JOINT___RTHUMB,//130 +MOCAPNET_JOINT_RTHUMB,//131 +MOCAPNET_JOINT_FINGER1_2_R,//132 +MOCAPNET_JOINT_FINGER1_3_R,//133 +MOCAPNET_JOINT_ENDSITE_FINGER1_3_R,//134 +MOCAPNET_JOINT_LCOLLAR,//135 +MOCAPNET_JOINT_LSHOULDER,//136 +MOCAPNET_JOINT_LELBOW,//137 +MOCAPNET_JOINT_LHAND,//138 +MOCAPNET_JOINT_METACARPAL1_L,//139 +MOCAPNET_JOINT_FINGER2_1_L,//140 +MOCAPNET_JOINT_FINGER2_2_L,//141 +MOCAPNET_JOINT_FINGER2_3_L,//142 +MOCAPNET_JOINT_ENDSITE_FINGER2_3_L,//143 +MOCAPNET_JOINT_METACARPAL2_L,//144 +MOCAPNET_JOINT_FINGER3_1_L,//145 +MOCAPNET_JOINT_FINGER3_2_L,//146 +MOCAPNET_JOINT_FINGER3_3_L,//147 +MOCAPNET_JOINT_ENDSITE_FINGER3_3_L,//148 +MOCAPNET_JOINT___METACARPAL3_L,//149 +MOCAPNET_JOINT_METACARPAL3_L,//150 +MOCAPNET_JOINT_FINGER4_1_L,//151 +MOCAPNET_JOINT_FINGER4_2_L,//152 +MOCAPNET_JOINT_FINGER4_3_L,//153 +MOCAPNET_JOINT_ENDSITE_FINGER4_3_L,//154 +MOCAPNET_JOINT___METACARPAL4_L,//155 +MOCAPNET_JOINT_METACARPAL4_L,//156 +MOCAPNET_JOINT_FINGER5_1_L,//157 +MOCAPNET_JOINT_FINGER5_2_L,//158 +MOCAPNET_JOINT_FINGER5_3_L,//159 +MOCAPNET_JOINT_ENDSITE_FINGER5_3_L,//160 +MOCAPNET_JOINT___LTHUMB,//161 +MOCAPNET_JOINT_LTHUMB,//162 +MOCAPNET_JOINT_FINGER1_2_L,//163 +MOCAPNET_JOINT_FINGER1_3_L,//164 +MOCAPNET_JOINT_ENDSITE_FINGER1_3_L,//165 +MOCAPNET_JOINT_RBUTTOCK,//166 +MOCAPNET_JOINT_RHIP,//167 +MOCAPNET_JOINT_RKNEE,//168 +MOCAPNET_JOINT_RFOOT,//169 +MOCAPNET_JOINT_TOE1_1_R,//170 +MOCAPNET_JOINT_TOE1_2_R,//171 +MOCAPNET_JOINT_ENDSITE_TOE1_2_R,//172 +MOCAPNET_JOINT_TOE2_1_R,//173 +MOCAPNET_JOINT_TOE2_2_R,//174 +MOCAPNET_JOINT_TOE2_3_R,//175 +MOCAPNET_JOINT_ENDSITE_TOE2_3_R,//176 +MOCAPNET_JOINT_TOE3_1_R,//177 +MOCAPNET_JOINT_TOE3_2_R,//178 +MOCAPNET_JOINT_TOE3_3_R,//179 +MOCAPNET_JOINT_ENDSITE_TOE3_3_R,//180 +MOCAPNET_JOINT_TOE4_1_R,//181 +MOCAPNET_JOINT_TOE4_2_R,//182 +MOCAPNET_JOINT_TOE4_3_R,//183 +MOCAPNET_JOINT_ENDSITE_TOE4_3_R,//184 +MOCAPNET_JOINT_TOE5_1_R,//185 +MOCAPNET_JOINT_TOE5_2_R,//186 +MOCAPNET_JOINT_TOE5_3_R,//187 +MOCAPNET_JOINT_ENDSITE_TOE5_3_R,//188 +MOCAPNET_JOINT_LBUTTOCK,//189 +MOCAPNET_JOINT_LHIP,//190 +MOCAPNET_JOINT_LKNEE,//191 +MOCAPNET_JOINT_LFOOT,//192 +MOCAPNET_JOINT_TOE1_1_L,//193 +MOCAPNET_JOINT_TOE1_2_L,//194 +MOCAPNET_JOINT_ENDSITE_TOE1_2_L,//195 +MOCAPNET_JOINT_TOE2_1_L,//196 +MOCAPNET_JOINT_TOE2_2_L,//197 +MOCAPNET_JOINT_TOE2_3_L,//198 +MOCAPNET_JOINT_ENDSITE_TOE2_3_L,//199 +MOCAPNET_JOINT_TOE3_1_L,//200 +MOCAPNET_JOINT_TOE3_2_L,//201 +MOCAPNET_JOINT_TOE3_3_L,//202 +MOCAPNET_JOINT_ENDSITE_TOE3_3_L,//203 +MOCAPNET_JOINT_TOE4_1_L,//204 +MOCAPNET_JOINT_TOE4_2_L,//205 +MOCAPNET_JOINT_TOE4_3_L,//206 +MOCAPNET_JOINT_ENDSITE_TOE4_3_L,//207 +MOCAPNET_JOINT_TOE5_1_L,//208 +MOCAPNET_JOINT_TOE5_2_L,//209 +MOCAPNET_JOINT_TOE5_3_L,//210 +MOCAPNET_JOINT_ENDSITE_TOE5_3_L//211 +}; + + + + +/** + * @brief An array with string labels for what each element of an input should be after concatenating uncompressed and compressed input. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +static const char * MocapNETOutputArrayNames[] = +{ +"hip_Xposition", // 0 +"hip_Yposition", // 1 +"hip_Zposition", // 2 +"hip_Zrotation", // 3 +"hip_Yrotation", // 4 +"hip_Xrotation", // 5 +"abdomen_Zrotation", // 6 + "abdomen_Xrotation", // 7 + "abdomen_Yrotation", // 8 + "chest_Zrotation", // 9 + "chest_Xrotation", // 10 + "chest_Yrotation", // 11 + "neck_Zrotation", // 12 + "neck_Xrotation", // 13 + "neck_Yrotation", // 14 + "neck1_Zrotation", // 15 + "neck1_Xrotation", // 16 + "neck1_Yrotation", // 17 + "head_Zrotation", // 18 + "head_Xrotation", // 19 + "head_Yrotation", // 20 + "__jaw_Zrotation", // 21 + "__jaw_Xrotation", // 22 + "__jaw_Yrotation", // 23 + "jaw_Zrotation", // 24 + "jaw_Xrotation", // 25 + "jaw_Yrotation", // 26 + "special04_Zrotation", // 27 + "special04_Xrotation", // 28 + "special04_Yrotation", // 29 + "oris02_Zrotation", // 30 + "oris02_Xrotation", // 31 + "oris02_Yrotation", // 32 + "oris01_Zrotation", // 33 + "oris01_Xrotation", // 34 + "oris01_Yrotation", // 35 + "oris06.l_Zrotation", // 36 + "oris06.l_Xrotation", // 37 + "oris06.l_Yrotation", // 38 + "oris07.l_Zrotation", // 39 + "oris07.l_Xrotation", // 40 + "oris07.l_Yrotation", // 41 + "oris06.r_Zrotation", // 42 + "oris06.r_Xrotation", // 43 + "oris06.r_Yrotation", // 44 + "oris07.r_Zrotation", // 45 + "oris07.r_Xrotation", // 46 + "oris07.r_Yrotation", // 47 + "tongue00_Zrotation", // 48 + "tongue00_Xrotation", // 49 + "tongue00_Yrotation", // 50 + "tongue01_Zrotation", // 51 + "tongue01_Xrotation", // 52 + "tongue01_Yrotation", // 53 + "tongue02_Zrotation", // 54 + "tongue02_Xrotation", // 55 + "tongue02_Yrotation", // 56 + "tongue03_Zrotation", // 57 + "tongue03_Xrotation", // 58 + "tongue03_Yrotation", // 59 + "__tongue04_Zrotation", // 60 + "__tongue04_Xrotation", // 61 + "__tongue04_Yrotation", // 62 + "tongue04_Zrotation", // 63 + "tongue04_Xrotation", // 64 + "tongue04_Yrotation", // 65 + "tongue07.l_Zrotation", // 66 + "tongue07.l_Xrotation", // 67 + "tongue07.l_Yrotation", // 68 + "tongue07.r_Zrotation", // 69 + "tongue07.r_Xrotation", // 70 + "tongue07.r_Yrotation", // 71 + "tongue06.l_Zrotation", // 72 + "tongue06.l_Xrotation", // 73 + "tongue06.l_Yrotation", // 74 + "tongue06.r_Zrotation", // 75 + "tongue06.r_Xrotation", // 76 + "tongue06.r_Yrotation", // 77 + "tongue05.l_Zrotation", // 78 + "tongue05.l_Xrotation", // 79 + "tongue05.l_Yrotation", // 80 + "tongue05.r_Zrotation", // 81 + "tongue05.r_Xrotation", // 82 + "tongue05.r_Yrotation", // 83 + "__levator02.l_Zrotation", // 84 + "__levator02.l_Xrotation", // 85 + "__levator02.l_Yrotation", // 86 + "levator02.l_Zrotation", // 87 + "levator02.l_Xrotation", // 88 + "levator02.l_Yrotation", // 89 + "levator03.l_Zrotation", // 90 + "levator03.l_Xrotation", // 91 + "levator03.l_Yrotation", // 92 + "levator04.l_Zrotation", // 93 + "levator04.l_Xrotation", // 94 + "levator04.l_Yrotation", // 95 + "levator05.l_Zrotation", // 96 + "levator05.l_Xrotation", // 97 + "levator05.l_Yrotation", // 98 + "__levator02.r_Zrotation", // 99 + "__levator02.r_Xrotation", // 100 + "__levator02.r_Yrotation", // 101 + "levator02.r_Zrotation", // 102 + "levator02.r_Xrotation", // 103 + "levator02.r_Yrotation", // 104 + "levator03.r_Zrotation", // 105 + "levator03.r_Xrotation", // 106 + "levator03.r_Yrotation", // 107 + "levator04.r_Zrotation", // 108 + "levator04.r_Xrotation", // 109 + "levator04.r_Yrotation", // 110 + "levator05.r_Zrotation", // 111 + "levator05.r_Xrotation", // 112 + "levator05.r_Yrotation", // 113 + "__special01_Zrotation", // 114 + "__special01_Xrotation", // 115 + "__special01_Yrotation", // 116 + "special01_Zrotation", // 117 + "special01_Xrotation", // 118 + "special01_Yrotation", // 119 + "oris04.l_Zrotation", // 120 + "oris04.l_Xrotation", // 121 + "oris04.l_Yrotation", // 122 + "oris03.l_Zrotation", // 123 + "oris03.l_Xrotation", // 124 + "oris03.l_Yrotation", // 125 + "oris04.r_Zrotation", // 126 + "oris04.r_Xrotation", // 127 + "oris04.r_Yrotation", // 128 + "oris03.r_Zrotation", // 129 + "oris03.r_Xrotation", // 130 + "oris03.r_Yrotation", // 131 + "oris06_Zrotation", // 132 + "oris06_Xrotation", // 133 + "oris06_Yrotation", // 134 + "oris05_Zrotation", // 135 + "oris05_Xrotation", // 136 + "oris05_Yrotation", // 137 + "__special03_Zrotation", // 138 + "__special03_Xrotation", // 139 + "__special03_Yrotation", // 140 + "special03_Zrotation", // 141 + "special03_Xrotation", // 142 + "special03_Yrotation", // 143 + "__levator06.l_Zrotation", // 144 + "__levator06.l_Xrotation", // 145 + "__levator06.l_Yrotation", // 146 + "levator06.l_Zrotation", // 147 + "levator06.l_Xrotation", // 148 + "levator06.l_Yrotation", // 149 + "__levator06.r_Zrotation", // 150 + "__levator06.r_Xrotation", // 151 + "__levator06.r_Yrotation", // 152 + "levator06.r_Zrotation", // 153 + "levator06.r_Xrotation", // 154 + "levator06.r_Yrotation", // 155 + "special06.l_Zrotation", // 156 + "special06.l_Xrotation", // 157 + "special06.l_Yrotation", // 158 + "special05.l_Zrotation", // 159 + "special05.l_Xrotation", // 160 + "special05.l_Yrotation", // 161 + "eye.l_Zrotation", // 162 + "eye.l_Xrotation", // 163 + "eye.l_Yrotation", // 164 + "orbicularis03.l_Zrotation", // 165 + "orbicularis03.l_Xrotation", // 166 + "orbicularis03.l_Yrotation", // 167 + "orbicularis04.l_Zrotation", // 168 + "orbicularis04.l_Xrotation", // 169 + "orbicularis04.l_Yrotation", // 170 + "special06.r_Zrotation", // 171 + "special06.r_Xrotation", // 172 + "special06.r_Yrotation", // 173 + "special05.r_Zrotation", // 174 + "special05.r_Xrotation", // 175 + "special05.r_Yrotation", // 176 + "eye.r_Zrotation", // 177 + "eye.r_Xrotation", // 178 + "eye.r_Yrotation", // 179 + "orbicularis03.r_Zrotation", // 180 + "orbicularis03.r_Xrotation", // 181 + "orbicularis03.r_Yrotation", // 182 + "orbicularis04.r_Zrotation", // 183 + "orbicularis04.r_Xrotation", // 184 + "orbicularis04.r_Yrotation", // 185 + "__temporalis01.l_Zrotation", // 186 + "__temporalis01.l_Xrotation", // 187 + "__temporalis01.l_Yrotation", // 188 + "temporalis01.l_Zrotation", // 189 + "temporalis01.l_Xrotation", // 190 + "temporalis01.l_Yrotation", // 191 + "oculi02.l_Zrotation", // 192 + "oculi02.l_Xrotation", // 193 + "oculi02.l_Yrotation", // 194 + "oculi01.l_Zrotation", // 195 + "oculi01.l_Xrotation", // 196 + "oculi01.l_Yrotation", // 197 + "__temporalis01.r_Zrotation", // 198 + "__temporalis01.r_Xrotation", // 199 + "__temporalis01.r_Yrotation", // 200 + "temporalis01.r_Zrotation", // 201 + "temporalis01.r_Xrotation", // 202 + "temporalis01.r_Yrotation", // 203 + "oculi02.r_Zrotation", // 204 + "oculi02.r_Xrotation", // 205 + "oculi02.r_Yrotation", // 206 + "oculi01.r_Zrotation", // 207 + "oculi01.r_Xrotation", // 208 + "oculi01.r_Yrotation", // 209 + "__temporalis02.l_Zrotation", // 210 + "__temporalis02.l_Xrotation", // 211 + "__temporalis02.l_Yrotation", // 212 + "temporalis02.l_Zrotation", // 213 + "temporalis02.l_Xrotation", // 214 + "temporalis02.l_Yrotation", // 215 + "risorius02.l_Zrotation", // 216 + "risorius02.l_Xrotation", // 217 + "risorius02.l_Yrotation", // 218 + "risorius03.l_Zrotation", // 219 + "risorius03.l_Xrotation", // 220 + "risorius03.l_Yrotation", // 221 + "__temporalis02.r_Zrotation", // 222 + "__temporalis02.r_Xrotation", // 223 + "__temporalis02.r_Yrotation", // 224 + "temporalis02.r_Zrotation", // 225 + "temporalis02.r_Xrotation", // 226 + "temporalis02.r_Yrotation", // 227 + "risorius02.r_Zrotation", // 228 + "risorius02.r_Xrotation", // 229 + "risorius02.r_Yrotation", // 230 + "risorius03.r_Zrotation", // 231 + "risorius03.r_Xrotation", // 232 + "risorius03.r_Yrotation", // 233 + "rcollar_Zrotation", // 234 + "rcollar_Xrotation", // 235 + "rcollar_Yrotation", // 236 + "rshoulder_Zrotation", // 237 + "rshoulder_Xrotation", // 238 + "rshoulder_Yrotation", // 239 + "relbow_Zrotation", // 240 + "relbow_Xrotation", // 241 + "relbow_Yrotation", // 242 + "rhand_Zrotation", // 243 + "rhand_Xrotation", // 244 + "rhand_Yrotation", // 245 + "metacarpal1.r_Zrotation", // 246 + "metacarpal1.r_Xrotation", // 247 + "metacarpal1.r_Yrotation", // 248 + "finger2-1.r_Zrotation", // 249 + "finger2-1.r_Xrotation", // 250 + "finger2-1.r_Yrotation", // 251 + "finger2-2.r_Zrotation", // 252 + "finger2-2.r_Xrotation", // 253 + "finger2-2.r_Yrotation", // 254 + "finger2-3.r_Zrotation", // 255 + "finger2-3.r_Xrotation", // 256 + "finger2-3.r_Yrotation", // 257 + "metacarpal2.r_Zrotation", // 258 + "metacarpal2.r_Xrotation", // 259 + "metacarpal2.r_Yrotation", // 260 + "finger3-1.r_Zrotation", // 261 + "finger3-1.r_Xrotation", // 262 + "finger3-1.r_Yrotation", // 263 + "finger3-2.r_Zrotation", // 264 + "finger3-2.r_Xrotation", // 265 + "finger3-2.r_Yrotation", // 266 + "finger3-3.r_Zrotation", // 267 + "finger3-3.r_Xrotation", // 268 + "finger3-3.r_Yrotation", // 269 + "__metacarpal3.r_Zrotation", // 270 + "__metacarpal3.r_Xrotation", // 271 + "__metacarpal3.r_Yrotation", // 272 + "metacarpal3.r_Zrotation", // 273 + "metacarpal3.r_Xrotation", // 274 + "metacarpal3.r_Yrotation", // 275 + "finger4-1.r_Zrotation", // 276 + "finger4-1.r_Xrotation", // 277 + "finger4-1.r_Yrotation", // 278 + "finger4-2.r_Zrotation", // 279 + "finger4-2.r_Xrotation", // 280 + "finger4-2.r_Yrotation", // 281 + "finger4-3.r_Zrotation", // 282 + "finger4-3.r_Xrotation", // 283 + "finger4-3.r_Yrotation", // 284 + "__metacarpal4.r_Zrotation", // 285 + "__metacarpal4.r_Xrotation", // 286 + "__metacarpal4.r_Yrotation", // 287 + "metacarpal4.r_Zrotation", // 288 + "metacarpal4.r_Xrotation", // 289 + "metacarpal4.r_Yrotation", // 290 + "finger5-1.r_Zrotation", // 291 + "finger5-1.r_Xrotation", // 292 + "finger5-1.r_Yrotation", // 293 + "finger5-2.r_Zrotation", // 294 + "finger5-2.r_Xrotation", // 295 + "finger5-2.r_Yrotation", // 296 + "finger5-3.r_Zrotation", // 297 + "finger5-3.r_Xrotation", // 298 + "finger5-3.r_Yrotation", // 299 + "__rthumb_Zrotation", // 300 + "__rthumb_Xrotation", // 301 + "__rthumb_Yrotation", // 302 + "rthumb_Zrotation", // 303 + "rthumb_Xrotation", // 304 + "rthumb_Yrotation", // 305 + "finger1-2.r_Zrotation", // 306 + "finger1-2.r_Xrotation", // 307 + "finger1-2.r_Yrotation", // 308 + "finger1-3.r_Zrotation", // 309 + "finger1-3.r_Xrotation", // 310 + "finger1-3.r_Yrotation", // 311 + "lcollar_Zrotation", // 312 + "lcollar_Xrotation", // 313 + "lcollar_Yrotation", // 314 + "lshoulder_Zrotation", // 315 + "lshoulder_Xrotation", // 316 + "lshoulder_Yrotation", // 317 + "lelbow_Zrotation", // 318 + "lelbow_Xrotation", // 319 + "lelbow_Yrotation", // 320 + "lhand_Zrotation", // 321 + "lhand_Xrotation", // 322 + "lhand_Yrotation", // 323 + "metacarpal1.l_Zrotation", // 324 + "metacarpal1.l_Xrotation", // 325 + "metacarpal1.l_Yrotation", // 326 + "finger2-1.l_Zrotation", // 327 + "finger2-1.l_Xrotation", // 328 + "finger2-1.l_Yrotation", // 329 + "finger2-2.l_Zrotation", // 330 + "finger2-2.l_Xrotation", // 331 + "finger2-2.l_Yrotation", // 332 + "finger2-3.l_Zrotation", // 333 + "finger2-3.l_Xrotation", // 334 + "finger2-3.l_Yrotation", // 335 + "metacarpal2.l_Zrotation", // 336 + "metacarpal2.l_Xrotation", // 337 + "metacarpal2.l_Yrotation", // 338 + "finger3-1.l_Zrotation", // 339 + "finger3-1.l_Xrotation", // 340 + "finger3-1.l_Yrotation", // 341 + "finger3-2.l_Zrotation", // 342 + "finger3-2.l_Xrotation", // 343 + "finger3-2.l_Yrotation", // 344 + "finger3-3.l_Zrotation", // 345 + "finger3-3.l_Xrotation", // 346 + "finger3-3.l_Yrotation", // 347 + "__metacarpal3.l_Zrotation", // 348 + "__metacarpal3.l_Xrotation", // 349 + "__metacarpal3.l_Yrotation", // 350 + "metacarpal3.l_Zrotation", // 351 + "metacarpal3.l_Xrotation", // 352 + "metacarpal3.l_Yrotation", // 353 + "finger4-1.l_Zrotation", // 354 + "finger4-1.l_Xrotation", // 355 + "finger4-1.l_Yrotation", // 356 + "finger4-2.l_Zrotation", // 357 + "finger4-2.l_Xrotation", // 358 + "finger4-2.l_Yrotation", // 359 + "finger4-3.l_Zrotation", // 360 + "finger4-3.l_Xrotation", // 361 + "finger4-3.l_Yrotation", // 362 + "__metacarpal4.l_Zrotation", // 363 + "__metacarpal4.l_Xrotation", // 364 + "__metacarpal4.l_Yrotation", // 365 + "metacarpal4.l_Zrotation", // 366 + "metacarpal4.l_Xrotation", // 367 + "metacarpal4.l_Yrotation", // 368 + "finger5-1.l_Zrotation", // 369 + "finger5-1.l_Xrotation", // 370 + "finger5-1.l_Yrotation", // 371 + "finger5-2.l_Zrotation", // 372 + "finger5-2.l_Xrotation", // 373 + "finger5-2.l_Yrotation", // 374 + "finger5-3.l_Zrotation", // 375 + "finger5-3.l_Xrotation", // 376 + "finger5-3.l_Yrotation", // 377 + "__lthumb_Zrotation", // 378 + "__lthumb_Xrotation", // 379 + "__lthumb_Yrotation", // 380 + "lthumb_Zrotation", // 381 + "lthumb_Xrotation", // 382 + "lthumb_Yrotation", // 383 + "finger1-2.l_Zrotation", // 384 + "finger1-2.l_Xrotation", // 385 + "finger1-2.l_Yrotation", // 386 + "finger1-3.l_Zrotation", // 387 + "finger1-3.l_Xrotation", // 388 + "finger1-3.l_Yrotation", // 389 + "rbuttock_Zrotation", // 390 + "rbuttock_Xrotation", // 391 + "rbuttock_Yrotation", // 392 + "rhip_Zrotation", // 393 + "rhip_Xrotation", // 394 + "rhip_Yrotation", // 395 + "rknee_Zrotation", // 396 + "rknee_Xrotation", // 397 + "rknee_Yrotation", // 398 + "rfoot_Zrotation", // 399 + "rfoot_Xrotation", // 400 + "rfoot_Yrotation", // 401 + "toe1-1.r_Zrotation", // 402 + "toe1-1.r_Xrotation", // 403 + "toe1-1.r_Yrotation", // 404 + "toe1-2.r_Zrotation", // 405 + "toe1-2.r_Xrotation", // 406 + "toe1-2.r_Yrotation", // 407 + "toe2-1.r_Zrotation", // 408 + "toe2-1.r_Xrotation", // 409 + "toe2-1.r_Yrotation", // 410 + "toe2-2.r_Zrotation", // 411 + "toe2-2.r_Xrotation", // 412 + "toe2-2.r_Yrotation", // 413 + "toe2-3.r_Zrotation", // 414 + "toe2-3.r_Xrotation", // 415 + "toe2-3.r_Yrotation", // 416 + "toe3-1.r_Zrotation", // 417 + "toe3-1.r_Xrotation", // 418 + "toe3-1.r_Yrotation", // 419 + "toe3-2.r_Zrotation", // 420 + "toe3-2.r_Xrotation", // 421 + "toe3-2.r_Yrotation", // 422 + "toe3-3.r_Zrotation", // 423 + "toe3-3.r_Xrotation", // 424 + "toe3-3.r_Yrotation", // 425 + "toe4-1.r_Zrotation", // 426 + "toe4-1.r_Xrotation", // 427 + "toe4-1.r_Yrotation", // 428 + "toe4-2.r_Zrotation", // 429 + "toe4-2.r_Xrotation", // 430 + "toe4-2.r_Yrotation", // 431 + "toe4-3.r_Zrotation", // 432 + "toe4-3.r_Xrotation", // 433 + "toe4-3.r_Yrotation", // 434 + "toe5-1.r_Zrotation", // 435 + "toe5-1.r_Xrotation", // 436 + "toe5-1.r_Yrotation", // 437 + "toe5-2.r_Zrotation", // 438 + "toe5-2.r_Xrotation", // 439 + "toe5-2.r_Yrotation", // 440 + "toe5-3.r_Zrotation", // 441 + "toe5-3.r_Xrotation", // 442 + "toe5-3.r_Yrotation", // 443 + "lbuttock_Zrotation", // 444 + "lbuttock_Xrotation", // 445 + "lbuttock_Yrotation", // 446 + "lhip_Zrotation", // 447 + "lhip_Xrotation", // 448 + "lhip_Yrotation", // 449 + "lknee_Zrotation", // 450 + "lknee_Xrotation", // 451 + "lknee_Yrotation", // 452 + "lfoot_Zrotation", // 453 + "lfoot_Xrotation", // 454 + "lfoot_Yrotation", // 455 + "toe1-1.l_Zrotation", // 456 + "toe1-1.l_Xrotation", // 457 + "toe1-1.l_Yrotation", // 458 + "toe1-2.l_Zrotation", // 459 + "toe1-2.l_Xrotation", // 460 + "toe1-2.l_Yrotation", // 461 + "toe2-1.l_Zrotation", // 462 + "toe2-1.l_Xrotation", // 463 + "toe2-1.l_Yrotation", // 464 + "toe2-2.l_Zrotation", // 465 + "toe2-2.l_Xrotation", // 466 + "toe2-2.l_Yrotation", // 467 + "toe2-3.l_Zrotation", // 468 + "toe2-3.l_Xrotation", // 469 + "toe2-3.l_Yrotation", // 470 + "toe3-1.l_Zrotation", // 471 + "toe3-1.l_Xrotation", // 472 + "toe3-1.l_Yrotation", // 473 + "toe3-2.l_Zrotation", // 474 + "toe3-2.l_Xrotation", // 475 + "toe3-2.l_Yrotation", // 476 + "toe3-3.l_Zrotation", // 477 + "toe3-3.l_Xrotation", // 478 + "toe3-3.l_Yrotation", // 479 + "toe4-1.l_Zrotation", // 480 + "toe4-1.l_Xrotation", // 481 + "toe4-1.l_Yrotation", // 482 + "toe4-2.l_Zrotation", // 483 + "toe4-2.l_Xrotation", // 484 + "toe4-2.l_Yrotation", // 485 + "toe4-3.l_Zrotation", // 486 + "toe4-3.l_Xrotation", // 487 + "toe4-3.l_Yrotation", // 488 + "toe5-1.l_Zrotation", // 489 + "toe5-1.l_Xrotation", // 490 + "toe5-1.l_Yrotation", // 491 + "toe5-2.l_Zrotation", // 492 + "toe5-2.l_Xrotation", // 493 + "toe5-2.l_Yrotation", // 494 + "toe5-3.l_Zrotation", // 495 + "toe5-3.l_Xrotation", // 496 + "toe5-3.l_Yrotation" // 497 +}; + + + +/** + * @brief This is a programmer friendly enumerator of joint output extracted from MocapNET. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +enum MOCAPNET_Output_Joints +{ +MOCAPNET_OUTPUT_HIP_XPOSITION = 0, +MOCAPNET_OUTPUT_HIP_YPOSITION,//1 +MOCAPNET_OUTPUT_HIP_ZPOSITION,//2 +MOCAPNET_OUTPUT_HIP_ZROTATION,//3 +MOCAPNET_OUTPUT_HIP_YROTATION,//4 +MOCAPNET_OUTPUT_HIP_XROTATION,//5 +MOCAPNET_OUTPUT_ABDOMEN_ZROTATION,//6 +MOCAPNET_OUTPUT_ABDOMEN_XROTATION,//7 +MOCAPNET_OUTPUT_ABDOMEN_YROTATION,//8 +MOCAPNET_OUTPUT_CHEST_ZROTATION,//9 +MOCAPNET_OUTPUT_CHEST_XROTATION,//10 +MOCAPNET_OUTPUT_CHEST_YROTATION,//11 +MOCAPNET_OUTPUT_NECK_ZROTATION,//12 +MOCAPNET_OUTPUT_NECK_XROTATION,//13 +MOCAPNET_OUTPUT_NECK_YROTATION,//14 +MOCAPNET_OUTPUT_NECK1_ZROTATION,//15 +MOCAPNET_OUTPUT_NECK1_XROTATION,//16 +MOCAPNET_OUTPUT_NECK1_YROTATION,//17 +MOCAPNET_OUTPUT_HEAD_ZROTATION,//18 +MOCAPNET_OUTPUT_HEAD_XROTATION,//19 +MOCAPNET_OUTPUT_HEAD_YROTATION,//20 +MOCAPNET_OUTPUT___JAW_ZROTATION,//21 +MOCAPNET_OUTPUT___JAW_XROTATION,//22 +MOCAPNET_OUTPUT___JAW_YROTATION,//23 +MOCAPNET_OUTPUT_JAW_ZROTATION,//24 +MOCAPNET_OUTPUT_JAW_XROTATION,//25 +MOCAPNET_OUTPUT_JAW_YROTATION,//26 +MOCAPNET_OUTPUT_SPECIAL04_ZROTATION,//27 +MOCAPNET_OUTPUT_SPECIAL04_XROTATION,//28 +MOCAPNET_OUTPUT_SPECIAL04_YROTATION,//29 +MOCAPNET_OUTPUT_ORIS02_ZROTATION,//30 +MOCAPNET_OUTPUT_ORIS02_XROTATION,//31 +MOCAPNET_OUTPUT_ORIS02_YROTATION,//32 +MOCAPNET_OUTPUT_ORIS01_ZROTATION,//33 +MOCAPNET_OUTPUT_ORIS01_XROTATION,//34 +MOCAPNET_OUTPUT_ORIS01_YROTATION,//35 +MOCAPNET_OUTPUT_ORIS06_L_ZROTATION,//36 +MOCAPNET_OUTPUT_ORIS06_L_XROTATION,//37 +MOCAPNET_OUTPUT_ORIS06_L_YROTATION,//38 +MOCAPNET_OUTPUT_ORIS07_L_ZROTATION,//39 +MOCAPNET_OUTPUT_ORIS07_L_XROTATION,//40 +MOCAPNET_OUTPUT_ORIS07_L_YROTATION,//41 +MOCAPNET_OUTPUT_ORIS06_R_ZROTATION,//42 +MOCAPNET_OUTPUT_ORIS06_R_XROTATION,//43 +MOCAPNET_OUTPUT_ORIS06_R_YROTATION,//44 +MOCAPNET_OUTPUT_ORIS07_R_ZROTATION,//45 +MOCAPNET_OUTPUT_ORIS07_R_XROTATION,//46 +MOCAPNET_OUTPUT_ORIS07_R_YROTATION,//47 +MOCAPNET_OUTPUT_TONGUE00_ZROTATION,//48 +MOCAPNET_OUTPUT_TONGUE00_XROTATION,//49 +MOCAPNET_OUTPUT_TONGUE00_YROTATION,//50 +MOCAPNET_OUTPUT_TONGUE01_ZROTATION,//51 +MOCAPNET_OUTPUT_TONGUE01_XROTATION,//52 +MOCAPNET_OUTPUT_TONGUE01_YROTATION,//53 +MOCAPNET_OUTPUT_TONGUE02_ZROTATION,//54 +MOCAPNET_OUTPUT_TONGUE02_XROTATION,//55 +MOCAPNET_OUTPUT_TONGUE02_YROTATION,//56 +MOCAPNET_OUTPUT_TONGUE03_ZROTATION,//57 +MOCAPNET_OUTPUT_TONGUE03_XROTATION,//58 +MOCAPNET_OUTPUT_TONGUE03_YROTATION,//59 +MOCAPNET_OUTPUT___TONGUE04_ZROTATION,//60 +MOCAPNET_OUTPUT___TONGUE04_XROTATION,//61 +MOCAPNET_OUTPUT___TONGUE04_YROTATION,//62 +MOCAPNET_OUTPUT_TONGUE04_ZROTATION,//63 +MOCAPNET_OUTPUT_TONGUE04_XROTATION,//64 +MOCAPNET_OUTPUT_TONGUE04_YROTATION,//65 +MOCAPNET_OUTPUT_TONGUE07_L_ZROTATION,//66 +MOCAPNET_OUTPUT_TONGUE07_L_XROTATION,//67 +MOCAPNET_OUTPUT_TONGUE07_L_YROTATION,//68 +MOCAPNET_OUTPUT_TONGUE07_R_ZROTATION,//69 +MOCAPNET_OUTPUT_TONGUE07_R_XROTATION,//70 +MOCAPNET_OUTPUT_TONGUE07_R_YROTATION,//71 +MOCAPNET_OUTPUT_TONGUE06_L_ZROTATION,//72 +MOCAPNET_OUTPUT_TONGUE06_L_XROTATION,//73 +MOCAPNET_OUTPUT_TONGUE06_L_YROTATION,//74 +MOCAPNET_OUTPUT_TONGUE06_R_ZROTATION,//75 +MOCAPNET_OUTPUT_TONGUE06_R_XROTATION,//76 +MOCAPNET_OUTPUT_TONGUE06_R_YROTATION,//77 +MOCAPNET_OUTPUT_TONGUE05_L_ZROTATION,//78 +MOCAPNET_OUTPUT_TONGUE05_L_XROTATION,//79 +MOCAPNET_OUTPUT_TONGUE05_L_YROTATION,//80 +MOCAPNET_OUTPUT_TONGUE05_R_ZROTATION,//81 +MOCAPNET_OUTPUT_TONGUE05_R_XROTATION,//82 +MOCAPNET_OUTPUT_TONGUE05_R_YROTATION,//83 +MOCAPNET_OUTPUT___LEVATOR02_L_ZROTATION,//84 +MOCAPNET_OUTPUT___LEVATOR02_L_XROTATION,//85 +MOCAPNET_OUTPUT___LEVATOR02_L_YROTATION,//86 +MOCAPNET_OUTPUT_LEVATOR02_L_ZROTATION,//87 +MOCAPNET_OUTPUT_LEVATOR02_L_XROTATION,//88 +MOCAPNET_OUTPUT_LEVATOR02_L_YROTATION,//89 +MOCAPNET_OUTPUT_LEVATOR03_L_ZROTATION,//90 +MOCAPNET_OUTPUT_LEVATOR03_L_XROTATION,//91 +MOCAPNET_OUTPUT_LEVATOR03_L_YROTATION,//92 +MOCAPNET_OUTPUT_LEVATOR04_L_ZROTATION,//93 +MOCAPNET_OUTPUT_LEVATOR04_L_XROTATION,//94 +MOCAPNET_OUTPUT_LEVATOR04_L_YROTATION,//95 +MOCAPNET_OUTPUT_LEVATOR05_L_ZROTATION,//96 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+MOCAPNET_OUTPUT___LTHUMB_XROTATION,//379 +MOCAPNET_OUTPUT___LTHUMB_YROTATION,//380 +MOCAPNET_OUTPUT_LTHUMB_ZROTATION,//381 +MOCAPNET_OUTPUT_LTHUMB_XROTATION,//382 +MOCAPNET_OUTPUT_LTHUMB_YROTATION,//383 +MOCAPNET_OUTPUT_FINGER1_2_L_ZROTATION,//384 +MOCAPNET_OUTPUT_FINGER1_2_L_XROTATION,//385 +MOCAPNET_OUTPUT_FINGER1_2_L_YROTATION,//386 +MOCAPNET_OUTPUT_FINGER1_3_L_ZROTATION,//387 +MOCAPNET_OUTPUT_FINGER1_3_L_XROTATION,//388 +MOCAPNET_OUTPUT_FINGER1_3_L_YROTATION,//389 +MOCAPNET_OUTPUT_RBUTTOCK_ZROTATION,//390 +MOCAPNET_OUTPUT_RBUTTOCK_XROTATION,//391 +MOCAPNET_OUTPUT_RBUTTOCK_YROTATION,//392 +MOCAPNET_OUTPUT_RHIP_ZROTATION,//393 +MOCAPNET_OUTPUT_RHIP_XROTATION,//394 +MOCAPNET_OUTPUT_RHIP_YROTATION,//395 +MOCAPNET_OUTPUT_RKNEE_ZROTATION,//396 +MOCAPNET_OUTPUT_RKNEE_XROTATION,//397 +MOCAPNET_OUTPUT_RKNEE_YROTATION,//398 +MOCAPNET_OUTPUT_RFOOT_ZROTATION,//399 +MOCAPNET_OUTPUT_RFOOT_XROTATION,//400 +MOCAPNET_OUTPUT_RFOOT_YROTATION,//401 +MOCAPNET_OUTPUT_TOE1_1_R_ZROTATION,//402 +MOCAPNET_OUTPUT_TOE1_1_R_XROTATION,//403 +MOCAPNET_OUTPUT_TOE1_1_R_YROTATION,//404 +MOCAPNET_OUTPUT_TOE1_2_R_ZROTATION,//405 +MOCAPNET_OUTPUT_TOE1_2_R_XROTATION,//406 +MOCAPNET_OUTPUT_TOE1_2_R_YROTATION,//407 +MOCAPNET_OUTPUT_TOE2_1_R_ZROTATION,//408 +MOCAPNET_OUTPUT_TOE2_1_R_XROTATION,//409 +MOCAPNET_OUTPUT_TOE2_1_R_YROTATION,//410 +MOCAPNET_OUTPUT_TOE2_2_R_ZROTATION,//411 +MOCAPNET_OUTPUT_TOE2_2_R_XROTATION,//412 +MOCAPNET_OUTPUT_TOE2_2_R_YROTATION,//413 +MOCAPNET_OUTPUT_TOE2_3_R_ZROTATION,//414 +MOCAPNET_OUTPUT_TOE2_3_R_XROTATION,//415 +MOCAPNET_OUTPUT_TOE2_3_R_YROTATION,//416 +MOCAPNET_OUTPUT_TOE3_1_R_ZROTATION,//417 +MOCAPNET_OUTPUT_TOE3_1_R_XROTATION,//418 +MOCAPNET_OUTPUT_TOE3_1_R_YROTATION,//419 +MOCAPNET_OUTPUT_TOE3_2_R_ZROTATION,//420 +MOCAPNET_OUTPUT_TOE3_2_R_XROTATION,//421 +MOCAPNET_OUTPUT_TOE3_2_R_YROTATION,//422 +MOCAPNET_OUTPUT_TOE3_3_R_ZROTATION,//423 +MOCAPNET_OUTPUT_TOE3_3_R_XROTATION,//424 +MOCAPNET_OUTPUT_TOE3_3_R_YROTATION,//425 +MOCAPNET_OUTPUT_TOE4_1_R_ZROTATION,//426 +MOCAPNET_OUTPUT_TOE4_1_R_XROTATION,//427 +MOCAPNET_OUTPUT_TOE4_1_R_YROTATION,//428 +MOCAPNET_OUTPUT_TOE4_2_R_ZROTATION,//429 +MOCAPNET_OUTPUT_TOE4_2_R_XROTATION,//430 +MOCAPNET_OUTPUT_TOE4_2_R_YROTATION,//431 +MOCAPNET_OUTPUT_TOE4_3_R_ZROTATION,//432 +MOCAPNET_OUTPUT_TOE4_3_R_XROTATION,//433 +MOCAPNET_OUTPUT_TOE4_3_R_YROTATION,//434 +MOCAPNET_OUTPUT_TOE5_1_R_ZROTATION,//435 +MOCAPNET_OUTPUT_TOE5_1_R_XROTATION,//436 +MOCAPNET_OUTPUT_TOE5_1_R_YROTATION,//437 +MOCAPNET_OUTPUT_TOE5_2_R_ZROTATION,//438 +MOCAPNET_OUTPUT_TOE5_2_R_XROTATION,//439 +MOCAPNET_OUTPUT_TOE5_2_R_YROTATION,//440 +MOCAPNET_OUTPUT_TOE5_3_R_ZROTATION,//441 +MOCAPNET_OUTPUT_TOE5_3_R_XROTATION,//442 +MOCAPNET_OUTPUT_TOE5_3_R_YROTATION,//443 +MOCAPNET_OUTPUT_LBUTTOCK_ZROTATION,//444 +MOCAPNET_OUTPUT_LBUTTOCK_XROTATION,//445 +MOCAPNET_OUTPUT_LBUTTOCK_YROTATION,//446 +MOCAPNET_OUTPUT_LHIP_ZROTATION,//447 +MOCAPNET_OUTPUT_LHIP_XROTATION,//448 +MOCAPNET_OUTPUT_LHIP_YROTATION,//449 +MOCAPNET_OUTPUT_LKNEE_ZROTATION,//450 +MOCAPNET_OUTPUT_LKNEE_XROTATION,//451 +MOCAPNET_OUTPUT_LKNEE_YROTATION,//452 +MOCAPNET_OUTPUT_LFOOT_ZROTATION,//453 +MOCAPNET_OUTPUT_LFOOT_XROTATION,//454 +MOCAPNET_OUTPUT_LFOOT_YROTATION,//455 +MOCAPNET_OUTPUT_TOE1_1_L_ZROTATION,//456 +MOCAPNET_OUTPUT_TOE1_1_L_XROTATION,//457 +MOCAPNET_OUTPUT_TOE1_1_L_YROTATION,//458 +MOCAPNET_OUTPUT_TOE1_2_L_ZROTATION,//459 +MOCAPNET_OUTPUT_TOE1_2_L_XROTATION,//460 +MOCAPNET_OUTPUT_TOE1_2_L_YROTATION,//461 +MOCAPNET_OUTPUT_TOE2_1_L_ZROTATION,//462 +MOCAPNET_OUTPUT_TOE2_1_L_XROTATION,//463 +MOCAPNET_OUTPUT_TOE2_1_L_YROTATION,//464 +MOCAPNET_OUTPUT_TOE2_2_L_ZROTATION,//465 +MOCAPNET_OUTPUT_TOE2_2_L_XROTATION,//466 +MOCAPNET_OUTPUT_TOE2_2_L_YROTATION,//467 +MOCAPNET_OUTPUT_TOE2_3_L_ZROTATION,//468 +MOCAPNET_OUTPUT_TOE2_3_L_XROTATION,//469 +MOCAPNET_OUTPUT_TOE2_3_L_YROTATION,//470 +MOCAPNET_OUTPUT_TOE3_1_L_ZROTATION,//471 +MOCAPNET_OUTPUT_TOE3_1_L_XROTATION,//472 +MOCAPNET_OUTPUT_TOE3_1_L_YROTATION,//473 +MOCAPNET_OUTPUT_TOE3_2_L_ZROTATION,//474 +MOCAPNET_OUTPUT_TOE3_2_L_XROTATION,//475 +MOCAPNET_OUTPUT_TOE3_2_L_YROTATION,//476 +MOCAPNET_OUTPUT_TOE3_3_L_ZROTATION,//477 +MOCAPNET_OUTPUT_TOE3_3_L_XROTATION,//478 +MOCAPNET_OUTPUT_TOE3_3_L_YROTATION,//479 +MOCAPNET_OUTPUT_TOE4_1_L_ZROTATION,//480 +MOCAPNET_OUTPUT_TOE4_1_L_XROTATION,//481 +MOCAPNET_OUTPUT_TOE4_1_L_YROTATION,//482 +MOCAPNET_OUTPUT_TOE4_2_L_ZROTATION,//483 +MOCAPNET_OUTPUT_TOE4_2_L_XROTATION,//484 +MOCAPNET_OUTPUT_TOE4_2_L_YROTATION,//485 +MOCAPNET_OUTPUT_TOE4_3_L_ZROTATION,//486 +MOCAPNET_OUTPUT_TOE4_3_L_XROTATION,//487 +MOCAPNET_OUTPUT_TOE4_3_L_YROTATION,//488 +MOCAPNET_OUTPUT_TOE5_1_L_ZROTATION,//489 +MOCAPNET_OUTPUT_TOE5_1_L_XROTATION,//490 +MOCAPNET_OUTPUT_TOE5_1_L_YROTATION,//491 +MOCAPNET_OUTPUT_TOE5_2_L_ZROTATION,//492 +MOCAPNET_OUTPUT_TOE5_2_L_XROTATION,//493 +MOCAPNET_OUTPUT_TOE5_2_L_YROTATION,//494 +MOCAPNET_OUTPUT_TOE5_3_L_ZROTATION,//495 +MOCAPNET_OUTPUT_TOE5_3_L_XROTATION,//496 +MOCAPNET_OUTPUT_TOE5_3_L_YROTATION,//497 +//----------------------------- +MOCAPNET_OUTPUT_NUMBER +}; + + + + + + + + + +/** + * @brief This is a programmer friendly enumerator to access 3D output extracted from MocapNET + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +enum MNET_3D_Output_Joints +{ +MOCAPNET_3DPOINT_HIPX,//0 +MOCAPNET_3DPOINT_HIPY,//1 +MOCAPNET_3DPOINT_HIPZ,//2 +MOCAPNET_3DPOINT_ABDOMENX,//3 +MOCAPNET_3DPOINT_ABDOMENY,//4 +MOCAPNET_3DPOINT_ABDOMENZ,//5 +MOCAPNET_3DPOINT_CHESTX,//6 +MOCAPNET_3DPOINT_CHESTY,//7 +MOCAPNET_3DPOINT_CHESTZ,//8 +MOCAPNET_3DPOINT_NECKX,//9 +MOCAPNET_3DPOINT_NECKY,//10 +MOCAPNET_3DPOINT_NECKZ,//11 +MOCAPNET_3DPOINT_NECK1X,//12 +MOCAPNET_3DPOINT_NECK1Y,//13 +MOCAPNET_3DPOINT_NECK1Z,//14 +MOCAPNET_3DPOINT_HEADX,//15 +MOCAPNET_3DPOINT_HEADY,//16 +MOCAPNET_3DPOINT_HEADZ,//17 +MOCAPNET_3DPOINT___JAWX,//18 +MOCAPNET_3DPOINT___JAWY,//19 +MOCAPNET_3DPOINT___JAWZ,//20 +MOCAPNET_3DPOINT_JAWX,//21 +MOCAPNET_3DPOINT_JAWY,//22 +MOCAPNET_3DPOINT_JAWZ,//23 +MOCAPNET_3DPOINT_SPECIAL04X,//24 +MOCAPNET_3DPOINT_SPECIAL04Y,//25 +MOCAPNET_3DPOINT_SPECIAL04Z,//26 +MOCAPNET_3DPOINT_ORIS02X,//27 +MOCAPNET_3DPOINT_ORIS02Y,//28 +MOCAPNET_3DPOINT_ORIS02Z,//29 +MOCAPNET_3DPOINT_ORIS01X,//30 +MOCAPNET_3DPOINT_ORIS01Y,//31 +MOCAPNET_3DPOINT_ORIS01Z,//32 +MOCAPNET_3DPOINT_ENDSITE_ORIS01X,//33 +MOCAPNET_3DPOINT_ENDSITE_ORIS01Y,//34 +MOCAPNET_3DPOINT_ENDSITE_ORIS01Z,//35 +MOCAPNET_3DPOINT_ORIS06_LX,//36 +MOCAPNET_3DPOINT_ORIS06_LY,//37 +MOCAPNET_3DPOINT_ORIS06_LZ,//38 +MOCAPNET_3DPOINT_ORIS07_LX,//39 +MOCAPNET_3DPOINT_ORIS07_LY,//40 +MOCAPNET_3DPOINT_ORIS07_LZ,//41 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_LX,//42 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_LY,//43 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_LZ,//44 +MOCAPNET_3DPOINT_ORIS06_RX,//45 +MOCAPNET_3DPOINT_ORIS06_RY,//46 +MOCAPNET_3DPOINT_ORIS06_RZ,//47 +MOCAPNET_3DPOINT_ORIS07_RX,//48 +MOCAPNET_3DPOINT_ORIS07_RY,//49 +MOCAPNET_3DPOINT_ORIS07_RZ,//50 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_RX,//51 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_RY,//52 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_RZ,//53 +MOCAPNET_3DPOINT_TONGUE00X,//54 +MOCAPNET_3DPOINT_TONGUE00Y,//55 +MOCAPNET_3DPOINT_TONGUE00Z,//56 +MOCAPNET_3DPOINT_TONGUE01X,//57 +MOCAPNET_3DPOINT_TONGUE01Y,//58 +MOCAPNET_3DPOINT_TONGUE01Z,//59 +MOCAPNET_3DPOINT_TONGUE02X,//60 +MOCAPNET_3DPOINT_TONGUE02Y,//61 +MOCAPNET_3DPOINT_TONGUE02Z,//62 +MOCAPNET_3DPOINT_TONGUE03X,//63 +MOCAPNET_3DPOINT_TONGUE03Y,//64 +MOCAPNET_3DPOINT_TONGUE03Z,//65 +MOCAPNET_3DPOINT___TONGUE04X,//66 +MOCAPNET_3DPOINT___TONGUE04Y,//67 +MOCAPNET_3DPOINT___TONGUE04Z,//68 +MOCAPNET_3DPOINT_TONGUE04X,//69 +MOCAPNET_3DPOINT_TONGUE04Y,//70 +MOCAPNET_3DPOINT_TONGUE04Z,//71 +MOCAPNET_3DPOINT_ENDSITE_TONGUE04X,//72 +MOCAPNET_3DPOINT_ENDSITE_TONGUE04Y,//73 +MOCAPNET_3DPOINT_ENDSITE_TONGUE04Z,//74 +MOCAPNET_3DPOINT_TONGUE07_LX,//75 +MOCAPNET_3DPOINT_TONGUE07_LY,//76 +MOCAPNET_3DPOINT_TONGUE07_LZ,//77 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_LX,//78 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_LY,//79 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_LZ,//80 +MOCAPNET_3DPOINT_TONGUE07_RX,//81 +MOCAPNET_3DPOINT_TONGUE07_RY,//82 +MOCAPNET_3DPOINT_TONGUE07_RZ,//83 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_RX,//84 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_RY,//85 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_RZ,//86 +MOCAPNET_3DPOINT_TONGUE06_LX,//87 +MOCAPNET_3DPOINT_TONGUE06_LY,//88 +MOCAPNET_3DPOINT_TONGUE06_LZ,//89 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_LX,//90 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_LY,//91 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_LZ,//92 +MOCAPNET_3DPOINT_TONGUE06_RX,//93 +MOCAPNET_3DPOINT_TONGUE06_RY,//94 +MOCAPNET_3DPOINT_TONGUE06_RZ,//95 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_RX,//96 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_RY,//97 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_RZ,//98 +MOCAPNET_3DPOINT_TONGUE05_LX,//99 +MOCAPNET_3DPOINT_TONGUE05_LY,//100 +MOCAPNET_3DPOINT_TONGUE05_LZ,//101 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_LX,//102 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_LY,//103 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_LZ,//104 +MOCAPNET_3DPOINT_TONGUE05_RX,//105 +MOCAPNET_3DPOINT_TONGUE05_RY,//106 +MOCAPNET_3DPOINT_TONGUE05_RZ,//107 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_RX,//108 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_RY,//109 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_RZ,//110 +MOCAPNET_3DPOINT___LEVATOR02_LX,//111 +MOCAPNET_3DPOINT___LEVATOR02_LY,//112 +MOCAPNET_3DPOINT___LEVATOR02_LZ,//113 +MOCAPNET_3DPOINT_LEVATOR02_LX,//114 +MOCAPNET_3DPOINT_LEVATOR02_LY,//115 +MOCAPNET_3DPOINT_LEVATOR02_LZ,//116 +MOCAPNET_3DPOINT_LEVATOR03_LX,//117 +MOCAPNET_3DPOINT_LEVATOR03_LY,//118 +MOCAPNET_3DPOINT_LEVATOR03_LZ,//119 +MOCAPNET_3DPOINT_LEVATOR04_LX,//120 +MOCAPNET_3DPOINT_LEVATOR04_LY,//121 +MOCAPNET_3DPOINT_LEVATOR04_LZ,//122 +MOCAPNET_3DPOINT_LEVATOR05_LX,//123 +MOCAPNET_3DPOINT_LEVATOR05_LY,//124 +MOCAPNET_3DPOINT_LEVATOR05_LZ,//125 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_LX,//126 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_LY,//127 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_LZ,//128 +MOCAPNET_3DPOINT___LEVATOR02_RX,//129 +MOCAPNET_3DPOINT___LEVATOR02_RY,//130 +MOCAPNET_3DPOINT___LEVATOR02_RZ,//131 +MOCAPNET_3DPOINT_LEVATOR02_RX,//132 +MOCAPNET_3DPOINT_LEVATOR02_RY,//133 +MOCAPNET_3DPOINT_LEVATOR02_RZ,//134 +MOCAPNET_3DPOINT_LEVATOR03_RX,//135 +MOCAPNET_3DPOINT_LEVATOR03_RY,//136 +MOCAPNET_3DPOINT_LEVATOR03_RZ,//137 +MOCAPNET_3DPOINT_LEVATOR04_RX,//138 +MOCAPNET_3DPOINT_LEVATOR04_RY,//139 +MOCAPNET_3DPOINT_LEVATOR04_RZ,//140 +MOCAPNET_3DPOINT_LEVATOR05_RX,//141 +MOCAPNET_3DPOINT_LEVATOR05_RY,//142 +MOCAPNET_3DPOINT_LEVATOR05_RZ,//143 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_RX,//144 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_RY,//145 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_RZ,//146 +MOCAPNET_3DPOINT___SPECIAL01X,//147 +MOCAPNET_3DPOINT___SPECIAL01Y,//148 +MOCAPNET_3DPOINT___SPECIAL01Z,//149 +MOCAPNET_3DPOINT_SPECIAL01X,//150 +MOCAPNET_3DPOINT_SPECIAL01Y,//151 +MOCAPNET_3DPOINT_SPECIAL01Z,//152 +MOCAPNET_3DPOINT_ORIS04_LX,//153 +MOCAPNET_3DPOINT_ORIS04_LY,//154 +MOCAPNET_3DPOINT_ORIS04_LZ,//155 +MOCAPNET_3DPOINT_ORIS03_LX,//156 +MOCAPNET_3DPOINT_ORIS03_LY,//157 +MOCAPNET_3DPOINT_ORIS03_LZ,//158 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_LX,//159 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_LY,//160 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_LZ,//161 +MOCAPNET_3DPOINT_ORIS04_RX,//162 +MOCAPNET_3DPOINT_ORIS04_RY,//163 +MOCAPNET_3DPOINT_ORIS04_RZ,//164 +MOCAPNET_3DPOINT_ORIS03_RX,//165 +MOCAPNET_3DPOINT_ORIS03_RY,//166 +MOCAPNET_3DPOINT_ORIS03_RZ,//167 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_RX,//168 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_RY,//169 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_RZ,//170 +MOCAPNET_3DPOINT_ORIS06X,//171 +MOCAPNET_3DPOINT_ORIS06Y,//172 +MOCAPNET_3DPOINT_ORIS06Z,//173 +MOCAPNET_3DPOINT_ORIS05X,//174 +MOCAPNET_3DPOINT_ORIS05Y,//175 +MOCAPNET_3DPOINT_ORIS05Z,//176 +MOCAPNET_3DPOINT_ENDSITE_ORIS05X,//177 +MOCAPNET_3DPOINT_ENDSITE_ORIS05Y,//178 +MOCAPNET_3DPOINT_ENDSITE_ORIS05Z,//179 +MOCAPNET_3DPOINT___SPECIAL03X,//180 +MOCAPNET_3DPOINT___SPECIAL03Y,//181 +MOCAPNET_3DPOINT___SPECIAL03Z,//182 +MOCAPNET_3DPOINT_SPECIAL03X,//183 +MOCAPNET_3DPOINT_SPECIAL03Y,//184 +MOCAPNET_3DPOINT_SPECIAL03Z,//185 +MOCAPNET_3DPOINT___LEVATOR06_LX,//186 +MOCAPNET_3DPOINT___LEVATOR06_LY,//187 +MOCAPNET_3DPOINT___LEVATOR06_LZ,//188 +MOCAPNET_3DPOINT_LEVATOR06_LX,//189 +MOCAPNET_3DPOINT_LEVATOR06_LY,//190 +MOCAPNET_3DPOINT_LEVATOR06_LZ,//191 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_LX,//192 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_LY,//193 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_LZ,//194 +MOCAPNET_3DPOINT___LEVATOR06_RX,//195 +MOCAPNET_3DPOINT___LEVATOR06_RY,//196 +MOCAPNET_3DPOINT___LEVATOR06_RZ,//197 +MOCAPNET_3DPOINT_LEVATOR06_RX,//198 +MOCAPNET_3DPOINT_LEVATOR06_RY,//199 +MOCAPNET_3DPOINT_LEVATOR06_RZ,//200 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_RX,//201 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_RY,//202 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_RZ,//203 +MOCAPNET_3DPOINT_SPECIAL06_LX,//204 +MOCAPNET_3DPOINT_SPECIAL06_LY,//205 +MOCAPNET_3DPOINT_SPECIAL06_LZ,//206 +MOCAPNET_3DPOINT_SPECIAL05_LX,//207 +MOCAPNET_3DPOINT_SPECIAL05_LY,//208 +MOCAPNET_3DPOINT_SPECIAL05_LZ,//209 +MOCAPNET_3DPOINT_EYE_LX,//210 +MOCAPNET_3DPOINT_EYE_LY,//211 +MOCAPNET_3DPOINT_EYE_LZ,//212 +MOCAPNET_3DPOINT_ENDSITE_EYE_LX,//213 +MOCAPNET_3DPOINT_ENDSITE_EYE_LY,//214 +MOCAPNET_3DPOINT_ENDSITE_EYE_LZ,//215 +MOCAPNET_3DPOINT_ORBICULARIS03_LX,//216 +MOCAPNET_3DPOINT_ORBICULARIS03_LY,//217 +MOCAPNET_3DPOINT_ORBICULARIS03_LZ,//218 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_LX,//219 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_LY,//220 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_LZ,//221 +MOCAPNET_3DPOINT_ORBICULARIS04_LX,//222 +MOCAPNET_3DPOINT_ORBICULARIS04_LY,//223 +MOCAPNET_3DPOINT_ORBICULARIS04_LZ,//224 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_LX,//225 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_LY,//226 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_LZ,//227 +MOCAPNET_3DPOINT_SPECIAL06_RX,//228 +MOCAPNET_3DPOINT_SPECIAL06_RY,//229 +MOCAPNET_3DPOINT_SPECIAL06_RZ,//230 +MOCAPNET_3DPOINT_SPECIAL05_RX,//231 +MOCAPNET_3DPOINT_SPECIAL05_RY,//232 +MOCAPNET_3DPOINT_SPECIAL05_RZ,//233 +MOCAPNET_3DPOINT_EYE_RX,//234 +MOCAPNET_3DPOINT_EYE_RY,//235 +MOCAPNET_3DPOINT_EYE_RZ,//236 +MOCAPNET_3DPOINT_ENDSITE_EYE_RX,//237 +MOCAPNET_3DPOINT_ENDSITE_EYE_RY,//238 +MOCAPNET_3DPOINT_ENDSITE_EYE_RZ,//239 +MOCAPNET_3DPOINT_ORBICULARIS03_RX,//240 +MOCAPNET_3DPOINT_ORBICULARIS03_RY,//241 +MOCAPNET_3DPOINT_ORBICULARIS03_RZ,//242 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_RX,//243 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_RY,//244 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_RZ,//245 +MOCAPNET_3DPOINT_ORBICULARIS04_RX,//246 +MOCAPNET_3DPOINT_ORBICULARIS04_RY,//247 +MOCAPNET_3DPOINT_ORBICULARIS04_RZ,//248 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_RX,//249 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_RY,//250 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_RZ,//251 +MOCAPNET_3DPOINT___TEMPORALIS01_LX,//252 +MOCAPNET_3DPOINT___TEMPORALIS01_LY,//253 +MOCAPNET_3DPOINT___TEMPORALIS01_LZ,//254 +MOCAPNET_3DPOINT_TEMPORALIS01_LX,//255 +MOCAPNET_3DPOINT_TEMPORALIS01_LY,//256 +MOCAPNET_3DPOINT_TEMPORALIS01_LZ,//257 +MOCAPNET_3DPOINT_OCULI02_LX,//258 +MOCAPNET_3DPOINT_OCULI02_LY,//259 +MOCAPNET_3DPOINT_OCULI02_LZ,//260 +MOCAPNET_3DPOINT_OCULI01_LX,//261 +MOCAPNET_3DPOINT_OCULI01_LY,//262 +MOCAPNET_3DPOINT_OCULI01_LZ,//263 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_LX,//264 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_LY,//265 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_LZ,//266 +MOCAPNET_3DPOINT___TEMPORALIS01_RX,//267 +MOCAPNET_3DPOINT___TEMPORALIS01_RY,//268 +MOCAPNET_3DPOINT___TEMPORALIS01_RZ,//269 +MOCAPNET_3DPOINT_TEMPORALIS01_RX,//270 +MOCAPNET_3DPOINT_TEMPORALIS01_RY,//271 +MOCAPNET_3DPOINT_TEMPORALIS01_RZ,//272 +MOCAPNET_3DPOINT_OCULI02_RX,//273 +MOCAPNET_3DPOINT_OCULI02_RY,//274 +MOCAPNET_3DPOINT_OCULI02_RZ,//275 +MOCAPNET_3DPOINT_OCULI01_RX,//276 +MOCAPNET_3DPOINT_OCULI01_RY,//277 +MOCAPNET_3DPOINT_OCULI01_RZ,//278 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_RX,//279 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_RY,//280 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_RZ,//281 +MOCAPNET_3DPOINT___TEMPORALIS02_LX,//282 +MOCAPNET_3DPOINT___TEMPORALIS02_LY,//283 +MOCAPNET_3DPOINT___TEMPORALIS02_LZ,//284 +MOCAPNET_3DPOINT_TEMPORALIS02_LX,//285 +MOCAPNET_3DPOINT_TEMPORALIS02_LY,//286 +MOCAPNET_3DPOINT_TEMPORALIS02_LZ,//287 +MOCAPNET_3DPOINT_RISORIUS02_LX,//288 +MOCAPNET_3DPOINT_RISORIUS02_LY,//289 +MOCAPNET_3DPOINT_RISORIUS02_LZ,//290 +MOCAPNET_3DPOINT_RISORIUS03_LX,//291 +MOCAPNET_3DPOINT_RISORIUS03_LY,//292 +MOCAPNET_3DPOINT_RISORIUS03_LZ,//293 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_LX,//294 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_LY,//295 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_LZ,//296 +MOCAPNET_3DPOINT___TEMPORALIS02_RX,//297 +MOCAPNET_3DPOINT___TEMPORALIS02_RY,//298 +MOCAPNET_3DPOINT___TEMPORALIS02_RZ,//299 +MOCAPNET_3DPOINT_TEMPORALIS02_RX,//300 +MOCAPNET_3DPOINT_TEMPORALIS02_RY,//301 +MOCAPNET_3DPOINT_TEMPORALIS02_RZ,//302 +MOCAPNET_3DPOINT_RISORIUS02_RX,//303 +MOCAPNET_3DPOINT_RISORIUS02_RY,//304 +MOCAPNET_3DPOINT_RISORIUS02_RZ,//305 +MOCAPNET_3DPOINT_RISORIUS03_RX,//306 +MOCAPNET_3DPOINT_RISORIUS03_RY,//307 +MOCAPNET_3DPOINT_RISORIUS03_RZ,//308 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_RX,//309 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_RY,//310 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_RZ,//311 +MOCAPNET_3DPOINT_RCOLLARX,//312 +MOCAPNET_3DPOINT_RCOLLARY,//313 +MOCAPNET_3DPOINT_RCOLLARZ,//314 +MOCAPNET_3DPOINT_RSHOULDERX,//315 +MOCAPNET_3DPOINT_RSHOULDERY,//316 +MOCAPNET_3DPOINT_RSHOULDERZ,//317 +MOCAPNET_3DPOINT_RELBOWX,//318 +MOCAPNET_3DPOINT_RELBOWY,//319 +MOCAPNET_3DPOINT_RELBOWZ,//320 +MOCAPNET_3DPOINT_RHANDX,//321 +MOCAPNET_3DPOINT_RHANDY,//322 +MOCAPNET_3DPOINT_RHANDZ,//323 +MOCAPNET_3DPOINT_METACARPAL1_RX,//324 +MOCAPNET_3DPOINT_METACARPAL1_RY,//325 +MOCAPNET_3DPOINT_METACARPAL1_RZ,//326 +MOCAPNET_3DPOINT_FINGER2_1_RX,//327 +MOCAPNET_3DPOINT_FINGER2_1_RY,//328 +MOCAPNET_3DPOINT_FINGER2_1_RZ,//329 +MOCAPNET_3DPOINT_FINGER2_2_RX,//330 +MOCAPNET_3DPOINT_FINGER2_2_RY,//331 +MOCAPNET_3DPOINT_FINGER2_2_RZ,//332 +MOCAPNET_3DPOINT_FINGER2_3_RX,//333 +MOCAPNET_3DPOINT_FINGER2_3_RY,//334 +MOCAPNET_3DPOINT_FINGER2_3_RZ,//335 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_RX,//336 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_RY,//337 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_RZ,//338 +MOCAPNET_3DPOINT_METACARPAL2_RX,//339 +MOCAPNET_3DPOINT_METACARPAL2_RY,//340 +MOCAPNET_3DPOINT_METACARPAL2_RZ,//341 +MOCAPNET_3DPOINT_FINGER3_1_RX,//342 +MOCAPNET_3DPOINT_FINGER3_1_RY,//343 +MOCAPNET_3DPOINT_FINGER3_1_RZ,//344 +MOCAPNET_3DPOINT_FINGER3_2_RX,//345 +MOCAPNET_3DPOINT_FINGER3_2_RY,//346 +MOCAPNET_3DPOINT_FINGER3_2_RZ,//347 +MOCAPNET_3DPOINT_FINGER3_3_RX,//348 +MOCAPNET_3DPOINT_FINGER3_3_RY,//349 +MOCAPNET_3DPOINT_FINGER3_3_RZ,//350 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_RX,//351 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_RY,//352 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_RZ,//353 +MOCAPNET_3DPOINT___METACARPAL3_RX,//354 +MOCAPNET_3DPOINT___METACARPAL3_RY,//355 +MOCAPNET_3DPOINT___METACARPAL3_RZ,//356 +MOCAPNET_3DPOINT_METACARPAL3_RX,//357 +MOCAPNET_3DPOINT_METACARPAL3_RY,//358 +MOCAPNET_3DPOINT_METACARPAL3_RZ,//359 +MOCAPNET_3DPOINT_FINGER4_1_RX,//360 +MOCAPNET_3DPOINT_FINGER4_1_RY,//361 +MOCAPNET_3DPOINT_FINGER4_1_RZ,//362 +MOCAPNET_3DPOINT_FINGER4_2_RX,//363 +MOCAPNET_3DPOINT_FINGER4_2_RY,//364 +MOCAPNET_3DPOINT_FINGER4_2_RZ,//365 +MOCAPNET_3DPOINT_FINGER4_3_RX,//366 +MOCAPNET_3DPOINT_FINGER4_3_RY,//367 +MOCAPNET_3DPOINT_FINGER4_3_RZ,//368 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_RX,//369 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_RY,//370 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_RZ,//371 +MOCAPNET_3DPOINT___METACARPAL4_RX,//372 +MOCAPNET_3DPOINT___METACARPAL4_RY,//373 +MOCAPNET_3DPOINT___METACARPAL4_RZ,//374 +MOCAPNET_3DPOINT_METACARPAL4_RX,//375 +MOCAPNET_3DPOINT_METACARPAL4_RY,//376 +MOCAPNET_3DPOINT_METACARPAL4_RZ,//377 +MOCAPNET_3DPOINT_FINGER5_1_RX,//378 +MOCAPNET_3DPOINT_FINGER5_1_RY,//379 +MOCAPNET_3DPOINT_FINGER5_1_RZ,//380 +MOCAPNET_3DPOINT_FINGER5_2_RX,//381 +MOCAPNET_3DPOINT_FINGER5_2_RY,//382 +MOCAPNET_3DPOINT_FINGER5_2_RZ,//383 +MOCAPNET_3DPOINT_FINGER5_3_RX,//384 +MOCAPNET_3DPOINT_FINGER5_3_RY,//385 +MOCAPNET_3DPOINT_FINGER5_3_RZ,//386 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_RX,//387 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_RY,//388 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_RZ,//389 +MOCAPNET_3DPOINT___RTHUMBX,//390 +MOCAPNET_3DPOINT___RTHUMBY,//391 +MOCAPNET_3DPOINT___RTHUMBZ,//392 +MOCAPNET_3DPOINT_RTHUMBX,//393 +MOCAPNET_3DPOINT_RTHUMBY,//394 +MOCAPNET_3DPOINT_RTHUMBZ,//395 +MOCAPNET_3DPOINT_FINGER1_2_RX,//396 +MOCAPNET_3DPOINT_FINGER1_2_RY,//397 +MOCAPNET_3DPOINT_FINGER1_2_RZ,//398 +MOCAPNET_3DPOINT_FINGER1_3_RX,//399 +MOCAPNET_3DPOINT_FINGER1_3_RY,//400 +MOCAPNET_3DPOINT_FINGER1_3_RZ,//401 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_RX,//402 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_RY,//403 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_RZ,//404 +MOCAPNET_3DPOINT_LCOLLARX,//405 +MOCAPNET_3DPOINT_LCOLLARY,//406 +MOCAPNET_3DPOINT_LCOLLARZ,//407 +MOCAPNET_3DPOINT_LSHOULDERX,//408 +MOCAPNET_3DPOINT_LSHOULDERY,//409 +MOCAPNET_3DPOINT_LSHOULDERZ,//410 +MOCAPNET_3DPOINT_LELBOWX,//411 +MOCAPNET_3DPOINT_LELBOWY,//412 +MOCAPNET_3DPOINT_LELBOWZ,//413 +MOCAPNET_3DPOINT_LHANDX,//414 +MOCAPNET_3DPOINT_LHANDY,//415 +MOCAPNET_3DPOINT_LHANDZ,//416 +MOCAPNET_3DPOINT_METACARPAL1_LX,//417 +MOCAPNET_3DPOINT_METACARPAL1_LY,//418 +MOCAPNET_3DPOINT_METACARPAL1_LZ,//419 +MOCAPNET_3DPOINT_FINGER2_1_LX,//420 +MOCAPNET_3DPOINT_FINGER2_1_LY,//421 +MOCAPNET_3DPOINT_FINGER2_1_LZ,//422 +MOCAPNET_3DPOINT_FINGER2_2_LX,//423 +MOCAPNET_3DPOINT_FINGER2_2_LY,//424 +MOCAPNET_3DPOINT_FINGER2_2_LZ,//425 +MOCAPNET_3DPOINT_FINGER2_3_LX,//426 +MOCAPNET_3DPOINT_FINGER2_3_LY,//427 +MOCAPNET_3DPOINT_FINGER2_3_LZ,//428 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_LX,//429 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_LY,//430 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_LZ,//431 +MOCAPNET_3DPOINT_METACARPAL2_LX,//432 +MOCAPNET_3DPOINT_METACARPAL2_LY,//433 +MOCAPNET_3DPOINT_METACARPAL2_LZ,//434 +MOCAPNET_3DPOINT_FINGER3_1_LX,//435 +MOCAPNET_3DPOINT_FINGER3_1_LY,//436 +MOCAPNET_3DPOINT_FINGER3_1_LZ,//437 +MOCAPNET_3DPOINT_FINGER3_2_LX,//438 +MOCAPNET_3DPOINT_FINGER3_2_LY,//439 +MOCAPNET_3DPOINT_FINGER3_2_LZ,//440 +MOCAPNET_3DPOINT_FINGER3_3_LX,//441 +MOCAPNET_3DPOINT_FINGER3_3_LY,//442 +MOCAPNET_3DPOINT_FINGER3_3_LZ,//443 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_LX,//444 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_LY,//445 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_LZ,//446 +MOCAPNET_3DPOINT___METACARPAL3_LX,//447 +MOCAPNET_3DPOINT___METACARPAL3_LY,//448 +MOCAPNET_3DPOINT___METACARPAL3_LZ,//449 +MOCAPNET_3DPOINT_METACARPAL3_LX,//450 +MOCAPNET_3DPOINT_METACARPAL3_LY,//451 +MOCAPNET_3DPOINT_METACARPAL3_LZ,//452 +MOCAPNET_3DPOINT_FINGER4_1_LX,//453 +MOCAPNET_3DPOINT_FINGER4_1_LY,//454 +MOCAPNET_3DPOINT_FINGER4_1_LZ,//455 +MOCAPNET_3DPOINT_FINGER4_2_LX,//456 +MOCAPNET_3DPOINT_FINGER4_2_LY,//457 +MOCAPNET_3DPOINT_FINGER4_2_LZ,//458 +MOCAPNET_3DPOINT_FINGER4_3_LX,//459 +MOCAPNET_3DPOINT_FINGER4_3_LY,//460 +MOCAPNET_3DPOINT_FINGER4_3_LZ,//461 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_LX,//462 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_LY,//463 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_LZ,//464 +MOCAPNET_3DPOINT___METACARPAL4_LX,//465 +MOCAPNET_3DPOINT___METACARPAL4_LY,//466 +MOCAPNET_3DPOINT___METACARPAL4_LZ,//467 +MOCAPNET_3DPOINT_METACARPAL4_LX,//468 +MOCAPNET_3DPOINT_METACARPAL4_LY,//469 +MOCAPNET_3DPOINT_METACARPAL4_LZ,//470 +MOCAPNET_3DPOINT_FINGER5_1_LX,//471 +MOCAPNET_3DPOINT_FINGER5_1_LY,//472 +MOCAPNET_3DPOINT_FINGER5_1_LZ,//473 +MOCAPNET_3DPOINT_FINGER5_2_LX,//474 +MOCAPNET_3DPOINT_FINGER5_2_LY,//475 +MOCAPNET_3DPOINT_FINGER5_2_LZ,//476 +MOCAPNET_3DPOINT_FINGER5_3_LX,//477 +MOCAPNET_3DPOINT_FINGER5_3_LY,//478 +MOCAPNET_3DPOINT_FINGER5_3_LZ,//479 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_LX,//480 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_LY,//481 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_LZ,//482 +MOCAPNET_3DPOINT___LTHUMBX,//483 +MOCAPNET_3DPOINT___LTHUMBY,//484 +MOCAPNET_3DPOINT___LTHUMBZ,//485 +MOCAPNET_3DPOINT_LTHUMBX,//486 +MOCAPNET_3DPOINT_LTHUMBY,//487 +MOCAPNET_3DPOINT_LTHUMBZ,//488 +MOCAPNET_3DPOINT_FINGER1_2_LX,//489 +MOCAPNET_3DPOINT_FINGER1_2_LY,//490 +MOCAPNET_3DPOINT_FINGER1_2_LZ,//491 +MOCAPNET_3DPOINT_FINGER1_3_LX,//492 +MOCAPNET_3DPOINT_FINGER1_3_LY,//493 +MOCAPNET_3DPOINT_FINGER1_3_LZ,//494 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_LX,//495 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_LY,//496 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_LZ,//497 +MOCAPNET_3DPOINT_RBUTTOCKX,//498 +MOCAPNET_3DPOINT_RBUTTOCKY,//499 +MOCAPNET_3DPOINT_RBUTTOCKZ,//500 +MOCAPNET_3DPOINT_RHIPX,//501 +MOCAPNET_3DPOINT_RHIPY,//502 +MOCAPNET_3DPOINT_RHIPZ,//503 +MOCAPNET_3DPOINT_RKNEEX,//504 +MOCAPNET_3DPOINT_RKNEEY,//505 +MOCAPNET_3DPOINT_RKNEEZ,//506 +MOCAPNET_3DPOINT_RFOOTX,//507 +MOCAPNET_3DPOINT_RFOOTY,//508 +MOCAPNET_3DPOINT_RFOOTZ,//509 +MOCAPNET_3DPOINT_TOE1_1_RX,//510 +MOCAPNET_3DPOINT_TOE1_1_RY,//511 +MOCAPNET_3DPOINT_TOE1_1_RZ,//512 +MOCAPNET_3DPOINT_TOE1_2_RX,//513 +MOCAPNET_3DPOINT_TOE1_2_RY,//514 +MOCAPNET_3DPOINT_TOE1_2_RZ,//515 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_RX,//516 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_RY,//517 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_RZ,//518 +MOCAPNET_3DPOINT_TOE2_1_RX,//519 +MOCAPNET_3DPOINT_TOE2_1_RY,//520 +MOCAPNET_3DPOINT_TOE2_1_RZ,//521 +MOCAPNET_3DPOINT_TOE2_2_RX,//522 +MOCAPNET_3DPOINT_TOE2_2_RY,//523 +MOCAPNET_3DPOINT_TOE2_2_RZ,//524 +MOCAPNET_3DPOINT_TOE2_3_RX,//525 +MOCAPNET_3DPOINT_TOE2_3_RY,//526 +MOCAPNET_3DPOINT_TOE2_3_RZ,//527 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_RX,//528 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_RY,//529 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_RZ,//530 +MOCAPNET_3DPOINT_TOE3_1_RX,//531 +MOCAPNET_3DPOINT_TOE3_1_RY,//532 +MOCAPNET_3DPOINT_TOE3_1_RZ,//533 +MOCAPNET_3DPOINT_TOE3_2_RX,//534 +MOCAPNET_3DPOINT_TOE3_2_RY,//535 +MOCAPNET_3DPOINT_TOE3_2_RZ,//536 +MOCAPNET_3DPOINT_TOE3_3_RX,//537 +MOCAPNET_3DPOINT_TOE3_3_RY,//538 +MOCAPNET_3DPOINT_TOE3_3_RZ,//539 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_RX,//540 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_RY,//541 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_RZ,//542 +MOCAPNET_3DPOINT_TOE4_1_RX,//543 +MOCAPNET_3DPOINT_TOE4_1_RY,//544 +MOCAPNET_3DPOINT_TOE4_1_RZ,//545 +MOCAPNET_3DPOINT_TOE4_2_RX,//546 +MOCAPNET_3DPOINT_TOE4_2_RY,//547 +MOCAPNET_3DPOINT_TOE4_2_RZ,//548 +MOCAPNET_3DPOINT_TOE4_3_RX,//549 +MOCAPNET_3DPOINT_TOE4_3_RY,//550 +MOCAPNET_3DPOINT_TOE4_3_RZ,//551 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_RX,//552 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_RY,//553 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_RZ,//554 +MOCAPNET_3DPOINT_TOE5_1_RX,//555 +MOCAPNET_3DPOINT_TOE5_1_RY,//556 +MOCAPNET_3DPOINT_TOE5_1_RZ,//557 +MOCAPNET_3DPOINT_TOE5_2_RX,//558 +MOCAPNET_3DPOINT_TOE5_2_RY,//559 +MOCAPNET_3DPOINT_TOE5_2_RZ,//560 +MOCAPNET_3DPOINT_TOE5_3_RX,//561 +MOCAPNET_3DPOINT_TOE5_3_RY,//562 +MOCAPNET_3DPOINT_TOE5_3_RZ,//563 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_RX,//564 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_RY,//565 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_RZ,//566 +MOCAPNET_3DPOINT_LBUTTOCKX,//567 +MOCAPNET_3DPOINT_LBUTTOCKY,//568 +MOCAPNET_3DPOINT_LBUTTOCKZ,//569 +MOCAPNET_3DPOINT_LHIPX,//570 +MOCAPNET_3DPOINT_LHIPY,//571 +MOCAPNET_3DPOINT_LHIPZ,//572 +MOCAPNET_3DPOINT_LKNEEX,//573 +MOCAPNET_3DPOINT_LKNEEY,//574 +MOCAPNET_3DPOINT_LKNEEZ,//575 +MOCAPNET_3DPOINT_LFOOTX,//576 +MOCAPNET_3DPOINT_LFOOTY,//577 +MOCAPNET_3DPOINT_LFOOTZ,//578 +MOCAPNET_3DPOINT_TOE1_1_LX,//579 +MOCAPNET_3DPOINT_TOE1_1_LY,//580 +MOCAPNET_3DPOINT_TOE1_1_LZ,//581 +MOCAPNET_3DPOINT_TOE1_2_LX,//582 +MOCAPNET_3DPOINT_TOE1_2_LY,//583 +MOCAPNET_3DPOINT_TOE1_2_LZ,//584 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_LX,//585 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_LY,//586 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_LZ,//587 +MOCAPNET_3DPOINT_TOE2_1_LX,//588 +MOCAPNET_3DPOINT_TOE2_1_LY,//589 +MOCAPNET_3DPOINT_TOE2_1_LZ,//590 +MOCAPNET_3DPOINT_TOE2_2_LX,//591 +MOCAPNET_3DPOINT_TOE2_2_LY,//592 +MOCAPNET_3DPOINT_TOE2_2_LZ,//593 +MOCAPNET_3DPOINT_TOE2_3_LX,//594 +MOCAPNET_3DPOINT_TOE2_3_LY,//595 +MOCAPNET_3DPOINT_TOE2_3_LZ,//596 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_LX,//597 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_LY,//598 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_LZ,//599 +MOCAPNET_3DPOINT_TOE3_1_LX,//600 +MOCAPNET_3DPOINT_TOE3_1_LY,//601 +MOCAPNET_3DPOINT_TOE3_1_LZ,//602 +MOCAPNET_3DPOINT_TOE3_2_LX,//603 +MOCAPNET_3DPOINT_TOE3_2_LY,//604 +MOCAPNET_3DPOINT_TOE3_2_LZ,//605 +MOCAPNET_3DPOINT_TOE3_3_LX,//606 +MOCAPNET_3DPOINT_TOE3_3_LY,//607 +MOCAPNET_3DPOINT_TOE3_3_LZ,//608 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_LX,//609 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_LY,//610 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_LZ,//611 +MOCAPNET_3DPOINT_TOE4_1_LX,//612 +MOCAPNET_3DPOINT_TOE4_1_LY,//613 +MOCAPNET_3DPOINT_TOE4_1_LZ,//614 +MOCAPNET_3DPOINT_TOE4_2_LX,//615 +MOCAPNET_3DPOINT_TOE4_2_LY,//616 +MOCAPNET_3DPOINT_TOE4_2_LZ,//617 +MOCAPNET_3DPOINT_TOE4_3_LX,//618 +MOCAPNET_3DPOINT_TOE4_3_LY,//619 +MOCAPNET_3DPOINT_TOE4_3_LZ,//620 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_LX,//621 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_LY,//622 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_LZ,//623 +MOCAPNET_3DPOINT_TOE5_1_LX,//624 +MOCAPNET_3DPOINT_TOE5_1_LY,//625 +MOCAPNET_3DPOINT_TOE5_1_LZ,//626 +MOCAPNET_3DPOINT_TOE5_2_LX,//627 +MOCAPNET_3DPOINT_TOE5_2_LY,//628 +MOCAPNET_3DPOINT_TOE5_2_LZ,//629 +MOCAPNET_3DPOINT_TOE5_3_LX,//630 +MOCAPNET_3DPOINT_TOE5_3_LY,//631 +MOCAPNET_3DPOINT_TOE5_3_LZ,//632 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_LX,//633 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_LY,//634 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_LZ,//635 +//------------------------------------------------------------------- +MOCAPNET_3DPOINT_NUMBER +}; + + + + +/** + * @brief An array with BVH string labels + */ +static const char * MocapNET3DPositionalOutputArrayNames[] = +{ +"hip_Xposition", // 0 +"hip_Yposition", // 1 +"hip_Zposition", // 2 +"abdomen_Xposition", // 3 +"abdomen_Yposition", // 4 +"abdomen_Zposition", // 5 +"chest_Xposition", // 6 +"chest_Yposition", // 7 +"chest_Zposition", // 8 +"neck_Xposition", // 9 +"neck_Yposition", // 10 +"neck_Zposition", // 11 +"neck1_Xposition", // 12 +"neck1_Yposition", // 13 +"neck1_Zposition", // 14 +"head_Xposition", // 15 +"head_Yposition", // 16 +"head_Zposition", // 17 +"__jaw_Xposition", // 18 +"__jaw_Yposition", // 19 +"__jaw_Zposition", // 20 +"jaw_Xposition", // 21 +"jaw_Yposition", // 22 +"jaw_Zposition", // 23 +"special04_Xposition", // 24 +"special04_Yposition", // 25 +"special04_Zposition", // 26 +"oris02_Xposition", // 27 +"oris02_Yposition", // 28 +"oris02_Zposition", // 29 +"oris01_Xposition", // 30 +"oris01_Yposition", // 31 +"oris01_Zposition", // 32 +"endsite_oris01_Xposition", // 33 +"endsite_oris01_Yposition", // 34 +"endsite_oris01_Zposition", // 35 +"oris06.l_Xposition", // 36 +"oris06.l_Yposition", // 37 +"oris06.l_Zposition", // 38 +"oris07.l_Xposition", // 39 +"oris07.l_Yposition", // 40 +"oris07.l_Zposition", // 41 +"endsite_oris07.l_Xposition", // 42 +"endsite_oris07.l_Yposition", // 43 +"endsite_oris07.l_Zposition", // 44 +"oris06.r_Xposition", // 45 +"oris06.r_Yposition", // 46 +"oris06.r_Zposition", // 47 +"oris07.r_Xposition", // 48 +"oris07.r_Yposition", // 49 +"oris07.r_Zposition", // 50 +"endsite_oris07.r_Xposition", // 51 +"endsite_oris07.r_Yposition", // 52 +"endsite_oris07.r_Zposition", // 53 +"tongue00_Xposition", // 54 +"tongue00_Yposition", // 55 +"tongue00_Zposition", // 56 +"tongue01_Xposition", // 57 +"tongue01_Yposition", // 58 +"tongue01_Zposition", // 59 +"tongue02_Xposition", // 60 +"tongue02_Yposition", // 61 +"tongue02_Zposition", // 62 +"tongue03_Xposition", // 63 +"tongue03_Yposition", // 64 +"tongue03_Zposition", // 65 +"__tongue04_Xposition", // 66 +"__tongue04_Yposition", // 67 +"__tongue04_Zposition", // 68 +"tongue04_Xposition", // 69 +"tongue04_Yposition", // 70 +"tongue04_Zposition", // 71 +"endsite_tongue04_Xposition", // 72 +"endsite_tongue04_Yposition", // 73 +"endsite_tongue04_Zposition", // 74 +"tongue07.l_Xposition", // 75 +"tongue07.l_Yposition", // 76 +"tongue07.l_Zposition", // 77 +"endsite_tongue07.l_Xposition", // 78 +"endsite_tongue07.l_Yposition", // 79 +"endsite_tongue07.l_Zposition", // 80 +"tongue07.r_Xposition", // 81 +"tongue07.r_Yposition", // 82 +"tongue07.r_Zposition", // 83 +"endsite_tongue07.r_Xposition", // 84 +"endsite_tongue07.r_Yposition", // 85 +"endsite_tongue07.r_Zposition", // 86 +"tongue06.l_Xposition", // 87 +"tongue06.l_Yposition", // 88 +"tongue06.l_Zposition", // 89 +"endsite_tongue06.l_Xposition", // 90 +"endsite_tongue06.l_Yposition", // 91 +"endsite_tongue06.l_Zposition", // 92 +"tongue06.r_Xposition", // 93 +"tongue06.r_Yposition", // 94 +"tongue06.r_Zposition", // 95 +"endsite_tongue06.r_Xposition", // 96 +"endsite_tongue06.r_Yposition", // 97 +"endsite_tongue06.r_Zposition", // 98 +"tongue05.l_Xposition", // 99 +"tongue05.l_Yposition", // 100 +"tongue05.l_Zposition", // 101 +"endsite_tongue05.l_Xposition", // 102 +"endsite_tongue05.l_Yposition", // 103 +"endsite_tongue05.l_Zposition", // 104 +"tongue05.r_Xposition", // 105 +"tongue05.r_Yposition", // 106 +"tongue05.r_Zposition", // 107 +"endsite_tongue05.r_Xposition", // 108 +"endsite_tongue05.r_Yposition", // 109 +"endsite_tongue05.r_Zposition", // 110 +"__levator02.l_Xposition", // 111 +"__levator02.l_Yposition", // 112 +"__levator02.l_Zposition", // 113 +"levator02.l_Xposition", // 114 +"levator02.l_Yposition", // 115 +"levator02.l_Zposition", // 116 +"levator03.l_Xposition", // 117 +"levator03.l_Yposition", // 118 +"levator03.l_Zposition", // 119 +"levator04.l_Xposition", // 120 +"levator04.l_Yposition", // 121 +"levator04.l_Zposition", // 122 +"levator05.l_Xposition", // 123 +"levator05.l_Yposition", // 124 +"levator05.l_Zposition", // 125 +"endsite_levator05.l_Xposition", // 126 +"endsite_levator05.l_Yposition", // 127 +"endsite_levator05.l_Zposition", // 128 +"__levator02.r_Xposition", // 129 +"__levator02.r_Yposition", // 130 +"__levator02.r_Zposition", // 131 +"levator02.r_Xposition", // 132 +"levator02.r_Yposition", // 133 +"levator02.r_Zposition", // 134 +"levator03.r_Xposition", // 135 +"levator03.r_Yposition", // 136 +"levator03.r_Zposition", // 137 +"levator04.r_Xposition", // 138 +"levator04.r_Yposition", // 139 +"levator04.r_Zposition", // 140 +"levator05.r_Xposition", // 141 +"levator05.r_Yposition", // 142 +"levator05.r_Zposition", // 143 +"endsite_levator05.r_Xposition", // 144 +"endsite_levator05.r_Yposition", // 145 +"endsite_levator05.r_Zposition", // 146 +"__special01_Xposition", // 147 +"__special01_Yposition", // 148 +"__special01_Zposition", // 149 +"special01_Xposition", // 150 +"special01_Yposition", // 151 +"special01_Zposition", // 152 +"oris04.l_Xposition", // 153 +"oris04.l_Yposition", // 154 +"oris04.l_Zposition", // 155 +"oris03.l_Xposition", // 156 +"oris03.l_Yposition", // 157 +"oris03.l_Zposition", // 158 +"endsite_oris03.l_Xposition", // 159 +"endsite_oris03.l_Yposition", // 160 +"endsite_oris03.l_Zposition", // 161 +"oris04.r_Xposition", // 162 +"oris04.r_Yposition", // 163 +"oris04.r_Zposition", // 164 +"oris03.r_Xposition", // 165 +"oris03.r_Yposition", // 166 +"oris03.r_Zposition", // 167 +"endsite_oris03.r_Xposition", // 168 +"endsite_oris03.r_Yposition", // 169 +"endsite_oris03.r_Zposition", // 170 +"oris06_Xposition", // 171 +"oris06_Yposition", // 172 +"oris06_Zposition", // 173 +"oris05_Xposition", // 174 +"oris05_Yposition", // 175 +"oris05_Zposition", // 176 +"endsite_oris05_Xposition", // 177 +"endsite_oris05_Yposition", // 178 +"endsite_oris05_Zposition", // 179 +"__special03_Xposition", // 180 +"__special03_Yposition", // 181 +"__special03_Zposition", // 182 +"special03_Xposition", // 183 +"special03_Yposition", // 184 +"special03_Zposition", // 185 +"__levator06.l_Xposition", // 186 +"__levator06.l_Yposition", // 187 +"__levator06.l_Zposition", // 188 +"levator06.l_Xposition", // 189 +"levator06.l_Yposition", // 190 +"levator06.l_Zposition", // 191 +"endsite_levator06.l_Xposition", // 192 +"endsite_levator06.l_Yposition", // 193 +"endsite_levator06.l_Zposition", // 194 +"__levator06.r_Xposition", // 195 +"__levator06.r_Yposition", // 196 +"__levator06.r_Zposition", // 197 +"levator06.r_Xposition", // 198 +"levator06.r_Yposition", // 199 +"levator06.r_Zposition", // 200 +"endsite_levator06.r_Xposition", // 201 +"endsite_levator06.r_Yposition", // 202 +"endsite_levator06.r_Zposition", // 203 +"special06.l_Xposition", // 204 +"special06.l_Yposition", // 205 +"special06.l_Zposition", // 206 +"special05.l_Xposition", // 207 +"special05.l_Yposition", // 208 +"special05.l_Zposition", // 209 +"eye.l_Xposition", // 210 +"eye.l_Yposition", // 211 +"eye.l_Zposition", // 212 +"endsite_eye.l_Xposition", // 213 +"endsite_eye.l_Yposition", // 214 +"endsite_eye.l_Zposition", // 215 +"orbicularis03.l_Xposition", // 216 +"orbicularis03.l_Yposition", // 217 +"orbicularis03.l_Zposition", // 218 +"endsite_orbicularis03.l_Xposition", // 219 +"endsite_orbicularis03.l_Yposition", // 220 +"endsite_orbicularis03.l_Zposition", // 221 +"orbicularis04.l_Xposition", // 222 +"orbicularis04.l_Yposition", // 223 +"orbicularis04.l_Zposition", // 224 +"endsite_orbicularis04.l_Xposition", // 225 +"endsite_orbicularis04.l_Yposition", // 226 +"endsite_orbicularis04.l_Zposition", // 227 +"special06.r_Xposition", // 228 +"special06.r_Yposition", // 229 +"special06.r_Zposition", // 230 +"special05.r_Xposition", // 231 +"special05.r_Yposition", // 232 +"special05.r_Zposition", // 233 +"eye.r_Xposition", // 234 +"eye.r_Yposition", // 235 +"eye.r_Zposition", // 236 +"endsite_eye.r_Xposition", // 237 +"endsite_eye.r_Yposition", // 238 +"endsite_eye.r_Zposition", // 239 +"orbicularis03.r_Xposition", // 240 +"orbicularis03.r_Yposition", // 241 +"orbicularis03.r_Zposition", // 242 +"endsite_orbicularis03.r_Xposition", // 243 +"endsite_orbicularis03.r_Yposition", // 244 +"endsite_orbicularis03.r_Zposition", // 245 +"orbicularis04.r_Xposition", // 246 +"orbicularis04.r_Yposition", // 247 +"orbicularis04.r_Zposition", // 248 +"endsite_orbicularis04.r_Xposition", // 249 +"endsite_orbicularis04.r_Yposition", // 250 +"endsite_orbicularis04.r_Zposition", // 251 +"__temporalis01.l_Xposition", // 252 +"__temporalis01.l_Yposition", // 253 +"__temporalis01.l_Zposition", // 254 +"temporalis01.l_Xposition", // 255 +"temporalis01.l_Yposition", // 256 +"temporalis01.l_Zposition", // 257 +"oculi02.l_Xposition", // 258 +"oculi02.l_Yposition", // 259 +"oculi02.l_Zposition", // 260 +"oculi01.l_Xposition", // 261 +"oculi01.l_Yposition", // 262 +"oculi01.l_Zposition", // 263 +"endsite_oculi01.l_Xposition", // 264 +"endsite_oculi01.l_Yposition", // 265 +"endsite_oculi01.l_Zposition", // 266 +"__temporalis01.r_Xposition", // 267 +"__temporalis01.r_Yposition", // 268 +"__temporalis01.r_Zposition", // 269 +"temporalis01.r_Xposition", // 270 +"temporalis01.r_Yposition", // 271 +"temporalis01.r_Zposition", // 272 +"oculi02.r_Xposition", // 273 +"oculi02.r_Yposition", // 274 +"oculi02.r_Zposition", // 275 +"oculi01.r_Xposition", // 276 +"oculi01.r_Yposition", // 277 +"oculi01.r_Zposition", // 278 +"endsite_oculi01.r_Xposition", // 279 +"endsite_oculi01.r_Yposition", // 280 +"endsite_oculi01.r_Zposition", // 281 +"__temporalis02.l_Xposition", // 282 +"__temporalis02.l_Yposition", // 283 +"__temporalis02.l_Zposition", // 284 +"temporalis02.l_Xposition", // 285 +"temporalis02.l_Yposition", // 286 +"temporalis02.l_Zposition", // 287 +"risorius02.l_Xposition", // 288 +"risorius02.l_Yposition", // 289 +"risorius02.l_Zposition", // 290 +"risorius03.l_Xposition", // 291 +"risorius03.l_Yposition", // 292 +"risorius03.l_Zposition", // 293 +"endsite_risorius03.l_Xposition", // 294 +"endsite_risorius03.l_Yposition", // 295 +"endsite_risorius03.l_Zposition", // 296 +"__temporalis02.r_Xposition", // 297 +"__temporalis02.r_Yposition", // 298 +"__temporalis02.r_Zposition", // 299 +"temporalis02.r_Xposition", // 300 +"temporalis02.r_Yposition", // 301 +"temporalis02.r_Zposition", // 302 +"risorius02.r_Xposition", // 303 +"risorius02.r_Yposition", // 304 +"risorius02.r_Zposition", // 305 +"risorius03.r_Xposition", // 306 +"risorius03.r_Yposition", // 307 +"risorius03.r_Zposition", // 308 +"endsite_risorius03.r_Xposition", // 309 +"endsite_risorius03.r_Yposition", // 310 +"endsite_risorius03.r_Zposition", // 311 +"rcollar_Xposition", // 312 +"rcollar_Yposition", // 313 +"rcollar_Zposition", // 314 +"rshoulder_Xposition", // 315 +"rshoulder_Yposition", // 316 +"rshoulder_Zposition", // 317 +"relbow_Xposition", // 318 +"relbow_Yposition", // 319 +"relbow_Zposition", // 320 +"rhand_Xposition", // 321 +"rhand_Yposition", // 322 +"rhand_Zposition", // 323 +"metacarpal1.r_Xposition", // 324 +"metacarpal1.r_Yposition", // 325 +"metacarpal1.r_Zposition", // 326 +"finger2-1.r_Xposition", // 327 +"finger2-1.r_Yposition", // 328 +"finger2-1.r_Zposition", // 329 +"finger2-2.r_Xposition", // 330 +"finger2-2.r_Yposition", // 331 +"finger2-2.r_Zposition", // 332 +"finger2-3.r_Xposition", // 333 +"finger2-3.r_Yposition", // 334 +"finger2-3.r_Zposition", // 335 +"endsite_finger2-3.r_Xposition", // 336 +"endsite_finger2-3.r_Yposition", // 337 +"endsite_finger2-3.r_Zposition", // 338 +"metacarpal2.r_Xposition", // 339 +"metacarpal2.r_Yposition", // 340 +"metacarpal2.r_Zposition", // 341 +"finger3-1.r_Xposition", // 342 +"finger3-1.r_Yposition", // 343 +"finger3-1.r_Zposition", // 344 +"finger3-2.r_Xposition", // 345 +"finger3-2.r_Yposition", // 346 +"finger3-2.r_Zposition", // 347 +"finger3-3.r_Xposition", // 348 +"finger3-3.r_Yposition", // 349 +"finger3-3.r_Zposition", // 350 +"endsite_finger3-3.r_Xposition", // 351 +"endsite_finger3-3.r_Yposition", // 352 +"endsite_finger3-3.r_Zposition", // 353 +"__metacarpal3.r_Xposition", // 354 +"__metacarpal3.r_Yposition", // 355 +"__metacarpal3.r_Zposition", // 356 +"metacarpal3.r_Xposition", // 357 +"metacarpal3.r_Yposition", // 358 +"metacarpal3.r_Zposition", // 359 +"finger4-1.r_Xposition", // 360 +"finger4-1.r_Yposition", // 361 +"finger4-1.r_Zposition", // 362 +"finger4-2.r_Xposition", // 363 +"finger4-2.r_Yposition", // 364 +"finger4-2.r_Zposition", // 365 +"finger4-3.r_Xposition", // 366 +"finger4-3.r_Yposition", // 367 +"finger4-3.r_Zposition", // 368 +"endsite_finger4-3.r_Xposition", // 369 +"endsite_finger4-3.r_Yposition", // 370 +"endsite_finger4-3.r_Zposition", // 371 +"__metacarpal4.r_Xposition", // 372 +"__metacarpal4.r_Yposition", // 373 +"__metacarpal4.r_Zposition", // 374 +"metacarpal4.r_Xposition", // 375 +"metacarpal4.r_Yposition", // 376 +"metacarpal4.r_Zposition", // 377 +"finger5-1.r_Xposition", // 378 +"finger5-1.r_Yposition", // 379 +"finger5-1.r_Zposition", // 380 +"finger5-2.r_Xposition", // 381 +"finger5-2.r_Yposition", // 382 +"finger5-2.r_Zposition", // 383 +"finger5-3.r_Xposition", // 384 +"finger5-3.r_Yposition", // 385 +"finger5-3.r_Zposition", // 386 +"endsite_finger5-3.r_Xposition", // 387 +"endsite_finger5-3.r_Yposition", // 388 +"endsite_finger5-3.r_Zposition", // 389 +"__rthumb_Xposition", // 390 +"__rthumb_Yposition", // 391 +"__rthumb_Zposition", // 392 +"rthumb_Xposition", // 393 +"rthumb_Yposition", // 394 +"rthumb_Zposition", // 395 +"finger1-2.r_Xposition", // 396 +"finger1-2.r_Yposition", // 397 +"finger1-2.r_Zposition", // 398 +"finger1-3.r_Xposition", // 399 +"finger1-3.r_Yposition", // 400 +"finger1-3.r_Zposition", // 401 +"endsite_finger1-3.r_Xposition", // 402 +"endsite_finger1-3.r_Yposition", // 403 +"endsite_finger1-3.r_Zposition", // 404 +"lcollar_Xposition", // 405 +"lcollar_Yposition", // 406 +"lcollar_Zposition", // 407 +"lshoulder_Xposition", // 408 +"lshoulder_Yposition", // 409 +"lshoulder_Zposition", // 410 +"lelbow_Xposition", // 411 +"lelbow_Yposition", // 412 +"lelbow_Zposition", // 413 +"lhand_Xposition", // 414 +"lhand_Yposition", // 415 +"lhand_Zposition", // 416 +"metacarpal1.l_Xposition", // 417 +"metacarpal1.l_Yposition", // 418 +"metacarpal1.l_Zposition", // 419 +"finger2-1.l_Xposition", // 420 +"finger2-1.l_Yposition", // 421 +"finger2-1.l_Zposition", // 422 +"finger2-2.l_Xposition", // 423 +"finger2-2.l_Yposition", // 424 +"finger2-2.l_Zposition", // 425 +"finger2-3.l_Xposition", // 426 +"finger2-3.l_Yposition", // 427 +"finger2-3.l_Zposition", // 428 +"endsite_finger2-3.l_Xposition", // 429 +"endsite_finger2-3.l_Yposition", // 430 +"endsite_finger2-3.l_Zposition", // 431 +"metacarpal2.l_Xposition", // 432 +"metacarpal2.l_Yposition", // 433 +"metacarpal2.l_Zposition", // 434 +"finger3-1.l_Xposition", // 435 +"finger3-1.l_Yposition", // 436 +"finger3-1.l_Zposition", // 437 +"finger3-2.l_Xposition", // 438 +"finger3-2.l_Yposition", // 439 +"finger3-2.l_Zposition", // 440 +"finger3-3.l_Xposition", // 441 +"finger3-3.l_Yposition", // 442 +"finger3-3.l_Zposition", // 443 +"endsite_finger3-3.l_Xposition", // 444 +"endsite_finger3-3.l_Yposition", // 445 +"endsite_finger3-3.l_Zposition", // 446 +"__metacarpal3.l_Xposition", // 447 +"__metacarpal3.l_Yposition", // 448 +"__metacarpal3.l_Zposition", // 449 +"metacarpal3.l_Xposition", // 450 +"metacarpal3.l_Yposition", // 451 +"metacarpal3.l_Zposition", // 452 +"finger4-1.l_Xposition", // 453 +"finger4-1.l_Yposition", // 454 +"finger4-1.l_Zposition", // 455 +"finger4-2.l_Xposition", // 456 +"finger4-2.l_Yposition", // 457 +"finger4-2.l_Zposition", // 458 +"finger4-3.l_Xposition", // 459 +"finger4-3.l_Yposition", // 460 +"finger4-3.l_Zposition", // 461 +"endsite_finger4-3.l_Xposition", // 462 +"endsite_finger4-3.l_Yposition", // 463 +"endsite_finger4-3.l_Zposition", // 464 +"__metacarpal4.l_Xposition", // 465 +"__metacarpal4.l_Yposition", // 466 +"__metacarpal4.l_Zposition", // 467 +"metacarpal4.l_Xposition", // 468 +"metacarpal4.l_Yposition", // 469 +"metacarpal4.l_Zposition", // 470 +"finger5-1.l_Xposition", // 471 +"finger5-1.l_Yposition", // 472 +"finger5-1.l_Zposition", // 473 +"finger5-2.l_Xposition", // 474 +"finger5-2.l_Yposition", // 475 +"finger5-2.l_Zposition", // 476 +"finger5-3.l_Xposition", // 477 +"finger5-3.l_Yposition", // 478 +"finger5-3.l_Zposition", // 479 +"endsite_finger5-3.l_Xposition", // 480 +"endsite_finger5-3.l_Yposition", // 481 +"endsite_finger5-3.l_Zposition", // 482 +"__lthumb_Xposition", // 483 +"__lthumb_Yposition", // 484 +"__lthumb_Zposition", // 485 +"lthumb_Xposition", // 486 +"lthumb_Yposition", // 487 +"lthumb_Zposition", // 488 +"finger1-2.l_Xposition", // 489 +"finger1-2.l_Yposition", // 490 +"finger1-2.l_Zposition", // 491 +"finger1-3.l_Xposition", // 492 +"finger1-3.l_Yposition", // 493 +"finger1-3.l_Zposition", // 494 +"endsite_finger1-3.l_Xposition", // 495 +"endsite_finger1-3.l_Yposition", // 496 +"endsite_finger1-3.l_Zposition", // 497 +"rbuttock_Xposition", // 498 +"rbuttock_Yposition", // 499 +"rbuttock_Zposition", // 500 +"rhip_Xposition", // 501 +"rhip_Yposition", // 502 +"rhip_Zposition", // 503 +"rknee_Xposition", // 504 +"rknee_Yposition", // 505 +"rknee_Zposition", // 506 +"rfoot_Xposition", // 507 +"rfoot_Yposition", // 508 +"rfoot_Zposition", // 509 +"toe1-1.r_Xposition", // 510 +"toe1-1.r_Yposition", // 511 +"toe1-1.r_Zposition", // 512 +"toe1-2.r_Xposition", // 513 +"toe1-2.r_Yposition", // 514 +"toe1-2.r_Zposition", // 515 +"endsite_toe1-2.r_Xposition", // 516 +"endsite_toe1-2.r_Yposition", // 517 +"endsite_toe1-2.r_Zposition", // 518 +"toe2-1.r_Xposition", // 519 +"toe2-1.r_Yposition", // 520 +"toe2-1.r_Zposition", // 521 +"toe2-2.r_Xposition", // 522 +"toe2-2.r_Yposition", // 523 +"toe2-2.r_Zposition", // 524 +"toe2-3.r_Xposition", // 525 +"toe2-3.r_Yposition", // 526 +"toe2-3.r_Zposition", // 527 +"endsite_toe2-3.r_Xposition", // 528 +"endsite_toe2-3.r_Yposition", // 529 +"endsite_toe2-3.r_Zposition", // 530 +"toe3-1.r_Xposition", // 531 +"toe3-1.r_Yposition", // 532 +"toe3-1.r_Zposition", // 533 +"toe3-2.r_Xposition", // 534 +"toe3-2.r_Yposition", // 535 +"toe3-2.r_Zposition", // 536 +"toe3-3.r_Xposition", // 537 +"toe3-3.r_Yposition", // 538 +"toe3-3.r_Zposition", // 539 +"endsite_toe3-3.r_Xposition", // 540 +"endsite_toe3-3.r_Yposition", // 541 +"endsite_toe3-3.r_Zposition", // 542 +"toe4-1.r_Xposition", // 543 +"toe4-1.r_Yposition", // 544 +"toe4-1.r_Zposition", // 545 +"toe4-2.r_Xposition", // 546 +"toe4-2.r_Yposition", // 547 +"toe4-2.r_Zposition", // 548 +"toe4-3.r_Xposition", // 549 +"toe4-3.r_Yposition", // 550 +"toe4-3.r_Zposition", // 551 +"endsite_toe4-3.r_Xposition", // 552 +"endsite_toe4-3.r_Yposition", // 553 +"endsite_toe4-3.r_Zposition", // 554 +"toe5-1.r_Xposition", // 555 +"toe5-1.r_Yposition", // 556 +"toe5-1.r_Zposition", // 557 +"toe5-2.r_Xposition", // 558 +"toe5-2.r_Yposition", // 559 +"toe5-2.r_Zposition", // 560 +"toe5-3.r_Xposition", // 561 +"toe5-3.r_Yposition", // 562 +"toe5-3.r_Zposition", // 563 +"endsite_toe5-3.r_Xposition", // 564 +"endsite_toe5-3.r_Yposition", // 565 +"endsite_toe5-3.r_Zposition", // 566 +"lbuttock_Xposition", // 567 +"lbuttock_Yposition", // 568 +"lbuttock_Zposition", // 569 +"lhip_Xposition", // 570 +"lhip_Yposition", // 571 +"lhip_Zposition", // 572 +"lknee_Xposition", // 573 +"lknee_Yposition", // 574 +"lknee_Zposition", // 575 +"lfoot_Xposition", // 576 +"lfoot_Yposition", // 577 +"lfoot_Zposition", // 578 +"toe1-1.l_Xposition", // 579 +"toe1-1.l_Yposition", // 580 +"toe1-1.l_Zposition", // 581 +"toe1-2.l_Xposition", // 582 +"toe1-2.l_Yposition", // 583 +"toe1-2.l_Zposition", // 584 +"endsite_toe1-2.l_Xposition", // 585 +"endsite_toe1-2.l_Yposition", // 586 +"endsite_toe1-2.l_Zposition", // 587 +"toe2-1.l_Xposition", // 588 +"toe2-1.l_Yposition", // 589 +"toe2-1.l_Zposition", // 590 +"toe2-2.l_Xposition", // 591 +"toe2-2.l_Yposition", // 592 +"toe2-2.l_Zposition", // 593 +"toe2-3.l_Xposition", // 594 +"toe2-3.l_Yposition", // 595 +"toe2-3.l_Zposition", // 596 +"endsite_toe2-3.l_Xposition", // 597 +"endsite_toe2-3.l_Yposition", // 598 +"endsite_toe2-3.l_Zposition", // 599 +"toe3-1.l_Xposition", // 600 +"toe3-1.l_Yposition", // 601 +"toe3-1.l_Zposition", // 602 +"toe3-2.l_Xposition", // 603 +"toe3-2.l_Yposition", // 604 +"toe3-2.l_Zposition", // 605 +"toe3-3.l_Xposition", // 606 +"toe3-3.l_Yposition", // 607 +"toe3-3.l_Zposition", // 608 +"endsite_toe3-3.l_Xposition", // 609 +"endsite_toe3-3.l_Yposition", // 610 +"endsite_toe3-3.l_Zposition", // 611 +"toe4-1.l_Xposition", // 612 +"toe4-1.l_Yposition", // 613 +"toe4-1.l_Zposition", // 614 +"toe4-2.l_Xposition", // 615 +"toe4-2.l_Yposition", // 616 +"toe4-2.l_Zposition", // 617 +"toe4-3.l_Xposition", // 618 +"toe4-3.l_Yposition", // 619 +"toe4-3.l_Zposition", // 620 +"endsite_toe4-3.l_Xposition", // 621 +"endsite_toe4-3.l_Yposition", // 622 +"endsite_toe4-3.l_Zposition", // 623 +"toe5-1.l_Xposition", // 624 +"toe5-1.l_Yposition", // 625 +"toe5-1.l_Zposition", // 626 +"toe5-2.l_Xposition", // 627 +"toe5-2.l_Yposition", // 628 +"toe5-2.l_Zposition", // 629 +"toe5-3.l_Xposition", // 630 +"toe5-3.l_Yposition", // 631 +"toe5-3.l_Zposition", // 632 +"endsite_toe5-3.l_Xposition", // 633 +"endsite_toe5-3.l_Yposition", // 634 +"endsite_toe5-3.l_Zposition"// 635 +}; + +/** + * @brief This is a structure to encode model limits, not currently used + */ +struct MocapNETModelLimits +{ + int numberOfLimits; + float minimumYaw1; + float maximumYaw1; + float minimumYaw2; + float maximumYaw2; + int isFlipped; +}; + +/** + * @brief This is a MocapNET orientation. + */ +enum MOCAPNET_Orientation +{ + MOCAPNET_ORIENTATION_NONE=0, + MOCAPNET_ORIENTATION_FRONT, + MOCAPNET_ORIENTATION_BACK, + MOCAPNET_ORIENTATION_LEFT, + MOCAPNET_ORIENTATION_RIGHT, + //----------------------------- + MOCAPNET_ORIENTATION_NUMBER +}; + +/** + * @brief This is an array of names for all uncompressed inputs expected from MocapNET. + * Please notice that these 171 values correspond to triplets of 57 x,y,v ( v for visibility ) information for each joint. + */ +static const char * MocapNETOrientationNames[] = +{ + "None", + "Front", + "Back", + "Left", + "Right" +}; + + +/** + * @brief Each part of our 3D pose output is solved by a dedicated ensemble, this structure organizes this data + */ +struct MocapNET2SolutionPart +{ + char partName[64]; + //----------------------------------------- + int hasOrientationScan; + struct ButterWorth directionSignals[MOCAPNET_ORIENTATION_NUMBER]; + float orientationClassifications[4]; + //----------------------------------------- + int perform2DAlignmentBeforeEvaluation; + //----------------------------------------- + std::vector result; + std::vector positionalInput; + std::vector NSDM; + std::vector neuralNetworkReadyInput; + std::vector lastNeuralNetworkReadyInput; + //----------------------------------------- + struct TensorflowInstance models[16]; + unsigned int mode; + unsigned int loadedModels; + struct MocapNETModelLimits modelLimits[16]; + //----------------------------------------- + unsigned int * selectionIndex; + unsigned int selectionIndexLength; +}; + + +#if USE_BVH + #include "../../../dependencies/RGBDAcquisition/tools/PThreadWorkerPool/pthreadWorkerPool.h" +#endif + + +/** + * @brief MocapNET consists of separate classes/ensembles that are invoked for particular orientations. + * This structure holds the required tensorflow instances to make MocapNET work. + */ +struct MocapNET2 +{ + //This is discontinued until further notice.. + char useRemoteMocapNETServer; + char remoteMocapNETServerURL[128]; + int remoteMocapNETServerPort; + void * remoteContext; + //-------------------------------- + + //-------------------------------- + unsigned int indexesPopulated; + //python3 exportCPPCodeFromJSONConfiguration.py --front upperbody --config dataset/upperbody_configuration.json + unsigned int upperBodySelectionIndexesArePopulated; + unsigned int upperBodySelectionIndexes[mocapNET_InputLength_WithoutNSDM_upperbody]; + //python3 exportCPPCodeFromJSONConfiguration.py --front lowerbody --config dataset/lowerbody_configuration.json + unsigned int lowerBodySelectionIndexesArePopulated; + unsigned int lowerBodySelectionIndexes[mocapNET_InputLength_WithoutNSDM_lowerbody]; + //python3 exportCPPCodeFromJSONConfiguration.py --front body --config dataset/body_configuration.json + unsigned int bodySelectionIndexesArePopulated; + unsigned int bodySelectionIndexes[mocapNET_InputLength_WithoutNSDM_body]; + //TODO: add hands, face here.. + //-------------------------------- + + unsigned int framesReceived; + + //Body cut in two domains + //------------------------------------- + struct MocapNET2SolutionPart upperBody; + struct MocapNET2SolutionPart lowerBody; + //Full Body + struct MocapNET2SolutionPart body; + //Hands + struct MocapNET2SolutionPart leftHand; + struct MocapNET2SolutionPart rightHand; + //Face + struct MocapNET2SolutionPart face; + //-------------------------------- + float neuralNetworkFramerate; + //-------------------------------- + + //Inverse Kinematics settings + //-------------------------------- + float learningRate; + float spring; + unsigned int iterations; + unsigned int epochs; + float inverseKinematicsFramerate; + //-------------------------------- + + //Solutions + //-------------------------------- + std::vector penultimateSolution; + std::vector previousSolution; + std::vector currentSolution; + //-------------------------------- + + //-------------------------------- + struct ButterWorth outputSignals[MOCAPNET_OUTPUT_NUMBER]; + //-------------------------------- + + + float orientationClassifications[MOCAPNET_ORIENTATION_NUMBER]; + float lastSkeletonOrientation; + int orientation; + + struct PoseHistory poseHistoryStorage; + + //Gestures + //------------------------------------- + unsigned int gesturesMasterSwitch; + unsigned int lastActivatedGesture; + unsigned int gestureTimestamp; + struct GestureDatabase recognizedGestures; + void * newGestureEventCallback; + + //Poses + //------------------------------------- + unsigned int activePose; + unsigned int lastActivatedPose; + struct PoseDatabase recognizedPoses; + void * newPoseEventCallback; + + //Artifacts + //------------------------------------- + struct sceneArtifacts artifacts; + + //Options + //------------------------------------- + struct MocapNET2Options * options; + + #if USE_BVH + struct workerPool threadPool; + #endif + +}; + +void requestRealtimePriority(); + +/** + * @brief Experimental: Register a function that will get called when a gesture event is detected, please keep in mind that the gesture system is under construction + * @param Pointer to a loaded MocapNET instance + * @param Pointer to the function that will get called + * @retval 1=Success/0=Failure + */ +int registerGestureEventCallbackWithMocapNET(struct MocapNET2 * mnet,void * callback); + + +/** + * @brief Since the C/C++ code in this repository depends on a seperate python neural network trainer, and the networks can differ depending on training parameters (and this is a research project) the series of 2D joints + * can often change. It is also very expensive to do string matching on every frame, so before evaluating any input this call has to be executed in order to perform the correct array associations and from then on + * we can pass 2D data without searching for labels on each frame. It needs to be called before any runMocapNET2 call + * @param MocapNET instance + * @param a skeletonSerialized structure that holds our 2D input + * @param switch that enables lower body associations + * @param switch that enables hand associations + * @retval 1=Success/0=Failure + */ +int initializeMocapNET2InputAssociation( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace + ); + +/** + * @brief Load a MocapNET from .pb files on disk + * @ingroup mocapnet + * @param Pointer to a struct MocapNET that will hold the tensorflow instances on load. + * @param Description of MocapNET session + * @retval 1 = Success loading the files , 0 = Failure + */ +int loadMocapNET2(struct MocapNET2 * mnet, const char * description); + + +/** + * @brief run MocapNET on an input vector that has the correct formatting. If getting data from an external source + * the prepareMocapNETInputFromUncompressedInput function could be used to prepare the input for this function. + * @param Pointer to a valid and populated MocapNET instance + * @param Vector of input values according to MocapNETUncompressedAndCompressedArrayNames + * @retval 1=Success,0=Failure + */ +std::vector runMocapNET2( + struct MocapNET2 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ); + +/** + * @brief Deallocate tensorflow instances and free memory + * @param Pointer to a valid and populated MocapNET instance + * @retval 1=Success,0=Failure + */ +int unloadMocapNET2(struct MocapNET2 * mnet); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/postProcessing/outputFiltering.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/postProcessing/outputFiltering.hpp new file mode 100644 index 0000000..c791a07 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/postProcessing/outputFiltering.hpp @@ -0,0 +1,123 @@ +#ifndef _BUTTERWORTH_FILTER_H_INCLUDED +#define _BUTTERWORTH_FILTER_H_INCLUDED + +/** @file outputFiltering.hpp +* @brief From Wikipedia : The Butterworth filter is a type of signal processing filter designed to have a frequency response as flat as possible in the passband. +* It is also referred to as a maximally flat magnitude filter. +* It was first described in 1930 by the British engineer and physicist Stephen Butterworth in his paper entitled "On the Theory of Filter Amplifiers" +* https://en.wikipedia.org/wiki/Butterworth_filter +* +* The frequency response of the Butterworth filter is maximally flat (i.e. has no ripples) in the passband and rolls off towards zero in the stopband. +* That's why it is used as a post-processing step if you don't disable it from the GUI. It should be noted that this is a relatively new addition to the codebase ( 30 -10-2019 ) +* the original BMVC 2019 paper ( https://www.youtube.com/watch?v=fH5e-KMBvM0 ) did not have any post processing done..! +* +* However some sort of filtering had to be added after numerous comments regarding signal noise. And here it is, in a header-only vanilla C compatible version. +* Thanks to Stelios Piperakis ( https://github.com/mrsp ) for giving me the initial code implementation that this filter is based on +* +* @author Ammar Qammaz (AmmarkoV) +*/ + + +#include + +/** + * @brief The complete state of a Butterworth filter instance + */ +struct ButterWorth +{ + //https://en.wikipedia.org/wiki/Butterworth_filter + //https://github.com/mrsp/serow/blob/master/src/butterworthLPF.cpp + float unfilteredValue; + float filteredValue; + //----------- + char initialized; + //----------- + float a; + float fx; + float fs; + float a1; + float a2; + float b0; + float b1; + float b2; + float ff; + float ita; + float q; + int i; + float y_p; + float y_pp; + float x_p; + float x_pp; +}; + +/** + * @brief Initialize a "sensor" using fsampling/fcutoff values + * @param Butterworth filter instance + * @param frequency of sampling + * @param frequency of cutoff + */ +static void initButterWorth(struct ButterWorth * sensor,float fsampling,float fcutoff) +{ + sensor->fs = fsampling; + sensor->fx = fcutoff; + + sensor->i = 0; + sensor->ff = (float) sensor->fx/sensor->fs; + sensor->ita = (float) 1.0/tan((float) 3.14159265359 * sensor->ff); + sensor->q = 1.41421356237; + sensor->b0 = (float) 1.0 / (1.0 + sensor->q*sensor->ita + sensor->ita*sensor->ita); + sensor->b1 = 2*sensor->b0; + sensor->b2 = sensor->b0; + sensor->a1 = 2.0 * (sensor->ita*sensor->ita - 1.0) * sensor->b0; + sensor->a2 = -(1.0 - sensor->q*sensor->ita + sensor->ita*sensor->ita) * sensor->b0; + sensor->a =(float) (2.0*3.14159265359*sensor->ff)/(2.0*3.14159265359*sensor->ff+1.0); +} + + +/** + * @brief Filter a new incoming value and get the result + * @param Butterworth filter instance + * @param Unfiltered input value + * @retval Filtered output value + */ +static float filter(struct ButterWorth * sensor,float unfilteredValue) +{ + sensor->unfilteredValue = unfilteredValue; + + float y = sensor->unfilteredValue; + float out; + if ((sensor->i>2)&&(1)) + { + out = sensor->b0 * y + sensor->b1 * sensor->y_p + sensor->b2* sensor->y_pp + sensor->a1 * sensor->x_p + sensor->a2 * sensor->x_pp; + } + else + { + out = sensor->x_p + sensor->a * (y - sensor->x_p); + sensor->i=sensor->i+1; + } + + sensor->y_pp = sensor->y_p; + sensor->y_p = y; + sensor->x_pp = sensor->x_p; + sensor->x_p = out; + + sensor->filteredValue = out; + + if (!sensor->initialized) + { + //Do a warmup.. + //Make sure we dont start from 0 + + sensor->initialized=1; + for (unsigned int i=0; i<5; i++) + { + filter(sensor,unfilteredValue); + } + + return filter(sensor,unfilteredValue); + } + + return out; +} + +#endif \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/qualityControl/qualityControl.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/qualityControl/qualityControl.cpp new file mode 100644 index 0000000..31e7b07 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/qualityControl/qualityControl.cpp @@ -0,0 +1,7 @@ +#include "qualityControl.hpp" +#include + +//./GroundTruthDumper --from dataset/MotionCapture/01/01_02.bvh --selectJoints 1 13 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand --selectJoints 0 13 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand --selectJoints 1 14 hip abdomen chest neck rhip rknee rfoot lhip lknee lfoot toe1-2.r toe5-3.r toe1-2.l toe5-3.l --hide2DLocationOfJoints 0 6 abdomen chest toe1-2.r toe5-3.r toe1-2.l toe5-3.l --selectJoints 1 16 rhand finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --selectJoints 1 16 lhand finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --occlusions --setPositionRotation 0 0 2000 0 0 0 --csv ./ quality.csv 2d+bvh + + +//./GroundTruthDumper --from dataset/MotionCapture/02/02_05.bvh --selectJoints 1 13 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand --selectJoints 0 13 hip eye.r eye.l abdomen chest neck head rshoulder relbow rhand lshoulder lelbow lhand --selectJoints 1 14 hip abdomen chest neck rhip rknee rfoot lhip lknee lfoot toe1-2.r toe5-3.r toe1-2.l toe5-3.l --hide2DLocationOfJoints 0 6 abdomen chest toe1-2.r toe5-3.r toe1-2.l toe5-3.l --selectJoints 1 16 rhand finger5-1.r finger5-2.r finger5-3.r finger4-1.r finger4-2.r finger4-3.r finger3-1.r finger3-2.r finger3-3.r finger2-1.r finger2-2.r finger2-3.r rthumb finger1-2.r finger1-3.r --selectJoints 1 16 lhand finger5-1.l finger5-2.l finger5-3.l finger4-1.l finger4-2.l finger4-3.l finger3-1.l finger3-2.l finger3-3.l finger2-1.l finger2-2.l finger2-3.l lthumb finger1-2.l finger1-3.l --occlusions --offsetPositionRotation 0 800 1500 0 0 0 --csv ./ quality.csv 2d+bvh diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/qualityControl/qualityControl.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/qualityControl/qualityControl.hpp new file mode 100644 index 0000000..84a1624 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/qualityControl/qualityControl.hpp @@ -0,0 +1,2 @@ +#pragma once + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/remoteExecution.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/remoteExecution.cpp new file mode 100644 index 0000000..adecdb1 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/remoteExecution.cpp @@ -0,0 +1,146 @@ +#include "remoteExecution.hpp" +#include "../../../dependencies/InputParser/InputParser_C.h" + + +#if USE_NETWORKING + #include "../../../dependencies/AmmarServer/src/AmmClient/AmmClient.h" +#endif + + +#include +#include +#include + + +#define SEND_RECV_BUFFER_SIZE 16000 + +char * strstrDoubleNewlineLocal(char * request,unsigned int requestLength,unsigned int * endOfLine) +{ + if (request==0) { return request; } + if (requestLength==0) { return request; } + if (endOfLine==0) { return request; } + + char * ptrA=request; + char * ptrB=request+1; + + char * ptrEnd = request + requestLength; + + //fprintf(stderr,"\strstrDoubleNewline for 13 10 13 10 on a buffer with %u bytes of data : ",requestLength); + while (ptrB remoteExecution(struct MocapNET2 * mnet,const std::vector &mnetInput) +{ + std::vector result; + + #if USE_NETWORKING + fprintf(stderr,"remoteExecution :\n"); + //----------------------------------------------------- + char requestBuffer[SEND_RECV_BUFFER_SIZE+1]={0}; + char part[128]={0}; + + snprintf(requestBuffer,SEND_RECV_BUFFER_SIZE,"control.html?skeleton="); + for (int i=0; iremoteContext; + + char resultBuffer[SEND_RECV_BUFFER_SIZE+1]={0}; + unsigned int filecontentSize=SEND_RECV_BUFFER_SIZE; + + if ( + AmmClient_RecvFile( + instance, + requestBuffer, + resultBuffer, + &filecontentSize, + 1,//keepAlive, + 0// reallyFastImplementation + ) + ) + { + unsigned int startOfData=0; + char * onlyResults = strstrDoubleNewlineLocal(resultBuffer,filecontentSize,&startOfData); + if (onlyResults==0) { + fprintf(stderr,"Couldnt find body.. \n"); + onlyResults=resultBuffer; + } + + //fprintf(stderr,"Got back:\n %s \n\n",resultBuffer); + + struct InputParserC * ipc = InputParser_Create(4096,1); + InputParser_SetDelimeter(ipc,0,','); + + if (ipc!=0) + { + result.clear(); + //fprintf(stderr,"Disregarding headers we have: %s \n\n",onlyResults); + int numberOfArguments = InputParser_SeperateWords(ipc,onlyResults,1); + //fprintf(stderr,"Skeleton Arguments : %u\n",numberOfArguments); + for (int argument=0; argument +#include + +#include "mocapnet2.hpp" + +std::vector remoteExecution(struct MocapNET2 * mnet,const std::vector &mnetInput); + + +void * intializeRemoteExecution(const char * ip,unsigned int port,unsigned int socketTimeoutSeconds); +int stopRemoteExecution(void * instance); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/body.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/body.cpp new file mode 100644 index 0000000..bdd6f93 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/body.cpp @@ -0,0 +1,307 @@ +#include "body.hpp" + +#include "../../MocapNETLib2/config.h" +#include "../../MocapNETLib2/IO/conversions.hpp" +#include "../../MocapNETLib2/tools.hpp" +#include "../../MocapNETLib2/core/core.hpp" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +int mocapnetBody_initializeAssociations(struct MocapNET2 * mnet, struct skeletonSerialized * input) +{ + return initializeAssociationsForSubsetOfSkeleton( + &mnet->bodySelectionIndexesArePopulated, + mnet->bodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_body, + mocapNET_body, + input + ); +} + + + +int mocapnetBody_initialize(struct MocapNET2 * mnet,const char * filename,float qualitySetting,unsigned int mode,unsigned int forceCPU) +{ + char modelPath[1024]= {0}; + int result = 0; + int modelNumber=0; + + + switch (mode) + { + case 3: + mnet->body.mode=3; + fprintf(stderr,RED "Fatal: Mode 3 No longer supported on MocapNET 2 \n" NORMAL); + exit(0); + break; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + case 5: + mnet->body.mode=5; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + // Regular 3 Model setup such as the BMVC 2019 Work.. + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/categorize_body_all.pb",mode,qualitySetting); + if (fileExists(modelPath)) + { + result += loadTensorflowInstance(&mnet->body.models[modelNumber] ,modelPath ,"input_all" ,"result_all/concat",forceCPU); + mnet->body.modelLimits[modelNumber].isFlipped=0; + mnet->body.modelLimits[modelNumber].numberOfLimits=1; + mnet->body.modelLimits[modelNumber].minimumYaw1=-360.0; + mnet->body.modelLimits[modelNumber].maximumYaw1=360.0; + modelNumber+=1; + } else + { + fprintf(stderr,YELLOW "Could not find a pose categorization classifier for Whole Body..\n " NORMAL ); + } + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/body_front.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->body.models[modelNumber],modelPath,"input_front","result_front/concat",forceCPU); + mnet->body.modelLimits[modelNumber].isFlipped=0; + mnet->body.modelLimits[modelNumber].numberOfLimits=1; + mnet->body.modelLimits[modelNumber].minimumYaw1=-45.0; + mnet->body.modelLimits[modelNumber].maximumYaw1=45.0; + modelNumber+=1; + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/body_back.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->body.models[modelNumber],modelPath,"input_back","result_back/concat",forceCPU); + mnet->body.modelLimits[modelNumber].isFlipped=0; + mnet->body.modelLimits[modelNumber].numberOfLimits=1; + mnet->body.modelLimits[modelNumber].minimumYaw1=135.0;//-90.0; + mnet->body.modelLimits[modelNumber].maximumYaw1=225.0;//-270.0; + modelNumber+=1; + + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/body_left.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->body.models[modelNumber] ,modelPath ,"input_left" ,"result_left/concat",forceCPU); + mnet->body.modelLimits[modelNumber].isFlipped=0; + mnet->body.modelLimits[modelNumber].numberOfLimits=1; + mnet->body.modelLimits[modelNumber].minimumYaw1=-135.0; + mnet->body.modelLimits[modelNumber].maximumYaw1=45.0; + modelNumber+=1; + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/body_right.pb",mode,qualitySetting);// + result += loadTensorflowInstance(&mnet->body.models[modelNumber] ,modelPath ,"input_right" ,"result_right/concat",forceCPU); + mnet->body.modelLimits[modelNumber].isFlipped=0; + mnet->body.modelLimits[modelNumber].numberOfLimits=1; + mnet->body.modelLimits[modelNumber].minimumYaw1=45.0; + mnet->body.modelLimits[modelNumber].maximumYaw1=135.0; + modelNumber+=1; + + + + if(result==modelNumber) // make this 5 + { + result=1; + mnet->body.loadedModels=modelNumber; //make this 5 + } + else + { + result=0; + } + break; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + + default: + fprintf(stderr,RED "You requested a MocapNET configuration that is incorrect ( mode=%u )\n" NORMAL , mode); + return 0; + break; + }; + + if (result) + { + fprintf(stderr,"Caching networks after initialization to be ready for use..\n"); + std::vector emptyValues; + for (int i=0; ibody.loadedModels; i++ ) + { + std::vector prediction = predictTensorflow(&mnet->body.models[i],emptyValues); + if (prediction.size()>0) + { + fprintf(stderr, GREEN "Caching model %u (%s) was successful\n" NORMAL ,i,mnet->body.models[i].modelPath); + } + else + { + fprintf(stderr,RED "Caching model %u (%s) was unsuccessful\n" NORMAL,i,mnet->body.models[i].modelPath); + } + } + + } + + return result; +} + + + +int mocapnetBody_unload(struct MocapNET2 * mnet) +{ + unsigned int result=0; + for (int i=0; ibody.loadedModels; i++ ) + { + result+=unloadTensorflow(&mnet->body.models[i]); + } + + if (result==mnet->body.loadedModels) { result=1; } else + { result=0; } + + mnet->body.loadedModels=0; + + return result; +} + + + +std::vector mocapnetBody_evaluateInput(struct MocapNET2 * mnet,struct skeletonSerialized * input) +{ + + + mnet->body.positionalInput = deriveMocapNET2InputUsingAssociations( + mnet, + input, + &mnet->bodySelectionIndexesArePopulated, + mnet->bodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_body, + mocapNET_body, + 0 // Enable to debug input + ); + + + mnet->body.NSDM = bodyCreateNDSM(mnet->body.positionalInput,1/*NSDM Positional*/,0/*NSDM Angular */,1 /*Use Normalization*/); + mnet->body.neuralNetworkReadyInput.clear(); + mnet->body.neuralNetworkReadyInput.insert(mnet->body.neuralNetworkReadyInput.end(),mnet->body.positionalInput.begin(), mnet->body.positionalInput.end()); + mnet->body.neuralNetworkReadyInput.insert(mnet->body.neuralNetworkReadyInput.end(),mnet->body.NSDM.begin(), mnet->body.NSDM.end()); + //---------------------------------------------------------------------------------------------- + //The code block above should have done everything needed to make a 749 element vector + //If input is not correct then we should stop right here + //---------------------------------------------------------------------------------------------- + if (mnet->body.neuralNetworkReadyInput.size()!=MNET_BODY_IN_NUMBER) + { + fprintf(stderr,RED "MocapNET: Incorrect size of MocapNET input .. \n" NORMAL); + std::vector emptyResult; + return emptyResult; + } + + int orientationReceived =localOrientationExtraction(&mnet->body,mnet->body.neuralNetworkReadyInput); + if (orientationReceived!=MOCAPNET_ORIENTATION_NONE) + { + mnet->orientation=orientationReceived; + mnet->orientationClassifications[0]= mnet->body.orientationClassifications[0]; + mnet->orientationClassifications[1]= mnet->body.orientationClassifications[1]; + mnet->orientationClassifications[2]= mnet->body.orientationClassifications[2]; + mnet->orientationClassifications[3]= mnet->body.orientationClassifications[3]; + } + //First run the network that can predict the orientation of the skeleton so we will use the correct + //network to get the full BVH result from it + //---------------------------------------------------------------------------------------------- + std::vector result = localExecution( + &mnet->body, + mnet->body.neuralNetworkReadyInput, + mnet->orientation, + NN_ORIENTATIONS_TRAINED_AROUND_ZERO_AND_REQUIRE_TRICK /*Body Requires orientation trick*/ + ); + mnet->body.result = result; + + return result; +} + + +int mocapnetBody_fillResultVector(std::vector &result ,std::vector resultBody) +{ + if (resultBody.size()==MOCAPNET_BODY_OUTPUT_NUMBER) + { //This is output that targets the old body only armature..! + result[MOCAPNET_OUTPUT_HIP_XPOSITION]=resultBody[MOCAPNET_BODY_OUTPUT_HIP_XPOSITION]; + result[MOCAPNET_OUTPUT_HIP_YPOSITION]=resultBody[MOCAPNET_BODY_OUTPUT_HIP_YPOSITION]; + result[MOCAPNET_OUTPUT_HIP_ZPOSITION]=resultBody[MOCAPNET_BODY_OUTPUT_HIP_ZPOSITION]; + result[MOCAPNET_OUTPUT_HIP_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_HIP_ZROTATION]; + result[MOCAPNET_OUTPUT_HIP_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_HIP_YROTATION]; + result[MOCAPNET_OUTPUT_HIP_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_HIP_XROTATION]; + result[MOCAPNET_OUTPUT_ABDOMEN_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_ABDOMEN_ZROTATION]; + result[MOCAPNET_OUTPUT_ABDOMEN_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_ABDOMEN_XROTATION]; + result[MOCAPNET_OUTPUT_ABDOMEN_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_ABDOMEN_YROTATION]; + result[MOCAPNET_OUTPUT_CHEST_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_CHEST_ZROTATION]; + result[MOCAPNET_OUTPUT_CHEST_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_CHEST_XROTATION]; + result[MOCAPNET_OUTPUT_CHEST_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_CHEST_YROTATION]; + result[MOCAPNET_OUTPUT_NECK_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_NECK_ZROTATION]; + result[MOCAPNET_OUTPUT_NECK_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_NECK_XROTATION]; + result[MOCAPNET_OUTPUT_NECK_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_NECK_YROTATION]; + result[MOCAPNET_OUTPUT_HEAD_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_HEAD_ZROTATION]; + result[MOCAPNET_OUTPUT_HEAD_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_HEAD_XROTATION]; + result[MOCAPNET_OUTPUT_HEAD_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_HEAD_YROTATION]; + result[MOCAPNET_OUTPUT_EYE_L_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_EYE_L_ZROTATION]; + result[MOCAPNET_OUTPUT_EYE_L_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_EYE_L_XROTATION]; + result[MOCAPNET_OUTPUT_EYE_L_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_EYE_L_YROTATION]; + result[MOCAPNET_OUTPUT_EYE_R_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_EYE_R_ZROTATION]; + result[MOCAPNET_OUTPUT_EYE_R_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_EYE_R_XROTATION]; + result[MOCAPNET_OUTPUT_EYE_R_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_EYE_R_YROTATION]; + result[MOCAPNET_OUTPUT_RSHOULDER_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RSHOULDER_ZROTATION]; + result[MOCAPNET_OUTPUT_RSHOULDER_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RSHOULDER_XROTATION]; + result[MOCAPNET_OUTPUT_RSHOULDER_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RSHOULDER_YROTATION]; + result[MOCAPNET_OUTPUT_RELBOW_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RELBOW_ZROTATION]; + result[MOCAPNET_OUTPUT_RELBOW_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RELBOW_XROTATION]; + result[MOCAPNET_OUTPUT_RELBOW_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RELBOW_YROTATION]; + result[MOCAPNET_OUTPUT_RHAND_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RHAND_ZROTATION]; + result[MOCAPNET_OUTPUT_RHAND_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RHAND_XROTATION]; + result[MOCAPNET_OUTPUT_RHAND_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RHAND_YROTATION]; + result[MOCAPNET_OUTPUT_LSHOULDER_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LSHOULDER_ZROTATION]; + result[MOCAPNET_OUTPUT_LSHOULDER_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LSHOULDER_XROTATION]; + result[MOCAPNET_OUTPUT_LSHOULDER_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LSHOULDER_YROTATION]; + result[MOCAPNET_OUTPUT_LELBOW_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LELBOW_ZROTATION]; + result[MOCAPNET_OUTPUT_LELBOW_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LELBOW_XROTATION]; + result[MOCAPNET_OUTPUT_LELBOW_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LELBOW_YROTATION]; + result[MOCAPNET_OUTPUT_LHAND_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LHAND_ZROTATION]; + result[MOCAPNET_OUTPUT_LHAND_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LHAND_XROTATION]; + result[MOCAPNET_OUTPUT_LHAND_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LHAND_YROTATION]; + result[MOCAPNET_OUTPUT_RHIP_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RHIP_ZROTATION]; + result[MOCAPNET_OUTPUT_RHIP_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RHIP_XROTATION]; + result[MOCAPNET_OUTPUT_RHIP_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RHIP_YROTATION]; + result[MOCAPNET_OUTPUT_RKNEE_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RKNEE_ZROTATION]; + result[MOCAPNET_OUTPUT_RKNEE_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RKNEE_XROTATION]; + result[MOCAPNET_OUTPUT_RKNEE_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RKNEE_YROTATION]; + result[MOCAPNET_OUTPUT_RFOOT_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RFOOT_ZROTATION]; + result[MOCAPNET_OUTPUT_RFOOT_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RFOOT_XROTATION]; + result[MOCAPNET_OUTPUT_RFOOT_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_RFOOT_YROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_R_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE1_2_R_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_R_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE1_2_R_XROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_R_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE1_2_R_YROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_R_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE5_3_R_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_R_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE5_3_R_XROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_R_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE5_3_R_YROTATION]; + result[MOCAPNET_OUTPUT_LHIP_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LHIP_ZROTATION]; + result[MOCAPNET_OUTPUT_LHIP_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LHIP_XROTATION]; + result[MOCAPNET_OUTPUT_LHIP_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LHIP_YROTATION]; + result[MOCAPNET_OUTPUT_LKNEE_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LKNEE_ZROTATION]; + result[MOCAPNET_OUTPUT_LKNEE_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LKNEE_XROTATION]; + result[MOCAPNET_OUTPUT_LKNEE_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LKNEE_YROTATION]; + result[MOCAPNET_OUTPUT_LFOOT_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LFOOT_ZROTATION]; + result[MOCAPNET_OUTPUT_LFOOT_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LFOOT_XROTATION]; + result[MOCAPNET_OUTPUT_LFOOT_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_LFOOT_YROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_L_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE1_2_L_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_L_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE1_2_L_XROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_L_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE1_2_L_YROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_L_ZROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE5_3_L_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_L_XROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE5_3_L_XROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_L_YROTATION]=resultBody[MOCAPNET_BODY_OUTPUT_TOE5_3_L_YROTATION]; + return 1; + } else + { + fprintf(stderr,YELLOW "MocapNET: Execution yielded %lu items ( expected %u ) for body don't know what to do with them.. \n" NORMAL,resultBody.size(),MOCAPNET_BODY_OUTPUT_NUMBER); + } + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/body.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/body.hpp new file mode 100644 index 0000000..f2d9867 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/body.hpp @@ -0,0 +1,106 @@ +#pragma once +/** @file body.hpp + * @brief Code that handles regressing the whole body using the legacy ( MocapNET 1 ) engine that encoded the whole body at once.. + * This is no longer used since the body has been split in upper and lower body to more effectively treat occlusions + * however the code remains here as documentation and for backwards compatibility + * @author Ammar Qammaz (AmmarkoV) + */ + + +#include "../mocapnet2.hpp" + + +/** + * @brief An array with string labels for what each element of an input should be after concatenating uncompressed and compressed input. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ + /* +enum MOCAPNET_BODY_Output_Joints +{ +MOCAPNET_BODY_OUTPUT_HIP_XPOSITION=0, //0 +MOCAPNET_BODY_OUTPUT_HIP_YPOSITION, //1 +MOCAPNET_BODY_OUTPUT_HIP_ZPOSITION, //2 +MOCAPNET_BODY_OUTPUT_HIP_ZROTATION, //3 +MOCAPNET_BODY_OUTPUT_HIP_YROTATION, //4 +MOCAPNET_BODY_OUTPUT_HIP_XROTATION, //5 +MOCAPNET_BODY_OUTPUT_ABDOMEN_ZROTATION, //6 +MOCAPNET_BODY_OUTPUT_ABDOMEN_XROTATION, //7 +MOCAPNET_BODY_OUTPUT_ABDOMEN_YROTATION, //8 +MOCAPNET_BODY_OUTPUT_CHEST_ZROTATION, //9 +MOCAPNET_BODY_OUTPUT_CHEST_XROTATION, //10 +MOCAPNET_BODY_OUTPUT_CHEST_YROTATION, //11 +MOCAPNET_BODY_OUTPUT_NECK_ZROTATION, //12 +MOCAPNET_BODY_OUTPUT_NECK_XROTATION, //13 +MOCAPNET_BODY_OUTPUT_NECK_YROTATION, //14 +MOCAPNET_BODY_OUTPUT_HEAD_ZROTATION, //15 +MOCAPNET_BODY_OUTPUT_HEAD_XROTATION, //16 +MOCAPNET_BODY_OUTPUT_HEAD_YROTATION, //17 +MOCAPNET_BODY_OUTPUT_EYE_L_ZROTATION, //18 +MOCAPNET_BODY_OUTPUT_EYE_L_XROTATION, //19 +MOCAPNET_BODY_OUTPUT_EYE_L_YROTATION, //20 +MOCAPNET_BODY_OUTPUT_EYE_R_ZROTATION, //21 +MOCAPNET_BODY_OUTPUT_EYE_R_XROTATION, //22 +MOCAPNET_BODY_OUTPUT_EYE_R_YROTATION, //23 +MOCAPNET_BODY_OUTPUT_RSHOULDER_ZROTATION,//24 +MOCAPNET_BODY_OUTPUT_RSHOULDER_XROTATION,//25 +MOCAPNET_BODY_OUTPUT_RSHOULDER_YROTATION,//26 +MOCAPNET_BODY_OUTPUT_RELBOW_ZROTATION, //27 +MOCAPNET_BODY_OUTPUT_RELBOW_XROTATION, //28 +MOCAPNET_BODY_OUTPUT_RELBOW_YROTATION, //29 +MOCAPNET_BODY_OUTPUT_RHAND_ZROTATION, //30 +MOCAPNET_BODY_OUTPUT_RHAND_XROTATION, //31 +MOCAPNET_BODY_OUTPUT_RHAND_YROTATION, //32 +MOCAPNET_BODY_OUTPUT_LSHOULDER_ZROTATION,//33 +MOCAPNET_BODY_OUTPUT_LSHOULDER_XROTATION,//34 +MOCAPNET_BODY_OUTPUT_LSHOULDER_YROTATION,//35 +MOCAPNET_BODY_OUTPUT_LELBOW_ZROTATION,//36 +MOCAPNET_BODY_OUTPUT_LELBOW_XROTATION,//37 +MOCAPNET_BODY_OUTPUT_LELBOW_YROTATION,//38 +MOCAPNET_BODY_OUTPUT_LHAND_ZROTATION,//39 +MOCAPNET_BODY_OUTPUT_LHAND_XROTATION,//40 +MOCAPNET_BODY_OUTPUT_LHAND_YROTATION,//41 +MOCAPNET_BODY_OUTPUT_RHIP_ZROTATION,//42 +MOCAPNET_BODY_OUTPUT_RHIP_XROTATION,//43 +MOCAPNET_BODY_OUTPUT_RHIP_YROTATION,//44 +MOCAPNET_BODY_OUTPUT_RKNEE_ZROTATION,//45 +MOCAPNET_BODY_OUTPUT_RKNEE_XROTATION,//46 +MOCAPNET_BODY_OUTPUT_RKNEE_YROTATION,//47 +MOCAPNET_BODY_OUTPUT_RFOOT_ZROTATION,//48 +MOCAPNET_BODY_OUTPUT_RFOOT_XROTATION,//49 +MOCAPNET_BODY_OUTPUT_RFOOT_YROTATION,//50 +MOCAPNET_BODY_OUTPUT_TOE1_2_R_ZROTATION,//51 +MOCAPNET_BODY_OUTPUT_TOE1_2_R_XROTATION,//52 +MOCAPNET_BODY_OUTPUT_TOE1_2_R_YROTATION,//53 +MOCAPNET_BODY_OUTPUT_TOE5_3_R_ZROTATION,//54 +MOCAPNET_BODY_OUTPUT_TOE5_3_R_XROTATION,//55 +MOCAPNET_BODY_OUTPUT_TOE5_3_R_YROTATION,//56 +MOCAPNET_BODY_OUTPUT_LHIP_ZROTATION,//57 +MOCAPNET_BODY_OUTPUT_LHIP_XROTATION,//58 +MOCAPNET_BODY_OUTPUT_LHIP_YROTATION,//59 +MOCAPNET_BODY_OUTPUT_LKNEE_ZROTATION,//60 +MOCAPNET_BODY_OUTPUT_LKNEE_XROTATION,//61 +MOCAPNET_BODY_OUTPUT_LKNEE_YROTATION,//62 +MOCAPNET_BODY_OUTPUT_LFOOT_ZROTATION,//63 +MOCAPNET_BODY_OUTPUT_LFOOT_XROTATION,//64 +MOCAPNET_BODY_OUTPUT_LFOOT_YROTATION,//65 +MOCAPNET_BODY_OUTPUT_TOE1_2_L_ZROTATION,//66 +MOCAPNET_BODY_OUTPUT_TOE1_2_L_XROTATION,//67 +MOCAPNET_BODY_OUTPUT_TOE1_2_L_YROTATION,//68 +MOCAPNET_BODY_OUTPUT_TOE5_3_L_ZROTATION,//69 +MOCAPNET_BODY_OUTPUT_TOE5_3_L_XROTATION,//70 +MOCAPNET_BODY_OUTPUT_TOE5_3_L_YROTATION,//71 +//----------------------------- +MOCAPNET_BODY_OUTPUT_NUMBER// = 72 +}; +*/ + +int mocapnetBody_initializeAssociations(struct MocapNET2 * mnet, struct skeletonSerialized * input); + +int mocapnetBody_initialize(struct MocapNET2 * mnet,const char * filename,float qualitySetting,unsigned int mode,unsigned int forceCPU); + +int mocapnetBody_unload(struct MocapNET2 * mnet); + +int mocapnetBody_fillResultVector(std::vector &finalResultVector,std::vector resultBody); + +std::vector mocapnetBody_evaluateInput(struct MocapNET2 * mnet,struct skeletonSerialized * input); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/lowerBody.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/lowerBody.cpp new file mode 100644 index 0000000..b1888af --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/lowerBody.cpp @@ -0,0 +1,310 @@ +#include "lowerBody.hpp" + +#include "../../MocapNETLib2/config.h" +#include "../../MocapNETLib2/IO/conversions.hpp" +#include "../../MocapNETLib2/tools.hpp" +#include "../../MocapNETLib2/core/core.hpp" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +int mocapnetLowerBody_initializeAssociations(struct MocapNET2 * mnet, struct skeletonSerialized * input) +{ + return initializeAssociationsForSubsetOfSkeleton( + &mnet->lowerBodySelectionIndexesArePopulated, + mnet->lowerBodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_lowerbody, + mocapNET_lowerbody, + input + ); +} + + + +int mocapnetLowerBody_initialize(struct MocapNET2 * mnet,const char * filename,float qualitySetting,unsigned int mode,unsigned int forceCPU) +{ + char modelPath[1024]= {0}; + int result = 0; + + int modelNumber=0; + + switch (mode) + { + case 3: + mnet->lowerBody.mode=3; + fprintf(stderr,RED "Fatal: Mode 3 No longer supported on MocapNET 2 \n" NORMAL); + exit(0); + break; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + case 5: + mnet->lowerBody.mode=5; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + // Regular 3 Model setup such as the BMVC 2019 Work.. + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/categorize_lowerbody_all.pb",mode,qualitySetting); + if (fileExists(modelPath)) + { + result += loadTensorflowInstance(&mnet->lowerBody.models[modelNumber] ,modelPath ,"input_all" ,"result_all/concat",forceCPU); + mnet->lowerBody.modelLimits[modelNumber].isFlipped=0; + mnet->lowerBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->lowerBody.modelLimits[modelNumber].minimumYaw1=-360.0; + mnet->lowerBody.modelLimits[modelNumber].maximumYaw1=360.0; + modelNumber+=1; + } else + { + fprintf(stderr,YELLOW "Could not find a pose categorization classifier for LowerBody..\n " NORMAL ); + } + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/lowerbody_front.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->lowerBody.models[modelNumber],modelPath,"input_front","result_front/concat",forceCPU); + mnet->lowerBody.modelLimits[modelNumber].isFlipped=0; + mnet->lowerBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->lowerBody.modelLimits[modelNumber].minimumYaw1=-45.0; + mnet->lowerBody.modelLimits[modelNumber].maximumYaw1=45.0; + modelNumber+=1; + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/lowerbody_back.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->lowerBody.models[modelNumber],modelPath,"input_back","result_back/concat",forceCPU); + mnet->lowerBody.modelLimits[modelNumber].isFlipped=0; + mnet->lowerBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->lowerBody.modelLimits[modelNumber].minimumYaw1=135.0;//-90.0; + mnet->lowerBody.modelLimits[modelNumber].maximumYaw1=225.0;//-270.0; + modelNumber+=1; + + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/lowerbody_left.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->lowerBody.models[modelNumber] ,modelPath ,"input_left" ,"result_left/concat",forceCPU); + mnet->lowerBody.modelLimits[modelNumber].isFlipped=0; + mnet->lowerBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->lowerBody.modelLimits[modelNumber].minimumYaw1=-135.0; + mnet->lowerBody.modelLimits[modelNumber].maximumYaw1=45.0; + modelNumber+=1; + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/lowerbody_right.pb",mode,qualitySetting);// + result += loadTensorflowInstance(&mnet->lowerBody.models[modelNumber] ,modelPath ,"input_right" ,"result_right/concat",forceCPU); + mnet->lowerBody.modelLimits[modelNumber].isFlipped=0; + mnet->lowerBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->lowerBody.modelLimits[modelNumber].minimumYaw1=45.0; + mnet->lowerBody.modelLimits[modelNumber].maximumYaw1=135.0; + modelNumber+=1; + + + + if(result==modelNumber) + { + result=1; + mnet->lowerBody.loadedModels=modelNumber; + } + else + { + result=0; + } + break; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + + default: + fprintf(stderr,RED "You requested a MocapNET configuration that is incorrect ( mode=%u )\n" NORMAL , mode); + return 0; + break; + }; + + if (result) + { + fprintf(stderr,"Caching networks after initialization to be ready for use..\n"); + std::vector emptyValues; + for (int i=0; ilowerBody.loadedModels; i++ ) + { + std::vector prediction = predictTensorflow(&mnet->lowerBody.models[i],emptyValues); + if (prediction.size()>0) + { + fprintf(stderr, GREEN "Caching model %u (%s) was successful\n" NORMAL ,i,mnet->lowerBody.models[i].modelPath); + } + else + { + fprintf(stderr,RED "Caching model %u (%s) was unsuccessful\n" NORMAL,i,mnet->lowerBody.models[i].modelPath); + } + } + + } + + return result; +} + + + +int mocapnetLowerBody_unload(struct MocapNET2 * mnet) +{ + unsigned int result=0; + for (int i=0; ilowerBody.loadedModels; i++ ) + { + result+=unloadTensorflow(&mnet->lowerBody.models[i]); + } + + if (result==mnet->lowerBody.loadedModels) { result=1; } else + { result=0; } + + mnet->lowerBody.loadedModels=0; + + return result; +} + + + +std::vector mocapnetLowerBody_evaluateInput(struct MocapNET2 * mnet,struct skeletonSerialized * input) +{ + mnet->lowerBody.positionalInput = deriveMocapNET2InputUsingAssociations( + mnet, + input, + &mnet->lowerBodySelectionIndexesArePopulated, + mnet->lowerBodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_lowerbody, + mocapNET_lowerbody, + 0 // Enable to debug input + ); + + + + + mnet->lowerBody.NSDM = lowerbodyCreateNDSM(mnet->lowerBody.positionalInput,0/*NSDM Positional*/,1/*NSDM Angular */,0 /*Use Normalization*/); + + /* + fprintf(stderr,"LowerBodyNSDM:\n"); + for (unsigned int y=0; y<17; y++) + { + for (unsigned int x=0; x<17; x++) + { + fprintf(stderr,"%0.1f ",mnet->lowerBody.NSDM[y*17+x]); + } + fprintf(stderr,"\n"); + }*/ + + mnet->lowerBody.neuralNetworkReadyInput.clear(); + mnet->lowerBody.neuralNetworkReadyInput.insert(mnet->lowerBody.neuralNetworkReadyInput.end(),mnet->lowerBody.positionalInput.begin(), mnet->lowerBody.positionalInput.end()); + mnet->lowerBody.neuralNetworkReadyInput.insert(mnet->lowerBody.neuralNetworkReadyInput.end(),mnet->lowerBody.NSDM.begin(), mnet->lowerBody.NSDM.end()); + //---------------------------------------------------------------------------------------------- + //The code block above should have done everything needed to make a 749 element vector + //If input is not correct then we should stop right here + //---------------------------------------------------------------------------------------------- + if (mnet->lowerBody.neuralNetworkReadyInput.size()!=MNET_LOWERBODY_IN_NUMBER) + { + fprintf(stderr,RED "MocapNET: Incorrect size of MocapNET input .. \n" NORMAL); + std::vector emptyResult; + return emptyResult; + } + + /* + * Skip extra calculations by relying on upperbody orientation.. + * + int orientationReceived =localOrientationExtraction(&mnet->lowerBody,mnet->lowerBody.neuralNetworkReadyInput); + if (orientationReceived!=MOCAPNET_ORIENTATION_NONE) + { + mnet->orientation=orientationReceived; + mnet->orientationClassifications[0]= mnet->lowerBody.orientationClassifications[0]; + mnet->orientationClassifications[1]= mnet->lowerBody.orientationClassifications[1]; + mnet->orientationClassifications[2]= mnet->lowerBody.orientationClassifications[2]; + mnet->orientationClassifications[3]= mnet->lowerBody.orientationClassifications[3]; + } + */ + if ( + vectorcmp( + mnet->lowerBody.lastNeuralNetworkReadyInput, + mnet->lowerBody.neuralNetworkReadyInput, + 0.1 + )!=0 + ) + { + //First run the network that can predict the orientation of the skeleton so we will use the correct + //network to get the full BVH result from it + //---------------------------------------------------------------------------------------------- + std::vector result = localExecution( + &mnet->lowerBody, + mnet->lowerBody.neuralNetworkReadyInput, + mnet->orientation, + NN_ORIENTATIONS_TRAINED_AROUND_ZERO_AND_REQUIRE_TRICK /*Body Requires orientation trick*/ + ); + mnet->lowerBody.result = result; + mnet->lowerBody.lastNeuralNetworkReadyInput = mnet->lowerBody.neuralNetworkReadyInput; + + return result; + } else + { + fprintf(stderr,GREEN "Nothing changed on lower body, returning previous result.. \n" NORMAL); + return mnet->lowerBody.result; + } +} + + +int mocapnetLowerBody_fillResultVector(std::vector &result ,std::vector resultBody) +{ + if (resultBody.size()==MOCAPNET_LOWERBODY_OUTPUT_NUMBER) + { //This is output that targets the old body only armature..! + //result[MOCAPNET_OUTPUT_HIP_XPOSITION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_HIP_XPOSITION]; + //result[MOCAPNET_OUTPUT_HIP_YPOSITION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_HIP_YPOSITION]; + //result[MOCAPNET_OUTPUT_HIP_ZPOSITION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_HIP_ZPOSITION]; + //result[MOCAPNET_OUTPUT_HIP_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_HIP_ZROTATION]; + //result[MOCAPNET_OUTPUT_HIP_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_HIP_YROTATION]; + //result[MOCAPNET_OUTPUT_HIP_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_HIP_XROTATION]; + //result[MOCAPNET_OUTPUT_ABDOMEN_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_ABDOMEN_ZROTATION]; + //result[MOCAPNET_OUTPUT_ABDOMEN_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_ABDOMEN_XROTATION]; + //result[MOCAPNET_OUTPUT_ABDOMEN_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_ABDOMEN_YROTATION]; + //result[MOCAPNET_OUTPUT_CHEST_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_CHEST_ZROTATION]; + //result[MOCAPNET_OUTPUT_CHEST_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_CHEST_XROTATION]; + //result[MOCAPNET_OUTPUT_CHEST_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_CHEST_YROTATION]; + //result[MOCAPNET_OUTPUT_NECK_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_NECK_ZROTATION]; + //result[MOCAPNET_OUTPUT_NECK_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_NECK_XROTATION]; + //result[MOCAPNET_OUTPUT_NECK_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_NECK_YROTATION]; + result[MOCAPNET_OUTPUT_RHIP_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RHIP_ZROTATION]; + result[MOCAPNET_OUTPUT_RHIP_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RHIP_XROTATION]; + result[MOCAPNET_OUTPUT_RHIP_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RHIP_YROTATION]; + result[MOCAPNET_OUTPUT_RKNEE_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RKNEE_ZROTATION]; + result[MOCAPNET_OUTPUT_RKNEE_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RKNEE_XROTATION]; + result[MOCAPNET_OUTPUT_RKNEE_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RKNEE_YROTATION]; + result[MOCAPNET_OUTPUT_RFOOT_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RFOOT_ZROTATION]; + result[MOCAPNET_OUTPUT_RFOOT_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RFOOT_XROTATION]; + result[MOCAPNET_OUTPUT_RFOOT_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_RFOOT_YROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_R_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_R_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_R_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_R_XROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_R_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_R_YROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_R_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_R_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_R_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_R_XROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_R_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_R_YROTATION]; + result[MOCAPNET_OUTPUT_LHIP_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LHIP_ZROTATION]; + result[MOCAPNET_OUTPUT_LHIP_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LHIP_XROTATION]; + result[MOCAPNET_OUTPUT_LHIP_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LHIP_YROTATION]; + result[MOCAPNET_OUTPUT_LKNEE_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LKNEE_ZROTATION]; + result[MOCAPNET_OUTPUT_LKNEE_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LKNEE_XROTATION]; + result[MOCAPNET_OUTPUT_LKNEE_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LKNEE_YROTATION]; + result[MOCAPNET_OUTPUT_LFOOT_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LFOOT_ZROTATION]; + result[MOCAPNET_OUTPUT_LFOOT_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LFOOT_XROTATION]; + result[MOCAPNET_OUTPUT_LFOOT_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_LFOOT_YROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_L_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_L_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_L_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_L_XROTATION]; + result[MOCAPNET_OUTPUT_TOE1_2_L_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE1_2_L_YROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_L_ZROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_L_ZROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_L_XROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_L_XROTATION]; + result[MOCAPNET_OUTPUT_TOE5_3_L_YROTATION]=resultBody[MOCAPNET_LOWERBODY_OUTPUT_TOE5_3_L_YROTATION]; + return 1; + } else + { + fprintf(stderr,YELLOW "MocapNET: Execution yielded %lu items ( expected %u ) for lower body don't know what to do with them.. \n" NORMAL,resultBody.size(),MOCAPNET_LOWERBODY_OUTPUT_NUMBER); + } + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/lowerBody.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/lowerBody.hpp new file mode 100644 index 0000000..794e605 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/lowerBody.hpp @@ -0,0 +1,53 @@ +#pragma once +/** @file lowerBody.hpp + * @brief Code that handles getting 2D joints packed in their skeletonSerialized and vector formats and can run the tensorflow code retrieving a 3D BVH motion frame that estimates the human lower body + * @author Ammar Qammaz (AmmarkoV) + */ + +#include "../mocapnet2.hpp" + +/** + * @brief Since the C/C++ code in this repository depends on a seperate python trainer, and the networks can differ depending on training parameters (and this is a research project) the series of 2D joints + * can often change. It is also very expensive to do string matching on every frame, so before evaluating any input this call has to be executed in order to perform the correct array associations and from then on + * we can pass 2D data without searching for labels on each frame. It needs to be called before any mocapnetLowerBody_evaluateInput call + * @param MocapNET instance + * @param a skeletonSerialized structure that holds our 2D input + * @retval 1=Success/0=Failure + */ +int mocapnetLowerBody_initializeAssociations(struct MocapNET2 * mnet, struct skeletonSerialized * input); + +/** + * @brief This call loads and initializes the required Tensorflow models for the specific configuration requested, it needs to be called before any mocapnetLowerBody_evaluateInput call + * @param MocapNET instance + * @param This parameter is currently omitted + * @param MocapNET 1 supported multiple quality settings, however for MocapNET2 you should default to 1.0 + * @param MocapNET 1 used 3 ensembles, MocapNET 2 uses 5 ensembles, so mode should default to 5 + * @param The network can be executed on the GPU or the CPU, if you supply 1 you will force CPU execution, if not MocapNET will try to run it on your GPU ( if tensorflow finds it ) + * @retval 1=Success/0=Failure + */ +int mocapnetLowerBody_initialize(struct MocapNET2 * mnet,const char * filename,float qualitySetting,unsigned int mode,unsigned int forceCPU); + +/** + * @brief This call deallocates the tensorflow models + * @param MocapNET instance + * @retval 1=Success/0=Failure + */ +int mocapnetLowerBody_unload(struct MocapNET2 * mnet); + +/** + * @brief This call inserts the 3D pose extracted from tensorflow into the final resulting BVH vector + * @param The final BVH vector that we want to populate with lower body data + * @param The lower body result + * @retval 1=Success/0=Failure + */ +int mocapnetLowerBody_fillResultVector(std::vector &finalResultVector,std::vector resultBody); + +/** + * @brief This call converts 2D input that is formatted on a skeletonSerialized structure to a 3D pose vector. You need to call mocapnetLowerBody_initializeAssociations and mocapnetLowerBody_initialize before calling this function + * and if you want to convert the output result to the final result vector you need to use the mocapnetLowerBody_fillResultVector call. + * @param MocapNET instance + * @param a skeletonSerialized structure that holds our 2D input + * correct ensemble for the perceived orientation. + * @retval 3D pose output that needs to be processed through mocapnetLowerBody_fillResultVector to fill the final BVH buffer + */ +std::vector mocapnetLowerBody_evaluateInput(struct MocapNET2 * mnet,struct skeletonSerialized * input); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/upperBody.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/upperBody.cpp new file mode 100644 index 0000000..5e444bb --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/upperBody.cpp @@ -0,0 +1,404 @@ +#include "upperBody.hpp" + +#include "../../MocapNETLib2/config.h" +#include "../../MocapNETLib2/IO/conversions.hpp" +#include "../../MocapNETLib2/core/core.hpp" +#include "../../MocapNETLib2/tools.hpp" +#include "../tools.hpp" + +#include "../visualization/visualization.hpp" //debug alignment + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +int mocapnetUpperBody_initializeAssociations(struct MocapNET2 * mnet, struct skeletonSerialized * input) +{ + return initializeAssociationsForSubsetOfSkeleton( + &mnet->upperBodySelectionIndexesArePopulated, + mnet->upperBodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_upperbody, + mocapNET_upperbody, + input + ); +} + + + +int mocapnetUpperBody_initialize(struct MocapNET2 * mnet,const char * filename,float qualitySetting,unsigned int mode,unsigned int forceCPU) +{ + char modelPath[1025]= {0}; + int result = 0; + + int modelNumber=0; + switch (mode) + { + case 3: + mnet->upperBody.mode=3; + fprintf(stderr,RED "Fatal: Mode 3 No longer supported on MocapNET 2 \n" NORMAL); + exit(0); + break; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + case 5: + mnet->upperBody.mode=5; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + // Regular 3 Model setup such as the BMVC 2019 Work.. + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/upperbody_front.pb",mode,qualitySetting); + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/categorize_upperbody_all.pb",mode,qualitySetting); + if (fileExists(modelPath)) + { + //result += loadTensorflowInstance(&mnet->upperBody.models[modelNumber],modelPath,"input_front","result_front/concat",forceCPU); + result += loadTensorflowInstance(&mnet->upperBody.models[modelNumber],modelPath,"input_all","result_all/concat",forceCPU); + mnet->upperBody.modelLimits[modelNumber].isFlipped=0; + mnet->upperBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->upperBody.modelLimits[modelNumber].minimumYaw1=-360.0; + mnet->upperBody.modelLimits[modelNumber].maximumYaw1=360.0; + modelNumber+=1; + } + else + { + fprintf(stderr,YELLOW "Could not find a pose categorization classifier for UpperBody..\n " NORMAL ); + } + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/upperbody_front.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->upperBody.models[modelNumber],modelPath,"input_front","result_front/concat",forceCPU); + mnet->upperBody.modelLimits[modelNumber].isFlipped=0; + mnet->upperBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->upperBody.modelLimits[modelNumber].minimumYaw1=-45.0; + mnet->upperBody.modelLimits[modelNumber].maximumYaw1=45.0; + modelNumber+=1; + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/upperbody_back.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->upperBody.models[modelNumber],modelPath,"input_back","result_back/concat",forceCPU); + mnet->upperBody.modelLimits[modelNumber].isFlipped=0; + mnet->upperBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->upperBody.modelLimits[modelNumber].minimumYaw1=135.0;//-90.0; + mnet->upperBody.modelLimits[modelNumber].maximumYaw1=225.0;//-270.0; + modelNumber+=1; + + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/upperbody_left.pb",mode,qualitySetting); + result += loadTensorflowInstance(&mnet->upperBody.models[modelNumber],modelPath,"input_left","result_left/concat",forceCPU); + mnet->upperBody.modelLimits[modelNumber].isFlipped=0; + mnet->upperBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->upperBody.modelLimits[modelNumber].minimumYaw1=-135.0; + mnet->upperBody.modelLimits[modelNumber].maximumYaw1=45.0; + modelNumber+=1; + + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + snprintf(modelPath,1024,"dataset/combinedModel/mocapnet2/mode%u/%0.1f/upperbody_right.pb",mode,qualitySetting);// + result += loadTensorflowInstance(&mnet->upperBody.models[modelNumber],modelPath,"input_right","result_right/concat",forceCPU); + mnet->upperBody.modelLimits[modelNumber].isFlipped=0; + mnet->upperBody.modelLimits[modelNumber].numberOfLimits=1; + mnet->upperBody.modelLimits[modelNumber].minimumYaw1=45.0; + mnet->upperBody.modelLimits[modelNumber].maximumYaw1=135.0; + modelNumber+=1; + + + + if(result==modelNumber) // make this 5 + { + result=1; + mnet->upperBody.loadedModels=modelNumber; //make this 5 + } + else + { + result=0; + } + break; + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + //-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + + + default: + fprintf(stderr,RED "You requested a MocapNET configuration that is incorrect ( mode=%u )\n" NORMAL, mode); + return 0; + break; + }; + + if (result) + { + fprintf(stderr,"Caching networks after initialization to be ready for use..\n"); + std::vector emptyValues; + for (int i=0; iupperBody.loadedModels; i++ ) + { + std::vector prediction = predictTensorflow(&mnet->upperBody.models[i],emptyValues); + if (prediction.size()>0) + { + fprintf(stderr, GREEN "Caching model %u (%s) was successful\n" NORMAL,i,mnet->upperBody.models[i].modelPath); + } + else + { + fprintf(stderr,RED "Caching model %u (%s) was unsuccessful\n" NORMAL,i,mnet->upperBody.models[i].modelPath); + } + } + + } + + return result; +} + + + +int mocapnetUpperBody_unload(struct MocapNET2 * mnet) +{ + unsigned int result=0; + for (int i=0; iupperBody.loadedModels; i++ ) + { + result+=unloadTensorflow(&mnet->upperBody.models[i]); + } + + if (result==mnet->upperBody.loadedModels) + { + result=1; + } + else + { + result=0; + } + + mnet->upperBody.loadedModels=0; + + return result; +} + + + +int mocapnetUpperBody_getOrientation(struct MocapNET2 * mnet,struct skeletonSerialized * input) +{ + mnet->upperBody.positionalInput = deriveMocapNET2InputUsingAssociations( + mnet, + input, + &mnet->upperBodySelectionIndexesArePopulated, + mnet->upperBodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_upperbody, + mocapNET_upperbody, + 0 // Enable to debug input + ); + + if (mnet->upperBody.perform2DAlignmentBeforeEvaluation) + { + unsigned int pivotJoint = 0; //Hip + unsigned int referenceJoint = 1; //Neck + + //std::vector original2DPoints = mnet->upperBody.positionalInput; + //rotate2DPointsBasedOnJointAsCenter(mnet->upperBody.positionalInput,20,0); + + //Force alignment of middle finger to make it easier on the neural network mnet->upperBody.positionalInput + + float angleToRotate = getAngleToAlignToZero(mnet->upperBody.positionalInput,pivotJoint,referenceJoint); + fprintf(stderr,YELLOW "Correcting upperbody skeleton by rotating it %0.2f degrees\n" NORMAL,angleToRotate); + rotate2DPointsBasedOnJointAsCenter(mnet->upperBody.positionalInput,angleToRotate,0); + + //debug2DPointAlignment("debugUpperAlignment",original2DPoints,mnet->upperBody.positionalInput,pivotJoint,referenceJoint,800,600); + } + + + mnet->upperBody.NSDM = upperbodyCreateNDSM(mnet->upperBody.positionalInput,0/*NSDM Positional*/,1/*NSDM Angular */,0/*Do Scale Compensation*/); + + mnet->upperBody.neuralNetworkReadyInput.clear(); + mnet->upperBody.neuralNetworkReadyInput.insert(mnet->upperBody.neuralNetworkReadyInput.end(),mnet->upperBody.positionalInput.begin(), mnet->upperBody.positionalInput.end()); + mnet->upperBody.neuralNetworkReadyInput.insert(mnet->upperBody.neuralNetworkReadyInput.end(),mnet->upperBody.NSDM.begin(), mnet->upperBody.NSDM.end()); + //---------------------------------------------------------------------------------------------- + //The code block above should have done everything needed to make a 749 element vector + //If input is not correct then we should stop right here + //---------------------------------------------------------------------------------------------- + if (mnet->upperBody.neuralNetworkReadyInput.size()!=MNET_UPPERBODY_IN_NUMBER) + { + fprintf(stderr,RED "MocapNET: Incorrect size of MocapNET input .. \n" NORMAL); + return 0; + } + + int orientationReceived = MOCAPNET_ORIENTATION_NONE; + + if (mnet->options->forceFront) + { + mnet->orientation = MOCAPNET_ORIENTATION_FRONT; + } else + if (mnet->options->forceLeft) + { + mnet->orientation = MOCAPNET_ORIENTATION_LEFT; + } else + if (mnet->options->forceBack) + { + mnet->orientation = MOCAPNET_ORIENTATION_BACK; + } else + if (mnet->options->forceRight) + { + mnet->orientation = MOCAPNET_ORIENTATION_RIGHT; + } else + { + orientationReceived = localOrientationExtraction(&mnet->upperBody,mnet->upperBody.neuralNetworkReadyInput); + if (orientationReceived!=MOCAPNET_ORIENTATION_NONE) + { + mnet->orientation=orientationReceived; + + mnet->orientationClassifications[0]= mnet->upperBody.orientationClassifications[0]; + mnet->orientationClassifications[1]= mnet->upperBody.orientationClassifications[1]; + mnet->orientationClassifications[2]= mnet->upperBody.orientationClassifications[2]; + mnet->orientationClassifications[3]= mnet->upperBody.orientationClassifications[3]; + } else + { + fprintf(stderr,RED "MocapNET: Incorrect orientation retrieved from upperbody .. \n" NORMAL); + } + } + + return orientationReceived; +} + + +std::vector mocapnetUpperBody_evaluateInput(struct MocapNET2 * mnet,struct skeletonSerialized * input) +{ + mnet->upperBody.positionalInput = deriveMocapNET2InputUsingAssociations( + mnet, + input, + &mnet->upperBodySelectionIndexesArePopulated, + mnet->upperBodySelectionIndexes, + mocapNET_InputLength_WithoutNSDM_upperbody, + mocapNET_upperbody, + 0 // Enable to debug input + ); + + if (mnet->upperBody.perform2DAlignmentBeforeEvaluation) + { + unsigned int pivotJoint = 0; //Hip + unsigned int referenceJoint = 1; //Neck + + //std::vector original2DPoints = mnet->upperBody.positionalInput; + //rotate2DPointsBasedOnJointAsCenter(mnet->upperBody.positionalInput,20,0); + + //Force alignment of middle finger to make it easier on the neural network mnet->upperBody.positionalInput + + float angleToRotate = getAngleToAlignToZero(mnet->upperBody.positionalInput,pivotJoint,referenceJoint); + fprintf(stderr,YELLOW "Correcting upperbody skeleton by rotating it %0.2f degrees\n" NORMAL,angleToRotate); + rotate2DPointsBasedOnJointAsCenter(mnet->upperBody.positionalInput,angleToRotate,0); + + //debug2DPointAlignment("debugUpperAlignment",original2DPoints,mnet->upperBody.positionalInput,pivotJoint,referenceJoint,800,600); + } + + mnet->upperBody.NSDM = upperbodyCreateNDSM(mnet->upperBody.positionalInput,0/*NSDM Positional*/,1/*NSDM Angular */,0/*Do Scale Compensation*/); + mnet->upperBody.neuralNetworkReadyInput.clear(); + mnet->upperBody.neuralNetworkReadyInput.insert(mnet->upperBody.neuralNetworkReadyInput.end(),mnet->upperBody.positionalInput.begin(), mnet->upperBody.positionalInput.end()); + mnet->upperBody.neuralNetworkReadyInput.insert(mnet->upperBody.neuralNetworkReadyInput.end(),mnet->upperBody.NSDM.begin(), mnet->upperBody.NSDM.end()); + //---------------------------------------------------------------------------------------------- + //The code block above should have done everything needed to make a 749 element vector + //If input is not correct then we should stop right here + //---------------------------------------------------------------------------------------------- + if (mnet->upperBody.neuralNetworkReadyInput.size()!=MNET_UPPERBODY_IN_NUMBER) + { + fprintf(stderr,RED "MocapNET: Incorrect size of MocapNET input .. \n" NORMAL); + std::vector emptyResult; + return emptyResult; + } + + if ( + vectorcmp( + mnet->upperBody.lastNeuralNetworkReadyInput, + mnet->upperBody.neuralNetworkReadyInput, + 0.1 + )!=0 + ) + { + //First run the network that can predict the orientation of the skeleton so we will use the correct + //network to get the full BVH result from it + //---------------------------------------------------------------------------------------------- + std::vector result = localExecution( + &mnet->upperBody, + mnet->upperBody.neuralNetworkReadyInput, + mnet->orientation, + NN_ORIENTATIONS_TRAINED_AROUND_ZERO_AND_REQUIRE_TRICK /*Body Requires orientation trick*/ + ); + + mnet->upperBody.result = result; + mnet->upperBody.lastNeuralNetworkReadyInput = mnet->upperBody.neuralNetworkReadyInput; + + return result; + } else + { + fprintf(stderr,GREEN "Nothing changed on upper body, returning previous result.. \n" NORMAL); + return mnet->upperBody.result; + } +} + + +int mocapnetUpperBody_fillResultVector(std::vector &result,std::vector resultUpperBody) +{ + if (resultUpperBody.size()==MOCAPNET_UPPERBODY_OUTPUT_NUMBER) + { + //This is output that targets the old body only armature..! + //---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + result[MOCAPNET_OUTPUT_HIP_XPOSITION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HIP_XPOSITION]; + result[MOCAPNET_OUTPUT_HIP_YPOSITION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HIP_YPOSITION]; + result[MOCAPNET_OUTPUT_HIP_ZPOSITION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HIP_ZPOSITION]; + result[MOCAPNET_OUTPUT_HIP_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HIP_ZROTATION]; + result[MOCAPNET_OUTPUT_HIP_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HIP_YROTATION]; + result[MOCAPNET_OUTPUT_HIP_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HIP_XROTATION]; + //Abdomen/Chest encoders seem to be wrong, we overwrite them.. + //---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + result[MOCAPNET_OUTPUT_ABDOMEN_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_ZROTATION]; + result[MOCAPNET_OUTPUT_ABDOMEN_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_XROTATION]; + result[MOCAPNET_OUTPUT_ABDOMEN_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_YROTATION]; + result[MOCAPNET_OUTPUT_ABDOMEN_ZROTATION]=(float) 0.0; + result[MOCAPNET_OUTPUT_ABDOMEN_XROTATION]=(float) 0.0; + result[MOCAPNET_OUTPUT_ABDOMEN_YROTATION]=(float) 0.0; + result[MOCAPNET_OUTPUT_CHEST_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_CHEST_ZROTATION]; + result[MOCAPNET_OUTPUT_CHEST_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_CHEST_XROTATION]; + result[MOCAPNET_OUTPUT_CHEST_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_CHEST_YROTATION]; + result[MOCAPNET_OUTPUT_CHEST_ZROTATION]=(float) 0.0; + result[MOCAPNET_OUTPUT_CHEST_XROTATION]=(float) 0.0; + result[MOCAPNET_OUTPUT_CHEST_YROTATION]=(float) 0.0; + //---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- + result[MOCAPNET_OUTPUT_NECK_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_NECK_ZROTATION]; + result[MOCAPNET_OUTPUT_NECK_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_NECK_XROTATION]; + result[MOCAPNET_OUTPUT_NECK_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_NECK_YROTATION]; + result[MOCAPNET_OUTPUT_HEAD_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HEAD_ZROTATION]; + result[MOCAPNET_OUTPUT_HEAD_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HEAD_XROTATION]; + result[MOCAPNET_OUTPUT_HEAD_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_HEAD_YROTATION]; + result[MOCAPNET_OUTPUT_EYE_L_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_EYE_L_ZROTATION]; + result[MOCAPNET_OUTPUT_EYE_L_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_EYE_L_XROTATION]; + result[MOCAPNET_OUTPUT_EYE_L_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_EYE_L_YROTATION]; + result[MOCAPNET_OUTPUT_EYE_R_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_EYE_R_ZROTATION]; + result[MOCAPNET_OUTPUT_EYE_R_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_EYE_R_XROTATION]; + result[MOCAPNET_OUTPUT_EYE_R_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_EYE_R_YROTATION]; + result[MOCAPNET_OUTPUT_RSHOULDER_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_ZROTATION]; + result[MOCAPNET_OUTPUT_RSHOULDER_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_XROTATION]; + result[MOCAPNET_OUTPUT_RSHOULDER_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_YROTATION]; + result[MOCAPNET_OUTPUT_RELBOW_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RELBOW_ZROTATION]; + result[MOCAPNET_OUTPUT_RELBOW_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RELBOW_XROTATION]; + result[MOCAPNET_OUTPUT_RELBOW_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RELBOW_YROTATION]; + result[MOCAPNET_OUTPUT_RHAND_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RHAND_ZROTATION]; + result[MOCAPNET_OUTPUT_RHAND_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RHAND_XROTATION]; + result[MOCAPNET_OUTPUT_RHAND_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_RHAND_YROTATION]; + result[MOCAPNET_OUTPUT_LSHOULDER_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_ZROTATION]; + result[MOCAPNET_OUTPUT_LSHOULDER_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_XROTATION]; + result[MOCAPNET_OUTPUT_LSHOULDER_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_YROTATION]; + result[MOCAPNET_OUTPUT_LELBOW_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LELBOW_ZROTATION]; + result[MOCAPNET_OUTPUT_LELBOW_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LELBOW_XROTATION]; + result[MOCAPNET_OUTPUT_LELBOW_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LELBOW_YROTATION]; + result[MOCAPNET_OUTPUT_LHAND_ZROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LHAND_ZROTATION]; + result[MOCAPNET_OUTPUT_LHAND_XROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LHAND_XROTATION]; + result[MOCAPNET_OUTPUT_LHAND_YROTATION]=resultUpperBody[MOCAPNET_UPPERBODY_OUTPUT_LHAND_YROTATION]; + return 1; + } + else + { + fprintf(stderr,YELLOW "MocapNET: Execution yielded %lu items ( expected %u ) for upper body don't know what to do with them.. \n" NORMAL,resultUpperBody.size(),MOCAPNET_UPPERBODY_OUTPUT_NUMBER); + } + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/upperBody.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/upperBody.hpp new file mode 100644 index 0000000..c89da83 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/solutionParts/upperBody.hpp @@ -0,0 +1,58 @@ +#pragma once +/** @file upperBody.hpp + * @brief Code that handles getting 2D joints packed in their skeletonSerialized and vector formats and can run the tensorflow code retrieving a 3D BVH motion frame that estimates the human upper body + * @author Ammar Qammaz (AmmarkoV) + */ + +#include "../mocapnet2.hpp" + +/** + * @brief Since the C/C++ code in this repository depends on a seperate python trainer, and the networks can differ depending on training parameters (and this is a research project) the series of 2D joints + * can often change. It is also very expensive to do string matching on every frame, so before evaluating any input this call has to be executed in order to perform the correct array associations and from then on + * we can pass 2D data without searching for labels on each frame. It needs to be called before any mocapnetUpperBody_evaluateInput call + * @param MocapNET instance + * @param a skeletonSerialized structure that holds our 2D input + * @retval 1=Success/0=Failure + */ +int mocapnetUpperBody_initializeAssociations(struct MocapNET2 * mnet, struct skeletonSerialized * input); + +/** + * @brief This call loads and initializes the required Tensorflow models for the specific configuration requested, it needs to be called before any mocapnetUpperBody_evaluateInput call + * @param MocapNET instance + * @param This parameter is currently omitted + * @param MocapNET 1 supported multiple quality settings, however for MocapNET2 you should default to 1.0 + * @param MocapNET 1 used 3 ensembles, MocapNET 2 uses 5 ensembles, so mode should default to 5 + * @param The network can be executed on the GPU or the CPU, if you supply 1 you will force CPU execution, if not MocapNET will try to run it on your GPU ( if tensorflow finds it ) + * @retval 1=Success/0=Failure + */ +int mocapnetUpperBody_initialize(struct MocapNET2 * mnet,const char * filename,float qualitySetting,unsigned int mode,unsigned int forceCPU); + +/** + * @brief This call deallocates the tensorflow models + * @param MocapNET instance + * @retval 1=Success/0=Failure + */ +int mocapnetUpperBody_unload(struct MocapNET2 * mnet); + +/** + * @brief This call inserts the 3D pose extracted from tensorflow into the final resulting BVH vector + * @param The final BVH vector that we want to populate with upper body data + * @param The upper body result + * @retval 1=Success/0=Failure + */ +int mocapnetUpperBody_fillResultVector(std::vector &finalResultVector,std::vector resultBody); + + + +int mocapnetUpperBody_getOrientation(struct MocapNET2 * mnet,struct skeletonSerialized * input); + +/** + * @brief This call converts 2D input that is formatted on a skeletonSerialized structure to a 3D pose vector. You need to call mocapnetUpperBody_initializeAssociations and mocapnetUpperBody_initialize before calling this function + * and if you want to convert the output result to the final result vector you need to use the mocapnetUpperBody_fillResultVector call. + * @param MocapNET instance + * @param a skeletonSerialized structure that holds our 2D input + * @param If this flag is set to 1 then the 2D input will be treated as front facing, otherwise the orientation classifier will decide on the orientation and call the + * correct ensemble for the perceived orientation. + * @retval 3D pose output that needs to be processed through mocapnetUpperBody_fillResultVector to fill the final BVH buffer + */ +std::vector mocapnetUpperBody_evaluateInput(struct MocapNET2 * mnet,struct skeletonSerialized * input); diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/tools.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/tools.cpp new file mode 100644 index 0000000..b993391 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/tools.cpp @@ -0,0 +1,498 @@ +#include "tools.hpp" + +#include +#include +#include +#include +#include +#include +#include //tolower +#include +#include + + +unsigned long tickBaseMN = 0; + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int vectorcmp(std::vector vA,std::vector vB, float maximumDistance) +{ + //fprintf(stderr,"vectorcmp %u vs %u elements \n",vA.size(), vB.size()); + if (vA.size()>vB.size()) { return -1; } + else + if (vA.size()maximumDistance) { return -1; } + } else + if (vA[i]>vB[i]) + { + if (vA[i]-vB[i]>maximumDistance) { return 1; } + } + } + } + return 0; +} + + +int nsleep(long nanoseconds) +{ + struct timespec req, rem; + + req.tv_sec = 0; + req.tv_nsec = nanoseconds; + + return nanosleep(&req , &rem); +} + + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +float getJoint2DDistance_tools(float aX,float aY,float bX,float bY) +{ + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return (float) sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + + +float getAngleToAlignToZero_tools(float aX,float aY,float bX,float bY) +{ + if ( (aX==bX) && (aY==bY) ) { return 0; } + + + //Bigger magnitudes.. + aX=100*aX; + aY=100*aY; + bX=100*bX; + bY=100*bY; + + //We have points a, b and c and we want to calculate angle b + float lengthBetweenAAndB = getJoint2DDistance_tools(aX,aY,bX,bY); + + + //We align vertically.. , Point C is B offset in Y direction + float cX = bX; + float cY = bY - lengthBetweenAAndB; + + //fprintf(stderr,"We want to align A(%0.2f,%0.2f) to C(%0.2f,%0.2f) with pivot B(%0.2f,%0.2f)\n",aX,aY,cX,cY,bX,bY); + //fprintf(stderr,"length AB = %0.2f\n",lengthBetweenAAndB); + //fprintf(stderr,"bY = %0.2f\n",bY); + //fprintf(stderr,"cY = %0.2f = %0.2f - %0.2f\n",cY,bY,lengthBetweenAAndB); + + + //Calulate vector a->b + float abX = bX - aX; + float abY = bY - aY; + + //calculate vector c->b + float cbX = bX - cX; + float cbY = bY - cY; + + + float dot = (abX * cbX + abY * cbY); // dot product + float cross = (abX * cbY - abY * cbX); // cross product + + float alpha = atan2(cross, dot); + + //fprintf(stderr,"Angle is %0.2f rad or %0.2f degrees \n",alpha,alpha*goFromRadToDegrees); + return (float) alpha;// * goFromRadToDegrees ; +} + + + +float getAngleToAlignToZero(std::vector &positions,unsigned int centerJoint,unsigned int referenceJoint) +{ + //We have points a, b and c and we want to calculate angle b + float aX= positions[referenceJoint*3+0]; + float aY= positions[referenceJoint*3+1]; + + float bX= positions[centerJoint*3+0]; + float bY= positions[centerJoint*3+1]; + + return getAngleToAlignToZero_tools(aX,aY,bX,bY); +} + + + +int rotate2DPointsBasedOnJointAsCenter(std::vector &positions,float angle,unsigned int centerJoint) +{ + if (positions.size()%3!=0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: incorrect positions.. \n" NORMAL); + return 0; + } + + if (positions.size()<=centerJoint*3) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: centerJoint out of bounds.. \n" NORMAL); + return 0; + } + + float s = sin((float) angle * goFromDegreesToRad ); + float c = cos((float) angle * goFromDegreesToRad ); + + float cx=positions[centerJoint*3+0]; + float cy=positions[centerJoint*3+1]; + float cVisibility=positions[centerJoint*3+2]; + + if (cVisibility==0.0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: .. \n" NORMAL); + return 0; + } + + for (unsigned int jID=0; jID ",jX,jY,cx,cy,angle); + + // translate point back to origin: + jX -= cx; + jY -= cy; + + // rotate point + float xnew = jX * c - jY * s; + float ynew = jX * s + jY * c; + + // translate point back: + positions[jID*3+0] = xnew + cx; + positions[jID*3+1] = ynew + cy; + + //fprintf(stderr,"%0.2f,%0.2f\n",positions[jID*3+0],positions[jID*3+1]); + } + + + return 1; +} + + + +void normalize2DPointWhileAlsoMatchingTrainingAspectRatio( + float * x, + float * y, + unsigned int currentWidth, + unsigned int currentHeight, + unsigned int respectTrainingAspectRatio, + unsigned int trainingWidth, + unsigned int trainingHeight +) +{ + if ( (*x==0) && (*y==0) ) + { + //Only Fix Aspect Ratio on visible points to preserve 0,0,0 that are + //important to the neural network.. + return; + } + + if (!respectTrainingAspectRatio) + { + *x=(float) *x/currentWidth; + *y=(float) *y/currentHeight; + return; + } + + //fprintf(stderr,YELLOW "normalizeWhileAlsoMatchingTrainingAspectRatio\n" NORMAL); + unsigned int addX=0,addY=0; + unsigned int targetWidth=currentWidth,targetHeight=currentHeight; + float currentAspectRatio = (float) currentWidth/currentHeight; + float trainingAspectRatio = (float) trainingWidth/trainingHeight; + + //fprintf(stderr,"Will try to correct aspect ratio from %0.2f(%ux%u) to %0.2f (%ux%u)\n",currentAspectRatio,currentWidth,currentHeight,trainingAspectRatio,trainingWidth,trainingHeight); + + + if (currentHeight=currentHeight) + { + addY=(unsigned int) (targetHeight-currentHeight)/2; + } + else + { + //Turns out we will have to enlarge X instead of englarging Y + addY=0; + targetHeight=currentHeight; + targetWidth=(unsigned int)currentHeight*trainingAspectRatio; + addX=(unsigned int) (targetWidth-currentWidth)/2; + } + } + else if (currentWidth<=currentHeight) + { + targetWidth=(unsigned int)currentHeight*trainingAspectRatio; + if (targetWidth>=currentWidth) + { + addX=(unsigned int) (targetWidth-currentWidth)/2; + } + else + { + //Turns out we will have to enlarge Y instead of englarging X + addX=0; + targetWidth=currentWidth; + targetHeight=(unsigned int) currentWidth/trainingAspectRatio; + addY=(unsigned int) (targetHeight-currentHeight)/2; + } + } + + //fprintf(stderr,"Target resolution is %ux%u to Y\n",targetWidth,targetHeight); + //fprintf(stderr,"Will add %u to X and %u to Y to achieve it\n",addX,addY); + + float targetAspectRatio=(float) targetWidth/targetHeight; + if ((unsigned int) targetAspectRatio/100!= (unsigned int) trainingAspectRatio/100) + { + fprintf(stderr,"Failed to perfectly match training aspect ratio (%0.5f), managed to reach (%0.5f)\n",trainingAspectRatio,targetAspectRatio); + } + + + *x=(float) (*x+addX)/targetWidth; + *y=(float) (*y+addY)/targetHeight; + +} + + +int convertStringToLowercase(char * target,unsigned int targetLength,const char * source) +{ + if (source==0) + { + return 0; + } + unsigned int sourceLength = strlen(source); + + if (targetLength<=sourceLength) + { + return 0; + } + + for (int i=0; i mocapNETInput) +{ + unsigned int numberOfZeros=0; + + for (int i=0; i +#include + +const float goFromRadToDegrees=(float) 180.0 / M_PI; +const float goFromDegreesToRad=(float) M_PI / 180.0; + + +int vectorcmp(std::vector vA,std::vector vB, float maximumDistance); + +int nsleep(long nanoseconds); + +/** + * @brief Get euclidean 2D distance between a pair of points + * @ingroup tools + * @param Point A, X coordinate + * @param Point A, Y coordinate + * @param Point B, X coordinate + * @param Point B, Y coordinate + * @retval Output rotation value + */ + float getJoint2DDistance_tools(float aX,float aY,float bX,float bY); + +/** + * @brief This is a very crucial function that converts a pair of 2D points to an NSRM rotation + * @ingroup tools + * @param Point A, X coordinate + * @param Point A, Y coordinate + * @param Point B, X coordinate + * @param Point B, Y coordinate + * @retval Output rotation value angle in rads + */ +float getAngleToAlignToZero_tools(float aX,float aY,float bX,float bY) ; + +/** + * @brief This is a wrapper for the getAngleToAlignToZero_tools function that applies it in a vector of positions while also optionally centering everything using a reference joint. + * @ingroup tools + * @param Vector of 2D points + * @param Index to the 2D point to be treated as alignment center + * @param Index to reference joint + * @retval Output rotation value angle in rads + */ +float getAngleToAlignToZero(std::vector &positions,unsigned int centerJoint,unsigned int referenceJoint); + + +/** + * @brief This call aligns 2D points based on a single point that will be used as a pivot. + * @ingroup tools + * @param Vector of 2D points + * @param Angle to rotate all points at + * @param Index to the 2D point to be treated as alignment center + * @retval 1=Success/0=Failure + */ +int rotate2DPointsBasedOnJointAsCenter(std::vector &positions,float angle,unsigned int centerJoint); + + +void normalize2DPointWhileAlsoMatchingTrainingAspectRatio( + float * x, + float * y, + unsigned int currentWidth, + unsigned int currentHeight, + unsigned int respectTrainingAspectRatio, + unsigned int trainingWidth, + unsigned int trainingHeight + ); + +int convertStringToLowercase( + char * target, + unsigned int targetLength, + const char * source + ); + + +int findFirstFormattedFileInDirectory( + const char * path, + const char * formatString, + const char * label, + unsigned int * frameIDOutput + ); + +/** + * @brief Count the number of empty ( zero ) elements in a vector + * @ingroup tools + * @param Vector + * @retval Number of zeros encountered + */ +unsigned int getNumberOfEmptyElements(std::vector mocapNETInput); + +/** + * @brief Execute a shell command + * @ingroup tools + * @param CString to execute + * @param Pointer to array that will contain our result + * @param Size of array that will contain our result + * @param If we have more than one lines of output which one we want? + * @bug This function executes a string in the shell and this means that it is dangerous + * @retval 1 = Success loading the files , 0 = Failure + */ +int executeCommandLineNum( + const char * command , + char * what2GetBack , + unsigned int what2GetBackMaxSize, + unsigned int lineNumber + ); + +/** + * @brief Get the CPU name + * @param CString where the CPU name should be stored + * @param Size of array that will contain our result + * @ingroup tools + * @retval 1=Success/0=Failure + */ +int getCPUName(char * str,unsigned int maxLength); + +/** + * @brief Get the GPU name + * @param CString where the GPU name should be stored + * @param Size of array that will contain our result + * @ingroup tools + * @retval 1=Success/0=Failure + */ +int getGPUName(char * str,unsigned int maxLength); + +/** + * @brief Get uptime in microseconds + * @ingroup tools + * @retval Microseconds of system uptime + */ +unsigned long GetTickCountMicrosecondsMN(); + +/** + * @brief Get uptime in milliseconds + * @ingroup tools + * @retval Milliseconds of system uptime + */ +unsigned long GetTickCountMillisecondsMN(); + +/** + * @brief Check if a directory exists on our filesystem + * @ingroup tools + * @retval 1=Exists/0=Doesnt Exist + */ +char directoryExists(const char * folder); + +/** + * @brief Check if a filename exists on our filesystem + * @ingroup tools + * @retval 1=Exists/0=Doesnt Exist + */ +char fileExists(const char * filename); + +/** + * @brief Convert start and end time to a framerate ( frames per second ) + * @ingroup demo + * @retval Will return a framerate from two millisecond timestamps, if no time duration has been passed there is no division by zero. + */ +float convertStartEndTimeFromMicrosecondsToFPS(unsigned long startTime, unsigned long endTime); + +/** + * @brief Get the dimensions of an image by relying on the identify tool + * @ingroup tools + * @param CString with path to the image file to get dimensions for + * @param Pointer to an unsigned int that will hold the width of the image file we specified + * @param Pointer to an unsigned int that will hold the height of the image file we specified + * @retval 1=Success/0=Failure + */ +int getImageWidthHeight( + const char * filename, + unsigned int * width , + unsigned int * height + ); + + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/allInOne.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/allInOne.cpp new file mode 100644 index 0000000..a376e35 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/allInOne.cpp @@ -0,0 +1,382 @@ +#include "allInOne.hpp" +#include "visualization.hpp" +#include "widgets.hpp" +#include "drawSkeleton.hpp" +#include "opengl.hpp" + + +#include "../IO/jsonRead.hpp" +#include "../IO/bvh.hpp" +#include "../tools.hpp" +#include "../mocapnet2.hpp" + + +std::vector frameRateHistoryTotal; +std::vector frameRateIKHistory; +std::vector frameRateMNET2History; + + +void appendToHistory(std::vector & frameRateHistory, float newValue, unsigned int maximumValues) +{ + //Keep framerate history + frameRateHistory.push_back(newValue); + if (frameRateHistory.size()>maximumValues) + { + frameRateHistory.erase(frameRateHistory.begin()); + } +} + + +#if USE_OPENCV +cv::Mat openGLAllInOneFramePermanentMat; + + +cv::Mat offsetImageWithPadding(const Mat& originalImage, int offsetX, int offsetY, Scalar backgroundColour) +{ + cv::Mat padded = Mat(originalImage.rows + 2 * abs(offsetY), originalImage.cols + 2 * abs(offsetX), CV_8UC3, backgroundColour); + originalImage.copyTo(padded(Rect(abs(offsetX), abs(offsetY), originalImage.cols, originalImage.rows))); + return Mat(padded,Rect(abs(offsetX) + offsetX, abs(offsetY) + offsetY, originalImage.cols, originalImage.rows)); +} + + +#endif + + +int extraWindow( + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView + ) +{ + //cv::Mat image = cv::Mat(800, 800, CV_8UC3); + cv::Mat image = cv::Mat::zeros(1920, 1080, CV_8UC3); + + //Switch that controls drawing skeleton labels + int draw3DSkeletonJointLabels = 1; + + //This is the 3D skeleton visualized to match the RGB image + //drawSkeleton(image,points2DOutputGUIRealView,-1000,0,draw3DSkeletonJointLabels); + + //This is the 3D skeleton nailed to be visualized to match the RGB image + drawSkeleton(image,points2DOutputGUIForcedView,-800,0,draw3DSkeletonJointLabels); + + cv::imshow("New Visualization",image); + return 1; +} + + + +int visualizeAllInOne( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + struct MocapNET2Options * options, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + unsigned int numberOfMissingJoints +) +{ + +#if USE_OPENCV + int success=0; + char finalFilename[2048]= {0}; + + + //Handle Background + //----------------------------------------------------------------------------------- + if (alreadyLoadedImage==0) + { + char formatString[256]= {0}; + snprintf(formatString,256,"%%s/%%s%%0%uu.jpg",serialLength); + + //colorFrame_0_00001.jpg + snprintf(finalFilename,2048,formatString,path,label,frameNumber/*Frame ID*/); + //snprintf(finalFilename,2048,"%s/colorFrame_0_%05d.jpg",path,frameNumber+1); + } + + int showFramerate=30; //30 or 0 + + float scale=1.0; + cv::Mat image; + cv::Rect roi; + cv::Mat destinationROI; + + if ( (fileExists(finalFilename) ) || (alreadyLoadedImage!=0) ) + { + if (alreadyLoadedImage!=0) + { + image = *alreadyLoadedImage; + + if ( + ( image.size().width < 640 ) || + ( image.size().height < 480 ) + ) + { + fprintf(stderr,"Very small given frame ( %ux%u..\n",image.size().width,image.size().height); + return 0; + } + + //cv::imshow(windowName,image); // + //cv::waitKey(0); + } + else + if (fileExists(finalFilename) ) + { + image = imread(finalFilename,cv::IMREAD_COLOR); // older versions might want the CV_LOAD_IMAGE_COLOR flag + } + //----------------------------------------------------------------------------------- + + + //The skeleton offset controls offset for the 3D skeleton rendering on top of the image + //Offsets are wrong for resolutions other than 1920x1080 since they are hardcoded.. + float offsetX = (float) -1*image.size().width/4; + float offsetY = 0.0;// (float) -1*image.size().height/1080; + + //If you uncomment the next like the 3D overlay will stay exactly on top of the skeleton + //offsetX=0; + + + //--------------------------------------------------------------------- + // OpenGL overlay stuff + //--------------------------------------------------------------------- + cv::Mat * openGLMatForVisualization = 0; + if (options->useOpenGLVisualization) { + //fprintf(stderr,"updateOpenGLView\n"); + updateOpenGLView(mnet->currentSolution); + + //fprintf(stderr,"visualizeOpenGL\n"); + unsigned int openGLFrameWidth=width,openGLFrameHeight=height; + char * openGLFrame = visualizeOpenGL(&openGLFrameWidth,&openGLFrameHeight); + //===================================================================== + if (openGLFrame!=0) + { + fprintf(stderr,"Got Back an OpenGL frame..!\n"); + cv::Mat openGLMat(openGLFrameHeight, openGLFrameWidth, CV_8UC3); + unsigned char * initialPointer = openGLMat.data; + openGLMat.data=(unsigned char * ) openGLFrame; + + cv::cvtColor(openGLMat,openGLAllInOneFramePermanentMat,cv::COLOR_RGB2BGR); + //cv::imshow("OpenGL",openGLAllInOneFramePermanentMat); + openGLMatForVisualization = &openGLAllInOneFramePermanentMat; + openGLMat.data=initialPointer; + } + //===================================================================== + } + + if (openGLMatForVisualization!=0) + { + cv::Mat * glMat = (cv::Mat *) openGLMatForVisualization; + //float alpha=0.3; + //image=cv::max(*glMat,image); + + //example use with black borders along the right hand side and top: + cv::Mat offsetImage = offsetImageWithPadding(*glMat, +offsetX, 0 , Scalar(0,0,0)); + image=overlay(image,offsetImage); + //image=cv::max(offsetImage,image); + //cv::addWeighted(offsetImage,1.0,image,0.8,0.0,image); + //cv::add(offsetImage,image,image); + } + //--------------------------------------------------------------------------------- + + + //This is the 2D visualization sitting on top of the RGB image + //fprintf(stderr,"2D Visualization skeleton(%0.2f,%0.2f) rendered on %ux%u image\n",skeleton->width,skeleton->height,image.size().width,image.size().height); + visualizeSkeletonSerialized( + image, + skeleton, + (mnet->leftHand.loadedModels>0), + (mnet->rightHand.loadedModels>0), + options->doFace, + 0,0, + image.size().width, + image.size().height + ); + + + //Switch that controls drawing skeleton labels + int draw3DSkeletonJointLabels = 0; + + //This is the 3D skeleton visualized to match the RGB image + drawSkeleton(image,points2DOutputGUIRealView,offsetX,offsetY,draw3DSkeletonJointLabels); + + //Uncomment to spawn an extra window with a centered skeleton..! + //extraWindow(points2DOutputGUIRealView,points2DOutputGUIForcedView); + + //If you dont want to see widgets just turn them off using this switch + int showWidgets = 1; + + + if (showWidgets) + { + int borderToScreen = 15; + int NSDMWidth=160; //body leftHand rightHand + int NSDMHeight=160; + + int positionX = image.size().width - NSDMWidth - borderToScreen; + int positionY = 40; + + cv::rectangle(image,cv::Rect(positionX-20,positionY-40,NSDMWidth+40,620+showFramerate),cv::Scalar(0,0,0),-1); + + visualizeNSDM(image,"Upper Body",mnet->upperBody.NSDM,1 /*angles*/,positionX,positionY,NSDMWidth,NSDMHeight); + positionY+=200; + visualizeNSDM(image,"Lower Body",mnet->lowerBody.NSDM,1 /*angles*/,positionX,positionY,NSDMWidth,NSDMHeight); + positionY+=200; + + + if ( (mnet->leftHand.loadedModels>0) || (mnet->rightHand.loadedModels>0) ) + { + int positionLX = borderToScreen; + int positionLY = 40; + cv::rectangle(image,cv::Rect(positionLX-20,positionLY-40,NSDMWidth+40,420),cv::Scalar(0,0,0),-1); + + visualizeNSDM(image,"Left Hand",mnet->leftHand.NSDM,1 /*angles*/,positionLX,positionLY,NSDMWidth,NSDMHeight); + positionLY+=200; + visualizeNSDM(image,"Right Hand",mnet->rightHand.NSDM,1 /*angles*/,positionLX,positionLY,NSDMWidth,NSDMHeight); + positionLY+=200; + } + + + visualizeOrientation( + image,"Orientation", + mnet->currentSolution[4], + mnet->orientationClassifications[0], + mnet->orientationClassifications[1], + mnet->orientationClassifications[2], + mnet->orientationClassifications[3], + positionX, + positionY, + NSDMWidth, + NSDMHeight + ); + + + if (showFramerate>0) + { + unsigned int framerateWidgetWidth = NSDMWidth + 40; + unsigned int framerateWidgetHeight = NSDMHeight; + + + //Keep framerate history + appendToHistory(frameRateHistoryTotal,options->totalLoopFPS,framerateWidgetWidth); + appendToHistory(frameRateIKHistory,mnet->inverseKinematicsFramerate,framerateWidgetWidth); + appendToHistory(frameRateMNET2History,mnet->neuralNetworkFramerate,framerateWidgetWidth); + + //Plot framerate history + plotFloatVector( + image, + 1,//Erase background + frameRateHistoryTotal, + positionX-20, + positionY+210, + framerateWidgetWidth, //Have same size as NSDM matrix + framerateWidgetHeight //Have same size as NSDM matrix + ); + /* + //Not enough space.. + plotFloatVector( + image, + 0,//Erase background + frameRateIKHistory, + positionX-20, + positionY+210, + framerateWidgetWidth, //Have same size as NSDM matrix + framerateWidgetHeight //Have same size as NSDM matrix + ); + plotFloatVector( + image, + 0,//Erase background + frameRateMNET2History, + positionX-20, + positionY+210, + framerateWidgetWidth, //Have same size as NSDM matrix + framerateWidgetHeight //Have same size as NSDM matrix + ); */ + + //----------------------------------------------------------------- + float thickness=1.7; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + cv::Scalar fontColor= cv::Scalar(255,255,255); + cv::Point txtPosition(positionX,positionY+190); + char fpsString[128]; + + snprintf(fpsString,128,"Framerate"); //2D->3D + cv::putText(image,fpsString,txtPosition,fontUsed,0.6,fontColor,thickness,8); + txtPosition.y+=30; + txtPosition.x+=10; + + snprintf(fpsString,128,"%0.2f fps",options->totalLoopFPS); + cv::putText(image,fpsString,txtPosition,fontUsed,0.8,fontColor,thickness,8); + + if ( + strlen(options->message)>0 + ) + { + txtPosition.y=60; + txtPosition.x=30; + fontColor= cv::Scalar(0,0,0); + cv::putText(image,options->message,txtPosition,fontUsed,1.8,fontColor,3.0,8); + txtPosition.x+=3; + txtPosition.y+=3; + fontColor= cv::Scalar(255,255,255); + cv::putText(image,options->message,txtPosition,fontUsed,1.8,fontColor,3.0,8); + } + + //----------------------------------------------------------------- + } + + + + } + + if(image.data!=0) + { + if ( image.size().height > image.size().width ) + { + scale=(float) 720/image.size().height; + } + else + { + scale=(float) 1024/image.size().width; + } + if (scale>1.0) + { + scale=1.0; + } + if (scale!=1.0) + { + cv::resize(image, image, cv::Size(0,0), scale, scale); + } + + //fprintf(stderr,"Image ( %u x %u )\n",image.size().width,image.size().height); + success=1; + } + + } + else + { + fprintf(stderr," Could not load %s image, cannot proceed to visualize it\n",finalFilename); + return 0; + } + + if (saveVisualization) + { + char filename[512]; + snprintf(filename,512,"vis%05u.jpg",frameNumber) ; + cv::imwrite(filename,image); + } + + cv::imshow(windowName,image); // + //Did not find a file to show .. + return success; +#else + fprintf(stderr,"OpenCV code not present in this build, cannot show visualization..\n"); +#endif + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/allInOne.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/allInOne.hpp new file mode 100644 index 0000000..39ee41b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/allInOne.hpp @@ -0,0 +1,35 @@ +#pragma once +/** @file allInOne.hpp + * @brief Code that handles GUI output and visualization using OpenCV. + * If OpenCV is not be available, CMake will not declare the USE_OPENCV compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; + +int visualizeAllInOne( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + struct MocapNET2Options * options, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + unsigned int numberOfMissingJoints +); + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/camera_ready.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/camera_ready.cpp new file mode 100644 index 0000000..217cd9e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/camera_ready.cpp @@ -0,0 +1,447 @@ +#include "camera_ready.hpp" +#include "visualization.hpp" +#include "drawSkeleton.hpp" +#include "widgets.hpp" + +#include "../IO/jsonRead.hpp" +#include "../IO/bvh.hpp" +#include "../tools.hpp" +#include "../mocapnet2.hpp" + + +Mat getPaddedROI(const Mat &input, int top_left_x, int top_left_y, int width, int height, Scalar paddingColor) +{ + int bottom_right_x = top_left_x + width; + int bottom_right_y = top_left_y + height; + + Mat output; + if (top_left_x < 0 || top_left_y < 0 || bottom_right_x > input.cols || bottom_right_y > input.rows) + { + // border padding will be required + int border_left = 0, border_right = 0, border_top = 0, border_bottom = 0; + + if (top_left_x < 0) + { + width = width + top_left_x; + border_left = -1 * top_left_x; + top_left_x = 0; + } + if (top_left_y < 0) + { + height = height + top_left_y; + border_top = -1 * top_left_y; + top_left_y = 0; + } + if (bottom_right_x > input.cols) + { + width = width - (bottom_right_x - input.cols); + border_right = bottom_right_x - input.cols; + } + if (bottom_right_y > input.rows) + { + height = height - (bottom_right_y - input.rows); + border_bottom = bottom_right_y - input.rows; + } + + Rect R(top_left_x, top_left_y, width, height); + copyMakeBorder(input(R), output, border_top, border_bottom, border_left, border_right, BORDER_CONSTANT, paddingColor); + } + else + { + // no border padding required + Rect R(top_left_x, top_left_y, width, height); + output = input(R); + } + return output; +} + +int getBoundingBox(struct skeletonSerialized * skeleton,cv::Point & pointMinimum,cv::Point & pointMaximum,unsigned int x,unsigned int y,unsigned int width,unsigned int height) +{ + if (skeleton==0) + { + return 0; + } + if (skeleton->skeletonBodyElements==0) + { + return 0; + } + + unsigned int jID = 3*3 ; + + float xNormalized = (float) skeleton->skeletonBody[jID+0].value / skeleton->width; + float yNormalized = (float) skeleton->skeletonBody[jID+1].value / skeleton->height; + + int performedUpdate=0; + pointMinimum.x = width;//x+xNormalized*width; + pointMinimum.y = height;//y+yNormalized*height; + pointMaximum.x = 0;//x+xNormalized*width; + pointMaximum.y = 0;//y+yNormalized*height; + + for (int jointID=3; jointIDskeletonBodyElements/3; jointID++) + { + jID = jointID*3 ; + + xNormalized = (float) skeleton->skeletonBody[jID+0].value / skeleton->width; + yNormalized = (float) skeleton->skeletonBody[jID+1].value / skeleton->height; + + if ( (xNormalized>0.0) && (yNormalized>0.0) ) + { + unsigned int fX = x+xNormalized*width; + unsigned int fY = y+yNormalized*height; + + if (fXpointMaximum.x) + { + pointMaximum.x = fX; + performedUpdate=1; + } + if (fY>pointMaximum.y) + { + pointMaximum.y = fY; + performedUpdate=1; + } + } + } + + + return performedUpdate; +} + + + + +int visualizeCameraReady( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + std::vector > points2DOutputGUIForcedViewSide, + unsigned int numberOfMissingJoints +) +{ + if (points2DOutputGUIRealView.size()==0) + { + return 0; + } + if (points2DOutputGUIForcedView.size()==0) + { + return 0; + } + if (points2DOutputGUIForcedViewSide.size()==0) + { + return 0; + } + if (path==0) + { + fprintf(stderr,"Can't visualize input without path to RGB images\n"); + return 0; + } + +#if USE_OPENCV + int success=0; + char finalFilename[2048]= {0}; + cv::Mat raytraced; + + + snprintf(finalFilename,256,"%s/raytraced2/%04u.png",path,frameNumber); + raytraced = imread(finalFilename,cv::IMREAD_COLOR); // older versions might want the CV_LOAD_IMAGE_COLOR flag + + if (alreadyLoadedImage==0) + { + + char formatString[256]= {0}; + snprintf(formatString,256,"%%s/%%s%%0%uu.jpg",serialLength); + + //colorFrame_0_00001.jpg + snprintf(finalFilename,2048,formatString,path,label,frameNumber/*Frame ID*/); + //snprintf(finalFilename,2048,"%s/colorFrame_0_%05d.jpg",path,frameNumber+1); + } + + + + float scale=1.0; + cv::Mat image; + cv::Rect roi; + cv::Mat destinationROI; + + if ( (fileExists(finalFilename) ) || (alreadyLoadedImage!=0) ) + { + if (alreadyLoadedImage!=0) + { + image = *alreadyLoadedImage; + + if ( + ( image.size().width < 640 ) || + ( image.size().height < 480 ) + ) + { + fprintf(stderr,"Very small given frame ( %ux%u..\n",image.size().width,image.size().height); + return 0; + } + + //cv::imshow(windowName,image); // + //cv::waitKey(0); + } + else + if (fileExists(finalFilename) ) + { + image = imread(finalFilename,cv::IMREAD_COLOR); // older versions might want the CV_LOAD_IMAGE_COLOR flag + } + + + if(image.data!=0) + { + if ( image.size().height > image.size().width ) + { + scale=(float) 720/image.size().height; + } + else + { + scale=(float) 1024/image.size().width; + } + if (scale>1.0) + { + scale=1.0; + } + if (scale!=1.0) + { + cv::resize(image, image, cv::Size(0,0), scale, scale); + } + + //fprintf(stderr,"Image ( %u x %u )\n",image.size().width,image.size().height); + success=1; + } + + } + else + { + fprintf(stderr," Could not load %s image, cannot proceed to visualize it\n",finalFilename); + return 0; + } + + + if (image.empty()) + { + return 0; + } + + //int offsetX=950; + int offsetX=650; + //cv::Mat visualization(image.size().height,offsetX+image.size().width, CV_8UC3, Scalar(0,0,0)); + cv::Mat visualization(1080,1920, CV_8UC3, Scalar(255,255,255)); + + //fprintf(stderr,"Visualization will be ( %u x %u )\n",visualization.size().width,visualization.size().height); + roi = cv::Rect( cv::Point(offsetX,0 ), cv::Size( image.size().width, image.size().height )); + destinationROI = visualization( roi ); + //image.copyTo( destinationROI ); + + + + //visualizeInput2DSkeletonFromSkeletonSerialized(visualization,skeleton,offsetX,0, image.size().width,image.size().height); + + //================================================================================== + unsigned int bbSizeWidth = 700; + unsigned int bbSizeHeight = 800; + + visualizeSkeletonSerialized( + image, + skeleton, + (mnet->leftHand.loadedModels>0), + (mnet->rightHand.loadedModels>0), + mnet->options->doFace, + 0,0, + image.size().width, + image.size().height + ); + + cv::Point ptMin(0,0),ptMax(0,0); + if ( getBoundingBox(skeleton,ptMin,ptMax,offsetX,0, image.size().width,image.size().height) ) + { + //cv::rectangle(visualization,pt1,pt2,cv::Scalar(0,0,0),-1,8,0); + ptMin.x -= offsetX; + ptMax.x -= offsetX; + + if (ptMin.x >= 20) + { + ptMin.x-=20; + } + if (ptMin.y >= 20) + { + ptMin.y-=20; + } + if (ptMax.x <= image.size().width+20) + { + ptMax.x+=20; + } + if (ptMax.y <= image.size().height+20) + { + ptMax.y+=20; + } + + } + else + { + ptMin.x=0; + ptMax.x=image.size().width-1; + ptMin.y=0; + ptMax.y=image.size().height-1; + + } + + + roi = cv::Rect(ptMin, cv::Size(ptMax.x-ptMin.x,ptMax.y-ptMin.y)); + //cv::Mat imageROI = image( roi ); + cv::Mat imageROI = getPaddedROI(image,ptMin.x,ptMin.y,ptMax.x-ptMin.x,ptMax.y-ptMin.y,cv::Scalar(123,123,123)); + + /* + float ratio = (float) imageROI.size().height / 800; + unsigned int originalImageWidth=(unsigned int) imageROI.size().width * ratio; + unsigned int originalImageHeight=(unsigned int) imageROI.size().height * ratio; + cv::resize(imageROI,imageROI,cv::Size(originalImageWidth,originalImageHeight)); + */ + + unsigned int tX = offsetX + 820 - ((unsigned int) ptMax.x-ptMin.x / 2); + unsigned int tY = 600 - ( (unsigned int) ptMax.y-ptMin.y / 2 ); + + if (tX + ptMax.x-ptMin.x > 1920 ) + { + tX=0; + } + if (tY + ptMax.y-ptMin.y > 1920 ) + { + tY=0; + } + + fprintf(stderr,"tx=%u,ty=%u\n",tX,tY); + cv::Point targetPosition(tX,tY); + roi = cv::Rect(targetPosition, cv::Size(ptMax.x-ptMin.x,ptMax.y-ptMin.y)); + + //destinationROI = visualization( roi ); + destinationROI = getPaddedROI(visualization,roi.x,roi.y,ptMax.x-ptMin.x,ptMax.y-ptMin.y,cv::Scalar(255,255,255)); + cv::addWeighted(destinationROI,0.0, imageROI, 1.0, 0.0, destinationROI); + //================================================================================== + + + + + + cv::Point pt1(offsetX,0); + cv::Point pt2(offsetX+00,image.size().height); + //cv::rectangle(visualization,pt1,pt2,cv::Scalar(0,0,0),-1,8,0); + + cv::Scalar color= cv::Scalar(123,123,123,123 /*Transparency here , although if the cv::Mat does not have an alpha channel it is useless*/); + cv::Scalar black= cv::Scalar(0,0,0,0 /*Transparency here , although if the cv::Mat does not have an alpha channel it is useless*/); + cv::Point txtPosition; + txtPosition.y=30; + float thickness=2.2; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + + + + + //FORCE VIEW..! + //numberOfMissingJoints=0; + + if (numberOfMissingJoints>40) + { + txtPosition.x=60; + txtPosition.y=350; + cv::putText(visualization,"Incomplete Data..!",txtPosition,fontUsed,1.8,color,thickness,8); + } + else + { + txtPosition.x=100; + //cv::putText(visualization,"Front View",txtPosition,fontUsed,1.1,color,thickness,8); + + txtPosition.x=400; + //cv::putText(visualization,"Side View",txtPosition,fontUsed,1.1,color,thickness,8); + + + drawSkeleton(visualization,points2DOutputGUIForcedView,-730,-230,0); + drawSkeleton(visualization,points2DOutputGUIForcedViewSide,-450,-230,0); + } + + + scaleSkeleton(points2DOutputGUIRealView,scale,scale); + //drawSkeleton(visualization,points2DOutputGUIRealView,offsetX-100,0,0); + + + if (!raytraced.empty()) + { + unsigned int clipSizeWidth = 700; + unsigned int clipSizeHeight = 800; + cv::Mat clippedRaytraced; + roi = cv::Rect( cv::Point(600,150),cv::Size(clipSizeWidth,clipSizeHeight)); + clippedRaytraced = raytraced(roi); + + clipSizeWidth=(unsigned int) clipSizeWidth*0.6; + clipSizeHeight=(unsigned int) clipSizeHeight*0.6; + cv::resize(clippedRaytraced,clippedRaytraced,cv::Size(clipSizeWidth,clipSizeHeight)); + + cv::Mat clippedVisualization; + roi = cv::Rect( cv::Point(1380,100),cv::Size(clipSizeWidth,clipSizeHeight)); + clippedVisualization = visualization(roi); + + cv::addWeighted(clippedVisualization,0.0, clippedRaytraced, 1.0, 0.0, clippedVisualization); + } + + + int NSDMWidth=160; //body leftHand rightHand + int NSDMHeight=160; + + int positionY = 80; + visualizeNSDM(visualization,"Upper Body",mnet->upperBody.NSDM,1 /*angles*/,770,positionY,NSDMWidth,NSDMHeight); + positionY+=200; + visualizeNSDM(visualization,"Lower Body",mnet->lowerBody.NSDM,1 /*angles*/,770,positionY,NSDMWidth,NSDMHeight); + positionY+=200; + + visualizeOrientation( + visualization,"Orientation", + mnet->currentSolution[4], //Orientation value + mnet->orientationClassifications[0], + mnet->orientationClassifications[1], + mnet->orientationClassifications[2], + mnet->orientationClassifications[3], + 770, + positionY, + NSDMWidth, + NSDMHeight + ); + + + cv::imshow(windowName,visualization); // + + if (saveVisualization) + { + char filename[1024]; + snprintf(filename,1024,"cr%05u.jpg",frameNumber); + imwrite(filename,visualization); + } + + //Did not find a file to show .. + return success; +#else + fprintf(stderr,"OpenCV code not present in this build, cannot show visualization..\n"); +#endif + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/camera_ready.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/camera_ready.hpp new file mode 100644 index 0000000..27f7a28 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/camera_ready.hpp @@ -0,0 +1,33 @@ +#pragma once +/** @file camera_ready.hpp + * @brief Code that handles GUI output and visualization using OpenCV. + * If OpenCV is not be available, CMake will not declare the USE_OPENCV compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; + + int visualizeCameraReady( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + std::vector > points2DOutputGUIForcedViewSide, + unsigned int numberOfMissingJoints + ); +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/drawSkeleton.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/drawSkeleton.cpp new file mode 100644 index 0000000..336c27e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/drawSkeleton.cpp @@ -0,0 +1,851 @@ +#include "drawSkeleton.hpp" + + +#if USE_OPENCV +#if USE_BVH +#include "../IO/bvh.hpp" + +#define GOOFY_EYES 0 + + +void drawCircleIfPointsExist(cv::Mat &outputMat,cv::Point point,float radious,cv::Scalar color,float thickness) +{ + if ( (point.x!=0) || (point.y!=0) ) + { + cv::circle(outputMat,point,radious,color,thickness,8,0); + } + return; +} + + +void drawLineIfPointsExist(cv::Mat &outputMat,cv::Point start,cv::Point finish,cv::Scalar color,float thickness) +{ + if ( ( (start.x!=0) || (start.y!=0) ) && ( (finish.x!=0) || (finish.y!=0) ) ) + { + cv::line(outputMat,start,finish,color,thickness); + } + return; +} + +int visualizeSkeletonSerialized( + cv::Mat &outputMat, + struct skeletonSerialized * skeleton, + unsigned int drawLeftFingers, + unsigned int drawRightFingers, + unsigned int drawFace, + unsigned int x,unsigned int y, + unsigned int width,unsigned int height + ) +{ + /* + fprintf(stderr,"Visualize %u,%u - Skeleton ",skeletonWidth,skeletonHeight); + fprintf(stderr,"Offset %u,%u - ",x,y); + fprintf(stderr,"Joints %u - ",skeleton->skeletonBodyElements); + fprintf(stderr,"Dimensions %u,%u \n ",width,height); + char filename[512]; + */ + + #if USE_TRANSPARENCY + cv::Mat base; + cv::Mat overlay(outputMat.size().height,outputMat.size().width, CV_8UC3, Scalar(0,0,0)); + outputMat.copyTo(base); + cv::Mat * baseP = &base; + cv::Mat * overlayP = &overlay ; + cv::Mat * outputP = &outputMat; + #else + //Not using transparency you just write everything on output + //and get rid of the add weighted last step that takes 6% of CPU time + cv::Mat * baseP = &outputMat; + cv::Mat * overlayP = &outputMat ; + cv::Mat * outputP = &outputMat; + #endif + + cv::Scalar color = cv::Scalar(0,255,255); + cv::Point pt1 = cv::Point(x,y); + cv::Point pt2 = cv::Point(x+width,y+height); + cv::rectangle(*overlayP, pt1, pt2, color); + + float thickness=1.0; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + unsigned int pointsThatExist=0; + unsigned int pointsThatSeemToBeWronglyNormalized=0; + + + //Uncomment for a string to jID match + //for (int jointID=3; jointIDskeletonBodyElements; jointID++) + // { + // fprintf(stderr,"skeleton[%u]=%s\n",jointID,skeleton->skeletonHeader[jointID].str); + // } + + cv::Scalar faceColor(255,255,0); + cv::Scalar rightColor(0,255,0); + cv::Scalar leftColor(0,0,255); + + float faceThickness=2.0; + float fingerThickness=2.0; + float bodyThickness=2.0; + + + for (int jointID=3; jointIDskeletonBodyElements/3; jointID++) + { + unsigned int jID = jointID*3; + + float pointExists = skeleton->skeletonBody[jID+2].value; + + if (pointExists>0.0) + { + float xNormalized = (float) skeleton->skeletonBody[jID+0].value / skeleton->width; + float yNormalized = (float) skeleton->skeletonBody[jID+1].value / skeleton->height; + + if ( (xNormalized!=0.0) || (yNormalized!=0.0) ) + { + //First of all bring output point to the range of our mini + //screen + pt1.x = (unsigned int) (xNormalized * width); + pt1.y = (unsigned int) (yNormalized * height); + + //Check if we have a normalization error..! + if ( (pt1.x>width) || (pt1.y>height) ) + { + ++pointsThatSeemToBeWronglyNormalized; + } + + + //Finally put the point in the target position on the overlay + pt1.x += (unsigned int) x; + pt1.y += (unsigned int) y; + + //And draw it. + cv::circle(*overlayP,pt1,1,color,1,8,0); + ++pointsThatExist; + } + } + } + + + float xRatio = (float) width / skeleton->width; + float yRatio = (float) height / skeleton->height; + + unsigned int jID; + + + jID=16 * 3; + cv::Point reye(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=17 * 3; + cv::Point leye(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=18 * 3; + cv::Point rear(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=19 * 3; + cv::Point lear(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + + jID=1 * 3; + cv::Point head(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=2 * 3; + cv::Point neck(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + jID=9 * 3; + cv::Point hip(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + jID=3 * 3; + cv::Point rshoulder(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=4 * 3; + cv::Point relbow(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=5 * 3; + cv::Point rhand(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + jID=6 * 3; + cv::Point lshoulder(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=7 * 3; + cv::Point lelbow(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=8 * 3; + cv::Point lhand(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + jID=10 * 3; + cv::Point rhip(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=11 * 3; + cv::Point rknee(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=12 * 3; + cv::Point rfoot(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=25 * 3; + cv::Point rheel(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=23 * 3; + cv::Point rbigtoe(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=24 * 3; + cv::Point rsmalltoe(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + jID=13 * 3; + cv::Point lhip(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=14 * 3; + cv::Point lknee(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=15 * 3; + cv::Point lfoot(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=22 * 3; + cv::Point lheel(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=20 * 3; + cv::Point lbigtoe(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=21 * 3; + cv::Point lsmalltoe(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + + drawLineIfPointsExist(*overlayP,lshoulder,lelbow,leftColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lhand,lelbow,leftColor,bodyThickness); + + drawLineIfPointsExist(*overlayP,rshoulder,relbow,rightColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rhand,relbow,rightColor,bodyThickness); + + + drawLineIfPointsExist(*overlayP,lhip,hip,leftColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lhip,lknee,leftColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lknee,lfoot,leftColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lfoot,lheel,leftColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lfoot,lbigtoe,leftColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lfoot,lsmalltoe,leftColor,bodyThickness); + + drawLineIfPointsExist(*overlayP,rhip,hip,rightColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rhip,rknee,rightColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rknee,rfoot,rightColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rfoot,rheel,rightColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rfoot,rbigtoe,rightColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rfoot,rsmalltoe,rightColor,bodyThickness); + + + drawLineIfPointsExist(*overlayP,lshoulder,rshoulder,cv::Scalar(255,0,0),bodyThickness); + drawLineIfPointsExist(*overlayP,hip,rshoulder,cv::Scalar(255,0,0),bodyThickness); + drawLineIfPointsExist(*overlayP,hip,lshoulder,cv::Scalar(255,0,0),bodyThickness); + drawLineIfPointsExist(*overlayP,neck,rshoulder,cv::Scalar(255,0,0),bodyThickness); + drawLineIfPointsExist(*overlayP,neck,lshoulder,cv::Scalar(255,0,0),bodyThickness); + + //Also draw joints + drawCircleIfPointsExist(*overlayP,lhand,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,rhand,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,lshoulder,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,rshoulder,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,lelbow,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,relbow,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,lhip,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,rhip,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,lknee,5,cv::Scalar(255,0,255),bodyThickness); + drawCircleIfPointsExist(*overlayP,rknee,5,cv::Scalar(255,0,255),bodyThickness); + + + if (drawLeftFingers) + { + //Left Hand + //--------------------------------------------------------------------------------------------------------- + jID=28 * 3; + cv::Point lthumb(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=29 * 3; + cv::Point lthumb1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=30 * 3; + cv::Point lthumb2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=31 * 3; + cv::Point lthumbES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=32 * 3; + cv::Point lpointer(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=33 * 3; + cv::Point lpointer1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=34 * 3; + cv::Point lpointer2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=35 * 3; + cv::Point lpointerES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=36 * 3; + cv::Point lmiddle(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=37 * 3; + cv::Point lmiddle1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=38 * 3; + cv::Point lmiddle2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=39 * 3; + cv::Point lmiddleES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=40 * 3; + cv::Point lring(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=41 * 3; + cv::Point lring1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=42 * 3; + cv::Point lring2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=43 * 3; + cv::Point lringES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=44 * 3; + cv::Point lpinky(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=45 * 3; + cv::Point lpinky1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=46 * 3; + cv::Point lpinky2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=47 * 3; + cv::Point lpinkyES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + + + drawLineIfPointsExist(*overlayP,lhand,lthumb,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lhand,lpointer,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lhand,lmiddle,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lhand,lring,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lhand,lpinky,leftColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,lthumb,lthumb1,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lthumb1,lthumb2,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lthumb2,lthumbES,leftColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,lpointer,lpointer1,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lpointer1,lpointer2,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lpointer2,lpointerES,leftColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,lmiddle,lmiddle1,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lmiddle1,lmiddle2,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lmiddle2,lmiddleES,leftColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,lring,lring1,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lring1,lring2,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lring2,lringES,leftColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,lpinky,lpinky1,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lpinky1,lpinky2,leftColor,fingerThickness); + drawLineIfPointsExist(*overlayP,lpinky2,lpinkyES,leftColor,fingerThickness); + } + + + + + if (drawRightFingers) + { + //Right Hand + //--------------------------------------------------------------------------------------------------------- + jID=49 * 3; + cv::Point rthumb(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=50 * 3; + cv::Point rthumb1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=51 * 3; + cv::Point rthumb2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=52 * 3; + cv::Point rthumbES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=53 * 3; + cv::Point rpointer(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=54 * 3; + cv::Point rpointer1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=55 * 3; + cv::Point rpointer2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=56 * 3; + cv::Point rpointerES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=57 * 3; + cv::Point rmiddle(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=58 * 3; + cv::Point rmiddle1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=59 * 3; + cv::Point rmiddle2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=60 * 3; + cv::Point rmiddleES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=61 * 3; + cv::Point rring(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=62 * 3; + cv::Point rring1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=63 * 3; + cv::Point rring2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=64 * 3; + cv::Point rringES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + jID=65 * 3; + cv::Point rpinky(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=66 * 3; + cv::Point rpinky1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=67 * 3; + cv::Point rpinky2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=68 * 3; + cv::Point rpinkyES(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); //EndSite + //--------------------------------------------------------------------------------------------------------- + + + drawLineIfPointsExist(*overlayP,rhand,rthumb,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rhand,rpointer,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rhand,rmiddle,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rhand,rring,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rhand,rpinky,rightColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,rthumb,rthumb1,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rthumb1,rthumb2,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rthumb2,rthumbES,rightColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,rpointer,rpointer1,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rpointer1,rpointer2,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rpointer2,rpointerES,rightColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,rmiddle,rmiddle1,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rmiddle1,rmiddle2,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rmiddle2,rmiddleES,rightColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,rring,rring1,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rring1,rring2,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rring2,rringES,rightColor,fingerThickness); + + drawLineIfPointsExist(*overlayP,rpinky,rpinky1,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rpinky1,rpinky2,rightColor,fingerThickness); + drawLineIfPointsExist(*overlayP,rpinky2,rpinkyES,rightColor,fingerThickness); + } + + + if (drawFace) + { + //Face + //--------------------------------------------------------------------------------------------------------- + jID=69 * 3; + cv::Point rchin0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=70 * 3; + cv::Point rchin1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=71 * 3; + cv::Point rchin2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=72 * 3; + cv::Point rchin3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=73 * 3; + cv::Point rchin4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=74 * 3; + cv::Point rchin5(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=75 * 3; + cv::Point rchin6(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=76 * 3; + cv::Point rchin7(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + + + //--------------------------------------------------------------------------------------------------------- + jID=77 * 3; + cv::Point rchin(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + + + //--------------------------------------------------------------------------------------------------------- + jID=78 * 3; + cv::Point lchin7(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=79 * 3; + cv::Point lchin6(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=80 * 3; + cv::Point lchin5(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=81 * 3; + cv::Point lchin4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=82 * 3; + cv::Point lchin3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=83 * 3; + cv::Point lchin2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=84 * 3; + cv::Point lchin1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=85 * 3; + cv::Point lchin0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + + drawLineIfPointsExist(*overlayP,rchin0,rchin1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,rchin1,rchin2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,rchin3,rchin4,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,rchin4,rchin5,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,rchin5,rchin6,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,rchin6,rchin7,faceColor,faceThickness); + + drawLineIfPointsExist(*overlayP,rchin7,rchin,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,rchin,lchin7,faceColor,faceThickness); + + drawLineIfPointsExist(*overlayP,lchin0,lchin1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,lchin1,lchin2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,lchin3,lchin4,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,lchin4,lchin5,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,lchin5,lchin6,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,lchin6,lchin7,faceColor,faceThickness); + + drawLineIfPointsExist(*overlayP,neck,rchin,cv::Scalar(255,0,0),bodyThickness); + + drawLineIfPointsExist(*overlayP,neck,rchin,cv::Scalar(255,0,0),bodyThickness); + + drawLineIfPointsExist(*overlayP,neck,rchin,faceColor,bodyThickness); + drawLineIfPointsExist(*overlayP,rear,rchin2,faceColor,bodyThickness); + drawLineIfPointsExist(*overlayP,lear,lchin2,faceColor,bodyThickness); + //--------------------------------------------------------------------------------------------------------- + jID=86 * 3; + cv::Point reyebrow0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=87 * 3; + cv::Point reyebrow1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=88 * 3; + cv::Point reyebrow2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=89 * 3; + cv::Point reyebrow3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=90 * 3; + cv::Point reyebrow4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + + //--------------------------------------------------------------------------------------------------------- + jID=91 * 3; + cv::Point leyebrow4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=92 * 3; + cv::Point leyebrow3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=93 * 3; + cv::Point leyebrow2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=94 * 3; + cv::Point leyebrow1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=95 * 3; + cv::Point leyebrow0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + + + drawLineIfPointsExist(*overlayP,reyebrow0,reyebrow1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reyebrow1,reyebrow2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reyebrow2,reyebrow3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reyebrow3,reyebrow4,faceColor,faceThickness); + + drawLineIfPointsExist(*overlayP,leyebrow0,leyebrow1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leyebrow1,leyebrow2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leyebrow2,leyebrow3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leyebrow3,leyebrow4,faceColor,faceThickness); + + + + //--------------------------------------------------------------------------------------------------------- + jID=96 * 3; + cv::Point nosebone0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=97 * 3; + cv::Point nosebone1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=98 * 3; + cv::Point nosebone2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=99 * 3; + cv::Point nosebone3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + jID=100 * 3; + cv::Point nostrills0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=101 * 3; + cv::Point nostrills1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=102 * 3; + cv::Point nostrills2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=103 * 3; + cv::Point nostrills3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=104 * 3; + cv::Point nostrills4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + + + drawLineIfPointsExist(*overlayP,nosebone0,nosebone1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,nosebone1,nosebone2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,nosebone2,nosebone3,faceColor,faceThickness); + + drawLineIfPointsExist(*overlayP,nostrills0,nostrills1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,nostrills1,nostrills2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,nostrills2,nostrills3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,nostrills3,nostrills4,faceColor,faceThickness); + + + + + //--------------------------------------------------------------------------------------------------------- + jID=105 * 3; + cv::Point reye0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=106 * 3; + cv::Point reye1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=107 * 3; + cv::Point reye2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=108 * 3; + cv::Point reye3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=109 * 3; + cv::Point reye4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=110 * 3; + cv::Point reye5(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + drawLineIfPointsExist(*overlayP,reye0,reye1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reye1,reye2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reye2,reye3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reye3,reye4,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reye4,reye5,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,reye0,reye5,faceColor,faceThickness); + //--------------------------------------------------------------------------------------------------------- + + + + + + //--------------------------------------------------------------------------------------------------------- + jID=111 * 3; + cv::Point leye0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=112 * 3; + cv::Point leye1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=113 * 3; + cv::Point leye2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=114 * 3; + cv::Point leye3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=115 * 3; + cv::Point leye4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=116 * 3; + cv::Point leye5(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + drawLineIfPointsExist(*overlayP,leye0,leye1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leye1,leye2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leye2,leye3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leye3,leye4,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leye4,leye5,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,leye0,leye5,faceColor,faceThickness); + //--------------------------------------------------------------------------------------------------------- + + + + //--------------------------------------------------------------------------------------------------------- + jID=117 * 3; + cv::Point outmouth0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=118 * 3; + cv::Point outmouth1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=119 * 3; + cv::Point outmouth2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=120 * 3; + cv::Point outmouth3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=121 * 3; + cv::Point outmouth4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=122 * 3; + cv::Point outmouth5(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=123 * 3; + cv::Point outmouth6(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=124 * 3; + cv::Point outmouth7(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=125 * 3; + cv::Point outmouth8(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=126 * 3; + cv::Point outmouth9(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=127 * 3; + cv::Point outmouth10(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=128 * 3; + cv::Point outmouth11(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + drawLineIfPointsExist(*overlayP,outmouth0,outmouth1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth1,outmouth2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth2,outmouth3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth3,outmouth4,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth4,outmouth5,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth5,outmouth6,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth6,outmouth7,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth7,outmouth8,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth8,outmouth9,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth9,outmouth10,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth10,outmouth11,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,outmouth11,outmouth0,faceColor,faceThickness); + //--------------------------------------------------------------------------------------------------------- + + + //--------------------------------------------------------------------------------------------------------- + jID=129 * 3; + cv::Point inmouth0(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=130 * 3; + cv::Point inmouth1(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=131 * 3; + cv::Point inmouth2(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=132 * 3; + cv::Point inmouth3(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=133 * 3; + cv::Point inmouth4(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=134 * 3; + cv::Point inmouth5(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=135 * 3; + cv::Point inmouth6(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + jID=136 * 3; + cv::Point inmouth7(x+xRatio*skeleton->skeletonBody[jID+0].value,y+yRatio*skeleton->skeletonBody[jID+1].value); + //--------------------------------------------------------------------------------------------------------- + drawLineIfPointsExist(*overlayP,inmouth0,inmouth1,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth1,inmouth2,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth2,inmouth3,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth3,inmouth4,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth4,inmouth5,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth5,inmouth6,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth6,inmouth7,faceColor,faceThickness); + drawLineIfPointsExist(*overlayP,inmouth7,inmouth0,faceColor,faceThickness); + //--------------------------------------------------------------------------------------------------------- + + + #if GOOFY_EYES + cv::circle(*overlayP,reye,6,cv::Scalar(255,255,255),faceThickness,8,0); + cv::circle(*overlayP,reye,3,cv::Scalar(0,0,0),faceThickness,8,0); + cv::circle(*overlayP,leye,6,cv::Scalar(255,255,255),faceThickness,8,0); + cv::circle(*overlayP,leye,3,cv::Scalar(0,0,0),faceThickness,8,0); + #endif + } + else + { + drawLineIfPointsExist(*overlayP,reye,head,faceColor,bodyThickness); + drawLineIfPointsExist(*overlayP,leye,head,faceColor,bodyThickness); + drawLineIfPointsExist(*overlayP,neck,head,faceColor,bodyThickness); + + #if GOOFY_EYES + cv::circle(*overlayP,reye,6,cv::Scalar(255,255,255),faceThickness,8,0); + cv::circle(*overlayP,reye,3,cv::Scalar(0,0,0),faceThickness,8,0); + cv::circle(*overlayP,leye,6,cv::Scalar(255,255,255),faceThickness,8,0); + cv::circle(*overlayP,leye,3,cv::Scalar(0,0,0),faceThickness,8,0); + #endif + } + + + + thickness=1.0; + + if (pointsThatSeemToBeWronglyNormalized>0) + { + fprintf(stderr,"visualizeInput2DSkeletonFromSkeletonSerialized %u points seem to be incorrectly normalized\n",pointsThatSeemToBeWronglyNormalized); + fprintf(stderr,"signaled units are %0.2f x %0.2f \n",skeleton->width,skeleton->height); + } + + + if (pointsThatExist==0) + { + pt1.x=x+10; + pt1.y=y+10; + cv::putText(*overlayP,"No Valid 2D Points available",pt1,fontUsed,0.4,color,thickness,8); + } + + if (pointsThatSeemToBeWronglyNormalized>0) + { + pt1.x=x+10; + pt1.y=y+30; + cv::putText(*overlayP,"Invalid Normalized Input Points",pt1,fontUsed,0.4,color,thickness,8); + } + + #if USE_TRANSPARENCY + cv::addWeighted(*baseP,1.0, *overlayP, 0.5, 0.0, *outputMatP); + #endif + + return 1; +} + + + +int drawSkeleton(cv::Mat &outputMat,std::vector > points2DOutputGUIForcedView,float offsetX,float offsetY,int labels) +{ + if (points2DOutputGUIForcedView.size()==0) + { + return 0; + } + + + for (int jointID=0; jointID +#include +#include "../mocapnet2.hpp" + +#include "../applicationLogic/parseCommandlineOptions.hpp" + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; +#endif + + +#if USE_OPENCV +#if USE_BVH + +int visualizeSkeletonSerialized( + cv::Mat &outputMat, + struct skeletonSerialized * skeleton, + unsigned int drawLeftFingers, + unsigned int drawRightFingers, + unsigned int drawFace, + unsigned int x,unsigned int y, + unsigned int width,unsigned int height + ); + + int drawSkeleton(cv::Mat &outputMat,std::vector > points2DOutputGUIForcedView,float offsetX,float offsetY,int labels); +#endif +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/map.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/map.cpp new file mode 100644 index 0000000..653b9d0 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/map.cpp @@ -0,0 +1,331 @@ +#include "map.hpp" +#include "visualization.hpp" + +#include "../applicationLogic/artifactRecognition.hpp" + +#include "../IO/jsonRead.hpp" +#include "../IO/bvh.hpp" +#include "../IO/skeletonAbstraction.hpp" +#include "../tools.hpp" +#include "../mocapnet2.hpp" + + +std::vector locationHistory; + +int drawPerson(cv::Mat & img,float x,float y,float r) +{ + int maxLocationHistory=15; + cv::Scalar color= cv::Scalar(0,0,255); + cv::Scalar colorLook= cv::Scalar(100,0,90); + + unsigned int width = img.size().width; + unsigned int height = img.size().height; + + + cv::Point startPoint(0,0); + cv::Point endPoint(0,0); + + unsigned int x2D = x*2 + width/2; + unsigned int y2D = -1*y*2 + height/2; + + //Draw Cross + //----------------------------------------------- + startPoint.x = x2D - 10; + startPoint.y = y2D; + endPoint.x = x2D + 10; + endPoint.y = y2D; + cv::line(img,startPoint,endPoint,color,2.0); + //----------------------------------------------- + startPoint.x = x2D; + startPoint.y = y2D - 10; + endPoint.x = x2D; + endPoint.y = y2D + 10; + cv::line(img,startPoint,endPoint,color,2.0); + //----------------------------------------------- + + + float rad=((float) -1.0 * 3.1415/180*r); + startPoint.x=x2D; + startPoint.y=y2D; + endPoint.x=x2D+cos(rad)* (width/5); + endPoint.y=y2D+sin(rad)* (height/5); + cv::line(img,startPoint,endPoint,colorLook,2.0); + + + + //--------------------------------------------------------------------------------------------------------------------------------------------- + cv::Point p(x2D,y2D); + locationHistory.push_back(p); + if (locationHistory.size()>maxLocationHistory) + { + locationHistory.erase(locationHistory.begin()); + } + float stepColorD=(float) 255/maxLocationHistory; + for (int step=0; step1) + { + cv::line(img,locationHistory[step],locationHistory[step-1], cv::Scalar(0,0,stepColor), 1.0); + } + } + //--------------------------------------------------------------------------------------------------------------------------------------------- + + return 1; +} + + +int visualizeMap( + const char* windowName, + unsigned int frameNumber, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + std::vector result, + std::vector > points2DOutputGUIRealView, + unsigned int numberOfMissingJoints, + unsigned int gestureDetected, + const char * gestureName, + unsigned int gestureFrame +) +{ + fprintf(stderr,"visualizeMap called with %lu\n",points2DOutputGUIRealView.size()); + if (points2DOutputGUIRealView.size()==0) + { + return 0; + } +#if USE_OPENCV + + unsigned int gridSpace=30; + cv::Point startPoint(0,0); + cv::Point endPoint(0,height); + + + cv::Mat img(height,width, CV_8UC3, Scalar(60,30,30)); + + cv::Scalar lookingColor= cv::Scalar(255,255,0); + cv::Scalar activeColor= cv::Scalar(0,123,255); + cv::Scalar color= cv::Scalar(253,123,30); + + for (unsigned int x=0; x points3DFlatOutput=convertBVHFrameToFlat3DPoints(result); //,width,height + //writeCSVHeaderFromLabelsAndVectorOfVectors("out3DP.csv",MocapNETOutputArrayNames,MOCAPNET_OUTPUT_NUMBER,output3DPositions) ) + + float x = result[2]; + float y = result[0]; + float r = result[4]; + + float lElbowX = points3DFlatOutput[MOCAPNET_3DPOINT_LELBOWZ]; + float lElbowY = points3DFlatOutput[MOCAPNET_3DPOINT_LELBOWX]; + float lHandX = points3DFlatOutput[MOCAPNET_3DPOINT_LHANDZ]; + float lHandY = points3DFlatOutput[MOCAPNET_3DPOINT_LHANDX]; + + + float rElbowX = points3DFlatOutput[MOCAPNET_3DPOINT_RELBOWZ]; + float rElbowY = points3DFlatOutput[MOCAPNET_3DPOINT_RHANDX]; + float rHandX = points3DFlatOutput[MOCAPNET_3DPOINT_RHANDZ]; + float rHandY = points3DFlatOutput[MOCAPNET_3DPOINT_RHANDX]; + + float rad=((float) 1.0 * 3.1415/180*r); + float x2=x+cos(rad)* (width/5); + float y2=y+sin(rad)* (height/5); + + + + + //------------------------------------------------------------------------------- + // If gestures are enabled.. Draw them.. + //------------------------------------------------------------------------------- + if (gestureName!=0) + {//------------------------------------------------------------------------------- + if(gestureDetected) + { + if (gestureFrame>25) + { + snprintf(text,512,"%s (%u)",gestureName,gestureDetected); + cv::Point txtPosition(600,200); + cv::putText(img,text,txtPosition,fontUsed,1.5,cv::Scalar(0,255,255),thickness,8); + cv::circle(img,txtPosition,2*gestureFrame,cv::Scalar(0,255,255),3,8,0); + } + } + } + + + + //------------------------------------------------------------------------------- + // If poses are enabled.. Draw them.. + //------------------------------------------------------------------------------- + if(mnet->activePose>1) + { + snprintf(text,512,"%s (%u)",hardcodedPoseName[mnet->activePose],mnet->activePose); + cv::Point txtPosition(600,200); + cv::putText(img,text,txtPosition,fontUsed,1.5,cv::Scalar(0,255,255),thickness,8); + cv::circle(img,txtPosition,2*gestureFrame,cv::Scalar(0,255,255),3,8,0); + } + + + + + + + + for (unsigned int artifactID=0; artifactIDartifacts.numberOfArtifacts; artifactID++) + { + startPoint.x= mnet->artifacts.artifact[artifactID].x1*2 + width/2; + startPoint.y= mnet->artifacts.artifact[artifactID].y1*-2 + height/2; + endPoint.x= mnet->artifacts.artifact[artifactID].x2*2 + width/2; + endPoint.y= mnet->artifacts.artifact[artifactID].y2*-2 + height/2; + + + if (check3DArtifactCollision( + &mnet->artifacts.artifact[artifactID], + points3DFlatOutput[MOCAPNET_3DPOINT_LELBOWZ], + points3DFlatOutput[MOCAPNET_3DPOINT_LELBOWX], + points3DFlatOutput[MOCAPNET_3DPOINT_LELBOWY], + points3DFlatOutput[MOCAPNET_3DPOINT_LHANDZ], + points3DFlatOutput[MOCAPNET_3DPOINT_LHANDX], + points3DFlatOutput[MOCAPNET_3DPOINT_LHANDY] + ) + ) + { + fprintf(stderr,"Left Hand Collision %u\n",artifactID); + } else + //--------------------------------------------------------------------------------------------------------------------------------------------- + if (check3DArtifactCollision( + &mnet->artifacts.artifact[artifactID], + points3DFlatOutput[MOCAPNET_3DPOINT_RELBOWZ], + points3DFlatOutput[MOCAPNET_3DPOINT_RELBOWX], + points3DFlatOutput[MOCAPNET_3DPOINT_RELBOWY], + points3DFlatOutput[MOCAPNET_3DPOINT_RHANDZ], + points3DFlatOutput[MOCAPNET_3DPOINT_RHANDX], + points3DFlatOutput[MOCAPNET_3DPOINT_RHANDY] + ) + ) + { + fprintf(stderr,"Right Hand Collision %u\n",artifactID); + } else + //--------------------------------------------------------------------------------------------------------------------------------------------- + if (checkArtifactCollision(&mnet->artifacts.artifact[artifactID],x, y, r)) + { + fprintf(stderr,"ARTIFACT %u (%s) is collided \n",artifactID,mnet->artifacts.artifact[artifactID].label); + if ((mnet->artifacts.artifact[artifactID].activatesOnPosition) && (!mnet->artifacts.artifact[artifactID].active) ) + { + mnet->artifacts.artifact[artifactID].active=1; + int i=system(mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); + if (i==0) { fprintf(stderr,"Executed %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } else + { fprintf(stderr,"Failed to execute %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } + } + cv::rectangle(img,startPoint,endPoint,activeColor,-1,8,0); + snprintf(text,512,"%s (aid=%u)",mnet->artifacts.artifact[artifactID].label,artifactID); + startPoint.y+=10; + cv::putText(img,text,startPoint,fontUsed,0.4,cv::Scalar(0,0,255),thickness,8); + } else + //--------------------------------------------------------------------------------------------------------------------------------------------- + if (checkArtifactDirection(&mnet->artifacts.artifact[artifactID],x, y,x2,y2)) + { + fprintf(stderr,"ARTIFACT %u (%s) is looked at \n",artifactID,mnet->artifacts.artifact[artifactID].label); + if ( (mnet->artifacts.artifact[artifactID].activateOnGesture) && (mnet->activePose>1) ) + { + int i =system(mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); + if (i==0) { fprintf(stderr,"Executed Gesture %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } else + { fprintf(stderr,"Failed to execute gesture %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } + } else + if ((mnet->artifacts.artifact[artifactID].activatesOnLook) && (!mnet->artifacts.artifact[artifactID].active)) + { + mnet->artifacts.artifact[artifactID].active=1; + int i = system(mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); + if (i==0) { fprintf(stderr,"Executed %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } else + { fprintf(stderr,"Failed to execute gesture %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } + } + + cv::rectangle(img,startPoint,endPoint,lookingColor,-1,8,0); + snprintf(text,512,"%s (aid=%u)",mnet->artifacts.artifact[artifactID].label,artifactID); + startPoint.y+=10; + cv::putText(img,text,startPoint,fontUsed,0.4,cv::Scalar(0,255,0),thickness,8); + } + //--------------------------------------------------------------------------------------------------------------------------------------------- + else + { + fprintf(stderr,"ARTIFACT %u (%s) is inactive \n",artifactID,mnet->artifacts.artifact[artifactID].label); + if ((mnet->artifacts.artifact[artifactID].activatesOnPosition) && (mnet->artifacts.artifact[artifactID].active) ) + { + mnet->artifacts.artifact[artifactID].active=0; + int i = system(mnet->artifacts.artifact[artifactID].actionToExecuteOnDeactivation); + if (i==0) { fprintf(stderr,"Executed %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnDeactivation); } else + { fprintf(stderr,"Failed to execute %s \n",mnet->artifacts.artifact[artifactID].actionToExecuteOnActivation); } + } + cv::rectangle(img,startPoint,endPoint,color,-1,8,0); + snprintf(text,512,"%s (aid=%u)",mnet->artifacts.artifact[artifactID].label,artifactID); + startPoint.y+=10; + cv::putText(img,text,startPoint,fontUsed,0.4,cv::Scalar(255,0,0),thickness,8); + } + //--------------------------------------------------------------------------------------------------------------------------------------------- + } + + cv::Point leftHandStart(lElbowX*2 + width/2,-1* lElbowY*2 + height/2); + cv::Point leftHandEnd(lHandX*2 + width/2,-1* lHandY*2 + height/2); + cv::line(img,leftHandStart,leftHandEnd,cv::Scalar(0,0,255),2.0); + + + cv::Point rightHandStart(rElbowX*2 + width/2,-1* rElbowY*2 + height/2); + cv::Point rightHandEnd(rHandX*2 + width/2,-1* rHandY*2 + height/2); + cv::line(img,rightHandStart,rightHandEnd,cv::Scalar(0,255,0),2.0); + + + + drawPerson(img,x,y,r); + drawSkeleton(img,points2DOutputGUIRealView,-730,0,0); + + //Draw Camera ----------------------------------------------------- + startPoint.x = width - 50; + startPoint.y = (height/2) - 20; + endPoint.x = width - 100; + endPoint.y = (height/2) + 20; + cv::rectangle(img,startPoint,endPoint,color,2,8,0); + endPoint.y = (height/2) ; + startPoint.x = endPoint.x-30; + startPoint.y = endPoint.y+20; + cv::line(img,startPoint,endPoint,color,2.0); + startPoint.x = endPoint.x-30; + startPoint.y = endPoint.y-20; + cv::line(img,startPoint,endPoint,color,2.0); + endPoint.x = endPoint.x-30; + endPoint.y = endPoint.y+20; + cv::line(img,startPoint,endPoint,color,2.0); + //---------------------------------------------------------------------------- + + cv::imshow(windowName,img); + + return 1; +#else + fprintf(stderr,"OpenCV code not present in this build, cannot show visualization..\n"); +#endif + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/map.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/map.hpp new file mode 100644 index 0000000..fd39376 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/map.hpp @@ -0,0 +1,30 @@ +#pragma once +/** @file map.hpp + * @brief Code that handles GUI output and visualization using OpenCV. + * If OpenCV is not be available, CMake will not declare the USE_OPENCV compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; + + int visualizeMap( + const char* windowName, + unsigned int frameNumber, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + std::vector result, + std::vector > points2DOutputGUIRealView, + unsigned int numberOfMissingJoints, + unsigned int gestureDetected, + const char * gestureName, + unsigned int gestureFrame + ); +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/opengl.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/opengl.cpp new file mode 100644 index 0000000..0a728a1 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/opengl.cpp @@ -0,0 +1,182 @@ +#include +#include "opengl.hpp" +#include "../mocapnet2.hpp" + +#if USE_OPENGL + int openGLHasInitialization=0; + int openGLHasFailed=0; + #include "../../../../dependencies/RGBDAcquisition/opengl_acquisition_shared_library/OpenGLAcquisition.h" +#endif + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +int initializeOpenGLStuff(unsigned int openGLFrameWidth,unsigned int openGLFrameHeight) +{ + #if USE_OPENGL + if (openGLHasFailed) + { + fprintf(stderr,"visualizeOpenGL won't do anything because it has previously failed.. \n"); + return 0; + } + //------------------------------------------------------------------ + if (!openGLHasInitialization) + { + openGLHasInitialization=1; + openGLHasFailed=1; + if (startOpenGLModule(1,"")) //safe + { + if ( + createOpenGLDevice( + 0, + "dataset/human3D.conf", + openGLFrameWidth, + openGLFrameHeight, + 30 + ) + ) + { + fprintf(stderr,GREEN "Success creating an OpenGL context" NORMAL); + fprintf(stderr,GREEN "Requested rendering resolution was %ux%u\n" NORMAL,openGLFrameWidth,openGLFrameHeight); + openGLHasFailed=0; + return 1; + } + else + { + fprintf(stderr,"Failed creating an OpenGL context"); + return 0; + } + } else + { + fprintf(stderr,"Failed initializing OpenGL context"); + return 0; + } + } + #endif // USE_OPENGL + //------------------------------------------------------------------ + //------------------------------------------------------------------ + return 0; +} + +#if USE_OPENGL +#if USE_BVH +int changeOpenGLCalibration(struct calibration * calib) +{ + initializeOpenGLStuff(calib->width,calib->height); + return ( (setOpenGLColorCalibration(0,calib)) && (setOpenGLDepthCalibration(0,calib) ) ); +} +#endif +#endif + +char * visualizeOpenGL(unsigned int *openGLFrameWidth,unsigned int *openGLFrameHeight) +{ + #if USE_OPENGL + //fprintf(stderr,"visualizeOpenGL CALLED for %u x %u size \n",*openGLFrameWidth,*openGLFrameHeight); + + if (openGLHasFailed) + { + fprintf(stderr,"visualizeOpenGL won't do anything because it has previously failed.. \n"); + return 0; + } + //------------------------------------------------------------------ + initializeOpenGLStuff(*openGLFrameWidth,*openGLFrameHeight); + //------------------------------------------------------------------ + //------------------------------------------------------------------ + + fprintf(stderr,"Snap OpenGL frame..\n"); + snapOpenGLFrames(0); + fprintf(stderr,"Returning OpenGL frame..\n"); + return getOpenGLColorPixels(0); + #else + fprintf(stderr,"OpenGL code not included in this build..\n"); + return 0; + #endif + } + + +int clickOpenGLView(unsigned int x,unsigned int y,unsigned int state) +{ + #if USE_OPENGL + fprintf(stderr,"clickOpenGLView @ %u,%u\n",x,y); + return passUserInputOpenGL(0,0,state,x,y); + #else + fprintf(stderr,"OpenGL code not included in this build..\n"); + return 0; + #endif +} + + + + +int updateOpenGLView(std::vector bvhFrame) +{ + if (bvhFrame.size() +#include + + +/** +* @brief This is a list of joints from the BVH file to an OpenCOLLADA model created using makehuman and this target armature -> ( http://www.makehumancommunity.org/content/cmu_plus_face.html ) +* There is a 1:1 correspondance of this array and the BVH output joints (see MocapNETOutputJointNames ). Joints on this array that are marked as `-` or start with underscore ( _ ) are not active and are ignored + * This list must also be in sync with the human3D.conf file that holds the 3D scene that gets rendered.. + * To check it do : cat human3D.conf | grep Bone | cut -d':' -f2 | tr '\n' '~' | tr -d '[:blank:]' | sed -e 's/\~/"\n\"/g' + * To also check if the BVH header used is in sync as well with the names here you can do : cat dataset/headerWithHeadAndOneMotion.bvh | grep JOINT +*/ +static const char * OpenCOLLADANames[]= +{ +"Hips", // 0 "hip", +"Hips", // 3 "hip", +"Spine", // 6 "abdomen", + "Spine1", // 9 "chest", + "Neck", // 12 "neck", + "Neck1", // 15 "neck1", + "Head", // 18 "head", + "-", // 21 "__jaw", + "jaw", // 24 "jaw", + "special04", // 27 "special04", + "oris02", // 30 "oris02", + "oris01", // 33 "oris01", + "oris06_L", // 36 "oris06.l", + "oris07_L", // 39 "oris07.l", + "oris06_R", // 42 "oris06.r", + "oris07_R", // 45 "oris07.r", + "tongue00", // 48 "tongue00", + "tongue01", // 51 "tongue01", + "tongue02", // 54 "tongue02", + "tongue03", // 57 "tongue03", + "-", // 60 "__tongue04", + "tongue04", // 63 "tongue04", + "tongue07_L", // 66 "tongue07.l", + "tongue07_R", // 69 "tongue07.r", + "tongue06_L", // 72 "tongue06.l", + "tongue06_R", // 75 "tongue06.r", + "tongue05_L", // 78 "tongue05.l", + "tongue05_R", // 81 "tongue05.r", + "-", // 84 "__levator02.l", + "levator02_L", // 87 "levator02.l", + "levator03_L", // 90 "levator03.l", + "levator04_L", // 93 "levator04.l", + "levator05_L", // 96 "levator05.l", + "-", // 99 "__levator02.r", + "levator02_R", // 102 "levator02.r", + "levator03_R", // 105 "levator03.r", + "levator04_R", // 108 "levator04.r", + "levator05_R", // 111 "levator05.r", + "-", // 114 "__special01", + "special01", // 117 "special01", + "oris04_L", // 120 "oris04.l", + "oris03_L", // 123 "oris03.l", + "oris04_R", // 126 "oris04.r", + "oris03_R", // 129 "oris03.r", + "oris06", // 132 "oris06", + "oris05", // 135 "oris05", + "-", // 138 "__special03", + "special03", // 141 "special03", + "-", // 144 "__levator06.l", + "levator06_L", // 147 "levator06.l", + "-", // 150 "__levator06.r", + "levator06_R", // 153 "levator06.r", + "special06_L", // 156 "special06.l", + "special05_L", // 159 "special05.l", + "eye_L", // 162 "eye.l", + "orbicularis03_L", // 165 "orbicularis03.l", + "orbicularis04_L", // 168 "orbicularis04.l", + "special06_R", // 171 "special06.r", + "special05_R", // 174 "special05.r", + "eye_R", // 177 "eye.r", + "orbicularis03_R", // 180 "orbicularis03.r", + "orbicularis04_R", // 183 "orbicularis04.r", + "-", // 186 "__temporalis01.l", + "temporalis01_L", // 189 "temporalis01.l", + "oculi02_L", // 192 "oculi02.l", + "oculi01_L", // 195 "oculi01.l", + "__temporalis01_R", // 198 "__temporalis01.r", + "temporalis01_R", // 201 "temporalis01.r", + "oculi02_R", // 204 "oculi02.r", + "oculi01_R", // 207 "oculi01.r", + "__temporalis02_L", // 210 "__temporalis02.l", + "temporalis02_L", // 213 "temporalis02.l", + "risorius02_L", // 216 "risorius02.l", + "risorius03_L", // 219 "risorius03.l", + "__temporalis02_R", // 222 "__temporalis02.r", + "temporalis02_R", // 225 "temporalis02.r", + "risorius02_R", // 228 "risorius02.r", + "risorius03_R", // 231 "risorius03.r", + "RightShoulder", // 234 "rCollar", + "RightArm", // 237 "rShldr", + "RightForeArm", // 240 "rForeArm", + "RightHand", // 243"rHand", + "metacarpal1_R", // 246 "metacarpal1.r", + "finger2-1_R", // 249 "finger2-1.r", + "finger2-2_R", // 252 "finger2-2.r", + "finger2-3_R", // 255 "finger2-3.r", + "metacarpal2_R", // 258 "metacarpal2.r", + "finger3-1_R", // 261 "finger3-1.r", + "finger3-2_R", // 264 "finger3-2.r", + "finger3-3_R", // 267 "finger3-3.r", + "__metacarpal3_R", // 270 "__metacarpal3.r", + "metacarpal3_R", // 273 "metacarpal3.r", + "finger4-1_R", // 276 "finger4-1.r", + "finger4-2_R", // 279 "finger4-2.r", + "finger4-3_R", // 282 "finger4-3.r", + "__metacarpal4_R", // 285 "__metacarpal4.r", + "metacarpal4_R", // 288 "metacarpal4.r", + "finger5-1_R", // 291 "finger5-1.r", + "finger5-2_R", // 294 "finger5-2.r", + "finger5-3_R", // 297 "finger5-3.r", + "__rthumb", // 300 "__rthumb", + "RThumb", // 303 "rthumb", + "finger1-2_R", // 306 "finger1-2.r", + "finger1-3_R", // 309 "finger1-3.r", + "LeftShoulder", // 312 "lCollar", + "LeftArm", // 315 "lShldr", + "LeftForeArm", // 318 "lForeArm", + "LeftHand", // 321 "lHand", + "metacarpal1_L", // 324 "metacarpal1.l", + "finger2-1_L", // 327 "finger2-1.l", + "finger2-2_L", // 330 "finger2-2.l", + "finger2-3_L", // 333 "finger2-3.l", + "metacarpal2_L", // 336 "metacarpal2.l", + "finger3-1_L", // 339 "finger3-1.l", + "finger3-2_L", // 342 "finger3-2.l", + "finger3-3_L", // 345 "finger3-3.l", + "__metacarpal3_L", // 348 "__metacarpal3.l", + "metacarpal3_L", // 351 "metacarpal3.l", + "finger4-1_L", // 354 "finger4-1.l", + "finger4-2_L", // 357 "finger4-2.l", + "finger4-3_L", // 360 "finger4-3.l", + "__metacarpal4_L", // 363 "__metacarpal4.l", + "metacarpal4_L", // 366 "metacarpal4.l", + "finger5-1_L", // 369 "finger5-1.l", + "finger5-2_L", // 372 "finger5-2.l", + "finger5-3_L", // 375 "finger5-3.l", + "__lthumb", // 378 "__lthumb", + "LThumb", // 381 "lthumb", + "finger1-2_L", // 384 "finger1-2.l", + "finger1-3_L", // 387 "finger1-3.l", + "RHipJoint", // 390 "rButtock", + "RightUpLeg", // 393 "rThigh", + "RightLeg", // 396 "rShin", + "RightFoot", // 399 "rFoot", + "_toe1-1_R", // 402 "toe1-1.R", <- This does not currently exist in the makehuman model + "_toe1-2_R", // 405 "toe1-2.R", <- This does not currently exist in the makehuman model + "_toe2-1_R", // 408 "toe2-1.R", <- This does not currently exist in the makehuman model + "_toe2-2_R", // 411 "toe2-2.R", <- This does not currently exist in the makehuman model + "_toe2-3_R", // 414 "toe2-3.R", <- This does not currently exist in the makehuman model + "_toe3-1_R", // 417 "toe3-1.R", <- This does not currently exist in the makehuman model + "_toe3-2_R", // 420 "toe3-2.R", <- This does not currently exist in the makehuman model + "_toe3-3_R", // 423 "toe3-3.R", <- This does not currently exist in the makehuman model + "_toe4-1_R", // 426 "toe4-1.R", <- This does not currently exist in the makehuman model + "_toe4-2_R", // 429 "toe4-2.R", <- This does not currently exist in the makehuman model + "_toe4-3_R", // 432 "toe4-3.R", <- This does not currently exist in the makehuman model + "_toe5-1_R", // 435 "toe5-1.R", <- This does not currently exist in the makehuman model + "_toe5-2_R", // 438 "toe5-2.R", <- This does not currently exist in the makehuman model + "_toe5-3_R", // 441 "toe5-3.R", <- This does not currently exist in the makehuman model + "LHipJoint", // 444 "lButtock", + "LeftUpLeg", // 447 "lThigh", + "LeftLeg", // 450 "lShin", + "LeftFoot", // 453 "lFoot", + "_toe1-1_L", // 456 "toe1-1.L", <- This does not currently exist in the makehuman model + "_toe1-2_L", // 459 "toe1-2.L", <- This does not currently exist in the makehuman model + "_toe2-1_L", // 462 "toe2-1.L", <- This does not currently exist in the makehuman model + "_toe2-2_L", // 465 "toe2-2.L", <- This does not currently exist in the makehuman model + "_toe2-3_L", // 468 "toe2-3.L", <- This does not currently exist in the makehuman model + "_toe3-1_L", // 471 "toe3-1.L", <- This does not currently exist in the makehuman model + "_toe3-2_L", // 474 "toe3-2.L", <- This does not currently exist in the makehuman model + "_toe3-3_L", // 477 "toe3-3.L", <- This does not currently exist in the makehuman model + "_toe4-1_L", // 480 "toe4-1.L", <- This does not currently exist in the makehuman model + "_toe4-2_L", // 483 "toe4-2.L", <- This does not currently exist in the makehuman model + "_toe4-3_L", // 486 "toe4-3.L", <- This does not currently exist in the makehuman model + "_toe5-1_L", // 489 "toe5-1.L", <- This does not currently exist in the makehuman model + "_toe5-2_L", // 492 "toe5-2.L", <- This does not currently exist in the makehuman model + "_toe5-3_L", // 495 "toe5-3.L" <- This does not currently exist in the makehuman model + }; + + +#if USE_BVH +#include "../../../../dependencies/RGBDAcquisition/tools/Calibration/calibration.h" +int changeOpenGLCalibration(struct calibration * calib); +#endif + + +int clickOpenGLView(unsigned int x,unsigned int y,unsigned int state); + + +int initializeOpenGLStuff(unsigned int openGLFrameWidth,unsigned int openGLFrameHeight); + +/** + * @brief This function uses the file dataset/human3D.conf and the current scene state as updated using updateOpenGLView + * @param Pointer to a value that is both input and output width of the OpenGL frame. The renderer will try to use the provided width but it might not be exactly the same. + * @param Pointer to a value that is both input and output height of the OpenGL frame. The renderer will try to use the provided height but it might not be exactly the same. + * @ingroup tools + * @retval 0=Failure else a pointer to an RGB image that holds the OpenGL rendering + */ +char * visualizeOpenGL(unsigned int *openGLFrameWidth,unsigned int *openGLFrameHeight); + + +/** + * @brief This function provides the 3D pose extracted to the OpenGL context so that the armature will be rendered accordingly + * @param MocapNET BVH output + * @ingroup tools + * @retval 1=Success/0=Failure + */ +int updateOpenGLView(std::vector bvhFrame); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/rgb.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/rgb.cpp new file mode 100644 index 0000000..b89ff70 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/rgb.cpp @@ -0,0 +1,186 @@ + +#if USE_OPENCV + +#include "opencv2/opencv.hpp" +using namespace cv; + +int visualizeCameraIntensities(const char* windowName, cv::Mat &imgOriginal,int forceColors) +{ + float fontSize = 0.3; + unsigned int verticalSpace=25; + unsigned int horizontalSpace=10; + + unsigned int x=0,y=10; + char text[512]; + if ( (imgOriginal.rows!=0) && (imgOriginal.cols!=0) ) + { + cv::Mat img(32,32, CV_8UC3, cv::Scalar(0,0,0)); + cv::resize(imgOriginal, img, img.size() ,0,0,INTER_NEAREST); + //cv::imshow(windowName,img); + cv::Mat imgV((3+img.cols) * horizontalSpace *3 ,img.rows * verticalSpace, CV_8UC3,cv::Scalar(0,0,0)); + for(int r=0; r=3) + { + cv::Mat imageMerged; + + if (channelNumber==3) + { + channel[0]=cv::Mat::zeros(img.rows, img.cols, CV_8UC1); + cv::merge(channel,3,imageMerged); + cv::putText(imageMerged,"Green + Red channel", jointPoint, cv::FONT_HERSHEY_DUPLEX,fontSize, cv::Scalar(0,255,255), 0.2, cv::LINE_8); + } + else //Set blue channel to 0 + if (channelNumber==4) + { + channel[1]=cv::Mat::zeros(img.rows, img.cols, CV_8UC1); + cv::merge(channel,3,imageMerged); + cv::putText(imageMerged,"Blue + Red channel", jointPoint, cv::FONT_HERSHEY_DUPLEX,fontSize, cv::Scalar(255,0,255), 0.2, cv::LINE_8); + } + else //Set blue channel to 0 + if (channelNumber==5) + { + channel[2]=cv::Mat::zeros(img.rows, img.cols, CV_8UC1); + cv::merge(channel,3,imageMerged); + cv::putText(imageMerged,"Green + Blue channel", jointPoint, cv::FONT_HERSHEY_DUPLEX,fontSize, cv::Scalar(255,255,0), 0.2, cv::LINE_8); + } + else //Set blue channel to 0 + { + cv::merge(channel,3,imageMerged); + cv::putText(imageMerged,"Red + Green + Blue channel", jointPoint, cv::FONT_HERSHEY_DUPLEX,fontSize, cv::Scalar(255,255,255), 0.2, cv::LINE_8); + } + + cv::imshow(windowName,imageMerged); + } + + + return 1; + } + return 0; +} + + + +int visualizeCameraEdges(const char* windowName,cv::Mat &img) +{ + cv::Mat edges; + //cv::cvtColor(img, edges,BGR2GRAY); + + cv::Canny(edges, edges, 30, 60); + + cv::Point jointPoint(10,10); + float fontSize = 0.5; + cv::putText(edges,"Edges", jointPoint, cv::FONT_HERSHEY_DUPLEX,fontSize, cv::Scalar(255,255,255), 0.2, cv::LINE_8); + cv::imshow(windowName, edges); + return 1; +} + + + +int visualizeCameraFeatures(const char* windowName,cv::Mat &img) +{ + std::vector keypoints; + + cv::Mat imgCopy; //= img.clone(); + cv::resize(img, imgCopy, img.size() ,0,0,INTER_NEAREST); + cv::FAST(imgCopy,keypoints,100.0,false); + char coordinates[256]; + + + for (int i=0; i > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + unsigned int numberOfMissingJoints +) +{ + +#if USE_OPENCV + int success=0; + char finalFilename[2048]= {0}; + cv::Mat raytraced; + + + snprintf(finalFilename,256,"%s/raytraced2/%04u.png",path,frameNumber); + raytraced = imread(finalFilename,cv::IMREAD_COLOR); // older versions might want the CV_LOAD_IMAGE_COLOR flag + + if (alreadyLoadedImage==0) + { + + char formatString[256]= {0}; + snprintf(formatString,256,"%%s/%%s%%0%uu.jpg",serialLength); + + //colorFrame_0_00001.jpg + snprintf(finalFilename,2048,formatString,path,label,frameNumber/*Frame ID*/); + //snprintf(finalFilename,2048,"%s/colorFrame_0_%05d.jpg",path,frameNumber+1); + } + + int showFramerate=30; //30 or 0 + + float scale=1.0; + cv::Mat image; + cv::Rect roi; + cv::Mat destinationROI; + + if ( (fileExists(finalFilename) ) || (alreadyLoadedImage!=0) ) + { + if (alreadyLoadedImage!=0) + { + image = *alreadyLoadedImage; + + if ( + ( image.size().width < 640 ) || + ( image.size().height < 480 ) + ) + { + fprintf(stderr,"Very small given frame ( %ux%u..\n",image.size().width,image.size().height); + return 0; + } + + //cv::imshow(windowName,image); // + //cv::waitKey(0); + } + else + if (fileExists(finalFilename) ) + { + image = imread(finalFilename,cv::IMREAD_COLOR); // older versions might want the CV_LOAD_IMAGE_COLOR flag + } + + + + visualizeSkeletonSerialized( + image, + skeleton, + 1,//Show left hand + 1,//Show right hand + 1,//Show face + 0,0, + image.size().width, + image.size().height + ); + drawSkeleton(image,points2DOutputGUIRealView,-330,-0,0); + + + int NSDMWidth=160; //body leftHand rightHand + int NSDMHeight=160; + + int positionX = image.size().width - NSDMWidth - 100; + int positionY = 240; + + cv::rectangle(image,cv::Rect(positionX-20,positionY-40,NSDMWidth+40,620+showFramerate),cv::Scalar(0,0,0),-1); + + visualizeNSDM(image,"Upper Body",mnet->upperBody.NSDM,1 /*angles*/,positionX,positionY,NSDMWidth,NSDMHeight); + positionY+=200; + visualizeNSDM(image,"Lower Body",mnet->lowerBody.NSDM,1 /*angles*/,positionX,positionY,NSDMWidth,NSDMHeight); + positionY+=200; + + visualizeOrientation( + image,"Orientation", + mnet->currentSolution[4], //Orientation value + mnet->orientationClassifications[0], + mnet->orientationClassifications[1], + mnet->orientationClassifications[2], + mnet->orientationClassifications[3], + positionX, + positionY, + NSDMWidth, + NSDMHeight + ); + + + if (showFramerate>0) + { + //----------------------------------------------------------------- + float thickness=1.7; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + cv::Scalar fontColor= cv::Scalar(255,255,255); + cv::Point txtPosition(positionX+10,positionY+190); + char fpsString[128]; + snprintf(fpsString,128,"%0.2f fps",options->totalLoopFPS); + cv::putText(image,fpsString,txtPosition,fontUsed,0.8,fontColor,thickness,8); + //----------------------------------------------------------------- + } + + if(image.data!=0) + { + if ( image.size().height > image.size().width ) + { + scale=(float) 720/image.size().height; + } + else + { + scale=(float) 1024/image.size().width; + } + if (scale>1.0) + { + scale=1.0; + } + if (scale!=1.0) + { + cv::resize(image, image, cv::Size(0,0), scale, scale); + } + + //fprintf(stderr,"Image ( %u x %u )\n",image.size().width,image.size().height); + success=1; + } + + } + else + { + fprintf(stderr," Could not load %s image, cannot proceed to visualize it\n",finalFilename); + return 0; + } + + if (saveVisualization) + { + char filename[512]; + snprintf(filename,512,"vis%05u.jpg",frameNumber) ; + cv::imwrite(filename,image); + } + + cv::imshow(windowName,image); // + //Did not find a file to show .. + return success; +#else + fprintf(stderr,"OpenCV code not present in this build, cannot show visualization..\n"); +#endif + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/template.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/template.hpp new file mode 100644 index 0000000..8523d85 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/template.hpp @@ -0,0 +1,35 @@ +#pragma once +/** @file allInOne.hpp + * @brief Code that handles GUI output and visualization using OpenCV. + * If OpenCV is not be available, CMake will not declare the USE_OPENCV compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include + + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; + +int visualizeTemplate( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + struct MocapNET2Options * options, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + unsigned int numberOfMissingJoints +); + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/visualization.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/visualization.cpp new file mode 100644 index 0000000..84a997f --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/visualization.cpp @@ -0,0 +1,1827 @@ +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; +#endif + + +#include "visualization.hpp" +#include "opengl.hpp" +#include "map.hpp" +#include "allInOne.hpp" +#include "camera_ready.hpp" +#include "widgets.hpp" +#include "drawSkeleton.hpp" +#include "template.hpp" +#include "../IO/jsonRead.hpp" +#include "../IO/bvh.hpp" +#include "../tools.hpp" +#include "../mocapnet2.hpp" +//#include "../MocapNETLib2/NSDM/legacyNSDM.hpp" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +std::vector leftEndEffector; +std::vector rightEndEffector; + + +int debug2DPointAlignment( + const char * windowName, + std::vector original2DPoints, + std::vector rotated2DPoints, + unsigned int pivotPoint, + unsigned int referencePoint, + unsigned int width, + unsigned int height +) +{ +#if USE_OPENCV + if (original2DPoints.size()!=rotated2DPoints.size()) + { + return 0; + } + + cv::Mat img(height,width, CV_8UC3, cv::Scalar(0,0,0)); + + for (int jointID=0; jointIDskeletonHeaderElements/3; jID++) + { + + jointPoint.x=input->skeletonBody[jID*3+0].value * MocapNETTrainingWidth; + jointPoint.y=input->skeletonBody[jID*3+1].value * MocapNETTrainingHeight; + //fprintf(stderr,"jIDIN %s,%s(%u)=%u,%u\n",input->skeletonHeader[jID*3+0].str,input->skeletonHeader[jID*3+1].str,jID,jointPoint.x,jointPoint.y); + int thickness=-2; + cv::circle(visualization,jointPoint,3,cv::Scalar(255,0,255),thickness,8,0); + } + + for (unsigned int jID=3; jIDskeletonHeaderElements/3; jID++) + { + jointPoint.x=result->skeletonBody[jID*3+0].value * MocapNETTrainingWidth; + jointPoint.y=result->skeletonBody[jID*3+1].value * MocapNETTrainingHeight; + //fprintf(stderr,"jIDRES %s,%s(%u)=%u,%u\n",result->skeletonHeader[jID*3+0].str,result->skeletonHeader[jID*3+1].str,jID,jointPoint.x,jointPoint.y); + int thickness=-2; + cv::circle(visualization,jointPoint,3,cv::Scalar(255,0,0),thickness,8,0); + } + + cv::imshow("Reprojection Check",visualization); + return 1; + #endif + + return 0; + +} + + + +#if USE_OPENCV + +cv::Mat openGLFramePermanentMat; + + + +static const char * reprojectBVHNames[] = +{ + "lShldr", + "rShldr", + "lForeArm", + "rForeArm", + "lHand", + "rHand", + "lThigh", + "rThigh", + "lShin", + "rShin", + "lFoot", + "rFoot" +}; + +static int reproject2DIDs[] = +{ + BODY25_LShoulder, + BODY25_RShoulder, + BODY25_LElbow, + BODY25_RElbow, + BODY25_LWrist, + BODY25_RWrist, + BODY25_LHip, + BODY25_RHip, + BODY25_LKnee, + BODY25_RKnee, + BODY25_LAnkle, + BODY25_RAnkle +}; + +static int numberOfReprojectionChecks=12; + + + + +int visualizeSkeletonCorrespondence( + cv::Mat &imgO, + std::vector > points2DInput, + std::vector > points2DOutput, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +) +{ + //TODO: FIX THIS + return 0; + + if ( + (points2DInput.size()==0) + ) + { + fprintf(stderr,YELLOW "visualizeSkeletonCorrespondence cannot display something without the input 2D points\n" NORMAL); + return 0; + } + + if ( + (points2DOutput.size()==0) + ) + { + fprintf(stderr,YELLOW "visualizeSkeletonCorrespondence cannot display something without the output 2D points\n" NORMAL); + return 0; + } + + + height=1080; + width=1920; + int doFullReprojectionVisualization = 0; + + if (doFullReprojectionVisualization) + { + cv::Mat img(height,width, CV_8UC3, Scalar(0,0,0)); + + +//Just the points and text ( foreground ) + for (int jointID=0; jointID0.07) + { + //fprintf(stderr,RED); + + cv::Point jointPoint(x,y); + cv::putText(imgO, textWarning , jointPoint, cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar(0,0,255), 0.2, 8 ); + y+=15; + } + //fprintf(stderr,"%s\n" NORMAL,textWarning); + } + + + return 1; +} + + + + + + + + + + + +int drawEndEffectorTrack(cv::Mat &outputMat,std::vector > points2DOutputGUIForcedView) +{ + int maxEndEffectorHistory=10; + unsigned int jointIDLeftHand= getBVHJointIDFromJointName("lHand"); + cv::Point leftHand(points2DOutputGUIForcedView[jointIDLeftHand][0],points2DOutputGUIForcedView[jointIDLeftHand][1]); + leftEndEffector.push_back(leftHand); + if (leftEndEffector.size()>maxEndEffectorHistory) + { + leftEndEffector.erase(leftEndEffector.begin()); + } + + unsigned int jointIDRightHand= getBVHJointIDFromJointName("rHand"); + cv::Point rightHand(points2DOutputGUIForcedView[jointIDRightHand][0],points2DOutputGUIForcedView[jointIDRightHand][1]); + rightEndEffector.push_back(rightHand); + if (rightEndEffector.size()>maxEndEffectorHistory) + { + rightEndEffector.erase(rightEndEffector.begin()); + } + + float stepColorD=(float) 255/maxEndEffectorHistory; + for (int step=0; step1) + { + cv::line(outputMat,leftEndEffector[step],leftEndEffector[step-1], cv::Scalar(0,stepColor,stepColor), 1.0); + cv::line(outputMat,rightEndEffector[step],rightEndEffector[step-1], cv::Scalar(0,stepColor,stepColor), 1.0); + + } + cv::circle(outputMat,leftEndEffector[step],1,cv::Scalar(0,stepColor,stepColor),3,8,0); + cv::circle(outputMat,rightEndEffector[step],1,cv::Scalar(0,stepColor,stepColor),3,8,0); + } + + return 1; +} + + + + +cv::Scalar getColorFromIndex(unsigned int i) +{ + cv::Scalar color = cv::Scalar::all(255); + + i = i%107; + + color[0] = lineColorIndex[i*3+0]; + color[1] = lineColorIndex[i*3+1]; + color[2] = lineColorIndex[i*3+2]; + + + return color; +} + + +std::vector > place2DSkeletonElsewhere(unsigned int x,unsigned int y, +unsigned int width, +unsigned int height, +std::vector > skeleton2D) +{ + int jointID=0; + + for (jointID=0; jointID > history, std::vector > skeleton2D) +{ +#if USE_OPENCV + if (history.size()==0) + { + return 0; + } + + + unsigned int visualizeWidth=1170; + unsigned int visualizeHeight=1024; + cv::Mat img(visualizeHeight,visualizeWidth, CV_8UC3, cv::Scalar(0,0,0)); + + //drawSkeleton(img,place2DSkeletonElsewhere(450,350,200,200,skeleton2D),0/*No 2D skeleton*/,0.0,0.0,1); + + //Was 450 + drawSkeleton(img,place2DSkeletonElsewhere(0,150,200,200,skeleton2D),0.0,0.0,1); + + unsigned int widthOfGraphs=165; + unsigned int heightOfGraphs=100; + unsigned int i=0,joint=0; + + unsigned int plotPosX=0; + unsigned int plotPosY=0; + unsigned int shiftY=15; + + char labelOfPlot[512]; + + + for (i=1; i=MOCAPNET_OUTPUT___JAW_ZROTATION) && (joint<=MOCAPNET_OUTPUT_RISORIUS03_R_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_TOE1_1_R_ZROTATION) && (joint<=MOCAPNET_OUTPUT_LBUTTOCK_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_TOE1_1_L_ZROTATION) && (joint<=MOCAPNET_OUTPUT_NUMBER) ) || + + ( (joint>=MOCAPNET_OUTPUT_METACARPAL1_R_ZROTATION) && (joint<=MOCAPNET_OUTPUT_FINGER1_3_R_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_METACARPAL1_L_ZROTATION) && (joint<=MOCAPNET_OUTPUT_FINGER1_3_L_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_RBUTTOCK_ZROTATION) && (joint<=MOCAPNET_OUTPUT_RBUTTOCK_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_LBUTTOCK_ZROTATION) && (joint<=MOCAPNET_OUTPUT_LBUTTOCK_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_CHEST_ZROTATION) && (joint<=MOCAPNET_OUTPUT_CHEST_YROTATION) )|| + //( (joint>=MOCAPNET_OUTPUT_LEFTEYE_ZROTATION) && (joint<=MOCAPNET_OUTPUT_RIGHTEYE_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_RCOLLAR_ZROTATION) && (joint<=MOCAPNET_OUTPUT_RCOLLAR_YROTATION) ) || + ( (joint>=MOCAPNET_OUTPUT_LCOLLAR_ZROTATION) && (joint<=MOCAPNET_OUTPUT_LCOLLAR_YROTATION) ) + ) + { + //Don't plot hands for now.. + //Don't plot dead joints + } else + { + if (i==5) + { + cv::Point labelPosition(plotPosX, plotPosY); + float absoluteValue = history[history.size()-1][joint]; + float velocityValue = history[history.size()-1][joint] - history[history.size()-2][joint]; + float accelerationValue = velocityValue - ( history[history.size()-3][joint] - history[history.size()-4][joint] ); + + + snprintf(labelOfPlot,512,"%s -> %0.2f",MocapNETOutputArrayNames[joint],absoluteValue); + cv::putText(img,labelOfPlot, labelPosition, cv::FONT_HERSHEY_DUPLEX, 0.3, cv::Scalar::all(255), 0.2, 8 ); + + snprintf(labelOfPlot,512,"v %0.2f acc %0.2f",velocityValue,accelerationValue); + labelPosition.y+=10; + cv::putText(img,labelOfPlot, labelPosition, cv::FONT_HERSHEY_DUPLEX, 0.3, cv::Scalar::all(255), 0.2, 8 ); + } + + cv::Point jointPointPrev(plotPosX+ i-1, plotPosY+history[i-1][joint] + heightOfGraphs/2 ); + cv::Point jointPointNext(plotPosX+ i, plotPosY+history[i][joint] + heightOfGraphs/2); + cv::Scalar usedColor = getColorFromIndex(joint); + + if (joint==4) + { + //Bright RED color for the Y Orientation which is very important + usedColor=cv::Scalar(0,0,255); + } + + cv::line(img,jointPointPrev,jointPointNext,usedColor, 1.0); + plotPosY+=heightOfGraphs; + + if (plotPosY+heightOfGraphs+shiftY>1000) + { + plotPosX+=widthOfGraphs; + plotPosY=shiftY; + } + } + + } + } + + cv::imshow(windowName, img); + return 1; + #else + fprintf(stderr,"visualizeMotionHistory cannot be compiled without OpenCV\n"); + return 0; +#endif +} + + + + + + + +int visualizeInput2DSkeletonFromSkeletonStruct( + cv::Mat &outputMat, + struct skeletonStructure * skeleton, + unsigned int skeletonWidth, + unsigned int skeletonHeight, + unsigned int x,unsigned int y, + unsigned int width,unsigned int height + ) +{ + #if USE_TRANSPARENCY + cv::Mat base; + cv::Mat overlay(outputMat.size().height,outputMat.size().width, CV_8UC3, Scalar(0,0,0)); + outputMat.copyTo(base); + cv::Mat * baseP = &base; + cv::Mat * overlayP = &overlay ; + cv::Mat * outputP = &outputMat; + #else + //Not using transparency you just write everything on output + //and get rid of the add weighted last step that takes 6% of CPU time + cv::Mat * baseP = &outputMat; + cv::Mat * overlayP = &outputMat ; + cv::Mat * outputP = &outputMat; + #endif + + cv::Point parentPoint = cv::Point(x+100,y+100); + cv::Point targetPoint = cv::Point(x+100,y+100); + + cv::Scalar color; + + for (int jointID=0; jointIDbody.joint2D[jointID].x / skeletonWidth ; + float yNormalized = skeleton->body.joint2D[jointID].y / skeletonHeight ; + + float xParentNorm= skeleton->body.joint2D[Body25SkeletonJointsParentRelationMap[jointID]].x/skeletonWidth; + float yParentNorm= skeleton->body.joint2D[Body25SkeletonJointsParentRelationMap[jointID]].y/skeletonHeight; + + if ( (xNormalized!=0) && (yNormalized!=0) ) + { + + targetPoint.x = x+xNormalized *width; + targetPoint.y = y+yNormalized *height; + + parentPoint.x = x+xParentNorm*width; + parentPoint.y = y+yParentNorm*height; + //fprintf(stderr,"Point%u (%0.2f,%0.2f)",jointID,targetPoint.x, targetPoint.y ); + + //cv::Scalar(0,123,123) + cv::circle(*overlayP,targetPoint,3,cv::Scalar(255,0,255),3,8,0); + + switch(jointID) + { + case BODY25_LAnkle : color = cv::Scalar(0,0,255); break; + case BODY25_LKnee : color = cv::Scalar(0,0,255); break; + case BODY25_LHip : color = cv::Scalar(0,0,255); break; + case BODY25_LBigToe : color = cv::Scalar(0,0,255); break; + case BODY25_LSmallToe : color = cv::Scalar(0,0,255); break; + case BODY25_LElbow : color = cv::Scalar(0,0,255); break; + case BODY25_LShoulder : color = cv::Scalar(0,0,255); break; + case BODY25_LWrist : color = cv::Scalar(0,0,255); break; + case BODY25_RAnkle : color = cv::Scalar(0,255,0); break; + case BODY25_RKnee : color = cv::Scalar(0,255,0); break; + case BODY25_RHip : color = cv::Scalar(0,255,0); break; + case BODY25_RBigToe : color = cv::Scalar(0,255,0); break; + case BODY25_RSmallToe : color = cv::Scalar(0,255,0); break; + case BODY25_RElbow : color = cv::Scalar(0,255,0); break; + case BODY25_RShoulder : color = cv::Scalar(0,255,0); break; + case BODY25_RWrist : color = cv::Scalar(0,255,0); break; + + default : + color = cv::Scalar(255,0,0); + }; + + if ( (xParentNorm!=0) && (yParentNorm!=0) ) + { + cv::line(*overlayP,targetPoint,parentPoint,color,3.0); + } + } + } + + if (skeleton->leftHand.isPopulated) + { + for (int jointID=0; jointIDleftHand.joint2D[jointID].x / skeletonWidth ; + float yNormalized = skeleton->leftHand.joint2D[jointID].y / skeletonHeight ; + targetPoint.x = x+xNormalized *width; + targetPoint.y = y+yNormalized *height; + + cv::circle(*overlayP,targetPoint,1,cv::Scalar(0,0,255),1,8,0); + } + } + + if (skeleton->rightHand.isPopulated) + { + for (int jointID=0; jointIDrightHand.joint2D[jointID].x / skeletonWidth ; + float yNormalized = skeleton->rightHand.joint2D[jointID].y / skeletonHeight ; + targetPoint.x = x+xNormalized *width; + targetPoint.y = y+yNormalized *height; + + cv::circle(*overlayP,targetPoint,1,cv::Scalar(0,255,0),1,8,0); + } + } + + + if (skeleton->head.isPopulated) + { + for (int jointID=0; jointIDhead.joint2D[jointID].x / skeletonWidth ; + float yNormalized = skeleton->head.joint2D[jointID].y / skeletonHeight ; + targetPoint.x = x+xNormalized *width; + targetPoint.y = y+yNormalized *height; + + //fprintf(stderr,"head(%u,%u)",targetPoint.x,targetPoint.y); + cv::circle(*overlayP,targetPoint,1,cv::Scalar(0,255,255),1,8,0); + } + } + + #if USE_TRANSPARENCY + cv::addWeighted(*baseP,0.8, *overlayP, 0.3, 0.0, *outputMatP); + #endif + + return 1; +} + + + + + + +int visualizeInput2DSkeletonFromSkeletonSerialized( + cv::Mat &outputMat, + struct skeletonSerialized * skeleton, + unsigned int x,unsigned int y, + unsigned int width,unsigned int height + ) +{ + /* + fprintf(stderr,"Visualize %u,%u - Skeleton ",skeletonWidth,skeletonHeight); + fprintf(stderr,"Offset %u,%u - ",x,y); + fprintf(stderr,"Joints %u - ",skeleton->skeletonBodyElements); + fprintf(stderr,"Dimensions %u,%u \n ",width,height); + */ + + #if USE_TRANSPARENCY + cv::Mat base; + cv::Mat overlay(outputMat.size().height,outputMat.size().width, CV_8UC3, Scalar(0,0,0)); + outputMat.copyTo(base); + cv::Mat * baseP = &base; + cv::Mat * overlayP = &overlay ; + cv::Mat * outputP = &outputMat; + #else + //Not using transparency you just write everything on output + //and get rid of the add weighted last step that takes 6% of CPU time + cv::Mat * baseP = &outputMat; + cv::Mat * overlayP = &outputMat ; + cv::Mat * outputP = &outputMat; + #endif + + + cv::Scalar color = cv::Scalar(0,255,255); + cv::Point pt1 = cv::Point(x,y); + cv::Point pt2 = cv::Point(x+width,y+height); + cv::rectangle(*overlayP, pt1, pt2, color); + + + unsigned int pointsThatExist=0; + unsigned int pointsThatSeemToBeWronglyNormalized=0; + + for (int jointID=3; jointIDskeletonBodyElements/3; jointID++) + { + unsigned int jID = jointID*3 ; + + float pointExists = skeleton->skeletonBody[jID+2].value; + + if (pointExists>0.0) + { + float xNormalized = (float) skeleton->skeletonBody[jID+0].value / skeleton->width; + float yNormalized = (float) skeleton->skeletonBody[jID+1].value / skeleton->height; + + if ( (xNormalized!=0.0) || (yNormalized!=0.0) ) + { + //First of all bring output point to the range of our mini + //screen + pt1.x = (unsigned int) (xNormalized * width); + pt1.y = (unsigned int) (yNormalized * height); + + //Check if we have a normalization error..! + if ( (pt1.x>width) || (pt1.y>height) ) + { + /* + fprintf( + stderr,"Point %s,%s is out of bounds (%u,%u)\n", + skeleton->skeletonHeader[jID+0].str, + skeleton->skeletonHeader[jID+1].str, + pt1.x , + pt1.y + ); + */ + ++pointsThatSeemToBeWronglyNormalized; + } + + + //Finally put the point in the target position on the overlay + pt1.x += (unsigned int) x; + pt1.y += (unsigned int) y; + + //And draw it. + cv::circle(*overlayP,pt1,1,color,1,8,0); + ++pointsThatExist; + } + } + } + + if (pointsThatSeemToBeWronglyNormalized>0) + { + fprintf(stderr,"visualizeInput2DSkeletonFromSkeletonSerialized %u points seem to be incorrectly normalized\n",pointsThatSeemToBeWronglyNormalized); + fprintf(stderr,"signaled units are %0.2f x %0.2f \n",skeleton->width,skeleton->height); + } + + float thickness=1.0; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + if (pointsThatExist==0) + { + pt1.x=x+10; + pt1.y=y+10; + cv::putText(*overlayP,"No Valid 2D Points available",pt1,fontUsed,0.4,color,thickness,8); + } + + if (pointsThatSeemToBeWronglyNormalized>0) + { + pt1.x=x+10; + pt1.y=y+30; + cv::putText(*overlayP,"Invalid Normalized Input Points",pt1,fontUsed,0.4,color,thickness,8); + } + + #if USE_TRANSPARENCY + cv::addWeighted(*baseP,1.0,*overlayP, 0.5, 0.0,*outputMatP); + #endif + return 1; +} + + + + + + +int visualizeInput2DSkeletonFromVectorofVectors( + cv::Mat &outputMat, + std::vector > skeleton, + unsigned int skeletonWidth,unsigned int skeletonHeight, + unsigned int x,unsigned int y, + unsigned int width,unsigned int height + ) +{ + + + /* + fprintf(stderr,"Visualize %u,%u - Skeleton ",skeletonWidth,skeletonHeight); + fprintf(stderr,"Offset %u,%u - ",x,y); + fprintf(stderr,"Joints %lu - ",skeleton.size()); + fprintf(stderr,"Dimensions %u,%u \n ",width,height); + */ + + + + #if USE_TRANSPARENCY + cv::Mat base; + cv::Mat overlay(outputMat.size().height,outputMat.size().width, CV_8UC3, Scalar(0,0,0)); + outputMat.copyTo(base); + cv::Mat * baseP = &base; + cv::Mat * overlayP = &overlay ; + cv::Mat * outputP = &outputMat; + #else + //Not using transparency you just write everything on output + //and get rid of the add weighted last step that takes 6% of CPU time + cv::Mat * baseP = &outputMat; + cv::Mat * overlayP = &outputMat ; + cv::Mat * outputP = &outputMat; + #endif + + + cv::Scalar color = cv::Scalar(123,123,0); + cv::Point pt1 = cv::Point(x,y); + cv::Point pt2 = cv::Point(x+width,y+height); + cv::rectangle(*overlayP, pt1, pt2, color); + + + unsigned int pointsThatExist=0; + unsigned int pointsThatSeemToBeWronglyNormalized=0; + + for (int jID=0; jID0.0) + { + float xNormalized = (float) skeleton[jID][0] / skeletonWidth; + float yNormalized = (float) skeleton[jID][1] / skeletonHeight; + + if ( (xNormalized!=0.0) || (yNormalized!=0.0) ) + { + //First of all bring output point to the range of our mini + //screen + pt1.x = (unsigned int) (xNormalized * width); + pt1.y = (unsigned int) (yNormalized * height); + + //Check if we have a normalization error..! + if ( (pt1.x>width) || (pt1.y>height) ) + { + /* + fprintf( + stderr,"Point is out of bounds (%u,%u) / (%u,%u) \n", + (unsigned int) pt1.x , + (unsigned int) pt1.y , + width, + height + ); + */ + ++pointsThatSeemToBeWronglyNormalized; + } + + + //Finally put the point in the target position on the overlay + pt1.x += (unsigned int) x; + pt1.y += (unsigned int) y; + + //And draw it. + cv::circle(*overlayP,pt1,1,color,1,8,0); + ++pointsThatExist; + } + } + } + + if (pointsThatSeemToBeWronglyNormalized>0) + { + fprintf(stderr,"visualizeInput2DSkeletonFromSkeletonSerialized %u points seem to be incorrectly normalized\n",pointsThatSeemToBeWronglyNormalized); + fprintf(stderr,"signaled units are %u x %u \n",skeletonWidth,skeletonHeight); + } + + float thickness=1.0; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + if (pointsThatExist==0) + { + pt1.x=x+10; + pt1.y=y+10; + cv::putText(*overlayP,"No Valid 2D Points available",pt1,fontUsed,0.4,color,thickness,8); + } + + if (pointsThatSeemToBeWronglyNormalized>0) + { + pt1.x=x+10; + pt1.y=y+30; + cv::putText(*overlayP,"Invalid Normalized Input Points",pt1,fontUsed,0.4,color,thickness,8); + } + + #if USE_TRANSPARENCY + cv::addWeighted(*baseP,1.0,*overlayP, 0.5, 0.0,*outputMatP); + #endif + return 1; +} + + + + + + + + + + + +int scaleSkeleton( std::vector > &sk, float scaleX,float scaleY) +{ + for (int i=0; i=2) + { + sk[i][0]*=scaleX; + sk[i][1]*=scaleY; + } + } + return 1; +} + + +int visualizeInput( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + std::vector > points2DOutputGUIForcedViewSide, + unsigned int numberOfMissingJoints + ) +{ +#if USE_OPENCV + int success=0; + char finalFilename[2048]={0}; + + if (alreadyLoadedImage==0) + { + if (path==0) { fprintf(stderr,"Can't visualize input without path to RGB images\n"); return 0; } + + char formatString[256]= {0}; + snprintf(formatString,256,"%%s/%%s%%0%uu.jpg",serialLength); + + //colorFrame_0_00001.jpg + snprintf(finalFilename,2048,formatString,path,label,frameNumber+1/*Frame ID*/); + //snprintf(finalFilename,2048,"%s/colorFrame_0_%05d.jpg",path,frameNumber+1); + } + + + float scale=1.0; + cv::Mat image; + cv::Rect roi; + cv::Mat destinationROI; + + if ( (fileExists(finalFilename) ) || (alreadyLoadedImage!=0) ) + { + if (fileExists(finalFilename) ) + { + image = imread(finalFilename, cv::IMREAD_COLOR); // older versions might want the CV_LOAD_IMAGE_COLOR flag + } else + { + image = *alreadyLoadedImage; + } + + if(image.data!=0) + { + if ( image.size().height > image.size().width ) + { + scale=(float) 720/image.size().height; + } else + { + scale=(float) 1024/image.size().width; + } + if (scale>1.0) { scale=1.0; } + if (scale!=1.0) + { + cv::resize(image, image, cv::Size(0,0), scale, scale); + } + + //fprintf(stderr,"Image ( %u x %u )\n",image.size().width,image.size().height); + success=1; + } + + } else + { + fprintf(stderr," Could not load %s image, cannot proceed to visualize it\n",finalFilename); + return 0; + } + + + //int offsetX=950; + int offsetX=650; + cv::Mat visualization(image.size().height,offsetX+image.size().width, CV_8UC3, Scalar(0,0,0)); + //fprintf(stderr,"Visualization will be ( %u x %u )\n",visualization.size().width,visualization.size().height); + roi = cv::Rect( cv::Point(offsetX,0 ), cv::Size( image.size().width, image.size().height )); + destinationROI = visualization( roi ); + image.copyTo( destinationROI ); + + // visualizeInput2DSkeletonFromCOCOStruct(visualization,skeleton,width,height,offsetX,0, image.size().width,image.size().height); + visualizeInput2DSkeletonFromSkeletonSerialized(visualization,skeleton,offsetX,0, image.size().width,image.size().height); + + + cv::Point pt1(offsetX,0); + cv::Point pt2(offsetX+00,image.size().height); + cv::rectangle(visualization,pt1,pt2,cv::Scalar(0,0,0),-1,8,0); + + cv::Scalar color= cv::Scalar(123,123,123,123 /*Transparency here , although if the cv::Mat does not have an alpha channel it is useless*/); + cv::Scalar black= cv::Scalar(0,0,0,0 /*Transparency here , although if the cv::Mat does not have an alpha channel it is useless*/); + cv::Point txtPosition; + txtPosition.y=30; + float thickness=2.2; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + + //FORCE VIEW..! + //numberOfMissingJoints=0; + + if (numberOfMissingJoints>40) + { + txtPosition.x=60; + txtPosition.y=350; + cv::putText(visualization,"Incomplete Data..!",txtPosition,fontUsed,1.8,color,thickness,8); + } else + { + txtPosition.x=100; + cv::putText(visualization,"Front View",txtPosition,fontUsed,1.1,color,thickness,8); + + txtPosition.x=400; + cv::putText(visualization,"Side View",txtPosition,fontUsed,1.1,color,thickness,8); + + + drawSkeleton(visualization,points2DOutputGUIForcedView,-350,-50,0); + drawSkeleton(visualization,points2DOutputGUIForcedViewSide,-50,-50,0); + } + + txtPosition.x=offsetX+200; + txtPosition.y=30; + snprintf(finalFilename,2048,"Orientation Classifier Front(%0.1f)Back(%0.1f) Left(%0.1f) Right(%0.1f) ",mnet->orientationClassifications[0],mnet->orientationClassifications[1],mnet->orientationClassifications[2],mnet->orientationClassifications[3]); + cv::putText(visualization,finalFilename,txtPosition,fontUsed,0.7,black,thickness,8); + txtPosition.x+=2; + cv::putText(visualization,finalFilename,txtPosition,fontUsed,0.7,color,thickness,8); + txtPosition.x-=2; + + txtPosition.x+=200; + txtPosition.y=60; + snprintf(finalFilename,2048,"Perceived Orientation %s (%u) ",MocapNETOrientationNames[mnet->orientation],mnet->orientation); + cv::putText(visualization,finalFilename,txtPosition,fontUsed,0.7,black,thickness,8); + txtPosition.x+=2; + cv::putText(visualization,finalFilename,txtPosition,fontUsed,0.7,color,thickness,8); + txtPosition.x-=2; + + scaleSkeleton(points2DOutputGUIRealView,scale,scale); + drawSkeleton(visualization,points2DOutputGUIRealView,offsetX-100,0,0); + + + cv::imshow(windowName,visualization); + + if (saveVisualization) + { + char filename[1024]; + snprintf(filename,1024,"vis%05u.jpg",frameNumber); + imwrite(filename,visualization); + } + + //Did not find a file to show .. + return success; +#else + fprintf(stderr,"OpenCV code not present in this build, cannot show visualization..\n"); +#endif +return 0; +} + + + + + + + + + + + + +void spawnVisualizationWindow(const char* windowName,unsigned int width,unsigned int height) +{ + cv::Mat img(height,width, CV_8UC3, Scalar(0,0,0)); + cv::namedWindow(windowName,cv::WINDOW_AUTOSIZE); + imshow(windowName,img); +} + + + + + + + + + + + + + +int visualizePoints( + const char* windowName, + unsigned int frameNumber, + unsigned int skippedFrames, + signed int totalNumberOfFrames, + unsigned int numberOfFramesToGrab, + const char * CPUName, + const char * GPUName, + int drawFloor, + int drawNSDM, + float fpsTotal, + float fpsAcquisition, + float fpsInverseKinematics, + float fpsJoint2DEstimator, + float fpsMocapNET, + unsigned int mocapNETMode, + unsigned int width, + unsigned int height, + unsigned int handleMessages, + unsigned int deadInputPoints, + unsigned int gestureDetected, + const char * gestureName, + unsigned int gestureFrame, + float originalOrientation, + struct MocapNET2 * mnet, + struct skeletonSerialized * skeleton, + std::vector mocapNETInput, + std::vector mocapNETOutput, + std::vector mocapNETOutputWithGUIForcedView, + std::vector > points2DInput, + std::vector > points2DOutput, + std::vector > points2DOutputGUIForcedView, + int useOpenGLVisualization, + unsigned int save3DVisualization +) +{ + if ( (width<=0) || (height<=0) ) + { + fprintf(stderr,YELLOW "visualizePoints with image %ux%u\n" NORMAL,width,height); + return 0; + } + + +#if USE_OPENCV + char textInfo[512]; + cv::Scalar color= cv::Scalar(220,220,220,0 /*Transparency here , although if the cv::Mat does not have an alpha channel it is useless*/); + cv::Point txtPosition; + txtPosition.x=20; + txtPosition.y=20; + float thickness=1; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + int drawOrientation = drawNSDM; + + cv::Mat img(height,width, CV_8UC3, Scalar(0,0,0)); + if (mocapNETOutput.size()==0) + { + txtPosition.x=100; + txtPosition.y=400; + fprintf(stderr,YELLOW "Won't visualize empty neural network output for frame %u\n" NORMAL,frameNumber); + cv::putText(img,"No visible person..",txtPosition,fontUsed,2.8,cv::Scalar(255,255,255),thickness,8); + cv::imshow(windowName,img); + return 0; + } + + if (points2DOutput.size()==0) + { + txtPosition.x=100; + txtPosition.y=400; + fprintf(stderr,YELLOW "Won't visualize empty 2D points for frame %u\n" NORMAL,frameNumber); + cv::putText(img,"No 2D skeleton..",txtPosition,fontUsed,2.8,cv::Scalar(255,255,255),thickness,8); + cv::imshow(windowName,img); + return 0; + } + + + + //----------------------------------------------------------------------------------------------------------------------------- + // The yellow lines that track the hand movement! + int endEffectorHistory=1; + +//--------------------------------------------------------------------------------------------------------------------- +// Draw correspondance, post processing step to see if output is good +//--------------------------------------------------------------------------------------------------------------------- + int visualizeCorrespondence=1; + + if (visualizeCorrespondence) + { + visualizeSkeletonCorrespondence( + img, + points2DInput, + points2DOutput, + 750, //X + 50, //Y + width, + height + ); + } +//---------------------------------------------------------------------------------------------------------------------- + + + if (points2DOutputGUIForcedView.size()==0) + { + fprintf(stderr,"Can't visualize empty 2D projected points for frame %u ..\n",frameNumber); + return 0; + } +//fprintf(stderr,"visualizePoints %u points\n",points2DOutputGUIForcedView.size()); +//Just the lines ( background layer) + + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + // We print all the lines of text that give information + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + + + if (numberOfFramesToGrab>0) + { + snprintf(textInfo,512,"Grabber will stop after collecting %u frames",numberOfFramesToGrab); + } + else + { + snprintf(textInfo,512,"Live mode, looping forever will not produce a bvh file"); + } + //txtPosition.y+=30; + //cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + + + if (CPUName!=0) + { + if (strlen(CPUName)!=0) + { + snprintf(textInfo,512,"CPU:%s",CPUName); + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + } + + if (GPUName!=0) + { + if (strlen(GPUName)!=0) + { + snprintf(textInfo,512,"GPU:%s",GPUName); + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + } + + + /* These are covered by the new orientation visualization.. + if (mocapNETOutput.size()>4) + { + int foundDirection=0; + + snprintf(textInfo,512,"Orientation Classifier Front(%0.1f)Back(%0.1f) Left(%0.1f) Right(%0.1f) ",mnet->orientationClassifications[0],mnet->orientationClassifications[1],mnet->orientationClassifications[2],mnet->orientationClassifications[3]); + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + + if ( (mocapNETOutput[4]>=-45) && (mocapNETOutput[4]<=45) ) + { snprintf(textInfo,512,"Direction : Front (%0.2f->%0.2f) " , originalOrientation, mocapNETOutput[4]); foundDirection=1; } else + if ( (mocapNETOutput[4]<=-45) && (mocapNETOutput[4]>=-135) ) + { snprintf(textInfo,512,"Direction : Left (%0.2f->%0.2f) " , originalOrientation, mocapNETOutput[4]); foundDirection=1; } else + if ( (mocapNETOutput[4]>=45) && (mocapNETOutput[4]<=135) ) + { snprintf(textInfo,512,"Direction : Right (%0.2f->%0.2f) " , originalOrientation, mocapNETOutput[4]); foundDirection=1; } else + if ( (mocapNETOutput[4]<=-135) && (mocapNETOutput[4]>=-225) ) + { snprintf(textInfo,512,"Direction : Back A (%0.2f->%0.2f) " , originalOrientation, mocapNETOutput[4]); foundDirection=1; } else + if ( (mocapNETOutput[4]>=135) && (mocapNETOutput[4]<=225) ) + { snprintf(textInfo,512,"Direction : Back B (%0.2f->%0.2f) " , originalOrientation, mocapNETOutput[4]); foundDirection=1; } + if (foundDirection) + { + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + }*/ + + if (totalNumberOfFrames>0) + { + snprintf(textInfo,512,"Frame %u/%u",frameNumber,totalNumberOfFrames); + } + else + { + snprintf(textInfo,512,"Frame %u",frameNumber); + } + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + + + + if (skippedFrames>0) + { + txtPosition.y+=30; + snprintf(textInfo,512,"Skipped/Corrupted Frames %u",skippedFrames); + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + + if (fpsAcquisition!=0.0) + { + txtPosition.y+=30; + snprintf(textInfo,512,"Acquisition loop : %0.2f fps",fpsAcquisition); + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + + if (fpsJoint2DEstimator!=0.0) + { + txtPosition.y+=30; + snprintf(textInfo,512,"2D Joint Detector : %0.2f fps",fpsJoint2DEstimator); + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + + if (fpsInverseKinematics!=0.0) + { + txtPosition.y+=30; + + if (codeOptimizationsForIKEnabled()) + { + snprintf(textInfo,512,"Inverse Kinematics (SSE%u/MT:%u) : %0.2f fps",codeOptimizationsForIKEnabled(),mnet->options->doMultiThreadedIK,fpsInverseKinematics); + } else + { + snprintf(textInfo,512,"Inverse Kinematics (Unoptimized/MT:%u) : %0.2f fps",mnet->options->doMultiThreadedIK,fpsInverseKinematics); + } + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + } + + + txtPosition.y+=30; + snprintf(textInfo,512,"Neural Network (4 orientations) : %0.2f fps",fpsMocapNET); + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + + txtPosition.y+=30; + snprintf(textInfo,512,"Total : %0.2f fps",fpsTotal); + cv::putText(img,textInfo,txtPosition,fontUsed,0.8,color,thickness,8); + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + + + if (height<600) + { + //If the resolution is very small unclutter visualization.. + drawNSDM=0; + drawOrientation=0; + } + + //----------------------- + // NSDM matrix + //----------------------- + unsigned int NSDMWidth=120; //body leftHand rightHand + unsigned int NSDMHeight=120; + unsigned int NSDMPositionLeftColumnX = 20; + unsigned int NSDMPositionRightColumnX = 170; + + if (drawNSDM) + { + + int positionY = height-438; + + /* This is obsolete + if(mnet->body.loadedModels>0) + { + visualizeNSDM(img,"All Body",mnet->body.NSDM,2 ,20,positionY,NSDMWidth,NSDMHeight); + }*/ + + if(mnet->face.loadedModels>0) + { + visualizeNSDM(img,"Face",mnet->face.NSDM,2 /*regular*/,NSDMPositionRightColumnX,positionY,NSDMWidth,NSDMHeight); + } + + positionY+=160; + visualizeNSDM(img,"Upper Body",mnet->upperBody.NSDM,1 /*angles*/,NSDMPositionLeftColumnX,positionY,NSDMWidth,NSDMHeight); + visualizeNSDM(img,"Lower Body",mnet->lowerBody.NSDM,1 /*angles*/,NSDMPositionRightColumnX,positionY,NSDMWidth,NSDMHeight); + + positionY+=160; + if(mnet->leftHand.loadedModels>0) + { + visualizeNSDM(img,"Left Hand",mnet->leftHand.NSDM,1/*regular*/,NSDMPositionLeftColumnX,positionY,NSDMWidth,NSDMHeight); + } + + if(mnet->rightHand.loadedModels>0) + { + visualizeNSDM(img,"Right Hand",mnet->rightHand.NSDM,1 /*regular*/,NSDMPositionRightColumnX,positionY,NSDMWidth,NSDMHeight); + } + } + //----------------------- + + + if (drawOrientation) + { + int positionY = height-438; + visualizeOrientation( + img, + "Orientation", + mnet->currentSolution[4], //Orientation value + mnet->orientationClassifications[0], + mnet->orientationClassifications[1], + mnet->orientationClassifications[2], + mnet->orientationClassifications[3], + 20, + positionY, + NSDMWidth, + NSDMHeight + ); + + } + + + if (mocapNETOutput.size()>4) + { + drawScale(img,"Distance",width-34,height-238,-1*mocapNETOutput[2]*10,800,4000); + //drawScale(img,"Height",950,480,mocapNETOutput[1]*10,-500,500); + } + + //----------------------- + // OpenGL stuff + //----------------------- + cv::Mat * openGLMatForVisualization = 0; + if (useOpenGLVisualization) { + //fprintf(stderr,"updateOpenGLView\n"); + updateOpenGLView(mocapNETOutput); + + //fprintf(stderr,"visualizeOpenGL\n"); + unsigned int openGLFrameWidth=width,openGLFrameHeight=height; + char * openGLFrame = visualizeOpenGL(&openGLFrameWidth,&openGLFrameHeight); + //===================================================================== + if (openGLFrame!=0) + { + fprintf(stderr,"Got Back an OpenGL frame..!\n"); + cv::Mat openGLMat(openGLFrameHeight, openGLFrameWidth, CV_8UC3); + unsigned char * initialPointer = openGLMat.data; + openGLMat.data=(unsigned char * ) openGLFrame; + + cv::cvtColor(openGLMat,openGLFramePermanentMat,cv::COLOR_RGB2BGR); + //cv::imshow("OpenGL",openGLFramePermanentMat); + openGLMatForVisualization = &openGLFramePermanentMat; + openGLMat.data=initialPointer; + } + //===================================================================== + } + + if (openGLMatForVisualization!=0) + { + cv::Mat * glMat = (cv::Mat *) openGLMatForVisualization; + //float alpha=0.3; + //NOT NEEDED cv::addWeighted(*glMat, alpha, img, 1 - alpha, 0, img) ; + + img=cv::max(*glMat, img); + } else + { + + +//------------------------------------------------------------------------------------------ +// Draw floor as a grid of lines +//------------------------------------------------------------------------------------------ + if (drawFloor) + { + float floorX=0,floorY=0,floorZ=0; + + if (mocapNETOutputWithGUIForcedView.size()>5) + { + floorX=mocapNETOutputWithGUIForcedView[3]; + floorY=mocapNETOutputWithGUIForcedView[4]; + floorZ=mocapNETOutputWithGUIForcedView[5]; + } + + unsigned int floorDimension=20; + drawFloorFromPrimitives( + img, + floorX, + floorY, + floorZ, + floorDimension + ); + } +//------------------------------------------------------------------------------------------ + + + + //----------------------------------------------------------------------------------------------------------------------------- + if (endEffectorHistory) + { + drawEndEffectorTrack(img,points2DOutputGUIForcedView); + } + //----------------------------------------------------------------------------------------------------------------------------- + + + unsigned int offsetX=width-454; + unsigned int offsetY=height-262; + visualizeInput2DSkeletonFromSkeletonSerialized(img,skeleton,offsetX,offsetY,444,250); + + + //View .. + visualizeInput2DSkeletonFromVectorofVectors(img,points2DOutputGUIForcedView,width,height,offsetX,offsetY,444,250); + + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //The main star of the show , the skeleton.. + //drawSkeleton(img,points2DOutputGUIForcedView,0/*No 2D skeleton*/,0.0,0.0,1); + drawSkeleton(img,points2DOutputGUIForcedView,0.0,0.0,1); + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + //----------------------------------------------------------------------------------------------------------------------------- + + } + //----------------------- + + + + + //------------------------------------------------------------------------------- + // If gestures are enabled.. Draw them.. + //------------------------------------------------------------------------------- + if (gestureName!=0) + {//------------------------------------------------------------------------------- + if(gestureDetected) + { + if (gestureFrame<25) + { + snprintf(textInfo,512,"%s (%u)",gestureName,gestureDetected); + + if (endEffectorHistory) + {//If we track end effectors we will use their position to emit circles + if (leftEndEffector.size()>0) + { + cv::circle(img,leftEndEffector[leftEndEffector.size()-1],5*gestureFrame,cv::Scalar(0,255,255),3,8,0); + } + if (rightEndEffector.size()>0) + { + //Right is always left :P so there is space for the string + cv::putText(img,textInfo,rightEndEffector[rightEndEffector.size()-1],fontUsed,2.2,cv::Scalar(0,255,255),thickness,8); + cv::circle(img,rightEndEffector[rightEndEffector.size()-1],5*gestureFrame,cv::Scalar(0,255,255),3,8,0); + } + } else + { + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,1.5,cv::Scalar(0,255,255),thickness,8); + cv::circle(img,txtPosition,2*gestureFrame,cv::Scalar(0,255,255),3,8,0); + } + } + } else + { + //We did not detect a gesture.. Let's check the framerate + if (fpsTotal<13) + { + txtPosition.y+=30; + cv::putText(img,"Framerate is too slow for reliable gesture recognition..",txtPosition,fontUsed,0.8,color,thickness,8); + } + } + } //------------------------------------------------------------------------------- + + + + + + //------------------------------------------------------------------------------- + // If Poses are enabled.. Draw them.. + //------------------------------------------------------------------------------- + if(mnet->activePose>0) + { + snprintf(textInfo,512,"Pose : %s (%u)",hardcodedPoseName[mnet->activePose],mnet->activePose); + txtPosition.y+=30; + cv::putText(img,textInfo,txtPosition,fontUsed,1.5,cv::Scalar(0,255,255),thickness,8); + cv::circle(img,txtPosition,2*gestureFrame,cv::Scalar(0,255,255),3,8,0); + } + + + +/* + if (deadInputPoints>MAXIMUM_NUMBER_OF_NSDM_ELEMENTS_MISSING) + { + txtPosition.y+=30; + cv::putText(img,"Bad Input - Filtered out..",txtPosition,fontUsed,1.0,cv::Scalar(0,0,255),thickness,8); + } +*/ + + //At last we are able to show the window.. + if ( (img.size().width >0) && (img.size().height>0) ) { cv::imshow(windowName,img); } else + { std::cerr<<"Invalid visualization frame.. \n"; } + + + if (save3DVisualization) + { + char filename[512]; + snprintf(filename,512,"vis%05u.jpg",frameNumber) ; + cv::imwrite(filename,img); + } + + //Only handle messages if they are not handled elsewhere + if (handleMessages) + { cv::waitKey(1); } + + //Initially place window top left.. + //if (frameNumber==0) + // { cv::moveWindow(windowName,0,0); } + + + + return 1; +#else + fprintf(stderr,"OpenCV code not present in this build, cannot show visualization..\n"); + return 0; +#endif +} + + + + + +int visualizationCommander( + struct MocapNET2 * mnet, + struct MocapNET2Options * options, + struct skeletonSerialized * skeleton, + cv::Mat * frame, + std::vector result, + std::vector > exactMocapNET2DOutput, + unsigned int frameID, + unsigned int callingProcessPromisesToHandleMessages +) +{ + if (result.size()==0) { return 0; } + + + //Retreive gesture name to display it + char * gestureName=0; + if (options->doGestureDetection) + { + if (mnet->lastActivatedGesture>0) + { + gestureName=mnet->recognizedGestures.gesture[mnet->lastActivatedGesture-1].label; + } + } + + std::vector > points2DInput; + //Force Skeleton Position and orientation to make it more easily visualizable + std::vector resultCenteredOnScreen = result; + if (options->constrainPositionRotation>0) + { + if (resultCenteredOnScreen.size()>=MOCAPNET_OUTPUT_HIP_XROTATION) + { + float distance=0,rollValue=0,yawValue=0,pitchValue=0; + + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_XPOSITION]=0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_YPOSITION]=0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_ZPOSITION]=-190.0 - (float) distance; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_ZROTATION]=(float) rollValue; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_YROTATION]=(float) yawValue; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_XROTATION]=(float) pitchValue; + } + } + + std::vector > points2DOutput = convertBVHFrameTo2DPoints(resultCenteredOnScreen); // ,options->visWidth,options->visHeight + + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_XPOSITION]=0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_YPOSITION]=0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_ZPOSITION]=-190.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_ZROTATION]=(float) 0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_YROTATION]=(float) 0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_XROTATION]=(float) 0.0; + std::vector > points2DFrontOutput = convertBVHFrameTo2DPoints(resultCenteredOnScreen); // ,options->visWidth,options->visHeight + + if (options->datasetPath) + { + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_XPOSITION]=0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_YPOSITION]=0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_ZPOSITION]=-190.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_ZROTATION]=(float) 0.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_YROTATION]=(float) 90.0; + resultCenteredOnScreen[MOCAPNET_OUTPUT_HIP_XROTATION]=(float) 0.0; + std::vector > points2DOutputSide = convertBVHFrameTo2DPoints(resultCenteredOnScreen); // ,options->visWidth,options->visHeight + + + /* //Not needed + visualizeInput( + "Input Visualization", + frameID, + saveVisualization, + 0, + datasetPath, + label, + serialLength, + width, + height, + &skeleton, + &mnet, + exactMocapNET2DOutput, + points2DOutput, + points2DOutputSide, + numberOfMissingJoints + ); + */ + + /* This is ugly + visualizeCameraReady( + "Camera Ready", + frameID, + options->saveVisualization, + frame, + options->datasetPath, + options->label, + options->serialLength, + options->width, + options->height, + skeleton, + mnet, + exactMocapNET2DOutput, + points2DFrontOutput, + points2DOutputSide, + options->numberOfMissingJoints + ); */ + } + + + if (options->visualizationType==0) + { + std::vector inputValues; + + //Default visualization + visualizePoints( + "3D Points Output", + frameID, + 0, // broken frames + 0, // totalNumberOfFrames + 0, // numberOfFramesToGrab + options->CPUName, + options->GPUName, + 0,//drawFloor + 1,//drawNSDM + options->totalLoopFPS, //<- total fps + options->fpsAcquisition, + mnet->inverseKinematicsFramerate, + options->fps2DEstimator, + mnet->neuralNetworkFramerate, + options->mocapNETMode, + options->visWidth, + options->visHeight, + 1, + 0,//Dead Input Points + mnet->lastActivatedGesture, + gestureName, + mnet->recognizedGestures.gestureChecksPerformed - mnet->gestureTimestamp, //gesture stuff + mnet->lastSkeletonOrientation, + mnet, + skeleton, + inputValues, + result, + result, + points2DInput, + points2DOutput, + points2DOutput, + options->useOpenGLVisualization, + options->saveVisualization // No 3D visualization saved.. + ); + } + else if (options->visualizationType==1) + { + //Motion history ( graphs of each rotation ) + visualizeMotionHistory("3D Points Output",mnet->poseHistoryStorage.history,points2DFrontOutput); //points2DOutput + if (!callingProcessPromisesToHandleMessages) { visualizeHandleMessages(); } + } + else if (options->visualizationType==2) + { //Map visualization + std::vector inputValues; + visualizeMap( + "3D Points Output", + frameID, + options->width, + options->height, + skeleton, + mnet, + result, + exactMocapNET2DOutput, + options->numberOfMissingJoints, + mnet->lastActivatedGesture, + gestureName, + mnet->recognizedGestures.gestureChecksPerformed - mnet->gestureTimestamp//gesture stuff + ); + if (!callingProcessPromisesToHandleMessages) { visualizeHandleMessages(); } + } else + if (options->visualizationType==3) + {//All in one visualization + std::vector inputValues; + visualizeAllInOne( + "3D Points Output", + frameID, + options->saveVisualization, + frame, + options->datasetPath, + options->label, + options->serialLength, + options->width, + options->height, + skeleton, + mnet, + options, + exactMocapNET2DOutput, + points2DFrontOutput, + options->numberOfMissingJoints + ); + if (!callingProcessPromisesToHandleMessages) { visualizeHandleMessages(); } + } else + if (options->visualizationType==4) + { + //TEMPLATE VISUALIZATION + std::vector inputValues; + + visualizeTemplate( + "3D Points Output", + frameID, + options->saveVisualization, + 0, + options->datasetPath, + options->label, + options->serialLength, + options->width, + options->height, + skeleton, + mnet, + options, + exactMocapNET2DOutput, + points2DFrontOutput, + options->numberOfMissingJoints + ); + if (!callingProcessPromisesToHandleMessages) { visualizeHandleMessages(); } + } + else + { + fprintf(stderr,"Unhandled visualization type %u\n",options->visualizationType); + } + + return 1; +} + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/visualization.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/visualization.hpp new file mode 100644 index 0000000..fed7b06 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/visualization.hpp @@ -0,0 +1,262 @@ +#pragma once +/** @file visualization.hpp + * @brief Code that handles GUI output and visualization using OpenCV. + * If OpenCV is not be available, CMake will not declare the USE_OPENCV compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#include +#include +#include "../mocapnet2.hpp" + +#include "../applicationLogic/parseCommandlineOptions.hpp" + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; +#endif + +int debug2DPointAlignment( + const char * windowName, + std::vector original2DPoints, + std::vector rotated2DPoints, + unsigned int pivotPoint, + unsigned int referencePoint, + unsigned int width, + unsigned int height +); + + +//Using transparency penalizes performance taking 12% of live webcam demo looptime +//when tested using scripts/profile.sh +#define USE_TRANSPARENCY 0 + + + int visualizeHandleMessages(); + int visualizeMotionHistory(const char* windowName, std::vector > history, std::vector > skeleton2D); + +static const int lineColorIndex[] = +{ +247,252,253, +224,236,244, +191,211,230, +158,188,218, +140,150,198, +140,107,177, +136,65,157, +129,15,124, +77,0,75, +247,252,253, +229,245,249, +204,236,230, +153,216,201, +102,194,164, +65,174,118, +35,139,69, +0,109,44, +0,68,27, +247,252,240, +224,243,219, +204,235,197, +168,221,181, +123,204,196, +78,179,211, +43,140,190, +8,104,172, +8,64,129, +255,247,236, +254,232,200, +253,212,158, +253,187,132, +252,141,89, +239,101,72, +215,48,31, +179,0,0, +127,0,0, +255,247,251, +236,231,242, +208,209,230, +166,189,219, +116,169,207, +54,144,192, +5,112,176, +4,90,141, +2,56,88, +255,247,251, +236,226,240, +208,209,230, +166,189,219, +103,169,207, +54,144,192, +2,129,138, +1,108,89, +1,70,54, +247,244,249, +231,225,239, +212,185,218, +201,148,199, +223,101,176, +231,41,138, +206,18,86, +152,0,67, +103,0,31, +255,247,243, +253,224,221, +252,197,192, +250,159,181, +247,104,161, +221,52,151, +174,1,126, +122,1,119, +73,0,106, +255,255,229, +247,252,185, +217,240,163, +173,221,142, +120,198,121, +65,171,93, +35,132,67, +0,104,55, +0,69,41, +255,255,217, +237,248,177, +199,233,180, +127,205,187, +65,182,196, +29,145,192, +34,94,168, +37,52,148, +8,29,88, +255,255,229, +255,247,188, +254,227,145, +254,196,79, +254,153,41, +236,112,20, +204,76,2, +153,52,4, +102,37,6, +255,255,204, +255,237,160, +254,217,118, +254,178,76, +253,141,60, +252,78,42, +227,26,28, +189,0,38, +128,0,38 +}; + + +#if USE_OPENCV + +int visualizeNSDM( + cv::Mat &img, + const char * label, + std::vector NSDM, + unsigned int channelsPerNSDMElement, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +); + + +int visualizeInput2DSkeletonFromSkeletonSerialized( + cv::Mat &outputMat, + struct skeletonSerialized * skeleton, + unsigned int x,unsigned int y, + unsigned int width,unsigned int height + ); + + +int scaleSkeleton( std::vector > &sk, float scaleX,float scaleY); +int drawSkeleton(cv::Mat &outputMat,std::vector > points2DOutputGUIForcedView,float offsetX,float offsetY,int labels); +#endif + +int doReprojectionCheck(struct skeletonSerialized * input,struct skeletonSerialized * result); + +int visualizeInput( + const char* windowName, + unsigned int frameNumber, + unsigned int saveVisualization, + cv::Mat * alreadyLoadedImage, + const char * path, + const char * label, + unsigned int serialLength, + unsigned int width, + unsigned int height, + struct skeletonSerialized * skeleton, + struct MocapNET2 * mnet, + std::vector > points2DOutputGUIRealView, + std::vector > points2DOutputGUIForcedView, + std::vector > points2DOutputGUIForcedViewSide, + unsigned int numberOfMissingJoints + ); + + + +void spawnVisualizationWindow(const char* windowName,unsigned int width,unsigned int height); + +/** + * @brief Visualize MocapNET BVH output on a window + * @ingroup visualization + * @param CString with title of window + * @param Current frame number + * @param Framerate of Acquisition + * @param Framerate of 2D Joint estimator + * @param Framerate of MocapNET 3D Pose estimator + * @param Output window width + * @param Output window height + * @param MocapNET output BVH frame that we want to visualize + * @retval 1 = Success loading the files , 0 = Failure + */ +int visualizePoints( + const char* windowName, + unsigned int frameNumber, + unsigned int skippedFrames, + signed int totalNumberOfFrames, + unsigned int numberOfFramesToGrab, + const char * CPUName, + const char * GPUName, + int drawFloor, + int drawNSDM, + float fpsTotal, + float fpsAcquisition, + float fpsInverseKinematics, + float fpsJoint2DEstimator, + float fpsMocapNET, + unsigned int mocapNETMode, + unsigned int width, + unsigned int height, + unsigned int handleMessages, + unsigned int deadInputPoints, + unsigned int gestureDetected, + const char * gestureName, + unsigned int gestureFrame, + float originalOrientation, + struct MocapNET2 * mnet, + struct skeletonSerialized * skeleton, + std::vector mocapNETInput, + std::vector mocapNETOutput, + std::vector mocapNETOutputWithGUIForcedView, + std::vector > points2DInput, + std::vector > points2DOutput, + std::vector > points2DOutputGUIForcedView, + int useOpenGLVisualization, + unsigned int save3DVisualization + ); + + + + +int visualizationCommander( + struct MocapNET2 * mnet, + struct MocapNET2Options * options, + struct skeletonSerialized * skeleton, + cv::Mat * frame, + std::vector result, + std::vector > exactMocapNET2DOutput, + unsigned int frameID, + unsigned int callingProcessPromisesToHandleMessages +); \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/widgets.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/widgets.cpp new file mode 100644 index 0000000..2c48777 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/widgets.cpp @@ -0,0 +1,594 @@ +#include "widgets.hpp" + +#if USE_OPENCV + +#include "opencv2/opencv.hpp" +using namespace cv; + +#include "../IO/bvh.hpp" +#include "../tools.hpp" +#include "../mocapnet2.hpp" + + +cv::Mat overlay(cv::Mat base,cv::Mat overlay) +{ + //-------------------------------------- + if ( (base.size().width==overlay.size().width) && (base.size().height==overlay.size().height) ) + { + if ( ( base.channels()==3 ) && ( base.channels()==overlay.channels() ) ) + { + for (unsigned int y=0; y(y); + unsigned char * overlayP = overlay.ptr(y); + + for (unsigned int x=0; xy+200) { yPos=y+200; } + + cv::Point distancePos(x,yPos); + cv::circle(outputMat,distancePos,1,cv::Scalar(0,255,0),3,8,0); + + + char label[64]; + + snprintf(label,64,"%s",description); + startPoint.x=startPoint.x-20; + startPoint.y=startPoint.y-10; + cv::putText(outputMat,label, startPoint, cv::FONT_HERSHEY_DUPLEX, 0.3, cv::Scalar::all(255), 0.2, 8 ); + + if (value<0) { snprintf(label,64,"Negative!"); } else + if (value>maximum) { snprintf(label,64,"Too Large!"); } else + { snprintf(label,64,"%0.2f",value); } + distancePos.x=distancePos.x-20; + distancePos.y=distancePos.y-10; + cv::putText(outputMat,label, distancePos, cv::FONT_HERSHEY_DUPLEX, 0.3, cv::Scalar::all(255), 0.2, 8 ); + + return 1; +} + + +int plotFloatVector( + cv::Mat & img, + char cleanBackground, + std::vector history, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height + ) +{ + #if USE_OPENCV + if (history.size()<2) { return 0; } + + + unsigned int plotPosX=x; + unsigned int plotPosY=y; + unsigned int widthOfGraphs=width; + unsigned int heightOfGraphs=height; + unsigned int i=0,joint=0; + + if (cleanBackground) + { cv::rectangle(img,cv::Rect(x,y,width,height),cv::Scalar(0,0,0),-1); } + + float resolution = 2.0; + float speed=2.0; + unsigned int plotStart = 1; + unsigned int plotEnd = history.size(); + + if (speed!=1.0) + { + plotStart = (unsigned int ) history.size() / speed; + } + + for (i=plotStart; i= 60) { usedColor = cv::Scalar(0,255,0); } else + if (lastRecordedFramerate >30) { usedColor = cv::Scalar(0,255,255); } + + + cv::line(img,jointPointPrev,jointPointNext,usedColor,2.0); + + } + return 1; + #else + fprintf(stderr,"plotFloatVector cannot be compiled without OpenCV\n"); + return 0; +#endif +} + + +int visualizeOrientation( + cv::Mat &img, + const char * label, + float orientationDegrees, + float frontClass, + float backClass, + float leftClass, + float rightClass, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +) +{ + float thickness=2.5; + cv::Point startPoint(x,y); + cv::Point endPoint(x+width,y+height); + + int addSyntheticPoints=1; + int doScaleCompensation=0; + + thickness=1; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + cv::Scalar fontColor= cv::Scalar(255,255,255); + cv::Point txtPosition(x,y-15); + cv::putText(img,label,txtPosition,fontUsed,0.6,fontColor,thickness,8); + txtPosition.y+=10; + + + cv::Scalar color= cv::Scalar(0,0,255); + + + unsigned int cX = x + width / 2; + unsigned int cY = y + height / 2; + + startPoint.x=cX; + startPoint.y=cY; + + cv::Scalar color0 = cv::Scalar(0,0,255); + cv::Scalar color45 = cv::Scalar(0,0,255); + cv::Scalar color135 = cv::Scalar(0,0,255); + cv::Scalar color225 = cv::Scalar(0,0,255); + cv::Scalar color315 = cv::Scalar(0,0,255); + + cv::Scalar font0 = cv::Scalar(255,255,0); + cv::Scalar font45 = cv::Scalar(255,255,0); + cv::Scalar font135 = cv::Scalar(255,255,0); + cv::Scalar font225 = cv::Scalar(255,255,0); + cv::Scalar font315 = cv::Scalar(255,255,0); + + + if ( (frontClass>backClass) && (frontClass>leftClass) && (frontClass>rightClass) ) + { + color45=cv::Scalar(0,255,0); + font45=cv::Scalar(255,0,255); + } + else if ( (backClass>frontClass) && (backClass>leftClass) && (backClass>rightClass) ) + { + color225=cv::Scalar(0,255,0); + font225=cv::Scalar(255,0,255); + } + else if ( (leftClass>backClass) && (leftClass>frontClass) && (leftClass>rightClass) ) + { + color135=cv::Scalar(0,255,0); + font135=cv::Scalar(255,0,255); + } + else if ( (rightClass>backClass) && (rightClass>frontClass) && (rightClass>leftClass) ) + { + color0=cv::Scalar(0,255,0); + color315=color0; + font0=cv::Scalar(255,0,255); + font315=cv::Scalar(255,0,255); + } + + for (unsigned int i=0; i<360; i++) + { + float rad=((float) 3.1415/180*i); + endPoint.x=cX+cos(rad)* (width/2); + endPoint.y=cY+sin(rad)* (height/2); + + if (i==0) + { + color=color0; + } + else if (i==45) + { + color=color45; + } + else if (i==135) + { + color=color135; + } + else if (i==225) + { + color=color225; + } + else if (i==315) + { + color=color315; + } + + cv::line( + img, + startPoint, + endPoint, + color, + 2.0 + ); + + + } + + + + + //Draw an arrow in our orientation.. + unsigned int rad = height/2; + + int lineStyle = 0; // https://github.com/FORTH-ModelBasedTracker/MocapNET/issues/30 has problems with CV_AA + + cv::Point arrowPointing; + arrowPointing.x = (int)(startPoint.x + (rad+10) * cos((90.0+orientationDegrees) * CV_PI / 180.0)); + arrowPointing.y = (int)(startPoint.y + (rad+10) * sin((90.0+orientationDegrees) * CV_PI / 180.0)); + cv::line(img,startPoint,arrowPointing,cv::Scalar(0,255,255),4,lineStyle,0); + cv::Point arrowSide; + arrowSide.x = (int)(startPoint.x + (rad) * cos((90.0+orientationDegrees-5) * CV_PI / 180.0)); + arrowSide.y = (int)(startPoint.y + (rad) * sin((90.0+orientationDegrees-5) * CV_PI / 180.0)); + cv::line(img,arrowSide,arrowPointing,cv::Scalar(0,255,255),4,lineStyle,0); + arrowSide.x = (int)(startPoint.x + (rad) * cos((90.0+orientationDegrees+5) * CV_PI / 180.0)); + arrowSide.y = (int)(startPoint.y + (rad) * sin((90.0+orientationDegrees+5) * CV_PI / 180.0)); + cv::line(img,arrowSide,arrowPointing,cv::Scalar(0,255,255),4,lineStyle,0); + + thickness=2; + startPoint.x-= width / 7; + + char str[512]; + snprintf(str,512,"%0.2f",frontClass); + txtPosition = startPoint; + txtPosition.y += 5+height/4; + cv::putText(img,str,txtPosition,fontUsed,0.6,font45,thickness,8); + + snprintf(str,512,"%0.2f",backClass); + txtPosition = startPoint; + txtPosition.y -= height/4; + cv::putText(img,str,txtPosition,fontUsed,0.6,font225,thickness,8); + + snprintf(str,512,"%0.2f",rightClass); + txtPosition = startPoint; + txtPosition.x += width/4; + cv::putText(img,str,txtPosition,fontUsed,0.6,font0,thickness,8); + + snprintf(str,512,"%0.2f",leftClass); + txtPosition = startPoint; + txtPosition.x -= width/4; + cv::putText(img,str,txtPosition,fontUsed,0.6,font135,thickness,8); + + return 1; +} + + + + +int visualizeNSDM( + cv::Mat &img, + const char * label, + std::vector NSDM, + unsigned int channelsPerNSDMElement, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +) +{ + float thickness=2.5; + //cv::Point topLeft(x,y); + //cv::Point bottomRight(x+width,y+height); + //cv::line(img,topLeft,bottomRight, cv::Scalar(0,255,0),thickness); + //cv::rectangle(img, topLeft,bottomRight, cv::Scalar(0,255,0),thickness, 8, 0); + + int addSyntheticPoints=1; + int doScaleCompensation=0; + + thickness=1.5; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + cv::Scalar color= cv::Scalar(255,255,255,0 /*Transparency here , although if the cv::Mat does not have an alpha channel it is useless*/); + cv::Point txtPosition(x,y-15); + cv::putText(img,label,txtPosition,fontUsed,0.8,color,thickness,8); + txtPosition.y+=10; + + if (channelsPerNSDMElement==2) + { + cv::putText(img,"NSDM",txtPosition,fontUsed,0.3,color,thickness,8); + } + else if (channelsPerNSDMElement==1) + { + cv::putText(img,"NSRM",txtPosition,fontUsed,0.3,color,thickness,8); + } + + + if (NSDM.size()==0) + { + txtPosition.x+=(width/2)-30; + txtPosition.y+=height/2; + cv::putText(img,"N/A",txtPosition,fontUsed,0.8,cv::Scalar(0,0,255),thickness,8); + return 0; + } + + + if (NSDM.size()>0) + { + unsigned int xI,yI,item=0,dim=sqrt(NSDM.size()/channelsPerNSDMElement); + unsigned int boxX=width/dim,boxY=height/dim; + for (yI=0; yI %0.2f\n",item,NSDM[item]); + greenChannel = (float) 255.0 * (float) xVal/2;// /180.0; + blueChannel = (float) 255.0 * (float) yVal/2;// /180.0; + redChannel = (float) 255.0 * ( (xVal==0.0) && (yVal==0.0) ); + } + + + cv::rectangle( + img, + topLeft, + bottomRight, + cv::Scalar( + blueChannel, + greenChannel, + redChannel + ), + -1*thickness, + 8, + 0 + ); + + //Visualize error + if ( ( (xVal>2.0) || (yVal>2.0) ) && (channelsPerNSDMElement==2) ) + { + cv::Point topRight(x+xI*boxX+boxX,y+yI*boxY); + cv::Point bottomLeft(x+xI*boxX,y+yI*boxY+boxY); + cv::Point topMiddle(x+xI*boxX+boxX/2,y+yI*boxY); + cv::Point bottomMiddle(x+xI*boxX+boxX/2,y+yI*boxY+boxY); + cv::line(img,topLeft,bottomRight, cv::Scalar(0,0,255), 1.0); + cv::line(img,topRight,bottomLeft, cv::Scalar(0,0,255), 1.0); + cv::line(img,topMiddle,bottomMiddle, cv::Scalar(0,0,255), 1.0); + } + else if ( ( (xVal>1.5) || (yVal>1.5) ) && (channelsPerNSDMElement==2) ) + { + cv::Point topRight(x+xI*boxX+boxX,y+yI*boxY); + cv::Point bottomLeft(x+xI*boxX,y+yI*boxY+boxY); + cv::line(img,topLeft,bottomRight, cv::Scalar(0,0,255), 1.0); + cv::line(img,topRight,bottomLeft, cv::Scalar(0,0,255), 1.0); + } + else if ( ( (xVal>1.0) || (yVal>1.0) ) && (channelsPerNSDMElement==2) ) + { + cv::Point topRight(x+xI*boxX+boxX,y+yI*boxY); + cv::Point bottomLeft(x+xI*boxX,y+yI*boxY+boxY); + cv::line(img,topLeft,bottomRight, cv::Scalar(0,0,255), 1.0); + } + + item+=channelsPerNSDMElement; + } + } + return 1; + } + return 0; +} + + + +int visualizeNSDMAsBar( + cv::Mat &img, + std::vector NSDM, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +) +{ + float thickness=2.5; + //cv::Point topLeft(x,y); + //cv::Point bottomRight(x+width,y+height); + //cv::line(img,topLeft,bottomRight, cv::Scalar(0,255,0),thickness); + //cv::rectangle(img, topLeft,bottomRight, cv::Scalar(0,255,0),thickness, 8, 0); + + int addSyntheticPoints=1; + int doScaleCompensation=0; + + if (NSDM.size()>0) + { + + float thickness=1; + int fontUsed=cv::FONT_HERSHEY_SIMPLEX; + + + unsigned int xI,yI,item=0,dim=sqrt(NSDM.size()/2); + unsigned int errors=0; + + for (yI=0; yI0.9) + { + color=cv::Scalar(0,255,0); + } + else if (qualityPercentage>0.8) + { + color=cv::Scalar(0,123,123); + qualityPercentage-=0.2; + } + else + { + color=cv::Scalar(0,0,255); + qualityPercentage-=0.6; + } + + if (qualityPercentage>1.0) + { + qualityPercentage=1.0; + } + if (qualityPercentage<0.0) + { + qualityPercentage=0.0; + } + + cv::Point topLeft(x,y); + cv::Point bottomRight(x+(qualityPercentage*width),y+height); + + + cv::rectangle(img, topLeft,bottomRight,color,-thickness, 8, 0); + + cv::Point txtPosition(x,y-15); + cv::putText(img,"Quality : ",txtPosition,fontUsed,0.8,color,thickness,8); + return 1; + } + return 0; +} + + + + +int drawFloorFromPrimitives( + cv::Mat &img, + float roll, + float pitch, + float yaw, + unsigned int floorDimension +) +{ + std::vector > gridPoints2D = convert3DGridTo2DPoints( + roll, + pitch, + yaw, + floorDimension + ); + + cv::Point parentPoint(gridPoints2D[0][0],gridPoints2D[0][1]); + cv::Point verticalPoint(gridPoints2D[0][0],gridPoints2D[0][1]); + float m=10.0; //Minimum + + for (int jointID=0; jointIDm) && (jointPoint.y>m) ) + { + cv::circle(img,jointPoint,2,cv::Scalar(255,255,0),3,8,0); + if ( (jointPoint.x>m) && (jointPoint.y>m) && (verticalPoint.x>m) && (verticalPoint.y>m) ) + { + cv::line(img,jointPoint,verticalPoint, cv::Scalar(255,255,0), 1.0); + } + } + + + if (jointID%floorDimension!=0) + { + if ( (jointPoint.x>m) && (jointPoint.y>m) && (parentPoint.x>m) && (parentPoint.y>m) ) + { + cv::line(img,jointPoint,parentPoint, cv::Scalar(255,255,0), 1.0); + } + } + + parentPoint = jointPoint; + } + return 1; +} + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/widgets.hpp b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/widgets.hpp new file mode 100644 index 0000000..290f0e1 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/MocapNETLib2/visualization/widgets.hpp @@ -0,0 +1,73 @@ +#pragma once +/** @file widgets.hpp + * @brief Code that handles GUI output and visualization using OpenCV. + * If OpenCV is not be available, CMake will not declare the USE_OPENCV compilation flag and the whole code will not be included. + * @author Ammar Qammaz (AmmarkoV) + */ + +#if USE_OPENCV +#include "opencv2/opencv.hpp" +using namespace cv; + +cv::Mat overlay(cv::Mat base,cv::Mat overlay); + +int drawScale(cv::Mat &outputMat,const char * description,float x,float y,float value,float minimum,float maximum); + +int plotFloatVector( + cv::Mat & img, + char cleanBackground, + std::vector history, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height + ); + + +int visualizeOrientation( + cv::Mat &img, + const char * label, + float orientationDegrees, + float frontClass, + float backClass, + float leftClass, + float rightClass, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +); + + + +int visualizeNSDM( + cv::Mat &img, + const char * label, + std::vector NSDM, + unsigned int channelsPerNSDMElement, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +); + + + +int visualizeNSDMAsBar( + cv::Mat &img, + std::vector NSDM, + unsigned int x, + unsigned int y, + unsigned int width, + unsigned int height +); + + +int drawFloorFromPrimitives( + cv::Mat &img, + float roll, + float pitch, + float yaw, + unsigned int floorDimension +); +#endif \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/drawCSV/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/drawCSV/CMakeLists.txt new file mode 100644 index 0000000..e0d1c07 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/drawCSV/CMakeLists.txt @@ -0,0 +1,23 @@ +project( drawCSV ) +cmake_minimum_required( VERSION 2.8.13) +#cmake_minimum_required(VERSION 3.5) +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +#set_property(GLOBAL PROPERTY USE_FOLDERS ON) +set(CMAKE_CXX_STANDARD 11) +include_directories(${TENSORFLOW_INCLUDE_ROOT}) + + +add_executable(drawCSV drawCSV.cpp ) + +target_link_libraries(drawCSV rt dl m ${OpenCV_LIBRARIES} ${OPENGL_LIBS} Tensorflow TensorflowFramework MocapNETLib2 ${NETWORK_CLIENT_LIBRARIES} ${PNG_Libs} ${JPG_Libs} ) +set_target_properties(drawCSV PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(drawCSV PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/drawCSV/drawCSV.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/drawCSV/drawCSV.cpp new file mode 100644 index 0000000..a94fb9a --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/drawCSV/drawCSV.cpp @@ -0,0 +1,195 @@ +#include "opencv2/opencv.hpp" +/** @file bvhgui.cpp + * @brief This is a simple test file to play with the BVH armature + * #Hands up + * // ./BVHGUI --set 241 290.0 --set 242 80 --set 284 117 --set 283 113 + * #Close up hands / face + * // ./BVHGUI --set 1 -70 --set 2 -60 --set 241 290.0 --set 242 80 --set 282 187 --set 283 252 --set 284 112 + * @author Ammar Qammaz (AmmarkoV) + */ +#include +#include "../../MocapNET2/MocapNETLib2/mocapnet2.hpp" +#include "../../MocapNET2/MocapNETLib2/IO/bvh.hpp" +#include "../../MocapNET2/MocapNETLib2/visualization/visualization.hpp" +#include "../../MocapNET2/MocapNETLib2/visualization/opengl.hpp" +#include + +using namespace cv; + +int visualizeOpenGLEnabled=0; + +struct applicationState +{ + std::vectorbvhConfiguration; + std::vector > bvh2DPoints; + int rotation; + int selectedJoint,previousSelection; + int visualizationType,previousVisualizationType; + int stop; + int save; + int redraw; +}; + + +int controls(cv::Mat & controlMat,struct applicationState * state) +{ + cv::imshow("3D Control",controlMat); + cv::createTrackbar("Stop Demo", "3D Control", &state->stop, 1); + if (visualizeOpenGLEnabled) + { cv::createTrackbar("Visualization Type", "3D Control", &state->visualizationType, 1); } + cv::createTrackbar("Save", "3D Control", &state->save, 1); + cv::createTrackbar("Selected Joint", "3D Control", &state->selectedJoint,MOCAPNET_OUTPUT_NUMBER); + cv::createTrackbar("Value ", "3D Control", &state->rotation, 360); + return 1; +} + + + + + + +int main(int argc, char *argv[]) +{ + unsigned int visWidth=1024; + unsigned int visHeight=768; + + cv::Mat controlMat = Mat(Size(300,1),CV_8UC3, Scalar(0,0,0)); + memset(controlMat.data,255,300*1*3*sizeof(char)); + cv::Mat viewMat = Mat(Size(visWidth,visHeight),CV_8UC3, Scalar(0,0,0)); + struct applicationState state; + state.rotation=0; + state.previousSelection=0; + state.selectedJoint=0; + state.visualizationType=0; + state.stop=0; + state.save=0; + state.previousVisualizationType=2;// <- force redraw on first loop + state.redraw=1; + + + for (int i=0; ii+2) + { + unsigned int bvhJoint = atoi(argv[i+1]); + float bvhValue = atof(argv[i+2]); + if (bvhJoint > bvhFrames; + bvhFrames.push_back(state.bvhConfiguration); + state.save=0; + controls(controlMat,&state); + writeBVHFile( + "out.bvh", + bvhHeader, + 0,// int prependTPose, + bvhFrames + ); + } + + if (state.visualizationType!=state.previousVisualizationType) + { + state.redraw=1; + state.previousVisualizationType=state.visualizationType; + } + + if (state.selectedJoint!=state.previousSelection) + { + state.previousSelection=state.selectedJoint; + state.rotation=state.bvhConfiguration[state.selectedJoint]; + controls(controlMat,&state); + state.redraw=1; + } + + if ( state.bvhConfiguration[state.selectedJoint]!=state.rotation ) + { + state.bvhConfiguration[state.selectedJoint]=state.rotation; + state.redraw=1; + } + + if (state.redraw) + { + memset(viewMat.data,0,1024*768*3*sizeof(char)); + + + //cv::Mat * openGLMatForVisualization = 0; + if ( (visualizeOpenGLEnabled) && (state.visualizationType) ) + { + //fprintf(stderr,"updateOpenGLView\n"); + updateOpenGLView(state.bvhConfiguration); + + //fprintf(stderr,"visualizeOpenGL\n"); + unsigned int openGLFrameWidth=visWidth,openGLFrameHeight=visHeight; + char * openGLFrame = visualizeOpenGL(&openGLFrameWidth,&openGLFrameHeight); + //===================================================================== + if (openGLFrame!=0) + { + fprintf(stderr,"Got Back an OpenGL frame..!\n"); + cv::Mat openGLMat(openGLFrameHeight, openGLFrameWidth, CV_8UC3); + unsigned char * initialPointer = openGLMat.data; + openGLMat.data=(unsigned char * ) openGLFrame; + cv::cvtColor(openGLMat,viewMat,COLOR_RGB2BGR); + openGLMat.data=initialPointer; + }//===================================================================== + else + { + fprintf(stderr,"Failed getting an OpenGL frame..!\n"); + } + } + else + { + if (state.bvhConfiguration.size()>0) + { + state.bvh2DPoints = convertBVHFrameTo2DPoints(state.bvhConfiguration); //visWidth,visHeight ); + } + drawSkeleton(viewMat,state.bvh2DPoints,0,0,1); + } + + + cv::putText(viewMat,MocapNETOutputArrayNames[state.previousSelection], cv::Point(10,30), cv::FONT_HERSHEY_DUPLEX, 0.5, cv::Scalar::all(255), 0.2, 8 ); + + state.redraw=0; + } + + + imshow("BVH", viewMat); + + waitKey(150); + } + // the camera will be deinitialized automatically in VideoCapture destructor + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/reshapeCSVFileToMakeClassification/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/reshapeCSVFileToMakeClassification/CMakeLists.txt new file mode 100644 index 0000000..468235a --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/reshapeCSVFileToMakeClassification/CMakeLists.txt @@ -0,0 +1,20 @@ +project( ReshapeCSV ) +cmake_minimum_required( VERSION 2.8.13 ) +#cmake_minimum_required(VERSION 3.5) + +#find_package(OpenCV REQUIRED) +#INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + + + +add_executable(ReshapeCSV reshapeCSV.cpp ) +target_link_libraries(ReshapeCSV rt dl m Tensorflow TensorflowFramework MocapNETLib2 ) +#set_target_properties(TestCSV PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(ReshapeCSV PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/reshapeCSVFileToMakeClassification/reshapeCSV.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/reshapeCSVFileToMakeClassification/reshapeCSV.cpp new file mode 100644 index 0000000..e58e184 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/reshapeCSVFileToMakeClassification/reshapeCSV.cpp @@ -0,0 +1,95 @@ +/* + * Utility to extract BVH files straight from OpenPose JSON output + * Sample usage ./MocapNETCSV --from test.csv --visualize + */ + +#include +#include +#include +#include +#include + +#include "../MocapNETLib2/mocapnet2.hpp" +#include "../MocapNETLib2/core/core.hpp" +#include "../MocapNETLib2/IO/csvRead.hpp" + + +int main(int argc, char *argv[]) +{ + struct MocapNET2 mnet; + struct CSVFileContext csvT={0}; + unsigned long processedLines=0; + + fprintf(stderr,"Run : \n"); + fprintf(stderr,"./createRandomizedDataset.sh\n"); + fprintf(stderr,"to update the dataset/bvh_body_all.csv to the latest version..\n\n"); + + fprintf(stdout,"Front,Back,Left,Right\n"); + + + char csvFileInput[1024]; + snprintf(csvFileInput,1024,"dataset/bvh_body_all.csv"); + + if (argc==2) + { + snprintf(csvFileInput,1024,"%s",argv[1]); + } else + { + fprintf(stderr,"Please provide an argument i.e. \n ./reshapeCSV dataset/bvh_body_all.csv"); + return 0; + } + + + if (openCSVFile(&csvT,csvFileInput)) + { + fprintf(stderr,"CSV file had %u lines\n",csvT.lineNumber); + //---------------------------------------------------- + struct skeletonSerialized skeletonS= {0}; + while ( parseNextCSVCOCOSkeleton(&csvT,&skeletonS) ) + //---------------------------------------------------- + { + if (processedLines==0) + { initializeMocapNET2InputAssociation(&mnet,&skeletonS,1,1,1); } + + unsigned int correspondingClass = getMocapNETOrientationFromAngle(skeletonS.skeletonBody[4].value); + + if (processedLines%1000==0) { fprintf(stderr,"."); } + //fprintf(stderr,".[%0.2f/%u]",skeletonS.skeletonBody[4].value,correspondingClass); + + /* + enum MOCAPNET_Orientation + { + MOCAPNET_ORIENTATION_NONE=0, + MOCAPNET_ORIENTATION_FRONT, + MOCAPNET_ORIENTATION_BACK, + MOCAPNET_ORIENTATION_LEFT, + MOCAPNET_ORIENTATION_RIGHT, + //----------------------------- + MOCAPNET_ORIENTATION_NUMBER + }; + */ + + switch(correspondingClass) + { + //The series here is important for MocapNETLib2/core.cpp: + case MOCAPNET_ORIENTATION_NONE: fprintf(stdout,"0,0,0,0\n"); break; //Add erroneous categories to catch bugs + case MOCAPNET_ORIENTATION_FRONT: fprintf(stdout,"1,0,0,0\n"); break; + case MOCAPNET_ORIENTATION_BACK: fprintf(stdout,"0,1,0,0\n"); break; + case MOCAPNET_ORIENTATION_LEFT: fprintf(stdout,"0,0,1,0\n"); break; + case MOCAPNET_ORIENTATION_RIGHT: fprintf(stdout,"0,0,0,1\n"); break; + default : fprintf(stdout,"0,0,0,0\n"); break; //Add erroneous categories to catch bugs + }; + + ++processedLines; + } + //---------------------------------------------------- + closeCSVFile(&csvT); + } + + + fprintf(stderr,"\nDon't forget, Run : \n"); + fprintf(stderr,"./createRandomizedDataset.sh\n"); + fprintf(stderr,"to update the dataset/bvh_body_all.csv to the latest version..\n\n"); + + exit(0); +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/testCSV/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET2/testCSV/CMakeLists.txt new file mode 100644 index 0000000..fb19931 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/testCSV/CMakeLists.txt @@ -0,0 +1,20 @@ +project( TestCSV ) +cmake_minimum_required( VERSION 2.8.13 ) +#cmake_minimum_required(VERSION 3.5) + +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + + + +add_executable(TestCSV testCSV.cpp ) +target_link_libraries(TestCSV rt dl m Tensorflow TensorflowFramework MocapNETLib2 ) +#set_target_properties(TestCSV PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(TestCSV PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET2/testCSV/testCSV.cpp b/animation/MocapNET-kasisnu/src/MocapNET2/testCSV/testCSV.cpp new file mode 100644 index 0000000..ec7c6f8 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET2/testCSV/testCSV.cpp @@ -0,0 +1,53 @@ +/* + * Utility to extract BVH files straight from OpenPose JSON output + * Sample usage ./MocapNETCSV --from test.csv --visualize + */ + +#include +#include +#include +#include +#include + +#include "../MocapNETLib2/mocapnet2.hpp" +#include "../MocapNETLib2/IO/csvRead.hpp" + + +int main(int argc, char *argv[]) +{ + struct MocapNET2 mnet; + struct CSVFileContext csvT={0}; + + fprintf(stderr,"Run : \n"); + fprintf(stderr,"./convertOpenPoseJSONToCSV --from frames/GOPR3246.MP4-data/ -o newtest.csv\n"); + fprintf(stderr,"to update the newtest.csv to the latest version..\n\n"); + + + if (openCSVFile(&csvT,"newtest.csv")) + { + fprintf(stderr,"CSV file had %u lines\n",csvT.lineNumber); + //---------------------------------------------------- + struct skeletonSerialized skeletonS= {0}; + while ( parseNextCSVCOCOSkeleton(&csvT,&skeletonS) ) + //---------------------------------------------------- + { + initializeMocapNET2InputAssociation(&mnet,&skeletonS,1,1,1); + fprintf(stderr,"...\n"); + + fprintf(stderr,"Don't forget, Run : \n"); + fprintf(stderr,"./convertOpenPoseJSONToCSV --from frames/GOPR3246.MP4-data/ -o newtest.csv\n"); + fprintf(stderr,"to update the newtest.csv to the latest version..\n\n"); + + exit(0); + } + //---------------------------------------------------- + closeCSVFile(&csvT); + } + + + fprintf(stderr,"Don't forget, Run : \n"); + fprintf(stderr,"./convertOpenPoseJSONToCSV --from frames/GOPR3246.MP4-data/ -o newtest.csv\n"); + fprintf(stderr,"to update the newtest.csv to the latest version..\n\n"); + + exit(0); +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNET4Test/CMakeLists.txt b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNET4Test/CMakeLists.txt new file mode 100644 index 0000000..211cfab --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNET4Test/CMakeLists.txt @@ -0,0 +1,24 @@ +project( MocapNET4Test ) +cmake_minimum_required( VERSION 2.8.7 ) +#cmake_minimum_required(VERSION 3.5) +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + +#set_property(GLOBAL PROPERTY USE_FOLDERS ON) +set(CMAKE_CXX_STANDARD 11) +include_directories(${TENSORFLOW_INCLUDE_ROOT}) +include_directories(../MocapNETLib4) #<- includes in test.cpp + + +add_executable(MocapNET4Test test.cpp) + +target_link_libraries(MocapNET4Test rt dl m ${OpenCV_LIBRARIES} ${OPENGL_LIBS} JointEstimator2D Tensorflow TensorflowFramework MocapNETLib4 ${NETWORK_CLIENT_LIBRARIES} ${PNG_Libs} ${JPG_Libs} ) +set_target_properties(MocapNET4Test PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(MocapNET4Test PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNET4Test/test.cpp b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNET4Test/test.cpp new file mode 100644 index 0000000..dc2ffd3 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNET4Test/test.cpp @@ -0,0 +1,122 @@ + +/** @file livedemo.cpp + * @brief This is the main "demo" offered in this repository, it will take a stream from a webcam or video file using OpenCV and run +* 2D pose estimation + MocapNET giving you a nice 3D visualization as well as an output .bvh file + * @author Ammar Qammaz (AmmarkoV) + */ +#include +#include +//----------------------------------------------------------------- + +#include "PCA/PCA.h" +#include "JSON/readModelConfiguration.h" +#include "JSON/readListFile.h" + +#include "NSxM/NSxM.h" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + +void testPCA(struct PCAData * pca) +{ + float output[210]={0}; + int outputSize = 210; + + float input[458]={0}; + int inputSize = 458; + for (int i=0; i + + +/** + * @brief This is an array of names for all the new BODY25 body parts. + * This tries to mirror body_configuration.json and everything used is lowercase exactly for this reason.. + * it also has to be the same with the bvh file headerWithHeadAndOneMotion.bvh + */ +static const char * Body25Labels[] = +{ + "head", //0 + "neck", //1 + "rshoulder", //2 + "relbow", //3 + "rhand", //4 + "lshoulder", //5 + "lelbow", //6 + "lhand", //7 + "hip", //8 + "rhip", //9 + "rknee", //10 + "rfoot", //11 + "lhip", //12 + "lknee", //13 + "lfoot", //14 + "endsite_eye.r", //15 + "endsite_eye.l", //16 + "rear", //17 ========= No correspondance + "lear", //18 ========= No correspondance + "endsite_toe1-2.l",//19 + "endsite_toe5-3.l",//20 + "lheel", //21 ========= No correspondance + "endsite_toe1-2.r",//22 + "endsite_toe5-3.r",//23 + "rheel", //24 ========= No correspondance + "bkg", //25 ========= No correspondance + //================== + "End of Joint Names", + 0 +}; + + + + +#include //toupper +static int strcasecmp_route(const char * input1,const char * input2) +{ + if ( (input1==0) || (input2==0) ) + { + fprintf(stderr,"Error , calling strcasecmp_route with null parameters \n"); + return 1; + } + + #if CASE_SENSITIVE_OBJECT_NAMES + return strcmp(input1,input2); + #endif + + unsigned int len1 = strlen(input1); + unsigned int len2 = strlen(input2); + if (len1!=len2) + { + //mismatched lengths of strings , they can't be equal..! + return 1; + } + + char A; //<- character buffer for input1 + char B; //<- character buffer for input2 + unsigned int i=0; + while (iroutedValues!=0) { free(route->routedValues); route->routedValues=0; } + if (route->routingRules!=0) { free(route->routingRules); route->routingRules=0; } + route->numberOfRoutingRules=0; + route->resolved=0; + return 1; +} + + + +/** + * @brief generate Route from Labels + * @param Pointer to a model configuration + * @param Pointer to the route structure + * @param Labels to route + * @param Number of Labels to route + * @retval 1=Success/0=Failure + */ +static int generateRouteFromLabels( + struct ModelConfigurationData * config, + struct inputRouting * route, + const char * * incomingLabels, + unsigned int incomingLabelsLength + ) +{ + fprintf(stderr,GREEN "\ngenerateRouteFromLabels for %u labels and %u hierarchy elements\n" NORMAL,incomingLabelsLength,config->numberOfHierarchyElements); + + if (config==0) { return 0; } + if (incomingLabels==0) { return 0; } + if (route==0) { return 0; } + + + destroyRoute(route); + + route->numberOfRoutingRules = config->numberOfHierarchyElements; + + route->routedValues = (float*) malloc(sizeof(float) * 3 * route->numberOfRoutingRules); + route->routingRules = (int*) malloc(sizeof(int) * route->numberOfRoutingRules); + + if ( + (route->routedValues==0) || + (route->routingRules==0) + ) //Failed allocating memory.. + { + destroyRoute(route); + return 0; + } + + + memset(route->routedValues,0,sizeof(float) * 3 * route->numberOfRoutingRules); + memset(route->routingRules,0,sizeof(int) * route->numberOfRoutingRules); + + + int routingFailures = 0; + for (int trg=0; trgnumberOfHierarchyElements; trg++) + { + //fprintf(stderr,"trg %u/%u\n",trg,config->numberOfHierarchyElements); + int trgJointResolved = 0; + + for (int src=0; srchierarchyElements[trg].joint)==0) + { + fprintf(stderr,GREEN "MATCH %s (%u) to %s (%u) \n" NORMAL,incomingLabels[src],src,config->hierarchyElements[trg].joint,trg); + trgJointResolved = 1; + route->routedValues[trg*3+0] = 0.0;//Set everything to zero initially.. + route->routedValues[trg*3+1] = 0.0;//Set everything to zero initially.. + route->routedValues[trg*3+2] = 0.0;//Set everything to zero initially.. + route->routingRules[trg] = src; + } + } + + + if (!trgJointResolved) + { + fprintf(stderr,YELLOW "Could not match %s \n" NORMAL,config->hierarchyElements[trg].joint); + routingFailures+=1; + } + } + + if (routingFailures==0) + { + fprintf(stderr,GREEN "Successfully routed all %u input rules\n" NORMAL,route->numberOfRoutingRules); + } else + { + fprintf(stderr,RED "Failed routing %u out of %u input rules\n" NORMAL, routingFailures,route->numberOfRoutingRules); + } + + route->resolved = (routingFailures==0); + + return (routingFailures==0); +} + + + +static int routeInput( + float * preexistingOutput2DJoints, + int * output2DJointsLength, + struct ModelConfigurationData * config, + struct inputRouting * route, + float * raw2DPoints, + int raw2DPointsLength + ) +{ + if (route->resolved) + { + //if (raw2DPointsLength==route->numberOfRoutingRules) + { + float * output = preexistingOutput2DJoints; + + int val = 0; + for (int i=0; inumberOfRoutingRules; i++) + { + fprintf(stderr,"%s ",config->hierarchyElements[i].joint); + //Each rule has 3 values 2DX, 2DY, 2DVisibility + //-------------------------------------------------------------------------- + output[val]=raw2DPoints[(route->routingRules[i]*3) + 0]; + fprintf(stderr,"2DX=%0.2f ",output[val]); + val+=1; + //-------------------------------------------------------------------------- + output[val]=raw2DPoints[(route->routingRules[i]*3) + 1]; + fprintf(stderr,"2DY=%0.2f ",output[val]); + val+=1; + //-------------------------------------------------------------------------- + output[val]=raw2DPoints[(route->routingRules[i]*3) + 2]; + fprintf(stderr,"2DVisibility=%0.2f \n",output[val]); + val+=1; + //-------------------------------------------------------------------------- + } + return 1; + } + } + return 0; +} + + + +#ifdef __cplusplus +} +#endif + + + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/nxjson.c b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/nxjson.c new file mode 100644 index 0000000..84a0c3c --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/nxjson.c @@ -0,0 +1,389 @@ +/* + * Copyright (c) 2013 Yaroslav Stavnichiy + * + * This file is part of NXJSON. + * + * NXJSON is free software: you can redistribute it and/or modify + * it under the terms of the GNU Lesser General Public License + * as published by the Free Software Foundation, either version 3 + * of the License, or (at your option) any later version. + * + * NXJSON is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Lesser General Public License for more details. + * + * You should have received a copy of the GNU Lesser General Public + * License along with NXJSON. If not, see . + */ + +// this file can be #included in your code +#ifndef NXJSON_C +#define NXJSON_C + +#ifdef __cplusplus +extern "C" { +#endif + + +#include +#include +#include +#include +#include + +#include "nxjson.h" + +// redefine NX_JSON_CALLOC & NX_JSON_FREE to use custom allocator +#ifndef NX_JSON_CALLOC +#define NX_JSON_CALLOC() calloc(1, sizeof(nx_json)) +#define NX_JSON_FREE(json) free((void*)(json)) +#endif + +// redefine NX_JSON_REPORT_ERROR to use custom error reporting +#ifndef NX_JSON_REPORT_ERROR +#define NX_JSON_REPORT_ERROR(msg, p) fprintf(stderr, "NXJSON PARSE ERROR (%d): " msg " at %s\n", __LINE__, p) +#endif + +#define IS_WHITESPACE(c) ((unsigned char)(c)<=(unsigned char)' ') + +static const nx_json dummy={ NX_JSON_NULL }; + +static nx_json* create_json(nx_json_type type, const char* key, nx_json* parent) { + nx_json* js=NX_JSON_CALLOC(); + assert(js); + js->type=type; + js->key=key; + if (!parent->last_child) { + parent->child=parent->last_child=js; + } + else { + parent->last_child->next=js; + parent->last_child=js; + } + parent->length++; + return js; +} + +void nx_json_free(const nx_json* js) { + nx_json* p=js->child; + nx_json* p1; + while (p) { + p1=p->next; + nx_json_free(p); + p=p1; + } + NX_JSON_FREE(js); +} + +static int unicode_to_utf8(unsigned int codepoint, char* p, char** endp) { + // code from http://stackoverflow.com/a/4609989/697313 + if (codepoint<0x80) *p++=codepoint; + else if (codepoint<0x800) *p++=192+codepoint/64, *p++=128+codepoint%64; + else if (codepoint-0xd800u<0x800) return 0; // surrogate must have been treated earlier + else if (codepoint<0x10000) *p++=224+codepoint/4096, *p++=128+codepoint/64%64, *p++=128+codepoint%64; + else if (codepoint<0x110000) *p++=240+codepoint/262144, *p++=128+codepoint/4096%64, *p++=128+codepoint/64%64, *p++=128+codepoint%64; + else return 0; // error + *endp=p; + return 1; +} + +nx_json_unicode_encoder nx_json_unicode_to_utf8=unicode_to_utf8; + +static inline int hex_val(char c) { + if (c>='0' && c<='9') return c-'0'; + if (c>='a' && c<='f') return c-'a'+10; + if (c>='A' && c<='F') return c-'A'+10; + return -1; +} + +static char* unescape_string(char* s, char** end, nx_json_unicode_encoder encoder) { + char* p=s; + char* d=s; + char c; + while ((c=*p++)) { + if (c=='"') { + *d='\0'; + *end=p; + return s; + } + else if (c=='\\') { + switch (*p) { + case '\\': + case '/': + case '"': + *d++=*p++; + break; + case 'b': + *d++='\b'; p++; + break; + case 'f': + *d++='\f'; p++; + break; + case 'n': + *d++='\n'; p++; + break; + case 'r': + *d++='\r'; p++; + break; + case 't': + *d++='\t'; p++; + break; + case 'u': // unicode + if (!encoder) { + // leave untouched + *d++=c; + break; + } + char* ps=p-1; + int h1, h2, h3, h4; + if ((h1=hex_val(p[1]))<0 || (h2=hex_val(p[2]))<0 || (h3=hex_val(p[3]))<0 || (h4=hex_val(p[4]))<0) { + NX_JSON_REPORT_ERROR("invalid unicode escape", p-1); + return 0; + } + unsigned int codepoint=h1<<12|h2<<8|h3<<4|h4; + if ((codepoint & 0xfc00)==0xd800) { // high surrogate; need one more unicode to succeed + p+=6; + if (p[-1]!='\\' || *p!='u' || (h1=hex_val(p[1]))<0 || (h2=hex_val(p[2]))<0 || (h3=hex_val(p[3]))<0 || (h4=hex_val(p[4]))<0) { + NX_JSON_REPORT_ERROR("invalid unicode surrogate", ps); + return 0; + } + unsigned int codepoint2=h1<<12|h2<<8|h3<<4|h4; + if ((codepoint2 & 0xfc00)!=0xdc00) { + NX_JSON_REPORT_ERROR("invalid unicode surrogate", ps); + return 0; + } + codepoint=0x10000+((codepoint-0xd800)<<10)+(codepoint2-0xdc00); + } + if (!encoder(codepoint, d, &d)) { + NX_JSON_REPORT_ERROR("invalid codepoint", ps); + return 0; + } + p+=5; + break; + default: + // leave untouched + *d++=c; + break; + } + } + else { + *d++=c; + } + } + NX_JSON_REPORT_ERROR("no closing quote for string", s); + return 0; +} + +static char* skip_block_comment(char* p) { + // assume p[-2]=='/' && p[-1]=='*' + char* ps=p-2; + if (!*p) { + NX_JSON_REPORT_ERROR("endless comment", ps); + return 0; + } + REPEAT: + p=strchr(p+1, '/'); + if (!p) { + NX_JSON_REPORT_ERROR("endless comment", ps); + return 0; + } + if (p[-1]!='*') goto REPEAT; + return p+1; +} + +static char* parse_key(const char** key, char* p, nx_json_unicode_encoder encoder) { + // on '}' return with *p=='}' + char c; + while ((c=*p++)) { + if (c=='"') { + *key=unescape_string(p, &p, encoder); + if (!*key) return 0; // propagate error + while (*p && IS_WHITESPACE(*p)) p++; + if (*p==':') return p+1; + NX_JSON_REPORT_ERROR("unexpected chars", p); + return 0; + } + else if (IS_WHITESPACE(c) || c==',') { + // continue + } + else if (c=='}') { + return p-1; + } + else if (c=='/') { + if (*p=='/') { // line comment + char* ps=p-1; + p=strchr(p+1, '\n'); + if (!p) { + NX_JSON_REPORT_ERROR("endless comment", ps); + return 0; // error + } + p++; + } + else if (*p=='*') { // block comment + p=skip_block_comment(p+1); + if (!p) return 0; + } + else { + NX_JSON_REPORT_ERROR("unexpected chars", p-1); + return 0; // error + } + } + else { + NX_JSON_REPORT_ERROR("unexpected chars", p-1); + return 0; // error + } + } + NX_JSON_REPORT_ERROR("unexpected chars", p-1); + return 0; // error +} + +static char* parse_value(nx_json* parent, const char* key, char* p, nx_json_unicode_encoder encoder) { + nx_json* js; + while (1) { + switch (*p) { + case '\0': + NX_JSON_REPORT_ERROR("unexpected end of text", p); + return 0; // error + case ' ': case '\t': case '\n': case '\r': + case ',': + // skip + p++; + break; + case '{': + js=create_json(NX_JSON_OBJECT, key, parent); + p++; + while (1) { + const char* new_key; + p=parse_key(&new_key, p, encoder); + if (!p) return 0; // error + if (*p=='}') return p+1; // end of object + p=parse_value(js, new_key, p, encoder); + if (!p) return 0; // error + } + case '[': + js=create_json(NX_JSON_ARRAY, key, parent); + p++; + while (1) { + p=parse_value(js, 0, p, encoder); + if (!p) return 0; // error + if (*p==']') return p+1; // end of array + } + case ']': + return p; + case '"': + p++; + js=create_json(NX_JSON_STRING, key, parent); + js->text_value=unescape_string(p, &p, encoder); + if (!js->text_value) return 0; // propagate error + return p; + case '-': case '0': case '1': case '2': case '3': case '4': case '5': case '6': case '7': case '8': case '9': + { + js=create_json(NX_JSON_INTEGER, key, parent); + char* pe; + errno = 0; + js->int_value=strtoll(p, &pe, 0); + if (pe==p || errno==ERANGE) { + NX_JSON_REPORT_ERROR("invalid number", p); + return 0; // error + } + if (*pe=='.' || *pe=='e' || *pe=='E') { // double value + js->type=NX_JSON_DOUBLE; + errno = 0; + js->dbl_value=strtod(p, &pe); + if (pe==p || errno==ERANGE) { + NX_JSON_REPORT_ERROR("invalid number", p); + return 0; // error + } + } + else { + js->dbl_value=js->int_value; + } + return pe; + } + case 't': + if (!strncmp(p, "true", 4)) { + js=create_json(NX_JSON_BOOL, key, parent); + js->int_value=1; + return p+4; + } + NX_JSON_REPORT_ERROR("unexpected chars", p); + return 0; // error + case 'f': + if (!strncmp(p, "false", 5)) { + js=create_json(NX_JSON_BOOL, key, parent); + js->int_value=0; + return p+5; + } + NX_JSON_REPORT_ERROR("unexpected chars", p); + return 0; // error + case 'n': + if (!strncmp(p, "null", 4)) { + create_json(NX_JSON_NULL, key, parent); + return p+4; + } + NX_JSON_REPORT_ERROR("unexpected chars", p); + return 0; // error + case '/': // comment + if (p[1]=='/') { // line comment + char* ps=p; + p=strchr(p+2, '\n'); + if (!p) { + NX_JSON_REPORT_ERROR("endless comment", ps); + return 0; // error + } + p++; + } + else if (p[1]=='*') { // block comment + p=skip_block_comment(p+2); + if (!p) return 0; + } + else { + NX_JSON_REPORT_ERROR("unexpected chars", p); + return 0; // error + } + break; + default: + NX_JSON_REPORT_ERROR("unexpected chars", p); + return 0; // error + } + } +} + +const nx_json* nx_json_parse_utf8(char* text) { + return nx_json_parse(text, unicode_to_utf8); +} + +const nx_json* nx_json_parse(char* text, nx_json_unicode_encoder encoder) { + nx_json js={0}; + if (!parse_value(&js, 0, text, encoder)) { + if (js.child) nx_json_free(js.child); + return 0; + } + return js.child; +} + +const nx_json* nx_json_get(const nx_json* json, const char* key) { + if (!json || !key) return &dummy; // never return null + nx_json* js; + for (js=json->child; js; js=js->next) { + if (js->key && !strcmp(js->key, key)) return js; + } + return &dummy; // never return null +} + +const nx_json* nx_json_item(const nx_json* json, int idx) { + if (!json) return &dummy; // never return null + nx_json* js; + for (js=json->child; js; js=js->next) { + if (!idx--) return js; + } + return &dummy; // never return null +} + + +#ifdef __cplusplus +} +#endif + +#endif /* NXJSON_C */ diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/nxjson.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/nxjson.h new file mode 100644 index 0000000..f85bba2 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/nxjson.h @@ -0,0 +1,65 @@ +/* + * Copyright (c) 2013 Yaroslav Stavnichiy + * + * This file is part of NXJSON. + * + * NXJSON is free software: you can redistribute it and/or modify + * it under the terms of the GNU Lesser General Public License + * as published by the Free Software Foundation, either version 3 + * of the License, or (at your option) any later version. + * + * NXJSON is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Lesser General Public License for more details. + * + * You should have received a copy of the GNU Lesser General Public + * License along with NXJSON. If not, see . + */ + +#ifndef NXJSON_H +#define NXJSON_H + +#ifdef __cplusplus +extern "C" { +#endif + + +typedef enum nx_json_type { + NX_JSON_NULL, // this is null value + NX_JSON_OBJECT, // this is an object; properties can be found in child nodes + NX_JSON_ARRAY, // this is an array; items can be found in child nodes + NX_JSON_STRING, // this is a string; value can be found in text_value field + NX_JSON_INTEGER, // this is an integer; value can be found in int_value field + NX_JSON_DOUBLE, // this is a double; value can be found in dbl_value field + NX_JSON_BOOL // this is a boolean; value can be found in int_value field +} nx_json_type; + +typedef struct nx_json { + nx_json_type type; // type of json node, see above + const char* key; // key of the property; for object's children only + const char* text_value; // text value of STRING node + long long int_value; // the value of INTEGER or BOOL node + double dbl_value; // the value of DOUBLE node + int length; // number of children of OBJECT or ARRAY + struct nx_json* child; // points to first child + struct nx_json* next; // points to next child + struct nx_json* last_child; +} nx_json; + +typedef int (*nx_json_unicode_encoder)(unsigned int codepoint, char* p, char** endp); + +extern nx_json_unicode_encoder nx_json_unicode_to_utf8; + +const nx_json* nx_json_parse(char* text, nx_json_unicode_encoder encoder); +const nx_json* nx_json_parse_utf8(char* text); +void nx_json_free(const nx_json* js); +const nx_json* nx_json_get(const nx_json* json, const char* key); // get object's property by key +const nx_json* nx_json_item(const nx_json* json, int idx); // get array element by index + + +#ifdef __cplusplus +} +#endif + +#endif /* NXJSON_H */ diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/readListFile.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/readListFile.h new file mode 100644 index 0000000..e90c3d1 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/readListFile.h @@ -0,0 +1,173 @@ +/** @file readListFile.h + * @brief An implementation of reading a list from a text file + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef READ_LIST_FILE_H_INCLUDED +#define READ_LIST_FILE_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + +#include +#include + +struct listFileEntry +{ + int strLength; + char * str; +}; + +struct listFileData +{ + unsigned int numberOfEntries; + struct listFileEntry * entry; +}; + + +static int slowLineCounter(const char * filename) +{ + char ch; + int linesCount=0; + //open file in read more + FILE * fp=fopen(filename,"r"); + if(fp==NULL) { + printf("File \"%s\" does not exist!!!\n",filename); + return -1; + } + //read character by character and check for new line + while((ch=fgetc(fp))!=EOF) + { + if(ch=='\n') + linesCount++; + } + //close the file + fclose(fp); + + return linesCount; +} + +static int destroyListFile(struct listFileData * listOutput) +{ + fprintf(stderr,"destroying List File..! \n"); + if (listOutput!=0) + { + if (listOutput->entry!=0) + { + for (int i=0; inumberOfEntries; i++) + { + if (listOutput->entry[i].str!=0) + { + free(listOutput->entry[i].str); + listOutput->entry[i].strLength = 0; + } + } + //------------------------------------------------ + free(listOutput->entry); + listOutput->entry=0; + } + } + fprintf(stderr,"destroyed List File..! \n"); + return 1; +} + + + +static int printListFile(struct listFileData * listOutput,const char * label) +{ + if (listOutput==0) { return 0; } + if (listOutput->entry!=0) + { + printf("Listing %s\n",label); + printf("_______________________\n"); + for (int i=0; inumberOfEntries; i++) + { + printf("Line %u === `%s`\n",i,listOutput->entry[i].str); + } + printf("\n\n\n"); + return 1; + } + + + return 0; +} + +static int readListFile(struct listFileData * listOutput,const char * filename) +{ + if (listOutput==0) { return 0; } + + //We now know the number of entries + listOutput->numberOfEntries = slowLineCounter(filename); + + if (listOutput!=0) + { + //Clean up everything! + destroyListFile(listOutput); + } + + + listOutput->entry = (struct listFileEntry *) malloc(sizeof(struct listFileEntry) * listOutput->numberOfEntries ); + if (listOutput->entry==0) { return 0; } + + + char * line = NULL; + size_t len = 0; + ssize_t read = 0; + + FILE * fp = fopen(filename, "r"); + if (fp == NULL) + { return 0; } + + unsigned int entryNumber = 0; + int i=0; + while ((read=getline(&line, &len, fp)) != -1) + { + int stringLength = strlen(line); + + int stringLengthWithoutNull = stringLength-1; + while ( (stringLengthWithoutNull>0) && ( (line[stringLengthWithoutNull]==10) || (line[stringLengthWithoutNull]==13) ) ) + { + line[stringLengthWithoutNull]=0; + stringLengthWithoutNull-=1; + stringLength-=1; + } + + + //fprintf(stderr,"reading line %s (%u)..! \n",line,stringLength); + if (entryNumbernumberOfEntries) + { + listOutput->entry[i].str = (char *) malloc(sizeof(char) * (stringLength+2)); + if (listOutput->entry[i].str!=0) + { + listOutput->entry[i].strLength = stringLength; + strncpy(listOutput->entry[i].str,line,stringLength); + listOutput->entry[i].str[stringLength]=0; //Null termination + //snprintf(listOutput->entry[i].str,stringLength+1,"%s",line); + } + + ++entryNumber; + } + + + i+=1; + } + + fclose(fp); + if (line) + { free(line); } + return 1; +} + + +#ifdef __cplusplus +} +#endif + + + + +#endif + diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/readModelConfiguration.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/readModelConfiguration.h new file mode 100644 index 0000000..84b40b3 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/JSON/readModelConfiguration.h @@ -0,0 +1,753 @@ +/** @file readModelConfiguration.h + * @brief Parsing the JSON files accompanying models..! + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef READ_JSON_CONFIGURATION_H_INCLUDED +#define READ_JSON_CONFIGURATION_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + + +#include +#include +#include "nxjson.h" +#include "../tools.h" + +#define SMALL_STR 32 +#define BIG_STR 128 +#define MAX_JOINT_NAME 32 +#define MAX_DESCRIPTOR_ELEMENTS 64 +#define MAX_HIERARCHY_ELEMENTS 64 +#define MAX_BANNED_ELEMENTS 16 + +//-------------------------------------- +struct jointName +{ + char joint[MAX_JOINT_NAME]; +}; +//-------------------------------------- +struct jointPair +{ + char jointStart[MAX_JOINT_NAME]; + unsigned int jointStartID; + char jointEnd[MAX_JOINT_NAME]; + unsigned int jointEndID; +}; +//-------------------------------------- +struct jointDescriptorItem +{ + char joint[MAX_JOINT_NAME]; + unsigned int jointID; + char isVirtual; + float xOffset; + float yOffset; + char halfwayFromJointAndThis[MAX_JOINT_NAME]; + unsigned int secondTargetJointID; +}; +//-------------------------------------- +struct jointHierarchyItem +{ + char joint[MAX_JOINT_NAME]; + char inheritNetwork[MAX_JOINT_NAME]; + char parent[MAX_JOINT_NAME]; + unsigned int parentID; + unsigned int importance; + char immuneToSelfOcclusions; +}; +//-------------------------------------- + +struct ModelConfigurationData +{ + float version; + //-------------------------------------- + char backend[SMALL_STR]; + char precision[SMALL_STR]; + char BVH[BIG_STR]; + char outputDirectory[BIG_STR]; + unsigned int veryHighNumberOfEpochs; + unsigned int highNumberOfEpochs; + unsigned int defaultNumberOfEpochs; + unsigned int defaultBatchSize; + float learningRate; + float minEarlyStoppingDelta; + char activationFunction[SMALL_STR]; + float dropoutRate; + float lamda; + char useQuadLoss; + char useSquaredLoss; + unsigned int earlyStoppingPatience; + char rememberWeights; + char rememberConsecutiveWeights; + char useOnlineHardExampleMining; + unsigned int hardMiningEpochs; + unsigned int normalMiningEpochs; + unsigned int groupOutputs; + char ignoreOcclusions; + char NSDMNormalizationMasterSwitch; + char NSDMAlsoUseAlignmentAngles; + unsigned int neuralNetworkDepth; + char EDM; + char eNSRM; + char doPCA[BIG_STR]; + unsigned int PCADimensionsKept; + unsigned int padEnsembleInput; + //-------------------------------------- + struct jointPair normalizedBasedOn[3]; + unsigned int numberOfNormalizedBasedOnRules; + //-------------------------------------- + struct jointPair alignment[3]; + unsigned int numberOfAlignmentRules; + //-------------------------------------- + struct jointDescriptorItem descriptorElements[MAX_DESCRIPTOR_ELEMENTS]; + unsigned int numberOfDescriptorElements; + //-------------------------------------- + struct jointHierarchyItem hierarchyElements[MAX_HIERARCHY_ELEMENTS]; + unsigned int numberOfHierarchyElements; + //-------------------------------------- + struct jointName bannedJoints[MAX_BANNED_ELEMENTS]; + unsigned int numberOfBannedJoints; +}; + + +static void printModelConfigurationData(struct ModelConfigurationData* out) +{ + fprintf(stderr,"Version : %0.2f\n", out->version); + fprintf(stderr,"Backend : %s\n", out->backend); + fprintf(stderr,"Precision : %s\n", out->precision); + fprintf(stderr,"BVH : %s\n", out->BVH); + fprintf(stderr,"outputDirectory : %s\n", out->outputDirectory); + fprintf(stderr,"veryHighNumberOfEpochs : %u\n", out->veryHighNumberOfEpochs); + fprintf(stderr,"highNumberOfEpochs : %u\n", out->highNumberOfEpochs); + fprintf(stderr,"defaultNumberOfEpochs : %u\n", out->defaultNumberOfEpochs); + fprintf(stderr,"defaultBatchSize : %u\n", out->defaultBatchSize); + fprintf(stderr,"learningRate : %f\n", out->learningRate); + fprintf(stderr,"minEarlyStoppingDelta : %f\n", out->minEarlyStoppingDelta); + fprintf(stderr,"activationFunction : %s\n", out->activationFunction); + fprintf(stderr,"dropoutRate : %f\n", out->dropoutRate); + fprintf(stderr,"lamda : %f\n", out->lamda); + fprintf(stderr,"useQuadLoss : %u\n", out->useQuadLoss); + fprintf(stderr,"useSquaredLoss : %u\n", out->useSquaredLoss); + fprintf(stderr,"earlyStoppingPatience : %u\n", out->earlyStoppingPatience); + fprintf(stderr,"rememberWeights : %u\n", out->rememberWeights); + fprintf(stderr,"rememberConsecutiveWeights : %u\n", out->rememberConsecutiveWeights); + fprintf(stderr,"useOnlineHardExampleMining : %u\n", out->useOnlineHardExampleMining); + fprintf(stderr,"hardMiningEpochs : %u\n", out->hardMiningEpochs); + fprintf(stderr,"normalMiningEpochs : %u\n", out->normalMiningEpochs); + fprintf(stderr,"groupOutputs : %u\n", out->groupOutputs); + fprintf(stderr,"ignoreOcclusions : %u\n", out->ignoreOcclusions); + fprintf(stderr,"NSDMNormalizationMasterSwitch : %u\n",out->NSDMNormalizationMasterSwitch); + fprintf(stderr,"NSDMAlsoUseAlignmentAngles : %u\n", out->NSDMAlsoUseAlignmentAngles); + fprintf(stderr,"neuralNetworkDepth : %u\n", out->neuralNetworkDepth); + fprintf(stderr,"EDM : %u\n", out->EDM); + fprintf(stderr,"eNSRM : %u\n", out->eNSRM); + fprintf(stderr,"BVH : %s\n", out->doPCA); + fprintf(stderr,"PCADimensionsKept : %u\n", out->PCADimensionsKept); + fprintf(stderr,"padEnsembleInput : %u\n", out->padEnsembleInput); + + fprintf(stderr,"Normalization Rules : %u\n",out->numberOfNormalizedBasedOnRules); + for (int i=0; inumberOfNormalizedBasedOnRules; i++) + { + fprintf(stderr,"Rule %u : \n",i); + fprintf(stderr," jointStart:%s\n" ,out->normalizedBasedOn[i].jointStart); + fprintf(stderr," jointStartID:%u\n",out->normalizedBasedOn[i].jointStartID); + fprintf(stderr," jointEnd:%s\n" ,out->normalizedBasedOn[i].jointEnd); + fprintf(stderr," jointEndID:%u\n" ,out->normalizedBasedOn[i].jointEndID); + } + + fprintf(stderr,"Alignment Rules : %u\n",out->numberOfAlignmentRules); + for (int i=0; inumberOfAlignmentRules; i++) + { + fprintf(stderr,"Rule %u : \n",i); + fprintf(stderr," jointStart:%s\n" ,out->alignment[i].jointStart); + fprintf(stderr," jointStartID:%u\n",out->alignment[i].jointStartID); + fprintf(stderr," jointEnd:%s\n" ,out->alignment[i].jointEnd); + fprintf(stderr," jointEndID:%u\n" ,out->alignment[i].jointEndID); + } + + fprintf(stderr,"Descriptor Rules : %u\n",out->numberOfDescriptorElements); + for (int i=0; inumberOfDescriptorElements; i++) + { + fprintf(stderr,"Rule %u : \n",i); + fprintf(stderr," joint:%s\n" ,out->descriptorElements[i].joint); + fprintf(stderr," jointID:%u\n" ,out->descriptorElements[i].jointID); + fprintf(stderr," isVirtual:%u\n" ,out->descriptorElements[i].isVirtual); + fprintf(stderr," xOffset:%f\n" ,out->descriptorElements[i].xOffset); + fprintf(stderr," yOffset:%f\n" ,out->descriptorElements[i].yOffset); + fprintf(stderr," halfwayFromJointAndThis:%s\n",out->descriptorElements[i].halfwayFromJointAndThis); + fprintf(stderr," secondTargetJointID:%u\n" ,out->descriptorElements[i].secondTargetJointID); + } + + //TODO POPULATE JOINT ID! + + fprintf(stderr,"Hierarchy Rules : %u\n",out->numberOfHierarchyElements); + for (int i=0; inumberOfHierarchyElements; i++) + { + fprintf(stderr,"Rule %u : \n",i); + fprintf(stderr," joint:%s\n" ,out->hierarchyElements[i].joint); + fprintf(stderr," inheritNetwork:%s\n" ,out->hierarchyElements[i].inheritNetwork); + fprintf(stderr," parent:%s\n" ,out->hierarchyElements[i].parent); + fprintf(stderr," parentID:%u\n" ,out->hierarchyElements[i].parentID); + fprintf(stderr," importance:%u\n" ,out->hierarchyElements[i].importance); + fprintf(stderr," immuneToSelfOcclusions:%u\n" ,out->hierarchyElements[i].immuneToSelfOcclusions); + } + + + fprintf(stderr,"Banned Encoders Rules : %u\n",out->numberOfBannedJoints); + for (int i=0; inumberOfBannedJoints; i++) + { + fprintf(stderr,"Rule %u : \n",i); + fprintf(stderr," joint:%s\n" ,out->bannedJoints[i].joint); + } +} + + + + +static int resolveConfigurationData(struct ModelConfigurationData* config) +{ + if (config!=0) + { //Found a configuration to resolve.. + + printModelConfigurationData(config); + + fprintf(stderr,"Resolving %u NSxM matrix elements..\n",config->numberOfDescriptorElements); + fprintf(stderr,"Using %u hierarchy elements..\n",config->numberOfHierarchyElements); + + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + + for (unsigned int descID=0; descIDnumberOfDescriptorElements; descID++) + { + //Resolve NSxM to Joint ID + for (unsigned int jointID=0; jointIDnumberOfHierarchyElements; jointID++) + { + fprintf(stderr,"Trying to match %s(%u/%u) with %s(%u/%u)\n",config->descriptorElements[descID].joint,descID,config->numberOfDescriptorElements,config->hierarchyElements[jointID].joint,jointID,config->numberOfHierarchyElements); + + if (strcmp(config->descriptorElements[descID].joint,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->descriptorElements[descID].jointID = jointID; + break; + } + } + + //Resolve NSxM to Joint ID for halfway + for (int jointID=0; jointIDnumberOfHierarchyElements; jointID++) + { + if (config->descriptorElements[descID].isVirtual==2) + { + fprintf(stderr,"Trying to match %s(%u) with %s(%u)\n",config->descriptorElements[descID].halfwayFromJointAndThis,descID,config->hierarchyElements[jointID].joint,jointID); + + if (strcmp(config->descriptorElements[descID].halfwayFromJointAndThis,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->descriptorElements[descID].secondTargetJointID = jointID; + break; + } + } + } + } + + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + //--------------------------------------------------------------------------------------------------------------------------- + + + + + const int INVALID_VALUE=666; + + //Resolve normalized based on order.. + //--------------------------------------------------------------------------------------------------------------------------- + for (unsigned int descID=0; descIDnumberOfNormalizedBasedOnRules; descID++) + { + config->normalizedBasedOn[descID].jointStartID=INVALID_VALUE; + config->normalizedBasedOn[descID].jointEndID =INVALID_VALUE; + } + //--------------------------------------------------------------------------------------------------------------------------- + for (unsigned int descID=0; descIDnumberOfDescriptorElements; descID++) + { + for (int jointID=0; jointIDnumberOfHierarchyElements; jointID++) + { + fprintf(stderr,"Trying to match norm rule %s(%u/%u) with %s(%u/%u)\n",config->descriptorElements[descID].joint,descID,config->numberOfNormalizedBasedOnRules,config->hierarchyElements[jointID].joint,jointID,config->numberOfHierarchyElements); + + if (strcmp(config->normalizedBasedOn[descID].jointStart,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->normalizedBasedOn[descID].jointStartID = jointID; + } + if (strcmp(config->normalizedBasedOn[descID].jointEnd,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->normalizedBasedOn[descID].jointEndID = jointID; + } + } + } + //--------------------------------------------------------------------------------------------------------------------------- + for (unsigned int descID=0; descIDnumberOfNormalizedBasedOnRules; descID++) + { + if ( config->normalizedBasedOn[descID].jointStartID == INVALID_VALUE ) + { + fprintf(stderr,"Normalization Rule %u/%u for start of joint is unresolved, stopping..\n",descID,config->numberOfNormalizedBasedOnRules); + return 0; + } + if ( config->normalizedBasedOn[descID].jointEndID == INVALID_VALUE ) + { + fprintf(stderr,"Normalization Rule %u/%u for end of joint is unresolved, stopping..\n",descID,config->numberOfNormalizedBasedOnRules); + return 0; + } + } + //--------------------------------------------------------------------------------------------------------------------------- + + + // Resolve joint alignment.. + //--------------------------------------------------------------------------------------------------------------------------- + for (unsigned int descID=0; descIDnumberOfAlignmentRules; descID++) + { + config->alignment[descID].jointStartID=INVALID_VALUE; + config->alignment[descID].jointEndID =INVALID_VALUE; + } + //--------------------------------------------------------------------------------------------------------------------------- + for (unsigned int descID=0; descIDnumberOfAlignmentRules; descID++) + { + for (int jointID=0; jointIDnumberOfHierarchyElements; jointID++) + { + fprintf(stderr,"Trying to match norm rule %s(%u/%u) with %s(%u/%u)\n",config->descriptorElements[descID].joint,descID,config->numberOfNormalizedBasedOnRules,config->hierarchyElements[jointID].joint,jointID,config->numberOfHierarchyElements); + + if (strcmp(config->alignment[descID].jointStart,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->alignment[descID].jointStartID = jointID; + } + if (strcmp(config->alignment[descID].jointEnd,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->alignment[descID].jointEndID = jointID; + } + } + } + //--------------------------------------------------------------------------------------------------------------------------- + for (unsigned int descID=0; descIDnumberOfAlignmentRules; descID++) + { + if ( config->alignment[descID].jointStartID == INVALID_VALUE ) + { + fprintf(stderr,"Normalization Rule %u/%u for start of joint is unresolved, stopping..\n",descID,config->numberOfNormalizedBasedOnRules); + return 0; + } + if ( config->alignment[descID].jointEndID == INVALID_VALUE ) + { + fprintf(stderr,"Normalization Rule %u/%u for end of joint is unresolved, stopping..\n",descID,config->numberOfNormalizedBasedOnRules); + return 0; + } + } + //--------------------------------------------------------------------------------------------------------------------------- + + + + //Resolve parent order for OpenCV drawing.. + for (unsigned int jointID=0; jointIDnumberOfHierarchyElements; jointID++) + { + //Resolve NSxM to Joint ID + for (unsigned int parentID=0; parentIDnumberOfHierarchyElements; parentID++) + { + if (strcmp(config->hierarchyElements[parentID].joint,config->hierarchyElements[jointID].joint)==0) + { + fprintf(stderr,"Found\n"); + config->hierarchyElements[jointID].parentID = jointID; + break; + } + } + } + + + return 1; + } + return 0; +} + + + + + +static int loadModelConfigurationData(struct ModelConfigurationData* out,const char * jsonFilename) +{ + fprintf(stderr,"Loading Configuration file %s ...\n",jsonFilename); + unsigned int inputLength=0; + char* input = readFileToMemory(jsonFilename,&inputLength); + if (input!=0) + { + fprintf(stderr,"Parsing %s ...\n",jsonFilename); + const nx_json* json=nx_json_parse_utf8(input); + const nx_json* rule=0; + const nx_json* item=0; + + //------------------------------------------------------------------- + const nx_json* j = nx_json_get(json,"version"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { out->version = atof(j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"backend"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { snprintf(out->backend,SMALL_STR,"%s",j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"precision"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { snprintf(out->precision,SMALL_STR,"%s",j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"BVH"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { snprintf(out->BVH,BIG_STR,"%s",j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"OutputDirectory"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { snprintf(out->outputDirectory,BIG_STR,"%s",j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"veryHighNumberOfEpochs"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->veryHighNumberOfEpochs = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"highNumberOfEpochs"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->highNumberOfEpochs = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"defaultNumberOfEpochs"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->defaultNumberOfEpochs = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"defaultBatchSize"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->defaultBatchSize = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"learningRate"); + if ((j!=0) && (j->type==NX_JSON_DOUBLE)) { out->learningRate = (float) j->dbl_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"minEarlyStoppingDelta"); + if ((j!=0) && (j->type==NX_JSON_DOUBLE)) { out->minEarlyStoppingDelta = (float) j->dbl_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"activationFunction"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { snprintf(out->activationFunction,SMALL_STR,"%s",j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"dropoutRate"); + if ((j!=0) && (j->type==NX_JSON_DOUBLE)) { out->dropoutRate = (float) j->dbl_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"lamda"); + if ((j!=0) && (j->type==NX_JSON_DOUBLE)) { out->lamda = (float) j->dbl_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"useQuadLoss"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->useQuadLoss = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"useSquaredLoss"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->useSquaredLoss = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"earlyStoppingPatience"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->earlyStoppingPatience = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"rememberWeights"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->rememberWeights = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"rememberConsecutiveWeights"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->rememberConsecutiveWeights = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"useOnlineHardExampleMining"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->useOnlineHardExampleMining = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"hardMiningEpochs"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->hardMiningEpochs = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"normalMiningEpochs"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->normalMiningEpochs = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"groupOutputs"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->groupOutputs = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"ignoreOcclusions"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->ignoreOcclusions = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"NSDMNormalizationMasterSwitch"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->NSDMNormalizationMasterSwitch = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"NSDMAlsoUseAlignmentAngles"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->NSDMAlsoUseAlignmentAngles = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"neuralNetworkDepth"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->neuralNetworkDepth = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"EDM"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->EDM = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"eNSRM"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->eNSRM = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"doPCA"); + if ((j!=0) && (j->type==NX_JSON_STRING)) { snprintf(out->doPCA,BIG_STR,"%s",j->text_value); } + //------------------------------------------------------------------- + j = nx_json_get(json,"PCADimensionsKept"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->PCADimensionsKept = j->int_value; } + //------------------------------------------------------------------- + j = nx_json_get(json,"padEnsembleInput"); + if ((j!=0) && (j->type==NX_JSON_INTEGER)) { out->padEnsembleInput = j->int_value; } + //------------------------------------------------------------------- + //------------------------------------------------------------------- + //------------------------------------------------------------------- + fprintf(stderr,"Parsed Initial Variables...\n"); + + //Now to parse Normalization elements.. + //------------------------------------------------------------------- + j = nx_json_get(json,"NormalizeNSDMBasedOn"); + if (j!=0) { + out->numberOfNormalizedBasedOnRules = j->length; + if (out->numberOfNormalizedBasedOnRules>3) + { + fprintf(stderr,"Maximum Accepted Normalization rules are 3!"); + out->numberOfNormalizedBasedOnRules = 3; + } + + for (int i=0; inumberOfNormalizedBasedOnRules; i++) + { + rule = nx_json_item(j,i); + //--------------------------------------------------------------------------------------------------- + item = nx_json_get(rule,"jointStart"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->normalizedBasedOn[i].jointStart,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"jointStartID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->normalizedBasedOn[i].jointStartID = item->int_value; } + item = nx_json_get(rule,"jointEnd"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->normalizedBasedOn[i].jointEnd,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"jointEndID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->normalizedBasedOn[i].jointEndID = item->int_value; } + } + } + //------------------------------------------------------------------- + + + //Now to parse Alignment elements.. + //------------------------------------------------------------------- + j = nx_json_get(json,"Alignment"); + if (j!=0) { + out->numberOfAlignmentRules = j->length; + if (out->numberOfAlignmentRules>3) + { + fprintf(stderr,"Maximum Accepted Alignment rules are 3!"); + out->numberOfAlignmentRules = 3; + } + + for (int i=0; inumberOfAlignmentRules; i++) + { + rule = nx_json_item(j,i); + //--------------------------------------------------------------------------------------------------- + item = nx_json_get(rule,"jointStart"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->alignment[i].jointStart,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"jointStartID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->alignment[i].jointStartID = item->int_value; } + item = nx_json_get(rule,"jointEnd"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->alignment[i].jointEnd,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"jointEndID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->alignment[i].jointEndID = item->int_value; } + } + } + //------------------------------------------------------------------- + + + //Now to parse NSxM elements.. + //------------------------------------------------------------------- + j = nx_json_get(json,"NSDM"); + if (j!=0) { + out->numberOfDescriptorElements = j->length; + if (out->numberOfDescriptorElements>MAX_DESCRIPTOR_ELEMENTS) + { + fprintf(stderr,"Maximum Accepted NSxM rules are %u!",MAX_DESCRIPTOR_ELEMENTS); + out->numberOfDescriptorElements = MAX_DESCRIPTOR_ELEMENTS; + } + + for (int i=0; inumberOfDescriptorElements; i++) + { + rule = nx_json_item(j,i); + //--------------------------------------------------------------------------------------------------- + item = nx_json_get(rule,"joint"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->descriptorElements[i].joint,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"jointID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->descriptorElements[i].jointID = item->int_value; } + item = nx_json_get(rule,"isVirtual"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->descriptorElements[i].isVirtual = item->int_value; } + item = nx_json_get(rule,"xOffset"); + if ((item!=0) && (item->type==NX_JSON_DOUBLE)) { out->descriptorElements[i].xOffset = (float) item->dbl_value; } + item = nx_json_get(rule,"yOffset"); + if ((item!=0) && (item->type==NX_JSON_DOUBLE)) { out->descriptorElements[i].yOffset = (float) item->dbl_value; } + item = nx_json_get(rule,"halfWayFromThisAnd"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->descriptorElements[i].halfwayFromJointAndThis,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"secondTargetJointID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->descriptorElements[i].secondTargetJointID = item->int_value; } + } + } + //------------------------------------------------------------------- + + + + + //Now to parse Hierarchy elements.. + //------------------------------------------------------------------- + j = nx_json_get(json,"hierarchy"); + if (j!=0) { + out->numberOfHierarchyElements = j->length; + if (out->numberOfHierarchyElements>MAX_HIERARCHY_ELEMENTS) + { + fprintf(stderr,"Maximum Accepted Hierarchy rules are %u!",MAX_HIERARCHY_ELEMENTS); + out->numberOfHierarchyElements = MAX_HIERARCHY_ELEMENTS; + } + + for (int i=0; inumberOfHierarchyElements; i++) + { + rule = nx_json_item(j,i); + //--------------------------------------------------------------------------------------------------- + item = nx_json_get(rule,"joint"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->hierarchyElements[i].joint,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"inheritNetwork"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->hierarchyElements[i].inheritNetwork,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"parent"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->hierarchyElements[i].parent,MAX_JOINT_NAME,"%s",item->text_value); } + item = nx_json_get(rule,"parentID"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->hierarchyElements[i].parentID = item->int_value; } + item = nx_json_get(rule,"importance"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->hierarchyElements[i].importance = item->int_value; } + item = nx_json_get(rule,"immuneToSelfOcclusions"); + if ((item!=0) && (item->type==NX_JSON_INTEGER)) { out->hierarchyElements[i].immuneToSelfOcclusions = item->int_value; } + } + } + //------------------------------------------------------------------- + + + + + //Now to parse Hierarchy elements.. + //------------------------------------------------------------------- + j = nx_json_get(json,"banned"); + if (j!=0) { + out->numberOfBannedJoints = j->length; + if (out->numberOfBannedJoints>MAX_BANNED_ELEMENTS) + { + fprintf(stderr,"Maximum Accepted Banned rules are %u!",MAX_BANNED_ELEMENTS); + out->numberOfBannedJoints = MAX_BANNED_ELEMENTS; + } + + for (int i=0; inumberOfBannedJoints; i++) + { + rule = nx_json_item(j,i); + //--------------------------------------------------------------------------------------------------- + item = nx_json_get(rule,"output"); + if ((item!=0) && (item->type==NX_JSON_STRING)) { snprintf(out->bannedJoints[i].joint,MAX_JOINT_NAME,"%s",item->text_value); } + } + } + //------------------------------------------------------------------- + + return resolveConfigurationData(out); + } + return 0; +} + + + + + + + +static int getCompositePoint( + float * iXOut, + float * iYOut, + float * iVisibilityOut, + int * invalidPointOut, + struct ModelConfigurationData* rules, + int i, + float * points2D, + unsigned int points2DLength + ) +{ + if (i > rules->numberOfDescriptorElements) + { + fprintf(stderr,"Point %u out of bounds for descriptor elements\n",i); + return 0; + } + + int invalidPoint = 0; + int numberOfNSDMRules = rules->numberOfDescriptorElements; + int iJointID = rules->descriptorElements[i].jointID; + + if (iJointID*3 > points2DLength) + { + fprintf(stderr,"Unable to get composite point %u\n",i); + return 0; + } + + float iX = points2D[iJointID*3+0]; + float iY = points2D[iJointID*3+1]; + float iVisibility = points2D[iJointID*3+2]; + //--------------------------------------------------------------------------- + //In case we fall through.. + *iXOut = iX; + *iYOut = iY; + *iVisibilityOut = iVisibility; + *invalidPointOut = invalidPoint; + //--------------------------------------------------------------------------- + + + if ((iX!=0) || (iY!=0)) + { + //--------------------------------------------------------------------------- + // Synthetic Points + //--------------------------------------------------------------------------- + + if (rules->descriptorElements[i].isVirtual==1) + { + + iX=iX+rules->descriptorElements[i].xOffset; + iY=iY+rules->descriptorElements[i].yOffset; + } + else + if (rules->descriptorElements[i].isVirtual==2) + { + int secondTargetJointID = rules->descriptorElements[i].secondTargetJointID;// rules['NSDM'][i]['secondTargetJointID'] + float secondTargetX = points2D[secondTargetJointID*3+0]; + float secondTargetY = points2D[secondTargetJointID*3+1]; + if ((secondTargetX==0) && (secondTargetY==0)) + { + invalidPoint=1; + iX=0; + iY=0; + } + else + { + iX=(float) (iX+secondTargetX)/2; + iY=(float) (iY+secondTargetY)/2; + } + } + //--------------------------------------------------------------------------- + + //Return values.. + *iXOut = iX; + *iYOut = iY; + *iVisibilityOut = iVisibility; + *invalidPointOut = invalidPoint; + return 1; + } + return 0; +} + + + + + + + + + + + + + +#ifdef __cplusplus +} +#endif + + + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/EDM.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/EDM.h new file mode 100644 index 0000000..65e8e84 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/EDM.h @@ -0,0 +1,103 @@ +/** @file EDM.h + * @brief An implementation of an EDM descriptor + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef EDM_H_INCLUDED +#define EDM_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + +#include "../JSON/readModelConfiguration.h" +#include "NSxM.h" +#include + + +static int countEDMElements(int numberOfJointRules) +{ + int count = 0; + for (int i=0; ij) + { + count+=1; + } + } + } + return count; +} + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistanceEDM(float aX,float aY,float bX,float bY) +{ + if ( ((aX==0) && (aY==0)) || ((bX==0) && (bY==0)) ) + { + return 0.0; + } + //------------------------- + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + float result = sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); + + if (result!=result) + { + fprintf(stderr,"getJoint2DDistanceEDM yielded NaN..\n"); + return 0.0; + } + + return result; +} + +static int appendEDMElements( + float * input2DJoints, + unsigned int input2DJointsLength, + float * output, + struct ModelConfigurationData* rules + ) +{ + int numberOfJointRules = rules->numberOfDescriptorElements; + //---------------------- + float iX=0.0,iY=0.0,iVisibility=0.0; + int iInvalidPoint=0; + //---------------------- + float jX=0.0,jY=0.0,jVisibility=0.0; + int jInvalidPoint=0; + //---------------------- + int count = 0; + for (int i=0; ij) + { + getCompositePoint(&jX,&jY,&jVisibility,&jInvalidPoint,rules,j,input2DJoints,input2DJointsLength); + if ( (iInvalidPoint) && (jInvalidPoint) ) //<- Why is this AND and not OR ? also in EDM.py code + { + output[count] = 0.0; + } else + { + output[count] = getJoint2DDistanceEDM(iX,iY,jX,jY); + } + count+=1; + } + } + } + + return count; +} + +#ifdef __cplusplus +} +#endif + + + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/NSDM.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/NSDM.h new file mode 100644 index 0000000..7d044b7 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/NSDM.h @@ -0,0 +1,45 @@ +/** @file NSDM.h + * @brief An implementation of an NSRM descriptor + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef NSDM_H_INCLUDED +#define NSDM_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + + +#include "../JSON/readModelConfiguration.h" +#include "NSxM.h" +#include + +static int countNSDMElements(int numberOfJointRules) +{ + return 2*numberOfJointRules*numberOfJointRules; +} + + + +static int appendNSDMElements( + float * input2DJoints, + struct descriptor * output, + int numberOfJointRules + ) +{ + return 0; +} + + + +#ifdef __cplusplus +} +#endif + + + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/NSRM.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/NSRM.h new file mode 100644 index 0000000..5f74405 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/NSRM.h @@ -0,0 +1,343 @@ +/** @file NSRM.h + * @brief An implementation of an NSRM descriptor + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef NSRM_H_INCLUDED +#define NSRM_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + + +#include + + +const float goFromRadToDegrees=(float) 180.0 / M_PI; +const float goFromDegreesToRad=(float) M_PI / 180.0; + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistance_NSRM(float aX,float aY,float bX,float bY) +{ + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return (float) sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + + +static float getAngleToAlignToZero_NSRM(float aX,float aY,float bX,float bY) +{ + if ( (aX==bX) && (aY==bY) ) { return 0; } + + + //Bigger magnitudes.. + aX=100*aX; + aY=100*aY; + bX=100*bX; + bY=100*bY; + + //We have points a, b and c and we want to calculate angle b + float lengthBetweenAAndB = getJoint2DDistance_NSRM(aX,aY,bX,bY); + + + //We align vertically.. , Point C is B offset in Y direction + float cX = bX; + float cY = bY - lengthBetweenAAndB; + + //fprintf(stderr,"We want to align A(%0.2f,%0.2f) to C(%0.2f,%0.2f) with pivot B(%0.2f,%0.2f)\n",aX,aY,cX,cY,bX,bY); + //fprintf(stderr,"length AB = %0.2f\n",lengthBetweenAAndB); + //fprintf(stderr,"bY = %0.2f\n",bY); + //fprintf(stderr,"cY = %0.2f = %0.2f - %0.2f\n",cY,bY,lengthBetweenAAndB); + + + //Calulate vector a->b + float abX = bX - aX; + float abY = bY - aY; + + //calculate vector c->b + float cbX = bX - cX; + float cbY = bY - cY; + + + float dot = ((abX * cbX) + (abY * cbY)); // dot product + float cross = ((abX * cbY) - (abY * cbX)); // cross product + + float alpha = atan2(cross,dot); + + //fprintf(stderr,"Angle is %0.2f rad or %0.2f degrees \n",alpha,alpha*goFromRadToDegrees); + return (float) alpha;// * goFromRadToDegrees ; +} + + + +static float getAngleToAlignToZeroJoints_NSRM(float * positions,unsigned int centerJoint,unsigned int referenceJoint) +{ + //We have points a, b and c and we want to calculate angle b + float aX= positions[referenceJoint*3+0]; + float aY= positions[referenceJoint*3+1]; + + float bX= positions[centerJoint*3+0]; + float bY= positions[centerJoint*3+1]; + + return getAngleToAlignToZero_NSRM(aX,aY,bX,bY); +} + + +/* +static int rotate2DPointsBasedOnJointAsCenter_NSRM(float * positions,int positionsSize,float angle,unsigned int centerJoint) +{ + if (positionsSize%3!=0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: incorrect positions.. \n" NORMAL); + return 0; + } + + if (positionsSize<=centerJoint*3) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: centerJoint out of bounds.. \n" NORMAL); + return 0; + } + + float s = sin((float) angle * goFromDegreesToRad); + float c = cos((float) angle * goFromDegreesToRad); + //================================================= + float cx = positions[centerJoint*3+0]; + float cy = positions[centerJoint*3+1]; + float cVisibility = positions[centerJoint*3+2]; + //================================================= + + if (cVisibility==0.0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: cannot work without pivot joint.. \n" NORMAL); + return 0; + } + + for (unsigned int jID=0; jID ",jX,jY,cx,cy,angle); + + //Translate point back to origin: + jX -= cx; + jY -= cy; + + //Rotate point + float xnew = jX * c - jY * s; + float ynew = jX * s + jY * c; + + //Translate point back: + positions[jID*3+0] = xnew + cx; + positions[jID*3+1] = ynew + cy; + + //fprintf(stderr,"%0.2f,%0.2f\n",positions[jID*3+0],positions[jID*3+1]); + } + + + return 1; +}*/ + + + +static int countNSRMElements(int numberOfJointRules) +{ + return numberOfJointRules*numberOfJointRules; +} + + + + +static int rotate2DPointsBasedOnJointAsCenter_NSRM(float * positions,unsigned int positionsLength,float angle,unsigned int centerJoint) +{ + if (positionsLength%3!=0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: incorrect positions.. \n" NORMAL); + return 0; + } + + if (positionsLength<=centerJoint*3) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: centerJoint out of bounds.. \n" NORMAL); + return 0; + } + + float s = sin((float) angle * goFromDegreesToRad ); + float c = cos((float) angle * goFromDegreesToRad ); + + float cx=positions[centerJoint*3+0]; + float cy=positions[centerJoint*3+1]; + float cVisibility=positions[centerJoint*3+2]; + + if (cVisibility==0.0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: cannot work with invisible pivot joint.. \n" NORMAL); + return 0; + } + + for (unsigned int jID=0; jID ",jX,jY,cx,cy,angle); + + //Translate point back to origin: + jX -= cx; + jY -= cy; + + //Rotate point + float xnew = jX * c - jY * s; + float ynew = jX * s + jY * c; + + //Translate point back: + positions[jID*3+0] = xnew + cx; + positions[jID*3+1] = ynew + cy; + + //fprintf(stderr,"%0.2f,%0.2f\n",positions[jID*3+0],positions[jID*3+1]); + } + + + return 1; +} + + + +static float performNSRMAlignment(float * input2DJoints, + unsigned int input2DJointsLength, + struct ModelConfigurationData* rules) +{ + //Enforce alignment rule.. + //------------------------------------------------------------ + int pivotPoint = rules->alignment[0].jointStartID;// rules['Alignment'][0]['jointStartID'] + int referencePoint = rules->alignment[0].jointEndID; // rules['Alignment'][0]['jointEndID'] + //------------------------------------------------------------ + float pivotX = input2DJoints[pivotPoint*3+0]; + float pivotY = input2DJoints[pivotPoint*3+1]; + float pivotVisibility = input2DJoints[pivotPoint*3+2]; + + float referenceX = input2DJoints[referencePoint*3+0]; + float referenceY = input2DJoints[referencePoint*3+1]; + float referenceVisibility = input2DJoints[referencePoint*3+2]; + //------------------------------------------------------------ + + float angleToRotate = 0.0; + if ((pivotVisibility!=0) && (referenceVisibility!=0)) + { + angleToRotate = getAngleToAlignToZero_NSRM(pivotX,pivotY,referenceX,referenceY); + rotate2DPointsBasedOnJointAsCenter_NSRM(input2DJoints,input2DJointsLength,angleToRotate,pivotPoint); + } + + return angleToRotate; +} + + +static int appendNSRMElements( + float * input2DJoints, + unsigned int input2DJointsLength, + float * output, + struct ModelConfigurationData* rules, + float angleUsedToRotateInput + ) +{ + int numberOfJointRules = rules->numberOfDescriptorElements; + //---------------------- + float iX=0.0,iY=0.0,iVisibility=0.0; + int iInvalidPoint=0; + //---------------------- + float jX=0.0,jY=0.0,jVisibility=0.0; + int jInvalidPoint=0; + //---------------------- + + //----------------------------------------------------------------------------------------------------- + // ..Main NSRM parameters .. + //----------------------------------------------------------------------------------------------------- + int count = 0; + for (int i=0; ieNSRM) + { + count=0; + int iJointID = rules->descriptorElements[0].jointID; + iX = input2DJoints[iJointID*3+0]; + iY = input2DJoints[iJointID*3+1]; + //getCompositePoint(&iX,&iY,&iVisibility,&iInvalidPoint,rules,i,input2DJoints,input2DJointsLength); + for (int i=0; i0) + { + int jJointID = rules->descriptorElements[j].jointID; + jX = input2DJoints[jJointID*3+0]; + jY = input2DJoints[jJointID*3+1]; + //getCompositePoint(&jX,&jY,&jVisibility,&jInvalidPoint,rules,j,input2DJoints,input2DJointsLength); + output[count] = getJoint2DDistance_NSRM(iX,iY,jX,jY); + } + } + count+=1; + } + } + } + + + //NSDM Normalization rule does not apply on NSRM + //SKIPPED + //----------------------------------------------------------------------------------------------------- + + //Enforce alignment rule.. + //------------------------------------------------------------ + int iJointID = rules->alignment[0].jointStartID;// rules['Alignment'][0]['jointStartID'] + int jJointID = rules->alignment[0].jointEndID; // rules['Alignment'][0]['jointEndID'] + //------------------------------------------------------------ + float aX = input2DJoints[iJointID*3+0]; + float aY = input2DJoints[iJointID*3+1]; + float bX = input2DJoints[jJointID*3+0]; + float bY = input2DJoints[jJointID*3+1]; + //------------------------------------------------------------ + float alignmentAngle = getAngleToAlignToZero_NSRM(aX,aY,bX,bY); + //fprintf(stderr,"ALIGNMENT %u %u %f",iJointID,jJointID,alignmentAngle); + for (int i=0; i +#include +#include +#include + +#include "../JSON/readListFile.h" +#include "../JSON/readModelConfiguration.h" + +#include "../PCA/PCA.h" + +#include "../IO/inputRouting.h" + +#include "EDM.h" +#include "NSDM.h" +#include "NSRM.h" + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +struct label +{ + char * str; + unsigned int length; +}; + +struct descriptor +{ + float * routedInput; + int routedInputLength; + unsigned int numberOfElements; + unsigned int maxNumberOfElements; + struct label * labels; + float * values; +}; + + +static int destroyDescriptor( + struct descriptor * output + ) +{ + //----------------------------------------------- + if (output->values!=0) + { free(output->values); } + //----------------------------------------------- + for (int i=0; imaxNumberOfElements; i++) + { + if (output->labels[i].str!=0) + { + free(output->labels[i].str); + output->labels[i].str = 0; + output->labels[i].length = 0; + } + } + //----------------------------------------------- + return 1; +} + + + + +static int createDescriptor( + struct descriptor * output, + float * data2DRaw, + unsigned int data2DRawLength, + struct inputRouting * route, + struct ModelConfigurationData * config, + struct PCAData * pca, + struct listFileData * listInputJoints, + struct listFileData * listOutput + ) +{ + fprintf(stderr,GREEN "createDescriptor..\n" NORMAL); + if (output==0) { return 0; } + //--------------------------------------------------------- + int useEDM = config->EDM; + int useNSRM = config->eNSRM; + int alignPoints = config->NSDMAlsoUseAlignmentAngles; + int usePCADimensions = config->PCADimensionsKept; + int numberOfJointRules = config->numberOfDescriptorElements; + //--------------------------------------------------------- + + + if (output->routedInput==0) + { + output->routedInput = (float *) malloc(sizeof(float) * route->numberOfRoutingRules * 3); + output->routedInputLength = route->numberOfRoutingRules * 3; + } + + //-------------------------------------- + if ( + !routeInput( + output->routedInput, + &output->routedInputLength, + config, + route, + data2DRaw, + data2DRawLength + ) + ) + { + fprintf(stderr,RED "Unable to route 2D input..\n" NORMAL); + return 0; + } + //-------------------------------------- + float * data2D = output->routedInput; + unsigned int data2DLength = output->routedInputLength; + //-------------------------------------- + + if (listInputJoints->numberOfEntries!=data2DLength) + { + fprintf(stderr,RED "Mismatch of Input 2D Points Vs Neural Network 2D Joints..\n" NORMAL); + } + + for (int i=0; ioutput->maxNumberOfElements) + { + //Space already allocated but not enough, + //Destroy anything previously allocated + destroyDescriptor(output); + } + //--------------------------------------------------------- + if (output->maxNumberOfElements==0) + { //If operating on a newly allocated descriptor + output->maxNumberOfElements = neededSpace; + //--------------------------------------------------------- + output->values = (float*) malloc( sizeof(float) * output->maxNumberOfElements ); + if (output->values!=0) + { memset(output->values,0,sizeof(float) * output->maxNumberOfElements); } + //--------------------------------------------------------- + output->labels = (struct label *) malloc( sizeof(struct label) * output->maxNumberOfElements ); + if (output->labels!=0) + { memset(output->labels,0,sizeof(struct label) * output->maxNumberOfElements); } + //We have a clean output descriptor + } + //--------------------------------------------------------- + + + float * outputInPosition = output->values; + + //Copy all 2D Data this must happen before the alignment!.. + fprintf(stderr,"Copying %u 2D coordinates \n",data2DLength); + for (int i=0; ieNSRM)//alignPoints) + { + fprintf(stderr,"Aligning Points\n"); + //Do point alignment here.. + angleToRotate = performNSRMAlignment(data2D,data2DLength,config); + fprintf(stderr,YELLOW "Correcting skeleton by rotating it %0.2f degrees\n" NORMAL,angleToRotate); + } + + /* + fprintf(stderr," 2d = list()\n"); + for (int i=0; ivalues[i],i); + }*/ + + + if (useEDM) + { + fprintf(stderr,"Using EDM\n"); + //--------------------------------------------------------- + int EDMElements = appendEDMElements( + data2D, + data2DLength, + outputInPosition, + config + ); + outputInPosition += EDMElements; + //--------------------------------------------------------- + /* + fprintf(stderr," EDM = list()\n"); + for (int i=0; ivalues[i],i); + }*/ + } + + + if (useNSRM) + { + fprintf(stderr,"Using NSRM\n"); + //--------------------------------------------------------- + int NSRMElements = appendNSRMElements( + data2D, + data2DLength, + outputInPosition, + config, + angleToRotate + ); + outputInPosition += NSRMElements; + //--------------------------------------------------------- + /* + fprintf(stderr," NSRM = list()\n"); + for (int i=0; ivalues[i],i); + }*/ + } + output->values[169] += angleToRotate; + + fprintf(stderr,"Descriptor yielded %u elements : ",neededSpace); + output->numberOfElements = neededSpace; + for (int i=0; ivalues[i]>10.0) { fprintf(stderr,RED); } + fprintf(stderr,"%0.2f(#%u) ",output->values[i],i); + if (output->values[i]>10.0) { fprintf(stderr,NORMAL); } + } + fprintf(stderr,"\n"); + + if (config->doPCA) + { + /* + fprintf(stderr," PCA = list()\n"); + for (int i=0; ivalues[i],i); + }*/ + int finalOutputSize = config->PCADimensionsKept; + doPCATransform( + output->values, + &finalOutputSize, + pca, + output->values, + neededSpace, + config->PCADimensionsKept + ); + + fprintf(stderr,"PCA (mean %0.2f/std %0.2f) packed descriptor yielded %u elements : \n",pca->mean,pca->std,finalOutputSize); + for (int i=0; ivalues[i]); + } + output->numberOfElements = finalOutputSize; + } + + return 1; +} + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistanceNSxM(float* in,int jointA,int jointB) +{ + float aX=in[jointA*3+0]; + float aY=in[jointA*3+1]; + float bX=in[jointB*3+0]; + float bY=in[jointB*3+1]; + if ( ((aX==0) && (aY==0)) || ((bX==0) && (bY==0)) ) { + return 0.0; + } + + + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + + + + +/** @brief This is an array of names for all uncompressed 2D inputs expected. */ +static const unsigned int mocapNET_InputLength_WithoutNSDM_upperbody = 33; + +/** @brief Use rich diagonal, part of networks after 31-01-2021 */ +static const unsigned int richDiagonal_upperbody = 1; + +/** @brief An array of strings that contains the label for each expected input. */ +static const char * mocapNET_upperbody[] = +{ + "2DX_hip", //0 + "2DY_hip", //1 + "visible_hip", //2 + "2DX_neck", //3 + "2DY_neck", //4 + "visible_neck", //5 + "2DX_head", //6 + "2DY_head", //7 + "visible_head", //8 + "2DX_EndSite_eye.l", //9 + "2DY_EndSite_eye.l", //10 + "visible_EndSite_eye.l", //11 + "2DX_EndSite_eye.r", //12 + "2DY_EndSite_eye.r", //13 + "visible_EndSite_eye.r", //14 + "2DX_rshoulder", //15 + "2DY_rshoulder", //16 + "visible_rshoulder", //17 + "2DX_relbow", //18 + "2DY_relbow", //19 + "visible_relbow", //20 + "2DX_rhand", //21 + "2DY_rhand", //22 + "visible_rhand", //23 + "2DX_lshoulder", //24 + "2DY_lshoulder", //25 + "visible_lshoulder", //26 + "2DX_lelbow", //27 + "2DY_lelbow", //28 + "visible_lelbow", //29 + "2DX_lhand", //30 + "2DY_lhand", //31 + "visible_lhand", //32 +//This is where regular input ends and the NSDM data kicks in.. + "angleUsedFor2DRotation_0", //33 + "hipY-EndSite_eye.rY-Angle", //34 + "hipY-EndSite_eye.lY-Angle", //35 + "hipY-neckY-Angle", //36 + "hipY-rshoulderY-Angle", //37 + "hipY-halfway_rshoulder_and_relbowY-Angle", //38 + "hipY-relbowY-Angle", //39 + "hipY-halfway_relbow_and_rhandY-Angle", //40 + "hipY-rhandY-Angle", //41 + "hipY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //42 + "hipY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //43 + "hipY-lshoulderY-Angle", //44 + "hipY-halfway_lshoulder_and_lelbowY-Angle", //45 + "hipY-lelbowY-Angle", //46 + "hipY-halfway_lelbow_and_lhandY-Angle", //47 + "hipY-lhandY-Angle", //48 + "hipY-halfway_neck_and_hipY-Angle", //49 + "EndSite_eye.rY-hipY-Angle", //50 + "angleUsedFor2DRotation_1", //51 + "EndSite_eye.rY-EndSite_eye.lY-Angle", //52 + "EndSite_eye.rY-neckY-Angle", //53 + "EndSite_eye.rY-rshoulderY-Angle", //54 + "EndSite_eye.rY-halfway_rshoulder_and_relbowY-Angle", //55 + "EndSite_eye.rY-relbowY-Angle", //56 + "EndSite_eye.rY-halfway_relbow_and_rhandY-Angle", //57 + "EndSite_eye.rY-rhandY-Angle", //58 + "EndSite_eye.rY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //59 + "EndSite_eye.rY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //60 + "EndSite_eye.rY-lshoulderY-Angle", //61 + "EndSite_eye.rY-halfway_lshoulder_and_lelbowY-Angle", //62 + "EndSite_eye.rY-lelbowY-Angle", //63 + "EndSite_eye.rY-halfway_lelbow_and_lhandY-Angle", //64 + "EndSite_eye.rY-lhandY-Angle", //65 + "EndSite_eye.rY-halfway_neck_and_hipY-Angle", //66 + "EndSite_eye.lY-hipY-Angle", //67 + "EndSite_eye.lY-EndSite_eye.rY-Angle", //68 + "angleUsedFor2DRotation_2", //69 + "EndSite_eye.lY-neckY-Angle", //70 + "EndSite_eye.lY-rshoulderY-Angle", //71 + "EndSite_eye.lY-halfway_rshoulder_and_relbowY-Angle", //72 + "EndSite_eye.lY-relbowY-Angle", //73 + "EndSite_eye.lY-halfway_relbow_and_rhandY-Angle", //74 + "EndSite_eye.lY-rhandY-Angle", //75 + "EndSite_eye.lY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //76 + "EndSite_eye.lY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //77 + "EndSite_eye.lY-lshoulderY-Angle", //78 + "EndSite_eye.lY-halfway_lshoulder_and_lelbowY-Angle", //79 + "EndSite_eye.lY-lelbowY-Angle", //80 + "EndSite_eye.lY-halfway_lelbow_and_lhandY-Angle", //81 + "EndSite_eye.lY-lhandY-Angle", //82 + "EndSite_eye.lY-halfway_neck_and_hipY-Angle", //83 + "neckY-hipY-Angle", //84 + "neckY-EndSite_eye.rY-Angle", //85 + "neckY-EndSite_eye.lY-Angle", //86 + "angleUsedFor2DRotation_3", //87 + "neckY-rshoulderY-Angle", //88 + "neckY-halfway_rshoulder_and_relbowY-Angle", //89 + "neckY-relbowY-Angle", //90 + "neckY-halfway_relbow_and_rhandY-Angle", //91 + "neckY-rhandY-Angle", //92 + "neckY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //93 + "neckY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //94 + "neckY-lshoulderY-Angle", //95 + "neckY-halfway_lshoulder_and_lelbowY-Angle", //96 + "neckY-lelbowY-Angle", //97 + "neckY-halfway_lelbow_and_lhandY-Angle", //98 + "neckY-lhandY-Angle", //99 + "neckY-halfway_neck_and_hipY-Angle", //100 + "rshoulderY-hipY-Angle", //101 + "rshoulderY-EndSite_eye.rY-Angle", //102 + "rshoulderY-EndSite_eye.lY-Angle", //103 + "rshoulderY-neckY-Angle", //104 + "angleUsedFor2DRotation_4", //105 + "rshoulderY-halfway_rshoulder_and_relbowY-Angle", //106 + "rshoulderY-relbowY-Angle", //107 + "rshoulderY-halfway_relbow_and_rhandY-Angle", //108 + "rshoulderY-rhandY-Angle", //109 + "rshoulderY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //110 + "rshoulderY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //111 + "rshoulderY-lshoulderY-Angle", //112 + "rshoulderY-halfway_lshoulder_and_lelbowY-Angle", //113 + "rshoulderY-lelbowY-Angle", //114 + "rshoulderY-halfway_lelbow_and_lhandY-Angle", //115 + "rshoulderY-lhandY-Angle", //116 + "rshoulderY-halfway_neck_and_hipY-Angle", //117 + "halfway_rshoulder_and_relbowY-hipY-Angle", //118 + "halfway_rshoulder_and_relbowY-EndSite_eye.rY-Angle", //119 + "halfway_rshoulder_and_relbowY-EndSite_eye.lY-Angle", //120 + "halfway_rshoulder_and_relbowY-neckY-Angle", //121 + "halfway_rshoulder_and_relbowY-rshoulderY-Angle", //122 + "angleUsedFor2DRotation_5", //123 + "halfway_rshoulder_and_relbowY-relbowY-Angle", //124 + "halfway_rshoulder_and_relbowY-halfway_relbow_and_rhandY-Angle", //125 + "halfway_rshoulder_and_relbowY-rhandY-Angle", //126 + "halfway_rshoulder_and_relbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //127 + "halfway_rshoulder_and_relbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //128 + "halfway_rshoulder_and_relbowY-lshoulderY-Angle", //129 + "halfway_rshoulder_and_relbowY-halfway_lshoulder_and_lelbowY-Angle", //130 + "halfway_rshoulder_and_relbowY-lelbowY-Angle", //131 + "halfway_rshoulder_and_relbowY-halfway_lelbow_and_lhandY-Angle", //132 + "halfway_rshoulder_and_relbowY-lhandY-Angle", //133 + "halfway_rshoulder_and_relbowY-halfway_neck_and_hipY-Angle", //134 + "relbowY-hipY-Angle", //135 + "relbowY-EndSite_eye.rY-Angle", //136 + "relbowY-EndSite_eye.lY-Angle", //137 + "relbowY-neckY-Angle", //138 + "relbowY-rshoulderY-Angle", //139 + "relbowY-halfway_rshoulder_and_relbowY-Angle", //140 + "angleUsedFor2DRotation_6", //141 + "relbowY-halfway_relbow_and_rhandY-Angle", //142 + "relbowY-rhandY-Angle", //143 + "relbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //144 + "relbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //145 + "relbowY-lshoulderY-Angle", //146 + "relbowY-halfway_lshoulder_and_lelbowY-Angle", //147 + "relbowY-lelbowY-Angle", //148 + "relbowY-halfway_lelbow_and_lhandY-Angle", //149 + "relbowY-lhandY-Angle", //150 + "relbowY-halfway_neck_and_hipY-Angle", //151 + "halfway_relbow_and_rhandY-hipY-Angle", //152 + "halfway_relbow_and_rhandY-EndSite_eye.rY-Angle", //153 + "halfway_relbow_and_rhandY-EndSite_eye.lY-Angle", //154 + "halfway_relbow_and_rhandY-neckY-Angle", //155 + "halfway_relbow_and_rhandY-rshoulderY-Angle", //156 + "halfway_relbow_and_rhandY-halfway_rshoulder_and_relbowY-Angle", //157 + "halfway_relbow_and_rhandY-relbowY-Angle", //158 + "angleUsedFor2DRotation_7", //159 + "halfway_relbow_and_rhandY-rhandY-Angle", //160 + "halfway_relbow_and_rhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //161 + "halfway_relbow_and_rhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //162 + "halfway_relbow_and_rhandY-lshoulderY-Angle", //163 + "halfway_relbow_and_rhandY-halfway_lshoulder_and_lelbowY-Angle", //164 + "halfway_relbow_and_rhandY-lelbowY-Angle", //165 + "halfway_relbow_and_rhandY-halfway_lelbow_and_lhandY-Angle", //166 + "halfway_relbow_and_rhandY-lhandY-Angle", //167 + "halfway_relbow_and_rhandY-halfway_neck_and_hipY-Angle", //168 + "rhandY-hipY-Angle", //169 + "rhandY-EndSite_eye.rY-Angle", //170 + "rhandY-EndSite_eye.lY-Angle", //171 + "rhandY-neckY-Angle", //172 + "rhandY-rshoulderY-Angle", //173 + "rhandY-halfway_rshoulder_and_relbowY-Angle", //174 + "rhandY-relbowY-Angle", //175 + "rhandY-halfway_relbow_and_rhandY-Angle", //176 + "angleUsedFor2DRotation_8", //177 + "rhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //178 + "rhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //179 + "rhandY-lshoulderY-Angle", //180 + "rhandY-halfway_lshoulder_and_lelbowY-Angle", //181 + "rhandY-lelbowY-Angle", //182 + "rhandY-halfway_lelbow_and_lhandY-Angle", //183 + "rhandY-lhandY-Angle", //184 + "rhandY-halfway_neck_and_hipY-Angle", //185 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-hipY-Angle", //186 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-EndSite_eye.rY-Angle", //187 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-EndSite_eye.lY-Angle", //188 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-neckY-Angle", //189 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-rshoulderY-Angle", //190 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_rshoulder_and_relbowY-Angle", //191 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-relbowY-Angle", //192 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_relbow_and_rhandY-Angle", //193 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-rhandY-Angle", //194 + "angleUsedFor2DRotation_9", //195 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //196 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-lshoulderY-Angle", //197 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_lshoulder_and_lelbowY-Angle", //198 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-lelbowY-Angle", //199 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_lelbow_and_lhandY-Angle", //200 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-lhandY-Angle", //201 + "virtual_hip_x_minus_0_15_y_minus_0_15Y-halfway_neck_and_hipY-Angle", //202 + "virtual_hip_x_plus0_15_y_minus_0_15Y-hipY-Angle", //203 + "virtual_hip_x_plus0_15_y_minus_0_15Y-EndSite_eye.rY-Angle", //204 + "virtual_hip_x_plus0_15_y_minus_0_15Y-EndSite_eye.lY-Angle", //205 + "virtual_hip_x_plus0_15_y_minus_0_15Y-neckY-Angle", //206 + "virtual_hip_x_plus0_15_y_minus_0_15Y-rshoulderY-Angle", //207 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_rshoulder_and_relbowY-Angle", //208 + "virtual_hip_x_plus0_15_y_minus_0_15Y-relbowY-Angle", //209 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_relbow_and_rhandY-Angle", //210 + "virtual_hip_x_plus0_15_y_minus_0_15Y-rhandY-Angle", //211 + "virtual_hip_x_plus0_15_y_minus_0_15Y-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //212 + "angleUsedFor2DRotation_10", //213 + "virtual_hip_x_plus0_15_y_minus_0_15Y-lshoulderY-Angle", //214 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_lshoulder_and_lelbowY-Angle", //215 + "virtual_hip_x_plus0_15_y_minus_0_15Y-lelbowY-Angle", //216 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_lelbow_and_lhandY-Angle", //217 + "virtual_hip_x_plus0_15_y_minus_0_15Y-lhandY-Angle", //218 + "virtual_hip_x_plus0_15_y_minus_0_15Y-halfway_neck_and_hipY-Angle", //219 + "lshoulderY-hipY-Angle", //220 + "lshoulderY-EndSite_eye.rY-Angle", //221 + "lshoulderY-EndSite_eye.lY-Angle", //222 + "lshoulderY-neckY-Angle", //223 + "lshoulderY-rshoulderY-Angle", //224 + "lshoulderY-halfway_rshoulder_and_relbowY-Angle", //225 + "lshoulderY-relbowY-Angle", //226 + "lshoulderY-halfway_relbow_and_rhandY-Angle", //227 + "lshoulderY-rhandY-Angle", //228 + "lshoulderY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //229 + "lshoulderY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //230 + "angleUsedFor2DRotation_11", //231 + "lshoulderY-halfway_lshoulder_and_lelbowY-Angle", //232 + "lshoulderY-lelbowY-Angle", //233 + "lshoulderY-halfway_lelbow_and_lhandY-Angle", //234 + "lshoulderY-lhandY-Angle", //235 + "lshoulderY-halfway_neck_and_hipY-Angle", //236 + "halfway_lshoulder_and_lelbowY-hipY-Angle", //237 + "halfway_lshoulder_and_lelbowY-EndSite_eye.rY-Angle", //238 + "halfway_lshoulder_and_lelbowY-EndSite_eye.lY-Angle", //239 + "halfway_lshoulder_and_lelbowY-neckY-Angle", //240 + "halfway_lshoulder_and_lelbowY-rshoulderY-Angle", //241 + "halfway_lshoulder_and_lelbowY-halfway_rshoulder_and_relbowY-Angle", //242 + "halfway_lshoulder_and_lelbowY-relbowY-Angle", //243 + "halfway_lshoulder_and_lelbowY-halfway_relbow_and_rhandY-Angle", //244 + "halfway_lshoulder_and_lelbowY-rhandY-Angle", //245 + "halfway_lshoulder_and_lelbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //246 + "halfway_lshoulder_and_lelbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //247 + "halfway_lshoulder_and_lelbowY-lshoulderY-Angle", //248 + "angleUsedFor2DRotation_12", //249 + "halfway_lshoulder_and_lelbowY-lelbowY-Angle", //250 + "halfway_lshoulder_and_lelbowY-halfway_lelbow_and_lhandY-Angle", //251 + "halfway_lshoulder_and_lelbowY-lhandY-Angle", //252 + "halfway_lshoulder_and_lelbowY-halfway_neck_and_hipY-Angle", //253 + "lelbowY-hipY-Angle", //254 + "lelbowY-EndSite_eye.rY-Angle", //255 + "lelbowY-EndSite_eye.lY-Angle", //256 + "lelbowY-neckY-Angle", //257 + "lelbowY-rshoulderY-Angle", //258 + "lelbowY-halfway_rshoulder_and_relbowY-Angle", //259 + "lelbowY-relbowY-Angle", //260 + "lelbowY-halfway_relbow_and_rhandY-Angle", //261 + "lelbowY-rhandY-Angle", //262 + "lelbowY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //263 + "lelbowY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //264 + "lelbowY-lshoulderY-Angle", //265 + "lelbowY-halfway_lshoulder_and_lelbowY-Angle", //266 + "angleUsedFor2DRotation_13", //267 + "lelbowY-halfway_lelbow_and_lhandY-Angle", //268 + "lelbowY-lhandY-Angle", //269 + "lelbowY-halfway_neck_and_hipY-Angle", //270 + "halfway_lelbow_and_lhandY-hipY-Angle", //271 + "halfway_lelbow_and_lhandY-EndSite_eye.rY-Angle", //272 + "halfway_lelbow_and_lhandY-EndSite_eye.lY-Angle", //273 + "halfway_lelbow_and_lhandY-neckY-Angle", //274 + "halfway_lelbow_and_lhandY-rshoulderY-Angle", //275 + "halfway_lelbow_and_lhandY-halfway_rshoulder_and_relbowY-Angle", //276 + "halfway_lelbow_and_lhandY-relbowY-Angle", //277 + "halfway_lelbow_and_lhandY-halfway_relbow_and_rhandY-Angle", //278 + "halfway_lelbow_and_lhandY-rhandY-Angle", //279 + "halfway_lelbow_and_lhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //280 + "halfway_lelbow_and_lhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //281 + "halfway_lelbow_and_lhandY-lshoulderY-Angle", //282 + "halfway_lelbow_and_lhandY-halfway_lshoulder_and_lelbowY-Angle", //283 + "halfway_lelbow_and_lhandY-lelbowY-Angle", //284 + "angleUsedFor2DRotation_14", //285 + "halfway_lelbow_and_lhandY-lhandY-Angle", //286 + "halfway_lelbow_and_lhandY-halfway_neck_and_hipY-Angle", //287 + "lhandY-hipY-Angle", //288 + "lhandY-EndSite_eye.rY-Angle", //289 + "lhandY-EndSite_eye.lY-Angle", //290 + "lhandY-neckY-Angle", //291 + "lhandY-rshoulderY-Angle", //292 + "lhandY-halfway_rshoulder_and_relbowY-Angle", //293 + "lhandY-relbowY-Angle", //294 + "lhandY-halfway_relbow_and_rhandY-Angle", //295 + "lhandY-rhandY-Angle", //296 + "lhandY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //297 + "lhandY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //298 + "lhandY-lshoulderY-Angle", //299 + "lhandY-halfway_lshoulder_and_lelbowY-Angle", //300 + "lhandY-lelbowY-Angle", //301 + "lhandY-halfway_lelbow_and_lhandY-Angle", //302 + "angleUsedFor2DRotation_15", //303 + "lhandY-halfway_neck_and_hipY-Angle", //304 + "halfway_neck_and_hipY-hipY-Angle", //305 + "halfway_neck_and_hipY-EndSite_eye.rY-Angle", //306 + "halfway_neck_and_hipY-EndSite_eye.lY-Angle", //307 + "halfway_neck_and_hipY-neckY-Angle", //308 + "halfway_neck_and_hipY-rshoulderY-Angle", //309 + "halfway_neck_and_hipY-halfway_rshoulder_and_relbowY-Angle", //310 + "halfway_neck_and_hipY-relbowY-Angle", //311 + "halfway_neck_and_hipY-halfway_relbow_and_rhandY-Angle", //312 + "halfway_neck_and_hipY-rhandY-Angle", //313 + "halfway_neck_and_hipY-virtual_hip_x_minus_0_15_y_minus_0_15Y-Angle", //314 + "halfway_neck_and_hipY-virtual_hip_x_plus0_15_y_minus_0_15Y-Angle", //315 + "halfway_neck_and_hipY-lshoulderY-Angle", //316 + "halfway_neck_and_hipY-halfway_lshoulder_and_lelbowY-Angle", //317 + "halfway_neck_and_hipY-lelbowY-Angle", //318 + "halfway_neck_and_hipY-halfway_lelbow_and_lhandY-Angle", //319 + "halfway_neck_and_hipY-lhandY-Angle", //320 + "angleUsedFor2DRotation_16", //321 + "end" +}; +/** @brief Programmer friendly enumerator of expected inputs*/ +enum mocapNET_upperbody_enum +{ + MNET_UPPERBODY_IN_2DX_HIP = 0, //0 + MNET_UPPERBODY_IN_2DY_HIP, //1 + MNET_UPPERBODY_IN_VISIBLE_HIP, //2 + MNET_UPPERBODY_IN_2DX_NECK, //3 + MNET_UPPERBODY_IN_2DY_NECK, //4 + MNET_UPPERBODY_IN_VISIBLE_NECK, //5 + MNET_UPPERBODY_IN_2DX_HEAD, //6 + MNET_UPPERBODY_IN_2DY_HEAD, //7 + MNET_UPPERBODY_IN_VISIBLE_HEAD, //8 + MNET_UPPERBODY_IN_2DX_ENDSITE_EYE_L, //9 + MNET_UPPERBODY_IN_2DY_ENDSITE_EYE_L, //10 + MNET_UPPERBODY_IN_VISIBLE_ENDSITE_EYE_L, //11 + MNET_UPPERBODY_IN_2DX_ENDSITE_EYE_R, //12 + MNET_UPPERBODY_IN_2DY_ENDSITE_EYE_R, //13 + MNET_UPPERBODY_IN_VISIBLE_ENDSITE_EYE_R, //14 + MNET_UPPERBODY_IN_2DX_RSHOULDER, //15 + MNET_UPPERBODY_IN_2DY_RSHOULDER, //16 + MNET_UPPERBODY_IN_VISIBLE_RSHOULDER, //17 + MNET_UPPERBODY_IN_2DX_RELBOW, //18 + MNET_UPPERBODY_IN_2DY_RELBOW, //19 + MNET_UPPERBODY_IN_VISIBLE_RELBOW, //20 + MNET_UPPERBODY_IN_2DX_RHAND, //21 + MNET_UPPERBODY_IN_2DY_RHAND, //22 + MNET_UPPERBODY_IN_VISIBLE_RHAND, //23 + MNET_UPPERBODY_IN_2DX_LSHOULDER, //24 + MNET_UPPERBODY_IN_2DY_LSHOULDER, //25 + MNET_UPPERBODY_IN_VISIBLE_LSHOULDER, //26 + MNET_UPPERBODY_IN_2DX_LELBOW, //27 + MNET_UPPERBODY_IN_2DY_LELBOW, //28 + MNET_UPPERBODY_IN_VISIBLE_LELBOW, //29 + MNET_UPPERBODY_IN_2DX_LHAND, //30 + MNET_UPPERBODY_IN_2DY_LHAND, //31 + MNET_UPPERBODY_IN_VISIBLE_LHAND, //32 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_0, //33 + MNET_UPPERBODY_IN_HIPY_ENDSITE_EYE_RY_ANGLE, //34 + MNET_UPPERBODY_IN_HIPY_ENDSITE_EYE_LY_ANGLE, //35 + MNET_UPPERBODY_IN_HIPY_NECKY_ANGLE, //36 + MNET_UPPERBODY_IN_HIPY_RSHOULDERY_ANGLE, //37 + MNET_UPPERBODY_IN_HIPY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //38 + MNET_UPPERBODY_IN_HIPY_RELBOWY_ANGLE, //39 + MNET_UPPERBODY_IN_HIPY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //40 + MNET_UPPERBODY_IN_HIPY_RHANDY_ANGLE, //41 + MNET_UPPERBODY_IN_HIPY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //42 + MNET_UPPERBODY_IN_HIPY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //43 + MNET_UPPERBODY_IN_HIPY_LSHOULDERY_ANGLE, //44 + MNET_UPPERBODY_IN_HIPY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //45 + MNET_UPPERBODY_IN_HIPY_LELBOWY_ANGLE, //46 + MNET_UPPERBODY_IN_HIPY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //47 + MNET_UPPERBODY_IN_HIPY_LHANDY_ANGLE, //48 + MNET_UPPERBODY_IN_HIPY_HALFWAY_NECK_AND_HIPY_ANGLE, //49 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HIPY_ANGLE, //50 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_1, //51 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_ENDSITE_EYE_LY_ANGLE, //52 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_NECKY_ANGLE, //53 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_RSHOULDERY_ANGLE, //54 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //55 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_RELBOWY_ANGLE, //56 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //57 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_RHANDY_ANGLE, //58 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //59 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //60 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_LSHOULDERY_ANGLE, //61 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //62 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_LELBOWY_ANGLE, //63 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //64 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_LHANDY_ANGLE, //65 + MNET_UPPERBODY_IN_ENDSITE_EYE_RY_HALFWAY_NECK_AND_HIPY_ANGLE, //66 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HIPY_ANGLE, //67 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_ENDSITE_EYE_RY_ANGLE, //68 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_2, //69 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_NECKY_ANGLE, //70 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_RSHOULDERY_ANGLE, //71 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //72 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_RELBOWY_ANGLE, //73 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //74 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_RHANDY_ANGLE, //75 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //76 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //77 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_LSHOULDERY_ANGLE, //78 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //79 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_LELBOWY_ANGLE, //80 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //81 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_LHANDY_ANGLE, //82 + MNET_UPPERBODY_IN_ENDSITE_EYE_LY_HALFWAY_NECK_AND_HIPY_ANGLE, //83 + MNET_UPPERBODY_IN_NECKY_HIPY_ANGLE, //84 + MNET_UPPERBODY_IN_NECKY_ENDSITE_EYE_RY_ANGLE, //85 + MNET_UPPERBODY_IN_NECKY_ENDSITE_EYE_LY_ANGLE, //86 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_3, //87 + MNET_UPPERBODY_IN_NECKY_RSHOULDERY_ANGLE, //88 + MNET_UPPERBODY_IN_NECKY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //89 + MNET_UPPERBODY_IN_NECKY_RELBOWY_ANGLE, //90 + MNET_UPPERBODY_IN_NECKY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //91 + MNET_UPPERBODY_IN_NECKY_RHANDY_ANGLE, //92 + MNET_UPPERBODY_IN_NECKY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //93 + MNET_UPPERBODY_IN_NECKY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //94 + MNET_UPPERBODY_IN_NECKY_LSHOULDERY_ANGLE, //95 + MNET_UPPERBODY_IN_NECKY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //96 + MNET_UPPERBODY_IN_NECKY_LELBOWY_ANGLE, //97 + MNET_UPPERBODY_IN_NECKY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //98 + MNET_UPPERBODY_IN_NECKY_LHANDY_ANGLE, //99 + MNET_UPPERBODY_IN_NECKY_HALFWAY_NECK_AND_HIPY_ANGLE, //100 + MNET_UPPERBODY_IN_RSHOULDERY_HIPY_ANGLE, //101 + MNET_UPPERBODY_IN_RSHOULDERY_ENDSITE_EYE_RY_ANGLE, //102 + MNET_UPPERBODY_IN_RSHOULDERY_ENDSITE_EYE_LY_ANGLE, //103 + MNET_UPPERBODY_IN_RSHOULDERY_NECKY_ANGLE, //104 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_4, //105 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //106 + MNET_UPPERBODY_IN_RSHOULDERY_RELBOWY_ANGLE, //107 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //108 + MNET_UPPERBODY_IN_RSHOULDERY_RHANDY_ANGLE, //109 + MNET_UPPERBODY_IN_RSHOULDERY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //110 + MNET_UPPERBODY_IN_RSHOULDERY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //111 + MNET_UPPERBODY_IN_RSHOULDERY_LSHOULDERY_ANGLE, //112 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //113 + MNET_UPPERBODY_IN_RSHOULDERY_LELBOWY_ANGLE, //114 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //115 + MNET_UPPERBODY_IN_RSHOULDERY_LHANDY_ANGLE, //116 + MNET_UPPERBODY_IN_RSHOULDERY_HALFWAY_NECK_AND_HIPY_ANGLE, //117 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HIPY_ANGLE, //118 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_ENDSITE_EYE_RY_ANGLE, //119 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_ENDSITE_EYE_LY_ANGLE, //120 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_NECKY_ANGLE, //121 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_RSHOULDERY_ANGLE, //122 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_5, //123 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_RELBOWY_ANGLE, //124 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //125 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_RHANDY_ANGLE, //126 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //127 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //128 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_LSHOULDERY_ANGLE, //129 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //130 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_LELBOWY_ANGLE, //131 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //132 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_LHANDY_ANGLE, //133 + MNET_UPPERBODY_IN_HALFWAY_RSHOULDER_AND_RELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //134 + MNET_UPPERBODY_IN_RELBOWY_HIPY_ANGLE, //135 + MNET_UPPERBODY_IN_RELBOWY_ENDSITE_EYE_RY_ANGLE, //136 + MNET_UPPERBODY_IN_RELBOWY_ENDSITE_EYE_LY_ANGLE, //137 + MNET_UPPERBODY_IN_RELBOWY_NECKY_ANGLE, //138 + MNET_UPPERBODY_IN_RELBOWY_RSHOULDERY_ANGLE, //139 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //140 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_6, //141 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //142 + MNET_UPPERBODY_IN_RELBOWY_RHANDY_ANGLE, //143 + MNET_UPPERBODY_IN_RELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //144 + MNET_UPPERBODY_IN_RELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //145 + MNET_UPPERBODY_IN_RELBOWY_LSHOULDERY_ANGLE, //146 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //147 + MNET_UPPERBODY_IN_RELBOWY_LELBOWY_ANGLE, //148 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //149 + MNET_UPPERBODY_IN_RELBOWY_LHANDY_ANGLE, //150 + MNET_UPPERBODY_IN_RELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //151 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HIPY_ANGLE, //152 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_ENDSITE_EYE_RY_ANGLE, //153 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_ENDSITE_EYE_LY_ANGLE, //154 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_NECKY_ANGLE, //155 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_RSHOULDERY_ANGLE, //156 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //157 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_RELBOWY_ANGLE, //158 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_7, //159 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_RHANDY_ANGLE, //160 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //161 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //162 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_LSHOULDERY_ANGLE, //163 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //164 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_LELBOWY_ANGLE, //165 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //166 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_LHANDY_ANGLE, //167 + MNET_UPPERBODY_IN_HALFWAY_RELBOW_AND_RHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //168 + MNET_UPPERBODY_IN_RHANDY_HIPY_ANGLE, //169 + MNET_UPPERBODY_IN_RHANDY_ENDSITE_EYE_RY_ANGLE, //170 + MNET_UPPERBODY_IN_RHANDY_ENDSITE_EYE_LY_ANGLE, //171 + MNET_UPPERBODY_IN_RHANDY_NECKY_ANGLE, //172 + MNET_UPPERBODY_IN_RHANDY_RSHOULDERY_ANGLE, //173 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //174 + MNET_UPPERBODY_IN_RHANDY_RELBOWY_ANGLE, //175 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //176 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_8, //177 + MNET_UPPERBODY_IN_RHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //178 + MNET_UPPERBODY_IN_RHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //179 + MNET_UPPERBODY_IN_RHANDY_LSHOULDERY_ANGLE, //180 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //181 + MNET_UPPERBODY_IN_RHANDY_LELBOWY_ANGLE, //182 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //183 + MNET_UPPERBODY_IN_RHANDY_LHANDY_ANGLE, //184 + MNET_UPPERBODY_IN_RHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //185 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HIPY_ANGLE, //186 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ENDSITE_EYE_RY_ANGLE, //187 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ENDSITE_EYE_LY_ANGLE, //188 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_NECKY_ANGLE, //189 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_RSHOULDERY_ANGLE, //190 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //191 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_RELBOWY_ANGLE, //192 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //193 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_RHANDY_ANGLE, //194 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_9, //195 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //196 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_LSHOULDERY_ANGLE, //197 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //198 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_LELBOWY_ANGLE, //199 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //200 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_LHANDY_ANGLE, //201 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_HALFWAY_NECK_AND_HIPY_ANGLE, //202 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HIPY_ANGLE, //203 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ENDSITE_EYE_RY_ANGLE, //204 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ENDSITE_EYE_LY_ANGLE, //205 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_NECKY_ANGLE, //206 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_RSHOULDERY_ANGLE, //207 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //208 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_RELBOWY_ANGLE, //209 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //210 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_RHANDY_ANGLE, //211 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //212 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_10, //213 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_LSHOULDERY_ANGLE, //214 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //215 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_LELBOWY_ANGLE, //216 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //217 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_LHANDY_ANGLE, //218 + MNET_UPPERBODY_IN_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_HALFWAY_NECK_AND_HIPY_ANGLE, //219 + MNET_UPPERBODY_IN_LSHOULDERY_HIPY_ANGLE, //220 + MNET_UPPERBODY_IN_LSHOULDERY_ENDSITE_EYE_RY_ANGLE, //221 + MNET_UPPERBODY_IN_LSHOULDERY_ENDSITE_EYE_LY_ANGLE, //222 + MNET_UPPERBODY_IN_LSHOULDERY_NECKY_ANGLE, //223 + MNET_UPPERBODY_IN_LSHOULDERY_RSHOULDERY_ANGLE, //224 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //225 + MNET_UPPERBODY_IN_LSHOULDERY_RELBOWY_ANGLE, //226 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //227 + MNET_UPPERBODY_IN_LSHOULDERY_RHANDY_ANGLE, //228 + MNET_UPPERBODY_IN_LSHOULDERY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //229 + MNET_UPPERBODY_IN_LSHOULDERY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //230 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_11, //231 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //232 + MNET_UPPERBODY_IN_LSHOULDERY_LELBOWY_ANGLE, //233 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //234 + MNET_UPPERBODY_IN_LSHOULDERY_LHANDY_ANGLE, //235 + MNET_UPPERBODY_IN_LSHOULDERY_HALFWAY_NECK_AND_HIPY_ANGLE, //236 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HIPY_ANGLE, //237 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_ENDSITE_EYE_RY_ANGLE, //238 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_ENDSITE_EYE_LY_ANGLE, //239 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_NECKY_ANGLE, //240 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_RSHOULDERY_ANGLE, //241 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //242 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_RELBOWY_ANGLE, //243 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //244 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_RHANDY_ANGLE, //245 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //246 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //247 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_LSHOULDERY_ANGLE, //248 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_12, //249 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_LELBOWY_ANGLE, //250 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //251 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_LHANDY_ANGLE, //252 + MNET_UPPERBODY_IN_HALFWAY_LSHOULDER_AND_LELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //253 + MNET_UPPERBODY_IN_LELBOWY_HIPY_ANGLE, //254 + MNET_UPPERBODY_IN_LELBOWY_ENDSITE_EYE_RY_ANGLE, //255 + MNET_UPPERBODY_IN_LELBOWY_ENDSITE_EYE_LY_ANGLE, //256 + MNET_UPPERBODY_IN_LELBOWY_NECKY_ANGLE, //257 + MNET_UPPERBODY_IN_LELBOWY_RSHOULDERY_ANGLE, //258 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //259 + MNET_UPPERBODY_IN_LELBOWY_RELBOWY_ANGLE, //260 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //261 + MNET_UPPERBODY_IN_LELBOWY_RHANDY_ANGLE, //262 + MNET_UPPERBODY_IN_LELBOWY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //263 + MNET_UPPERBODY_IN_LELBOWY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //264 + MNET_UPPERBODY_IN_LELBOWY_LSHOULDERY_ANGLE, //265 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //266 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_13, //267 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //268 + MNET_UPPERBODY_IN_LELBOWY_LHANDY_ANGLE, //269 + MNET_UPPERBODY_IN_LELBOWY_HALFWAY_NECK_AND_HIPY_ANGLE, //270 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HIPY_ANGLE, //271 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_ENDSITE_EYE_RY_ANGLE, //272 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_ENDSITE_EYE_LY_ANGLE, //273 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_NECKY_ANGLE, //274 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_RSHOULDERY_ANGLE, //275 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //276 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_RELBOWY_ANGLE, //277 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //278 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_RHANDY_ANGLE, //279 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //280 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //281 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_LSHOULDERY_ANGLE, //282 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //283 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_LELBOWY_ANGLE, //284 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_14, //285 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_LHANDY_ANGLE, //286 + MNET_UPPERBODY_IN_HALFWAY_LELBOW_AND_LHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //287 + MNET_UPPERBODY_IN_LHANDY_HIPY_ANGLE, //288 + MNET_UPPERBODY_IN_LHANDY_ENDSITE_EYE_RY_ANGLE, //289 + MNET_UPPERBODY_IN_LHANDY_ENDSITE_EYE_LY_ANGLE, //290 + MNET_UPPERBODY_IN_LHANDY_NECKY_ANGLE, //291 + MNET_UPPERBODY_IN_LHANDY_RSHOULDERY_ANGLE, //292 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //293 + MNET_UPPERBODY_IN_LHANDY_RELBOWY_ANGLE, //294 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //295 + MNET_UPPERBODY_IN_LHANDY_RHANDY_ANGLE, //296 + MNET_UPPERBODY_IN_LHANDY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //297 + MNET_UPPERBODY_IN_LHANDY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //298 + MNET_UPPERBODY_IN_LHANDY_LSHOULDERY_ANGLE, //299 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //300 + MNET_UPPERBODY_IN_LHANDY_LELBOWY_ANGLE, //301 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //302 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_15, //303 + MNET_UPPERBODY_IN_LHANDY_HALFWAY_NECK_AND_HIPY_ANGLE, //304 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HIPY_ANGLE, //305 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_ENDSITE_EYE_RY_ANGLE, //306 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_ENDSITE_EYE_LY_ANGLE, //307 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_NECKY_ANGLE, //308 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_RSHOULDERY_ANGLE, //309 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_RSHOULDER_AND_RELBOWY_ANGLE, //310 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_RELBOWY_ANGLE, //311 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_RELBOW_AND_RHANDY_ANGLE, //312 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_RHANDY_ANGLE, //313 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15Y_ANGLE, //314 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15Y_ANGLE, //315 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_LSHOULDERY_ANGLE, //316 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_LSHOULDER_AND_LELBOWY_ANGLE, //317 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_LELBOWY_ANGLE, //318 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_HALFWAY_LELBOW_AND_LHANDY_ANGLE, //319 + MNET_UPPERBODY_IN_HALFWAY_NECK_AND_HIPY_LHANDY_ANGLE, //320 + MNET_UPPERBODY_IN_ANGLEUSEDFOR2DROTATION_16, //321 + MNET_UPPERBODY_IN_NUMBER +}; + +/** @brief Programmer friendly enumerator of expected outputs + TODO: CAREFULL!*/ +enum mocapNET_Output_upperbody_enum +{ + MOCAPNET_UPPERBODY_OUTPUT_HIP_XPOSITION = 0, //0 + MOCAPNET_UPPERBODY_OUTPUT_HIP_YPOSITION, //1 + MOCAPNET_UPPERBODY_OUTPUT_HIP_ZPOSITION, //2 + MOCAPNET_UPPERBODY_OUTPUT_HIP_ZROTATION, //3 + MOCAPNET_UPPERBODY_OUTPUT_HIP_YROTATION, //4 + MOCAPNET_UPPERBODY_OUTPUT_HIP_XROTATION, //5 + MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_ZROTATION, //6 + MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_XROTATION, //7 + MOCAPNET_UPPERBODY_OUTPUT_ABDOMEN_YROTATION, //8 + MOCAPNET_UPPERBODY_OUTPUT_CHEST_ZROTATION, //9 + MOCAPNET_UPPERBODY_OUTPUT_CHEST_XROTATION, //10 + MOCAPNET_UPPERBODY_OUTPUT_CHEST_YROTATION, //11 + MOCAPNET_UPPERBODY_OUTPUT_NECK_ZROTATION, //12 + MOCAPNET_UPPERBODY_OUTPUT_NECK_XROTATION, //13 + MOCAPNET_UPPERBODY_OUTPUT_NECK_YROTATION, //14 + MOCAPNET_UPPERBODY_OUTPUT_HEAD_ZROTATION, //15 + MOCAPNET_UPPERBODY_OUTPUT_HEAD_XROTATION, //16 + MOCAPNET_UPPERBODY_OUTPUT_HEAD_YROTATION, //17 + MOCAPNET_UPPERBODY_OUTPUT_EYE_L_ZROTATION, //18 + MOCAPNET_UPPERBODY_OUTPUT_EYE_L_XROTATION, //19 + MOCAPNET_UPPERBODY_OUTPUT_EYE_L_YROTATION, //20 + MOCAPNET_UPPERBODY_OUTPUT_EYE_R_ZROTATION, //21 + MOCAPNET_UPPERBODY_OUTPUT_EYE_R_XROTATION, //22 + MOCAPNET_UPPERBODY_OUTPUT_EYE_R_YROTATION, //23 + MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_ZROTATION, //24 + MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_XROTATION, //25 + MOCAPNET_UPPERBODY_OUTPUT_RSHOULDER_YROTATION, //26 + MOCAPNET_UPPERBODY_OUTPUT_RELBOW_ZROTATION, //27 + MOCAPNET_UPPERBODY_OUTPUT_RELBOW_XROTATION, //28 + MOCAPNET_UPPERBODY_OUTPUT_RELBOW_YROTATION, //29 + MOCAPNET_UPPERBODY_OUTPUT_RHAND_ZROTATION, //30 + MOCAPNET_UPPERBODY_OUTPUT_RHAND_XROTATION, //31 + MOCAPNET_UPPERBODY_OUTPUT_RHAND_YROTATION, //32 + MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_ZROTATION, //33 + MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_XROTATION, //34 + MOCAPNET_UPPERBODY_OUTPUT_LSHOULDER_YROTATION, //35 + MOCAPNET_UPPERBODY_OUTPUT_LELBOW_ZROTATION, //36 + MOCAPNET_UPPERBODY_OUTPUT_LELBOW_XROTATION, //37 + MOCAPNET_UPPERBODY_OUTPUT_LELBOW_YROTATION, //38 + MOCAPNET_UPPERBODY_OUTPUT_LHAND_ZROTATION, //39 + MOCAPNET_UPPERBODY_OUTPUT_LHAND_XROTATION, //40 + MOCAPNET_UPPERBODY_OUTPUT_LHAND_YROTATION, //41 + MOCAPNET_UPPERBODY_OUTPUT_NUMBER +}; + +/** @brief Programmer friendly enumerator of NSDM elments*/ +enum mocapNET_NSDM_upperbody_enum +{ + MNET_NSDM_UPPERBODY_HIP = 0, //0 + MNET_NSDM_UPPERBODY_ENDSITE_EYE_R, //1 + MNET_NSDM_UPPERBODY_ENDSITE_EYE_L, //2 + MNET_NSDM_UPPERBODY_NECK, //3 + MNET_NSDM_UPPERBODY_RSHOULDER, //4 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_RSHOULDER_AND_RELBOW, //5 + MNET_NSDM_UPPERBODY_RELBOW, //6 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_RELBOW_AND_RHAND, //7 + MNET_NSDM_UPPERBODY_RHAND, //8 + MNET_NSDM_UPPERBODY_VIRTUAL_HIP_X_MINUS_0_15_Y_MINUS_0_15, //9 + MNET_NSDM_UPPERBODY_VIRTUAL_HIP_X_PLUS0_15_Y_MINUS_0_15, //10 + MNET_NSDM_UPPERBODY_LSHOULDER, //11 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_LSHOULDER_AND_LELBOW, //12 + MNET_NSDM_UPPERBODY_LELBOW, //13 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_LELBOW_AND_LHAND, //14 + MNET_NSDM_UPPERBODY_LHAND, //15 + MNET_NSDM_UPPERBODY_VIRTUAL_HALFWAY_BETWEEN_NECK_AND_HIP, //16 + MNET_NSDM_UPPERBODY_NUMBER +}; + +/** @brief This is a lookup table to immediately resolve referred Joints*/ +static const int mocapNET_ResolveJoint_upperbody[] = +{ + 0, //0 + 4, //1 + 3, //2 + 1, //3 + 5, //4 + 5, //5 + 6, //6 + 6, //7 + 7, //8 + 0, //9 + 0, //10 + 8, //11 + 8, //12 + 9, //13 + 9, //14 + 10, //15 + 1, //16 + 0//end of array +}; + +/** @brief This is a lookup table to immediately resolve referred Joints of second targets*/ +static const int mocapNET_ResolveSecondTargetJoint_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 6, //5 + 0, //6 + 7, //7 + 0, //8 + 0, //9 + 0, //10 + 0, //11 + 9, //12 + 0, //13 + 10, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief This is the configuration of NSDM elements : + * A value of 0 is a normal 2D point + * A value of 1 is a 2D point plus some offset + * A value of 2 is a virtual point between two 2D points */ +static const int mocapNET_ArtificialJoint_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 2, //5 + 0, //6 + 2, //7 + 0, //8 + 1, //9 + 1, //10 + 0, //11 + 2, //12 + 0, //13 + 2, //14 + 0, //15 + 2, //16 + 0//end of array +}; + +/** @brief These are X offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_upperbody )*/ +static const float mocapNET_ArtificialJointXOffset_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + -0.15, //9 + 0.15, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief These are Y offsets for artificial joints of type 1 ( see mocapNET_ArtificialJoint_upperbody )*/ +static const float mocapNET_ArtificialJointYOffset_upperbody[] = +{ + 0, //0 + 0, //1 + 0, //2 + 0, //3 + 0, //4 + 0, //5 + 0, //6 + 0, //7 + 0, //8 + -0.15, //9 + -0.15, //10 + 0, //11 + 0, //12 + 0, //13 + 0, //14 + 0, //15 + 0, //16 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as starting points for scaling vectors*/ +static const int mocapNET_ScalingStart_upperbody[] = +{ + 0, //0 + 0, //1 + 0//end of array +}; + +/** @brief These are 2D Joints that are used as ending points for scaling vectors*/ +static const int mocapNET_ScalingEnd_upperbody[] = +{ + 5, //0 + 8, //1 + 0//end of array +}; + +/** @brief These is a 2D Joints that is used as alignment for the skeleton*/ +static const int mocapNET_AlignmentStart_upperbody[] = +{ + 0, //0 + 0//end of array +}; + +/** @brief These is a 2D Joints that is used as alignment for the skeleton*/ +static const int mocapNET_AlignmentEnd_upperbody[] = +{ + 1, //0 + 0//end of array +}; + +/** @brief This function can be used to debug NSDM input and find in a user friendly what is missing..!*/ +static int upperbodyCountMissingNSDMElements(std::vector mocapNETInput,int verbose) +{ + unsigned int numberOfZeros=0; + for (int i=0; i skeletonSerialized %s\n ",mocapNET_upperbody[i],labels[i]); + } +} + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +static float getJoint2DDistance_UPPERBODY(std::vector in,int jointA,int jointB) +{ + float aX=in[jointA*3+0]; + float aY=in[jointA*3+1]; + float bX=in[jointB*3+0]; + float bY=in[jointB*3+1]; + if ( ((aX==0) && (aY==0)) || ((bX==0) && (bY==0)) ) { + return 0.0; + } + + + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} +/* +static std::vector upperbodyCreateNDSM(std::vector in,float alignmentAngle2D,int havePositionalElements,int haveAngularElements,int doNormalization) +{ + std::vector result; + int secondTargetJointID; + float sIX,sIY,sJX,sJY; + for (int i=0; i0) + { + unsigned int numberOfDistanceSamples=0; + float sumOfDistanceSamples=0.0; + for ( int i=0; i0.0) + { + numberOfDistanceSamples=numberOfDistanceSamples+1; + sumOfDistanceSamples=sumOfDistanceSamples+distance; + } + } +//------------------------------------------------------------------------------------------------- + float scaleDistance=1.0; +//------------------------------------------------------------------------------------------------- + if (numberOfDistanceSamples>0) + { + scaleDistance=(float) sumOfDistanceSamples/numberOfDistanceSamples; + } +//------------------------------------------------------------------------------------------------- + if (scaleDistance!=1.0) + { + for (int i=0; imaxValue) { + maxValue=result[i]; + } + } + fprintf(stderr,"Original Min Value %0.2f, Max Value %0.2f \n",minValue,maxValue); + + + unsigned int iJointID=mocapNET_AlignmentStart_upperbody[0]; + unsigned int jJointID=mocapNET_AlignmentEnd_upperbody[0]; + float aX=in[iJointID*3+0]; + float aY=in[iJointID*3+1]; + float bX=in[jJointID*3+0]; + float bY=in[jJointID*3+1]; + float alignmentAngle=getAngleToAlignToZero_tools(aX,aY,bX,bY); + for (int i=0; imaxValue) { + maxValue=result[i]; + } + } + fprintf(stderr,"Aligned Min Value %0.2f, Max Value %0.2f \n",minValue,maxValue); + + + } +//------------------------------------------------------------------------------------------------- + + + } //If normalization is enabled.. + + +//New normalization code that overrides diagonal of Matrix + unsigned int elementID=0; + unsigned int firstJointID=mocapNET_ResolveJoint_upperbody[0]; + for (unsigned int i=0; i0) && (richDiagonal_upperbody) ) + { + unsigned int jJointID=mocapNET_ResolveJoint_upperbody[j]; + result[elementID]=getJoint2DDistance_UPPERBODY(in,firstJointID,jJointID); + } + } + elementID+=1; + } + } + return result; +} + +*/ \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/calculations.c b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/calculations.c new file mode 100644 index 0000000..5069948 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/calculations.c @@ -0,0 +1,137 @@ +#include "calculations.h" + +#include +#include + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +const float goFromRadToDegrees=(float) 180.0 / M_PI; +const float goFromDegreesToRad=(float) M_PI / 180.0; + + +/** @brief This function returns the euclidean distance between two input 2D joints and zero if either of them is invalid*/ +float getJoint2DDistance_tools(float aX,float aY,float bX,float bY) +{ + float xDistance=(float) bX-aX; + float yDistance=(float) bY-aY; + return (float) sqrt( (xDistance*xDistance) + (yDistance*yDistance) ); +} + + +float getAngleToAlignToZero_tools(float aX,float aY,float bX,float bY) +{ + if ( (aX==bX) && (aY==bY) ) { return 0; } + + + //Bigger magnitudes.. + aX=100*aX; + aY=100*aY; + bX=100*bX; + bY=100*bY; + + //We have points a, b and c and we want to calculate angle b + float lengthBetweenAAndB = getJoint2DDistance_tools(aX,aY,bX,bY); + + + //We align vertically.. , Point C is B offset in Y direction + float cX = bX; + float cY = bY - lengthBetweenAAndB; + + //fprintf(stderr,"We want to align A(%0.2f,%0.2f) to C(%0.2f,%0.2f) with pivot B(%0.2f,%0.2f)\n",aX,aY,cX,cY,bX,bY); + //fprintf(stderr,"length AB = %0.2f\n",lengthBetweenAAndB); + //fprintf(stderr,"bY = %0.2f\n",bY); + //fprintf(stderr,"cY = %0.2f = %0.2f - %0.2f\n",cY,bY,lengthBetweenAAndB); + + + //Calulate vector a->b + float abX = bX - aX; + float abY = bY - aY; + + //calculate vector c->b + float cbX = bX - cX; + float cbY = bY - cY; + + + float dot = (abX * cbX + abY * cbY); // dot product + float cross = (abX * cbY - abY * cbX); // cross product + + float alpha = atan2(cross, dot); + + //fprintf(stderr,"Angle is %0.2f rad or %0.2f degrees \n",alpha,alpha*goFromRadToDegrees); + return (float) alpha;// * goFromRadToDegrees ; +} + + + +float getAngleToAlignToZero(float *positions,unsigned int centerJoint,unsigned int referenceJoint) +{ + //We have points a, b and c and we want to calculate angle b + float aX= positions[referenceJoint*3+0]; + float aY= positions[referenceJoint*3+1]; + + float bX= positions[centerJoint*3+0]; + float bY= positions[centerJoint*3+1]; + + return getAngleToAlignToZero_tools(aX,aY,bX,bY); +} + + + +int rotate2DPointsBasedOnJointAsCenter(float * positions,unsigned int positionsLength,float angle,unsigned int centerJoint) +{ + if (positionsLength%3!=0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: incorrect positions.. \n" NORMAL); + return 0; + } + + if (positionsLength<=centerJoint*3) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: centerJoint out of bounds.. \n" NORMAL); + return 0; + } + + float s = sin((float) angle * goFromDegreesToRad ); + float c = cos((float) angle * goFromDegreesToRad ); + + float cx=positions[centerJoint*3+0]; + float cy=positions[centerJoint*3+1]; + float cVisibility=positions[centerJoint*3+2]; + + if (cVisibility==0.0) + { + fprintf(stderr,RED "rotate2DPointsBasedOnJointAsCenter: cannot work without pivot joint.. \n" NORMAL); + return 0; + } + + for (unsigned int jID=0; jID ",jX,jY,cx,cy,angle); + + //Translate point back to origin: + jX -= cx; + jY -= cy; + + //Rotate point + float xnew = jX * c - jY * s; + float ynew = jX * s + jY * c; + + //Translate point back: + positions[jID*3+0] = xnew + cx; + positions[jID*3+1] = ynew + cy; + + //fprintf(stderr,"%0.2f,%0.2f\n",positions[jID*3+0],positions[jID*3+1]); + } + + + return 1; +} + diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/calculations.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/calculations.h new file mode 100644 index 0000000..5ee5469 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/NSxM/calculations.h @@ -0,0 +1,27 @@ +/** @file calculations.h + * @brief calculations used for descriptors + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef CALCULATIONS_H_INCLUDED +#define CALCULATIONS_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + + +#include + +float getJoint2DDistance_tools(float aX,float aY,float bX,float bY); + +#ifdef __cplusplus +} +#endif + + + + +#endif diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/PCA/PCA.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/PCA/PCA.h new file mode 100644 index 0000000..b0d89a2 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/PCA/PCA.h @@ -0,0 +1,446 @@ +/** @file PCA.h + * @brief An implementation of a PCA data loader + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef PCA_H_INCLUDED +#define PCA_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + +#include +#include +#include "../JSON/nxjson.h" +#include "../tools.h" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + +struct eigenVectorOpt +{ + float __attribute__((aligned(16))) * value; +}; + +struct complexNumber +{ + float realPart; + float imaginaryPart; +}; + +struct eigenVector +{ + struct complexNumber * value; +}; + +struct PCAData +{ + unsigned int numberOfSamplesUsedToCreatePCA; + unsigned int numberOfEigenValues; + float mean; + float std; + struct complexNumber * eigenValues; + struct eigenVector * eigenVectors; + float * screeProportion; + float * screeCumulative; +}; + +//Complex Numbers +//http://ebooks.edu.gr/ebooks/v/html/8547/2754/Mathimatika-B-Lykeiou-ThSp_html-apli/index5_2.html +static struct complexNumber addComplexNumbers(struct complexNumber a,struct complexNumber b) +{ + struct complexNumber result={0}; + result.realPart = a.realPart + b.realPart; + result.imaginaryPart = a.imaginaryPart + b.imaginaryPart; + return result; +} + +static struct complexNumber multiplyComplexNumbers(struct complexNumber a,struct complexNumber b) +{ + struct complexNumber result={0}; + // (A + B i) * (C + D i) = (AC-BD) + (AD+BC) i + // (a.realPart + a.imaginaryPart i) * (b.realPart + b.imaginaryPart i) = (a.realPart b.realPart - a.imaginaryPart b.imaginaryPart) + (a.realPart b.imaginaryPart + a.imaginaryPart b.realPart) i + result.realPart = (a.realPart*b.realPart) - (a.imaginaryPart*b.imaginaryPart); + result.imaginaryPart = (a.realPart*b.imaginaryPart) + (a.imaginaryPart*b.realPart); + return result; +} + +static char * recognizeComplexNumberStart(char * complexNumber) +{ + int l = strlen(complexNumber); + char * res = complexNumber; + //All Complex Numbers end with j + //------------------------------- + //We expect something like : + // -4.1106844e-12-4.560428e-12j + // or + // -4.1106844e-12-4.560428e+12j + // or + // -4.1106844e-12-4.560428j + // or + // -4.1106844e-12+48j + //------------------------------- + if (l>2) + { + if (complexNumber[l-1]=='j') + { + l=l-2; + while (l>0) + { + if ( + (complexNumber[l]=='+') || + (complexNumber[l]=='-') + ) + { + //We have reache a +/- symbol there are three cases : + //Case A ) if the previous character is an e then it is a scientific notation complex number! + //Case B ) if not then we found the complex number start! + //Case C ) if l is 0 then we reached the start of the string! + if (l>0) + { + if (complexNumber[l-1]!='e') + { + //This is not a scientific notation so Case B we found our result! + res = complexNumber+l; + break; + } + } + } + l=l-1; + } + } + } + return res; +} + + + + + + + +static struct complexNumber parseComplexNumber(const char * complexNumberString) +{ + struct complexNumber result={0}; + //--------------------------------------------------------------------------------------------- + if ( complexNumberString == 0 ) { return result; } + if (complexNumberString[0]=='(') { complexNumberString++; } //Point to second element to skip ( + + char localBuffer[128]; + snprintf(localBuffer,128,"%s",complexNumberString); + + + float imaginaryPart = 0.0; + char * seperator = strchr(localBuffer,')'); + if (seperator!=0) { *seperator = 0; } //Trim last ) + + + seperator = strstr(localBuffer,"+0j"); + if (seperator!=0) + { + //This is the easy case which means that the value is a clear float value and the +0j can be easily discarded! + *seperator = 0; + } else + { + unsigned int l = strlen(localBuffer); + if (localBuffer[l-1]=='j') + { + fprintf(stderr,"Parsing complex number `%s`\n",localBuffer); + seperator = recognizeComplexNumberStart(localBuffer); + if (seperator!=0) + { + *seperator = 0; + fprintf(stderr,"Imaginary part `%s` / keeping real part `%s` \n",seperator+1,localBuffer); + char * imaginaryPartString = seperator+1; + int lastCharacter = strlen(imaginaryPartString); + if (imaginaryPartString[lastCharacter-1]=='j') + { imaginaryPartString[lastCharacter-1]=0; } + imaginaryPart = strtof(imaginaryPartString,NULL); + } + } + } + + float cleanedFloat = strtof(localBuffer,NULL); + //fprintf(stderr,"Cleaned float is %f \n",cleanedFloat); + if (cleanedFloat!=cleanedFloat) + { + //If we got a NaN value then clean it.. + cleanedFloat = 0.0; + } + + + result.realPart = cleanedFloat; + result.imaginaryPart = imaginaryPart; + + return result; +} + + +static int unloadPCAData(struct PCAData* pca) +{ + if (pca!=0) + { + if (pca->screeProportion!=0) { free(pca->screeProportion); pca->screeProportion=0; } + if (pca->screeCumulative!=0) { free(pca->screeCumulative); pca->screeCumulative=0; } + if (pca->eigenValues!=0) { free(pca->eigenValues); pca->eigenValues=0; } + if (pca->eigenVectors!=0) + { + for (int i=0; inumberOfEigenValues; i++) + { + free(pca->eigenVectors[i].value); + } + free(pca->eigenVectors); + } + return 1; + } + return 0; +} + +static int loadPCADataFromJSON(struct PCAData* output, const char * jsonFilename) +{ + fprintf(stderr,"Loading PCA file %s ...\n",jsonFilename); + unsigned int inputLength=0; + char* input = readFileToMemory(jsonFilename,&inputLength); + if (input!=0) + { + //fprintf(stderr,"JSON DATA %s ...\n",input); + fprintf(stderr,"Parsing %s ...\n",jsonFilename); + const nx_json* json=nx_json_parse_utf8(input); + + //------------------------------------------------------------------- + const nx_json* j = nx_json_get(json,"numberOfSamplesFittedOn"); + fprintf(stderr,"key(%s)/type(%u)\n",j->key,j->type); + output->numberOfSamplesUsedToCreatePCA = (unsigned int) atoi(j->text_value); + //------------------------------------------------------------------- + j = nx_json_get(json,"expectedInputs"); + fprintf(stderr,"key(%s)/type(%u)\n",j->key,j->type); + output->numberOfEigenValues = (unsigned int) atoi(j->text_value); + //------------------------------------------------------------------- + j = nx_json_get(json,"std"); + fprintf(stderr,"key(%s)/type(%u)\n",j->key,j->type); + output->std = (float) atof(j->text_value); + j = nx_json_get(json,"mean"); + fprintf(stderr,"key(%s)/type(%u)\n",j->key,j->type); + output->mean = (float) atof(j->text_value); + //------------------------------------------------------------------- + + //Our Summary..! + //---------------------------------------------------------------------------------------------------------------------------- + fprintf(stderr,"Number Of Samples %u\n",output->numberOfSamplesUsedToCreatePCA); + fprintf(stderr,"Number Of Eigen Values %u\n",output->numberOfEigenValues); + fprintf(stderr,"Mean %0.2f / Std %0.2f\n",output->mean,output->std ); + + //We have now allocated enough space and are ready to parse all incoming values.. + //---------------------------------------------------------------------------------------------------------------------------- + output->eigenValues = (struct complexNumber*) malloc(sizeof(struct complexNumber) * output->numberOfEigenValues); + output->eigenVectors = (struct eigenVector*) malloc(sizeof(struct eigenVector) * output->numberOfEigenValues); + if (output->eigenVectors) + { + for (int i=0; inumberOfEigenValues; i++) + { + output->eigenVectors[i].value = (struct complexNumber*) malloc(sizeof(struct complexNumber) * output->numberOfEigenValues); + } + } + output->screeProportion = (float*) malloc(sizeof(float) * output->numberOfEigenValues); + output->screeCumulative = (float*) malloc(sizeof(float) * output->numberOfEigenValues); + //---------------------------------------------------------------------------------------------------------------------------- + + + j = nx_json_get(json,"eigenvalues"); + fprintf(stderr,"We encountered %u eigenvalues (header says %u) \n",j->length,output->numberOfEigenValues); + if (j->length == output->numberOfEigenValues) + { + //We have a correct number of eigenvalues so let's read them! + for (int idx=0; idxnumberOfEigenValues; idx++) + { + const nx_json* item = nx_json_item(j,idx); + //fprintf(stderr,"key(%s)/index(%u)/type(%u)\n",j->key,idx,item->type); + output->eigenValues[idx] = parseComplexNumber(item->text_value); + } + } + + + const nx_json* jY = nx_json_get(json,"eigenvectors"); + if (jY->length == output->numberOfEigenValues) + { + fprintf(stderr,"We encountered %u eigenvectors (header says %u) \n",jY->length,output->numberOfEigenValues); + //We have a correct number of eigenvalues so let's read them! + for (int idy=0; idynumberOfEigenValues; idy++) + { + const nx_json* itemY = nx_json_item(jY,idy); + if (itemY->length == output->numberOfEigenValues) + { + for (int idx=0; idxnumberOfEigenValues; idx++) + { + const nx_json* itemX = nx_json_item(itemY,idx); + //fprintf(stderr,"key(%s)/index(%u)/type(%u)\n",j->key,idx,item->type); + output->eigenVectors[idx].value[idy] = parseComplexNumber(itemX->text_value); + } + } else + { + fprintf(stderr,"Eigen Vector %d has an incorrect number of values (%u)\n",idy,itemY->length); + } + } + } + + + j = nx_json_get(json,"scree_proportion"); + fprintf(stderr,"We encountered %u scree_proportions (header says %u) \n",j->length,output->numberOfEigenValues); + if (j->length == output->numberOfEigenValues) + { + //We have a correct number of eigenvalues so let's read them! + for (int idx=0; idxnumberOfEigenValues; idx++) + { + const nx_json* item = nx_json_item(j,idx); + //fprintf(stderr,"key(%s)/index(%u)/type(%u)\n",j->key,idx,item->type); + output->screeProportion[idx] = strtof(item->text_value,NULL); + } + } + + + j = nx_json_get(json,"scree_cumulative"); + fprintf(stderr,"We encountered %u scree_cumulatives (header says %u) \n",j->length,output->numberOfEigenValues); + if (j->length == output->numberOfEigenValues) + { + //We have a correct number of eigenvalues so let's read them! + for (int idx=0; idxnumberOfEigenValues; idx++) + { + const nx_json* item = nx_json_item(j,idx); + //fprintf(stderr,"key(%s)/index(%u)/type(%u)\n",j->key,idx,item->type); + output->screeCumulative[idx] = strtof(item->text_value,NULL); + } + } + + nx_json_free(json); + free(input); + + return 1; + } + return 0; +} + + +float dotProduct(float * vect_A, float * vect_B, int n) +{ + float product = 0.0; + + for (int i = 0; i < n; i++) + { product += vect_A[i] * vect_B[i]; } + + return product; +} + + + +static int doPCATransform(float * output,int * outputSize,struct PCAData* pca,float * inputRaw,int inputSize,int selectedPCADimensions) +{ + if (pca==0) { return 0; } + if (pca->numberOfEigenValues!=inputSize) { fprintf(stderr, RED "PCA: Shape given as input (%d,) is not aligned with PCA loaded (%u,%d) \n" NORMAL,inputSize,pca->numberOfEigenValues,*outputSize); return 0; } + + float mean = pca->mean; + float std = pca->std; + if (std == 0.0 ) { std=1.0; } //Don't ever divide by zero + + fprintf(stderr," sample = list()\n"); + for (int i=0; iinputSize) + { + selectedPCADimensions = inputSize; + } + + fprintf(stderr,"We want dot product of %u dimensions : \n",selectedPCADimensions); + fprintf(stderr,"Input of size 0->%u\n",inputSize); + fprintf(stderr,"EigenVector of size 0->%u\n",pca->numberOfEigenValues); + + /* + * input is 1 x 458 + eigenvectors is 458 x 458 + result is 1 x 210 + + data is 1 x 461 + eigenvectors is 461 x 461 + result is 1 x 210 + a = [[0, 1 , 2]] +b = [[4, 1], [3, 2], [10,10] ] +#[[23 22]] +print(np.dot(a,b)) + * ./MocapNET4TestD + + * python3 DNN_Tensorflow2/principleComponentAnalysis.py + */ + + for (int i=0; inumberOfEigenValues; j++) + { + //fprintf(stderr,"%0.2f * %0.2f \n",input[j],pca->eigenVectors[i].value[j]); + //output[i] += input[j] * pca->eigenVectors[i].value[j]; + //output[i] += input[j] * pca->eigenVectors[i].value[j].realPart; + struct complexNumber thisOutputComplex = multiplyComplexNumbers(inputComplex[j],pca->eigenVectors[i].value[j]); + outputComplex[i].realPart += thisOutputComplex.realPart; + outputComplex[i].imaginaryPart += thisOutputComplex.imaginaryPart; + } + //--------------------------------- + } + + //We have gone through all of the complex arithmetic, now we will keep only the real part + for (int i=0; i Median is ",median,"Mean is ",mean," Std is ",std," Var is ",var) + + sys.exit(0) + +class PCA(): + def __init__(self, + inputData:np.array=np.array([]), + savedFile:str="" + ): + self.mean = 0.0 + self.std = 1.0 + self.eigenvalues = np.array([]) + self.eigenvectors = np.array([]) + self.proportional = list() + self.cumulative = list() + self.numberOfSamplesFittedOn = 0 + self.expectedInputs = 0 + + if (savedFile!=""): + self.load(savedFile) + elif inputData.size != 0: + self.fit(inputData) + else: + print("No PCA input given..!") + + def ok(self): + return self.numberOfSamplesFittedOn!=0 + + def getNumberOfExpectedSamples(self): + #return len(self.eigenvalues) + return np.size(self.eigenvalues, axis = 0) + + def fit(self,data): + #doPCAUsingSKLearn(data,"Test") + #getStatsPerColumn(data) + #print(data) + + self.numberOfSamplesFittedOn = data.shape[0] + + print("Doing PCA fit on ",self.numberOfSamplesFittedOn) + print(" please wait .. ") + + #Standardize data + #------------------------------------------------------------------------------------------------- + self.mean = data.mean() + data = data - self.mean + # Normalize + self.std = data.std() + if (self.std!=0.0): + data = data / self.std + #print('Data Mean : ',self.mean,'STD: ',self.std) + #------------------------------------------------------------------------------------------------- + + #Take the matrix, transpose it, and multiply the transposed matrix. This is the covariance matrix. + covarianceMatrix = np.dot(data.T,data) + + #Get an array of computed eigenvalues and a matrix whose columns are the normalized eigenvectors corresponding to the eigenvalues in that order. + #In this step it is important to make sure that the eigenvalues and its eigenvectors are sorted in descending order (from largest to smallest). Sort the eigenvalues and then the eigenvectors, accordingly. + self.eigenvalues, self.eigenvectors = np.linalg.eig(covarianceMatrix) + + #Sort eigenvectors according to eigenvalues + idx = self.eigenvalues.argsort()[::-1] + self.eigenvalues = self.eigenvalues[idx] + self.eigenvectors = self.eigenvectors[:,idx] + + #Assign P to the matrix of eigenvectors and D to the diagonal matrix with eigenvalues on the diagonal and values of zero everywhere else. + #The eigenvalues on the diagonal of D will be associated with the corresponding column in P. + D = np.diag(self.eigenvalues) + P = self.eigenvectors + + self.expectedInputs = len(self.eigenvalues) + + #1. Calculate the proportion of variance explained by each feature + sumOfEigenvalues = np.sum(self.eigenvalues) + self.proportional = [i/sumOfEigenvalues for i in self.eigenvalues] + #2. Calculate the cumulative variance + self.cumulative = [np.sum(self.proportional[:i+1]) for i in range(len(self.proportional))] + + def transform(self,data,selectedPCADimensions=0): + if (self.numberOfSamplesFittedOn==0): + print("Can't transform input with no PCA loaded ..") + return data + #Normalize input data + data = data - self.mean + if (self.std!=0.0): + data = data / self.std + #Do transform.. + if (selectedPCADimensions==0): + #Transform using all PCA components + return np.dot(data,self.eigenvectors) + else: + #print("eigenvectors is ",eigenvectors.shape[0]," x ",eigenvectors.shape[1]) + return data.dot(self.eigenvectors[:,:selectedPCADimensions]) + + def save(self,filename): + print("Saving PCA to ",filename) + outputDict = dict() + #------------------------------------------ + outputDict["numberOfSamplesFittedOn"]= str(self.numberOfSamplesFittedOn) + outputDict["expectedInputs"] = str(self.expectedInputs) + outputDict["mean"] = str(self.mean) + outputDict["std"] = str(self.std) + outputDict["eigenvalues"] = list() + outputDict["eigenvectors"] = list() + outputDict["scree_proportion"] = list() + outputDict["scree_cumulative"] = list() + #------------------------------------------ + for v in range(0,len(self.proportional)): + outputDict["scree_proportion"].append(str(self.proportional[v])) + outputDict["scree_cumulative"].append(str(self.cumulative[v])) + #------------------------------------------ + print("eigenvalues ",self.eigenvalues.shape[0]) + for v in range(0,self.eigenvalues.shape[0]): + outputDict["eigenvalues"].append(str(self.eigenvalues[v])) + #------------------------------------------ + print("eigenvectors ",self.eigenvectors.shape[0]," x ",self.eigenvectors.shape[1]) + for r in range(0,self.eigenvectors.shape[0]): + thisRow = list() + for c in range(0,self.eigenvectors.shape[1]): + thisRow.append(str(self.eigenvectors[r,c])) + outputDict["eigenvectors"].append(thisRow) + #------------------------------------------ + import json + json_obj = json.dumps(outputDict) + file = open(filename,'w',encoding="utf-8") + file.write(json_obj) + file.close() + + def load(self,filename): + print("Loading PCA from ",filename) + import json + file = open(filename,'r',encoding="utf-8") + data = json.load(file) + #----------------------------------------------------- + self.numberOfSamplesFittedOn = int(data["numberOfSamplesFittedOn"]) + self.expectedInputs = int(data["expectedInputs"]) + self.mean = float(data["mean"]) + self.std = float(data["std"]) + #----------------------------------------------------- + numberOfEigenValues = len(data["eigenvalues"]) + print("Eigen values = ",numberOfEigenValues) + self.eigenvalues = np.full([numberOfEigenValues],fill_value=0,dtype=np.complex_,order='C') + for i in range(0,numberOfEigenValues): + self.eigenvalues[i] = complex(data["eigenvalues"][i]) + #----------------------------------------------------- + numberOfEigenVectors = len(data["eigenvectors"]) + print("Eigen vectors = ",numberOfEigenVectors) + self.eigenvectors = np.full([numberOfEigenVectors,numberOfEigenVectors],fill_value=0,dtype=np.complex_,order='C') + for r in range(0,numberOfEigenVectors): + for c in range(0,numberOfEigenVectors): + self.eigenvectors[r,c] = complex(data["eigenvectors"][r][c]) + #----------------------------------------------------- + file.close() + return self.mean,self.std,self.eigenvalues,self.eigenvectors + + + def visualize(self,data,saveToFile="",onlyScreePlotNDimensions=0,label="PCA",colors=list(),colorLabel="Highlighting PC-4",viewAzimuth=45,viewElevation=45,showScree=1): + import matplotlib.pyplot as plt + + font = {'family' : 'normal', + 'weight' : 'bold', + 'size' : 28} + + plt.rc('font', **font) + plt.rc('xtick', labelsize=15) + plt.rc('ytick', labelsize=15) + # === Plot ========================================================================= + fig = plt.figure() + fig.set_size_inches(19.2, 10.8, forward=True) + + if (showScree==1): + ax2 = fig.add_subplot(1, 2, 1) + ax1 = fig.add_subplot(1, 2, 2,projection='3d') + else: + ax1 = fig.add_subplot(1, 1, 1,projection='3d') + #=================================================================================== + + #Number of PCA components to plot on first plot (our plot is 3D so max is 4 if we dont have a color ..! ) + keepNDimensions = 3 + if (len(colors)==0): + keepNDimensions = 4 + + #Do transform of our input using the PCA dimensions as new basis + #=================================================================================== + transformedData = self.transform(data,selectedPCADimensions=keepNDimensions).real + #=================================================================================== + + if (len(colors)==0): + colors = transformedData[:,3] + colorLabel = "highlighting PC-4" + else: + print("Using provided set of colorValues") + keepNDimensions = 3 + + #If there is no limit on Scree plot dimensions then plot all + if (onlyScreePlotNDimensions==0): + onlyScreePlotNDimensions = len(eigenvalues) + #=================================================================================== + plottedEigenValues = self.eigenvalues + plottedEigenValues=list() + for i in range(0,onlyScreePlotNDimensions): + plottedEigenValues.append(self.eigenvalues[i]) + #=================================================================================== + #1. Calculate the proportion of variance explained by each feature + sum_eigenvalues = np.sum(plottedEigenValues) + prop_var = [i/sum_eigenvalues for i in plottedEigenValues] + #2. Calculate the cumulative variance + cum_var = [np.sum(prop_var[:i+1]) for i in range(len(prop_var))] + #=================================================================================== + + ax1.view_init(viewAzimuth,viewElevation) + #=================================================================================== + ax1.scatter(transformedData[:,0],transformedData[:,1],transformedData[:,2],c=colors) + #=================================================================================== + + # Adding title, xlabel and ylabel + ax1.set_title('PCA %s %s '%(label,colorLabel)) # Title of the plot + ax1.set_xlabel('PC-1 (%0.2f %%) '% (100.0*float(prop_var[0])),labelpad=30) # X-Label + ax1.set_ylabel('PC-2 (%0.2f %%) '% (100.0*float(prop_var[1])),labelpad=30) # Y-Label + ax1.set_zlabel('PC-3 (%0.2f %%) '% (100.0*float(prop_var[2])),labelpad=30) # Z-Label + #ax1.tick_params(axis='x', pad=5) #fine tune numbers of plot + #=================================================================================== + #=================================================================================== + #=================================================================================== + if (showScree==1): + # Plot scree plot from PCA + x_labels = ['PC{}'.format(i+1) for i in range(len(prop_var))] + ax2.plot(x_labels, prop_var, marker='o', markersize=6, color='skyblue', linewidth=2, label='Proportion of variance') + ax2.plot(x_labels, cum_var, marker='o', color='orange', linewidth=2, label="Cumulative variance") + ax2.legend() + ax2.set_title('Scree plot %s '%label) + ax2.set_xlabel('Principal components') + ax2.set_ylabel('Proportion of variance') + #=================================================================================== + #=================================================================================== + #=================================================================================== + + plt.subplots_adjust(left=0.05, right=0.95, top=0.95, bottom=0.08) + + if (saveToFile!=""): + fig.savefig(saveToFile) + else: + plt.show() + + +if __name__ == '__main__': + pca = PCA(savedFile="../../../../dataset/combinedModel/mocapnet4/mode1/1.0/step1_upperbody_all/upperbody_all.pca") + inptR = [1.0] * 458 + inpt =np.asarray(inptR,dtype=np.float32) + outLength = 210 + out = pca.transform(inpt,selectedPCADimensions=outLength) + for i in range(0,outLength): + print("%u = %0.6f" % (i,out[i])) + diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/config.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/config.h new file mode 100644 index 0000000..9fe3f2e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/config.h @@ -0,0 +1,46 @@ +#ifndef MOCAPNET_CONFIGURATION_H_INCLUDED +#define MOCAPNET_CONFIGURATION_H_INCLUDED + +#ifdef __cplusplus +extern "C" +{ +#endif + +//Neural network orientations centered around 0 +#define NN_ORIENTATIONS_TRAINED_AROUND_ZERO_AND_REQUIRE_TRICK 0 + +//Also swap bvh rotations before IK step +#define APPLY_BVH_FIX_TO_IK_INPUT 0 + +//Test swapped +#define SWAP_LEFT_RIGHT_ENSEMBLES 0 + +//Hands mode ( 1 / 3 (deprecated) / 5 ) +#define HANDS_MODE 1 + +//Use flip for RHand Regression..! +#define RHAND_FLIP 1 + + +//Limits synced to scripts/createRandomizedDatset.sh +const float FRONT_MIN_ORIENTATION = -45.0; +const float FRONT_MAX_ORIENTATION = 45.0; +//-------------------------------- +const float BACK_MIN_ORIENTATION = 135.0; +const float BACK_MAX_ORIENTATION = 225.0; +const float BACK_ALT_MIN_ORIENTATION = -225; +const float BACK_ALT_MAX_ORIENTATION = -135; +//-------------------------------- +const float LEFT_MIN_ORIENTATION = -135.0; +const float LEFT_MAX_ORIENTATION = -45.0; +//-------------------------------- +const float RIGHT_MIN_ORIENTATION = 45.0; +const float RIGHT_MAX_ORIENTATION = 135.0; +//-------------------------------- + + +#ifdef __cplusplus +} +#endif + +#endif // MOCAPNET_CONFIGURATION_H_INCLUDED diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/mocapnet4.cpp b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/mocapnet4.cpp new file mode 100644 index 0000000..1917d2a --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/mocapnet4.cpp @@ -0,0 +1,61 @@ +//MOCAPNET2 ------------------------------------ +#include "../MocapNETLib4/mocapnet4.h" +//---------------------------------------------- +#include "../MocapNETLib4/config.h" +#include "../MocapNETLib4/JSON/readModelConfiguration.h" +//---------------------------------------------- + +#include "tools.h" +#include "../../../dependencies/nxjson/nxjson.h" + +#include +#include + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + + + +int loadMocapNET4( + struct MocapNET4 * mnet, + const char * description + ) +{ + + unsigned int length = 0; + char * data = readFileToMemory("dataset/combinedModel/mocapnet4/mode1/1.0/step1_upperbody_all",&length); + + struct ModelConfigurationData modelConfiguration={0}; + loadModelConfigurationData(&modelConfiguration,"dataset/combinedModel/mocapnet4/mode1/1.0/step1_upperbody_all/upperbody_configuration.json"); + + return 0; +} + + + +std::vector runMocapNET4( + struct MocapNET4 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ) +{ + std::vector emptyResult; + return emptyResult; +} + + + + +int unloadMocapNET4(struct MocapNET4 * mnet) +{ + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/mocapnet4.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/mocapnet4.h new file mode 100644 index 0000000..9cca20e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/mocapnet4.h @@ -0,0 +1,3464 @@ +#pragma once +/** @file mocapnet4.hpp + * @brief The MocapNET C library + * As seen in https://www.youtube.com/watch?v=fH5e-KMBvM0 , the MocapNET network requires two types of input. + * The first is an uncompressed list of (x,y,v) joints and the second an NSDM array. To add to those the output consists of BVH + * frames that must be accompanied by a header. This library internally handles all of these details. + * @author Ammar Qammaz (AmmarkoV) + */ + + +#include +#include + +/** + * @brief MocapNET version + */ +static const char MocapNETVersion[] = { "4.0" }; + +/** + * @brief MocapNET has been trained on 1920x1080 frames, so all the received coordinates are normalized in the +* 0..1 range based on that. This means that the NN learns the X and Y variations. If a joint lies at pixel 500,500 +* it will be represented as 500/1920 , 500/1080. +* Now if a user uses another configuration, let's say a vertical (portrait) feed where the resolution is 1080x1920 +* the 2D points will get normalized at 500/1080 , 500/1920 and the resulting 2D joint cloud won't work as well +* This is why it is better to change the aspect ratio while normalizing + */ +static const unsigned int MocapNETTrainingWidth=1920, MocapNETTrainingHeight=1080; + + + +/** + * @brief MocapNET output joint names that correspond to the BVH file + * These should correspond to `cat dataset/headerWithHeadAndOneMotion.bvh | grep JOINT` +*/ +static const char * MocapNETOutputJointNames[] = +{ +"hip", +"abdomen", +"chest", +"neck", +"neck1", +"head", +"__jaw", +"jaw", +"special04", +"oris02", +"oris01", +"oris06.l", +"oris07.l", +"oris06.r", +"oris07.r", +"tongue00", +"tongue01", +"tongue02", +"tongue03", +"__tongue04", +"tongue04", +"tongue07.l", +"tongue07.r", +"tongue06.l", +"tongue06.r", +"tongue05.l", +"tongue05.r", +"__levator02.l", +"levator02.l", +"levator03.l", +"levator04.l", +"levator05.l", +"__levator02.r", +"levator02.r", +"levator03.r", +"levator04.r", +"levator05.r", +"__special01", +"special01", +"oris04.l", +"oris03.l", +"oris04.r", +"oris03.r", +"oris06", +"oris05", +"__special03", +"special03", +"__levator06.l", +"levator06.l", +"__levator06.r", +"levator06.r", +"special06.l", +"special05.l", +"eye.l", +"orbicularis03.l", +"orbicularis04.l", +"special06.r", +"special05.r", +"eye.r", +"orbicularis03.r", +"orbicularis04.r", +"__temporalis01.l", +"temporalis01.l", +"oculi02.l", +"oculi01.l", +"__temporalis01.r", +"temporalis01.r", +"oculi02.r", +"oculi01.r", +"__temporalis02.l", +"temporalis02.l", +"risorius02.l", +"risorius03.l", +"__temporalis02.r", +"temporalis02.r", +"risorius02.r", +"risorius03.r", +"rCollar", +"rShldr", +"rForeArm", +"rHand", +"metacarpal1.r", +"finger2-1.r", +"finger2-2.r", +"finger2-3.r", +"metacarpal2.r", +"finger3-1.r", +"finger3-2.r", +"finger3-3.r", +"__metacarpal3.r", +"metacarpal3.r", +"finger4-1.r", +"finger4-2.r", +"finger4-3.r", +"__metacarpal4.r", +"metacarpal4.r", +"finger5-1.r", +"finger5-2.r", +"finger5-3.r", +"rthumbBase", +"rthumb", +"finger1-2.r", +"finger1-3.r", +"lCollar", +"lShldr", +"lForeArm", +"lHand", +"metacarpal1.l", +"finger2-1.l", +"finger2-2.l", +"finger2-3.l", +"metacarpal2.l", +"finger3-1.l", +"finger3-2.l", +"finger3-3.l", +"__metacarpal3.l", +"metacarpal3.l", +"finger4-1.l", +"finger4-2.l", +"finger4-3.l", +"__metacarpal4.l", +"metacarpal4.l", +"finger5-1.l", +"finger5-2.l", +"finger5-3.l", +"lthumbBase", +"lthumb", +"finger1-2.l", +"finger1-3.l", +"rButtock", +"rThigh", +"rShin", +"rFoot", +"toe1-1.R", +"toe1-2.R", +"toe2-1.R", +"toe2-2.R", +"toe2-3.R", +"toe3-1.R", +"toe3-2.R", +"toe3-3.R", +"toe4-1.R", +"toe4-2.R", +"toe4-3.R", +"toe5-1.R", +"toe5-2.R", +"toe5-3.R", +"lButtock", +"lThigh", +"lShin", +"lFoot", +"toe1-1.L", +"toe1-2.L", +"toe2-1.L", +"toe2-2.L", +"toe2-3.L", +"toe3-1.L", +"toe3-2.L", +"toe3-3.L", +"toe4-1.L", +"toe4-2.L", +"toe4-3.L", +"toe5-1.L", +"toe5-2.L", +"toe5-3.L" +}; + + + + + + +/** + * @brief This is a programmer friendly enumerator of joint output extracted from MocapNET. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +enum MOCAPNET_Output_Joint_Name_ENUM +{ +MOCAPNET_OUTPUT_JOINT_HIP, +MOCAPNET_OUTPUT_JOINT_ABDOMEN, +MOCAPNET_OUTPUT_JOINT_CHEST, +MOCAPNET_OUTPUT_JOINT_NECK, +MOCAPNET_OUTPUT_JOINT_NECK1, +MOCAPNET_OUTPUT_JOINT_HEAD, +MOCAPNET_OUTPUT_JOINT___JAW, +MOCAPNET_OUTPUT_JOINT_JAW, +MOCAPNET_OUTPUT_JOINT_SPECIAL04, +MOCAPNET_OUTPUT_JOINT_ORIS02, +MOCAPNET_OUTPUT_JOINT_ORIS01, +MOCAPNET_OUTPUT_JOINT_ORIS06_L, +MOCAPNET_OUTPUT_JOINT_ORIS07_L, +MOCAPNET_OUTPUT_JOINT_ORIS06_R, +MOCAPNET_OUTPUT_JOINT_ORIS07_R, +MOCAPNET_OUTPUT_JOINT_TONGUE00, +MOCAPNET_OUTPUT_JOINT_TONGUE01, +MOCAPNET_OUTPUT_JOINT_TONGUE02, +MOCAPNET_OUTPUT_JOINT_TONGUE03, +MOCAPNET_OUTPUT_JOINT___TONGUE04, +MOCAPNET_OUTPUT_JOINT_TONGUE04, +MOCAPNET_OUTPUT_JOINT_TONGUE07_L, +MOCAPNET_OUTPUT_JOINT_TONGUE07_R, +MOCAPNET_OUTPUT_JOINT_TONGUE06_L, +MOCAPNET_OUTPUT_JOINT_TONGUE06_R, +MOCAPNET_OUTPUT_JOINT_TONGUE05_L, +MOCAPNET_OUTPUT_JOINT_TONGUE05_R, +MOCAPNET_OUTPUT_JOINT___LEVATOR02_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR02_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR03_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR04_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR05_L, +MOCAPNET_OUTPUT_JOINT___LEVATOR02_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR02_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR03_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR04_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR05_R, +MOCAPNET_OUTPUT_JOINT___SPECIAL01, +MOCAPNET_OUTPUT_JOINT_SPECIAL01, +MOCAPNET_OUTPUT_JOINT_ORIS04_L, +MOCAPNET_OUTPUT_JOINT_ORIS03_L, +MOCAPNET_OUTPUT_JOINT_ORIS04_R, +MOCAPNET_OUTPUT_JOINT_ORIS03_R, +MOCAPNET_OUTPUT_JOINT_ORIS06, +MOCAPNET_OUTPUT_JOINT_ORIS05, +MOCAPNET_OUTPUT_JOINT___SPECIAL03, +MOCAPNET_OUTPUT_JOINT_SPECIAL03, +MOCAPNET_OUTPUT_JOINT___LEVATOR06_L, +MOCAPNET_OUTPUT_JOINT_LEVATOR06_L, +MOCAPNET_OUTPUT_JOINT___LEVATOR06_R, +MOCAPNET_OUTPUT_JOINT_LEVATOR06_R, +MOCAPNET_OUTPUT_JOINT_SPECIAL06_L, +MOCAPNET_OUTPUT_JOINT_SPECIAL05_L, +MOCAPNET_OUTPUT_JOINT_EYE_L, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS03_L, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS04_L, +MOCAPNET_OUTPUT_JOINT_SPECIAL06_R, +MOCAPNET_OUTPUT_JOINT_SPECIAL05_R, +MOCAPNET_OUTPUT_JOINT_EYE_R, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS03_R, +MOCAPNET_OUTPUT_JOINT_ORBICULARIS04_R, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS01_L, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS01_L, +MOCAPNET_OUTPUT_JOINT_OCULI02_L, +MOCAPNET_OUTPUT_JOINT_OCULI01_L, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS01_R, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS01_R, +MOCAPNET_OUTPUT_JOINT_OCULI02_R, +MOCAPNET_OUTPUT_JOINT_OCULI01_R, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS02_L, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS02_L, +MOCAPNET_OUTPUT_JOINT_RISORIUS02_L, +MOCAPNET_OUTPUT_JOINT_RISORIUS03_L, +MOCAPNET_OUTPUT_JOINT___TEMPORALIS02_R, +MOCAPNET_OUTPUT_JOINT_TEMPORALIS02_R, +MOCAPNET_OUTPUT_JOINT_RISORIUS02_R, +MOCAPNET_OUTPUT_JOINT_RISORIUS03_R, +MOCAPNET_OUTPUT_JOINT_RCOLLAR, +MOCAPNET_OUTPUT_JOINT_RSHLDR, +MOCAPNET_OUTPUT_JOINT_RFOREARM, +MOCAPNET_OUTPUT_JOINT_RHAND, +MOCAPNET_OUTPUT_JOINT_METACARPAL1_R, +MOCAPNET_OUTPUT_JOINT_FINGER2_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER2_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER2_3_R, +MOCAPNET_OUTPUT_JOINT_METACARPAL2_R, +MOCAPNET_OUTPUT_JOINT_FINGER3_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER3_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER3_3_R, +MOCAPNET_OUTPUT_JOINT___METACARPAL3_R, +MOCAPNET_OUTPUT_JOINT_METACARPAL3_R, +MOCAPNET_OUTPUT_JOINT_FINGER4_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER4_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER4_3_R, +MOCAPNET_OUTPUT_JOINT___METACARPAL4_R, +MOCAPNET_OUTPUT_JOINT_METACARPAL4_R, +MOCAPNET_OUTPUT_JOINT_FINGER5_1_R, +MOCAPNET_OUTPUT_JOINT_FINGER5_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER5_3_R, +MOCAPNET_OUTPUT_JOINT_RTHUMBBASE, +MOCAPNET_OUTPUT_JOINT_RTHUMB, +MOCAPNET_OUTPUT_JOINT_FINGER1_2_R, +MOCAPNET_OUTPUT_JOINT_FINGER1_3_R, +MOCAPNET_OUTPUT_JOINT_LCOLLAR, +MOCAPNET_OUTPUT_JOINT_LSHLDR, +MOCAPNET_OUTPUT_JOINT_LFOREARM, +MOCAPNET_OUTPUT_JOINT_LHAND, +MOCAPNET_OUTPUT_JOINT_METACARPAL1_L, +MOCAPNET_OUTPUT_JOINT_FINGER2_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER2_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER2_3_L, +MOCAPNET_OUTPUT_JOINT_METACARPAL2_L, +MOCAPNET_OUTPUT_JOINT_FINGER3_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER3_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER3_3_L, +MOCAPNET_OUTPUT_JOINT___METACARPAL3_L, +MOCAPNET_OUTPUT_JOINT_METACARPAL3_L, +MOCAPNET_OUTPUT_JOINT_FINGER4_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER4_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER4_3_L, +MOCAPNET_OUTPUT_JOINT___METACARPAL4_L, +MOCAPNET_OUTPUT_JOINT_METACARPAL4_L, +MOCAPNET_OUTPUT_JOINT_FINGER5_1_L, +MOCAPNET_OUTPUT_JOINT_FINGER5_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER5_3_L, +MOCAPNET_OUTPUT_JOINT_LTHUMBBASE, +MOCAPNET_OUTPUT_JOINT_LTHUMB, +MOCAPNET_OUTPUT_JOINT_FINGER1_2_L, +MOCAPNET_OUTPUT_JOINT_FINGER1_3_L, +MOCAPNET_OUTPUT_JOINT_RBUTTOCK, +MOCAPNET_OUTPUT_JOINT_RTHIGH, +MOCAPNET_OUTPUT_JOINT_RSHIN, +MOCAPNET_OUTPUT_JOINT_RFOOT, +MOCAPNET_OUTPUT_JOINT_TOE1_1_R, +MOCAPNET_OUTPUT_JOINT_TOE1_2_R, +MOCAPNET_OUTPUT_JOINT_TOE2_1_R, +MOCAPNET_OUTPUT_JOINT_TOE2_2_R, +MOCAPNET_OUTPUT_JOINT_TOE2_3_R, +MOCAPNET_OUTPUT_JOINT_TOE3_1_R, +MOCAPNET_OUTPUT_JOINT_TOE3_2_R, +MOCAPNET_OUTPUT_JOINT_TOE3_3_R, +MOCAPNET_OUTPUT_JOINT_TOE4_1_R, +MOCAPNET_OUTPUT_JOINT_TOE4_2_R, +MOCAPNET_OUTPUT_JOINT_TOE4_3_R, +MOCAPNET_OUTPUT_JOINT_TOE5_1_R, +MOCAPNET_OUTPUT_JOINT_TOE5_2_R, +MOCAPNET_OUTPUT_JOINT_TOE5_3_R, +MOCAPNET_OUTPUT_JOINT_LBUTTOCK, +MOCAPNET_OUTPUT_JOINT_LTHIGH, +MOCAPNET_OUTPUT_JOINT_LSHIN, +MOCAPNET_OUTPUT_JOINT_LFOOT, +MOCAPNET_OUTPUT_JOINT_TOE1_1_L, +MOCAPNET_OUTPUT_JOINT_TOE1_2_L, +MOCAPNET_OUTPUT_JOINT_TOE2_1_L, +MOCAPNET_OUTPUT_JOINT_TOE2_2_L, +MOCAPNET_OUTPUT_JOINT_TOE2_3_L, +MOCAPNET_OUTPUT_JOINT_TOE3_1_L, +MOCAPNET_OUTPUT_JOINT_TOE3_2_L, +MOCAPNET_OUTPUT_JOINT_TOE3_3_L, +MOCAPNET_OUTPUT_JOINT_TOE4_1_L, +MOCAPNET_OUTPUT_JOINT_TOE4_2_L, +MOCAPNET_OUTPUT_JOINT_TOE4_3_L, +MOCAPNET_OUTPUT_JOINT_TOE5_1_L, +MOCAPNET_OUTPUT_JOINT_TOE5_2_L, +MOCAPNET_OUTPUT_JOINT_TOE5_3_L +}; + + + + +/** + * @brief This is a programmer friendly enumerator to access 3D output extracted from the BVH file_ + * Use _/GroundTruthDumper __from dataset/headerWithHeadAndOneMotion_bvh __printc to extract this automatically + */ +enum MOCAPNET_2D_Output_Joints +{ +MOCAPNET_2DPOINT_HIPX,//0 +MOCAPNET_2DPOINT_HIPY,//1 +MOCAPNET_2DPOINT_ABDOMENX,//2 +MOCAPNET_2DPOINT_ABDOMENY,//3 +MOCAPNET_2DPOINT_CHESTX,//4 +MOCAPNET_2DPOINT_CHESTY,//5 +MOCAPNET_2DPOINT_NECKX,//6 +MOCAPNET_2DPOINT_NECKY,//7 +MOCAPNET_2DPOINT_NECK1X,//8 +MOCAPNET_2DPOINT_NECK1Y,//9 +MOCAPNET_2DPOINT_HEADX,//10 +MOCAPNET_2DPOINT_HEADY,//11 +MOCAPNET_2DPOINT___JAWX,//12 +MOCAPNET_2DPOINT___JAWY,//13 +MOCAPNET_2DPOINT_JAWX,//14 +MOCAPNET_2DPOINT_JAWY,//15 +MOCAPNET_2DPOINT_SPECIAL04X,//16 +MOCAPNET_2DPOINT_SPECIAL04Y,//17 +MOCAPNET_2DPOINT_ORIS02X,//18 +MOCAPNET_2DPOINT_ORIS02Y,//19 +MOCAPNET_2DPOINT_ORIS01X,//20 +MOCAPNET_2DPOINT_ORIS01Y,//21 +MOCAPNET_2DPOINT_ENDSITE_ORIS01X,//22 +MOCAPNET_2DPOINT_ENDSITE_ORIS01Y,//23 +MOCAPNET_2DPOINT_ORIS06_LX,//24 +MOCAPNET_2DPOINT_ORIS06_LY,//25 +MOCAPNET_2DPOINT_ORIS07_LX,//26 +MOCAPNET_2DPOINT_ORIS07_LY,//27 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_LX,//28 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_LY,//29 +MOCAPNET_2DPOINT_ORIS06_RX,//30 +MOCAPNET_2DPOINT_ORIS06_RY,//31 +MOCAPNET_2DPOINT_ORIS07_RX,//32 +MOCAPNET_2DPOINT_ORIS07_RY,//33 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_RX,//34 +MOCAPNET_2DPOINT_ENDSITE_ORIS07_RY,//35 +MOCAPNET_2DPOINT_TONGUE00X,//36 +MOCAPNET_2DPOINT_TONGUE00Y,//37 +MOCAPNET_2DPOINT_TONGUE01X,//38 +MOCAPNET_2DPOINT_TONGUE01Y,//39 +MOCAPNET_2DPOINT_TONGUE02X,//40 +MOCAPNET_2DPOINT_TONGUE02Y,//41 +MOCAPNET_2DPOINT_TONGUE03X,//42 +MOCAPNET_2DPOINT_TONGUE03Y,//43 +MOCAPNET_2DPOINT___TONGUE04X,//44 +MOCAPNET_2DPOINT___TONGUE04Y,//45 +MOCAPNET_2DPOINT_TONGUE04X,//46 +MOCAPNET_2DPOINT_TONGUE04Y,//47 +MOCAPNET_2DPOINT_ENDSITE_TONGUE04X,//48 +MOCAPNET_2DPOINT_ENDSITE_TONGUE04Y,//49 +MOCAPNET_2DPOINT_TONGUE07_LX,//50 +MOCAPNET_2DPOINT_TONGUE07_LY,//51 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_LX,//52 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_LY,//53 +MOCAPNET_2DPOINT_TONGUE07_RX,//54 +MOCAPNET_2DPOINT_TONGUE07_RY,//55 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_RX,//56 +MOCAPNET_2DPOINT_ENDSITE_TONGUE07_RY,//57 +MOCAPNET_2DPOINT_TONGUE06_LX,//58 +MOCAPNET_2DPOINT_TONGUE06_LY,//59 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_LX,//60 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_LY,//61 +MOCAPNET_2DPOINT_TONGUE06_RX,//62 +MOCAPNET_2DPOINT_TONGUE06_RY,//63 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_RX,//64 +MOCAPNET_2DPOINT_ENDSITE_TONGUE06_RY,//65 +MOCAPNET_2DPOINT_TONGUE05_LX,//66 +MOCAPNET_2DPOINT_TONGUE05_LY,//67 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_LX,//68 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_LY,//69 +MOCAPNET_2DPOINT_TONGUE05_RX,//70 +MOCAPNET_2DPOINT_TONGUE05_RY,//71 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_RX,//72 +MOCAPNET_2DPOINT_ENDSITE_TONGUE05_RY,//73 +MOCAPNET_2DPOINT___LEVATOR02_LX,//74 +MOCAPNET_2DPOINT___LEVATOR02_LY,//75 +MOCAPNET_2DPOINT_LEVATOR02_LX,//76 +MOCAPNET_2DPOINT_LEVATOR02_LY,//77 +MOCAPNET_2DPOINT_LEVATOR03_LX,//78 +MOCAPNET_2DPOINT_LEVATOR03_LY,//79 +MOCAPNET_2DPOINT_LEVATOR04_LX,//80 +MOCAPNET_2DPOINT_LEVATOR04_LY,//81 +MOCAPNET_2DPOINT_LEVATOR05_LX,//82 +MOCAPNET_2DPOINT_LEVATOR05_LY,//83 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_LX,//84 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_LY,//85 +MOCAPNET_2DPOINT___LEVATOR02_RX,//86 +MOCAPNET_2DPOINT___LEVATOR02_RY,//87 +MOCAPNET_2DPOINT_LEVATOR02_RX,//88 +MOCAPNET_2DPOINT_LEVATOR02_RY,//89 +MOCAPNET_2DPOINT_LEVATOR03_RX,//90 +MOCAPNET_2DPOINT_LEVATOR03_RY,//91 +MOCAPNET_2DPOINT_LEVATOR04_RX,//92 +MOCAPNET_2DPOINT_LEVATOR04_RY,//93 +MOCAPNET_2DPOINT_LEVATOR05_RX,//94 +MOCAPNET_2DPOINT_LEVATOR05_RY,//95 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_RX,//96 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR05_RY,//97 +MOCAPNET_2DPOINT___SPECIAL01X,//98 +MOCAPNET_2DPOINT___SPECIAL01Y,//99 +MOCAPNET_2DPOINT_SPECIAL01X,//100 +MOCAPNET_2DPOINT_SPECIAL01Y,//101 +MOCAPNET_2DPOINT_ORIS04_LX,//102 +MOCAPNET_2DPOINT_ORIS04_LY,//103 +MOCAPNET_2DPOINT_ORIS03_LX,//104 +MOCAPNET_2DPOINT_ORIS03_LY,//105 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_LX,//106 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_LY,//107 +MOCAPNET_2DPOINT_ORIS04_RX,//108 +MOCAPNET_2DPOINT_ORIS04_RY,//109 +MOCAPNET_2DPOINT_ORIS03_RX,//110 +MOCAPNET_2DPOINT_ORIS03_RY,//111 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_RX,//112 +MOCAPNET_2DPOINT_ENDSITE_ORIS03_RY,//113 +MOCAPNET_2DPOINT_ORIS06X,//114 +MOCAPNET_2DPOINT_ORIS06Y,//115 +MOCAPNET_2DPOINT_ORIS05X,//116 +MOCAPNET_2DPOINT_ORIS05Y,//117 +MOCAPNET_2DPOINT_ENDSITE_ORIS05X,//118 +MOCAPNET_2DPOINT_ENDSITE_ORIS05Y,//119 +MOCAPNET_2DPOINT___SPECIAL03X,//120 +MOCAPNET_2DPOINT___SPECIAL03Y,//121 +MOCAPNET_2DPOINT_SPECIAL03X,//122 +MOCAPNET_2DPOINT_SPECIAL03Y,//123 +MOCAPNET_2DPOINT___LEVATOR06_LX,//124 +MOCAPNET_2DPOINT___LEVATOR06_LY,//125 +MOCAPNET_2DPOINT_LEVATOR06_LX,//126 +MOCAPNET_2DPOINT_LEVATOR06_LY,//127 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_LX,//128 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_LY,//129 +MOCAPNET_2DPOINT___LEVATOR06_RX,//130 +MOCAPNET_2DPOINT___LEVATOR06_RY,//131 +MOCAPNET_2DPOINT_LEVATOR06_RX,//132 +MOCAPNET_2DPOINT_LEVATOR06_RY,//133 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_RX,//134 +MOCAPNET_2DPOINT_ENDSITE_LEVATOR06_RY,//135 +MOCAPNET_2DPOINT_SPECIAL06_LX,//136 +MOCAPNET_2DPOINT_SPECIAL06_LY,//137 +MOCAPNET_2DPOINT_SPECIAL05_LX,//138 +MOCAPNET_2DPOINT_SPECIAL05_LY,//139 +MOCAPNET_2DPOINT_EYE_LX,//140 +MOCAPNET_2DPOINT_EYE_LY,//141 +MOCAPNET_2DPOINT_ENDSITE_EYE_LX,//142 +MOCAPNET_2DPOINT_ENDSITE_EYE_LY,//143 +MOCAPNET_2DPOINT_ORBICULARIS03_LX,//144 +MOCAPNET_2DPOINT_ORBICULARIS03_LY,//145 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_LX,//146 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_LY,//147 +MOCAPNET_2DPOINT_ORBICULARIS04_LX,//148 +MOCAPNET_2DPOINT_ORBICULARIS04_LY,//149 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_LX,//150 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_LY,//151 +MOCAPNET_2DPOINT_SPECIAL06_RX,//152 +MOCAPNET_2DPOINT_SPECIAL06_RY,//153 +MOCAPNET_2DPOINT_SPECIAL05_RX,//154 +MOCAPNET_2DPOINT_SPECIAL05_RY,//155 +MOCAPNET_2DPOINT_EYE_RX,//156 +MOCAPNET_2DPOINT_EYE_RY,//157 +MOCAPNET_2DPOINT_ENDSITE_EYE_RX,//158 +MOCAPNET_2DPOINT_ENDSITE_EYE_RY,//159 +MOCAPNET_2DPOINT_ORBICULARIS03_RX,//160 +MOCAPNET_2DPOINT_ORBICULARIS03_RY,//161 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_RX,//162 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS03_RY,//163 +MOCAPNET_2DPOINT_ORBICULARIS04_RX,//164 +MOCAPNET_2DPOINT_ORBICULARIS04_RY,//165 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_RX,//166 +MOCAPNET_2DPOINT_ENDSITE_ORBICULARIS04_RY,//167 +MOCAPNET_2DPOINT___TEMPORALIS01_LX,//168 +MOCAPNET_2DPOINT___TEMPORALIS01_LY,//169 +MOCAPNET_2DPOINT_TEMPORALIS01_LX,//170 +MOCAPNET_2DPOINT_TEMPORALIS01_LY,//171 +MOCAPNET_2DPOINT_OCULI02_LX,//172 +MOCAPNET_2DPOINT_OCULI02_LY,//173 +MOCAPNET_2DPOINT_OCULI01_LX,//174 +MOCAPNET_2DPOINT_OCULI01_LY,//175 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_LX,//176 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_LY,//177 +MOCAPNET_2DPOINT___TEMPORALIS01_RX,//178 +MOCAPNET_2DPOINT___TEMPORALIS01_RY,//179 +MOCAPNET_2DPOINT_TEMPORALIS01_RX,//180 +MOCAPNET_2DPOINT_TEMPORALIS01_RY,//181 +MOCAPNET_2DPOINT_OCULI02_RX,//182 +MOCAPNET_2DPOINT_OCULI02_RY,//183 +MOCAPNET_2DPOINT_OCULI01_RX,//184 +MOCAPNET_2DPOINT_OCULI01_RY,//185 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_RX,//186 +MOCAPNET_2DPOINT_ENDSITE_OCULI01_RY,//187 +MOCAPNET_2DPOINT___TEMPORALIS02_LX,//188 +MOCAPNET_2DPOINT___TEMPORALIS02_LY,//189 +MOCAPNET_2DPOINT_TEMPORALIS02_LX,//190 +MOCAPNET_2DPOINT_TEMPORALIS02_LY,//191 +MOCAPNET_2DPOINT_RISORIUS02_LX,//192 +MOCAPNET_2DPOINT_RISORIUS02_LY,//193 +MOCAPNET_2DPOINT_RISORIUS03_LX,//194 +MOCAPNET_2DPOINT_RISORIUS03_LY,//195 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_LX,//196 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_LY,//197 +MOCAPNET_2DPOINT___TEMPORALIS02_RX,//198 +MOCAPNET_2DPOINT___TEMPORALIS02_RY,//199 +MOCAPNET_2DPOINT_TEMPORALIS02_RX,//200 +MOCAPNET_2DPOINT_TEMPORALIS02_RY,//201 +MOCAPNET_2DPOINT_RISORIUS02_RX,//202 +MOCAPNET_2DPOINT_RISORIUS02_RY,//203 +MOCAPNET_2DPOINT_RISORIUS03_RX,//204 +MOCAPNET_2DPOINT_RISORIUS03_RY,//205 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_RX,//206 +MOCAPNET_2DPOINT_ENDSITE_RISORIUS03_RY,//207 +MOCAPNET_2DPOINT_RCOLLARX,//208 +MOCAPNET_2DPOINT_RCOLLARY,//209 +MOCAPNET_2DPOINT_RSHOULDERX,//210 +MOCAPNET_2DPOINT_RSHOULDERY,//211 +MOCAPNET_2DPOINT_RELBOWX,//212 +MOCAPNET_2DPOINT_RELBOWY,//213 +MOCAPNET_2DPOINT_RHANDX,//214 +MOCAPNET_2DPOINT_RHANDY,//215 +MOCAPNET_2DPOINT_METACARPAL1_RX,//216 +MOCAPNET_2DPOINT_METACARPAL1_RY,//217 +MOCAPNET_2DPOINT_FINGER2_1_RX,//218 +MOCAPNET_2DPOINT_FINGER2_1_RY,//219 +MOCAPNET_2DPOINT_FINGER2_2_RX,//220 +MOCAPNET_2DPOINT_FINGER2_2_RY,//221 +MOCAPNET_2DPOINT_FINGER2_3_RX,//222 +MOCAPNET_2DPOINT_FINGER2_3_RY,//223 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_RX,//224 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_RY,//225 +MOCAPNET_2DPOINT_METACARPAL2_RX,//226 +MOCAPNET_2DPOINT_METACARPAL2_RY,//227 +MOCAPNET_2DPOINT_FINGER3_1_RX,//228 +MOCAPNET_2DPOINT_FINGER3_1_RY,//229 +MOCAPNET_2DPOINT_FINGER3_2_RX,//230 +MOCAPNET_2DPOINT_FINGER3_2_RY,//231 +MOCAPNET_2DPOINT_FINGER3_3_RX,//232 +MOCAPNET_2DPOINT_FINGER3_3_RY,//233 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_RX,//234 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_RY,//235 +MOCAPNET_2DPOINT___METACARPAL3_RX,//236 +MOCAPNET_2DPOINT___METACARPAL3_RY,//237 +MOCAPNET_2DPOINT_METACARPAL3_RX,//238 +MOCAPNET_2DPOINT_METACARPAL3_RY,//239 +MOCAPNET_2DPOINT_FINGER4_1_RX,//240 +MOCAPNET_2DPOINT_FINGER4_1_RY,//241 +MOCAPNET_2DPOINT_FINGER4_2_RX,//242 +MOCAPNET_2DPOINT_FINGER4_2_RY,//243 +MOCAPNET_2DPOINT_FINGER4_3_RX,//244 +MOCAPNET_2DPOINT_FINGER4_3_RY,//245 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_RX,//246 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_RY,//247 +MOCAPNET_2DPOINT___METACARPAL4_RX,//248 +MOCAPNET_2DPOINT___METACARPAL4_RY,//249 +MOCAPNET_2DPOINT_METACARPAL4_RX,//250 +MOCAPNET_2DPOINT_METACARPAL4_RY,//251 +MOCAPNET_2DPOINT_FINGER5_1_RX,//252 +MOCAPNET_2DPOINT_FINGER5_1_RY,//253 +MOCAPNET_2DPOINT_FINGER5_2_RX,//254 +MOCAPNET_2DPOINT_FINGER5_2_RY,//255 +MOCAPNET_2DPOINT_FINGER5_3_RX,//256 +MOCAPNET_2DPOINT_FINGER5_3_RY,//257 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_RX,//258 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_RY,//259 +MOCAPNET_2DPOINT_RTHUMBBASEX,//260 +MOCAPNET_2DPOINT_RTHUMBBASEY,//261 +MOCAPNET_2DPOINT_RTHUMBX,//262 +MOCAPNET_2DPOINT_RTHUMBY,//263 +MOCAPNET_2DPOINT_FINGER1_2_RX,//264 +MOCAPNET_2DPOINT_FINGER1_2_RY,//265 +MOCAPNET_2DPOINT_FINGER1_3_RX,//266 +MOCAPNET_2DPOINT_FINGER1_3_RY,//267 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_RX,//268 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_RY,//269 +MOCAPNET_2DPOINT_LCOLLARX,//270 +MOCAPNET_2DPOINT_LCOLLARY,//271 +MOCAPNET_2DPOINT_LSHOULDERX,//272 +MOCAPNET_2DPOINT_LSHOULDERY,//273 +MOCAPNET_2DPOINT_LELBOWX,//274 +MOCAPNET_2DPOINT_LELBOWY,//275 +MOCAPNET_2DPOINT_LHANDX,//276 +MOCAPNET_2DPOINT_LHANDY,//277 +MOCAPNET_2DPOINT_METACARPAL1_LX,//278 +MOCAPNET_2DPOINT_METACARPAL1_LY,//279 +MOCAPNET_2DPOINT_FINGER2_1_LX,//280 +MOCAPNET_2DPOINT_FINGER2_1_LY,//281 +MOCAPNET_2DPOINT_FINGER2_2_LX,//282 +MOCAPNET_2DPOINT_FINGER2_2_LY,//283 +MOCAPNET_2DPOINT_FINGER2_3_LX,//284 +MOCAPNET_2DPOINT_FINGER2_3_LY,//285 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_LX,//286 +MOCAPNET_2DPOINT_ENDSITE_FINGER2_3_LY,//287 +MOCAPNET_2DPOINT_METACARPAL2_LX,//288 +MOCAPNET_2DPOINT_METACARPAL2_LY,//289 +MOCAPNET_2DPOINT_FINGER3_1_LX,//290 +MOCAPNET_2DPOINT_FINGER3_1_LY,//291 +MOCAPNET_2DPOINT_FINGER3_2_LX,//292 +MOCAPNET_2DPOINT_FINGER3_2_LY,//293 +MOCAPNET_2DPOINT_FINGER3_3_LX,//294 +MOCAPNET_2DPOINT_FINGER3_3_LY,//295 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_LX,//296 +MOCAPNET_2DPOINT_ENDSITE_FINGER3_3_LY,//297 +MOCAPNET_2DPOINT___METACARPAL3_LX,//298 +MOCAPNET_2DPOINT___METACARPAL3_LY,//299 +MOCAPNET_2DPOINT_METACARPAL3_LX,//300 +MOCAPNET_2DPOINT_METACARPAL3_LY,//301 +MOCAPNET_2DPOINT_FINGER4_1_LX,//302 +MOCAPNET_2DPOINT_FINGER4_1_LY,//303 +MOCAPNET_2DPOINT_FINGER4_2_LX,//304 +MOCAPNET_2DPOINT_FINGER4_2_LY,//305 +MOCAPNET_2DPOINT_FINGER4_3_LX,//306 +MOCAPNET_2DPOINT_FINGER4_3_LY,//307 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_LX,//308 +MOCAPNET_2DPOINT_ENDSITE_FINGER4_3_LY,//309 +MOCAPNET_2DPOINT___METACARPAL4_LX,//310 +MOCAPNET_2DPOINT___METACARPAL4_LY,//311 +MOCAPNET_2DPOINT_METACARPAL4_LX,//312 +MOCAPNET_2DPOINT_METACARPAL4_LY,//313 +MOCAPNET_2DPOINT_FINGER5_1_LX,//314 +MOCAPNET_2DPOINT_FINGER5_1_LY,//315 +MOCAPNET_2DPOINT_FINGER5_2_LX,//316 +MOCAPNET_2DPOINT_FINGER5_2_LY,//317 +MOCAPNET_2DPOINT_FINGER5_3_LX,//318 +MOCAPNET_2DPOINT_FINGER5_3_LY,//319 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_LX,//320 +MOCAPNET_2DPOINT_ENDSITE_FINGER5_3_LY,//321 +MOCAPNET_2DPOINT_LTHUMBBASEX,//322 +MOCAPNET_2DPOINT_LTHUMBBASEY,//323 +MOCAPNET_2DPOINT_LTHUMBX,//324 +MOCAPNET_2DPOINT_LTHUMBY,//325 +MOCAPNET_2DPOINT_FINGER1_2_LX,//326 +MOCAPNET_2DPOINT_FINGER1_2_LY,//327 +MOCAPNET_2DPOINT_FINGER1_3_LX,//328 +MOCAPNET_2DPOINT_FINGER1_3_LY,//329 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_LX,//330 +MOCAPNET_2DPOINT_ENDSITE_FINGER1_3_LY,//331 +MOCAPNET_2DPOINT_RBUTTOCKX,//332 +MOCAPNET_2DPOINT_RBUTTOCKY,//333 +MOCAPNET_2DPOINT_RHIPX,//334 +MOCAPNET_2DPOINT_RHIPY,//335 +MOCAPNET_2DPOINT_RKNEEX,//336 +MOCAPNET_2DPOINT_RKNEEY,//337 +MOCAPNET_2DPOINT_RFOOTX,//338 +MOCAPNET_2DPOINT_RFOOTY,//339 +MOCAPNET_2DPOINT_TOE1_1_RX,//340 +MOCAPNET_2DPOINT_TOE1_1_RY,//341 +MOCAPNET_2DPOINT_TOE1_2_RX,//342 +MOCAPNET_2DPOINT_TOE1_2_RY,//343 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_RX,//344 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_RY,//345 +MOCAPNET_2DPOINT_TOE2_1_RX,//346 +MOCAPNET_2DPOINT_TOE2_1_RY,//347 +MOCAPNET_2DPOINT_TOE2_2_RX,//348 +MOCAPNET_2DPOINT_TOE2_2_RY,//349 +MOCAPNET_2DPOINT_TOE2_3_RX,//350 +MOCAPNET_2DPOINT_TOE2_3_RY,//351 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_RX,//352 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_RY,//353 +MOCAPNET_2DPOINT_TOE3_1_RX,//354 +MOCAPNET_2DPOINT_TOE3_1_RY,//355 +MOCAPNET_2DPOINT_TOE3_2_RX,//356 +MOCAPNET_2DPOINT_TOE3_2_RY,//357 +MOCAPNET_2DPOINT_TOE3_3_RX,//358 +MOCAPNET_2DPOINT_TOE3_3_RY,//359 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_RX,//360 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_RY,//361 +MOCAPNET_2DPOINT_TOE4_1_RX,//362 +MOCAPNET_2DPOINT_TOE4_1_RY,//363 +MOCAPNET_2DPOINT_TOE4_2_RX,//364 +MOCAPNET_2DPOINT_TOE4_2_RY,//365 +MOCAPNET_2DPOINT_TOE4_3_RX,//366 +MOCAPNET_2DPOINT_TOE4_3_RY,//367 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_RX,//368 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_RY,//369 +MOCAPNET_2DPOINT_TOE5_1_RX,//370 +MOCAPNET_2DPOINT_TOE5_1_RY,//371 +MOCAPNET_2DPOINT_TOE5_2_RX,//372 +MOCAPNET_2DPOINT_TOE5_2_RY,//373 +MOCAPNET_2DPOINT_TOE5_3_RX,//374 +MOCAPNET_2DPOINT_TOE5_3_RY,//375 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_RX,//376 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_RY,//377 +MOCAPNET_2DPOINT_LBUTTOCKX,//378 +MOCAPNET_2DPOINT_LBUTTOCKY,//379 +MOCAPNET_2DPOINT_LHIPX,//380 +MOCAPNET_2DPOINT_LHIPY,//381 +MOCAPNET_2DPOINT_LKNEEX,//382 +MOCAPNET_2DPOINT_LKNEEY,//383 +MOCAPNET_2DPOINT_LFOOTX,//384 +MOCAPNET_2DPOINT_LFOOTY,//385 +MOCAPNET_2DPOINT_TOE1_1_LX,//386 +MOCAPNET_2DPOINT_TOE1_1_LY,//387 +MOCAPNET_2DPOINT_TOE1_2_LX,//388 +MOCAPNET_2DPOINT_TOE1_2_LY,//389 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_LX,//390 +MOCAPNET_2DPOINT_ENDSITE_TOE1_2_LY,//391 +MOCAPNET_2DPOINT_TOE2_1_LX,//392 +MOCAPNET_2DPOINT_TOE2_1_LY,//393 +MOCAPNET_2DPOINT_TOE2_2_LX,//394 +MOCAPNET_2DPOINT_TOE2_2_LY,//395 +MOCAPNET_2DPOINT_TOE2_3_LX,//396 +MOCAPNET_2DPOINT_TOE2_3_LY,//397 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_LX,//398 +MOCAPNET_2DPOINT_ENDSITE_TOE2_3_LY,//399 +MOCAPNET_2DPOINT_TOE3_1_LX,//400 +MOCAPNET_2DPOINT_TOE3_1_LY,//401 +MOCAPNET_2DPOINT_TOE3_2_LX,//402 +MOCAPNET_2DPOINT_TOE3_2_LY,//403 +MOCAPNET_2DPOINT_TOE3_3_LX,//404 +MOCAPNET_2DPOINT_TOE3_3_LY,//405 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_LX,//406 +MOCAPNET_2DPOINT_ENDSITE_TOE3_3_LY,//407 +MOCAPNET_2DPOINT_TOE4_1_LX,//408 +MOCAPNET_2DPOINT_TOE4_1_LY,//409 +MOCAPNET_2DPOINT_TOE4_2_LX,//410 +MOCAPNET_2DPOINT_TOE4_2_LY,//411 +MOCAPNET_2DPOINT_TOE4_3_LX,//412 +MOCAPNET_2DPOINT_TOE4_3_LY,//413 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_LX,//414 +MOCAPNET_2DPOINT_ENDSITE_TOE4_3_LY,//415 +MOCAPNET_2DPOINT_TOE5_1_LX,//416 +MOCAPNET_2DPOINT_TOE5_1_LY,//417 +MOCAPNET_2DPOINT_TOE5_2_LX,//418 +MOCAPNET_2DPOINT_TOE5_2_LY,//419 +MOCAPNET_2DPOINT_TOE5_3_LX,//420 +MOCAPNET_2DPOINT_TOE5_3_LY,//421 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_LX,//422 +MOCAPNET_2DPOINT_ENDSITE_TOE5_3_LY//423 +}; + + + + +/** + * @brief This is a programmer friendly enumerator to access 3D output extracted from the BVH file_ + * Use _/GroundTruthDumper __from dataset/headerWithHeadAndOneMotion_bvh __printc to extract this automatically + */ +enum MOCAPNET_JointHierarchy_Joints +{ +MOCAPNET_JOINT_HIP,//0 +MOCAPNET_JOINT_ABDOMEN,//1 +MOCAPNET_JOINT_CHEST,//2 +MOCAPNET_JOINT_NECK,//3 +MOCAPNET_JOINT_NECK1,//4 +MOCAPNET_JOINT_HEAD,//5 +MOCAPNET_JOINT___JAW,//6 +MOCAPNET_JOINT_JAW,//7 +MOCAPNET_JOINT_SPECIAL04,//8 +MOCAPNET_JOINT_ORIS02,//9 +MOCAPNET_JOINT_ORIS01,//10 +MOCAPNET_JOINT_ENDSITE_ORIS01,//11 +MOCAPNET_JOINT_ORIS06_L,//12 +MOCAPNET_JOINT_ORIS07_L,//13 +MOCAPNET_JOINT_ENDSITE_ORIS07_L,//14 +MOCAPNET_JOINT_ORIS06_R,//15 +MOCAPNET_JOINT_ORIS07_R,//16 +MOCAPNET_JOINT_ENDSITE_ORIS07_R,//17 +MOCAPNET_JOINT_TONGUE00,//18 +MOCAPNET_JOINT_TONGUE01,//19 +MOCAPNET_JOINT_TONGUE02,//20 +MOCAPNET_JOINT_TONGUE03,//21 +MOCAPNET_JOINT___TONGUE04,//22 +MOCAPNET_JOINT_TONGUE04,//23 +MOCAPNET_JOINT_ENDSITE_TONGUE04,//24 +MOCAPNET_JOINT_TONGUE07_L,//25 +MOCAPNET_JOINT_ENDSITE_TONGUE07_L,//26 +MOCAPNET_JOINT_TONGUE07_R,//27 +MOCAPNET_JOINT_ENDSITE_TONGUE07_R,//28 +MOCAPNET_JOINT_TONGUE06_L,//29 +MOCAPNET_JOINT_ENDSITE_TONGUE06_L,//30 +MOCAPNET_JOINT_TONGUE06_R,//31 +MOCAPNET_JOINT_ENDSITE_TONGUE06_R,//32 +MOCAPNET_JOINT_TONGUE05_L,//33 +MOCAPNET_JOINT_ENDSITE_TONGUE05_L,//34 +MOCAPNET_JOINT_TONGUE05_R,//35 +MOCAPNET_JOINT_ENDSITE_TONGUE05_R,//36 +MOCAPNET_JOINT___LEVATOR02_L,//37 +MOCAPNET_JOINT_LEVATOR02_L,//38 +MOCAPNET_JOINT_LEVATOR03_L,//39 +MOCAPNET_JOINT_LEVATOR04_L,//40 +MOCAPNET_JOINT_LEVATOR05_L,//41 +MOCAPNET_JOINT_ENDSITE_LEVATOR05_L,//42 +MOCAPNET_JOINT___LEVATOR02_R,//43 +MOCAPNET_JOINT_LEVATOR02_R,//44 +MOCAPNET_JOINT_LEVATOR03_R,//45 +MOCAPNET_JOINT_LEVATOR04_R,//46 +MOCAPNET_JOINT_LEVATOR05_R,//47 +MOCAPNET_JOINT_ENDSITE_LEVATOR05_R,//48 +MOCAPNET_JOINT___SPECIAL01,//49 +MOCAPNET_JOINT_SPECIAL01,//50 +MOCAPNET_JOINT_ORIS04_L,//51 +MOCAPNET_JOINT_ORIS03_L,//52 +MOCAPNET_JOINT_ENDSITE_ORIS03_L,//53 +MOCAPNET_JOINT_ORIS04_R,//54 +MOCAPNET_JOINT_ORIS03_R,//55 +MOCAPNET_JOINT_ENDSITE_ORIS03_R,//56 +MOCAPNET_JOINT_ORIS06,//57 +MOCAPNET_JOINT_ORIS05,//58 +MOCAPNET_JOINT_ENDSITE_ORIS05,//59 +MOCAPNET_JOINT___SPECIAL03,//60 +MOCAPNET_JOINT_SPECIAL03,//61 +MOCAPNET_JOINT___LEVATOR06_L,//62 +MOCAPNET_JOINT_LEVATOR06_L,//63 +MOCAPNET_JOINT_ENDSITE_LEVATOR06_L,//64 +MOCAPNET_JOINT___LEVATOR06_R,//65 +MOCAPNET_JOINT_LEVATOR06_R,//66 +MOCAPNET_JOINT_ENDSITE_LEVATOR06_R,//67 +MOCAPNET_JOINT_SPECIAL06_L,//68 +MOCAPNET_JOINT_SPECIAL05_L,//69 +MOCAPNET_JOINT_EYE_L,//70 +MOCAPNET_JOINT_ENDSITE_EYE_L,//71 +MOCAPNET_JOINT_ORBICULARIS03_L,//72 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS03_L,//73 +MOCAPNET_JOINT_ORBICULARIS04_L,//74 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS04_L,//75 +MOCAPNET_JOINT_SPECIAL06_R,//76 +MOCAPNET_JOINT_SPECIAL05_R,//77 +MOCAPNET_JOINT_EYE_R,//78 +MOCAPNET_JOINT_ENDSITE_EYE_R,//79 +MOCAPNET_JOINT_ORBICULARIS03_R,//80 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS03_R,//81 +MOCAPNET_JOINT_ORBICULARIS04_R,//82 +MOCAPNET_JOINT_ENDSITE_ORBICULARIS04_R,//83 +MOCAPNET_JOINT___TEMPORALIS01_L,//84 +MOCAPNET_JOINT_TEMPORALIS01_L,//85 +MOCAPNET_JOINT_OCULI02_L,//86 +MOCAPNET_JOINT_OCULI01_L,//87 +MOCAPNET_JOINT_ENDSITE_OCULI01_L,//88 +MOCAPNET_JOINT___TEMPORALIS01_R,//89 +MOCAPNET_JOINT_TEMPORALIS01_R,//90 +MOCAPNET_JOINT_OCULI02_R,//91 +MOCAPNET_JOINT_OCULI01_R,//92 +MOCAPNET_JOINT_ENDSITE_OCULI01_R,//93 +MOCAPNET_JOINT___TEMPORALIS02_L,//94 +MOCAPNET_JOINT_TEMPORALIS02_L,//95 +MOCAPNET_JOINT_RISORIUS02_L,//96 +MOCAPNET_JOINT_RISORIUS03_L,//97 +MOCAPNET_JOINT_ENDSITE_RISORIUS03_L,//98 +MOCAPNET_JOINT___TEMPORALIS02_R,//99 +MOCAPNET_JOINT_TEMPORALIS02_R,//100 +MOCAPNET_JOINT_RISORIUS02_R,//101 +MOCAPNET_JOINT_RISORIUS03_R,//102 +MOCAPNET_JOINT_ENDSITE_RISORIUS03_R,//103 +MOCAPNET_JOINT_RCOLLAR,//104 +MOCAPNET_JOINT_RSHOULDER,//105 +MOCAPNET_JOINT_RELBOW,//106 +MOCAPNET_JOINT_RHAND,//107 +MOCAPNET_JOINT_METACARPAL1_R,//108 +MOCAPNET_JOINT_FINGER2_1_R,//109 +MOCAPNET_JOINT_FINGER2_2_R,//110 +MOCAPNET_JOINT_FINGER2_3_R,//111 +MOCAPNET_JOINT_ENDSITE_FINGER2_3_R,//112 +MOCAPNET_JOINT_METACARPAL2_R,//113 +MOCAPNET_JOINT_FINGER3_1_R,//114 +MOCAPNET_JOINT_FINGER3_2_R,//115 +MOCAPNET_JOINT_FINGER3_3_R,//116 +MOCAPNET_JOINT_ENDSITE_FINGER3_3_R,//117 +MOCAPNET_JOINT___METACARPAL3_R,//118 +MOCAPNET_JOINT_METACARPAL3_R,//119 +MOCAPNET_JOINT_FINGER4_1_R,//120 +MOCAPNET_JOINT_FINGER4_2_R,//121 +MOCAPNET_JOINT_FINGER4_3_R,//122 +MOCAPNET_JOINT_ENDSITE_FINGER4_3_R,//123 +MOCAPNET_JOINT___METACARPAL4_R,//124 +MOCAPNET_JOINT_METACARPAL4_R,//125 +MOCAPNET_JOINT_FINGER5_1_R,//126 +MOCAPNET_JOINT_FINGER5_2_R,//127 +MOCAPNET_JOINT_FINGER5_3_R,//128 +MOCAPNET_JOINT_ENDSITE_FINGER5_3_R,//129 +MOCAPNET_JOINT_RTHUMBBASE,//130 +MOCAPNET_JOINT_RTHUMB,//131 +MOCAPNET_JOINT_FINGER1_2_R,//132 +MOCAPNET_JOINT_FINGER1_3_R,//133 +MOCAPNET_JOINT_ENDSITE_FINGER1_3_R,//134 +MOCAPNET_JOINT_LCOLLAR,//135 +MOCAPNET_JOINT_LSHOULDER,//136 +MOCAPNET_JOINT_LELBOW,//137 +MOCAPNET_JOINT_LHAND,//138 +MOCAPNET_JOINT_METACARPAL1_L,//139 +MOCAPNET_JOINT_FINGER2_1_L,//140 +MOCAPNET_JOINT_FINGER2_2_L,//141 +MOCAPNET_JOINT_FINGER2_3_L,//142 +MOCAPNET_JOINT_ENDSITE_FINGER2_3_L,//143 +MOCAPNET_JOINT_METACARPAL2_L,//144 +MOCAPNET_JOINT_FINGER3_1_L,//145 +MOCAPNET_JOINT_FINGER3_2_L,//146 +MOCAPNET_JOINT_FINGER3_3_L,//147 +MOCAPNET_JOINT_ENDSITE_FINGER3_3_L,//148 +MOCAPNET_JOINT___METACARPAL3_L,//149 +MOCAPNET_JOINT_METACARPAL3_L,//150 +MOCAPNET_JOINT_FINGER4_1_L,//151 +MOCAPNET_JOINT_FINGER4_2_L,//152 +MOCAPNET_JOINT_FINGER4_3_L,//153 +MOCAPNET_JOINT_ENDSITE_FINGER4_3_L,//154 +MOCAPNET_JOINT___METACARPAL4_L,//155 +MOCAPNET_JOINT_METACARPAL4_L,//156 +MOCAPNET_JOINT_FINGER5_1_L,//157 +MOCAPNET_JOINT_FINGER5_2_L,//158 +MOCAPNET_JOINT_FINGER5_3_L,//159 +MOCAPNET_JOINT_ENDSITE_FINGER5_3_L,//160 +MOCAPNET_JOINT_LTHUMBBASE,//161 +MOCAPNET_JOINT_LTHUMB,//162 +MOCAPNET_JOINT_FINGER1_2_L,//163 +MOCAPNET_JOINT_FINGER1_3_L,//164 +MOCAPNET_JOINT_ENDSITE_FINGER1_3_L,//165 +MOCAPNET_JOINT_RBUTTOCK,//166 +MOCAPNET_JOINT_RHIP,//167 +MOCAPNET_JOINT_RKNEE,//168 +MOCAPNET_JOINT_RFOOT,//169 +MOCAPNET_JOINT_TOE1_1_R,//170 +MOCAPNET_JOINT_TOE1_2_R,//171 +MOCAPNET_JOINT_ENDSITE_TOE1_2_R,//172 +MOCAPNET_JOINT_TOE2_1_R,//173 +MOCAPNET_JOINT_TOE2_2_R,//174 +MOCAPNET_JOINT_TOE2_3_R,//175 +MOCAPNET_JOINT_ENDSITE_TOE2_3_R,//176 +MOCAPNET_JOINT_TOE3_1_R,//177 +MOCAPNET_JOINT_TOE3_2_R,//178 +MOCAPNET_JOINT_TOE3_3_R,//179 +MOCAPNET_JOINT_ENDSITE_TOE3_3_R,//180 +MOCAPNET_JOINT_TOE4_1_R,//181 +MOCAPNET_JOINT_TOE4_2_R,//182 +MOCAPNET_JOINT_TOE4_3_R,//183 +MOCAPNET_JOINT_ENDSITE_TOE4_3_R,//184 +MOCAPNET_JOINT_TOE5_1_R,//185 +MOCAPNET_JOINT_TOE5_2_R,//186 +MOCAPNET_JOINT_TOE5_3_R,//187 +MOCAPNET_JOINT_ENDSITE_TOE5_3_R,//188 +MOCAPNET_JOINT_LBUTTOCK,//189 +MOCAPNET_JOINT_LHIP,//190 +MOCAPNET_JOINT_LKNEE,//191 +MOCAPNET_JOINT_LFOOT,//192 +MOCAPNET_JOINT_TOE1_1_L,//193 +MOCAPNET_JOINT_TOE1_2_L,//194 +MOCAPNET_JOINT_ENDSITE_TOE1_2_L,//195 +MOCAPNET_JOINT_TOE2_1_L,//196 +MOCAPNET_JOINT_TOE2_2_L,//197 +MOCAPNET_JOINT_TOE2_3_L,//198 +MOCAPNET_JOINT_ENDSITE_TOE2_3_L,//199 +MOCAPNET_JOINT_TOE3_1_L,//200 +MOCAPNET_JOINT_TOE3_2_L,//201 +MOCAPNET_JOINT_TOE3_3_L,//202 +MOCAPNET_JOINT_ENDSITE_TOE3_3_L,//203 +MOCAPNET_JOINT_TOE4_1_L,//204 +MOCAPNET_JOINT_TOE4_2_L,//205 +MOCAPNET_JOINT_TOE4_3_L,//206 +MOCAPNET_JOINT_ENDSITE_TOE4_3_L,//207 +MOCAPNET_JOINT_TOE5_1_L,//208 +MOCAPNET_JOINT_TOE5_2_L,//209 +MOCAPNET_JOINT_TOE5_3_L,//210 +MOCAPNET_JOINT_ENDSITE_TOE5_3_L//211 +}; + + + + +/** + * @brief An array with string labels for what each element of an input should be after concatenating uncompressed and compressed input. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +static const char * MocapNETOutputArrayNames[] = +{ +"hip_Xposition", // 0 +"hip_Yposition", // 1 +"hip_Zposition", // 2 +"hip_Zrotation", // 3 +"hip_Yrotation", // 4 +"hip_Xrotation", // 5 +"abdomen_Zrotation", // 6 + "abdomen_Xrotation", // 7 + "abdomen_Yrotation", // 8 + "chest_Zrotation", // 9 + "chest_Xrotation", // 10 + "chest_Yrotation", // 11 + "neck_Zrotation", // 12 + "neck_Xrotation", // 13 + "neck_Yrotation", // 14 + "neck1_Zrotation", // 15 + "neck1_Xrotation", // 16 + "neck1_Yrotation", // 17 + "head_Zrotation", // 18 + "head_Xrotation", // 19 + "head_Yrotation", // 20 + "__jaw_Zrotation", // 21 + "__jaw_Xrotation", // 22 + "__jaw_Yrotation", // 23 + "jaw_Zrotation", // 24 + "jaw_Xrotation", // 25 + "jaw_Yrotation", // 26 + "special04_Zrotation", // 27 + "special04_Xrotation", // 28 + "special04_Yrotation", // 29 + "oris02_Zrotation", // 30 + "oris02_Xrotation", // 31 + "oris02_Yrotation", // 32 + "oris01_Zrotation", // 33 + "oris01_Xrotation", // 34 + "oris01_Yrotation", // 35 + "oris06.l_Zrotation", // 36 + "oris06.l_Xrotation", // 37 + "oris06.l_Yrotation", // 38 + "oris07.l_Zrotation", // 39 + "oris07.l_Xrotation", // 40 + "oris07.l_Yrotation", // 41 + "oris06.r_Zrotation", // 42 + "oris06.r_Xrotation", // 43 + "oris06.r_Yrotation", // 44 + "oris07.r_Zrotation", // 45 + "oris07.r_Xrotation", // 46 + "oris07.r_Yrotation", // 47 + "tongue00_Zrotation", // 48 + "tongue00_Xrotation", // 49 + "tongue00_Yrotation", // 50 + "tongue01_Zrotation", // 51 + "tongue01_Xrotation", // 52 + "tongue01_Yrotation", // 53 + "tongue02_Zrotation", // 54 + "tongue02_Xrotation", // 55 + "tongue02_Yrotation", // 56 + "tongue03_Zrotation", // 57 + "tongue03_Xrotation", // 58 + "tongue03_Yrotation", // 59 + "__tongue04_Zrotation", // 60 + "__tongue04_Xrotation", // 61 + "__tongue04_Yrotation", // 62 + "tongue04_Zrotation", // 63 + "tongue04_Xrotation", // 64 + "tongue04_Yrotation", // 65 + "tongue07.l_Zrotation", // 66 + "tongue07.l_Xrotation", // 67 + "tongue07.l_Yrotation", // 68 + "tongue07.r_Zrotation", // 69 + "tongue07.r_Xrotation", // 70 + "tongue07.r_Yrotation", // 71 + "tongue06.l_Zrotation", // 72 + "tongue06.l_Xrotation", // 73 + "tongue06.l_Yrotation", // 74 + "tongue06.r_Zrotation", // 75 + "tongue06.r_Xrotation", // 76 + "tongue06.r_Yrotation", // 77 + "tongue05.l_Zrotation", // 78 + "tongue05.l_Xrotation", // 79 + "tongue05.l_Yrotation", // 80 + "tongue05.r_Zrotation", // 81 + "tongue05.r_Xrotation", // 82 + "tongue05.r_Yrotation", // 83 + "__levator02.l_Zrotation", // 84 + "__levator02.l_Xrotation", // 85 + "__levator02.l_Yrotation", // 86 + "levator02.l_Zrotation", // 87 + "levator02.l_Xrotation", // 88 + "levator02.l_Yrotation", // 89 + "levator03.l_Zrotation", // 90 + "levator03.l_Xrotation", // 91 + "levator03.l_Yrotation", // 92 + "levator04.l_Zrotation", // 93 + "levator04.l_Xrotation", // 94 + "levator04.l_Yrotation", // 95 + "levator05.l_Zrotation", // 96 + "levator05.l_Xrotation", // 97 + "levator05.l_Yrotation", // 98 + "__levator02.r_Zrotation", // 99 + "__levator02.r_Xrotation", // 100 + "__levator02.r_Yrotation", // 101 + "levator02.r_Zrotation", // 102 + "levator02.r_Xrotation", // 103 + "levator02.r_Yrotation", // 104 + "levator03.r_Zrotation", // 105 + "levator03.r_Xrotation", // 106 + "levator03.r_Yrotation", // 107 + "levator04.r_Zrotation", // 108 + "levator04.r_Xrotation", // 109 + "levator04.r_Yrotation", // 110 + "levator05.r_Zrotation", // 111 + "levator05.r_Xrotation", // 112 + "levator05.r_Yrotation", // 113 + "__special01_Zrotation", // 114 + "__special01_Xrotation", // 115 + "__special01_Yrotation", // 116 + "special01_Zrotation", // 117 + "special01_Xrotation", // 118 + "special01_Yrotation", // 119 + "oris04.l_Zrotation", // 120 + "oris04.l_Xrotation", // 121 + "oris04.l_Yrotation", // 122 + "oris03.l_Zrotation", // 123 + "oris03.l_Xrotation", // 124 + "oris03.l_Yrotation", // 125 + "oris04.r_Zrotation", // 126 + "oris04.r_Xrotation", // 127 + "oris04.r_Yrotation", // 128 + "oris03.r_Zrotation", // 129 + "oris03.r_Xrotation", // 130 + "oris03.r_Yrotation", // 131 + "oris06_Zrotation", // 132 + "oris06_Xrotation", // 133 + "oris06_Yrotation", // 134 + "oris05_Zrotation", // 135 + "oris05_Xrotation", // 136 + "oris05_Yrotation", // 137 + "__special03_Zrotation", // 138 + "__special03_Xrotation", // 139 + "__special03_Yrotation", // 140 + "special03_Zrotation", // 141 + "special03_Xrotation", // 142 + "special03_Yrotation", // 143 + "__levator06.l_Zrotation", // 144 + "__levator06.l_Xrotation", // 145 + "__levator06.l_Yrotation", // 146 + "levator06.l_Zrotation", // 147 + "levator06.l_Xrotation", // 148 + "levator06.l_Yrotation", // 149 + "__levator06.r_Zrotation", // 150 + "__levator06.r_Xrotation", // 151 + "__levator06.r_Yrotation", // 152 + "levator06.r_Zrotation", // 153 + "levator06.r_Xrotation", // 154 + "levator06.r_Yrotation", // 155 + "special06.l_Zrotation", // 156 + "special06.l_Xrotation", // 157 + "special06.l_Yrotation", // 158 + "special05.l_Zrotation", // 159 + "special05.l_Xrotation", // 160 + "special05.l_Yrotation", // 161 + "eye.l_Zrotation", // 162 + "eye.l_Xrotation", // 163 + "eye.l_Yrotation", // 164 + "orbicularis03.l_Zrotation", // 165 + "orbicularis03.l_Xrotation", // 166 + "orbicularis03.l_Yrotation", // 167 + "orbicularis04.l_Zrotation", // 168 + "orbicularis04.l_Xrotation", // 169 + "orbicularis04.l_Yrotation", // 170 + "special06.r_Zrotation", // 171 + "special06.r_Xrotation", // 172 + "special06.r_Yrotation", // 173 + "special05.r_Zrotation", // 174 + "special05.r_Xrotation", // 175 + "special05.r_Yrotation", // 176 + "eye.r_Zrotation", // 177 + "eye.r_Xrotation", // 178 + "eye.r_Yrotation", // 179 + "orbicularis03.r_Zrotation", // 180 + "orbicularis03.r_Xrotation", // 181 + "orbicularis03.r_Yrotation", // 182 + "orbicularis04.r_Zrotation", // 183 + "orbicularis04.r_Xrotation", // 184 + "orbicularis04.r_Yrotation", // 185 + "__temporalis01.l_Zrotation", // 186 + "__temporalis01.l_Xrotation", // 187 + "__temporalis01.l_Yrotation", // 188 + "temporalis01.l_Zrotation", // 189 + "temporalis01.l_Xrotation", // 190 + "temporalis01.l_Yrotation", // 191 + "oculi02.l_Zrotation", // 192 + "oculi02.l_Xrotation", // 193 + "oculi02.l_Yrotation", // 194 + "oculi01.l_Zrotation", // 195 + "oculi01.l_Xrotation", // 196 + "oculi01.l_Yrotation", // 197 + "__temporalis01.r_Zrotation", // 198 + "__temporalis01.r_Xrotation", // 199 + "__temporalis01.r_Yrotation", // 200 + "temporalis01.r_Zrotation", // 201 + "temporalis01.r_Xrotation", // 202 + "temporalis01.r_Yrotation", // 203 + "oculi02.r_Zrotation", // 204 + "oculi02.r_Xrotation", // 205 + "oculi02.r_Yrotation", // 206 + "oculi01.r_Zrotation", // 207 + "oculi01.r_Xrotation", // 208 + "oculi01.r_Yrotation", // 209 + "__temporalis02.l_Zrotation", // 210 + "__temporalis02.l_Xrotation", // 211 + "__temporalis02.l_Yrotation", // 212 + "temporalis02.l_Zrotation", // 213 + "temporalis02.l_Xrotation", // 214 + "temporalis02.l_Yrotation", // 215 + "risorius02.l_Zrotation", // 216 + "risorius02.l_Xrotation", // 217 + "risorius02.l_Yrotation", // 218 + "risorius03.l_Zrotation", // 219 + "risorius03.l_Xrotation", // 220 + "risorius03.l_Yrotation", // 221 + "__temporalis02.r_Zrotation", // 222 + "__temporalis02.r_Xrotation", // 223 + "__temporalis02.r_Yrotation", // 224 + "temporalis02.r_Zrotation", // 225 + "temporalis02.r_Xrotation", // 226 + "temporalis02.r_Yrotation", // 227 + "risorius02.r_Zrotation", // 228 + "risorius02.r_Xrotation", // 229 + "risorius02.r_Yrotation", // 230 + "risorius03.r_Zrotation", // 231 + "risorius03.r_Xrotation", // 232 + "risorius03.r_Yrotation", // 233 + "rcollar_Zrotation", // 234 + "rcollar_Xrotation", // 235 + "rcollar_Yrotation", // 236 + "rshoulder_Zrotation", // 237 + "rshoulder_Xrotation", // 238 + "rshoulder_Yrotation", // 239 + "relbow_Zrotation", // 240 + "relbow_Xrotation", // 241 + "relbow_Yrotation", // 242 + "rhand_Zrotation", // 243 + "rhand_Xrotation", // 244 + "rhand_Yrotation", // 245 + "metacarpal1.r_Zrotation", // 246 + "metacarpal1.r_Xrotation", // 247 + "metacarpal1.r_Yrotation", // 248 + "finger2-1.r_Zrotation", // 249 + "finger2-1.r_Xrotation", // 250 + "finger2-1.r_Yrotation", // 251 + "finger2-2.r_Zrotation", // 252 + "finger2-2.r_Xrotation", // 253 + "finger2-2.r_Yrotation", // 254 + "finger2-3.r_Zrotation", // 255 + "finger2-3.r_Xrotation", // 256 + "finger2-3.r_Yrotation", // 257 + "metacarpal2.r_Zrotation", // 258 + "metacarpal2.r_Xrotation", // 259 + "metacarpal2.r_Yrotation", // 260 + "finger3-1.r_Zrotation", // 261 + "finger3-1.r_Xrotation", // 262 + "finger3-1.r_Yrotation", // 263 + "finger3-2.r_Zrotation", // 264 + "finger3-2.r_Xrotation", // 265 + "finger3-2.r_Yrotation", // 266 + "finger3-3.r_Zrotation", // 267 + "finger3-3.r_Xrotation", // 268 + "finger3-3.r_Yrotation", // 269 + "__metacarpal3.r_Zrotation", // 270 + "__metacarpal3.r_Xrotation", // 271 + "__metacarpal3.r_Yrotation", // 272 + "metacarpal3.r_Zrotation", // 273 + "metacarpal3.r_Xrotation", // 274 + "metacarpal3.r_Yrotation", // 275 + "finger4-1.r_Zrotation", // 276 + "finger4-1.r_Xrotation", // 277 + "finger4-1.r_Yrotation", // 278 + "finger4-2.r_Zrotation", // 279 + "finger4-2.r_Xrotation", // 280 + "finger4-2.r_Yrotation", // 281 + "finger4-3.r_Zrotation", // 282 + "finger4-3.r_Xrotation", // 283 + "finger4-3.r_Yrotation", // 284 + "__metacarpal4.r_Zrotation", // 285 + "__metacarpal4.r_Xrotation", // 286 + "__metacarpal4.r_Yrotation", // 287 + "metacarpal4.r_Zrotation", // 288 + "metacarpal4.r_Xrotation", // 289 + "metacarpal4.r_Yrotation", // 290 + "finger5-1.r_Zrotation", // 291 + "finger5-1.r_Xrotation", // 292 + "finger5-1.r_Yrotation", // 293 + "finger5-2.r_Zrotation", // 294 + "finger5-2.r_Xrotation", // 295 + "finger5-2.r_Yrotation", // 296 + "finger5-3.r_Zrotation", // 297 + "finger5-3.r_Xrotation", // 298 + "finger5-3.r_Yrotation", // 299 + "rthumbBase_Zrotation", // 300 + "rthumbBase_Xrotation", // 301 + "rthumbBase_Yrotation", // 302 + "rthumb_Zrotation", // 303 + "rthumb_Xrotation", // 304 + "rthumb_Yrotation", // 305 + "finger1-2.r_Zrotation", // 306 + "finger1-2.r_Xrotation", // 307 + "finger1-2.r_Yrotation", // 308 + "finger1-3.r_Zrotation", // 309 + "finger1-3.r_Xrotation", // 310 + "finger1-3.r_Yrotation", // 311 + "lcollar_Zrotation", // 312 + "lcollar_Xrotation", // 313 + "lcollar_Yrotation", // 314 + "lshoulder_Zrotation", // 315 + "lshoulder_Xrotation", // 316 + "lshoulder_Yrotation", // 317 + "lelbow_Zrotation", // 318 + "lelbow_Xrotation", // 319 + "lelbow_Yrotation", // 320 + "lhand_Zrotation", // 321 + "lhand_Xrotation", // 322 + "lhand_Yrotation", // 323 + "metacarpal1.l_Zrotation", // 324 + "metacarpal1.l_Xrotation", // 325 + "metacarpal1.l_Yrotation", // 326 + "finger2-1.l_Zrotation", // 327 + "finger2-1.l_Xrotation", // 328 + "finger2-1.l_Yrotation", // 329 + "finger2-2.l_Zrotation", // 330 + "finger2-2.l_Xrotation", // 331 + "finger2-2.l_Yrotation", // 332 + "finger2-3.l_Zrotation", // 333 + "finger2-3.l_Xrotation", // 334 + "finger2-3.l_Yrotation", // 335 + "metacarpal2.l_Zrotation", // 336 + "metacarpal2.l_Xrotation", // 337 + "metacarpal2.l_Yrotation", // 338 + "finger3-1.l_Zrotation", // 339 + "finger3-1.l_Xrotation", // 340 + "finger3-1.l_Yrotation", // 341 + "finger3-2.l_Zrotation", // 342 + "finger3-2.l_Xrotation", // 343 + "finger3-2.l_Yrotation", // 344 + "finger3-3.l_Zrotation", // 345 + "finger3-3.l_Xrotation", // 346 + "finger3-3.l_Yrotation", // 347 + "__metacarpal3.l_Zrotation", // 348 + "__metacarpal3.l_Xrotation", // 349 + "__metacarpal3.l_Yrotation", // 350 + "metacarpal3.l_Zrotation", // 351 + "metacarpal3.l_Xrotation", // 352 + "metacarpal3.l_Yrotation", // 353 + "finger4-1.l_Zrotation", // 354 + "finger4-1.l_Xrotation", // 355 + "finger4-1.l_Yrotation", // 356 + "finger4-2.l_Zrotation", // 357 + "finger4-2.l_Xrotation", // 358 + "finger4-2.l_Yrotation", // 359 + "finger4-3.l_Zrotation", // 360 + "finger4-3.l_Xrotation", // 361 + "finger4-3.l_Yrotation", // 362 + "__metacarpal4.l_Zrotation", // 363 + "__metacarpal4.l_Xrotation", // 364 + "__metacarpal4.l_Yrotation", // 365 + "metacarpal4.l_Zrotation", // 366 + "metacarpal4.l_Xrotation", // 367 + "metacarpal4.l_Yrotation", // 368 + "finger5-1.l_Zrotation", // 369 + "finger5-1.l_Xrotation", // 370 + "finger5-1.l_Yrotation", // 371 + "finger5-2.l_Zrotation", // 372 + "finger5-2.l_Xrotation", // 373 + "finger5-2.l_Yrotation", // 374 + "finger5-3.l_Zrotation", // 375 + "finger5-3.l_Xrotation", // 376 + "finger5-3.l_Yrotation", // 377 + "lthumbBase_Zrotation", // 378 + "lthumbBase_Xrotation", // 379 + "lthumbBase_Yrotation", // 380 + "lthumb_Zrotation", // 381 + "lthumb_Xrotation", // 382 + "lthumb_Yrotation", // 383 + "finger1-2.l_Zrotation", // 384 + "finger1-2.l_Xrotation", // 385 + "finger1-2.l_Yrotation", // 386 + "finger1-3.l_Zrotation", // 387 + "finger1-3.l_Xrotation", // 388 + "finger1-3.l_Yrotation", // 389 + "rbuttock_Zrotation", // 390 + "rbuttock_Xrotation", // 391 + "rbuttock_Yrotation", // 392 + "rhip_Zrotation", // 393 + "rhip_Xrotation", // 394 + "rhip_Yrotation", // 395 + "rknee_Zrotation", // 396 + "rknee_Xrotation", // 397 + "rknee_Yrotation", // 398 + "rfoot_Zrotation", // 399 + "rfoot_Xrotation", // 400 + "rfoot_Yrotation", // 401 + "toe1-1.r_Zrotation", // 402 + "toe1-1.r_Xrotation", // 403 + "toe1-1.r_Yrotation", // 404 + "toe1-2.r_Zrotation", // 405 + "toe1-2.r_Xrotation", // 406 + "toe1-2.r_Yrotation", // 407 + "toe2-1.r_Zrotation", // 408 + "toe2-1.r_Xrotation", // 409 + "toe2-1.r_Yrotation", // 410 + "toe2-2.r_Zrotation", // 411 + "toe2-2.r_Xrotation", // 412 + "toe2-2.r_Yrotation", // 413 + "toe2-3.r_Zrotation", // 414 + "toe2-3.r_Xrotation", // 415 + "toe2-3.r_Yrotation", // 416 + "toe3-1.r_Zrotation", // 417 + "toe3-1.r_Xrotation", // 418 + "toe3-1.r_Yrotation", // 419 + "toe3-2.r_Zrotation", // 420 + "toe3-2.r_Xrotation", // 421 + "toe3-2.r_Yrotation", // 422 + "toe3-3.r_Zrotation", // 423 + "toe3-3.r_Xrotation", // 424 + "toe3-3.r_Yrotation", // 425 + "toe4-1.r_Zrotation", // 426 + "toe4-1.r_Xrotation", // 427 + "toe4-1.r_Yrotation", // 428 + "toe4-2.r_Zrotation", // 429 + "toe4-2.r_Xrotation", // 430 + "toe4-2.r_Yrotation", // 431 + "toe4-3.r_Zrotation", // 432 + "toe4-3.r_Xrotation", // 433 + "toe4-3.r_Yrotation", // 434 + "toe5-1.r_Zrotation", // 435 + "toe5-1.r_Xrotation", // 436 + "toe5-1.r_Yrotation", // 437 + "toe5-2.r_Zrotation", // 438 + "toe5-2.r_Xrotation", // 439 + "toe5-2.r_Yrotation", // 440 + "toe5-3.r_Zrotation", // 441 + "toe5-3.r_Xrotation", // 442 + "toe5-3.r_Yrotation", // 443 + "lbuttock_Zrotation", // 444 + "lbuttock_Xrotation", // 445 + "lbuttock_Yrotation", // 446 + "lhip_Zrotation", // 447 + "lhip_Xrotation", // 448 + "lhip_Yrotation", // 449 + "lknee_Zrotation", // 450 + "lknee_Xrotation", // 451 + "lknee_Yrotation", // 452 + "lfoot_Zrotation", // 453 + "lfoot_Xrotation", // 454 + "lfoot_Yrotation", // 455 + "toe1-1.l_Zrotation", // 456 + "toe1-1.l_Xrotation", // 457 + "toe1-1.l_Yrotation", // 458 + "toe1-2.l_Zrotation", // 459 + "toe1-2.l_Xrotation", // 460 + "toe1-2.l_Yrotation", // 461 + "toe2-1.l_Zrotation", // 462 + "toe2-1.l_Xrotation", // 463 + "toe2-1.l_Yrotation", // 464 + "toe2-2.l_Zrotation", // 465 + "toe2-2.l_Xrotation", // 466 + "toe2-2.l_Yrotation", // 467 + "toe2-3.l_Zrotation", // 468 + "toe2-3.l_Xrotation", // 469 + "toe2-3.l_Yrotation", // 470 + "toe3-1.l_Zrotation", // 471 + "toe3-1.l_Xrotation", // 472 + "toe3-1.l_Yrotation", // 473 + "toe3-2.l_Zrotation", // 474 + "toe3-2.l_Xrotation", // 475 + "toe3-2.l_Yrotation", // 476 + "toe3-3.l_Zrotation", // 477 + "toe3-3.l_Xrotation", // 478 + "toe3-3.l_Yrotation", // 479 + "toe4-1.l_Zrotation", // 480 + "toe4-1.l_Xrotation", // 481 + "toe4-1.l_Yrotation", // 482 + "toe4-2.l_Zrotation", // 483 + "toe4-2.l_Xrotation", // 484 + "toe4-2.l_Yrotation", // 485 + "toe4-3.l_Zrotation", // 486 + "toe4-3.l_Xrotation", // 487 + "toe4-3.l_Yrotation", // 488 + "toe5-1.l_Zrotation", // 489 + "toe5-1.l_Xrotation", // 490 + "toe5-1.l_Yrotation", // 491 + "toe5-2.l_Zrotation", // 492 + "toe5-2.l_Xrotation", // 493 + "toe5-2.l_Yrotation", // 494 + "toe5-3.l_Zrotation", // 495 + "toe5-3.l_Xrotation", // 496 + "toe5-3.l_Yrotation" // 497 +}; + + + +/** + * @brief This is a programmer friendly enumerator of joint output extracted from MocapNET. + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +enum MOCAPNET_Output_Joints +{ +MOCAPNET_OUTPUT_HIP_XPOSITION = 0, +MOCAPNET_OUTPUT_HIP_YPOSITION,//1 +MOCAPNET_OUTPUT_HIP_ZPOSITION,//2 +MOCAPNET_OUTPUT_HIP_ZROTATION,//3 +MOCAPNET_OUTPUT_HIP_YROTATION,//4 +MOCAPNET_OUTPUT_HIP_XROTATION,//5 +MOCAPNET_OUTPUT_ABDOMEN_ZROTATION,//6 +MOCAPNET_OUTPUT_ABDOMEN_XROTATION,//7 +MOCAPNET_OUTPUT_ABDOMEN_YROTATION,//8 +MOCAPNET_OUTPUT_CHEST_ZROTATION,//9 +MOCAPNET_OUTPUT_CHEST_XROTATION,//10 +MOCAPNET_OUTPUT_CHEST_YROTATION,//11 +MOCAPNET_OUTPUT_NECK_ZROTATION,//12 +MOCAPNET_OUTPUT_NECK_XROTATION,//13 +MOCAPNET_OUTPUT_NECK_YROTATION,//14 +MOCAPNET_OUTPUT_NECK1_ZROTATION,//15 +MOCAPNET_OUTPUT_NECK1_XROTATION,//16 +MOCAPNET_OUTPUT_NECK1_YROTATION,//17 +MOCAPNET_OUTPUT_HEAD_ZROTATION,//18 +MOCAPNET_OUTPUT_HEAD_XROTATION,//19 +MOCAPNET_OUTPUT_HEAD_YROTATION,//20 +MOCAPNET_OUTPUT___JAW_ZROTATION,//21 +MOCAPNET_OUTPUT___JAW_XROTATION,//22 +MOCAPNET_OUTPUT___JAW_YROTATION,//23 +MOCAPNET_OUTPUT_JAW_ZROTATION,//24 +MOCAPNET_OUTPUT_JAW_XROTATION,//25 +MOCAPNET_OUTPUT_JAW_YROTATION,//26 +MOCAPNET_OUTPUT_SPECIAL04_ZROTATION,//27 +MOCAPNET_OUTPUT_SPECIAL04_XROTATION,//28 +MOCAPNET_OUTPUT_SPECIAL04_YROTATION,//29 +MOCAPNET_OUTPUT_ORIS02_ZROTATION,//30 +MOCAPNET_OUTPUT_ORIS02_XROTATION,//31 +MOCAPNET_OUTPUT_ORIS02_YROTATION,//32 +MOCAPNET_OUTPUT_ORIS01_ZROTATION,//33 +MOCAPNET_OUTPUT_ORIS01_XROTATION,//34 +MOCAPNET_OUTPUT_ORIS01_YROTATION,//35 +MOCAPNET_OUTPUT_ORIS06_L_ZROTATION,//36 +MOCAPNET_OUTPUT_ORIS06_L_XROTATION,//37 +MOCAPNET_OUTPUT_ORIS06_L_YROTATION,//38 +MOCAPNET_OUTPUT_ORIS07_L_ZROTATION,//39 +MOCAPNET_OUTPUT_ORIS07_L_XROTATION,//40 +MOCAPNET_OUTPUT_ORIS07_L_YROTATION,//41 +MOCAPNET_OUTPUT_ORIS06_R_ZROTATION,//42 +MOCAPNET_OUTPUT_ORIS06_R_XROTATION,//43 +MOCAPNET_OUTPUT_ORIS06_R_YROTATION,//44 +MOCAPNET_OUTPUT_ORIS07_R_ZROTATION,//45 +MOCAPNET_OUTPUT_ORIS07_R_XROTATION,//46 +MOCAPNET_OUTPUT_ORIS07_R_YROTATION,//47 +MOCAPNET_OUTPUT_TONGUE00_ZROTATION,//48 +MOCAPNET_OUTPUT_TONGUE00_XROTATION,//49 +MOCAPNET_OUTPUT_TONGUE00_YROTATION,//50 +MOCAPNET_OUTPUT_TONGUE01_ZROTATION,//51 +MOCAPNET_OUTPUT_TONGUE01_XROTATION,//52 +MOCAPNET_OUTPUT_TONGUE01_YROTATION,//53 +MOCAPNET_OUTPUT_TONGUE02_ZROTATION,//54 +MOCAPNET_OUTPUT_TONGUE02_XROTATION,//55 +MOCAPNET_OUTPUT_TONGUE02_YROTATION,//56 +MOCAPNET_OUTPUT_TONGUE03_ZROTATION,//57 +MOCAPNET_OUTPUT_TONGUE03_XROTATION,//58 +MOCAPNET_OUTPUT_TONGUE03_YROTATION,//59 +MOCAPNET_OUTPUT___TONGUE04_ZROTATION,//60 +MOCAPNET_OUTPUT___TONGUE04_XROTATION,//61 +MOCAPNET_OUTPUT___TONGUE04_YROTATION,//62 +MOCAPNET_OUTPUT_TONGUE04_ZROTATION,//63 +MOCAPNET_OUTPUT_TONGUE04_XROTATION,//64 +MOCAPNET_OUTPUT_TONGUE04_YROTATION,//65 +MOCAPNET_OUTPUT_TONGUE07_L_ZROTATION,//66 +MOCAPNET_OUTPUT_TONGUE07_L_XROTATION,//67 +MOCAPNET_OUTPUT_TONGUE07_L_YROTATION,//68 +MOCAPNET_OUTPUT_TONGUE07_R_ZROTATION,//69 +MOCAPNET_OUTPUT_TONGUE07_R_XROTATION,//70 +MOCAPNET_OUTPUT_TONGUE07_R_YROTATION,//71 +MOCAPNET_OUTPUT_TONGUE06_L_ZROTATION,//72 +MOCAPNET_OUTPUT_TONGUE06_L_XROTATION,//73 +MOCAPNET_OUTPUT_TONGUE06_L_YROTATION,//74 +MOCAPNET_OUTPUT_TONGUE06_R_ZROTATION,//75 +MOCAPNET_OUTPUT_TONGUE06_R_XROTATION,//76 +MOCAPNET_OUTPUT_TONGUE06_R_YROTATION,//77 +MOCAPNET_OUTPUT_TONGUE05_L_ZROTATION,//78 +MOCAPNET_OUTPUT_TONGUE05_L_XROTATION,//79 +MOCAPNET_OUTPUT_TONGUE05_L_YROTATION,//80 +MOCAPNET_OUTPUT_TONGUE05_R_ZROTATION,//81 +MOCAPNET_OUTPUT_TONGUE05_R_XROTATION,//82 +MOCAPNET_OUTPUT_TONGUE05_R_YROTATION,//83 +MOCAPNET_OUTPUT___LEVATOR02_L_ZROTATION,//84 +MOCAPNET_OUTPUT___LEVATOR02_L_XROTATION,//85 +MOCAPNET_OUTPUT___LEVATOR02_L_YROTATION,//86 +MOCAPNET_OUTPUT_LEVATOR02_L_ZROTATION,//87 +MOCAPNET_OUTPUT_LEVATOR02_L_XROTATION,//88 +MOCAPNET_OUTPUT_LEVATOR02_L_YROTATION,//89 +MOCAPNET_OUTPUT_LEVATOR03_L_ZROTATION,//90 +MOCAPNET_OUTPUT_LEVATOR03_L_XROTATION,//91 +MOCAPNET_OUTPUT_LEVATOR03_L_YROTATION,//92 +MOCAPNET_OUTPUT_LEVATOR04_L_ZROTATION,//93 +MOCAPNET_OUTPUT_LEVATOR04_L_XROTATION,//94 +MOCAPNET_OUTPUT_LEVATOR04_L_YROTATION,//95 +MOCAPNET_OUTPUT_LEVATOR05_L_ZROTATION,//96 +MOCAPNET_OUTPUT_LEVATOR05_L_XROTATION,//97 +MOCAPNET_OUTPUT_LEVATOR05_L_YROTATION,//98 +MOCAPNET_OUTPUT___LEVATOR02_R_ZROTATION,//99 +MOCAPNET_OUTPUT___LEVATOR02_R_XROTATION,//100 +MOCAPNET_OUTPUT___LEVATOR02_R_YROTATION,//101 +MOCAPNET_OUTPUT_LEVATOR02_R_ZROTATION,//102 +MOCAPNET_OUTPUT_LEVATOR02_R_XROTATION,//103 +MOCAPNET_OUTPUT_LEVATOR02_R_YROTATION,//104 +MOCAPNET_OUTPUT_LEVATOR03_R_ZROTATION,//105 +MOCAPNET_OUTPUT_LEVATOR03_R_XROTATION,//106 +MOCAPNET_OUTPUT_LEVATOR03_R_YROTATION,//107 +MOCAPNET_OUTPUT_LEVATOR04_R_ZROTATION,//108 +MOCAPNET_OUTPUT_LEVATOR04_R_XROTATION,//109 +MOCAPNET_OUTPUT_LEVATOR04_R_YROTATION,//110 +MOCAPNET_OUTPUT_LEVATOR05_R_ZROTATION,//111 +MOCAPNET_OUTPUT_LEVATOR05_R_XROTATION,//112 +MOCAPNET_OUTPUT_LEVATOR05_R_YROTATION,//113 +MOCAPNET_OUTPUT___SPECIAL01_ZROTATION,//114 +MOCAPNET_OUTPUT___SPECIAL01_XROTATION,//115 +MOCAPNET_OUTPUT___SPECIAL01_YROTATION,//116 +MOCAPNET_OUTPUT_SPECIAL01_ZROTATION,//117 +MOCAPNET_OUTPUT_SPECIAL01_XROTATION,//118 +MOCAPNET_OUTPUT_SPECIAL01_YROTATION,//119 +MOCAPNET_OUTPUT_ORIS04_L_ZROTATION,//120 +MOCAPNET_OUTPUT_ORIS04_L_XROTATION,//121 +MOCAPNET_OUTPUT_ORIS04_L_YROTATION,//122 +MOCAPNET_OUTPUT_ORIS03_L_ZROTATION,//123 +MOCAPNET_OUTPUT_ORIS03_L_XROTATION,//124 +MOCAPNET_OUTPUT_ORIS03_L_YROTATION,//125 +MOCAPNET_OUTPUT_ORIS04_R_ZROTATION,//126 +MOCAPNET_OUTPUT_ORIS04_R_XROTATION,//127 +MOCAPNET_OUTPUT_ORIS04_R_YROTATION,//128 +MOCAPNET_OUTPUT_ORIS03_R_ZROTATION,//129 +MOCAPNET_OUTPUT_ORIS03_R_XROTATION,//130 +MOCAPNET_OUTPUT_ORIS03_R_YROTATION,//131 +MOCAPNET_OUTPUT_ORIS06_ZROTATION,//132 +MOCAPNET_OUTPUT_ORIS06_XROTATION,//133 +MOCAPNET_OUTPUT_ORIS06_YROTATION,//134 +MOCAPNET_OUTPUT_ORIS05_ZROTATION,//135 +MOCAPNET_OUTPUT_ORIS05_XROTATION,//136 +MOCAPNET_OUTPUT_ORIS05_YROTATION,//137 +MOCAPNET_OUTPUT___SPECIAL03_ZROTATION,//138 +MOCAPNET_OUTPUT___SPECIAL03_XROTATION,//139 +MOCAPNET_OUTPUT___SPECIAL03_YROTATION,//140 +MOCAPNET_OUTPUT_SPECIAL03_ZROTATION,//141 +MOCAPNET_OUTPUT_SPECIAL03_XROTATION,//142 +MOCAPNET_OUTPUT_SPECIAL03_YROTATION,//143 +MOCAPNET_OUTPUT___LEVATOR06_L_ZROTATION,//144 +MOCAPNET_OUTPUT___LEVATOR06_L_XROTATION,//145 +MOCAPNET_OUTPUT___LEVATOR06_L_YROTATION,//146 +MOCAPNET_OUTPUT_LEVATOR06_L_ZROTATION,//147 +MOCAPNET_OUTPUT_LEVATOR06_L_XROTATION,//148 +MOCAPNET_OUTPUT_LEVATOR06_L_YROTATION,//149 +MOCAPNET_OUTPUT___LEVATOR06_R_ZROTATION,//150 +MOCAPNET_OUTPUT___LEVATOR06_R_XROTATION,//151 +MOCAPNET_OUTPUT___LEVATOR06_R_YROTATION,//152 +MOCAPNET_OUTPUT_LEVATOR06_R_ZROTATION,//153 +MOCAPNET_OUTPUT_LEVATOR06_R_XROTATION,//154 +MOCAPNET_OUTPUT_LEVATOR06_R_YROTATION,//155 +MOCAPNET_OUTPUT_SPECIAL06_L_ZROTATION,//156 +MOCAPNET_OUTPUT_SPECIAL06_L_XROTATION,//157 +MOCAPNET_OUTPUT_SPECIAL06_L_YROTATION,//158 +MOCAPNET_OUTPUT_SPECIAL05_L_ZROTATION,//159 +MOCAPNET_OUTPUT_SPECIAL05_L_XROTATION,//160 +MOCAPNET_OUTPUT_SPECIAL05_L_YROTATION,//161 +MOCAPNET_OUTPUT_EYE_L_ZROTATION,//162 +MOCAPNET_OUTPUT_EYE_L_XROTATION,//163 +MOCAPNET_OUTPUT_EYE_L_YROTATION,//164 +MOCAPNET_OUTPUT_ORBICULARIS03_L_ZROTATION,//165 +MOCAPNET_OUTPUT_ORBICULARIS03_L_XROTATION,//166 +MOCAPNET_OUTPUT_ORBICULARIS03_L_YROTATION,//167 +MOCAPNET_OUTPUT_ORBICULARIS04_L_ZROTATION,//168 +MOCAPNET_OUTPUT_ORBICULARIS04_L_XROTATION,//169 +MOCAPNET_OUTPUT_ORBICULARIS04_L_YROTATION,//170 +MOCAPNET_OUTPUT_SPECIAL06_R_ZROTATION,//171 +MOCAPNET_OUTPUT_SPECIAL06_R_XROTATION,//172 +MOCAPNET_OUTPUT_SPECIAL06_R_YROTATION,//173 +MOCAPNET_OUTPUT_SPECIAL05_R_ZROTATION,//174 +MOCAPNET_OUTPUT_SPECIAL05_R_XROTATION,//175 +MOCAPNET_OUTPUT_SPECIAL05_R_YROTATION,//176 +MOCAPNET_OUTPUT_EYE_R_ZROTATION,//177 +MOCAPNET_OUTPUT_EYE_R_XROTATION,//178 +MOCAPNET_OUTPUT_EYE_R_YROTATION,//179 +MOCAPNET_OUTPUT_ORBICULARIS03_R_ZROTATION,//180 +MOCAPNET_OUTPUT_ORBICULARIS03_R_XROTATION,//181 +MOCAPNET_OUTPUT_ORBICULARIS03_R_YROTATION,//182 +MOCAPNET_OUTPUT_ORBICULARIS04_R_ZROTATION,//183 +MOCAPNET_OUTPUT_ORBICULARIS04_R_XROTATION,//184 +MOCAPNET_OUTPUT_ORBICULARIS04_R_YROTATION,//185 +MOCAPNET_OUTPUT___TEMPORALIS01_L_ZROTATION,//186 +MOCAPNET_OUTPUT___TEMPORALIS01_L_XROTATION,//187 +MOCAPNET_OUTPUT___TEMPORALIS01_L_YROTATION,//188 +MOCAPNET_OUTPUT_TEMPORALIS01_L_ZROTATION,//189 +MOCAPNET_OUTPUT_TEMPORALIS01_L_XROTATION,//190 +MOCAPNET_OUTPUT_TEMPORALIS01_L_YROTATION,//191 +MOCAPNET_OUTPUT_OCULI02_L_ZROTATION,//192 +MOCAPNET_OUTPUT_OCULI02_L_XROTATION,//193 +MOCAPNET_OUTPUT_OCULI02_L_YROTATION,//194 +MOCAPNET_OUTPUT_OCULI01_L_ZROTATION,//195 +MOCAPNET_OUTPUT_OCULI01_L_XROTATION,//196 +MOCAPNET_OUTPUT_OCULI01_L_YROTATION,//197 +MOCAPNET_OUTPUT___TEMPORALIS01_R_ZROTATION,//198 +MOCAPNET_OUTPUT___TEMPORALIS01_R_XROTATION,//199 +MOCAPNET_OUTPUT___TEMPORALIS01_R_YROTATION,//200 +MOCAPNET_OUTPUT_TEMPORALIS01_R_ZROTATION,//201 +MOCAPNET_OUTPUT_TEMPORALIS01_R_XROTATION,//202 +MOCAPNET_OUTPUT_TEMPORALIS01_R_YROTATION,//203 +MOCAPNET_OUTPUT_OCULI02_R_ZROTATION,//204 +MOCAPNET_OUTPUT_OCULI02_R_XROTATION,//205 +MOCAPNET_OUTPUT_OCULI02_R_YROTATION,//206 +MOCAPNET_OUTPUT_OCULI01_R_ZROTATION,//207 +MOCAPNET_OUTPUT_OCULI01_R_XROTATION,//208 +MOCAPNET_OUTPUT_OCULI01_R_YROTATION,//209 +MOCAPNET_OUTPUT___TEMPORALIS02_L_ZROTATION,//210 +MOCAPNET_OUTPUT___TEMPORALIS02_L_XROTATION,//211 +MOCAPNET_OUTPUT___TEMPORALIS02_L_YROTATION,//212 +MOCAPNET_OUTPUT_TEMPORALIS02_L_ZROTATION,//213 +MOCAPNET_OUTPUT_TEMPORALIS02_L_XROTATION,//214 +MOCAPNET_OUTPUT_TEMPORALIS02_L_YROTATION,//215 +MOCAPNET_OUTPUT_RISORIUS02_L_ZROTATION,//216 +MOCAPNET_OUTPUT_RISORIUS02_L_XROTATION,//217 +MOCAPNET_OUTPUT_RISORIUS02_L_YROTATION,//218 +MOCAPNET_OUTPUT_RISORIUS03_L_ZROTATION,//219 +MOCAPNET_OUTPUT_RISORIUS03_L_XROTATION,//220 +MOCAPNET_OUTPUT_RISORIUS03_L_YROTATION,//221 +MOCAPNET_OUTPUT___TEMPORALIS02_R_ZROTATION,//222 +MOCAPNET_OUTPUT___TEMPORALIS02_R_XROTATION,//223 +MOCAPNET_OUTPUT___TEMPORALIS02_R_YROTATION,//224 +MOCAPNET_OUTPUT_TEMPORALIS02_R_ZROTATION,//225 +MOCAPNET_OUTPUT_TEMPORALIS02_R_XROTATION,//226 +MOCAPNET_OUTPUT_TEMPORALIS02_R_YROTATION,//227 +MOCAPNET_OUTPUT_RISORIUS02_R_ZROTATION,//228 +MOCAPNET_OUTPUT_RISORIUS02_R_XROTATION,//229 +MOCAPNET_OUTPUT_RISORIUS02_R_YROTATION,//230 +MOCAPNET_OUTPUT_RISORIUS03_R_ZROTATION,//231 +MOCAPNET_OUTPUT_RISORIUS03_R_XROTATION,//232 +MOCAPNET_OUTPUT_RISORIUS03_R_YROTATION,//233 +MOCAPNET_OUTPUT_RCOLLAR_ZROTATION,//234 +MOCAPNET_OUTPUT_RCOLLAR_XROTATION,//235 +MOCAPNET_OUTPUT_RCOLLAR_YROTATION,//236 +MOCAPNET_OUTPUT_RSHOULDER_ZROTATION,//237 +MOCAPNET_OUTPUT_RSHOULDER_XROTATION,//238 +MOCAPNET_OUTPUT_RSHOULDER_YROTATION,//239 +MOCAPNET_OUTPUT_RELBOW_ZROTATION,//240 +MOCAPNET_OUTPUT_RELBOW_XROTATION,//241 +MOCAPNET_OUTPUT_RELBOW_YROTATION,//242 +MOCAPNET_OUTPUT_RHAND_ZROTATION,//243 +MOCAPNET_OUTPUT_RHAND_XROTATION,//244 +MOCAPNET_OUTPUT_RHAND_YROTATION,//245 +MOCAPNET_OUTPUT_METACARPAL1_R_ZROTATION,//246 +MOCAPNET_OUTPUT_METACARPAL1_R_XROTATION,//247 +MOCAPNET_OUTPUT_METACARPAL1_R_YROTATION,//248 +MOCAPNET_OUTPUT_FINGER2_1_R_ZROTATION,//249 +MOCAPNET_OUTPUT_FINGER2_1_R_XROTATION,//250 +MOCAPNET_OUTPUT_FINGER2_1_R_YROTATION,//251 +MOCAPNET_OUTPUT_FINGER2_2_R_ZROTATION,//252 +MOCAPNET_OUTPUT_FINGER2_2_R_XROTATION,//253 +MOCAPNET_OUTPUT_FINGER2_2_R_YROTATION,//254 +MOCAPNET_OUTPUT_FINGER2_3_R_ZROTATION,//255 +MOCAPNET_OUTPUT_FINGER2_3_R_XROTATION,//256 +MOCAPNET_OUTPUT_FINGER2_3_R_YROTATION,//257 +MOCAPNET_OUTPUT_METACARPAL2_R_ZROTATION,//258 +MOCAPNET_OUTPUT_METACARPAL2_R_XROTATION,//259 +MOCAPNET_OUTPUT_METACARPAL2_R_YROTATION,//260 +MOCAPNET_OUTPUT_FINGER3_1_R_ZROTATION,//261 +MOCAPNET_OUTPUT_FINGER3_1_R_XROTATION,//262 +MOCAPNET_OUTPUT_FINGER3_1_R_YROTATION,//263 +MOCAPNET_OUTPUT_FINGER3_2_R_ZROTATION,//264 +MOCAPNET_OUTPUT_FINGER3_2_R_XROTATION,//265 +MOCAPNET_OUTPUT_FINGER3_2_R_YROTATION,//266 +MOCAPNET_OUTPUT_FINGER3_3_R_ZROTATION,//267 +MOCAPNET_OUTPUT_FINGER3_3_R_XROTATION,//268 +MOCAPNET_OUTPUT_FINGER3_3_R_YROTATION,//269 +MOCAPNET_OUTPUT___METACARPAL3_R_ZROTATION,//270 +MOCAPNET_OUTPUT___METACARPAL3_R_XROTATION,//271 +MOCAPNET_OUTPUT___METACARPAL3_R_YROTATION,//272 +MOCAPNET_OUTPUT_METACARPAL3_R_ZROTATION,//273 +MOCAPNET_OUTPUT_METACARPAL3_R_XROTATION,//274 +MOCAPNET_OUTPUT_METACARPAL3_R_YROTATION,//275 +MOCAPNET_OUTPUT_FINGER4_1_R_ZROTATION,//276 +MOCAPNET_OUTPUT_FINGER4_1_R_XROTATION,//277 +MOCAPNET_OUTPUT_FINGER4_1_R_YROTATION,//278 +MOCAPNET_OUTPUT_FINGER4_2_R_ZROTATION,//279 +MOCAPNET_OUTPUT_FINGER4_2_R_XROTATION,//280 +MOCAPNET_OUTPUT_FINGER4_2_R_YROTATION,//281 +MOCAPNET_OUTPUT_FINGER4_3_R_ZROTATION,//282 +MOCAPNET_OUTPUT_FINGER4_3_R_XROTATION,//283 +MOCAPNET_OUTPUT_FINGER4_3_R_YROTATION,//284 +MOCAPNET_OUTPUT___METACARPAL4_R_ZROTATION,//285 +MOCAPNET_OUTPUT___METACARPAL4_R_XROTATION,//286 +MOCAPNET_OUTPUT___METACARPAL4_R_YROTATION,//287 +MOCAPNET_OUTPUT_METACARPAL4_R_ZROTATION,//288 +MOCAPNET_OUTPUT_METACARPAL4_R_XROTATION,//289 +MOCAPNET_OUTPUT_METACARPAL4_R_YROTATION,//290 +MOCAPNET_OUTPUT_FINGER5_1_R_ZROTATION,//291 +MOCAPNET_OUTPUT_FINGER5_1_R_XROTATION,//292 +MOCAPNET_OUTPUT_FINGER5_1_R_YROTATION,//293 +MOCAPNET_OUTPUT_FINGER5_2_R_ZROTATION,//294 +MOCAPNET_OUTPUT_FINGER5_2_R_XROTATION,//295 +MOCAPNET_OUTPUT_FINGER5_2_R_YROTATION,//296 +MOCAPNET_OUTPUT_FINGER5_3_R_ZROTATION,//297 +MOCAPNET_OUTPUT_FINGER5_3_R_XROTATION,//298 +MOCAPNET_OUTPUT_FINGER5_3_R_YROTATION,//299 +MOCAPNET_OUTPUT_RTHUMBBASE_ZROTATION,//300 +MOCAPNET_OUTPUT_RTHUMBBASE_XROTATION,//301 +MOCAPNET_OUTPUT_RTHUMBBASE_YROTATION,//302 +MOCAPNET_OUTPUT_RTHUMB_ZROTATION,//303 +MOCAPNET_OUTPUT_RTHUMB_XROTATION,//304 +MOCAPNET_OUTPUT_RTHUMB_YROTATION,//305 +MOCAPNET_OUTPUT_FINGER1_2_R_ZROTATION,//306 +MOCAPNET_OUTPUT_FINGER1_2_R_XROTATION,//307 +MOCAPNET_OUTPUT_FINGER1_2_R_YROTATION,//308 +MOCAPNET_OUTPUT_FINGER1_3_R_ZROTATION,//309 +MOCAPNET_OUTPUT_FINGER1_3_R_XROTATION,//310 +MOCAPNET_OUTPUT_FINGER1_3_R_YROTATION,//311 +MOCAPNET_OUTPUT_LCOLLAR_ZROTATION,//312 +MOCAPNET_OUTPUT_LCOLLAR_XROTATION,//313 +MOCAPNET_OUTPUT_LCOLLAR_YROTATION,//314 +MOCAPNET_OUTPUT_LSHOULDER_ZROTATION,//315 +MOCAPNET_OUTPUT_LSHOULDER_XROTATION,//316 +MOCAPNET_OUTPUT_LSHOULDER_YROTATION,//317 +MOCAPNET_OUTPUT_LELBOW_ZROTATION,//318 +MOCAPNET_OUTPUT_LELBOW_XROTATION,//319 +MOCAPNET_OUTPUT_LELBOW_YROTATION,//320 +MOCAPNET_OUTPUT_LHAND_ZROTATION,//321 +MOCAPNET_OUTPUT_LHAND_XROTATION,//322 +MOCAPNET_OUTPUT_LHAND_YROTATION,//323 +MOCAPNET_OUTPUT_METACARPAL1_L_ZROTATION,//324 +MOCAPNET_OUTPUT_METACARPAL1_L_XROTATION,//325 +MOCAPNET_OUTPUT_METACARPAL1_L_YROTATION,//326 +MOCAPNET_OUTPUT_FINGER2_1_L_ZROTATION,//327 +MOCAPNET_OUTPUT_FINGER2_1_L_XROTATION,//328 +MOCAPNET_OUTPUT_FINGER2_1_L_YROTATION,//329 +MOCAPNET_OUTPUT_FINGER2_2_L_ZROTATION,//330 +MOCAPNET_OUTPUT_FINGER2_2_L_XROTATION,//331 +MOCAPNET_OUTPUT_FINGER2_2_L_YROTATION,//332 +MOCAPNET_OUTPUT_FINGER2_3_L_ZROTATION,//333 +MOCAPNET_OUTPUT_FINGER2_3_L_XROTATION,//334 +MOCAPNET_OUTPUT_FINGER2_3_L_YROTATION,//335 +MOCAPNET_OUTPUT_METACARPAL2_L_ZROTATION,//336 +MOCAPNET_OUTPUT_METACARPAL2_L_XROTATION,//337 +MOCAPNET_OUTPUT_METACARPAL2_L_YROTATION,//338 +MOCAPNET_OUTPUT_FINGER3_1_L_ZROTATION,//339 +MOCAPNET_OUTPUT_FINGER3_1_L_XROTATION,//340 +MOCAPNET_OUTPUT_FINGER3_1_L_YROTATION,//341 +MOCAPNET_OUTPUT_FINGER3_2_L_ZROTATION,//342 +MOCAPNET_OUTPUT_FINGER3_2_L_XROTATION,//343 +MOCAPNET_OUTPUT_FINGER3_2_L_YROTATION,//344 +MOCAPNET_OUTPUT_FINGER3_3_L_ZROTATION,//345 +MOCAPNET_OUTPUT_FINGER3_3_L_XROTATION,//346 +MOCAPNET_OUTPUT_FINGER3_3_L_YROTATION,//347 +MOCAPNET_OUTPUT___METACARPAL3_L_ZROTATION,//348 +MOCAPNET_OUTPUT___METACARPAL3_L_XROTATION,//349 +MOCAPNET_OUTPUT___METACARPAL3_L_YROTATION,//350 +MOCAPNET_OUTPUT_METACARPAL3_L_ZROTATION,//351 +MOCAPNET_OUTPUT_METACARPAL3_L_XROTATION,//352 +MOCAPNET_OUTPUT_METACARPAL3_L_YROTATION,//353 +MOCAPNET_OUTPUT_FINGER4_1_L_ZROTATION,//354 +MOCAPNET_OUTPUT_FINGER4_1_L_XROTATION,//355 +MOCAPNET_OUTPUT_FINGER4_1_L_YROTATION,//356 +MOCAPNET_OUTPUT_FINGER4_2_L_ZROTATION,//357 +MOCAPNET_OUTPUT_FINGER4_2_L_XROTATION,//358 +MOCAPNET_OUTPUT_FINGER4_2_L_YROTATION,//359 +MOCAPNET_OUTPUT_FINGER4_3_L_ZROTATION,//360 +MOCAPNET_OUTPUT_FINGER4_3_L_XROTATION,//361 +MOCAPNET_OUTPUT_FINGER4_3_L_YROTATION,//362 +MOCAPNET_OUTPUT___METACARPAL4_L_ZROTATION,//363 +MOCAPNET_OUTPUT___METACARPAL4_L_XROTATION,//364 +MOCAPNET_OUTPUT___METACARPAL4_L_YROTATION,//365 +MOCAPNET_OUTPUT_METACARPAL4_L_ZROTATION,//366 +MOCAPNET_OUTPUT_METACARPAL4_L_XROTATION,//367 +MOCAPNET_OUTPUT_METACARPAL4_L_YROTATION,//368 +MOCAPNET_OUTPUT_FINGER5_1_L_ZROTATION,//369 +MOCAPNET_OUTPUT_FINGER5_1_L_XROTATION,//370 +MOCAPNET_OUTPUT_FINGER5_1_L_YROTATION,//371 +MOCAPNET_OUTPUT_FINGER5_2_L_ZROTATION,//372 +MOCAPNET_OUTPUT_FINGER5_2_L_XROTATION,//373 +MOCAPNET_OUTPUT_FINGER5_2_L_YROTATION,//374 +MOCAPNET_OUTPUT_FINGER5_3_L_ZROTATION,//375 +MOCAPNET_OUTPUT_FINGER5_3_L_XROTATION,//376 +MOCAPNET_OUTPUT_FINGER5_3_L_YROTATION,//377 +MOCAPNET_OUTPUT_LTHUMBBASE_ZROTATION,//378 +MOCAPNET_OUTPUT_LTHUMBBASE_XROTATION,//379 +MOCAPNET_OUTPUT_LTHUMBBASE_YROTATION,//380 +MOCAPNET_OUTPUT_LTHUMB_ZROTATION,//381 +MOCAPNET_OUTPUT_LTHUMB_XROTATION,//382 +MOCAPNET_OUTPUT_LTHUMB_YROTATION,//383 +MOCAPNET_OUTPUT_FINGER1_2_L_ZROTATION,//384 +MOCAPNET_OUTPUT_FINGER1_2_L_XROTATION,//385 +MOCAPNET_OUTPUT_FINGER1_2_L_YROTATION,//386 +MOCAPNET_OUTPUT_FINGER1_3_L_ZROTATION,//387 +MOCAPNET_OUTPUT_FINGER1_3_L_XROTATION,//388 +MOCAPNET_OUTPUT_FINGER1_3_L_YROTATION,//389 +MOCAPNET_OUTPUT_RBUTTOCK_ZROTATION,//390 +MOCAPNET_OUTPUT_RBUTTOCK_XROTATION,//391 +MOCAPNET_OUTPUT_RBUTTOCK_YROTATION,//392 +MOCAPNET_OUTPUT_RHIP_ZROTATION,//393 +MOCAPNET_OUTPUT_RHIP_XROTATION,//394 +MOCAPNET_OUTPUT_RHIP_YROTATION,//395 +MOCAPNET_OUTPUT_RKNEE_ZROTATION,//396 +MOCAPNET_OUTPUT_RKNEE_XROTATION,//397 +MOCAPNET_OUTPUT_RKNEE_YROTATION,//398 +MOCAPNET_OUTPUT_RFOOT_ZROTATION,//399 +MOCAPNET_OUTPUT_RFOOT_XROTATION,//400 +MOCAPNET_OUTPUT_RFOOT_YROTATION,//401 +MOCAPNET_OUTPUT_TOE1_1_R_ZROTATION,//402 +MOCAPNET_OUTPUT_TOE1_1_R_XROTATION,//403 +MOCAPNET_OUTPUT_TOE1_1_R_YROTATION,//404 +MOCAPNET_OUTPUT_TOE1_2_R_ZROTATION,//405 +MOCAPNET_OUTPUT_TOE1_2_R_XROTATION,//406 +MOCAPNET_OUTPUT_TOE1_2_R_YROTATION,//407 +MOCAPNET_OUTPUT_TOE2_1_R_ZROTATION,//408 +MOCAPNET_OUTPUT_TOE2_1_R_XROTATION,//409 +MOCAPNET_OUTPUT_TOE2_1_R_YROTATION,//410 +MOCAPNET_OUTPUT_TOE2_2_R_ZROTATION,//411 +MOCAPNET_OUTPUT_TOE2_2_R_XROTATION,//412 +MOCAPNET_OUTPUT_TOE2_2_R_YROTATION,//413 +MOCAPNET_OUTPUT_TOE2_3_R_ZROTATION,//414 +MOCAPNET_OUTPUT_TOE2_3_R_XROTATION,//415 +MOCAPNET_OUTPUT_TOE2_3_R_YROTATION,//416 +MOCAPNET_OUTPUT_TOE3_1_R_ZROTATION,//417 +MOCAPNET_OUTPUT_TOE3_1_R_XROTATION,//418 +MOCAPNET_OUTPUT_TOE3_1_R_YROTATION,//419 +MOCAPNET_OUTPUT_TOE3_2_R_ZROTATION,//420 +MOCAPNET_OUTPUT_TOE3_2_R_XROTATION,//421 +MOCAPNET_OUTPUT_TOE3_2_R_YROTATION,//422 +MOCAPNET_OUTPUT_TOE3_3_R_ZROTATION,//423 +MOCAPNET_OUTPUT_TOE3_3_R_XROTATION,//424 +MOCAPNET_OUTPUT_TOE3_3_R_YROTATION,//425 +MOCAPNET_OUTPUT_TOE4_1_R_ZROTATION,//426 +MOCAPNET_OUTPUT_TOE4_1_R_XROTATION,//427 +MOCAPNET_OUTPUT_TOE4_1_R_YROTATION,//428 +MOCAPNET_OUTPUT_TOE4_2_R_ZROTATION,//429 +MOCAPNET_OUTPUT_TOE4_2_R_XROTATION,//430 +MOCAPNET_OUTPUT_TOE4_2_R_YROTATION,//431 +MOCAPNET_OUTPUT_TOE4_3_R_ZROTATION,//432 +MOCAPNET_OUTPUT_TOE4_3_R_XROTATION,//433 +MOCAPNET_OUTPUT_TOE4_3_R_YROTATION,//434 +MOCAPNET_OUTPUT_TOE5_1_R_ZROTATION,//435 +MOCAPNET_OUTPUT_TOE5_1_R_XROTATION,//436 +MOCAPNET_OUTPUT_TOE5_1_R_YROTATION,//437 +MOCAPNET_OUTPUT_TOE5_2_R_ZROTATION,//438 +MOCAPNET_OUTPUT_TOE5_2_R_XROTATION,//439 +MOCAPNET_OUTPUT_TOE5_2_R_YROTATION,//440 +MOCAPNET_OUTPUT_TOE5_3_R_ZROTATION,//441 +MOCAPNET_OUTPUT_TOE5_3_R_XROTATION,//442 +MOCAPNET_OUTPUT_TOE5_3_R_YROTATION,//443 +MOCAPNET_OUTPUT_LBUTTOCK_ZROTATION,//444 +MOCAPNET_OUTPUT_LBUTTOCK_XROTATION,//445 +MOCAPNET_OUTPUT_LBUTTOCK_YROTATION,//446 +MOCAPNET_OUTPUT_LHIP_ZROTATION,//447 +MOCAPNET_OUTPUT_LHIP_XROTATION,//448 +MOCAPNET_OUTPUT_LHIP_YROTATION,//449 +MOCAPNET_OUTPUT_LKNEE_ZROTATION,//450 +MOCAPNET_OUTPUT_LKNEE_XROTATION,//451 +MOCAPNET_OUTPUT_LKNEE_YROTATION,//452 +MOCAPNET_OUTPUT_LFOOT_ZROTATION,//453 +MOCAPNET_OUTPUT_LFOOT_XROTATION,//454 +MOCAPNET_OUTPUT_LFOOT_YROTATION,//455 +MOCAPNET_OUTPUT_TOE1_1_L_ZROTATION,//456 +MOCAPNET_OUTPUT_TOE1_1_L_XROTATION,//457 +MOCAPNET_OUTPUT_TOE1_1_L_YROTATION,//458 +MOCAPNET_OUTPUT_TOE1_2_L_ZROTATION,//459 +MOCAPNET_OUTPUT_TOE1_2_L_XROTATION,//460 +MOCAPNET_OUTPUT_TOE1_2_L_YROTATION,//461 +MOCAPNET_OUTPUT_TOE2_1_L_ZROTATION,//462 +MOCAPNET_OUTPUT_TOE2_1_L_XROTATION,//463 +MOCAPNET_OUTPUT_TOE2_1_L_YROTATION,//464 +MOCAPNET_OUTPUT_TOE2_2_L_ZROTATION,//465 +MOCAPNET_OUTPUT_TOE2_2_L_XROTATION,//466 +MOCAPNET_OUTPUT_TOE2_2_L_YROTATION,//467 +MOCAPNET_OUTPUT_TOE2_3_L_ZROTATION,//468 +MOCAPNET_OUTPUT_TOE2_3_L_XROTATION,//469 +MOCAPNET_OUTPUT_TOE2_3_L_YROTATION,//470 +MOCAPNET_OUTPUT_TOE3_1_L_ZROTATION,//471 +MOCAPNET_OUTPUT_TOE3_1_L_XROTATION,//472 +MOCAPNET_OUTPUT_TOE3_1_L_YROTATION,//473 +MOCAPNET_OUTPUT_TOE3_2_L_ZROTATION,//474 +MOCAPNET_OUTPUT_TOE3_2_L_XROTATION,//475 +MOCAPNET_OUTPUT_TOE3_2_L_YROTATION,//476 +MOCAPNET_OUTPUT_TOE3_3_L_ZROTATION,//477 +MOCAPNET_OUTPUT_TOE3_3_L_XROTATION,//478 +MOCAPNET_OUTPUT_TOE3_3_L_YROTATION,//479 +MOCAPNET_OUTPUT_TOE4_1_L_ZROTATION,//480 +MOCAPNET_OUTPUT_TOE4_1_L_XROTATION,//481 +MOCAPNET_OUTPUT_TOE4_1_L_YROTATION,//482 +MOCAPNET_OUTPUT_TOE4_2_L_ZROTATION,//483 +MOCAPNET_OUTPUT_TOE4_2_L_XROTATION,//484 +MOCAPNET_OUTPUT_TOE4_2_L_YROTATION,//485 +MOCAPNET_OUTPUT_TOE4_3_L_ZROTATION,//486 +MOCAPNET_OUTPUT_TOE4_3_L_XROTATION,//487 +MOCAPNET_OUTPUT_TOE4_3_L_YROTATION,//488 +MOCAPNET_OUTPUT_TOE5_1_L_ZROTATION,//489 +MOCAPNET_OUTPUT_TOE5_1_L_XROTATION,//490 +MOCAPNET_OUTPUT_TOE5_1_L_YROTATION,//491 +MOCAPNET_OUTPUT_TOE5_2_L_ZROTATION,//492 +MOCAPNET_OUTPUT_TOE5_2_L_XROTATION,//493 +MOCAPNET_OUTPUT_TOE5_2_L_YROTATION,//494 +MOCAPNET_OUTPUT_TOE5_3_L_ZROTATION,//495 +MOCAPNET_OUTPUT_TOE5_3_L_XROTATION,//496 +MOCAPNET_OUTPUT_TOE5_3_L_YROTATION,//497 +//----------------------------- +MOCAPNET_OUTPUT_NUMBER +}; + + + + + + + + + +/** + * @brief This is a programmer friendly enumerator to access 3D output extracted from MocapNET + * Use ./GroundTruthDumper --from dataset/headerWithHeadAndOneMotion.bvh --printc + * to extract this automatically + */ +enum MNET_3D_Output_Joints +{ +MOCAPNET_3DPOINT_HIPX,//0 +MOCAPNET_3DPOINT_HIPY,//1 +MOCAPNET_3DPOINT_HIPZ,//2 +MOCAPNET_3DPOINT_ABDOMENX,//3 +MOCAPNET_3DPOINT_ABDOMENY,//4 +MOCAPNET_3DPOINT_ABDOMENZ,//5 +MOCAPNET_3DPOINT_CHESTX,//6 +MOCAPNET_3DPOINT_CHESTY,//7 +MOCAPNET_3DPOINT_CHESTZ,//8 +MOCAPNET_3DPOINT_NECKX,//9 +MOCAPNET_3DPOINT_NECKY,//10 +MOCAPNET_3DPOINT_NECKZ,//11 +MOCAPNET_3DPOINT_NECK1X,//12 +MOCAPNET_3DPOINT_NECK1Y,//13 +MOCAPNET_3DPOINT_NECK1Z,//14 +MOCAPNET_3DPOINT_HEADX,//15 +MOCAPNET_3DPOINT_HEADY,//16 +MOCAPNET_3DPOINT_HEADZ,//17 +MOCAPNET_3DPOINT___JAWX,//18 +MOCAPNET_3DPOINT___JAWY,//19 +MOCAPNET_3DPOINT___JAWZ,//20 +MOCAPNET_3DPOINT_JAWX,//21 +MOCAPNET_3DPOINT_JAWY,//22 +MOCAPNET_3DPOINT_JAWZ,//23 +MOCAPNET_3DPOINT_SPECIAL04X,//24 +MOCAPNET_3DPOINT_SPECIAL04Y,//25 +MOCAPNET_3DPOINT_SPECIAL04Z,//26 +MOCAPNET_3DPOINT_ORIS02X,//27 +MOCAPNET_3DPOINT_ORIS02Y,//28 +MOCAPNET_3DPOINT_ORIS02Z,//29 +MOCAPNET_3DPOINT_ORIS01X,//30 +MOCAPNET_3DPOINT_ORIS01Y,//31 +MOCAPNET_3DPOINT_ORIS01Z,//32 +MOCAPNET_3DPOINT_ENDSITE_ORIS01X,//33 +MOCAPNET_3DPOINT_ENDSITE_ORIS01Y,//34 +MOCAPNET_3DPOINT_ENDSITE_ORIS01Z,//35 +MOCAPNET_3DPOINT_ORIS06_LX,//36 +MOCAPNET_3DPOINT_ORIS06_LY,//37 +MOCAPNET_3DPOINT_ORIS06_LZ,//38 +MOCAPNET_3DPOINT_ORIS07_LX,//39 +MOCAPNET_3DPOINT_ORIS07_LY,//40 +MOCAPNET_3DPOINT_ORIS07_LZ,//41 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_LX,//42 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_LY,//43 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_LZ,//44 +MOCAPNET_3DPOINT_ORIS06_RX,//45 +MOCAPNET_3DPOINT_ORIS06_RY,//46 +MOCAPNET_3DPOINT_ORIS06_RZ,//47 +MOCAPNET_3DPOINT_ORIS07_RX,//48 +MOCAPNET_3DPOINT_ORIS07_RY,//49 +MOCAPNET_3DPOINT_ORIS07_RZ,//50 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_RX,//51 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_RY,//52 +MOCAPNET_3DPOINT_ENDSITE_ORIS07_RZ,//53 +MOCAPNET_3DPOINT_TONGUE00X,//54 +MOCAPNET_3DPOINT_TONGUE00Y,//55 +MOCAPNET_3DPOINT_TONGUE00Z,//56 +MOCAPNET_3DPOINT_TONGUE01X,//57 +MOCAPNET_3DPOINT_TONGUE01Y,//58 +MOCAPNET_3DPOINT_TONGUE01Z,//59 +MOCAPNET_3DPOINT_TONGUE02X,//60 +MOCAPNET_3DPOINT_TONGUE02Y,//61 +MOCAPNET_3DPOINT_TONGUE02Z,//62 +MOCAPNET_3DPOINT_TONGUE03X,//63 +MOCAPNET_3DPOINT_TONGUE03Y,//64 +MOCAPNET_3DPOINT_TONGUE03Z,//65 +MOCAPNET_3DPOINT___TONGUE04X,//66 +MOCAPNET_3DPOINT___TONGUE04Y,//67 +MOCAPNET_3DPOINT___TONGUE04Z,//68 +MOCAPNET_3DPOINT_TONGUE04X,//69 +MOCAPNET_3DPOINT_TONGUE04Y,//70 +MOCAPNET_3DPOINT_TONGUE04Z,//71 +MOCAPNET_3DPOINT_ENDSITE_TONGUE04X,//72 +MOCAPNET_3DPOINT_ENDSITE_TONGUE04Y,//73 +MOCAPNET_3DPOINT_ENDSITE_TONGUE04Z,//74 +MOCAPNET_3DPOINT_TONGUE07_LX,//75 +MOCAPNET_3DPOINT_TONGUE07_LY,//76 +MOCAPNET_3DPOINT_TONGUE07_LZ,//77 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_LX,//78 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_LY,//79 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_LZ,//80 +MOCAPNET_3DPOINT_TONGUE07_RX,//81 +MOCAPNET_3DPOINT_TONGUE07_RY,//82 +MOCAPNET_3DPOINT_TONGUE07_RZ,//83 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_RX,//84 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_RY,//85 +MOCAPNET_3DPOINT_ENDSITE_TONGUE07_RZ,//86 +MOCAPNET_3DPOINT_TONGUE06_LX,//87 +MOCAPNET_3DPOINT_TONGUE06_LY,//88 +MOCAPNET_3DPOINT_TONGUE06_LZ,//89 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_LX,//90 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_LY,//91 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_LZ,//92 +MOCAPNET_3DPOINT_TONGUE06_RX,//93 +MOCAPNET_3DPOINT_TONGUE06_RY,//94 +MOCAPNET_3DPOINT_TONGUE06_RZ,//95 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_RX,//96 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_RY,//97 +MOCAPNET_3DPOINT_ENDSITE_TONGUE06_RZ,//98 +MOCAPNET_3DPOINT_TONGUE05_LX,//99 +MOCAPNET_3DPOINT_TONGUE05_LY,//100 +MOCAPNET_3DPOINT_TONGUE05_LZ,//101 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_LX,//102 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_LY,//103 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_LZ,//104 +MOCAPNET_3DPOINT_TONGUE05_RX,//105 +MOCAPNET_3DPOINT_TONGUE05_RY,//106 +MOCAPNET_3DPOINT_TONGUE05_RZ,//107 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_RX,//108 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_RY,//109 +MOCAPNET_3DPOINT_ENDSITE_TONGUE05_RZ,//110 +MOCAPNET_3DPOINT___LEVATOR02_LX,//111 +MOCAPNET_3DPOINT___LEVATOR02_LY,//112 +MOCAPNET_3DPOINT___LEVATOR02_LZ,//113 +MOCAPNET_3DPOINT_LEVATOR02_LX,//114 +MOCAPNET_3DPOINT_LEVATOR02_LY,//115 +MOCAPNET_3DPOINT_LEVATOR02_LZ,//116 +MOCAPNET_3DPOINT_LEVATOR03_LX,//117 +MOCAPNET_3DPOINT_LEVATOR03_LY,//118 +MOCAPNET_3DPOINT_LEVATOR03_LZ,//119 +MOCAPNET_3DPOINT_LEVATOR04_LX,//120 +MOCAPNET_3DPOINT_LEVATOR04_LY,//121 +MOCAPNET_3DPOINT_LEVATOR04_LZ,//122 +MOCAPNET_3DPOINT_LEVATOR05_LX,//123 +MOCAPNET_3DPOINT_LEVATOR05_LY,//124 +MOCAPNET_3DPOINT_LEVATOR05_LZ,//125 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_LX,//126 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_LY,//127 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_LZ,//128 +MOCAPNET_3DPOINT___LEVATOR02_RX,//129 +MOCAPNET_3DPOINT___LEVATOR02_RY,//130 +MOCAPNET_3DPOINT___LEVATOR02_RZ,//131 +MOCAPNET_3DPOINT_LEVATOR02_RX,//132 +MOCAPNET_3DPOINT_LEVATOR02_RY,//133 +MOCAPNET_3DPOINT_LEVATOR02_RZ,//134 +MOCAPNET_3DPOINT_LEVATOR03_RX,//135 +MOCAPNET_3DPOINT_LEVATOR03_RY,//136 +MOCAPNET_3DPOINT_LEVATOR03_RZ,//137 +MOCAPNET_3DPOINT_LEVATOR04_RX,//138 +MOCAPNET_3DPOINT_LEVATOR04_RY,//139 +MOCAPNET_3DPOINT_LEVATOR04_RZ,//140 +MOCAPNET_3DPOINT_LEVATOR05_RX,//141 +MOCAPNET_3DPOINT_LEVATOR05_RY,//142 +MOCAPNET_3DPOINT_LEVATOR05_RZ,//143 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_RX,//144 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_RY,//145 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR05_RZ,//146 +MOCAPNET_3DPOINT___SPECIAL01X,//147 +MOCAPNET_3DPOINT___SPECIAL01Y,//148 +MOCAPNET_3DPOINT___SPECIAL01Z,//149 +MOCAPNET_3DPOINT_SPECIAL01X,//150 +MOCAPNET_3DPOINT_SPECIAL01Y,//151 +MOCAPNET_3DPOINT_SPECIAL01Z,//152 +MOCAPNET_3DPOINT_ORIS04_LX,//153 +MOCAPNET_3DPOINT_ORIS04_LY,//154 +MOCAPNET_3DPOINT_ORIS04_LZ,//155 +MOCAPNET_3DPOINT_ORIS03_LX,//156 +MOCAPNET_3DPOINT_ORIS03_LY,//157 +MOCAPNET_3DPOINT_ORIS03_LZ,//158 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_LX,//159 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_LY,//160 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_LZ,//161 +MOCAPNET_3DPOINT_ORIS04_RX,//162 +MOCAPNET_3DPOINT_ORIS04_RY,//163 +MOCAPNET_3DPOINT_ORIS04_RZ,//164 +MOCAPNET_3DPOINT_ORIS03_RX,//165 +MOCAPNET_3DPOINT_ORIS03_RY,//166 +MOCAPNET_3DPOINT_ORIS03_RZ,//167 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_RX,//168 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_RY,//169 +MOCAPNET_3DPOINT_ENDSITE_ORIS03_RZ,//170 +MOCAPNET_3DPOINT_ORIS06X,//171 +MOCAPNET_3DPOINT_ORIS06Y,//172 +MOCAPNET_3DPOINT_ORIS06Z,//173 +MOCAPNET_3DPOINT_ORIS05X,//174 +MOCAPNET_3DPOINT_ORIS05Y,//175 +MOCAPNET_3DPOINT_ORIS05Z,//176 +MOCAPNET_3DPOINT_ENDSITE_ORIS05X,//177 +MOCAPNET_3DPOINT_ENDSITE_ORIS05Y,//178 +MOCAPNET_3DPOINT_ENDSITE_ORIS05Z,//179 +MOCAPNET_3DPOINT___SPECIAL03X,//180 +MOCAPNET_3DPOINT___SPECIAL03Y,//181 +MOCAPNET_3DPOINT___SPECIAL03Z,//182 +MOCAPNET_3DPOINT_SPECIAL03X,//183 +MOCAPNET_3DPOINT_SPECIAL03Y,//184 +MOCAPNET_3DPOINT_SPECIAL03Z,//185 +MOCAPNET_3DPOINT___LEVATOR06_LX,//186 +MOCAPNET_3DPOINT___LEVATOR06_LY,//187 +MOCAPNET_3DPOINT___LEVATOR06_LZ,//188 +MOCAPNET_3DPOINT_LEVATOR06_LX,//189 +MOCAPNET_3DPOINT_LEVATOR06_LY,//190 +MOCAPNET_3DPOINT_LEVATOR06_LZ,//191 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_LX,//192 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_LY,//193 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_LZ,//194 +MOCAPNET_3DPOINT___LEVATOR06_RX,//195 +MOCAPNET_3DPOINT___LEVATOR06_RY,//196 +MOCAPNET_3DPOINT___LEVATOR06_RZ,//197 +MOCAPNET_3DPOINT_LEVATOR06_RX,//198 +MOCAPNET_3DPOINT_LEVATOR06_RY,//199 +MOCAPNET_3DPOINT_LEVATOR06_RZ,//200 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_RX,//201 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_RY,//202 +MOCAPNET_3DPOINT_ENDSITE_LEVATOR06_RZ,//203 +MOCAPNET_3DPOINT_SPECIAL06_LX,//204 +MOCAPNET_3DPOINT_SPECIAL06_LY,//205 +MOCAPNET_3DPOINT_SPECIAL06_LZ,//206 +MOCAPNET_3DPOINT_SPECIAL05_LX,//207 +MOCAPNET_3DPOINT_SPECIAL05_LY,//208 +MOCAPNET_3DPOINT_SPECIAL05_LZ,//209 +MOCAPNET_3DPOINT_EYE_LX,//210 +MOCAPNET_3DPOINT_EYE_LY,//211 +MOCAPNET_3DPOINT_EYE_LZ,//212 +MOCAPNET_3DPOINT_ENDSITE_EYE_LX,//213 +MOCAPNET_3DPOINT_ENDSITE_EYE_LY,//214 +MOCAPNET_3DPOINT_ENDSITE_EYE_LZ,//215 +MOCAPNET_3DPOINT_ORBICULARIS03_LX,//216 +MOCAPNET_3DPOINT_ORBICULARIS03_LY,//217 +MOCAPNET_3DPOINT_ORBICULARIS03_LZ,//218 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_LX,//219 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_LY,//220 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_LZ,//221 +MOCAPNET_3DPOINT_ORBICULARIS04_LX,//222 +MOCAPNET_3DPOINT_ORBICULARIS04_LY,//223 +MOCAPNET_3DPOINT_ORBICULARIS04_LZ,//224 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_LX,//225 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_LY,//226 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_LZ,//227 +MOCAPNET_3DPOINT_SPECIAL06_RX,//228 +MOCAPNET_3DPOINT_SPECIAL06_RY,//229 +MOCAPNET_3DPOINT_SPECIAL06_RZ,//230 +MOCAPNET_3DPOINT_SPECIAL05_RX,//231 +MOCAPNET_3DPOINT_SPECIAL05_RY,//232 +MOCAPNET_3DPOINT_SPECIAL05_RZ,//233 +MOCAPNET_3DPOINT_EYE_RX,//234 +MOCAPNET_3DPOINT_EYE_RY,//235 +MOCAPNET_3DPOINT_EYE_RZ,//236 +MOCAPNET_3DPOINT_ENDSITE_EYE_RX,//237 +MOCAPNET_3DPOINT_ENDSITE_EYE_RY,//238 +MOCAPNET_3DPOINT_ENDSITE_EYE_RZ,//239 +MOCAPNET_3DPOINT_ORBICULARIS03_RX,//240 +MOCAPNET_3DPOINT_ORBICULARIS03_RY,//241 +MOCAPNET_3DPOINT_ORBICULARIS03_RZ,//242 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_RX,//243 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_RY,//244 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS03_RZ,//245 +MOCAPNET_3DPOINT_ORBICULARIS04_RX,//246 +MOCAPNET_3DPOINT_ORBICULARIS04_RY,//247 +MOCAPNET_3DPOINT_ORBICULARIS04_RZ,//248 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_RX,//249 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_RY,//250 +MOCAPNET_3DPOINT_ENDSITE_ORBICULARIS04_RZ,//251 +MOCAPNET_3DPOINT___TEMPORALIS01_LX,//252 +MOCAPNET_3DPOINT___TEMPORALIS01_LY,//253 +MOCAPNET_3DPOINT___TEMPORALIS01_LZ,//254 +MOCAPNET_3DPOINT_TEMPORALIS01_LX,//255 +MOCAPNET_3DPOINT_TEMPORALIS01_LY,//256 +MOCAPNET_3DPOINT_TEMPORALIS01_LZ,//257 +MOCAPNET_3DPOINT_OCULI02_LX,//258 +MOCAPNET_3DPOINT_OCULI02_LY,//259 +MOCAPNET_3DPOINT_OCULI02_LZ,//260 +MOCAPNET_3DPOINT_OCULI01_LX,//261 +MOCAPNET_3DPOINT_OCULI01_LY,//262 +MOCAPNET_3DPOINT_OCULI01_LZ,//263 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_LX,//264 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_LY,//265 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_LZ,//266 +MOCAPNET_3DPOINT___TEMPORALIS01_RX,//267 +MOCAPNET_3DPOINT___TEMPORALIS01_RY,//268 +MOCAPNET_3DPOINT___TEMPORALIS01_RZ,//269 +MOCAPNET_3DPOINT_TEMPORALIS01_RX,//270 +MOCAPNET_3DPOINT_TEMPORALIS01_RY,//271 +MOCAPNET_3DPOINT_TEMPORALIS01_RZ,//272 +MOCAPNET_3DPOINT_OCULI02_RX,//273 +MOCAPNET_3DPOINT_OCULI02_RY,//274 +MOCAPNET_3DPOINT_OCULI02_RZ,//275 +MOCAPNET_3DPOINT_OCULI01_RX,//276 +MOCAPNET_3DPOINT_OCULI01_RY,//277 +MOCAPNET_3DPOINT_OCULI01_RZ,//278 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_RX,//279 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_RY,//280 +MOCAPNET_3DPOINT_ENDSITE_OCULI01_RZ,//281 +MOCAPNET_3DPOINT___TEMPORALIS02_LX,//282 +MOCAPNET_3DPOINT___TEMPORALIS02_LY,//283 +MOCAPNET_3DPOINT___TEMPORALIS02_LZ,//284 +MOCAPNET_3DPOINT_TEMPORALIS02_LX,//285 +MOCAPNET_3DPOINT_TEMPORALIS02_LY,//286 +MOCAPNET_3DPOINT_TEMPORALIS02_LZ,//287 +MOCAPNET_3DPOINT_RISORIUS02_LX,//288 +MOCAPNET_3DPOINT_RISORIUS02_LY,//289 +MOCAPNET_3DPOINT_RISORIUS02_LZ,//290 +MOCAPNET_3DPOINT_RISORIUS03_LX,//291 +MOCAPNET_3DPOINT_RISORIUS03_LY,//292 +MOCAPNET_3DPOINT_RISORIUS03_LZ,//293 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_LX,//294 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_LY,//295 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_LZ,//296 +MOCAPNET_3DPOINT___TEMPORALIS02_RX,//297 +MOCAPNET_3DPOINT___TEMPORALIS02_RY,//298 +MOCAPNET_3DPOINT___TEMPORALIS02_RZ,//299 +MOCAPNET_3DPOINT_TEMPORALIS02_RX,//300 +MOCAPNET_3DPOINT_TEMPORALIS02_RY,//301 +MOCAPNET_3DPOINT_TEMPORALIS02_RZ,//302 +MOCAPNET_3DPOINT_RISORIUS02_RX,//303 +MOCAPNET_3DPOINT_RISORIUS02_RY,//304 +MOCAPNET_3DPOINT_RISORIUS02_RZ,//305 +MOCAPNET_3DPOINT_RISORIUS03_RX,//306 +MOCAPNET_3DPOINT_RISORIUS03_RY,//307 +MOCAPNET_3DPOINT_RISORIUS03_RZ,//308 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_RX,//309 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_RY,//310 +MOCAPNET_3DPOINT_ENDSITE_RISORIUS03_RZ,//311 +MOCAPNET_3DPOINT_RCOLLARX,//312 +MOCAPNET_3DPOINT_RCOLLARY,//313 +MOCAPNET_3DPOINT_RCOLLARZ,//314 +MOCAPNET_3DPOINT_RSHOULDERX,//315 +MOCAPNET_3DPOINT_RSHOULDERY,//316 +MOCAPNET_3DPOINT_RSHOULDERZ,//317 +MOCAPNET_3DPOINT_RELBOWX,//318 +MOCAPNET_3DPOINT_RELBOWY,//319 +MOCAPNET_3DPOINT_RELBOWZ,//320 +MOCAPNET_3DPOINT_RHANDX,//321 +MOCAPNET_3DPOINT_RHANDY,//322 +MOCAPNET_3DPOINT_RHANDZ,//323 +MOCAPNET_3DPOINT_METACARPAL1_RX,//324 +MOCAPNET_3DPOINT_METACARPAL1_RY,//325 +MOCAPNET_3DPOINT_METACARPAL1_RZ,//326 +MOCAPNET_3DPOINT_FINGER2_1_RX,//327 +MOCAPNET_3DPOINT_FINGER2_1_RY,//328 +MOCAPNET_3DPOINT_FINGER2_1_RZ,//329 +MOCAPNET_3DPOINT_FINGER2_2_RX,//330 +MOCAPNET_3DPOINT_FINGER2_2_RY,//331 +MOCAPNET_3DPOINT_FINGER2_2_RZ,//332 +MOCAPNET_3DPOINT_FINGER2_3_RX,//333 +MOCAPNET_3DPOINT_FINGER2_3_RY,//334 +MOCAPNET_3DPOINT_FINGER2_3_RZ,//335 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_RX,//336 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_RY,//337 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_RZ,//338 +MOCAPNET_3DPOINT_METACARPAL2_RX,//339 +MOCAPNET_3DPOINT_METACARPAL2_RY,//340 +MOCAPNET_3DPOINT_METACARPAL2_RZ,//341 +MOCAPNET_3DPOINT_FINGER3_1_RX,//342 +MOCAPNET_3DPOINT_FINGER3_1_RY,//343 +MOCAPNET_3DPOINT_FINGER3_1_RZ,//344 +MOCAPNET_3DPOINT_FINGER3_2_RX,//345 +MOCAPNET_3DPOINT_FINGER3_2_RY,//346 +MOCAPNET_3DPOINT_FINGER3_2_RZ,//347 +MOCAPNET_3DPOINT_FINGER3_3_RX,//348 +MOCAPNET_3DPOINT_FINGER3_3_RY,//349 +MOCAPNET_3DPOINT_FINGER3_3_RZ,//350 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_RX,//351 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_RY,//352 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_RZ,//353 +MOCAPNET_3DPOINT___METACARPAL3_RX,//354 +MOCAPNET_3DPOINT___METACARPAL3_RY,//355 +MOCAPNET_3DPOINT___METACARPAL3_RZ,//356 +MOCAPNET_3DPOINT_METACARPAL3_RX,//357 +MOCAPNET_3DPOINT_METACARPAL3_RY,//358 +MOCAPNET_3DPOINT_METACARPAL3_RZ,//359 +MOCAPNET_3DPOINT_FINGER4_1_RX,//360 +MOCAPNET_3DPOINT_FINGER4_1_RY,//361 +MOCAPNET_3DPOINT_FINGER4_1_RZ,//362 +MOCAPNET_3DPOINT_FINGER4_2_RX,//363 +MOCAPNET_3DPOINT_FINGER4_2_RY,//364 +MOCAPNET_3DPOINT_FINGER4_2_RZ,//365 +MOCAPNET_3DPOINT_FINGER4_3_RX,//366 +MOCAPNET_3DPOINT_FINGER4_3_RY,//367 +MOCAPNET_3DPOINT_FINGER4_3_RZ,//368 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_RX,//369 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_RY,//370 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_RZ,//371 +MOCAPNET_3DPOINT___METACARPAL4_RX,//372 +MOCAPNET_3DPOINT___METACARPAL4_RY,//373 +MOCAPNET_3DPOINT___METACARPAL4_RZ,//374 +MOCAPNET_3DPOINT_METACARPAL4_RX,//375 +MOCAPNET_3DPOINT_METACARPAL4_RY,//376 +MOCAPNET_3DPOINT_METACARPAL4_RZ,//377 +MOCAPNET_3DPOINT_FINGER5_1_RX,//378 +MOCAPNET_3DPOINT_FINGER5_1_RY,//379 +MOCAPNET_3DPOINT_FINGER5_1_RZ,//380 +MOCAPNET_3DPOINT_FINGER5_2_RX,//381 +MOCAPNET_3DPOINT_FINGER5_2_RY,//382 +MOCAPNET_3DPOINT_FINGER5_2_RZ,//383 +MOCAPNET_3DPOINT_FINGER5_3_RX,//384 +MOCAPNET_3DPOINT_FINGER5_3_RY,//385 +MOCAPNET_3DPOINT_FINGER5_3_RZ,//386 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_RX,//387 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_RY,//388 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_RZ,//389 +MOCAPNET_3DPOINT_RTHUMBBASEX,//390 +MOCAPNET_3DPOINT_RTHUMBBASEY,//391 +MOCAPNET_3DPOINT_RTHUMBBASEZ,//392 +MOCAPNET_3DPOINT_RTHUMBX,//393 +MOCAPNET_3DPOINT_RTHUMBY,//394 +MOCAPNET_3DPOINT_RTHUMBZ,//395 +MOCAPNET_3DPOINT_FINGER1_2_RX,//396 +MOCAPNET_3DPOINT_FINGER1_2_RY,//397 +MOCAPNET_3DPOINT_FINGER1_2_RZ,//398 +MOCAPNET_3DPOINT_FINGER1_3_RX,//399 +MOCAPNET_3DPOINT_FINGER1_3_RY,//400 +MOCAPNET_3DPOINT_FINGER1_3_RZ,//401 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_RX,//402 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_RY,//403 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_RZ,//404 +MOCAPNET_3DPOINT_LCOLLARX,//405 +MOCAPNET_3DPOINT_LCOLLARY,//406 +MOCAPNET_3DPOINT_LCOLLARZ,//407 +MOCAPNET_3DPOINT_LSHOULDERX,//408 +MOCAPNET_3DPOINT_LSHOULDERY,//409 +MOCAPNET_3DPOINT_LSHOULDERZ,//410 +MOCAPNET_3DPOINT_LELBOWX,//411 +MOCAPNET_3DPOINT_LELBOWY,//412 +MOCAPNET_3DPOINT_LELBOWZ,//413 +MOCAPNET_3DPOINT_LHANDX,//414 +MOCAPNET_3DPOINT_LHANDY,//415 +MOCAPNET_3DPOINT_LHANDZ,//416 +MOCAPNET_3DPOINT_METACARPAL1_LX,//417 +MOCAPNET_3DPOINT_METACARPAL1_LY,//418 +MOCAPNET_3DPOINT_METACARPAL1_LZ,//419 +MOCAPNET_3DPOINT_FINGER2_1_LX,//420 +MOCAPNET_3DPOINT_FINGER2_1_LY,//421 +MOCAPNET_3DPOINT_FINGER2_1_LZ,//422 +MOCAPNET_3DPOINT_FINGER2_2_LX,//423 +MOCAPNET_3DPOINT_FINGER2_2_LY,//424 +MOCAPNET_3DPOINT_FINGER2_2_LZ,//425 +MOCAPNET_3DPOINT_FINGER2_3_LX,//426 +MOCAPNET_3DPOINT_FINGER2_3_LY,//427 +MOCAPNET_3DPOINT_FINGER2_3_LZ,//428 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_LX,//429 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_LY,//430 +MOCAPNET_3DPOINT_ENDSITE_FINGER2_3_LZ,//431 +MOCAPNET_3DPOINT_METACARPAL2_LX,//432 +MOCAPNET_3DPOINT_METACARPAL2_LY,//433 +MOCAPNET_3DPOINT_METACARPAL2_LZ,//434 +MOCAPNET_3DPOINT_FINGER3_1_LX,//435 +MOCAPNET_3DPOINT_FINGER3_1_LY,//436 +MOCAPNET_3DPOINT_FINGER3_1_LZ,//437 +MOCAPNET_3DPOINT_FINGER3_2_LX,//438 +MOCAPNET_3DPOINT_FINGER3_2_LY,//439 +MOCAPNET_3DPOINT_FINGER3_2_LZ,//440 +MOCAPNET_3DPOINT_FINGER3_3_LX,//441 +MOCAPNET_3DPOINT_FINGER3_3_LY,//442 +MOCAPNET_3DPOINT_FINGER3_3_LZ,//443 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_LX,//444 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_LY,//445 +MOCAPNET_3DPOINT_ENDSITE_FINGER3_3_LZ,//446 +MOCAPNET_3DPOINT___METACARPAL3_LX,//447 +MOCAPNET_3DPOINT___METACARPAL3_LY,//448 +MOCAPNET_3DPOINT___METACARPAL3_LZ,//449 +MOCAPNET_3DPOINT_METACARPAL3_LX,//450 +MOCAPNET_3DPOINT_METACARPAL3_LY,//451 +MOCAPNET_3DPOINT_METACARPAL3_LZ,//452 +MOCAPNET_3DPOINT_FINGER4_1_LX,//453 +MOCAPNET_3DPOINT_FINGER4_1_LY,//454 +MOCAPNET_3DPOINT_FINGER4_1_LZ,//455 +MOCAPNET_3DPOINT_FINGER4_2_LX,//456 +MOCAPNET_3DPOINT_FINGER4_2_LY,//457 +MOCAPNET_3DPOINT_FINGER4_2_LZ,//458 +MOCAPNET_3DPOINT_FINGER4_3_LX,//459 +MOCAPNET_3DPOINT_FINGER4_3_LY,//460 +MOCAPNET_3DPOINT_FINGER4_3_LZ,//461 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_LX,//462 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_LY,//463 +MOCAPNET_3DPOINT_ENDSITE_FINGER4_3_LZ,//464 +MOCAPNET_3DPOINT___METACARPAL4_LX,//465 +MOCAPNET_3DPOINT___METACARPAL4_LY,//466 +MOCAPNET_3DPOINT___METACARPAL4_LZ,//467 +MOCAPNET_3DPOINT_METACARPAL4_LX,//468 +MOCAPNET_3DPOINT_METACARPAL4_LY,//469 +MOCAPNET_3DPOINT_METACARPAL4_LZ,//470 +MOCAPNET_3DPOINT_FINGER5_1_LX,//471 +MOCAPNET_3DPOINT_FINGER5_1_LY,//472 +MOCAPNET_3DPOINT_FINGER5_1_LZ,//473 +MOCAPNET_3DPOINT_FINGER5_2_LX,//474 +MOCAPNET_3DPOINT_FINGER5_2_LY,//475 +MOCAPNET_3DPOINT_FINGER5_2_LZ,//476 +MOCAPNET_3DPOINT_FINGER5_3_LX,//477 +MOCAPNET_3DPOINT_FINGER5_3_LY,//478 +MOCAPNET_3DPOINT_FINGER5_3_LZ,//479 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_LX,//480 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_LY,//481 +MOCAPNET_3DPOINT_ENDSITE_FINGER5_3_LZ,//482 +MOCAPNET_3DPOINT_LTHUMBBASEX,//483 +MOCAPNET_3DPOINT_LTHUMBBASEY,//484 +MOCAPNET_3DPOINT_LTHUMBBASEZ,//485 +MOCAPNET_3DPOINT_LTHUMBX,//486 +MOCAPNET_3DPOINT_LTHUMBY,//487 +MOCAPNET_3DPOINT_LTHUMBZ,//488 +MOCAPNET_3DPOINT_FINGER1_2_LX,//489 +MOCAPNET_3DPOINT_FINGER1_2_LY,//490 +MOCAPNET_3DPOINT_FINGER1_2_LZ,//491 +MOCAPNET_3DPOINT_FINGER1_3_LX,//492 +MOCAPNET_3DPOINT_FINGER1_3_LY,//493 +MOCAPNET_3DPOINT_FINGER1_3_LZ,//494 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_LX,//495 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_LY,//496 +MOCAPNET_3DPOINT_ENDSITE_FINGER1_3_LZ,//497 +MOCAPNET_3DPOINT_RBUTTOCKX,//498 +MOCAPNET_3DPOINT_RBUTTOCKY,//499 +MOCAPNET_3DPOINT_RBUTTOCKZ,//500 +MOCAPNET_3DPOINT_RHIPX,//501 +MOCAPNET_3DPOINT_RHIPY,//502 +MOCAPNET_3DPOINT_RHIPZ,//503 +MOCAPNET_3DPOINT_RKNEEX,//504 +MOCAPNET_3DPOINT_RKNEEY,//505 +MOCAPNET_3DPOINT_RKNEEZ,//506 +MOCAPNET_3DPOINT_RFOOTX,//507 +MOCAPNET_3DPOINT_RFOOTY,//508 +MOCAPNET_3DPOINT_RFOOTZ,//509 +MOCAPNET_3DPOINT_TOE1_1_RX,//510 +MOCAPNET_3DPOINT_TOE1_1_RY,//511 +MOCAPNET_3DPOINT_TOE1_1_RZ,//512 +MOCAPNET_3DPOINT_TOE1_2_RX,//513 +MOCAPNET_3DPOINT_TOE1_2_RY,//514 +MOCAPNET_3DPOINT_TOE1_2_RZ,//515 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_RX,//516 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_RY,//517 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_RZ,//518 +MOCAPNET_3DPOINT_TOE2_1_RX,//519 +MOCAPNET_3DPOINT_TOE2_1_RY,//520 +MOCAPNET_3DPOINT_TOE2_1_RZ,//521 +MOCAPNET_3DPOINT_TOE2_2_RX,//522 +MOCAPNET_3DPOINT_TOE2_2_RY,//523 +MOCAPNET_3DPOINT_TOE2_2_RZ,//524 +MOCAPNET_3DPOINT_TOE2_3_RX,//525 +MOCAPNET_3DPOINT_TOE2_3_RY,//526 +MOCAPNET_3DPOINT_TOE2_3_RZ,//527 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_RX,//528 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_RY,//529 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_RZ,//530 +MOCAPNET_3DPOINT_TOE3_1_RX,//531 +MOCAPNET_3DPOINT_TOE3_1_RY,//532 +MOCAPNET_3DPOINT_TOE3_1_RZ,//533 +MOCAPNET_3DPOINT_TOE3_2_RX,//534 +MOCAPNET_3DPOINT_TOE3_2_RY,//535 +MOCAPNET_3DPOINT_TOE3_2_RZ,//536 +MOCAPNET_3DPOINT_TOE3_3_RX,//537 +MOCAPNET_3DPOINT_TOE3_3_RY,//538 +MOCAPNET_3DPOINT_TOE3_3_RZ,//539 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_RX,//540 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_RY,//541 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_RZ,//542 +MOCAPNET_3DPOINT_TOE4_1_RX,//543 +MOCAPNET_3DPOINT_TOE4_1_RY,//544 +MOCAPNET_3DPOINT_TOE4_1_RZ,//545 +MOCAPNET_3DPOINT_TOE4_2_RX,//546 +MOCAPNET_3DPOINT_TOE4_2_RY,//547 +MOCAPNET_3DPOINT_TOE4_2_RZ,//548 +MOCAPNET_3DPOINT_TOE4_3_RX,//549 +MOCAPNET_3DPOINT_TOE4_3_RY,//550 +MOCAPNET_3DPOINT_TOE4_3_RZ,//551 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_RX,//552 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_RY,//553 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_RZ,//554 +MOCAPNET_3DPOINT_TOE5_1_RX,//555 +MOCAPNET_3DPOINT_TOE5_1_RY,//556 +MOCAPNET_3DPOINT_TOE5_1_RZ,//557 +MOCAPNET_3DPOINT_TOE5_2_RX,//558 +MOCAPNET_3DPOINT_TOE5_2_RY,//559 +MOCAPNET_3DPOINT_TOE5_2_RZ,//560 +MOCAPNET_3DPOINT_TOE5_3_RX,//561 +MOCAPNET_3DPOINT_TOE5_3_RY,//562 +MOCAPNET_3DPOINT_TOE5_3_RZ,//563 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_RX,//564 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_RY,//565 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_RZ,//566 +MOCAPNET_3DPOINT_LBUTTOCKX,//567 +MOCAPNET_3DPOINT_LBUTTOCKY,//568 +MOCAPNET_3DPOINT_LBUTTOCKZ,//569 +MOCAPNET_3DPOINT_LHIPX,//570 +MOCAPNET_3DPOINT_LHIPY,//571 +MOCAPNET_3DPOINT_LHIPZ,//572 +MOCAPNET_3DPOINT_LKNEEX,//573 +MOCAPNET_3DPOINT_LKNEEY,//574 +MOCAPNET_3DPOINT_LKNEEZ,//575 +MOCAPNET_3DPOINT_LFOOTX,//576 +MOCAPNET_3DPOINT_LFOOTY,//577 +MOCAPNET_3DPOINT_LFOOTZ,//578 +MOCAPNET_3DPOINT_TOE1_1_LX,//579 +MOCAPNET_3DPOINT_TOE1_1_LY,//580 +MOCAPNET_3DPOINT_TOE1_1_LZ,//581 +MOCAPNET_3DPOINT_TOE1_2_LX,//582 +MOCAPNET_3DPOINT_TOE1_2_LY,//583 +MOCAPNET_3DPOINT_TOE1_2_LZ,//584 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_LX,//585 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_LY,//586 +MOCAPNET_3DPOINT_ENDSITE_TOE1_2_LZ,//587 +MOCAPNET_3DPOINT_TOE2_1_LX,//588 +MOCAPNET_3DPOINT_TOE2_1_LY,//589 +MOCAPNET_3DPOINT_TOE2_1_LZ,//590 +MOCAPNET_3DPOINT_TOE2_2_LX,//591 +MOCAPNET_3DPOINT_TOE2_2_LY,//592 +MOCAPNET_3DPOINT_TOE2_2_LZ,//593 +MOCAPNET_3DPOINT_TOE2_3_LX,//594 +MOCAPNET_3DPOINT_TOE2_3_LY,//595 +MOCAPNET_3DPOINT_TOE2_3_LZ,//596 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_LX,//597 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_LY,//598 +MOCAPNET_3DPOINT_ENDSITE_TOE2_3_LZ,//599 +MOCAPNET_3DPOINT_TOE3_1_LX,//600 +MOCAPNET_3DPOINT_TOE3_1_LY,//601 +MOCAPNET_3DPOINT_TOE3_1_LZ,//602 +MOCAPNET_3DPOINT_TOE3_2_LX,//603 +MOCAPNET_3DPOINT_TOE3_2_LY,//604 +MOCAPNET_3DPOINT_TOE3_2_LZ,//605 +MOCAPNET_3DPOINT_TOE3_3_LX,//606 +MOCAPNET_3DPOINT_TOE3_3_LY,//607 +MOCAPNET_3DPOINT_TOE3_3_LZ,//608 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_LX,//609 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_LY,//610 +MOCAPNET_3DPOINT_ENDSITE_TOE3_3_LZ,//611 +MOCAPNET_3DPOINT_TOE4_1_LX,//612 +MOCAPNET_3DPOINT_TOE4_1_LY,//613 +MOCAPNET_3DPOINT_TOE4_1_LZ,//614 +MOCAPNET_3DPOINT_TOE4_2_LX,//615 +MOCAPNET_3DPOINT_TOE4_2_LY,//616 +MOCAPNET_3DPOINT_TOE4_2_LZ,//617 +MOCAPNET_3DPOINT_TOE4_3_LX,//618 +MOCAPNET_3DPOINT_TOE4_3_LY,//619 +MOCAPNET_3DPOINT_TOE4_3_LZ,//620 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_LX,//621 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_LY,//622 +MOCAPNET_3DPOINT_ENDSITE_TOE4_3_LZ,//623 +MOCAPNET_3DPOINT_TOE5_1_LX,//624 +MOCAPNET_3DPOINT_TOE5_1_LY,//625 +MOCAPNET_3DPOINT_TOE5_1_LZ,//626 +MOCAPNET_3DPOINT_TOE5_2_LX,//627 +MOCAPNET_3DPOINT_TOE5_2_LY,//628 +MOCAPNET_3DPOINT_TOE5_2_LZ,//629 +MOCAPNET_3DPOINT_TOE5_3_LX,//630 +MOCAPNET_3DPOINT_TOE5_3_LY,//631 +MOCAPNET_3DPOINT_TOE5_3_LZ,//632 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_LX,//633 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_LY,//634 +MOCAPNET_3DPOINT_ENDSITE_TOE5_3_LZ,//635 +//------------------------------------------------------------------- +MOCAPNET_3DPOINT_NUMBER +}; + + + + +/** + * @brief An array with BVH string labels + */ +static const char * MocapNET3DPositionalOutputArrayNames[] = +{ +"hip_Xposition", // 0 +"hip_Yposition", // 1 +"hip_Zposition", // 2 +"abdomen_Xposition", // 3 +"abdomen_Yposition", // 4 +"abdomen_Zposition", // 5 +"chest_Xposition", // 6 +"chest_Yposition", // 7 +"chest_Zposition", // 8 +"neck_Xposition", // 9 +"neck_Yposition", // 10 +"neck_Zposition", // 11 +"neck1_Xposition", // 12 +"neck1_Yposition", // 13 +"neck1_Zposition", // 14 +"head_Xposition", // 15 +"head_Yposition", // 16 +"head_Zposition", // 17 +"__jaw_Xposition", // 18 +"__jaw_Yposition", // 19 +"__jaw_Zposition", // 20 +"jaw_Xposition", // 21 +"jaw_Yposition", // 22 +"jaw_Zposition", // 23 +"special04_Xposition", // 24 +"special04_Yposition", // 25 +"special04_Zposition", // 26 +"oris02_Xposition", // 27 +"oris02_Yposition", // 28 +"oris02_Zposition", // 29 +"oris01_Xposition", // 30 +"oris01_Yposition", // 31 +"oris01_Zposition", // 32 +"endsite_oris01_Xposition", // 33 +"endsite_oris01_Yposition", // 34 +"endsite_oris01_Zposition", // 35 +"oris06.l_Xposition", // 36 +"oris06.l_Yposition", // 37 +"oris06.l_Zposition", // 38 +"oris07.l_Xposition", // 39 +"oris07.l_Yposition", // 40 +"oris07.l_Zposition", // 41 +"endsite_oris07.l_Xposition", // 42 +"endsite_oris07.l_Yposition", // 43 +"endsite_oris07.l_Zposition", // 44 +"oris06.r_Xposition", // 45 +"oris06.r_Yposition", // 46 +"oris06.r_Zposition", // 47 +"oris07.r_Xposition", // 48 +"oris07.r_Yposition", // 49 +"oris07.r_Zposition", // 50 +"endsite_oris07.r_Xposition", // 51 +"endsite_oris07.r_Yposition", // 52 +"endsite_oris07.r_Zposition", // 53 +"tongue00_Xposition", // 54 +"tongue00_Yposition", // 55 +"tongue00_Zposition", // 56 +"tongue01_Xposition", // 57 +"tongue01_Yposition", // 58 +"tongue01_Zposition", // 59 +"tongue02_Xposition", // 60 +"tongue02_Yposition", // 61 +"tongue02_Zposition", // 62 +"tongue03_Xposition", // 63 +"tongue03_Yposition", // 64 +"tongue03_Zposition", // 65 +"__tongue04_Xposition", // 66 +"__tongue04_Yposition", // 67 +"__tongue04_Zposition", // 68 +"tongue04_Xposition", // 69 +"tongue04_Yposition", // 70 +"tongue04_Zposition", // 71 +"endsite_tongue04_Xposition", // 72 +"endsite_tongue04_Yposition", // 73 +"endsite_tongue04_Zposition", // 74 +"tongue07.l_Xposition", // 75 +"tongue07.l_Yposition", // 76 +"tongue07.l_Zposition", // 77 +"endsite_tongue07.l_Xposition", // 78 +"endsite_tongue07.l_Yposition", // 79 +"endsite_tongue07.l_Zposition", // 80 +"tongue07.r_Xposition", // 81 +"tongue07.r_Yposition", // 82 +"tongue07.r_Zposition", // 83 +"endsite_tongue07.r_Xposition", // 84 +"endsite_tongue07.r_Yposition", // 85 +"endsite_tongue07.r_Zposition", // 86 +"tongue06.l_Xposition", // 87 +"tongue06.l_Yposition", // 88 +"tongue06.l_Zposition", // 89 +"endsite_tongue06.l_Xposition", // 90 +"endsite_tongue06.l_Yposition", // 91 +"endsite_tongue06.l_Zposition", // 92 +"tongue06.r_Xposition", // 93 +"tongue06.r_Yposition", // 94 +"tongue06.r_Zposition", // 95 +"endsite_tongue06.r_Xposition", // 96 +"endsite_tongue06.r_Yposition", // 97 +"endsite_tongue06.r_Zposition", // 98 +"tongue05.l_Xposition", // 99 +"tongue05.l_Yposition", // 100 +"tongue05.l_Zposition", // 101 +"endsite_tongue05.l_Xposition", // 102 +"endsite_tongue05.l_Yposition", // 103 +"endsite_tongue05.l_Zposition", // 104 +"tongue05.r_Xposition", // 105 +"tongue05.r_Yposition", // 106 +"tongue05.r_Zposition", // 107 +"endsite_tongue05.r_Xposition", // 108 +"endsite_tongue05.r_Yposition", // 109 +"endsite_tongue05.r_Zposition", // 110 +"__levator02.l_Xposition", // 111 +"__levator02.l_Yposition", // 112 +"__levator02.l_Zposition", // 113 +"levator02.l_Xposition", // 114 +"levator02.l_Yposition", // 115 +"levator02.l_Zposition", // 116 +"levator03.l_Xposition", // 117 +"levator03.l_Yposition", // 118 +"levator03.l_Zposition", // 119 +"levator04.l_Xposition", // 120 +"levator04.l_Yposition", // 121 +"levator04.l_Zposition", // 122 +"levator05.l_Xposition", // 123 +"levator05.l_Yposition", // 124 +"levator05.l_Zposition", // 125 +"endsite_levator05.l_Xposition", // 126 +"endsite_levator05.l_Yposition", // 127 +"endsite_levator05.l_Zposition", // 128 +"__levator02.r_Xposition", // 129 +"__levator02.r_Yposition", // 130 +"__levator02.r_Zposition", // 131 +"levator02.r_Xposition", // 132 +"levator02.r_Yposition", // 133 +"levator02.r_Zposition", // 134 +"levator03.r_Xposition", // 135 +"levator03.r_Yposition", // 136 +"levator03.r_Zposition", // 137 +"levator04.r_Xposition", // 138 +"levator04.r_Yposition", // 139 +"levator04.r_Zposition", // 140 +"levator05.r_Xposition", // 141 +"levator05.r_Yposition", // 142 +"levator05.r_Zposition", // 143 +"endsite_levator05.r_Xposition", // 144 +"endsite_levator05.r_Yposition", // 145 +"endsite_levator05.r_Zposition", // 146 +"__special01_Xposition", // 147 +"__special01_Yposition", // 148 +"__special01_Zposition", // 149 +"special01_Xposition", // 150 +"special01_Yposition", // 151 +"special01_Zposition", // 152 +"oris04.l_Xposition", // 153 +"oris04.l_Yposition", // 154 +"oris04.l_Zposition", // 155 +"oris03.l_Xposition", // 156 +"oris03.l_Yposition", // 157 +"oris03.l_Zposition", // 158 +"endsite_oris03.l_Xposition", // 159 +"endsite_oris03.l_Yposition", // 160 +"endsite_oris03.l_Zposition", // 161 +"oris04.r_Xposition", // 162 +"oris04.r_Yposition", // 163 +"oris04.r_Zposition", // 164 +"oris03.r_Xposition", // 165 +"oris03.r_Yposition", // 166 +"oris03.r_Zposition", // 167 +"endsite_oris03.r_Xposition", // 168 +"endsite_oris03.r_Yposition", // 169 +"endsite_oris03.r_Zposition", // 170 +"oris06_Xposition", // 171 +"oris06_Yposition", // 172 +"oris06_Zposition", // 173 +"oris05_Xposition", // 174 +"oris05_Yposition", // 175 +"oris05_Zposition", // 176 +"endsite_oris05_Xposition", // 177 +"endsite_oris05_Yposition", // 178 +"endsite_oris05_Zposition", // 179 +"__special03_Xposition", // 180 +"__special03_Yposition", // 181 +"__special03_Zposition", // 182 +"special03_Xposition", // 183 +"special03_Yposition", // 184 +"special03_Zposition", // 185 +"__levator06.l_Xposition", // 186 +"__levator06.l_Yposition", // 187 +"__levator06.l_Zposition", // 188 +"levator06.l_Xposition", // 189 +"levator06.l_Yposition", // 190 +"levator06.l_Zposition", // 191 +"endsite_levator06.l_Xposition", // 192 +"endsite_levator06.l_Yposition", // 193 +"endsite_levator06.l_Zposition", // 194 +"__levator06.r_Xposition", // 195 +"__levator06.r_Yposition", // 196 +"__levator06.r_Zposition", // 197 +"levator06.r_Xposition", // 198 +"levator06.r_Yposition", // 199 +"levator06.r_Zposition", // 200 +"endsite_levator06.r_Xposition", // 201 +"endsite_levator06.r_Yposition", // 202 +"endsite_levator06.r_Zposition", // 203 +"special06.l_Xposition", // 204 +"special06.l_Yposition", // 205 +"special06.l_Zposition", // 206 +"special05.l_Xposition", // 207 +"special05.l_Yposition", // 208 +"special05.l_Zposition", // 209 +"eye.l_Xposition", // 210 +"eye.l_Yposition", // 211 +"eye.l_Zposition", // 212 +"endsite_eye.l_Xposition", // 213 +"endsite_eye.l_Yposition", // 214 +"endsite_eye.l_Zposition", // 215 +"orbicularis03.l_Xposition", // 216 +"orbicularis03.l_Yposition", // 217 +"orbicularis03.l_Zposition", // 218 +"endsite_orbicularis03.l_Xposition", // 219 +"endsite_orbicularis03.l_Yposition", // 220 +"endsite_orbicularis03.l_Zposition", // 221 +"orbicularis04.l_Xposition", // 222 +"orbicularis04.l_Yposition", // 223 +"orbicularis04.l_Zposition", // 224 +"endsite_orbicularis04.l_Xposition", // 225 +"endsite_orbicularis04.l_Yposition", // 226 +"endsite_orbicularis04.l_Zposition", // 227 +"special06.r_Xposition", // 228 +"special06.r_Yposition", // 229 +"special06.r_Zposition", // 230 +"special05.r_Xposition", // 231 +"special05.r_Yposition", // 232 +"special05.r_Zposition", // 233 +"eye.r_Xposition", // 234 +"eye.r_Yposition", // 235 +"eye.r_Zposition", // 236 +"endsite_eye.r_Xposition", // 237 +"endsite_eye.r_Yposition", // 238 +"endsite_eye.r_Zposition", // 239 +"orbicularis03.r_Xposition", // 240 +"orbicularis03.r_Yposition", // 241 +"orbicularis03.r_Zposition", // 242 +"endsite_orbicularis03.r_Xposition", // 243 +"endsite_orbicularis03.r_Yposition", // 244 +"endsite_orbicularis03.r_Zposition", // 245 +"orbicularis04.r_Xposition", // 246 +"orbicularis04.r_Yposition", // 247 +"orbicularis04.r_Zposition", // 248 +"endsite_orbicularis04.r_Xposition", // 249 +"endsite_orbicularis04.r_Yposition", // 250 +"endsite_orbicularis04.r_Zposition", // 251 +"__temporalis01.l_Xposition", // 252 +"__temporalis01.l_Yposition", // 253 +"__temporalis01.l_Zposition", // 254 +"temporalis01.l_Xposition", // 255 +"temporalis01.l_Yposition", // 256 +"temporalis01.l_Zposition", // 257 +"oculi02.l_Xposition", // 258 +"oculi02.l_Yposition", // 259 +"oculi02.l_Zposition", // 260 +"oculi01.l_Xposition", // 261 +"oculi01.l_Yposition", // 262 +"oculi01.l_Zposition", // 263 +"endsite_oculi01.l_Xposition", // 264 +"endsite_oculi01.l_Yposition", // 265 +"endsite_oculi01.l_Zposition", // 266 +"__temporalis01.r_Xposition", // 267 +"__temporalis01.r_Yposition", // 268 +"__temporalis01.r_Zposition", // 269 +"temporalis01.r_Xposition", // 270 +"temporalis01.r_Yposition", // 271 +"temporalis01.r_Zposition", // 272 +"oculi02.r_Xposition", // 273 +"oculi02.r_Yposition", // 274 +"oculi02.r_Zposition", // 275 +"oculi01.r_Xposition", // 276 +"oculi01.r_Yposition", // 277 +"oculi01.r_Zposition", // 278 +"endsite_oculi01.r_Xposition", // 279 +"endsite_oculi01.r_Yposition", // 280 +"endsite_oculi01.r_Zposition", // 281 +"__temporalis02.l_Xposition", // 282 +"__temporalis02.l_Yposition", // 283 +"__temporalis02.l_Zposition", // 284 +"temporalis02.l_Xposition", // 285 +"temporalis02.l_Yposition", // 286 +"temporalis02.l_Zposition", // 287 +"risorius02.l_Xposition", // 288 +"risorius02.l_Yposition", // 289 +"risorius02.l_Zposition", // 290 +"risorius03.l_Xposition", // 291 +"risorius03.l_Yposition", // 292 +"risorius03.l_Zposition", // 293 +"endsite_risorius03.l_Xposition", // 294 +"endsite_risorius03.l_Yposition", // 295 +"endsite_risorius03.l_Zposition", // 296 +"__temporalis02.r_Xposition", // 297 +"__temporalis02.r_Yposition", // 298 +"__temporalis02.r_Zposition", // 299 +"temporalis02.r_Xposition", // 300 +"temporalis02.r_Yposition", // 301 +"temporalis02.r_Zposition", // 302 +"risorius02.r_Xposition", // 303 +"risorius02.r_Yposition", // 304 +"risorius02.r_Zposition", // 305 +"risorius03.r_Xposition", // 306 +"risorius03.r_Yposition", // 307 +"risorius03.r_Zposition", // 308 +"endsite_risorius03.r_Xposition", // 309 +"endsite_risorius03.r_Yposition", // 310 +"endsite_risorius03.r_Zposition", // 311 +"rcollar_Xposition", // 312 +"rcollar_Yposition", // 313 +"rcollar_Zposition", // 314 +"rshoulder_Xposition", // 315 +"rshoulder_Yposition", // 316 +"rshoulder_Zposition", // 317 +"relbow_Xposition", // 318 +"relbow_Yposition", // 319 +"relbow_Zposition", // 320 +"rhand_Xposition", // 321 +"rhand_Yposition", // 322 +"rhand_Zposition", // 323 +"metacarpal1.r_Xposition", // 324 +"metacarpal1.r_Yposition", // 325 +"metacarpal1.r_Zposition", // 326 +"finger2-1.r_Xposition", // 327 +"finger2-1.r_Yposition", // 328 +"finger2-1.r_Zposition", // 329 +"finger2-2.r_Xposition", // 330 +"finger2-2.r_Yposition", // 331 +"finger2-2.r_Zposition", // 332 +"finger2-3.r_Xposition", // 333 +"finger2-3.r_Yposition", // 334 +"finger2-3.r_Zposition", // 335 +"endsite_finger2-3.r_Xposition", // 336 +"endsite_finger2-3.r_Yposition", // 337 +"endsite_finger2-3.r_Zposition", // 338 +"metacarpal2.r_Xposition", // 339 +"metacarpal2.r_Yposition", // 340 +"metacarpal2.r_Zposition", // 341 +"finger3-1.r_Xposition", // 342 +"finger3-1.r_Yposition", // 343 +"finger3-1.r_Zposition", // 344 +"finger3-2.r_Xposition", // 345 +"finger3-2.r_Yposition", // 346 +"finger3-2.r_Zposition", // 347 +"finger3-3.r_Xposition", // 348 +"finger3-3.r_Yposition", // 349 +"finger3-3.r_Zposition", // 350 +"endsite_finger3-3.r_Xposition", // 351 +"endsite_finger3-3.r_Yposition", // 352 +"endsite_finger3-3.r_Zposition", // 353 +"__metacarpal3.r_Xposition", // 354 +"__metacarpal3.r_Yposition", // 355 +"__metacarpal3.r_Zposition", // 356 +"metacarpal3.r_Xposition", // 357 +"metacarpal3.r_Yposition", // 358 +"metacarpal3.r_Zposition", // 359 +"finger4-1.r_Xposition", // 360 +"finger4-1.r_Yposition", // 361 +"finger4-1.r_Zposition", // 362 +"finger4-2.r_Xposition", // 363 +"finger4-2.r_Yposition", // 364 +"finger4-2.r_Zposition", // 365 +"finger4-3.r_Xposition", // 366 +"finger4-3.r_Yposition", // 367 +"finger4-3.r_Zposition", // 368 +"endsite_finger4-3.r_Xposition", // 369 +"endsite_finger4-3.r_Yposition", // 370 +"endsite_finger4-3.r_Zposition", // 371 +"__metacarpal4.r_Xposition", // 372 +"__metacarpal4.r_Yposition", // 373 +"__metacarpal4.r_Zposition", // 374 +"metacarpal4.r_Xposition", // 375 +"metacarpal4.r_Yposition", // 376 +"metacarpal4.r_Zposition", // 377 +"finger5-1.r_Xposition", // 378 +"finger5-1.r_Yposition", // 379 +"finger5-1.r_Zposition", // 380 +"finger5-2.r_Xposition", // 381 +"finger5-2.r_Yposition", // 382 +"finger5-2.r_Zposition", // 383 +"finger5-3.r_Xposition", // 384 +"finger5-3.r_Yposition", // 385 +"finger5-3.r_Zposition", // 386 +"endsite_finger5-3.r_Xposition", // 387 +"endsite_finger5-3.r_Yposition", // 388 +"endsite_finger5-3.r_Zposition", // 389 +"rthumbBase_Xposition", // 390 +"rthumbBase_Yposition", // 391 +"rthumbBase_Zposition", // 392 +"rthumb_Xposition", // 393 +"rthumb_Yposition", // 394 +"rthumb_Zposition", // 395 +"finger1-2.r_Xposition", // 396 +"finger1-2.r_Yposition", // 397 +"finger1-2.r_Zposition", // 398 +"finger1-3.r_Xposition", // 399 +"finger1-3.r_Yposition", // 400 +"finger1-3.r_Zposition", // 401 +"endsite_finger1-3.r_Xposition", // 402 +"endsite_finger1-3.r_Yposition", // 403 +"endsite_finger1-3.r_Zposition", // 404 +"lcollar_Xposition", // 405 +"lcollar_Yposition", // 406 +"lcollar_Zposition", // 407 +"lshoulder_Xposition", // 408 +"lshoulder_Yposition", // 409 +"lshoulder_Zposition", // 410 +"lelbow_Xposition", // 411 +"lelbow_Yposition", // 412 +"lelbow_Zposition", // 413 +"lhand_Xposition", // 414 +"lhand_Yposition", // 415 +"lhand_Zposition", // 416 +"metacarpal1.l_Xposition", // 417 +"metacarpal1.l_Yposition", // 418 +"metacarpal1.l_Zposition", // 419 +"finger2-1.l_Xposition", // 420 +"finger2-1.l_Yposition", // 421 +"finger2-1.l_Zposition", // 422 +"finger2-2.l_Xposition", // 423 +"finger2-2.l_Yposition", // 424 +"finger2-2.l_Zposition", // 425 +"finger2-3.l_Xposition", // 426 +"finger2-3.l_Yposition", // 427 +"finger2-3.l_Zposition", // 428 +"endsite_finger2-3.l_Xposition", // 429 +"endsite_finger2-3.l_Yposition", // 430 +"endsite_finger2-3.l_Zposition", // 431 +"metacarpal2.l_Xposition", // 432 +"metacarpal2.l_Yposition", // 433 +"metacarpal2.l_Zposition", // 434 +"finger3-1.l_Xposition", // 435 +"finger3-1.l_Yposition", // 436 +"finger3-1.l_Zposition", // 437 +"finger3-2.l_Xposition", // 438 +"finger3-2.l_Yposition", // 439 +"finger3-2.l_Zposition", // 440 +"finger3-3.l_Xposition", // 441 +"finger3-3.l_Yposition", // 442 +"finger3-3.l_Zposition", // 443 +"endsite_finger3-3.l_Xposition", // 444 +"endsite_finger3-3.l_Yposition", // 445 +"endsite_finger3-3.l_Zposition", // 446 +"__metacarpal3.l_Xposition", // 447 +"__metacarpal3.l_Yposition", // 448 +"__metacarpal3.l_Zposition", // 449 +"metacarpal3.l_Xposition", // 450 +"metacarpal3.l_Yposition", // 451 +"metacarpal3.l_Zposition", // 452 +"finger4-1.l_Xposition", // 453 +"finger4-1.l_Yposition", // 454 +"finger4-1.l_Zposition", // 455 +"finger4-2.l_Xposition", // 456 +"finger4-2.l_Yposition", // 457 +"finger4-2.l_Zposition", // 458 +"finger4-3.l_Xposition", // 459 +"finger4-3.l_Yposition", // 460 +"finger4-3.l_Zposition", // 461 +"endsite_finger4-3.l_Xposition", // 462 +"endsite_finger4-3.l_Yposition", // 463 +"endsite_finger4-3.l_Zposition", // 464 +"__metacarpal4.l_Xposition", // 465 +"__metacarpal4.l_Yposition", // 466 +"__metacarpal4.l_Zposition", // 467 +"metacarpal4.l_Xposition", // 468 +"metacarpal4.l_Yposition", // 469 +"metacarpal4.l_Zposition", // 470 +"finger5-1.l_Xposition", // 471 +"finger5-1.l_Yposition", // 472 +"finger5-1.l_Zposition", // 473 +"finger5-2.l_Xposition", // 474 +"finger5-2.l_Yposition", // 475 +"finger5-2.l_Zposition", // 476 +"finger5-3.l_Xposition", // 477 +"finger5-3.l_Yposition", // 478 +"finger5-3.l_Zposition", // 479 +"endsite_finger5-3.l_Xposition", // 480 +"endsite_finger5-3.l_Yposition", // 481 +"endsite_finger5-3.l_Zposition", // 482 +"lthumbBase_Xposition", // 483 +"lthumbBase_Yposition", // 484 +"lthumbBase_Zposition", // 485 +"lthumb_Xposition", // 486 +"lthumb_Yposition", // 487 +"lthumb_Zposition", // 488 +"finger1-2.l_Xposition", // 489 +"finger1-2.l_Yposition", // 490 +"finger1-2.l_Zposition", // 491 +"finger1-3.l_Xposition", // 492 +"finger1-3.l_Yposition", // 493 +"finger1-3.l_Zposition", // 494 +"endsite_finger1-3.l_Xposition", // 495 +"endsite_finger1-3.l_Yposition", // 496 +"endsite_finger1-3.l_Zposition", // 497 +"rbuttock_Xposition", // 498 +"rbuttock_Yposition", // 499 +"rbuttock_Zposition", // 500 +"rhip_Xposition", // 501 +"rhip_Yposition", // 502 +"rhip_Zposition", // 503 +"rknee_Xposition", // 504 +"rknee_Yposition", // 505 +"rknee_Zposition", // 506 +"rfoot_Xposition", // 507 +"rfoot_Yposition", // 508 +"rfoot_Zposition", // 509 +"toe1-1.r_Xposition", // 510 +"toe1-1.r_Yposition", // 511 +"toe1-1.r_Zposition", // 512 +"toe1-2.r_Xposition", // 513 +"toe1-2.r_Yposition", // 514 +"toe1-2.r_Zposition", // 515 +"endsite_toe1-2.r_Xposition", // 516 +"endsite_toe1-2.r_Yposition", // 517 +"endsite_toe1-2.r_Zposition", // 518 +"toe2-1.r_Xposition", // 519 +"toe2-1.r_Yposition", // 520 +"toe2-1.r_Zposition", // 521 +"toe2-2.r_Xposition", // 522 +"toe2-2.r_Yposition", // 523 +"toe2-2.r_Zposition", // 524 +"toe2-3.r_Xposition", // 525 +"toe2-3.r_Yposition", // 526 +"toe2-3.r_Zposition", // 527 +"endsite_toe2-3.r_Xposition", // 528 +"endsite_toe2-3.r_Yposition", // 529 +"endsite_toe2-3.r_Zposition", // 530 +"toe3-1.r_Xposition", // 531 +"toe3-1.r_Yposition", // 532 +"toe3-1.r_Zposition", // 533 +"toe3-2.r_Xposition", // 534 +"toe3-2.r_Yposition", // 535 +"toe3-2.r_Zposition", // 536 +"toe3-3.r_Xposition", // 537 +"toe3-3.r_Yposition", // 538 +"toe3-3.r_Zposition", // 539 +"endsite_toe3-3.r_Xposition", // 540 +"endsite_toe3-3.r_Yposition", // 541 +"endsite_toe3-3.r_Zposition", // 542 +"toe4-1.r_Xposition", // 543 +"toe4-1.r_Yposition", // 544 +"toe4-1.r_Zposition", // 545 +"toe4-2.r_Xposition", // 546 +"toe4-2.r_Yposition", // 547 +"toe4-2.r_Zposition", // 548 +"toe4-3.r_Xposition", // 549 +"toe4-3.r_Yposition", // 550 +"toe4-3.r_Zposition", // 551 +"endsite_toe4-3.r_Xposition", // 552 +"endsite_toe4-3.r_Yposition", // 553 +"endsite_toe4-3.r_Zposition", // 554 +"toe5-1.r_Xposition", // 555 +"toe5-1.r_Yposition", // 556 +"toe5-1.r_Zposition", // 557 +"toe5-2.r_Xposition", // 558 +"toe5-2.r_Yposition", // 559 +"toe5-2.r_Zposition", // 560 +"toe5-3.r_Xposition", // 561 +"toe5-3.r_Yposition", // 562 +"toe5-3.r_Zposition", // 563 +"endsite_toe5-3.r_Xposition", // 564 +"endsite_toe5-3.r_Yposition", // 565 +"endsite_toe5-3.r_Zposition", // 566 +"lbuttock_Xposition", // 567 +"lbuttock_Yposition", // 568 +"lbuttock_Zposition", // 569 +"lhip_Xposition", // 570 +"lhip_Yposition", // 571 +"lhip_Zposition", // 572 +"lknee_Xposition", // 573 +"lknee_Yposition", // 574 +"lknee_Zposition", // 575 +"lfoot_Xposition", // 576 +"lfoot_Yposition", // 577 +"lfoot_Zposition", // 578 +"toe1-1.l_Xposition", // 579 +"toe1-1.l_Yposition", // 580 +"toe1-1.l_Zposition", // 581 +"toe1-2.l_Xposition", // 582 +"toe1-2.l_Yposition", // 583 +"toe1-2.l_Zposition", // 584 +"endsite_toe1-2.l_Xposition", // 585 +"endsite_toe1-2.l_Yposition", // 586 +"endsite_toe1-2.l_Zposition", // 587 +"toe2-1.l_Xposition", // 588 +"toe2-1.l_Yposition", // 589 +"toe2-1.l_Zposition", // 590 +"toe2-2.l_Xposition", // 591 +"toe2-2.l_Yposition", // 592 +"toe2-2.l_Zposition", // 593 +"toe2-3.l_Xposition", // 594 +"toe2-3.l_Yposition", // 595 +"toe2-3.l_Zposition", // 596 +"endsite_toe2-3.l_Xposition", // 597 +"endsite_toe2-3.l_Yposition", // 598 +"endsite_toe2-3.l_Zposition", // 599 +"toe3-1.l_Xposition", // 600 +"toe3-1.l_Yposition", // 601 +"toe3-1.l_Zposition", // 602 +"toe3-2.l_Xposition", // 603 +"toe3-2.l_Yposition", // 604 +"toe3-2.l_Zposition", // 605 +"toe3-3.l_Xposition", // 606 +"toe3-3.l_Yposition", // 607 +"toe3-3.l_Zposition", // 608 +"endsite_toe3-3.l_Xposition", // 609 +"endsite_toe3-3.l_Yposition", // 610 +"endsite_toe3-3.l_Zposition", // 611 +"toe4-1.l_Xposition", // 612 +"toe4-1.l_Yposition", // 613 +"toe4-1.l_Zposition", // 614 +"toe4-2.l_Xposition", // 615 +"toe4-2.l_Yposition", // 616 +"toe4-2.l_Zposition", // 617 +"toe4-3.l_Xposition", // 618 +"toe4-3.l_Yposition", // 619 +"toe4-3.l_Zposition", // 620 +"endsite_toe4-3.l_Xposition", // 621 +"endsite_toe4-3.l_Yposition", // 622 +"endsite_toe4-3.l_Zposition", // 623 +"toe5-1.l_Xposition", // 624 +"toe5-1.l_Yposition", // 625 +"toe5-1.l_Zposition", // 626 +"toe5-2.l_Xposition", // 627 +"toe5-2.l_Yposition", // 628 +"toe5-2.l_Zposition", // 629 +"toe5-3.l_Xposition", // 630 +"toe5-3.l_Yposition", // 631 +"toe5-3.l_Zposition", // 632 +"endsite_toe5-3.l_Xposition", // 633 +"endsite_toe5-3.l_Yposition", // 634 +"endsite_toe5-3.l_Zposition"// 635 +}; + +/** + * @brief This is a structure to encode model limits, not currently used + */ +struct MocapNETModelLimits +{ + int numberOfLimits; + float minimumYaw1; + float maximumYaw1; + float minimumYaw2; + float maximumYaw2; + int isFlipped; +}; + +/** + * @brief This is a MocapNET orientation. + */ +enum MOCAPNET_Orientation +{ + MOCAPNET_ORIENTATION_NONE=0, + MOCAPNET_ORIENTATION_FRONT, + MOCAPNET_ORIENTATION_BACK, + MOCAPNET_ORIENTATION_LEFT, + MOCAPNET_ORIENTATION_RIGHT, + //----------------------------- + MOCAPNET_ORIENTATION_NUMBER +}; + +/** + * @brief This is an array of names for all uncompressed inputs expected from MocapNET. + * Please notice that these 171 values correspond to triplets of 57 x,y,v ( v for visibility ) information for each joint. + */ +static const char * MocapNETOrientationNames[] = +{ + "None", + "Front", + "Back", + "Left", + "Right" +}; + + +/** + * @brief Each part of our 3D pose output is solved by a dedicated ensemble, this structure organizes this data + */ +struct MocapNET2SolutionPart +{ + int test; +}; + + +#if USE_BVH + #include "../../../dependencies/RGBDAcquisition/tools/PThreadWorkerPool/pthreadWorkerPool.h" +#endif + + +/** + * @brief MocapNET consists of separate classes/ensembles that are invoked for particular orientations. + * This structure holds the required tensorflow instances to make MocapNET work. + */ +struct MocapNET4 +{ + int test; +}; + +/** + * @brief Load a MocapNET from .pb files on disk + * @ingroup mocapnet + * @param Pointer to a struct MocapNET that will hold the tensorflow instances on load. + * @param Description of instance + * @retval 1 = Success loading the files , 0 = Failure + */ +int loadMocapNET4( + struct MocapNET4 * mnet, + const char * description + ); + + +/** + * @brief run MocapNET on an input vector that has the correct formatting. If getting data from an external source + * the prepareMocapNETInputFromUncompressedInput function could be used to prepare the input for this function. + * @param Pointer to a valid and populated MocapNET instance + * @param Vector of input values according to MocapNETUncompressedAndCompressedArrayNames + * @retval 1=Success,0=Failure + */ +std::vector runMocapNET4( + struct MocapNET4 * mnet, + struct skeletonSerialized * input, + int doLowerbody, + int doHands, + int doFace, + int doGestureDetection, + unsigned int useInverseKinematics, + int doOutputFiltering + ); + +/** + * @brief Deallocate tensorflow instances and free memory + * @param Pointer to a valid and populated MocapNET instance + * @retval 1=Success,0=Failure + */ +int unloadMocapNET4(struct MocapNET4 * mnet); + diff --git a/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/tools.h b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/tools.h new file mode 100644 index 0000000..68be579 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/MocapNET4/MocapNETLib4/tools.h @@ -0,0 +1,86 @@ +/** @file tools.h + * @brief Various Tools! + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef MNET4_TOOLS_H_INCLUDED +#define MNET4_TOOLS_H_INCLUDED + + +#ifdef __cplusplus +extern "C" +{ +#endif + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +#include +#include + + +static char * readFileToMemory(const char * filename,unsigned int *length) +{ + if (filename==0) + { + fprintf(stderr,RED "No Path Given to readFileToMemory\n" NORMAL); + return 0; + } + + *length = 0; + FILE * pFile = fopen ( filename , "rb" ); + + if (pFile==0) + { + fprintf(stderr,RED "readFileToMemory failed\n" NORMAL); + fprintf(stderr,RED "Could not read file %s \n" NORMAL,filename); + return 0; + } + + // obtain file size: + fseek (pFile , 0 , SEEK_END); + unsigned long lSize = ftell (pFile); + rewind (pFile); + + // allocate memory to contain the whole file: + unsigned long bufferSize = sizeof(char)*(lSize+1); + char * buffer = (char*) malloc (bufferSize); + if (buffer == 0 ) + { + fprintf(stderr,RED "Could not allocate enough memory for file %s \n" NORMAL,filename); + fclose(pFile); + return 0; + } + + // copy the file into the buffer: + size_t result = fread (buffer,1,lSize,pFile); + if (result != lSize) + { + free(buffer); + fprintf(stderr,RED "Could not read the whole file onto memory %s \n" NORMAL,filename); + fclose(pFile); + return 0; + } + + /* the whole file is now loaded in the memory buffer. */ + + // terminate + fclose (pFile); + + buffer[lSize]=0; //Null Terminate Buffer! + *length = (unsigned int) lSize; + return buffer; +} + + +#ifdef __cplusplus +} +#endif + + + +#endif \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/README.md b/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/README.md new file mode 100644 index 0000000..07d46e2 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/README.md @@ -0,0 +1,3 @@ +Since Tensorflow is under very active development and undergoes frequent "changes" this is an abstraction layer so that all the tensorflow crazyness is handled transparently. +In the end what we need is to just give a vector of numbers run the network and get back a vector of numbers. +We don't care about all the internals of tensorflow diff --git a/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp b/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp new file mode 100644 index 0000000..fdd6acd --- /dev/null +++ b/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.cpp @@ -0,0 +1,219 @@ +#include "neuralNetworkAbstraction.hpp" + +#include + + +void neuralNetworkPrintVersion() +{ + #if USE_TENSORFLOW1 + //========================= + //========================= + fprintf(stderr,"TF1 wrapper / Tensorflow version %s\n",TF_Version()); + //========================= + //========================= + #elif USE_TENSORFLOW2 + //========================= + //========================= + fprintf(stderr,"TF2 wrapper / Tensorflow version %s\n",TF_Version()); + //========================= + //========================= + #else + fprintf(stderr,"No Tensorflow support\n"); + #endif +} + +char * neuralNetworkGetPath(struct neuralNetworkModel * nn) +{ + #if USE_TENSORFLOW1 + //========================= + //========================= + return nn->model.modelPath; + //========================= + //========================= + #elif USE_TENSORFLOW2 + //========================= + //========================= + return nn->model.modelPath; + //========================= + //========================= + #else + return 0; + #endif +} + +int neuralNetworkLoad( + struct neuralNetworkModel * nn, + const char * filename, + const char * inputTensor, + const char * outputTensor, + unsigned int numberOfInputElements, + unsigned int numberOfOutputElements, + unsigned int forceCPU + ) +{ + #ifdef USE_TENSORFLOW1 + //========================= + //========================= + return loadTensorflowInstance(&nn->model,filename,inputTensor,outputTensor,forceCPU); + //========================= + //========================= + #elif USE_TENSORFLOW2 + //========================= + //========================= + char checkForTF2Path[2048]; + snprintf(checkForTF2Path,2048,"%s/saved_model.pb",filename); + + if (tf2_fileExists(checkForTF2Path)) + { + fprintf(stderr,"neuralNetworkLoad: attempting to load a TF2 style saved model (%s)..\n",checkForTF2Path); + //This means we are talking about a TF2 "saved model" model + return tf2_loadModel(&nn->model,filename,inputTensor,numberOfInputElements,numberOfOutputElements,forceCPU); + } else + if (tf2_fileExists(filename)) + { + //This means we are talking about a TF1 style "frozen graph" model + return tf2_loadFrozenGraph(&nn->model,filename,inputTensor,outputTensor,numberOfInputElements,numberOfOutputElements,forceCPU); + } else + { + fprintf(stderr,"Error: neuralNetworkLoad could not find a tf2/tf1 model to load..\n"); + } + //========================= + //========================= + #endif + return 0; +} + + +int neuralNetworkUnload( + struct neuralNetworkModel * nn + ) +{ + #ifdef USE_TENSORFLOW1 + //========================= + //========================= + return unloadTensorflow(&nn->model); + //========================= + //========================= + #elif USE_TENSORFLOW2 + //========================= + //========================= + return tf2_unloadModel(&nn->model); + //========================= + //========================= + #else + return 0; + #endif +} + +std::vector neuralNetworkExecute(struct neuralNetworkModel * nn,std::vector input) +{ + std::vector result; + result.clear(); + + + #ifdef USE_TENSORFLOW1 + //========================= + //========================= + result = predictTensorflow(&nn->model,input); + //========================= + //========================= + #elif USE_TENSORFLOW2 + //========================= + //========================= + if (input.size() != nn->model.inputElements) + { + fprintf(stderr,"neuralNetworkExecute: Inconsistent input .. \n"); + return result; + } + + //Cast input vector in a regular C memory block and hold the dimension data.. + //------------------------------------------------------------------------------------------- + unsigned int ndata = sizeof(float)*1*nn->model.inputElements; //number of bytes not number of element + int ndims = 2; + int64_t dims[] = {1,nn->model.inputElements}; + + + unsigned int newBufferSize = ndata; + if ( (nn->model.buffer!=0) && (nn->model.bufferSize < newBufferSize) ) + { + //Do reallocation here.. + float * newBuffer = (float*) realloc (nn->model.buffer,newBufferSize); + if (newBuffer!=nn->model.buffer) + { + nn->model.bufferSize = newBufferSize; + } + } else + if (nn->model.buffer==0) + { + newBufferSize = ndata; + nn->model.bufferSize = newBufferSize; + nn->model.buffer = (float *) malloc(newBufferSize); + } + + if (nn->model.buffer!=0) + { + for(int i=0; imodel.inputElements; i++) + { + nn->model.buffer[i] = input[i]; + } + //------------------------------------------------------------------------------------------- + + //Actually run the neural network.. + //------------------------------------------------------------------------------------------- + if ( + tf2_run( + &nn->model, + dims, + ndims, + nn->model.buffer, + nn->model.bufferSize + ) + ) + //------------------------------------------------------------------------------------------- + { + //Cast ouput from TF tensor data to a vector + //------------------------------------------------------------------------------------------- + void* buff = TF_TensorData(nn->model.outputValues[0]); + size_t tensorByteSize = TF_TensorByteSize(nn->model.outputValues[0]); + unsigned long tensorElements = TF_TensorElementCount(nn->model.outputValues[0]); + + if (tensorElementsmodel.outputElements) + { + fprintf(stderr,"neuralNetworkExecute: Incorrect number of output elements, expected %u found %lu \n",nn->model.outputElements,tensorElements); + } else + if (buff!=0) + { //If we have a buffer + if (nn->model.outputIsHalfFloats) + { + //Convert float16 back to float32 + float16* output = (float16*) buff; + for (unsigned int i=0; imodel.outputElements; i++) + { + result.push_back(convertFloat16ToFloat32(output[i])); + } + } else + { + //Regular float32 output that can be immediately pushed back to the vector + float* output = (float*) buff; + for (unsigned int i=0; imodel.outputElements; i++) + { + result.push_back(output[i]); + } + } + } + } else + { + fprintf(stderr,"Failed running the network..\n"); + } + //------------------------------------------------------------------------------------------- + } //We have a data buffer.. + else + { + fprintf(stderr,"Could not allocate enough memory to run %s\n",nn->model.modelPath); + } + //========================= + //========================= + #endif + + return result; +} diff --git a/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.hpp b/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.hpp new file mode 100644 index 0000000..cf31a15 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/NeuralNetworkAbstractionLayer/neuralNetworkAbstraction.hpp @@ -0,0 +1,50 @@ +#pragma once + +#include + + +#ifdef USE_TENSORFLOW1 + #include "../Tensorflow/tensorflow.hpp" +#elif USE_TENSORFLOW2 + #include "../Tensorflow2/tensorflow2.h" +#endif + +struct neuralNetworkModel +{ +#ifdef USE_TENSORFLOW1 + struct TensorflowInstance model; +#elif USE_TENSORFLOW2 + struct Tensorflow2Instance model; +#else + #warning "No tensorflow support signaled through a C define.." + void * model; +#endif +}; + + +void neuralNetworkPrintVersion(); + +char * neuralNetworkGetPath(struct neuralNetworkModel * nn); + +int neuralNetworkLoad( + struct neuralNetworkModel * nn, + const char * filename, + const char * inputTensor, + const char * outputTensor, + unsigned int numberOfInputElements, + unsigned int numberOfOutputElements, + unsigned int forceCPU + ); + + + + +int neuralNetworkUnload( + struct neuralNetworkModel * nn + ); + + +std::vector neuralNetworkExecute( + struct neuralNetworkModel * nn, + std::vector input + ); diff --git a/animation/MocapNET-kasisnu/src/Tensorflow/createTensorflowConfigurationForC.py b/animation/MocapNET-kasisnu/src/Tensorflow/createTensorflowConfigurationForC.py new file mode 100644 index 0000000..b41ecb1 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow/createTensorflowConfigurationForC.py @@ -0,0 +1,20 @@ +import tensorflow as tf +gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5) +#config = tf.ConfigProto(gpu_options=gpu_options) + +config = tf.ConfigProto( + device_count={'CPU' : 1, 'GPU' : 0}, + allow_soft_placement=True, + log_device_placement=False + ); + +serialized = config.SerializeToString() +c = list(map(hex, serialized)) + + +print("uint8_t config[] = {") +print(c) +print("};") +print("TF_SetConfig(opts, (void*)config, ",len(serialized),", status);};") +#['0x32', '0x9', '0x9', '0x0', '0x0', '0x0', '0x0', '0x0', '0x0', '0xe0', '0x3f'] + diff --git a/animation/MocapNET-kasisnu/src/Tensorflow/tensorflow.cpp b/animation/MocapNET-kasisnu/src/Tensorflow/tensorflow.cpp new file mode 100644 index 0000000..cc37f10 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow/tensorflow.cpp @@ -0,0 +1,462 @@ +/** @file tensorflow.cpp + * @brief This is a Tensorflow C-API wrapper.. Since Tensorflow is pretty volatile this whole file is kind of + * half-finished and will probably have to change to reflect changes to the tensorflow project. This code needs a serious cleanup when the Tensorflow C API is + * completely stable. + * If you are not familiar with Tensorflow for C check https://github.com/iwatake2222/CNN_NumberDetector/tree/master/03_Tensorflow_C + * @bug Tensorflow and the TF_GraphGetTensorShape ( https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h#L239 ) returns -1,-1 as the dimensions of some tensors. In order to work around this the expected dimensions are given as variables + * from the MocapNET client applications + * @author Ammar Qammaz (AmmarkoV) + */ + +#include "tensorflow.hpp" +#include "tf_utils.hpp" +#include + +#include +#include +#include // include this header for uint64_t +/* +std::vector get_tensor_shape(tensorflow::Tensor& tensor) +{ + std::vector shape; + int num_dimensions = tensor.shape().dims() + for(int ii_dim=0; ii_dim +#include +#include + + +unsigned long tickBaseTF = 0; + + +unsigned long GetTickCountMicroseconds() +{ + struct timespec ts; + if ( clock_gettime(CLOCK_MONOTONIC,&ts) != 0) + { + return 0; + } + + if (tickBaseTF==0) + { + tickBaseTF = ts.tv_sec*1000000 + ts.tv_nsec/1000; + return 0; + } + + return ( ts.tv_sec*1000000 + ts.tv_nsec/1000 ) - tickBaseTF; +} + + + +unsigned long GetTickCountMilliseconds() +{ + return (unsigned long) GetTickCountMicroseconds()/1000; +} + + + + + + +int checkAndDeallocate(TF_Status * s,const char * label) +{ + if (TF_GetCode(s) != TF_OK) + { + fprintf(stderr,RED "Error %s \n" NORMAL,label ); + TF_DeleteStatus(s); + return 0; + } + return 1; +} + + +void listNodes(const char * label , TF_Graph* graph) +{ + size_t pos = 0; + TF_Operation* oper; + std::cout << "Nodes list : \n"; + while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) + { + std::cout << label<<" - "<outputTensor=nullptr; + + //-------------------------------------------------------------------------------------------------------------- + net->graph = tf_utils::LoadGraph(filename); + if (net->graph == nullptr) + { + fprintf(stderr,RED "Can't load graph %s \n" NORMAL,filename); + return 0; + } + //tf_utils::PrintOp(net->graph); + //-------------------------------------------------------------------------------------------------------------- + net->input_operation = {TF_GraphOperationByName(net->graph, inputTensor), 0}; + if (net->input_operation.oper == nullptr) + { + listNodes(filename,net->graph); + fprintf(stderr,RED "Can't init input for %s \n" NORMAL,filename); + return 0; + } + std::cout << " Input Tensor for " <graph,inputTensor,0,0); + + net->output_operation = {TF_GraphOperationByName(net->graph, outputTensor), 0}; + if (net->output_operation.oper == nullptr) + { + listNodes(filename,net->graph); + fprintf(stderr,RED "Can't init output for %s \n" NORMAL,filename); + return 0; + } + std::cout << " Output Tensor for " <graph,outputTensor,0,0); + //-------------------------------------------------------------------------------------------------------------- + + snprintf(net->modelPath,1024,"%s",filename); + snprintf(net->inputLayerName,512,"%s",inputTensor); + snprintf(net->outputLayerName,512,"%s",outputTensor); + + //-------------------------------------------------------------------------------------------------------------- + net->status = TF_NewStatus(); + net->options = TF_NewSessionOptions(); + if (forceCPU) + { + //How do you end up with this byte array you might ask ? + uint8_t config[] = { 0xa,0x7,0xa,0x3,0x43,0x50,0x55,0x10,0x1,0xa,0x7,0xa,0x3,0x47,0x50,0x55,0x10,0x0,0x38,0x1}; + //Good Question, you use the python code and extract the configuration bytes and copy paste them here.. + //https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/src/Tensorflow/createTensorflowConfigurationForC.py + /*net->session = tf.ConfigProto( + device_count={'CPU' : 1, 'GPU' : 0}, + allow_soft_placement=True, + log_device_placement=False + );*/ + + TF_SetConfig(net->options, (void*)config, 20 , net->status); + } + + net->session = TF_NewSession(net->graph,net->options,net->status); + + TF_DeleteSessionOptions(net->options); + if (TF_GetCode(net->status) != TF_OK) + { + TF_DeleteStatus(net->status); + return 0; + } + //-------------------------------------------------------------------------------------------------------------- + + return 1; +} + +int unloadTensorflow(struct TensorflowInstance * net) +{ + //------------------------------------ + TF_CloseSession(net->session, net->status); + if (TF_GetCode(net->status) != TF_OK) + { + std::cout << "Error closing all session"; + TF_DeleteStatus(net->status); + return 0; + } + TF_DeleteSession(net->session, net->status); + if (TF_GetCode(net->status) != TF_OK) + { + std::cout << "Error delete session"; + TF_DeleteStatus(net->status); + return 0; + } + tf_utils::DeleteGraph(net->graph); + tf_utils::DeleteTensor(net->inputTensor); + tf_utils::DeleteTensor(net->outputTensor); + //------------------------------------ + + TF_DeleteStatus(net->status); + return 1; +} + + + + + +std::vector predictTensorflow(struct TensorflowInstance * net,std::vector input) +{ + const int printInput=0; + const int printOutput=0; + + std::vector result; + result.clear(); + + TF_Tensor* output_tensor = nullptr; + + unsigned int inputSize=input.size(); + std::vector input_dims = {1,inputSize}; + std::vector input_vals = input; + + //---------------------------------------- + if (printInput) + { + //---------------------------------------- + std::cout << "Input vals: "; + for (int i=0; isession, + nullptr, // Run options. + &net->input_operation, &input_tensor, 1, // Input tensors, input tensor values, number of inputs. + &net->output_operation, &output_tensor, 1, // Output tensors, output tensor values, number of outputs. + nullptr, 0, // Target operations, number of targets. + nullptr, // Run metadata. + net->status // Output status. + ); + + if (!checkAndDeallocate(net->status,"at predictTensorflow while running session")) + { + fprintf(stderr,RED "Possibly a wrong number of arguments given ( %lu )\n" NORMAL,input.size()); + return result; + } + + + const auto data = static_cast(TF_TensorData(output_tensor)); + + + int64_t outputSizeA[4]; + TF_GraphGetTensorShape( + net->graph, + net->output_operation, + outputSizeA, 2, + net->status + ); + + + if (!checkAndDeallocate(net->status,"TF_GraphGetTensorShape")) + { + return result; + } + + + + + int outputSize = outputSizeA[1]; + //---------------------------------------- + if (printOutput) + { + //---------------------------------------- + std::cout << "Output vals "< > predictTensorflowOnArrayOfHeatmaps( + struct TensorflowInstance * net, + unsigned int width , + unsigned int height , + float * data, + unsigned int heatmapWidth, + unsigned int heatmapHeight, + unsigned int numberOfOutputTensors +) +{ + std::vector > matrix; //This function output + + TF_Tensor* output_tensor = nullptr; + std::vector input_dims = {1,height,width,3}; + + + TF_Tensor* input_tensor = tf_utils::CreateTensor( + TF_FLOAT, + input_dims.data(), 4, + data , width * height * 3 * sizeof(float) + ); + + TF_SessionRun( net->session, + nullptr, // Run options. + &net->input_operation, &input_tensor, 1, // Input tensors, input tensor values, number of inputs. + &net->output_operation, &output_tensor, 1, // Output tensors, output tensor values, number of outputs. + nullptr, 0, // Target operations, number of targets. + nullptr, // Run metadata. + net->status // Output status. + ); + + if (TF_GetCode(net->status) != TF_OK) + { + fprintf(stderr,RED "predictTensorflowOnArrayOfHeatmaps: Error running session %u ( %s ) \n" NORMAL, TF_GetCode(net->status),TF_Message(net->status)); + TF_DeleteStatus(net->status); + tf_utils::DeleteTensor(input_tensor); + return matrix; + } + + if (output_tensor==nullptr) + { + fprintf(stderr,RED "Error retrieving output..\n" NORMAL); + TF_DeleteStatus(net->status); + tf_utils::DeleteTensor(input_tensor); + return matrix; + } + + + /* + TF_Output outputO = TF_Output{net->operation, 0}; + TF_Status* s = TF_NewStatus(); + //TF_Output feed_out_0 = TF_Output{output_tensor, 0}; + int num_dims; + + // Fetch the shape, it should be completely unknown. + num_dims = TF_GraphGetTensorNumDims(net->graph,outputO, s); + fprintf(stderr,"Number of dimensions is %u \n",num_dims); + */ + + //=============================================================================== + int64_t outputSizeA[32]= {0}; + TF_GraphGetTensorShape( + net->graph, + net->output_operation, + outputSizeA, numberOfOutputTensors, + net->status + ); + + + if (TF_GetCode(net->status) != TF_OK) + { + fprintf(stderr,RED "Error TF_GraphGetTensorShape for output, numberOfOutputTensors is probably wrong..! \n" NORMAL); + TF_DeleteStatus(net->status); + tf_utils::DeleteTensor(input_tensor); + return matrix; + } + + + //=============================================================================== + + //std::cout << "OutputA vals "<graph, net->outputLayerName); + TF_Output operation_out = {operation, 0}; + int64_t outputSizeB[32]; + TF_OperationGetAttrShape( + operation, + "shape", + outputSizeB, 4, + net->status + ); + std::cout << "OutputB "<outputLayerName<<" vals "<(TF_TensorData(output_tensor)); + if (out_p!=nullptr) + { + //Rows and columns should be automatically extracted,however + //tensorflow and TF_GraphGetTensorShape returns -1,-1 as their dimensions + //https://github.com/tensorflow/tensorflow/blob/master/tensorflow/c/c_api.h#L239 + unsigned int rows = heatmapWidth; //outputSizeA[numberOfOutputTensors-3]; + unsigned int cols = heatmapHeight; //outputSizeA[numberOfOutputTensors-2]; + unsigned int hm = outputSizeA[numberOfOutputTensors-1]; + + //For each of the output heatmaps + for(int i=0; i heatmap(rows*cols); + //For each of the rows of a particular heatmap + for(int r=0; r // TensorFlow C API header +#include + + +/** + * @brief A structure that holds all of the relevant information for a tensorflow instance + * + * This is used to simplify context switching and reduce the complexity of the library + * + */ +struct TensorflowInstance +{ + char modelPath[1024]; + char inputLayerName[512]; + char outputLayerName[512]; + + TF_Graph* graph; + TF_Session* session; + TF_Operation* operation; + TF_Tensor* inputTensor; + TF_Tensor* outputTensor; + TF_Output input_operation; + TF_Output output_operation; + + TF_Status* status; + TF_SessionOptions* options; +}; + +/** + * @brief Get the number of Ticks in Microseconds, to be used as a performance counter + * @ingroup tensorflow + * @retval The number of microseconds since system boot + */ +unsigned long GetTickCountMicroseconds(); + + +/** + * @brief Get the number of Ticks in Milliseconds, to be used as a performance counter + * @ingroup tensorflow + * @retval The number of milliseconds since system boot + */ +unsigned long GetTickCountMilliseconds(); + + +/** + * @brief List nodes of the TF_Graph by printing them in stdout + * @ingroup tensorflow + * @param Label of printed output + * @param Pointer to TF_Graph struct + * @retval No return value + */ +void listNodes(const char * label , TF_Graph* graph); + + +/** + * @brief Load a tensorflow instance from a .pb file + * @ingroup tensorflow + * @param Pointer to a struct TensorflowInstance that will hold the tensorflow instance on load. + * @param Path to .pb file + * @param Name of input tensor, i.e. input_1 + * @param Name of output tensor, i.e. output_1 + * @retval 1 = Success loading the file , 0 = Failure + */ +int loadTensorflowInstance( + struct TensorflowInstance * net, + const char * filename, + const char * inputTensor, + const char * outputTensor, + unsigned int forceCPU + ); + +/** + * @brief Evaluate an input vector through the neural network and return an output vector + * @ingroup tensorflow + * @param Pointer to a struct TensorflowInstance that holds a loaded tensorflow instance. + * @param Input vector of floats + * @retval Output vector of floats, Empty vector in case of failure + */ +std::vector predictTensorflow(struct TensorflowInstance * net,std::vector input); + + + +/** + * @brief Evaluate an input image through a network that outputs a vector of heatmaps + * @ingroup tensorflow + * @param Pointer to a struct TensorflowInstance that holds a loaded tensorflow instance. + * @param Width of input image + * @param Height of input image + * @param Pixels of input image + * @retval Output vector of vectors of floats, That correspond to the heatmaps + * @bug TF_GraphGetTensorShape returns -1 for some networks. + */ +std::vector > predictTensorflowOnArrayOfHeatmaps( + struct TensorflowInstance * net, + unsigned int width , + unsigned int height , + float * data, + unsigned int heatmapWidth, + unsigned int heatmapHeight, + unsigned int numberOfOutputTensors + ); + + +/** + * @brief Clean tensorflow instance from memory and deallocate it + * @ingroup tensorflow + * @param Pointer to a struct TensorflowInstance that holds a loaded tensorflow instance. + * @retval 1 = Success saving the file , 0 = Failure + */ +int unloadTensorflow(struct TensorflowInstance * net); diff --git a/animation/MocapNET-kasisnu/src/Tensorflow/tf_utils.cpp b/animation/MocapNET-kasisnu/src/Tensorflow/tf_utils.cpp new file mode 100644 index 0000000..cdf1c5b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow/tf_utils.cpp @@ -0,0 +1,505 @@ +// Licensed under the MIT License . +// SPDX-License-Identifier: MIT +// Copyright (c) 2018 - 2019 Daniil Goncharov . +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all +// copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +#include "tf_utils.hpp" +#include +#include +#include +#include +#include + +namespace tf_utils +{ + +namespace +{ +static void DeallocateBuffer(void* data, size_t) +{ + std::free(data); +} + +static TF_Buffer* ReadBufferFromFile(const char* file) +{ + std::ifstream f(file, std::ios::binary); + if (f.fail() || !f.is_open()) + { + return nullptr; + } + + f.seekg(0, std::ios::end); + const auto fsize = f.tellg(); + f.seekg(0, std::ios::beg); + + if (fsize < 1) + { + f.close(); + return nullptr; + } + + char* data = static_cast(std::malloc(fsize)); + f.read(data, fsize); + f.close(); + + TF_Buffer* buf = TF_NewBuffer(); + buf->data = data; + buf->length = fsize; + buf->data_deallocator = DeallocateBuffer; + + return buf; +} + +} // namespace tf_utils:: + +TF_Graph* LoadGraph(const char* graphPath) +{ + fprintf(stderr,"LoadGraph %s using TensorFlow Version: %s\n",graphPath,TF_Version()); + if (graphPath == nullptr) + { + fprintf(stderr,"Cannot load graph with null path..\n"); + return nullptr; + } + + TF_Buffer* buffer = ReadBufferFromFile(graphPath); + if (buffer == nullptr) + { + fprintf(stderr,"Cannot read buffer from file %s ..\n",graphPath); + return nullptr; + } + + TF_Graph* graph = TF_NewGraph(); + TF_Status* status = TF_NewStatus(); + TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions(); + + TF_GraphImportGraphDef(graph, buffer, opts, status); + TF_DeleteImportGraphDefOptions(opts); + TF_DeleteBuffer(buffer); + + if (TF_GetCode(status) != TF_OK) + { + fprintf(stderr,"Error importing graph definition.. (%u)\n",TF_GetCode(status)); + switch (TF_GetCode(status)) + { + case TF_INVALID_ARGUMENT : + fprintf(stderr,"Invalid Argument in graph..\n"); + break; + }; + TF_DeleteGraph(graph); + graph = nullptr; + } + + TF_DeleteStatus(status); + + return graph; +} + +void DeleteGraph(TF_Graph* graph) +{ + TF_DeleteGraph(graph); +} + +TF_Session* CreateSession(TF_Graph* graph) +{ + TF_Status* status = TF_NewStatus(); + TF_SessionOptions* options = TF_NewSessionOptions(); + TF_Session* session = TF_NewSession(graph, options, status); + TF_DeleteSessionOptions(options); + + if (TF_GetCode(status) != TF_OK) + { + DeleteSession(session); + TF_DeleteStatus(status); + return nullptr; + } + TF_DeleteStatus(status); + + return session; +} + +void DeleteSession(TF_Session* session) +{ + TF_Status* status = TF_NewStatus(); + TF_CloseSession(session, status); + if (TF_GetCode(status) != TF_OK) + { + TF_CloseSession(session, status); + } + TF_DeleteSession(session, status); + if (TF_GetCode(status) != TF_OK) + { + TF_DeleteSession(session, status); + } + TF_DeleteStatus(status); +} + +TF_Code RunSession(TF_Session* session, + const TF_Output* inputs, TF_Tensor* const* input_tensors, std::size_t ninputs, + const TF_Output* outputs, TF_Tensor** output_tensors, std::size_t noutputs) +{ + if (session == nullptr || + inputs == nullptr || input_tensors == nullptr || + outputs == nullptr || output_tensors == nullptr) + { + return TF_INVALID_ARGUMENT; + } + + TF_Status* status = TF_NewStatus(); + TF_SessionRun(session, + nullptr, // Run options. + inputs, input_tensors, static_cast(ninputs), // Input tensors, input tensor values, number of inputs. + outputs, output_tensors, static_cast(noutputs), // Output tensors, output tensor values, number of outputs. + nullptr, 0, // Target operations, number of targets. + nullptr, // Run metadata. + status // Output status. + ); + + TF_Code code = TF_GetCode(status); + TF_DeleteStatus(status); + return code; +} + +TF_Code RunSession(TF_Session* session, + const std::vector& inputs, const std::vector& input_tensors, + const std::vector& outputs, std::vector& output_tensors) +{ + return RunSession(session, + inputs.data(), input_tensors.data(), input_tensors.size(), + outputs.data(), output_tensors.data(), output_tensors.size()); +} + +TF_Tensor* CreateTensor(TF_DataType data_type, + const std::int64_t* dims, std::size_t num_dims, + const void* data, std::size_t len) +{ + if (dims == nullptr) + { + return nullptr; + } + + TF_Tensor* tensor = TF_AllocateTensor(data_type, dims, static_cast(num_dims), len); + if (tensor == nullptr) + { + return nullptr; + } + + void* tensor_data = TF_TensorData(tensor); + if (tensor_data == nullptr) + { + TF_DeleteTensor(tensor); + return nullptr; + } + + if (data != nullptr) + { + std::memcpy(tensor_data, data, std::min(len, TF_TensorByteSize(tensor))); + } + + return tensor; +} + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::int64_t* dims, std::size_t num_dims) +{ + return CreateTensor(data_type, dims, num_dims, nullptr, 0); +} + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::vector& dims) +{ + return CreateEmptyTensor(data_type, dims.data(), dims.size()); +} + +void DeleteTensor(TF_Tensor* tensor) +{ + if (tensor != nullptr) + { + TF_DeleteTensor(tensor); + } +} + +void DeleteTensors(const std::vector& tensors) +{ + for (auto t : tensors) + { + TF_DeleteTensor(t); + } +} + +void SetTensorsData(TF_Tensor* tensor, const void* data, std::size_t len) +{ + void* tensor_data = TF_TensorData(tensor); + if (tensor_data != nullptr) + { + std::memcpy(tensor_data, data, std::min(len, TF_TensorByteSize(tensor))); + } +} + + + + + + +const char* TFDataTypeToString(TF_DataType data_type) +{ + switch (data_type) + { + case TF_FLOAT: + return "TF_FLOAT"; + case TF_DOUBLE: + return "TF_DOUBLE"; + case TF_INT32: + return "TF_INT32"; + case TF_UINT8: + return "TF_UINT8"; + case TF_INT16: + return "TF_INT16"; + case TF_INT8: + return "TF_INT8"; + case TF_STRING: + return "TF_STRING"; + case TF_COMPLEX64: + return "TF_COMPLEX64"; + case TF_INT64: + return "TF_INT64"; + case TF_BOOL: + return "TF_BOOL"; + case TF_QINT8: + return "TF_QINT8"; + case TF_QUINT8: + return "TF_QUINT8"; + case TF_QINT32: + return "TF_QINT32"; + case TF_BFLOAT16: + return "TF_BFLOAT16"; + case TF_QINT16: + return "TF_QINT16"; + case TF_QUINT16: + return "TF_QUINT16"; + case TF_UINT16: + return "TF_UINT16"; + case TF_COMPLEX128: + return "TF_COMPLEX128"; + case TF_HALF: + return "TF_HALF"; + case TF_RESOURCE: + return "TF_RESOURCE"; + case TF_VARIANT: + return "TF_VARIANT"; + case TF_UINT32: + return "TF_UINT32"; + case TF_UINT64: + return "TF_UINT64"; + default: + return "Unknown"; + } +} + +void PrintInputs(TF_Graph*, TF_Operation* op) +{ + const int num_inputs = TF_OperationNumInputs(op); + + for (int i = 0; i < num_inputs; ++i) + { + const TF_Input input = {op, i}; + const TF_DataType type = TF_OperationInputType(input); + std::cout << "Input: " << i << " type: " << TFDataTypeToString(type) << std::endl; + } +} + +void PrintOutputs(TF_Graph* graph, TF_Operation* op) +{ + const int num_outputs = TF_OperationNumOutputs(op); + TF_Status* status = TF_NewStatus(); + + for (int i = 0; i < num_outputs; ++i) + { + const TF_Output output = {op, i}; + const TF_DataType type = TF_OperationOutputType(output); + const int num_dims = TF_GraphGetTensorNumDims(graph, output, status); + + if (TF_GetCode(status) != TF_OK) + { + std::cout << "Can't get tensor dimensionality" << std::endl; + continue; + } + + std::cout << " dims: " << num_dims<<"\n"; + + if (num_dims <= 0) + { + std::cout << " []" << std::endl;; + continue; + } + + std::vector dims(num_dims); + + std::cout << "Output: " << i << " type: " << TFDataTypeToString(type); + TF_GraphGetTensorShape(graph, output, dims.data(), num_dims, status); + + if (TF_GetCode(status) != TF_OK) + { + std::cout << "Can't get get tensor shape" << std::endl; + continue; + } + + std::cout << " ["; + for (int d = 0; d < num_dims; ++d) + { + std::cout << dims[d]; + if (d < num_dims - 1) + { + std::cout << ", "; + } + } + std::cout << "]" << std::endl; + } + + TF_DeleteStatus(status); +} + +void PrintTensorInfo(TF_Graph* graph, const char* layer_name,int printInputs,int printOutputs) +{ + //std::cout << "PrintTensorInfo\n"; + std::cout << "Tensor: " << layer_name; + TF_Operation* op = TF_GraphOperationByName(graph, layer_name); + + if (op == nullptr) + { + std::cout << "Could not get " << layer_name << std::endl; + return; + } + + const int num_inputs = TF_OperationNumInputs(op); + const int num_outputs = TF_OperationNumOutputs(op); + std::cout << " inputs: " << num_inputs << "\n outputs: " << num_outputs << std::endl; + + if (printInputs) + { + PrintInputs(graph, op); + } + if (printOutputs) + { + PrintOutputs(graph, op); + } +} + + + +void PrintOpInputs(TF_Graph*, TF_Operation* op) +{ + const int num_inputs = TF_OperationNumInputs(op); + + std::cout << "Number inputs: " << num_inputs << std::endl; + + for (int i = 0; i < num_inputs; ++i) + { + const TF_Input input = {op, i}; + const TF_DataType type = TF_OperationInputType(input); + std::cout << std::to_string(i) << " type : " << TFDataTypeToString(type) << std::endl; + } +} + +void PrintOpOutputs(TF_Graph* graph, TF_Operation* op) +{ + TF_Status* status = TF_NewStatus(); + const int num_outputs = TF_OperationNumOutputs(op); + + std::cout << "Number outputs: " << num_outputs << std::endl; + + for (int i = 0; i < num_outputs; ++i) + { + const TF_Output output = {op, i}; + const TF_DataType type = TF_OperationOutputType(output); + std::cout << std::to_string(i) << " type : " << TFDataTypeToString(type); + + const int num_dims = TF_GraphGetTensorNumDims(graph, output, status); + + if (TF_GetCode(status) != TF_OK) + { + std::cout << "Can't get tensor dimensionality" << std::endl; + continue; + } + + std::cout << " dims: " << num_dims; + + if (num_dims <= 0) + { + std::cout << " []" << std::endl;; + continue; + } + + std::vector dims(num_dims); + TF_GraphGetTensorShape(graph, output, dims.data(), num_dims, status); + + if (TF_GetCode(status) != TF_OK) + { + std::cout << "Can't get get tensor shape" << std::endl; + continue; + } + + std::cout << " ["; + for (int j = 0; j < num_dims; ++j) + { + std::cout << dims[j]; + if (j < num_dims - 1) + { + std::cout << ","; + } + } + std::cout << "]" << std::endl; + } + + TF_DeleteStatus(status); +} + +void PrintOp(TF_Graph* graph) +{ + TF_Operation* op; + std::size_t pos = 0; + + while ((op = TF_GraphNextOperation(graph, &pos)) != nullptr) + { + const char* name = TF_OperationName(op); + const char* type = TF_OperationOpType(op); + const char* device = TF_OperationDevice(op); + + const int num_outputs = TF_OperationNumOutputs(op); + const int num_inputs = TF_OperationNumInputs(op); + + std::cout << pos << ": " << name << " type: " << type << " device: " << device << " number inputs: " << num_inputs << " number outputs: " << num_outputs << std::endl; + + PrintOpInputs(graph, op); + PrintOpOutputs(graph, op); + std::cout << std::endl; + } +} + +int listNodesMN(const char * label , TF_Graph* graph) +{ + size_t pos = 0; + TF_Operation* oper; + fprintf(stdout, "Nodes list : \n"); + while ((oper = TF_GraphNextOperation(graph, &pos)) != nullptr) + { + fprintf(stderr," %s - %s \n",label,TF_OperationName(oper)); + } + return 1; +} + +} // namespace tf_utils diff --git a/animation/MocapNET-kasisnu/src/Tensorflow/tf_utils.hpp b/animation/MocapNET-kasisnu/src/Tensorflow/tf_utils.hpp new file mode 100644 index 0000000..e353891 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow/tf_utils.hpp @@ -0,0 +1,113 @@ +// Licensed under the MIT License . +// SPDX-License-Identifier: MIT +// Copyright (c) 2018 - 2019 Daniil Goncharov . +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the "Software"), to deal +// in the Software without restriction, including without limitation the rights +// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +// copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in all +// copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +// SOFTWARE. + +#pragma once + +#if defined(_MSC_VER) +# if !defined(COMPILER_MSVC) +# define COMPILER_MSVC // Set MSVC visibility of exported symbols in the shared library. +# endif +# pragma warning(push) +# pragma warning(disable : 4190) +#endif +#include // TensorFlow C API header +#include +#include +#include + +namespace tf_utils { + +TF_Graph* LoadGraph(const char* graphPath); + +void DeleteGraph(TF_Graph* graph); + +TF_Session* CreateSession(TF_Graph* graph); + +void DeleteSession(TF_Session* session); + +TF_Code RunSession(TF_Session* session, + const TF_Output* inputs, TF_Tensor* const* input_tensors, std::size_t ninputs, + const TF_Output* outputs, TF_Tensor** output_tensors, std::size_t noutputs); + +TF_Code RunSession(TF_Session* session, + const std::vector& inputs, const std::vector& input_tensors, + const std::vector& outputs, std::vector& output_tensors); + +TF_Tensor* CreateTensor(TF_DataType data_type, + const std::int64_t* dims, std::size_t num_dims, + const void* data, std::size_t len); + +template +TF_Tensor* CreateTensor(TF_DataType data_type, const std::vector& dims, const std::vector& data) { + return CreateTensor(data_type, + dims.data(), dims.size(), + data.data(), data.size() * sizeof(T)); +} + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::int64_t* dims, std::size_t num_dims); + +TF_Tensor* CreateEmptyTensor(TF_DataType data_type, const std::vector& dims); + +void DeleteTensor(TF_Tensor* tensor); + +void DeleteTensors(const std::vector& tensors); + +void SetTensorsData(TF_Tensor* tensor, const void* data, std::size_t len); + +void PrintTensorInfo(TF_Graph* graph, const char* layer_name,int printInputs,int printOutputs); + +void PrintOp(TF_Graph* graph); + +int listNodesMN(const char * label , TF_Graph* graph); + +template +void SetTensorsData(TF_Tensor* tensor, const std::vector& data) { + SetTensorsData(tensor, data.data(), data.size() * sizeof(T)); +} + +template +std::vector GetTensorsData(const TF_Tensor* tensor) { + auto data = static_cast(TF_TensorData(tensor)); + if (data == nullptr) { + return {}; + } + + return {data, data + (TF_TensorByteSize(tensor) / TF_DataTypeSize(TF_TensorType(tensor)))}; +} + +template +std::vector > GetTensorsData(const std::vector& tensors) { + std::vector > data; + data.reserve(tensors.size()); + for (const auto t : tensors) { + data.push_back(GetTensorsData(t)); + } + + return data; +} + +} // namespace tf_utils + +#if defined(_MSC_VER) +# pragma warning(pop) +#endif + diff --git a/animation/MocapNET-kasisnu/src/Tensorflow2/CMakeLists.txt b/animation/MocapNET-kasisnu/src/Tensorflow2/CMakeLists.txt new file mode 100644 index 0000000..074901d --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow2/CMakeLists.txt @@ -0,0 +1,16 @@ +project( Tensorflow2 ) +cmake_minimum_required( VERSION 2.8.7 ) +#cmake_minimum_required(VERSION 3.5) + + +add_executable(Tensorflow2 testtf2.cpp tensorflow2.h ) +target_link_libraries(Tensorflow2 rt dl m Tensorflow TensorflowFramework MocapNETLib2 ) +set_target_properties(Tensorflow2 PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(Tensorflow2 PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/Tensorflow2/tensorflow2.h b/animation/MocapNET-kasisnu/src/Tensorflow2/tensorflow2.h new file mode 100644 index 0000000..f05d376 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow2/tensorflow2.h @@ -0,0 +1,592 @@ +/** @file tensorflow2.h + * @brief A header-only tensorflow 2.x wrapper automization to make your neuralnetwork-lives easier. + * Repository : https://github.com/AmmarkoV/PThreadWorkerPool + * @author Ammar Qammaz (AmmarkoV) + */ + +#ifndef TENSORFLOW2_H_INCLUDED +#define TENSORFLOW2_H_INCLUDED + +#ifdef __cplusplus +extern "C" +{ +#endif + +#include // TensorFlow C API header + +#include +#include + + +//#define NORMAL "\033[0m" +//#define BLACK "\033[30m" /* Black */ +//#define RED "\033[31m" /* Red */ +//#define GREEN "\033[32m" /* Green */ +//#define YELLOW "\033[33m" /* Yellow */ + + +typedef unsigned short float16; + + +/** + * @brief A structure that holds all of the relevant information for a tensorflow instance + * This is used to simplify context switching and reduce the complexity of the library + */ +struct Tensorflow2Instance +{ + char modelPath[1025]; + + //Network instance + //------------------------- + TF_Graph* graph; + TF_Status* status; + TF_Session* session; + TF_SessionOptions* sessionOptions; + + //Input + //------------------------- + TF_Output input_operation; + TF_Output* input; + TF_Tensor* inputTensor; + TF_Tensor** inputValues; + char inputIsHalfFloats; + unsigned int inputElements; + int numberOfInputTensors; + + //Output + //------------------------- + TF_Output output_operation; + TF_Output* output; + TF_Tensor* outputTensor; + TF_Tensor** outputValues; + char outputIsHalfFloats; + unsigned int outputElements; + int numberOfOutputTensors; + + //Buffer for itermediate data + //------------------------- + unsigned int bufferSize; + float * buffer; +}; + +static void tf2_noOpDeallocator(void* data, size_t a, void* b) {} + + +static void tf2_deallocateBuffer(void* data, size_t) +{ + if (data!=0) + { + fprintf(stderr,"tf2_deallocateBuffer\n"); + free(data); + } +} + + +#if INTEL_OPTIMIZATIONS +#include +#include + +//https://software.intel.com/content/www/us/en/develop/documentation/cpp-compiler-developer-guide-and-reference/top/compiler-reference/intrinsics/intrinsics-for-converting-half-floats/intrinsics-for-converting-half-floats-1.html +//AVX +//This intrinsic takes a 32-bit float value, x , and converts it to a half-float value, which is returned. +//__m128 _mm_cvtph_ps(__m128i x); +//This intrinsic takes four packed half-float values and converts them to four 32-bit float values, which are returned. The upper 64-bits of x are ignored. The lower 64-bits are taken as four 16-bit float values for conversion. +//__m128i _mm_cvtps_ph(_m128 x, int imm); + +static float16 convertFloat32ToFloat16(float f) +{ + /* + (_MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) // round to nearest, and suppress exceptions + (_MM_FROUND_TO_NEG_INF |_MM_FROUND_NO_EXC) // round down, and suppress exceptions + (_MM_FROUND_TO_POS_INF |_MM_FROUND_NO_EXC) // round up, and suppress exceptions + (_MM_FROUND_TO_ZERO |_MM_FROUND_NO_EXC) // truncate, and suppress exceptions + _MM_FROUND_CUR_DIRECTION // use MXCSR.RC; see _MM_SET_ROUNDING_MODE + * */ + int imm = _MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC; + //https://software.intel.com/sites/landingpage/IntrinsicsGuide/#text=_cvtss_sh&expand=4979,5019,4979,1898 + return _cvtss_sh(f,imm); +} + +static float convertFloat16ToFloat32(float16 h) +{ + //https://software.intel.com/sites/landingpage/IntrinsicsGuide/#text=_cvtsh_ss&expand=4979,5019,4979,1898,1873 + return _cvtsh_ss(h); +} +#else +static float16 convertFloat32ToFloat16(float f) +{ + //https://en.wikipedia.org/wiki/Half-precision_floating-point_format + //https://en.wikipedia.org/wiki/IEEE_754-2008_revision + //https://stackoverflow.com/questions/3026441/float32-to-float16/3026505 + #warning "convertFloat32ToFloat16 is not correctly implemented on non SSE equipped builds" + unsigned int * fltInt32 = ( unsigned int * ) &f; + float16 h; + + h = (*fltInt32 >> 31) << 5; + unsigned short tmp = (*fltInt32 >> 23) & 0xff; + tmp = (tmp - 0x70) & ((unsigned int)((int)(0x70 - tmp) >> 4) >> 27); + h = (h | tmp) << 10; + h |= (*fltInt32 >> 13) & 0x3ff; + + return h; +} + +static float convertFloat16ToFloat32(float16 h) +{ + #warning "convertFloat16ToFloat32 is not properly implemented on non SSE equipped builds" + fprintf(stderr,"convertFloat16ToFloat32 not working.. "); + //http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + float f = ((h&0x8000)<<16) | (((h&0x7c00)+0x1C000)<<13) | ((h&0x03FF)<<13); + return f; +} +#endif + + + +static int tf2_checkModelUsingExternalTool(const char * path) +{ + char commandToCheck[4096]; + snprintf(commandToCheck,2000,"saved_model_cli show --dir %s --tag_set serve --signature_def serving_default",path); + int i = system(commandToCheck); + + return (i==0); +} + + + + + +static int tf2_setCPUExecutionSessionConfiguration(struct Tensorflow2Instance * tf2i) +{ + //How do you end up with this byte array you might ask ? + unsigned char config[] = {0xa,0x7,0xa,0x3,0x43,0x50,0x55,0x10,0x1,0xa,0x7,0xa,0x3,0x47,0x50,0x55,0x10,0x0,0x38,0x1}; + //Good Question, you use the python code and extract the configuration bytes and copy paste them here.. + //https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/master/src/Tensorflow/createTensorflowConfigurationForC.py + /*net->session = tf.ConfigProto( + device_count={'CPU' : 1, 'GPU' : 0}, + allow_soft_placement=True, + log_device_placement=False + );*/ + + TF_SetConfig(tf2i->sessionOptions, (void*)config, 20 , tf2i->status); + return (TF_GetCode(tf2i->status) == TF_OK); +} + + +static char tf2_fileExists(const char * filename) +{ + FILE *fp = fopen(filename,"r"); + if( fp ) { /* exists */ fclose(fp); return 1; } + return 0; +} + +//https://github.com/AmmarkoV/AmmarServer/blob/master/src/AmmServerlib/AString/AString.c#L235 +static char * tf2_readFileToMem(const char * filename,unsigned int *length ) +{ + if (length==0) { return 0; } + if (filename==0) { return 0; } + //----------------------------------------- + + *length = 0; + FILE * pFile = fopen ( filename , "rb" ); + + if (pFile==0) + { + fprintf(stderr,"ERROR tf2_readFileToMem: failed\n"); + fprintf(stderr,"Could not read file %s \n",filename); + return 0; + } + + // obtain file size: + fseek (pFile , 0 , SEEK_END); + unsigned long lSize = ftell (pFile); + rewind (pFile); + + // allocate memory to contain the whole file: + unsigned long bufferSize = sizeof(char)*(lSize+1); + char * buffer = (char*) malloc (bufferSize); + if (buffer == 0 ) + { + fprintf(stderr,"ERROR tf2_readFileToMem: Could not allocate enough memory for file %s \n",filename); + fclose(pFile); + return 0; + } + + // copy the file into the buffer: + size_t result = fread (buffer,1,lSize,pFile); + if (result != lSize) + { + free(buffer); + fprintf(stderr,"ERROR tf2_readFileToMem: Could not read the whole file onto memory %s \n",filename); + fclose(pFile); + return 0; + } + + /* the whole file is now loaded in the memory buffer. */ + + // terminate + fclose (pFile); + + buffer[lSize]=0; //Null Terminate Buffer! + *length = (unsigned int) lSize; + return buffer; +} + + + + +static int tf2_allocateIOTensors( + struct Tensorflow2Instance * tf2i, + const char * inputTensorName, + const char * outputTensorName, + unsigned int inputElements, + unsigned int outputElements, + unsigned int forceCPU + ) +{ + //Allocate input tensor + //-------------------------------------------------------------------------------- + tf2i->numberOfInputTensors = 1; + tf2i->input = (TF_Output*) malloc(sizeof(TF_Output) * tf2i->numberOfInputTensors); + tf2i->input_operation = {TF_GraphOperationByName(tf2i->graph,inputTensorName), 0}; + if(tf2i->input_operation.oper == NULL) + { + fprintf(stderr,"ERROR: Failed TF_GraphOperationByName %s\n",inputTensorName); + return 0; + } + tf2i->input[0] = tf2i->input_operation; + //-------------------------------------------------------------------------------- + + + //Allocate Output tensor + //-------------------------------------------------------------------------------- + tf2i->numberOfOutputTensors = 1; + tf2i->output = (TF_Output*) malloc(sizeof(TF_Output) * tf2i->numberOfOutputTensors); + + tf2i->output_operation = {TF_GraphOperationByName(tf2i->graph,outputTensorName), 0}; + if(tf2i->output_operation.oper == NULL) + { + fprintf(stderr,"ERROR: Failed TF_GraphOperationByName %s\n",outputTensorName); + return 0; + } + tf2i->output[0] = tf2i->output_operation; + //-------------------------------------------------------------------------------- + + + //Allocate memory for input & output data + tf2i->inputValues = (TF_Tensor**) malloc(sizeof(TF_Tensor*)*tf2i->numberOfInputTensors); + tf2i->outputValues = (TF_Tensor**) malloc(sizeof(TF_Tensor*)*tf2i->numberOfOutputTensors); + return ( (tf2i->inputValues!=0) && (tf2i->outputValues!=0) ); +} + + + +static int tf2_loadFrozenGraph( + struct Tensorflow2Instance * tf2i, + const char* graphPath, + const char * inputTensorName, + const char * outputTensorName, + unsigned int inputElements, + unsigned int outputElements, + unsigned int forceCPU + ) +{ + if (graphPath == 0) + { + fprintf(stderr,"Cannot load graph with null path..\n"); + return 0; + } + fprintf(stderr,"LoadGraph %s using TensorFlow Version: %s\n",graphPath,TF_Version()); + + unsigned int graphInMemorySize=0; + char * graphInMemory = tf2_readFileToMem(graphPath,&graphInMemorySize); + + if (graphInMemory == 0) + { + fprintf(stderr,"Cannot read buffer from file %s ..\n",graphPath); + return 0; + } + + + //Remember parameters.. + snprintf(tf2i->modelPath,1024,"%s",graphPath); + tf2i->inputElements=inputElements; + tf2i->outputElements=outputElements; + + tf2i->graph = TF_NewGraph(); + tf2i->status = TF_NewStatus(); + TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions(); + + + TF_Buffer* tfBufferWithGraph = TF_NewBuffer(); + tfBufferWithGraph->data = graphInMemory; + tfBufferWithGraph->length = graphInMemorySize; + tfBufferWithGraph->data_deallocator = tf2_deallocateBuffer; + + TF_GraphImportGraphDef(tf2i->graph,tfBufferWithGraph,opts,tf2i->status); + TF_DeleteImportGraphDefOptions(opts); + + + if (TF_GetCode(tf2i->status) != TF_OK) + { + fprintf(stderr,"ERROR importing graph definition.. (%u)\n",TF_GetCode(tf2i->status)); + switch (TF_GetCode(tf2i->status)) + { + case TF_INVALID_ARGUMENT : + fprintf(stderr,"Invalid Argument in graph..\n"); + break; + }; + TF_DeleteGraph(tf2i->graph); + tf2i->graph = 0; + } + + TF_DeleteBuffer(tfBufferWithGraph); + graphInMemorySize=0; + + + //-------------------------------------------------------------------------------------------------------------- + tf2i->sessionOptions = TF_NewSessionOptions(); + if (forceCPU) { tf2_setCPUExecutionSessionConfiguration(tf2i); } + + + tf2i->session = TF_NewSession(tf2i->graph,tf2i->sessionOptions,tf2i->status); + //-------------------------------------------------------------------------------------------------------------- + + return tf2_allocateIOTensors( + tf2i, + inputTensorName, + outputTensorName, + inputElements, + outputElements, + forceCPU + ); +} + + + +static int tf2_loadModel( + struct Tensorflow2Instance * tf2i, + const char * path, + const char * inputTensor, + unsigned int inputElements, + unsigned int outputElements, + unsigned int forceCPU + ) +{ + //https://medium.com/analytics-vidhya/deploying-tensorflow-2-1-as-c-c-executable-1d090845055c + //Tensorflow boilerplate names, fingers crossed that they don't change them.. + char inputTensorName[1025]={0}; //= {"serving_default_input_front"}; //Default input layer + snprintf(inputTensorName,1024,"serving_default_%s",inputTensor); + //snprintf(inputTensorName,1024,"%s",inputTensor); + + char outputTensorName[1025]={0}; //= {"StatefulPartitionedCall"}; //Default output layer + snprintf(outputTensorName,1024,"StatefulPartitionedCall"); + + //-------------------------------------------------------------------------- + int ntags = 1; + + //char tags[1025]={0}; //= {"StatefulPartitionedCall"}; //Default output layer + //snprintf(tags,1024,"serve"); + //const char* tags = "serve"; // default model serving tag; can change in future + const char* tags[] = {"serve"}; + //-------------------------------------------------------------------------- + + //-------------------------------------------------------------------------- + fprintf(stderr,"Loading model : %s | Input : %s | Output : %s\n",path,inputTensorName,outputTensorName); + //-------------------------------------------------------------------------- + + + //Remember parameters.. + snprintf(tf2i->modelPath,1024,"%s",path); + tf2i->inputElements=inputElements; + tf2i->outputElements=outputElements; + + //Allocate stuff.. + tf2i->graph = TF_NewGraph(); + tf2i->status = TF_NewStatus(); + tf2i->sessionOptions = TF_NewSessionOptions(); + if (forceCPU) { tf2_setCPUExecutionSessionConfiguration(tf2i); } + + TF_Buffer* runOptions = NULL; + + //Actual loading from a saved model assuming : + //https://www.tensorflow.org/api_docs/python/tf/saved_model/save + tf2i->session = TF_LoadSessionFromSavedModel( + tf2i->sessionOptions, + runOptions, + path, + tags, + ntags, + tf2i->graph, + NULL, + tf2i->status + ); + if(TF_GetCode(tf2i->status) != TF_OK) + { + fprintf(stderr,"Error loading model : %s \n",TF_Message(tf2i->status)); + + + tf2_checkModelUsingExternalTool(tf2i->modelPath); + + return 0; + } + + return tf2_allocateIOTensors( + tf2i, + inputTensorName, + outputTensorName, + inputElements, + outputElements, + forceCPU + ); +} + + +static int tf2_unloadModel(struct Tensorflow2Instance * tf2i) +{ + // //Free memory + TF_DeleteGraph(tf2i->graph); + if(TF_GetCode(tf2i->status) != TF_OK) + { + fprintf(stderr,"tf2_unloadModel: Error deleting graph (%s) \n",TF_Message(tf2i->status)); + return 0; + } + + + TF_DeleteSession(tf2i->session, tf2i->status); + if(TF_GetCode(tf2i->status) != TF_OK) + { + fprintf(stderr,"tf2_unloadModel: Error deleting session (%s) \n",TF_Message(tf2i->status)); + return 0; + } + + + TF_DeleteSessionOptions(tf2i->sessionOptions); + if(TF_GetCode(tf2i->status) != TF_OK) + { + fprintf(stderr,"tf2_unloadModel: Error deleting session options (%s) \n",TF_Message(tf2i->status)); + return 0; + } + + + TF_DeleteStatus(tf2i->status); + //Can't check status having deleted the status .. + + + if (tf2i->buffer!=0) + { + free(tf2i->buffer); + tf2i->buffer=0; + tf2i->bufferSize=0; + } + return 1; +} + +static int tf2_run( + struct Tensorflow2Instance * tf2i, + int64_t * dimensions, + unsigned int dimensionsNumber, + float * data, + unsigned int dataSizeBytes + ) +{ + int result = 0; + TF_Tensor* int_tensor = NULL; + + + if (tf2i->inputIsHalfFloats) + { + //In an attempt not to perform unneccessary reallocations + //I rewrite the float array with halfs.. + unsigned int dataSizeElements = dataSizeBytes / sizeof(float); + unsigned int convertedDataSizeBytes = sizeof(float16) * dataSizeElements; + + float16 * dataAsFloat16 = (float16*) data; + float buffer=0.0; + + for (int i=0; iinputValues[0] = int_tensor; + + //Run the Session + TF_SessionRun( + tf2i->session, + NULL, //run options + tf2i->input, tf2i->inputValues, tf2i->numberOfInputTensors, + tf2i->output, tf2i->outputValues, tf2i->numberOfOutputTensors, + NULL, 0, + NULL, //Metadata + tf2i->status + ); + + if(TF_GetCode(tf2i->status) != TF_OK) + { + fprintf(stderr,"tf2_run: Error running %s network, %s\n",tf2i->modelPath,TF_Message(tf2i->status)); + + fprintf(stderr,"dimensionsNumber=%u\n",dimensionsNumber); + fprintf(stderr,"dataSizeBytes=%u\n",dataSizeBytes); + tf2_checkModelUsingExternalTool(tf2i->modelPath); + + result = 0; + } + + TF_DeleteTensor(int_tensor); + tf2i->inputValues[0] =0; + int_tensor=0; + result=1; + } + + return result; +} + + +#ifdef __cplusplus +} +#endif + +#endif \ No newline at end of file diff --git a/animation/MocapNET-kasisnu/src/Tensorflow2/testtf2.cpp b/animation/MocapNET-kasisnu/src/Tensorflow2/testtf2.cpp new file mode 100644 index 0000000..59d2859 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Tensorflow2/testtf2.cpp @@ -0,0 +1,315 @@ +#include +#include +#include +#include +#include + +#include "tensorflow2.h" + + +#define NORMAL "\033[0m" +#define BLACK "\033[30m" /* Black */ +#define RED "\033[31m" /* Red */ +#define GREEN "\033[32m" /* Green */ +#define YELLOW "\033[33m" /* Yellow */ + + +float rand_FloatRange(float a, float b) +{ + return ((b - a) * ((float)rand() / RAND_MAX)) + a; +} + + +int testFloat16() +{ + unsigned int maxErrors = 10000; + unsigned int errors=0; + for (int i=0; i0.4) + { + fprintf(stderr,"%f ",distance); + ++errors; + } + } + + fprintf(stderr,"\n\n%u errors / %0.2f %% \n",errors,(float) errors*100/maxErrors); + return (errors==0); +} + + +int testUpperBody() +{ + const char* savedModelDirectory = "/home/ammar/Documents/Programming/DNNTracker/tensorflow2GPU/src/tmp.pb/"; + + int runOnCPU=1; + + int ndims = 2; + int64_t dims[] = {1,322}; + //----------------------------- + float data[1*322]; + for(int i=0; i< (1*322); i++) + { + data[i] = 1.00; + } + int ndata = sizeof(float)*1*322 ; //number of bytes not number of element + //----------------------------- + + + unsigned int numberOfInputElements = 322; + unsigned int numberOfOutputElements = 42; + + char inputTensor[]={"input_front"}; + char outputTensor[]={"result_front/concat"}; + + //Tensorflow v2 test.. + struct Tensorflow2Instance upperbodyFrontv2={0}; + if ( tf2_loadModel(&upperbodyFrontv2,savedModelDirectory,inputTensor,numberOfInputElements,numberOfOutputElements,runOnCPU) ) + { + if ( tf2_run(&upperbodyFrontv2,dims,ndims,data,ndata) ) + { + + void* buff = TF_TensorData(upperbodyFrontv2.outputValues[0]); + float* offsets = (float*)buff; + printf("Result Tensor v2:\n"); + for(int i=0;i0) { printf(","); } + printf("%f",offsets[i]); + } + printf("\n"); + + } else + { + fprintf(stderr,"Failed tf2_run\n"); + } + + printf("Deallocating v2 \n"); + tf2_unloadModel(&upperbodyFrontv2); + } else + { + fprintf(stderr,"Failed tf2_loadModel\n"); + } + + + + + + + //Tensorflow v1 test.. + struct Tensorflow2Instance upperbodyFrontv1={0}; + if ( tf2_loadFrozenGraph(&upperbodyFrontv1,"dataset/combinedModel/mocapnet2/mode5/1.0/upperbody_front.pb",inputTensor,outputTensor,numberOfInputElements,numberOfOutputElements,runOnCPU) ) + { + if ( tf2_run(&upperbodyFrontv1,dims,ndims,data,ndata) ) + { + fprintf(stderr,"Ready to run..!\n"); + tf2_run(&upperbodyFrontv1,dims,ndims,data,ndata); + + void* buff = TF_TensorData(upperbodyFrontv1.outputValues[0]); + float* offsets = (float*)buff; + printf("Result Tensor v1:\n"); + for(int i=0;i0) { printf(","); } + printf("%f",offsets[i]); + } + printf("\n"); + } + + + printf("Deallocating v1 \n"); + tf2_unloadModel(&upperbodyFrontv1); + } + + + + printf("Done\n"); + + + + + + return 0; +} + + + + +#include +int executeCommandLineAndRetreiveAllResults(const char * command , char * what2GetBack , unsigned int what2GetBackMaxSize, unsigned long * what2GetBackSize) +{ + /* Open the command for reading. */ + FILE * f = popen(command, "r"); + if (f == 0) + { + fprintf(stderr,"Failed to run command (%s) \n",command); + return 0; + } + + + size_t contentSize = fread(what2GetBack, 1 , what2GetBackMaxSize, f); + *what2GetBackSize = contentSize; + + /* close */ + pclose(f); + return 1; +} + +int getInfoFast(const char * field,char * buffer,unsigned int bufferSize,char terminatorCharacter) +{ + int fieldOffset = strlen(field); + char * foundAt = strstr(buffer,field); + + if (foundAt!=0) + { + foundAt += fieldOffset; + char * termination = strchr(foundAt,terminatorCharacter); + if (termination!=0) + { + char retainOldTerminator = *termination; + *termination = 0; + fprintf(stderr,"Field `%s` => `%s` \n",field,foundAt); + *termination =retainOldTerminator; + } + } + + return 0; +} + + + + +int testHands() +{ +// const char* savedModelDirectory = "/home/ammar/Documents/Programming/DNNTracker/DNNTracker/dataset/combinedModel/mocapnet2/mode1/1.0/rhand_half_all.pb"; + const char* savedModelDirectory = "/home/ammar/Documents/Programming/DNNTracker/DNNTracker/dataset/combinedModel/mocapnet2/mode1/1.0/lhand_all.pb"; + + #define NUMBER_OF_OUTPUTS 52 + #define NUMBER_OF_INPUTS 319 + int runOnCPU=1; + + int ndims = 2; + int64_t dims[] = {1,NUMBER_OF_INPUTS}; + //----------------------------- + float data[1*NUMBER_OF_INPUTS]; + for(int i=0; i< (1*NUMBER_OF_INPUTS); i++) + { + data[i] = 1.00; + } + int ndata = sizeof(float)*1*NUMBER_OF_INPUTS ; //number of bytes not number of element + //----------------------------- + + + //Check using external tool and a parser + //------------------------------------- + tf2_checkModelUsingExternalTool(savedModelDirectory); + /* + + static int maxOutputLength=4096; + unsigned long outputSize=0; + char output[maxOutputLength]={0}; + executeCommandLineAndRetreiveAllResults(commandToCheck,output,maxOutputLength,&outputSize); + fprintf(stderr,"Retrieved.. %s\n",output); + + getInfoFast("inputs['",output,outputSize,'\''); + getInfoFast("shape: ",output,outputSize,10); + getInfoFast("name: ",output,outputSize,10); + + char * outputTensorsStart = strstr(output,"output(s):"); + if (outputTensorsStart!=0) + { + getInfoFast("outputs['",outputTensorsStart,outputSize,'\''); + getInfoFast("shape: ",outputTensorsStart,outputSize,10); + getInfoFast("name: ",output,outputSize,10); + } + //------------------------------------- + */ + unsigned int numberOfInputElements = NUMBER_OF_INPUTS; + unsigned int numberOfOutputElements = NUMBER_OF_OUTPUTS; + + //https://medium.com/analytics-vidhya/deploying-tensorflow-2-1-as-c-c-executable-1d090845055c + char inputTensor[]={"input_all"}; //{"input_front"}; + char outputTensor[]={"result_all/concat"}; //{"result_front/concat"}; + + //Tensorflow v2 test.. + struct Tensorflow2Instance upperbodyFrontv2={0}; + if ( tf2_loadModel(&upperbodyFrontv2,savedModelDirectory,inputTensor,numberOfInputElements,numberOfOutputElements,runOnCPU) ) + { + upperbodyFrontv2.inputIsHalfFloats=1; + if ( tf2_run(&upperbodyFrontv2,dims,ndims,data,ndata) ) + { + + void* buff = TF_TensorData(upperbodyFrontv2.outputValues[0]); + float* offsets = (float*)buff; + printf("Result Tensor v2:\n"); + for(int i=0; i0) { printf(","); } + printf("%f",offsets[i]); + } + printf("\n"); + + } else + { + fprintf(stderr,"Failed tf2_run\n"); + } + + printf("Deallocating v2 \n"); + tf2_unloadModel(&upperbodyFrontv2); + } else + { + fprintf(stderr,RED "Failed tf2_loadModel\n" NORMAL); + } + + + + + //Tensorflow v1 test.. + struct Tensorflow2Instance upperbodyFrontv1={0}; + if ( tf2_loadFrozenGraph(&upperbodyFrontv1,"dataset/combinedModel/mocapnet2/mode1/1.0/_rhand_all.pb",inputTensor,outputTensor,numberOfInputElements,numberOfOutputElements,runOnCPU) ) + { + if ( tf2_run(&upperbodyFrontv1,dims,ndims,data,ndata) ) + { + fprintf(stderr,"Ready to run..!\n"); + tf2_run(&upperbodyFrontv1,dims,ndims,data,ndata); + + void* buff = TF_TensorData(upperbodyFrontv1.outputValues[0]); + float* offsets = (float*)buff; + printf("Result Tensor v1:\n"); + for(int i=0; i0) { printf(","); } + printf("%f",offsets[i]); + } + printf("\n"); + } + + + printf("Deallocating v1 \n"); + tf2_unloadModel(&upperbodyFrontv1); + } + + + + printf("Done\n"); + + + + + + return 0; +} + + + +int main() +{ + testFloat16(); + + return testHands(); + + //return testUpperBody(); +} diff --git a/animation/MocapNET-kasisnu/src/Webcam/CMakeLists.txt b/animation/MocapNET-kasisnu/src/Webcam/CMakeLists.txt new file mode 100644 index 0000000..fcb66f6 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Webcam/CMakeLists.txt @@ -0,0 +1,19 @@ +project( OpenCVTest ) +#cmake_minimum_required( VERSION 2.8.7 ) +cmake_minimum_required(VERSION 3.5) +find_package(OpenCV REQUIRED) +INCLUDE_DIRECTORIES(${OpenCV_INCLUDE_DIRS}) + + + +add_executable(OpenCVTest webcam.cpp) +target_link_libraries(OpenCVTest rt dl m ${OpenCV_LIBRARIES} ) +set_target_properties(OpenCVTest PROPERTIES DEBUG_POSTFIX "D") + + +set_target_properties(OpenCVTest PROPERTIES + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_SOURCE_DIR}" + ) + diff --git a/animation/MocapNET-kasisnu/src/Webcam/webcam.cpp b/animation/MocapNET-kasisnu/src/Webcam/webcam.cpp new file mode 100644 index 0000000..6189570 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/Webcam/webcam.cpp @@ -0,0 +1,64 @@ +#include "opencv2/opencv.hpp" +/** @file webcam.cpp + * @brief This is a simple test file to make sure your camera or video files can be opened using OpenCV + * @author Ammar Qammaz (AmmarkoV) + */ +#include + + +using namespace cv; + +int main(int argc, char *argv[]) +{ + const char * webcam = 0; + for (int i=0; ii+1) + { + webcam = argv[i+1]; + } + } + } + + + VideoCapture cap(webcam); // open the default camera + if (webcam==0) + { + std::cerr<<"Trying to open webcam\n"; + cap.set(cv::CAP_PROP_FRAME_WIDTH,640); // In case of errors try CV_CAP_PROP_FRAME_WIDTH + cap.set(cv::CAP_PROP_FRAME_HEIGHT,480); // In case of errors try CV_CAP_PROP_FRAME_HEIGHT + } + else + { + std::cerr<<"Trying to open "<> frame; + if ( (frame.size().width>0) && (frame.size().height>0) ) + { + imshow("feed", frame); + } + else + { + std::cerr<<"Broken frame.. \n"; + } + waitKey(1); + } + // the camera will be deinitialized automatically in VideoCapture destructor + return 0; +} diff --git a/animation/MocapNET-kasisnu/src/python/2d_pose_estimation/readCOCO.py b/animation/MocapNET-kasisnu/src/python/2d_pose_estimation/readCOCO.py new file mode 100644 index 0000000..7a80f43 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/2d_pose_estimation/readCOCO.py @@ -0,0 +1,1300 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2023 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +#Dependencies should be : +#python3 -m pip install tensorflow==2.15.0 numpy tensorboard opencv-python wget + +import sys +import os +import numpy as np +import datetime +#---------------------------------------------- +useGPU = True +if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--cpu"): + useGPU = False +# Set CUDA_VISIBLE_DEVICES to an empty string to force TensorFlow to use the CPU +if (not useGPU): + os.environ['CUDA_VISIBLE_DEVICES'] = '' #<- Force CPU +#---------------------------------------------- +import cv2 +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras.callbacks import TensorBoard +from tensorflow.keras import layers, models +from tensorflow.keras.losses import Loss +from tensorflow.keras.metrics import Metric +from tensorflow.keras.layers import Input, DepthwiseConv2D, Flatten, Dropout, Conv2D, Conv2DTranspose, AvgPool2D, BatchNormalization, ReLU, Reshape, Dense, Add, UpSampling2D, MaxPooling2D +from tensorflow.keras.models import Model +#---------------------------------------------- +dataType = np.uint8 +dataTypeTF = tf.uint8 + +if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--float32"): + dataType = np.float32 + dataTypeTF = tf.float32 + if (sys.argv[i]=="--uint8"): + dataType = np.uint8 + dataTypeTF = tf.uint8 +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#------------------------------------------------------------------------------- +def retrieveModelOutputDimensions(model): + output_layer = model.layers[-1] # Assuming the output layer is the last layer + output_shape = output_layer.output_shape + output_size = (output_shape[1],output_shape[2]) + numberOfHeatmaps = output_shape[3] + print("Number of Heatmaps is ", numberOfHeatmaps) + print("Output Shape is ", output_size) + return output_shape[1],output_shape[2],output_shape[3] +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +def compose(*funcs): + from functools import reduce + if funcs: + return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs) + else: + raise ValueError('Composition of empty sequence not supported.') + +class Conv_Bn_Relu6(keras.layers.Layer): + def __init__(self, filters, kernel_size, strides, padding, name): + super(Conv_Bn_Relu6, self).__init__() + self._name = name + self.block = keras.Sequential() + if name.find('depthwise') == -1: + self.block.add(keras.layers.Conv2D(filters, kernel_size, strides, padding=padding)) + else: + self.block.add(keras.layers.DepthwiseConv2D(kernel_size, strides, padding=padding)) + self.block.add(keras.layers.BatchNormalization()) + if name.find('relu') != -1: + self.block.add(keras.layers.ReLU(6)) + def call(self, inputs, **kwargs): + return self.block(inputs) + +def block(x, filters, t, strides, name): + shortcut = x + + x = compose(Conv_Bn_Relu6(t * filters, (1, 1), (1, 1), 'same', name='{}_conv_bn_relu6'.format(name)), + Conv_Bn_Relu6(None, (3, 3), strides, 'same', name='{}_depthwiseconv_bn_relu6'.format(name)), + Conv_Bn_Relu6(filters, (1, 1), (1, 1), 'same', name='{}_conv_bn'.format(name)))(x) + + if shortcut.shape[-1] == filters and strides == (1, 1): + x = keras.layers.Add(name='{}_add'.format(name))([x, shortcut]) + + return x + +def add_block(x, filters, t, strides, n, name): + x = block(x, filters, t, strides, name='{}_1'.format(name)) + for i in range(n - 1): + x = block(x, filters, t, (1, 1), name='{}_{}'.format(name, i + 2)) + + return x + +#https://ustccoder.github.io/2020/03/22/feature_extraction%20MobileNet_V2/ +def create_keypoints_modelNew(inputHeight, inputWidth, inputChannels, outputWidth, outputHeight, numKeypoints, midSectionRepetitions=5, activation='swish'): + input_shape = (inputHeight, inputWidth, inputChannels) + input_tensor = Input(shape=input_shape) + #x = input_tensor + + # Create Rescaling layer + rescale_layer = tf.keras.layers.experimental.preprocessing.Rescaling(scale=1./255) + normalized_tensor = rescale_layer(input_tensor) + x = normalized_tensor + + x = mobilenet_block(x, filters=32, strides=(2, 2), activation=activation, name='block1') + x = add_block(x, filters=16, t=1, strides=(1, 1), n=1, name='block2') + x = add_block(x, filters=24, t=6, strides=(2, 2), n=2, name='block3') + x = add_block(x, filters=32, t=6, strides=(2, 2), n=3, name='block4') + x = add_block(x, filters=64, t=6, strides=(2, 2), n=4, name='block5') + x = add_block(x, filters=96, t=6, strides=(1, 1), n=3, name='block6') + x = add_block(x, filters=160, t=6, strides=(2, 2), n=3, name='block7') + x = add_block(x, filters=320, t=6, strides=(1, 1), n=1, name='block8') + + x = compose(Conv_Bn_Relu6(1280, (1, 1), (1, 1), 'same', name='conv2'),AvgPool2D(pool_size=4, name='global_averagepool'))(x) + + x = Dense(units=int((outputHeight * outputWidth * numKeypoints) // 9), activation=activation, name='dense')(x) + + x = Reshape((numKeypoints, outputHeight, outputWidth), name='reshape')(x) + + model = Model(inputs=input_tensor, outputs=x, name='MobileNet-V2-KeyPoints') + model.summary() + + return model +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#------------------------------------------------------------------------------- +def mobilenet_block(x, filters, strides, activation, dropoutRate, name=None): + x = DepthwiseConv2D(kernel_size = 3, strides = strides, padding = 'same', activation=activation)(x) #, activation=activation + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + x = Conv2D(filters = filters, kernel_size = 1, strides = 1, activation=activation)(x) # , activation=activation + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + if dropoutRate>0.0: + x = Dropout(dropoutRate)(x) + + return x + +# Define the CNN model for keypoints prediction +def create_keypoints_model(inputHeight, inputWidth, inputChannels, outputWidth, outputHeight, numKeypoints, midSectionRepetitions=5 ,activation='relu', dropoutRate=0.0, baseChannels = 64): + input_shape = (inputHeight, inputWidth, inputChannels) + + input_tensor = Input(shape=input_shape) + + # Create Rescaling layer + rescale_layer = tf.keras.layers.experimental.preprocessing.Rescaling(scale=1./255) + normalized_tensor = rescale_layer(input_tensor) + + + x = mobilenet_block(normalized_tensor, filters=baseChannels, strides=1, dropoutRate=dropoutRate, activation=activation) + #x = Dropout(0.2)(x) + x = mobilenet_block(x, filters=baseChannels*2, strides=2, dropoutRate=dropoutRate, activation=activation) + #x = Dropout(0.2)(x) + x = mobilenet_block(x, filters=baseChannels*3, strides=1, dropoutRate=dropoutRate, activation=activation) + #x = Dropout(0.2)(x) + x = mobilenet_block(x, filters=baseChannels*4, strides=2, dropoutRate=dropoutRate, activation=activation) + x = mobilenet_block(x, filters=baseChannels*5, strides=1, dropoutRate=dropoutRate, activation=activation) + x = mobilenet_block(x, filters=baseChannels*8, strides=2, dropoutRate=dropoutRate, activation=activation) + + for _ in range(midSectionRepetitions): + x = mobilenet_block(x, filters=baseChannels*16, strides=1, dropoutRate=dropoutRate, activation=activation) + + x = mobilenet_block(x, filters=baseChannels*24, strides=2, dropoutRate=dropoutRate, activation=activation) + x = mobilenet_block(x, filters=baseChannels*16, strides=1, dropoutRate=dropoutRate, activation=activation) + + x = AvgPool2D(pool_size=4)(x) # Adjust pool_size based on your desired output size + + x = Conv2DTranspose(filters=baseChannels*12, kernel_size=4, strides=2, padding='same', activation=activation)(x) + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + x = Conv2DTranspose(filters=baseChannels*10, kernel_size=4, strides=2, padding='same', activation=activation)(x) + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + x = Conv2DTranspose(filters=baseChannels*8, kernel_size=4, strides=2, padding='same', activation=activation)(x) + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + x = Conv2DTranspose(filters=baseChannels*3, kernel_size=4, strides=2, padding='same', activation=activation)(x) + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + x = Conv2DTranspose(filters=baseChannels, kernel_size=4, strides=2, padding='same', activation=activation)(x) + if (activation=='relu'): #Disabled after switching to swish/selu + x = BatchNormalization()(x) + x = ReLU()(x) + + x = Conv2D(filters=numKeypoints, kernel_size=1, strides=1, activation='linear')(x) + + # Adjust the Reshape layer based on the original output size + x = Reshape((outputHeight, outputWidth,numKeypoints))(x) # Reshape to the desired size + model = Model(inputs=input_tensor, outputs=x) + model.summary() + + return model +#------------------------------------------------------------------------------- +#------------------------------------------------------------------------------- +#------------------------------------------------------------------------------- +#------------------------------------------------------------------------------- +def Conv2D_BN_Leaky(*args, **kwargs): + conv_kwargs = { + 'use_bias': True, + 'padding': 'same', + 'kernel_initializer': 'he_normal' + } + conv_kwargs.update(kwargs) + return Conv2D(*args, **conv_kwargs) + +def resblock_module(tensor, num_filters): + skip = Conv2D_BN_Leaky(num_filters, (1, 1))(tensor) + + tensor = Conv2D_BN_Leaky(num_filters//2, (1, 1))(tensor) + tensor = Conv2D_BN_Leaky(num_filters//2, (3, 3), padding='same')(tensor) + tensor = Conv2D_BN_Leaky(num_filters, (1, 1))(tensor) + tensor = Add()([skip, tensor]) + + return tensor + +def hourglass_module(input_tensor, stage): + stage -= 1 + skip = resblock_module(input_tensor, 256) + tensor = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(input_tensor) + tensor = resblock_module(tensor, 256) + if stage == 0: + tensor = resblock_module(tensor, 256) + else: + tensor = hourglass_module(tensor, stage) + tensor = resblock_module(tensor, 256) + tensor = UpSampling2D(2)(tensor) + tensor = Add()([skip, tensor]) + + return tensor + +def front_module(input_tensor, num_filters=256): + tensor = Conv2D_BN_Leaky(num_filters//4, (7, 7), (2, 2), padding='same')(input_tensor) + tensor = resblock_module(tensor, num_filters//2) + tensor = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(tensor) + tensor = resblock_module(tensor, num_filters//2) + tensor = resblock_module(tensor, num_filters) + + return tensor + +def stack_module(input_tensor, num_points, num_filters=256, stage=4, activation="sigmoid", is_head=False): + tensor = hourglass_module(input_tensor, stage) + tensor = resblock_module(tensor, num_filters) + tensor = Conv2D_BN_Leaky(num_filters, (1, 1))(tensor) + outputs = Conv2D(num_points, (1, 1))(tensor) + if activation == "sigmoid": + outputs = tf.keras.layers.Activation("sigmoid")(outputs) + elif activation == "softmax": + outputs = tf.keras.layers.Softmax(axis=-1)(outputs) + + if is_head: + return outputs + else: + tensor = Conv2D(num_filters, (1, 1))(tensor) + skip = Conv2D(num_filters, (1, 1))(outputs) + tensor = Add()([skip, tensor]) + return outputs, tensor + +def create_hourglass_keypoints_model(input_shape, num_stacks=8, num_points=15, num_filters=256, stage=4, activation="sigmoid", pretrained_weights=None): + output_list = [] + inputs = Input(input_shape) + tensor = front_module(inputs, num_filters=num_filters) + + for _ in range(num_stacks - 1): + skip = tensor + outputs, tensor = stack_module( + tensor, num_points, + num_filters=num_filters, + stage=stage, + activation=activation) + tensor = Add()([skip, tensor]) + output_list.append(outputs) + + outputs = stack_module( + tensor, num_points, + num_filters=num_filters, + stage=stage, + activation=activation, + is_head=True) + output_list.append(outputs) + + model = tf.keras.models.Model(inputs, output_list) + + model.summary() + + x,y,num = retrieveModelOutputDimensions(model) + + return model +#------------------------------------------------------------------------------- +#------------------------------------------------------------------------------- +#------------------------------------------------------------------------------- +#------------------------------------------------------------------------------- +def logTrainingParameters(cfg, log_dir): + try: + # Create a summary writer + param_log = tf.summary.create_file_writer(log_dir) + + with param_log.as_default(): + # Convert the dictionary to a formatted string + params_str = "\n".join([f"{key}: {value}" for key, value in cfg.items()]) + # Log the parameters as text + tf.summary.text("Training Parameters", params_str, step=0) + except Exception as e: + print(f"Error storing logging parameters in tensorboard: {e}") +#------------------------------------------------------------------------------- +def logSomeInputsAndOutputs(inputs, outputs, labels, log_dir, samples=100): + try: + # Create a summary writer + image_log = tf.summary.create_file_writer(log_dir) + + with image_log.as_default(): + + sample_indices = np.random.choice(len(inputs), size=min(samples,len(inputs)), replace=False) + + for logID in sample_indices: + #Store the image as float32 [0..1] RGB to make sure tensorboard visualizes it correctly + image_as_float = inputs[logID].astype(np.float32) + image = image_as_float / 255.0 + + # Convert input and output arrays to TensorFlow tensors + bgr_image_tensor = tf.convert_to_tensor([image], dtype=tf.float32) + + # Swap BGR to RGB + rgb_image_tensor = tf.reverse(bgr_image_tensor, axis=[-1]) + + # Write input image summary + tf.summary.image(f"Image {logID} Input", rgb_image_tensor , step=logID) + + # Write output image summary + heatmapID = 0 + for heatmapID in range(0,18): + heatmap = outputs[logID,:,:,heatmapID] + #print(f"Heatmap {heatmapID} dimensions: {heatmap.shape}") + # Add batch and channel dimensions + heatmapS = np.squeeze(heatmap) + heatmapS = np.expand_dims(heatmapS, axis=-1) + heatmap_as_float = heatmapS.astype(np.float32) + output_image_tensor = tf.convert_to_tensor([heatmap_as_float], dtype=tf.float32) + + thisOutputlabel = "#%u" % heatmapID + if (heatmapID < len(labels)): + thisOutputlabel = labels[heatmapID] + + tf.summary.image(f"Image {logID} Output / {thisOutputlabel}", output_image_tensor, step=logID) + heatmapID = heatmapID + 1 + except Exception as e: + print(f"Error storing image in tensorboard: {e}") + sys.exit(1) +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< +#------------------------------------------------------------------------------- +def checkIfFileExists(filename): + return os.path.isfile(filename) +#------------------------------------------------------------------------------- +def convert_bytes(num): + #This function will convert bytes to MB.... GB... strings + step_unit = 1000.0 #1024 bad the size + for x in ['bytes', 'KB', 'MB', 'GB', 'TB']: + if num < step_unit: + return "%3.1f %s" % (num, x) + num /= step_unit +#------------------------------------------------------------------------------- +def printTFVersion(): + global useGPU + print("") + print("Tensorflow version : ",tf.__version__) + #print("Keras version : ",keras.__version__) <- no longer available in TF-2.13 + print("Numpy version : ",np.__version__) + #----------------------------- + from tensorflow.python.platform import build_info as tf_build_info + print("TF/CUDA version : ",tf_build_info.build_info['cuda_version']) + print("TF/CUDNN version : ",tf_build_info.build_info['cudnn_version']) + print("Use GPU : ",useGPU) + #----------------------------- + if useGPU: + physical_devices = tf.config.list_physical_devices('GPU') + if physical_devices: + gpuID = 0 + for gpu in physical_devices: + print("GPU #",gpuID," Name:", gpu.name) + try: + # Note: The following code may not be available in older versions of TensorFlow + memory_info = tf.config.experimental.get_memory_info('GPU:%u'%gpuID) + print("GPU #",gpuID," Memory Currently Used (in MB):", memory_info['current'] / (1024**2)) + print("GPU #",gpuID," Memory Peak Used (in MB):", memory_info['peak'] / (1024**2)) + except Exception as e: + print(f"Error getting memory info for GPU #{gpuID}: {e}") + gpuID += 1 + else: + print("No GPU available.") + print("") + #----------------------------- +#------------------------------------------------------------------------------- +class RSquaredMetric(Metric): + def __init__(self, name='r_squared', **kwargs): + super(RSquaredMetric, self).__init__(name=name, **kwargs) + self.ssr = self.add_weight(name='ssr', initializer='zeros') + self.sst = self.add_weight(name='sst', initializer='zeros') + + def update_state(self, y_true, y_pred, sample_weight=None): + # Explicitly cast y_true to float32 + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + + # Reshape y_true and y_pred to ensure they're 1D tensors + y_true = tf.reshape(y_true, [-1]) + y_pred = tf.reshape(y_pred, [-1]) + + # Calculate the sum of squares of residuals + ssr_update = tf.reduce_sum(tf.square(y_true - y_pred)) + self.ssr.assign_add(ssr_update) + + # Calculate the total sum of squares + mean_y_true = tf.reduce_mean(y_true) + sst_update = tf.reduce_sum(tf.square(y_true - mean_y_true)) + self.sst.assign_add(sst_update) + + def result(self): + return 1 - (self.ssr / self.sst) + + def reset_state(self): + self.ssr.assign(0.0) + self.sst.assign(0.0) +#------------------------------------------------------------------------------- +class PCKMetric(Metric): + def __init__(self, name='pck', threshold=0.01, **kwargs): + super(PCKMetric, self).__init__(name=name, **kwargs) + self.threshold = threshold + self.total_correct_keypoints = self.add_weight(name='total_correct_keypoints', initializer='zeros') + self.total_keypoints = self.add_weight(name='total_keypoints', initializer='zeros') + + def update_state(self, y_true, y_pred, sample_weight=None): + # Explicitly cast y_true to float32 + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + + # Assuming y_true and y_pred have shape (batch_size, num_keypoints*2) + batch_size = tf.shape(y_true)[0] + + # Reshape to (batch_size, num_keypoints, 2) + y_true = tf.reshape(y_true, (batch_size, -1, 2)) + y_pred = tf.reshape(y_pred, (batch_size, -1, 2)) + + # Calculate Euclidean distances between true and predicted keypoints + distances = tf.norm(y_true - y_pred, axis=-1) + + # Count correct keypoints within the threshold + correct_keypoints = tf.cast(tf.reduce_sum(tf.cast(distances <= self.threshold, tf.float32)), tf.float32) + + # Update total correct keypoints and total keypoints + self.total_correct_keypoints.assign_add(correct_keypoints) + self.total_keypoints.assign_add(tf.cast(tf.reduce_sum(tf.ones_like(distances)), tf.float32)) + + def result(self): + # Calculate the percentage of correct keypoints + return self.total_correct_keypoints / self.total_keypoints if self.total_keypoints > 0 else 0.0 + + def reset_state(self): + # Reset counts at the start of each epoch or batch + self.total_correct_keypoints.assign(0.0) + self.total_keypoints.assign(0.0) +#------------------------------------------------------------------------------- +# Define Focal Loss +# categorical_focal_loss and binary_focal_loss +# https://github.com/aldi-dimara/keras-focal-loss/blob/master/focal_loss.py +class FocalLoss(Loss): + def __init__(self, alpha=0.25, gamma=2.0, num_joints=17, **kwargs): + super(FocalLoss, self).__init__(**kwargs) + self.alpha = alpha + self.gamma = gamma + self.num_joints = num_joints + + def call(self, y_true, y_pred): + #batch_size = tf.shape(y_true)[0] + y_true = tf.cast(y_true, tf.float32)# / 255.0 + y_pred = tf.cast(y_pred, tf.float32)# / 255.0 + + # Split the y_true into joint heatmaps and background heatmap + y_true_joints = y_true[:, :, :, :self.num_joints] + y_true_background = y_true[:, :, :, self.num_joints:] + + # Split the y_pred into joint predictions and background prediction + y_pred_joints = y_pred[:, :, :, :self.num_joints] + y_pred_background = y_pred[:, :, :, self.num_joints:] + + # Calculate focal loss for joint heatmaps + joint_pos_mask = tf.cast(y_true_joints > 0, dtype=tf.float32) + joint_neg_mask = tf.cast(y_true_joints == 0, dtype=tf.float32) + + alpha_factor_joint = self.alpha * joint_pos_mask + (1 - self.alpha) * joint_neg_mask + focal_weight_joint = alpha_factor_joint * tf.pow(1 - y_pred_joints, self.gamma) + + joint_pos_loss = focal_weight_joint * tf.square(y_pred_joints - y_true_joints) + joint_neg_loss = (1 - focal_weight_joint) * tf.square(y_pred_joints) + + num_joint_pos = tf.reduce_sum(joint_pos_mask) + joint_pos_loss = tf.reduce_sum(joint_pos_loss) + joint_neg_loss = tf.reduce_sum(joint_neg_loss) + + joint_loss = tf.cond(tf.greater(num_joint_pos, 0), lambda: (joint_pos_loss + joint_neg_loss) / num_joint_pos, lambda: joint_neg_loss) + + # Calculate focal loss for the background heatmap + background_pos_mask = tf.cast(y_true_background > 0, dtype=tf.float32) + background_neg_mask = tf.cast(y_true_background == 0, dtype=tf.float32) + + alpha_factor_background = self.alpha * background_pos_mask + (1 - self.alpha) * background_neg_mask + focal_weight_background = alpha_factor_background * tf.pow(1 - y_pred_background, self.gamma) + + background_pos_loss = focal_weight_background * tf.square(y_pred_background - y_true_background) + background_neg_loss = (1 - focal_weight_background) * tf.square(y_pred_background) + + num_background_pos = tf.reduce_sum(background_pos_mask) + background_pos_loss = tf.reduce_sum(background_pos_loss) + background_neg_loss = tf.reduce_sum(background_neg_loss) + + background_loss = tf.cond(tf.greater(num_background_pos, 0), lambda: (background_pos_loss + background_neg_loss) / num_background_pos, lambda: background_neg_loss) + + # Combine joint and background losses + total_loss = joint_loss + background_loss + + return total_loss +# Define your custom loss function +def focal_loss(y_true, y_pred): + return FocalLoss()(y_true, y_pred) +#------------------------------------------------------------------------------- +#Define some more losses : https://github.com/stefanopini/simple-HRNet/blob/master/losses/loss.py#L58 +#https://github.com/microsoft/human-pose-estimation.pytorch/blob/master/lib/core/loss.py +class JointsMSELoss(Loss): + def __init__(self, num_joints=17, weight_factor=10.0, **kwargs): + super(JointsMSELoss, self).__init__(**kwargs) + self.num_joints = num_joints + self.weight_factor = weight_factor + self.criterion = tf.keras.losses.MeanSquaredError() + + def call(self, y_true, y_pred): + y_true = tf.cast(y_true, tf.float32)# / 255.0 + y_pred = tf.cast(y_pred, tf.float32)# / 255.0 + + # Split the y_true and y_pred into joint heatmaps and background heatmap + y_true_joints = y_true[:, :, :, :self.num_joints] + y_true_background = y_true[:, :, :, self.num_joints:] + + y_pred_joints = y_pred[:, :, :, :self.num_joints] + y_pred_background = y_pred[:, :, :, self.num_joints:] + + # Compute loss for joint heatmaps + joint_loss = 0.0 + for idx in range(self.num_joints): + heatmap_pred = y_pred_joints[:, :, :, idx] + heatmap_gt = y_true_joints[:, :, :, idx] + + # Calculate the weight for this joint + weight = tf.reduce_mean(heatmap_gt) * self.weight_factor + 1.0 + + joint_loss += 0.5 * self.criterion(heatmap_gt, heatmap_pred) * weight + + joint_loss /= tf.cast(self.num_joints, dtype=tf.float32) + + # Compute loss for the background heatmap + background_loss = 0.5 * self.criterion(y_true_background, y_pred_background) + + # Combine joint and background losses + total_loss = joint_loss + background_loss + + return total_loss +# Define your custom loss function +def jointsMSE_loss(y_true, y_pred): + return JointsMSELoss()(y_true, y_pred) +#------------------------------------------------------------------------------- +class VanillaMSELoss(tf.keras.losses.Loss): + def __init__(self, **kwargs): + super(VanillaMSELoss, self).__init__(**kwargs) + + def call(self, y_true, y_pred): + # Ensure both y_true and y_pred are cast to float32 + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + + # Compute the squared difference + squared_difference = tf.square(y_true - y_pred) + + # Compute the mean over all elements + mse_loss = tf.reduce_mean(squared_difference) + + return mse_loss + +# Define your custom loss function +def vanilla_mse_loss(y_true, y_pred): + return VanillaMSELoss()(y_true, y_pred) +#------------------------------------------------------------------------------- +class WeightedMSELoss(tf.keras.losses.Loss): + def __init__(self, last_heatmap_weight=2.0, **kwargs): + super(WeightedMSELoss, self).__init__(**kwargs) + self.last_heatmap_weight = last_heatmap_weight + + def call(self, y_true, y_pred): + # Ensure both y_true and y_pred are cast to float32 + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) + + # Compute the squared difference + squared_difference = tf.square(y_true - y_pred) + + # Apply weighting to the last heatmap + last_heatmap_weighted = squared_difference[..., -1] * self.last_heatmap_weight + squared_difference = tf.concat([squared_difference[..., :-1], tf.expand_dims(last_heatmap_weighted, axis=-1)], axis=-1) + + # Compute the mean over all elements + mse_loss = tf.reduce_mean(squared_difference) + + return mse_loss + +# Define your custom loss function +def weighted_mse_loss(y_true, y_pred): + return WeightedMSELoss(last_heatmap_weight=2.0)(y_true, y_pred) +#------------------------------------------------------------------------------- +def flip_data(inputs_tf, outputs_tf, flip_x=True, flip_y=True): + print("Flipping data X:",flip_x," Y:",flip_y) + # Flip along the X dimension if requested + if flip_x: + inputs_tf = tf.image.flip_left_right(inputs_tf) + outputs_tf = tf.image.flip_left_right(outputs_tf) + + # Flip along the Y dimension if requested + if flip_y: + inputs_tf = tf.image.flip_up_down(inputs_tf) + outputs_tf = tf.image.flip_up_down(outputs_tf) + + return inputs_tf, outputs_tf +#------------------------------------------------------------------------------- +def read_json_file(file_path): + import json + try: + with open(file_path, 'r') as file: + data = json.load(file) + return data + except FileNotFoundError: + print(f"Error: File '{file_path}' not found.") + except json.JSONDecodeError: + print(f"Error: Invalid JSON format in file '{file_path}'.") + except Exception as e: + print(f"Error: {e}") +#------------------------------------------------------------------------------- +def download_image(url, save_path): + import wget + try: + # Check if the file already exists at the save_path + if os.path.exists(save_path): + #print(f"File already exists at {save_path}. Skipping download.") + return True + else: + # Download the image using wget + wget.download(url, save_path) + print(f"\nImage downloaded successfully to {save_path}") + except Exception as e: + print(f"Error downloading image: {e}") + return False +#------------------------------------------------------------------------------- +def resize_image_with_borders(image, target_size=(300, 300)): + try: + # Get the original image size + originalWidth = image.shape[1] + originalHeight = image.shape[0] + #Notice that we use a different convention than OpenCV + newWidth = target_size[0] + newHeight = target_size[1] + + # Calculate the aspect ratios of the original and target sizes + aspect_ratio_original = originalWidth / originalHeight + aspect_ratio_target = newWidth / newHeight + + # Determine the resizing factor and size for maintaining the aspect ratio + if aspect_ratio_original > aspect_ratio_target: + newHeight = int(newWidth / aspect_ratio_original) + else: + newWidth = int(newHeight * aspect_ratio_original) + + # Resize the image while maintaining the aspect ratio + resized_image = cv2.resize(image, (newWidth, newHeight)) + + # Create a new image with a black background + new_image = np.zeros((target_size[1], target_size[0], 3), dtype=dataType) + + # Calculate the position to paste the resized image onto the new image + x_offset = (target_size[0] - newWidth) // 2 + y_offset = (target_size[1] - newHeight) // 2 + + # Paste the resized image onto the new image + new_image[y_offset:y_offset + newHeight, x_offset:x_offset + newWidth] = resized_image + + keypointXMultiplier = newWidth / originalWidth + keypointYMultiplier = newHeight / originalHeight + keypointXOffset = x_offset + keypointYOffset = y_offset + + return new_image, keypointXMultiplier, keypointYMultiplier, keypointXOffset, keypointYOffset + + except Exception as e: + print(f"Error resizing image: {e}") + return image, 0.0, 0.0, 0.0, 0.0 +#------------------------------------------------------------------------------- +def emptyImage(labels=None, target_size=(300, 300), output_target_size=(64, 64), heatmapActive=0, heatmapDeactivated=255): + # Create an empty black image + image = np.zeros((target_size[1], target_size[0], 3), dtype=dataType) + + # Fill the image with randomized grayscale values + image[:, :] = np.ones((target_size[1], target_size[0],3), dtype=dataType) * 255 + scale = np.random.rand() + image = image * (scale) + + heatmap = np.full((output_target_size[1], output_target_size[0], len(labels)+1), heatmapDeactivated, dtype=dataType) + heatmap[:,:,-1] = heatmapActive + + return image, heatmap +#------------------------------------------------------------------------------- +def syntheticImage(labels=None, target_size=(300, 300), output_target_size=(64, 64), heatmapActive=0, heatmapDeactivated=255): + # Create an empty black image + image = np.zeros((target_size[1], target_size[0], 3), dtype=dataType) + + # Fill the image with randomized grayscale values + image[:, :] = np.random.rand(target_size[1], target_size[0], 3) * 255 + + heatmap = np.full((output_target_size[1], output_target_size[0], len(labels)+1), heatmapDeactivated, dtype=dataType) + heatmap[:,:,-1] = heatmapActive + + return image, heatmap +#------------------------------------------------------------------------------- +def add_gaussian_noise(image, maxValue=1.0, magnitude=0.01): + # Generate Gaussian noise with the same shape as the input image + noise = np.random.normal(scale=magnitude, size=image.shape) + + # Add noise to the image + corrupted_image = image + noise + + # Clip values to be within [0, 1] + corrupted_image = np.clip(corrupted_image, 0, maxValue) + + return corrupted_image +#------------------------------------------------------------------------------- +def generate_one_hot_images(keypointsList,labels, target_size=(64, 64), heatmapActive=0, heatmapDeactivated=255, simple=True): + num_labels = len(labels) + numberOfHeatmaps = num_labels + 1 #Labels + Bkg label + heatmaps = np.full((target_size[1], target_size[0], numberOfHeatmaps), heatmapDeactivated, dtype=dataType) + heatmaps[:,:,-1] = heatmapActive + + """ + # Define the kernel outside the loop + kernel = np.array([[0.2, 0.4, 0.2], + [0.4, 1.0, 0.4], + [0.2, 0.4, 0.2]]) * heatmapActive + # Normalize the kernel to ensure that the total intensity remains the same + kernel /= np.sum(kernel) #Don;t normalize to boost gradient + """ + + for i in range(num_labels): + #For each joint Label for each keypoint list + for keypoints in keypointsList: + x = int(keypoints[i*3+0]*target_size[0]) + y = int(keypoints[i*3+1]*target_size[1]) + v = keypoints[i*3+2] + if (x!=0) and (y!=0) and (v!=0): + #if (simple): + heatmaps[y,x,i] = heatmapActive #1 hot + heatmaps[y,x,-1] = heatmapDeactivated + """ + else: + #Complex pattern for bigger training targets.. + if (y <= 1 or y >= target_size[1]-1 ) or (x <= 1 or x >= target_size[0]-1): + # If at the border, set the center pixel to v without interpolation + heatmap[y,x,i] = heatmapMagnitude + union_heatmap[y,x,-1] = 0 + else: + heatmap[y-1:y+2, x-1:x+2,i] += kernel #more spread.. + union_heatmap[y-1:y+2, x-1:x+2,-1] -= kernel + """ + #----------------------------------------------------------------------------- + + #add union heatmap making sure it is in range + #heatmaps = np.clip(heatmaps, 0, heatmapMagnitude) + return heatmaps +#------------------------------------------------------------------------------- +def processImage(inputDataset,outputDataset,sampleNumber,image_path, keypointsList=list(), labels=list(), target_size=(300, 300), output_target_size=(64,64), augment=False, heatmapActive=0, heatmapDeactivated=255): + # Step 1: Read the image using OpenCV + image_raw = cv2.imread(image_path) + # Step 2: Convert the image to float32 + image = image_raw.astype(dataType) + + #if (augment): + # image = add_gaussian_noise(image, maxValue=1.0, magnitude=0.01) # <- This is not working well.. + + originalWidth = image.shape[1] + originalHeight = image.shape[0] + keypointXMultiplier = 1.0 + keypointYMultiplier = 1.0 + keypointXOffset = 0.0 + keypointYOffset = 0.0 + + image, keypointXMultiplier, keypointYMultiplier, keypointXOffset, keypointYOffset = resize_image_with_borders(image,target_size) + inputDataset[sampleNumber,:,:,:] = image + resizedWidth = image.shape[1] + resizedHeight = image.shape[0] + + for keypoints in keypointsList: + numberOfJoints = len(keypoints) + for i in range(0,int(numberOfJoints/3)): + x = (keypointXMultiplier * keypoints[i*3+0]) + keypointXOffset + y = (keypointYMultiplier * keypoints[i*3+1]) + keypointYOffset + v = keypoints[i*3+2] + if v > 0: # Only correct visible keypoints + #Correct keypoints + keypoints[i*3+0] = x / target_size[0] + keypoints[i*3+1] = y / target_size[1] + + # Calculate the heatmaps + oneHotImages = generate_one_hot_images(keypointsList, labels, target_size=output_target_size, heatmapActive=heatmapActive, heatmapDeactivated=heatmapDeactivated) + outputDataset[sampleNumber,:,:,:] = oneHotImages + + return inputDataset , outputDataset +#------------------------------------------------------------------------------- +def compileJSONAssociations(json_data): + print("Compiling annotation->image associations") + annotations={} + for annotation in json_data['annotations']: + image_id = annotation['image_id'] + if image_id not in annotations: + annotations[image_id] = [] + annotations[image_id].append(annotation) + return annotations +#------------------------------------------------------------------------------- +def createTrainingSetFromJSONFile(jsonPath, + database, + cache_directory = 'cache/data/coco/val2017/', + start_index = 0, + end_index = None, + target_size = (200,200), + output_target_size = (120,120), + RGBMagnitude = 255, + heatmapActive = 0, + heatmapDeactivated = 255, + mem = 1.0, + visualize = False, + preloadedJSON = None, + preloadedAssociations = None + ): + # Call the function to read the JSON file + if (not preloadedJSON): + json_data = read_json_file(jsonPath) + print("JSON has ",len(json_data['annotations'])," annotations") + print("JSON has ",len(json_data['images'])," images") + else: + json_data = preloadedJSON + + augment = False + if ("train" in jsonPath) and (not preloadedJSON): + augment = True + + msgTick = 0 + labels = json_data['categories'][0]['keypoints'] + + # Check if the file was successfully read + if json_data: + if not os.path.exists(cache_directory): + # Create the 'cache' directory if it doesn't exist to allow downloading to work + os.makedirs(cache_directory) + print(f"Directory '{cache_directory}' created.") + + # Create a dictionary to organize annotations by image ID and optimize reading if not already populated + if (preloadedAssociations): + annotations_of_image = preloadedAssociations + else: + annotations_of_image = compileJSONAssociations(json_data) + + #Decide on the number of entries to read + end_index = end_index or len(json_data['images']) + if ("train" in jsonPath) and (mem!=1.0): + end_index = start_index + int(mem) + + #Allocate all of the I/O tensors in a contiguous memory block in a single step + inputDataset = np.zeros((end_index - start_index,*target_size,3), dtype=dataType) + outputDataset = np.zeros((end_index - start_index,*output_target_size,len(labels)+1), dtype=dataType) + + for imageID in range(start_index,end_index): + source = "%s/%s" % (database,json_data['images'][imageID]["file_name"]) + cacheTarget = "%s/%s" % (cache_directory,json_data['images'][imageID]["file_name"]) + download_image(source,cacheTarget) + + width = int(json_data['images'][imageID]["width"]) + height = int(json_data['images'][imageID]["height"]) + ID = int(json_data['images'][imageID]["id"]) + + keypointsList = list() + numberOfSkeletons = 0 + + if ID in annotations_of_image: + annotations = annotations_of_image[ID] + for annotation in annotations: + keypointsList.append(annotation["keypoints"]) + numberOfSkeletons = numberOfSkeletons + 1 + #break #<- Single person + + if (msgTick%31==0) and not preloadedJSON : + print(f"\r Image ID: {imageID} /",len(json_data['images']) , end=" ") + print(f"Width: {width}, Height: {height}, Skeletons: {numberOfSkeletons} \r",end=" ") + msgTick = msgTick + 1 + + inputDataset,outputDataset = processImage( + inputDataset, + outputDataset, + imageID, + cacheTarget, + keypointsList, + labels=labels, + target_size=target_size, + output_target_size = output_target_size, + heatmapActive = heatmapActive, + heatmapDeactivated = heatmapDeactivated, + augment=augment + ) + """ + if (augment) and not preloadedJSON: + #Add random noise Images.. + for syntheticID in range(100): + thisInput,thisOutputs = syntheticImage(labels=json_data['categories'][0]['keypoints'], + target_size=target_size, + output_target_size=output_target_size, + heatmapMagnitude=heatmapMagnitude + ) + inputs.append(thisInput) + outputs.append(thisOutputs) + + #Add random empty Images.. + for emptyID in range(100): + thisInput,thisOutputs = emptyImage(labels=json_data['categories'][0]['keypoints'], + target_size=target_size, + output_target_size=output_target_size, + heatmapMagnitude=heatmapMagnitude + ) + inputs.append(thisInput) + outputs.append(thisOutputs) + """ + return inputDataset,outputDataset,labels + return inputs,outputs,labels +#============================================================================================ +#============================================================================================ +#============================================================================================ +#============================================================================================ +#============================================================================================ +class TrainingDataGenerator(tf.keras.utils.Sequence): + def __init__(self, json_path, database, cache_directory, target_size, output_target_size, RGBMagnitude, heatmapActive, heatmapDeactivated, mem, visualize, batch_size): + self.json_path = json_path + self.json_data = read_json_file(json_path) + self.labels = self.json_data['categories'][0]['keypoints'] + self.associations = compileJSONAssociations(self.json_data) + self.database = database + self.cache_directory = cache_directory + self.target_size = target_size + self.output_target_size = output_target_size + self.RGBMagnitude = RGBMagnitude + self.heatmapActive = heatmapActive, + self.heatmapDeactivated = heatmapDeactivated, + self.mem = mem + self.visualize = visualize + self.batch_size = batch_size + + # Load JSON data + self.json_data = read_json_file(json_path) + self.indices = np.arange(len(self.json_data['images'])) + np.random.shuffle(self.indices) + + def __len__(self): + return len(self.indices) // self.batch_size + + def __getitem__(self, index): + start_index = index * self.batch_size + end_index = min(start_index + self.batch_size, len(self.indices)) + batch_indices = self.indices[start_index:end_index] + + xs = np.zeros((len(batch_indices), self.target_size[1], self.target_size[0], 3), dtype=dataType) + ys = np.zeros((len(batch_indices), len(self.labels) + 1, self.output_target_size[1], self.output_target_size[0]), dtype=dataType) + + for i, imageID in enumerate(batch_indices): + source = f"{self.database}/{self.json_data['images'][imageID]['file_name']}" + cacheTarget = f"{self.cache_directory}/{self.json_data['images'][imageID]['file_name']}" + + inputs, outputs, labels = createTrainingSetFromJSONFile( + self.json_path, + self.database, + self.cache_directory, + target_size = self.target_size, + output_target_size = self.output_target_size, + RGBMagnitude = self.RGBMagnitude, + heatmapActive = self.heatmapActive, + heatmapDeactivated = self.heatmapDeactivated, + mem = self.mem, + visualize = self.visualize, + start_index = imageID, + end_index = imageID + 1, + preloadedJSON = self.json_data, + preloadedAssociations = self.associations + ) + xs[i] = inputs[0] + ys[i] = outputs[0] + + return xs, ys + +def streamDataset(json_path, + database, + cache_directory = 'cache/data/coco/val2017/', + target_size = (200, 200), + output_target_size = (64, 64), + RGBMagnitude = 255, + heatmapActive = 0, + heatmapDeactivated = 255, + mem = 1.0, + visualize = False, + batch_size = 48, + shuffle_buffer_size = 100 + ): + json_data = read_json_file(json_path) + labels = json_data['categories'][0]['keypoints'] + datasetLength = len(json_data['images']) + + dataset_generator = TrainingDataGenerator(json_path, database, cache_directory, target_size, output_target_size, RGBMagnitude, heatmapActive, heatmapDeactivated, mem, visualize, batch_size) + + dataset = tf.data.Dataset.from_generator( + lambda: iter(dataset_generator), + output_signature=( + tf.TensorSpec(shape=(batch_size, target_size[1], target_size[0], 3), dtype=dataTypeTF), + tf.TensorSpec(shape=(batch_size, len(labels) + 1, output_target_size[1], output_target_size[0]), dtype=dataTypeTF) + ) + ) + + dataset = dataset.shuffle(buffer_size=shuffle_buffer_size) + dataset = dataset.repeat() # Add repeat to make the dataset repeat indefinitely + dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE) + + return dataset, datasetLength, labels +#============================================================================================ +#============================================================================================ +# Main Function +if __name__ == '__main__': + cfg = { + 'COCOTrainingJSONPath':'cache/data/annotations/person_keypoints_train2017.json', + 'COCOTrainingURI':'http://ammar.gr/COCO_HumanPose/data/coco/train2017', + 'COCOTrainingLocalCache':'cache/data/coco/train2017/', + + 'COCOValidationJSONPath':'cache/data/annotations/person_keypoints_val2017.json', + 'COCOValidationURI':'http://ammar.gr/COCO_HumanPose/data/coco/val2017', + 'COCOValidationLocalCache':'cache/data/coco/val2017/', + + 'inputWidth' :220, #140 + 'inputHeight' :220, #140 + 'outputWidth' :96, + 'outputHeight':96, + + 'dropoutRate':0.1, + 'midSectionRepetitions':5, + 'activation':'relu', + 'baseChannels' : 78, + + 'RGBMagnitude': 255, + 'heatmapActive': 0, + 'heatmapDeactivated': 255, + 'streamDataset': False, + 'streamBufferLength': 100, + + 'earlyStoppingPatience':3, + 'earlyStoppingMinDelta':0.0001, + 'datasetUsage':1.0, + 'learningRate':0.0005, + 'batchSize':24, + 'epochs':54, #54 + 'pCK_AP_Threshold':0.05, + 'loss':'mse' + } + + if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--mem"): + cfg['datasetUsage']=float(sys.argv[i+1]) + if (sys.argv[i]=="--stream"): + cfg['streamDataset'] = True + if (sys.argv[i]=="--clear"): + os.system("rm -rf 2d_pose_estimation/tensorboard") + os.system("rm 2d_pose_estimation.zip") + if (sys.argv[i]=="--test"): + model = create_keypoints_model( + cfg['inputHeight'], + cfg['inputWidth'], + 3, + cfg['outputWidth'], + cfg['outputHeight'], + 17+1, + midSectionRepetitions = cfg['midSectionRepetitions'], + activation = cfg['activation'], + baseChannels = cfg['baseChannels'], + dropoutRate = cfg['dropoutRate'] + ) + #hourglass_model = create_hourglass_keypoints_model( input_shape=(cfg['inputHeight'],cfg['inputWidth'],3), num_points=17+1) + sys.exit(0) + + # Set up TensorBoard logging + log_dir = "2d_pose_estimation/tensorboard/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") + tensorboard_callback = TensorBoard(log_dir=log_dir, histogram_freq=1) + + + #First of all create the Neural Network model + #Don't load other data in vain if this step fails due to bad configuration.. + #model = create_hourglass_keypoints_model( input_shape=(cfg['inputHeight'],cfg['inputWidth'],3), num_points=18) + model = create_keypoints_model( + cfg['inputHeight'], + cfg['inputWidth'], + 3, + cfg['outputWidth'], + cfg['outputHeight'], + 18, + midSectionRepetitions = cfg['midSectionRepetitions'], + activation = cfg['activation'], + baseChannels = cfg['baseChannels'], + dropoutRate = cfg['dropoutRate'] + ) + cfg['outputWidth'],cfg['outputHeight'],numHeatmaps = retrieveModelOutputDimensions(model) + + if (cfg['streamDataset']): + mem=1.0 #When streaming use everything.. + + #Validation data is not so big and is loaded first in memory.. + onlyTrainingData = True + if (checkIfFileExists(cfg['COCOValidationJSONPath'])): + onlyTrainingData = False + rawValInputs,rawValOutputs,outValLabels = createTrainingSetFromJSONFile( + cfg['COCOValidationJSONPath'], + cfg['COCOValidationURI'], + cfg['COCOValidationLocalCache'], + target_size=(cfg['inputHeight'],cfg['inputWidth']), + output_target_size=(cfg['outputHeight'],cfg['outputWidth']), + heatmapActive = cfg['heatmapActive'], + heatmapDeactivated = cfg['heatmapDeactivated'] + ) + val_inputs = tf.constant(np.array(rawValInputs), dtype=dataTypeTF) + val_outputs = tf.constant(np.array(rawValOutputs), dtype=dataTypeTF) + validationDataset = tf.data.Dataset.from_tensor_slices((val_inputs, val_outputs)) + logSomeInputsAndOutputs(rawValInputs, rawValOutputs, outValLabels, log_dir, samples=100) + + + #The training set is very large, so depending on the system there are two ways to use it + #try streaming it which is very slow due to I/O operations but can work on small VRAM systems + #or load it all in memory and use regular TF mechanisms to train on it + if (checkIfFileExists(cfg['COCOTrainingJSONPath'])): + if (cfg['streamDataset']): + # Modify the way you call the dataset + trainingDataset,trainingDatasetLength,outLabels = streamDataset( + cfg['COCOTrainingJSONPath'], + cfg['COCOTrainingURI'], + cfg['COCOTrainingLocalCache'], + target_size=(cfg['inputHeight'], cfg['inputWidth']), + output_target_size=(cfg['outputHeight'], cfg['outputWidth']), + RGBMagnitude=cfg['RGBMagnitude'], + heatmapActive = cfg['heatmapActive'], + heatmapDeactivated = cfg['heatmapDeactivated'], + batch_size=cfg['batchSize'], + shuffle_buffer_size=cfg['streamBufferLength'] + ) + stepsPerEpoch = trainingDatasetLength // cfg['batchSize'] + else: + rawInputs,rawOutputs,outLabels = createTrainingSetFromJSONFile( + cfg['COCOTrainingJSONPath'], + cfg['COCOTrainingURI'], + cfg['COCOTrainingLocalCache'], + target_size=(cfg['inputHeight'],cfg['inputWidth']), + output_target_size=(cfg['outputHeight'],cfg['outputWidth']), + RGBMagnitude=cfg['RGBMagnitude'], + heatmapActive = cfg['heatmapActive'], + heatmapDeactivated = cfg['heatmapDeactivated'], + mem=cfg['datasetUsage'] + ) + trainingDatasetLength = len(rawInputs) + print("Number of samples:", trainingDatasetLength ) + inputs = tf.constant(np.array(rawInputs) , dtype=dataTypeTF) + outputs = tf.constant(np.array(rawOutputs), dtype=dataTypeTF) + #inputs,outputs = flip_data(inputs,outputs,flip_x=True,flip_y=False) + print("Inputs shape:", inputs.shape) + print("Outputs shape:", outputs.shape) + trainingDataset = tf.data.Dataset.from_tensor_slices((inputs, outputs)).batch(cfg['batchSize']) + stepsPerEpoch = None #<- None is the default and lets TF manage this value + + + # Print the shapes of inputs and outputs + print("Training Configuration :", cfg) + logTrainingParameters(cfg,log_dir) + + + + # Initialize the Adam Optimizer using configuration + optimizer = tf.keras.optimizers.Adam(learning_rate=cfg['learningRate']) + + # Define EarlyStopping callback + from tensorflow.keras.callbacks import EarlyStopping + early_stopping = EarlyStopping( + #Regular loss monitoring + monitor = 'loss', # Monitor the loss metric + mode = 'min', # Mode should be 'min' because we want to minimize the loss metric + + #Smarter pck monitoring + #monitor = 'val_pck_metric', # Monitor the PCK metric + #mode = 'max', # Mode should be 'max' because we want to maximize the PCK metric + + patience = cfg['earlyStoppingPatience'], # Number of epochs with no improvement after which training will be stopped + min_delta = cfg['earlyStoppingMinDelta'], # Minimum change in the monitored quantity to qualify as an improvement + verbose = 1, # Set to 1 for more verbose output + restore_best_weights = True # Restore model weights from the epoch with the best value of the monitored quantity + ) + + # Create the PCKMetric to have a better grasp of what is happening with the model + pck_metric = PCKMetric(threshold=cfg['pCK_AP_Threshold']) + rsq_metric = RSquaredMetric() + + #Compile a model with the requested loss + if (cfg['loss']=="focal"): + model.compile(optimizer=optimizer, loss=focal_loss, metrics=[pck_metric,rsq_metric]) + elif (cfg['loss']=="mse"): + model.compile(optimizer=optimizer, loss=vanilla_mse_loss, metrics=[pck_metric,rsq_metric]) + elif (cfg['loss']=="jointsMSE"): + model.compile(optimizer=optimizer, loss=jointsMSE_loss, metrics=[pck_metric,rsq_metric]) + else: + model.compile(optimizer=optimizer, loss=cfg['loss'], metrics=[pck_metric,rsq_metric]) + + #Before starting training log TF Versions + printTFVersion() + + #Printout data in screen + #-------------------------------------------------------------------------------------------------------------------------------- + if dataType == np.float32: + bytesPerValue = 4 + else: + bytesPerValue = 1 + estimatedInputByteSize = cfg['inputWidth'] * cfg['inputHeight'] * 3 * trainingDatasetLength * bytesPerValue + estimatedOutputByteSize = cfg['outputWidth'] * cfg['outputHeight'] * (1+len(outLabels)) * trainingDatasetLength * bytesPerValue + print("Input Data Size : ",convert_bytes(estimatedInputByteSize)) + print("Output Data Size : ",convert_bytes(estimatedOutputByteSize)) + print("Total Data Size : ",convert_bytes(estimatedInputByteSize+estimatedOutputByteSize)) + #-------------------------------------------------------------------------------------------------------------------------------- + + # Train the model + if (onlyTrainingData): + model.fit( + trainingDataset, + batch_size = cfg['batchSize'], + epochs = cfg['epochs'], + validation_split = 0.2, + callbacks = [tensorboard_callback,early_stopping], + steps_per_epoch = stepsPerEpoch + ) + else: + model.fit( + trainingDataset, + batch_size = cfg['batchSize'], + epochs = cfg['epochs'], + validation_data = validationDataset.batch(cfg['batchSize']), + callbacks = [tensorboard_callback,early_stopping], + steps_per_epoch = stepsPerEpoch + ) + + # Save the trained model + print('Saving result model') + model.save('2d_pose_estimation', save_format='tf') + + print('PCK threshold was set to ',pck_metric.threshold) + + print('Training complete') + + os.system("date +\"%y-%m-%d_%H-%M-%S\" > 2d_pose_estimation/date.txt") #Tag date + os.system("zip -r 2d_pose_estimation.zip 2d_pose_estimation/") #Create zip of models + print('You can see a summary using :\n tensorboard --logdir=2d_pose_estimation/tensorboard --bind_all && firefox http://127.0.0.1:6006') + + print('Attempting to upload results (if you take too long it will timeout)') + os.system("scp -P 2222 2d_pose_estimation.zip ammar@ammar.gr:/home/ammar/public_html") + print("scp -P 2222 2d_pose_estimation.zip ammar@ammar.gr:/home/ammar/public_html") + diff --git a/animation/MocapNET-kasisnu/src/python/2d_pose_estimation/runCOCO.py b/animation/MocapNET-kasisnu/src/python/2d_pose_estimation/runCOCO.py new file mode 100644 index 0000000..b2289ac --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/2d_pose_estimation/runCOCO.py @@ -0,0 +1,177 @@ +import sys +import cv2 +import numpy as np +import tensorflow as tf +from tensorflow.keras.models import load_model +from readCOCO import resize_image_with_borders + + +dataType = np.uint8 # + +useGPU = True +if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--float32"): + dataType = np.float32 + if (sys.argv[i]=="--uint8"): + dataType = np.uint8 + if (sys.argv[i]=="--cpu"): + useGPU = False + +# Set CUDA_VISIBLE_DEVICES to an empty string to force TensorFlow to use the CPU +if (not useGPU): + os.environ['CUDA_VISIBLE_DEVICES'] = '' #<- Force CPU + +def load_keypoints_model(model_path): + from readCOCO import PCKMetric,RSquaredMetric,FocalLoss,focal_loss,JointsMSELoss,jointsMSE_loss,VanillaMSELoss,vanilla_mse_loss + model = load_model(model_path, custom_objects={'PCKMetric': PCKMetric, 'RSquaredMetric':RSquaredMetric, 'focal_loss': focal_loss, 'jointsMSE_loss': jointsMSE_loss, 'vanilla_mse_loss':vanilla_mse_loss}) + + # Check the shape of the input layer + input_layer = model.layers[0] # Assuming the input layer is the first layer + input_shape = input_layer.input_shape + input_size = (input_shape[0][1],input_shape[0][2]) + print("Input shape is ",input_size) + + # Get the output layer of the model + output_layer = model.layers[-1] # Assuming the output layer is the last layer + output_shape = output_layer.output_shape + output_size = (output_shape[2],output_shape[3]) + numberOfHeatmaps = output_shape[1] + print("Number of Heatmaps is ", numberOfHeatmaps) + print("Output Shape is ", output_size) + + return model,input_size,output_size,numberOfHeatmaps + +def preprocess_image(frame, target_size=(128, 128)): + # Resize the frame to the target size and normalize pixel values + # Step 1: Convert the image to float32 + image = frame.astype(dataType) + + #print("Input Image Range:", np.min(image), np.max(image)) + #print("Input Image Mean:", np.mean(image)) + #print("Input Image STD:", np.std(image)) + + # Step 2: Normalize the pixel values to be in the range [0, 1] + #image = image / 255 + image, keypointXMultiplier, keypointYMultiplier, keypointXOffset, keypointYOffset = resize_image_with_borders(image,target_size) + + return image + +def predict_keypoints(model, image): + # Use the model call for predictions + image_batch = np.expand_dims(image, axis=0) + predictions = model(image_batch, training=False) + return predictions[0] + + +def visualize_heatmaps(frame, frameNumber, heatmaps, keypoint_names, threshold=0.0): + i=0 + wnd_x = 0 + wnd_y = 0 + + #Scale back window + #rgb_uint8_image = frame * 255 + rgb_uint8_image = frame + + # Convert to uint8 type for display + rgb_uint8_image = np.uint8(rgb_uint8_image) + + # Display the result + cv2.imshow('RGB Input', rgb_uint8_image) + if (frameNumber==0): + cv2.moveWindow("RGB Input", wnd_x, wnd_y) + wnd_y+=231 + + + #print("Heatmaps ",heatmaps.shape) + for i in range(0,heatmaps.shape[2]): + heatmap = heatmaps[:,:,i] + resized_heatmap = np.array(heatmap,dtype=dataType) + + if (threshold>0.0): + # Create a boolean mask for values above the threshold + resized_heatmap[resized_heatmap <= threshold] = 0 + + #heatmap_uint8 = np.uint8(resized_heatmap) + #print("Heatmap ",i," shape ",heatmap.shape) + cv2.imshow('Heatmap %s'% keypoint_names[i], resized_heatmap) + if (frameNumber==0): + cv2.moveWindow('Heatmap %s'% keypoint_names[i], wnd_x, wnd_y) + wnd_y+=170 + if ( wnd_y > 900 ): + wnd_x+=384 + wnd_y =0 + + + i=i+1 + + cv2.waitKey(1) + +def webcam_keypoints_detection(model_path, keypoint_names, threshold=0.0): + # Load the 2D Pose Estimation model + keypoints_model,input_size,output_size,numberOfHeatmaps = load_keypoints_model(model_path) + + # Open a connection to the webcam (0 indicates the default camera) + cap = cv2.VideoCapture(0) + + frameNumber = 0 + + while True: + # Capture a single frame from the webcam + ret, frame = cap.read() + if not ret: + print("Failed to capture frame") + break + + # Preprocess the frame for the model + input_image = preprocess_image(frame, target_size=input_size) + + # Make predictions using the model + keypoints_predictions = predict_keypoints(keypoints_model, input_image) + + #i=0 + #for keypoint in keypoints_predictions: + # print("Prediction ",keypoint_names[i]," Range:", np.min(keypoint), np.max(keypoint)) + # print("Prediction ",keypoint_names[i]," Mean:", np.mean(keypoint)) + # print("Prediction ",keypoint_names[i]," STD:", np.std(keypoint)) + # i=i+1 + + # Visualize the heatmaps on the source image + visualize_heatmaps(input_image,frameNumber, keypoints_predictions, keypoint_names, threshold=threshold) + frameNumber = frameNumber + 1 + + # Release the webcam and close all OpenCV windows + cap.release() + cv2.destroyAllWindows() + +if __name__ == '__main__': + # Specify the path to the trained 2D Pose Estimation model + model_path = '2d_pose_estimation' + + # Specify the names of keypoints (change accordingly based on your model's keypoint order) + keypoint_names = [ + "nose", + "left_eye", + "right_eye", + "left_ear", + "right_ear", + "left_shoulder", + "right_shoulder", + "left_elbow", + "right_elbow", + "left_wrist", + "right_wrist", + "left_hip", + "right_hip", + "left_knee", + "right_knee", + "left_ankle", + "right_ankle", + "bkg" + ] + + print("Reported Keypoints : ",keypoint_names) + # Run the webcam keypoints detection + webcam_keypoints_detection(model_path, keypoint_names, threshold = 0.0) + diff --git a/animation/MocapNET-kasisnu/src/python/blender/blender_face.py b/animation/MocapNET-kasisnu/src/python/blender/blender_face.py new file mode 100644 index 0000000..0cad7cd --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/blender_face.py @@ -0,0 +1,1611 @@ +#Written by Ammar Qammaz 2022-2023 +#This is a Blender Python script that upon loaded can facilitate animating a skinned model created by +#the MakeHuman plugin for Blender ( http://static.makehumancommunity.org/mpfb.html ) +mnetPluginVersion=float(0.34) + +import bpy +from bpy.props import EnumProperty + +import os +import random +import math +import gc +import numpy as np +import array +import csv + + +#Steps to generate a good dataset! +#Load a BVH file and point to it +#Load an armature and point to it +#Run this python script in blender +#Run : sobolRandomDistributionForFace.py to generate a sobol/quasi-random dataset +#Point to the target dataset directory in Dataset Path +#Point to your dataset in Dataset Path: and click Create Dataset from CSV file +# Either : +# run mediapipeDumpHead2DFromRGB.py --from your dataset path +# or +# run associate2DFiles.py to remake associations , update them here and rely on them as 2D data +csvResolutionErrors = 0 + + +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + +class vertexHolder(): + def __init__(self,v,vID): + self.co = v.co + self.index = vID + +def timeDuration(startTimeInSeconds,endTimeInSeconds): + timeElapsed = endTimeInSeconds - startTimeInSeconds + timeUnit = "seconds" + if (timeElapsed>86400): + timeElapsed = timeElapsed / 86400 + timeUnit = "days" + elif (timeElapsed>3600): + timeElapsed = timeElapsed / 3600 + timeUnit = "hours" + return timeElapsed,timeUnit + +def printSelectedVertex(): + mode = bpy.context.active_object.mode + # Keep track of previous mode + bpy.ops.object.mode_set(mode='OBJECT') + # Go into object mode to update the selected vertices + + obj = bpy.context.object + # Get the currently select object + sel = np.zeros(len(obj.data.vertices), dtype=np.bool) + # Create a numpy array with empty values for each vertex + + obj.data.vertices.foreach_get('select', sel) + # Populate the array with True/False if the vertex is selected + + for ind in np.where(sel==True)[0]: + # Loop over each currently selected vertex + v = obj.data.vertices[ind] + print(bcolors.OKGREEN) + print('Vertex {} at position {} is selected'.format(v.index, v.co)) + print(bcolors.ENDC) + # If you just want the first one you can break directly here + # break + + bpy.ops.object.mode_set(mode=mode) + + +def combineCSVFiles(outputCSVPath,inputCSVPathList): + targetPath = bpy.context.scene.datasetPath + filenames = list() + #================================================= + for csvF in inputCSVPathList.keys(): + #print(sys.argv[i]) + csvFilename = "%s/2d_face_all_blender_%s.csv" % (targetPath,csvF) + filenames.append(csvFilename) + + numberOfFiles = len(filenames) + #================================================= + files = list() + for i in range(0,numberOfFiles): + f = open(filenames[i],'r') + files.append(f) + #================================================= + + #================================================= + f = open(outputCSVPath, 'w') + #================================================= + readingMoreLines = True + while readingMoreLines: + thisTotalLine = "" + for i in range(0,numberOfFiles): + line = files[i].readline().replace('\n', '') + #-------------------------------- + if (i!=0): + f.write(",") + f.write(line) + #-------------------------------- + if not line: + readingMoreLines = False + break + if (readingMoreLines): + f.write("\n") + #================================================= + f.close() + #================================================= + for i in range(0,numberOfFiles): + files[i].close() + #================================================= + + return + #----------------------------------------------------------------- + + +def write_csv_2d_data_header(csvFilename,vertices,verticeCSVWhitelist=dict()): + f = open(csvFilename, 'w') + print(csvFilename," Number of vertices :",len(vertices)) + for v in range(0,len(vertices)): + if (v>0): + f.write(',') + if 'label' in verticeCSVWhitelist: + f.write('2DX_%s,2DY_%s,visible_%s' % (verticeCSVWhitelist['label'][v],verticeCSVWhitelist['label'][v],verticeCSVWhitelist['label'][v])) + else: + f.write('2DX_v%u,2DY_v%u,visible_%u' % (v,v,v)) + f.write('\n') + f.close() + +def write_vertex_csv_2d_data(objName="",baseDirectory="/home/ammar/",fID=0,csvFile=True,svgFile=True,verticeCSVWhitelistForAllObjects=dict()): + import bpy_extras + from bpy_extras.object_utils import world_to_camera_view + + #--------------------------------------------------------------------------- + if (objName==""): + print("Cannot write_vertex_csv_2d_data without an obj name!") + return + #--------------------------------------------------------------------------- + + #--------------------------------------------------------------------------- + verticeCSVWhitelist = dict() + if (len(verticeCSVWhitelistForAllObjects.keys())>0 ): #There is a whitelist declared a.k.a. dict not empty! + if objName in verticeCSVWhitelistForAllObjects: + verticeCSVWhitelist = verticeCSVWhitelistForAllObjects[objName] + else: + #If vertice whitelists exist but not for the particular object then + #we completely ignore the object and just return.. + #print("No vertex whitelist for ",objName,end=" ") + #print("We assume that this means that the whole object is blacklisted! ") + return + #if there is no white list declared we go on as usual dumping everything.. + #--------------------------------------------------------------------------- + + # Get the active object + scene = bpy.context.scene + camera = scene.objects.get("Camera") #bpy.context.scene.camera + obj = scene.objects.get(objName) # "newgirl.body" + + #Apply modifiers + dg = bpy.context.evaluated_depsgraph_get() + obj = obj.evaluated_get(dg) + mesh = obj.to_mesh(preserve_all_data_layers=True, depsgraph=dg) + + #Point vertices to either all vertices or a specific list of vertices + if ('label' in verticeCSVWhitelist) and ('body' in verticeCSVWhitelist): + #We have an active list of vertices to select/transform so we will be economic + #print(verticeCSVWhitelist['label']) + numberOfVertices = len(verticeCSVWhitelist['label']) + vertices = list() + for i in range (0,numberOfVertices): + vertexID = int(verticeCSVWhitelist['body'][0][i]) + newV = vertexHolder(mesh.vertices[vertexID],vertexID) + newV.co = obj.matrix_world @ newV.co + vertices.append(newV) + else: + #Just transform all vertices and pass them all + mesh.transform(obj.matrix_world) # apply loc/rot/scale + vertices = mesh.vertices + + render_scale = scene.render.resolution_percentage / 100 + render_size = (int(scene.render.resolution_x * render_scale),int(scene.render.resolution_y * render_scale),) + width = render_size[0] + height = render_size[1] + + #Zoomed Debugging Vertices + zoom = bpy.context.scene.zoomSVG + if (zoom): + width = 10*width + height = 10*height + + #---------------------------------------------------------------------- + csvFilename = "%s/2d_face_all_blender_%s.csv" % (baseDirectory,objName) + if (fID==0): + write_csv_2d_data_header(csvFilename=csvFilename,vertices=vertices,verticeCSVWhitelist=verticeCSVWhitelist) + if (csvFile): + fCSV = open(csvFilename, 'a') + #---------------------------------------------------------------------- + if (svgFile): + f = open('%s/blender_%s_face_dataset_%04u.svg'%(baseDirectory,objName,fID), 'w') + f.write('\n'%(height,width)) + f.write('\n'%(width,height)) + #---------------------------------------------------------------------- + + + vNum = 0 + for v in vertices: + co_final= v.co# @ obj.matrix_world + # Get the 2D projection of the vertex + coords_2d = bpy_extras.object_utils.world_to_camera_view(bpy.context.scene,camera,co_final) + #print("world_to_camera_view :",coords_2d) + x = coords_2d.x * width + y = (1.0-coords_2d.y) * height + visible = 0.0 + if (0'%(round(x),round(y))); + if ('label' in verticeCSVWhitelist): + f.write('%u %s(%u)\n' % (round(x+2),round(y),vNum,verticeCSVWhitelist['label'][vNum],v.index)); + else: + f.write('%u\n' % (round(x),round(y),vNum)); + f.write('\n' % (x,y,vNum)); + #------------- + if (csvFile): + if (vNum>0): + fCSV.write(',') + fCSV.write('%f,%f,%0.1f' % (coords_2d.x,(1.0-coords_2d.y),visible)) + #------------- + vNum = vNum + 1 + + if (svgFile): + f.write('\n') + f.close() + #--------------------------------------------------------- + if (csvFile): + fCSV.write('\n') + fCSV.close() + #print("fID ",fID," number of vertices :",len(vertices)) + return True + + +def write_csv_2d_data_all_objects(baseDirectory="/home/ammar/",fID=0,csvFile=True,svgFile=True,verticeCSVWhitelistForAllObjects=dict()): + skinnedObjectName = bpy.context.scene.mnetTarget + for obj in bpy.data.objects[skinnedObjectName].children: + #print(skinnedObjectName," has a child ",obj.name) + write_vertex_csv_2d_data( + objName=obj.name, + baseDirectory=baseDirectory, + fID=fID, + csvFile=csvFile, + svgFile=svgFile, + verticeCSVWhitelistForAllObjects=verticeCSVWhitelistForAllObjects + ) + +def write_csv_3d_data_header(filename): + scene = bpy.context.scene + obj = scene.objects.get("newgirl.body") + f = open(filename, 'w') + vertices = obj.data.vertices + print("number of vertices :",len(vertices)) + for v in range(0,len(vertices)): + if (v>0): + f.write(',') + f.write('3DX_v%u,3DY_v%u,3DZ_v%u' % (v,v,v)) + f.write('\n') + f.close() + +def write_vertex_csv_3d_data(filename="/home/ammar/",fID=0): + return get_vertex_2d_projection(filename=filename,fID=fID) + import bpy_extras + from bpy_extras.object_utils import world_to_camera_view + #----------------------------------------------------------------- + scene = bpy.context.scene + camera = scene.objects.get("Camera") #bpy.context.scene.camera + obj = scene.objects.get("newgirl.body") + vertices = obj.data.vertices + #Apply modifiers + dg = bpy.context.evaluated_depsgraph_get() + obj = obj.evaluated_get(dg) + + mesh = obj.to_mesh(preserve_all_data_layers=True, depsgraph=dg) + #co = mesh.vertices[0].co + #co_final = obj.matrix_world @ co + + #mesh = obj.to_mesh(scene, True, 'PREVIEW') # apply modifiers with preview settings + mesh.transform(obj.matrix_world) # apply loc/rot/scale + vertices = mesh.vertices + #----------------------------------------------------------------- + """ + bpy.ops.export_scene.obj( + filepath="%s/blender_%04u.obj"%(filename,fID), + check_existing=False, + axis_forward='-Z', + axis_up='Y', + filter_glob="*.obj;*.mtl", + use_selection=False, + use_animation=False, + use_mesh_modifiers=True, + use_edges=True, + use_smooth_groups=False, + use_smooth_groups_bitflags=False, + use_normals=True, + use_uvs=True, + use_materials=True, + use_triangles=False, + use_nurbs=False, + use_vertex_groups=False, + use_blen_objects=True, + group_by_object=False, + group_by_material=False, + keep_vertex_order=False, + global_scale=1, + path_mode='AUTO' +)""" + #----------------------------------------------------------------- + csvFileName = '%s/blender.csv'%filename + if (fID==0): + write_csv_3d_data_header(csvFileName) + #----------------------------------------------------------------- + f = open(csvFileName,'a') + vNum = 0 + for v in vertices: + print("v :",v.co) + #----------------------------------------------------------------- + if (vNum!=0): + f.write(',') + f.write('%f,%f,%f' % (v.co.x,v.co.y,v.co.z)) + #----------------------------------------------------------------- + vNum = vNum + 1 + #----------------------------------------------------------------- + f.write('\n') + f.close() + print("number of vertices :",len(vertices)) + return True + + +# =================================================================================================================== +# =================================================================================================================== +# =================================================================================================================== +# =================================================================================================================== +def resolveCSVRowColumn(data,label,sampleID): + #--------------------------- + column = 0 + labelLowerCase = label.lower() + for columnLabel in data['label']: + if (columnLabel.lower()==labelLowerCase): + return float(data['body'][sampleID][column]) + column = column+1 + #--------------------------- + global csvResolutionErrors + csvResolutionErrors += 1 + if (csvResolutionErrors < 100): + print("Could not resolve ",label," sample ",sampleID) + elif (csvResolutionErrors == 100): + print("Could not resolve ",label," sample ",sampleID) + print("From now on will supress error output to speed up computation ") + elif (csvResolutionErrors % 30000 == 0): + print("Reminder : Could not resolve ",label," sample ",sampleID," surpressed ",csvResolutionErrors," errors.. ") + + return float(0.0) + +def convert_bytes(num): + """ + this function will convert bytes to MB.... GB... etc + """ + step_unit = 1000.0 #1024 bad the size + + for x in ['bytes', 'KB', 'MB', 'GB', 'TB']: + if num < step_unit: + return "%3.1f %s" % (num, x) + num /= step_unit + +def getNumberOfLines(filename): + print("Counting number of lines in file ",filename) + with open(filename) as f: + return sum(1 for line in f) + +def checkIfPathExists(filename): + import os + return os.path.exists(filename) + +def checkIfFileExists(filename): + import os + return os.path.isfile(filename) + +def readCSVFile(filename,memPercentage=1.0,useHalfFloats=0): + import os + import time + print("CSV file :",filename,"..\n") + + if (not checkIfFileExists(filename)): + print( bcolors.FAIL + "Input file "+filename+" does not exist, cannot read ground truth.." + bcolors.ENDC) + print("Current Directory was "+os.getcwd()) + return dict() + start = time.time() + + dtypeSelected=np.dtype(np.float32) + dtypeSelectedByteSize=int(dtypeSelected.itemsize) + if (useHalfFloats): + dtypeSelected=np.dtype(np.float16) + dtypeSelectedByteSize=int(dtypeSelected.itemsize) + + progress=0.0 + sampleNumber=0 + receivedHeader=False + inputNumberOfColumns=0 + outputNumberOfColumns=0 + + inputLabels=list() + + #------------------------------------------------------------------------------------------------- + numberOfSamplesInput=getNumberOfLines(filename)-1 + print(" Input file has ",numberOfSamplesInput," training samples\n") + #------------------------------------------------------------------------------------------------- + + + numberOfSamples = numberOfSamplesInput + numberOfSamplesLimit=int(numberOfSamples*memPercentage) + #------------------------------------------------------------------------------------------------- + if (memPercentage==0.0): + print("readCSVFile was asked to occupy 0 memory so this probably means we just want one record") + numberOfSamplesLimit=2 + if (memPercentage>1.0): + print("Memory Limit will be interpreted as a raw value..") + numberOfSamplesLimit=int(memPercentage) + #------------------------------------------------------------------------------------------------- + + + #--------------------------------- + thisInput = array.array('f') + #--------------------------------- + + fi = open(filename, "r") + readerIn = csv.reader( fi , delimiter =',', skipinitialspace=True) + for rowIn in readerIn: + #------------------------------------------------------ + if (not receivedHeader): #use header to get labels + #------------------------------------------------------ + inputNumberOfColumns=len(rowIn) + inputLabels = list(rowIn[i] for i in range(0,inputNumberOfColumns) ) + print("Number of Input elements : ",len(inputLabels)) + #------------------------------------------------------ + + if (memPercentage==0): + print("Will only return labels\n") + return {'label':inputLabels}; + + #--------------------------------- + # Allocate Lists + #--------------------------------- + for i in range(inputNumberOfColumns): + thisInput.append(0.0) + #--------------------------------- + + + #--------------------------------- + # Allocate Numpy Arrays + #--------------------------------- + inputSize=0 + startCompressed=0 + inputSize=inputNumberOfColumns + startCompressed=inputNumberOfColumns + + npInputBytesize=0+numberOfSamplesLimit * inputSize * dtypeSelectedByteSize + print(" Input file on disk has a shape of [",numberOfSamples,",",inputSize,"]") + print(" Input we will read has a shape of [",numberOfSamplesLimit,",",inputSize,"]") + print(" Input will occupy ",convert_bytes(npInputBytesize)," of RAM\n") + npInput = np.full([numberOfSamplesLimit,inputSize],fill_value=0,dtype=dtypeSelected,order='C') + #---------------------------------------------------------------------------------------------------------- + receivedHeader=True + #---------------------------------------------------------------------------------------------------------- + else: + #------------------------------------------- + # First convert our string INPUT to floats + #------------------------------------------- + for i in range(inputNumberOfColumns): + try: + thisInput[i]=float(rowIn[i]) + except: + thisInput[i]=0.0 + #------------------------------------------- + for num in range(0,inputNumberOfColumns): + npInput[sampleNumber,num]=float(thisInput[num]); + #------------------------------------------- + sampleNumber=sampleNumber+1 + + if (numberOfSamples>0): + progress=sampleNumber/numberOfSamplesLimit + + if (sampleNumber%1000==0) : + progressString = "%0.2f"%float(100*progress) + print("\rReading from disk (",sampleNumber,") - ",progressString," % \r", end="", flush=True) + + if (numberOfSamplesLimit<=sampleNumber): + print("\rStopping reading file to obey memory limit given by parameter --mem ",memPercentage,"\n") + break + #------------------------------------------- + fi.close() + del readerIn + gc.collect() + + + print("\n read, Samples: ",sampleNumber,", was expecting ",numberOfSamples," samples\n") + print(npInput.shape) + + totalNumberOfBytes=npInput.nbytes; + totalNumberOfGigaBytes=totalNumberOfBytes/1073741824; + print("GPU Size Occupied by data = ",totalNumberOfGigaBytes," GB \n") + + end = time.time() + print("Time elapsed : ",(end-start)/60," mins") + #--------------------------------------------------------------------- + return {'label':inputLabels, 'body':npInput }; + +# =================================================================================================================== +# =================================================================================================================== +# =================================================================================================================== +# =================================================================================================================== + + + +def retrieveSkinToBVHAssotiationDict(doFace=False): + r = dict() + if (doFace): + r["root"]="hip" + r["neck1"]="neck1" + r["head"]="head" + #r["__jaw"]="__jaw" + r["jaw"]="jaw" + #r["special04"]="special04" + #r["oris02"]="oris02" + r["oris01"]="oris01" #<-- + r["oris06.L"]="oris06.l" ##### + r["oris07.L"]="oris07.l" + r["oris06.R"]="oris06.r" ##### + r["oris07.R"]="oris07.r" + #r["tongue00"]="tongue00" + #r["tongue01"]="tongue01" + #r["tongue02"]="tongue02" + #r["tongue03"]="tongue03" + #r["__tongue04"]="__tongue04" + #r["tongue04"]="tongue04" + #r["tongue07.L"]="tongue07.l" + #r["tongue07.R"]="tongue07.r" + #r["tongue06.L"]="tongue06.l" + #r["tongue06.R"]="tongue06.r" + #r["tongue05.L"]="tongue05.l" + #r["tongue05.R"]="tongue05.r" + #r["__levator02.L"]="__levator02.l" + #r["levator02.L"]="levator02.l" + r["levator03.L"]="levator03.l" + #r["levator04.L"]="levator04.l" + #r["levator05.L"]="levator05.l" + #r["__levator02.R"]="__levator02.r" + #r["levator02.R"]="levator02.r" + r["levator03.R"]="levator03.r" + #r["levator04.R"]="levator04.r" + #r["levator05.R"]="levator05.r" + #r["__special01"]="__special01" + #r["special01"]="special01" + r["oris04.L"]="oris04.l" + r["oris03.L"]="oris03.l" + r["oris04.R"]="oris04.r" + r["oris03.R"]="oris03.r" + #r["oris06"]="oris06" + r["oris05"]="oris05" #<-- + #r["__special03"]="__special03" + #r["special03"]="special03" + #r["__levator06.L"]="__levator06.l" + r["levator06.L"]="levator06.l" + #r["__levator06.R"]="__levator06.r" + r["levator06.R"]="levator06.r" + #r["special06.L"]="special06.l" + #r["special05.L"]="special05.l" + r["eye.L"]="eye.l" + r["orbicularis03.L"]="orbicularis03.l" + r["orbicularis04.L"]="orbicularis04.l" + #r["special06.R"]="special06.r" + #r["special05.R"]="special05.r" + r["eye.R"]="eye.r" + r["orbicularis03.R"]="orbicularis03.r" + r["orbicularis04.R"]="orbicularis04.r" + #r["__temporalis01.L"]="__temporalis01.l" + #r["temporalis01.L"]="temporalis01.l" + #r["oculi02.L"]="oculi02.l" ## + r["oculi01.L"]="oculi01.l" + #r["__temporalis01.R"]="__temporalis01.r" + #r["temporalis01.R"]="temporalis01.r" + #r["oculi02.R"]="oculi02.r" ## + r["oculi01.R"]="oculi01.r" + #r["__temporalis02.L"]="__temporalis02.l" + #r["temporalis02.L"]="temporalis02.l" + #r["risorius02.L"]="risorius02.l" + #r["risorius03.L"]="risorius03.l" ## + #r["__temporalis02.R"]="__temporalis02.r" + #r["temporalis02.R"]="temporalis02.r" + #r["risorius02.R"]="risorius02.r" + #r["risorius03.R"]="risorius03.r" ## + return r + +def degToRad(degrees): + return degrees * math.pi / 180 + +def randomize_property(propName): + #prop = bpy.data.scenes[0][propName] + scene = bpy.data.scenes[0] + + prop = None + for p in scene.bl_rna.properties: + #print(p.name) + if (p.name == propName): + #print("FOUND ",propName) + prop=p + break + #else: + # print("`%s`!=`%s`" % (p.name,propName)) + + #print(type(prop)) + if isinstance(prop, bpy.types.FloatProperty): # and prop.has_min and prop.has_max: + # Property is a float with a defined range, use min and max attributes + min_value = prop.soft_min + max_value = prop.soft_max + random_value = random.uniform(min_value, max_value) + # Set the property to the random value + #print(propName,"randomize(%0.1f,%0.1f) = %0.2f "%(min_value,max_value,random_value)) + return random_value + else: + # Property is a different type, handle it differently + # (for example, generate a random value within a reasonable range for the property type) + print("Unable to use min/max for prop ",propName) + return 0.0 + +def dumpBVHFile(r,targetPath,frameID): + if (frameID==0): + f = open('%s/bvh_face_all.csv' % targetPath, 'w') + i=0 + for joint in r.keys(): + if (i>0): + f.write(',') + f.write(joint) + i=i+1 + f.write('\n') + f.close() + f = open('%s/bvh_face_all.csv' % targetPath, 'a') + i=0 + for joint in r.keys(): + if (i>0): + f.write(',') + f.write("%0.2f"%r[joint]) + i=i+1 + f.write('\n') + f.close() + +def setSkeletonRaw(jointName,z,x,y): + context = bpy.context + scene = context.scene + #------------------------------------------------------- + skinnedObjectName = bpy.context.scene.mnetSource + jointName = jointName.lower() + armature_obj = bpy.context.scene.objects.get(bpy.context.scene.mnetSource) + #skinnedObjectName = bpy.context.scene.mnetTarget + #print("skinnedObjectName",skinnedObjectName) + #------------------------------------------------------- + + + skinnedObject = scene.objects.get(skinnedObjectName) + if (skinnedObject is not None) : + # Get the joint object + armature = bpy.data.objects[skinnedObjectName] + bone = armature.pose.bones[jointName] + + # Set the rotation mode to ZXY + bone.rotation_mode = 'ZXY' + + # Set the rotation values + bone.rotation_euler = (degToRad(z),degToRad(x),degToRad(y)) + + #Animation set + #-------------------------------------------------------------------- + # Set the joint values for the current frame + armature_obj.pose.bones[jointName].rotation_euler = (degToRad(z),degToRad(x),degToRad(y)) + # Add a keyframe for the joint values + armature_obj.pose.bones[jointName].keyframe_insert(data_path="rotation_euler", index=-1) + + +def setSkeletonPositionRaw(jointName,x,y,z): + context = bpy.context + scene = context.scene + #------------------------------------------------------- + skinnedObjectName = bpy.context.scene.mnetSource + jointName = jointName.lower() + armature_obj = bpy.context.scene.objects.get(bpy.context.scene.mnetSource) + #skinnedObjectName = bpy.context.scene.mnetTarget + #print("skinnedObjectName",skinnedObjectName) + #------------------------------------------------------- + + skinnedObject = scene.objects.get(skinnedObjectName) + if (skinnedObject is not None) : + # Get the joint object + armature = bpy.data.objects[skinnedObjectName] + bone = armature.pose.bones[jointName] + + # Set the rotation mode to ZXY + #bone.rotation_mode = 'ZXY' + # Set the rotation values + #bone.rotation_euler = (degToRad(90.0),degToRad(0.0),degToRad(0.0)) + + #Animation set + #-------------------------------------------------------------------- + # Set the joint values for the current frame + armature_obj.pose.bones[jointName].location = (x,y,z) + # Add a keyframe for the joint values + armature_obj.pose.bones[jointName].keyframe_insert(data_path="location", index=-1) + + + + +class FaceBVHAnimationPanel(bpy.types.Panel): + """Creates a Panel in the Object properties window""" + bl_label = "Face BVH Animation Helper" + bl_idname = "OBJECT_PT_face_panel" + bl_space_type = 'PROPERTIES' + bl_region_type = 'WINDOW' + bl_context = "object" + + def draw(self, context): + context = bpy.context + scene = context.scene + layout = self.layout + + obj = context.object + + #layout = layout.split(factor=0.96, align=True) + #------------------------------------------------------------------ + #------------------------------------------------------------------ + row = layout.row() + row.label(text="Face BVH MocapNET Helper v%0.2f" % mnetPluginVersion, icon='WORLD_DATA') + #------------------------------------------------------------------ + row = layout.row() + row.label(text="BVH file to use as source: ") + row = layout.row() + row.prop_search(scene, "mnetSource", scene, "objects", icon='ARMATURE_DATA') + row = layout.row() + row.operator("face.face_op",text='Link BVH').action='LINKBVH' + #------------------------------------------------------------------ + row = layout.row() + row.label(text="Skinned Body to use as target: ") + row = layout.row() + row.prop_search(scene, "mnetTarget", scene, "objects", icon='OUTLINER_OB_ARMATURE') + #------------------------------------------------------------------ + row = layout.row() + row.label(text="Parts of armature to animate: ") + row = layout.row() + row.operator("face.face_op",text='Open Mouth').action='OPENMOUTH' + row.operator("face.face_op",text='Close Mouth').action='CLOSEMOUTH' + row = layout.row() + row.label(text="Positional Component : ") + row = layout.row() + row.operator("face.face_op",text='Open Eyes').action='OPENEYES' + row.operator("face.face_op",text='Close Eyes').action='CLOSEEYES' + + + row = layout.row() + row.label(text="Depth : ") + row = layout.row() + row.prop(scene, 'posX', slider=True) + row = layout.row() + row.prop(scene, 'posY', slider=True) + row = layout.row() + row.prop(scene, 'depth', slider=True) + + row = layout.row() + row.label(text="Neck : ") + row = layout.row() + row.prop(scene, 'neck1Z', slider=True) + row = layout.row() + row.prop(scene, 'neck1X', slider=True) + row = layout.row() + row.prop(scene, 'neck1Y', slider=True) + + + row = layout.row() + row.label(text="Eyes : ") + row = layout.row() + row.prop(scene, 'eyelidLUD', slider=True) + row = layout.row() + row.prop(scene, 'eyelidRUD', slider=True) + row = layout.row() + row.prop(scene, 'eyeLR', slider=True) + row = layout.row() + row.prop(scene, 'eyeUD', slider=True) + + + row = layout.row() + row.label(text="Nose : ") + row = layout.row() + row.prop(scene, 'noseLR', slider=True) + + + row = layout.row() + row.prop(scene, 'REyebrowInUD', slider=True) + row = layout.row() + row.prop(scene, 'LEyebrowInUD', slider=True) + + row = layout.row() + row.label(text="Mouth : ") + row = layout.row() + row.prop(scene, 'smileAD', slider=True) + row = layout.row() + row.prop(scene, 'mouthUD', slider=True) + row = layout.row() + row.prop(scene, 'mouthLR', slider=True) + row = layout.row() + row.prop(scene, 'mouthOC', slider=True) + row = layout.row() + row.prop(scene, 'moustacheLUD', slider=True) + row = layout.row() + row.prop(scene, 'moustacheRUD', slider=True) + + row = layout.row() + row.prop(scene, 'mouthTopL', slider=True) + row = layout.row() + row.prop(scene, 'mouthTopR', slider=True) + + row = layout.row() + row.prop(scene, 'mouthBotL', slider=True) + row = layout.row() + row.prop(scene, 'mouthBotR', slider=True) + + + row = layout.row() + row.label(text="Export controls : ") + row = layout.row() + row.prop(scene, 'dumpSVG') + row.prop(scene, 'zoomSVG') + row = layout.row() + row.prop(scene, 'dumpPNG') + row = layout.row() + row.prop(scene, 'dump2D') + row.prop(scene, 'dump3D') + row = layout.row() + row.prop(scene, 'dumpSpecificVertices') + + row = layout.row() + row.operator("face.face_op",text='Take Picture').action='PHOTO' + row = layout.row() + row.label(text="Path to store generated dataset : ") + row = layout.row() + row.prop_search(scene, "datasetPath", scene, "objects", icon='FILE_FOLDER') + row = layout.row() + row.prop(scene, 'randomFramesNumber', slider=True) + row = layout.row() + row.operator("face.face_op",text='Create Randomized Dataset').action='RANDOM' + + row = layout.row() + row.label(text="Path to load pre-generated dataset : ") + row = layout.row() + row.prop_search(scene, "readDatasetCSVPath", scene, "objects", icon='FILE_HIDDEN') + row = layout.row() + row.operator("face.face_op",text='Create Dataset from CSV file').action='LOADCSV' + row = layout.row() + row.operator("face.face_op",text='Just Render CSV Dataset').action='RENDERCSV' + + + + +class FaceBVHAnimation(bpy.types.Operator): + """Creates a Panel in the Object properties window""" + bl_label = "Face BVH Animation" + bl_idname = "face.face_op" + bl_description = 'MocapNET operation control' + bl_options = {'REGISTER', 'UNDO'} + + action: EnumProperty( + items=[ + ('LOADCSV', 'Load Pose Data From CSV File', 'Load Pose Data From CSV File'), + ('RENDERCSV', 'Render Pose Data From CSV File', 'Render Pose Data From CSV File'), + ('RANDOM', 'Create Randomized Data', 'Create Randomized Data'), + ('PHOTO', 'Take a Picture', 'Take a Picture'), + ('LINKBVH', 'Link BVH File to Face', 'Link BVH File to Face'), + ('OPENMOUTH', 'Link MocapNET to Skinned Model', 'Link MocapNET to Skinned Model'), + ('CLOSEMOUTH', 'Link MocapNET to Upper Body Only', 'Link MocapNET to Upper Body Only'), + ('OPENEYES', 'Link MocapNET to Face', 'Link MocapNET to Face'), + ('CLOSEEYES', 'Link MocapNET positional component', 'Link MocapNET positional component') + ] + ) + + @staticmethod + def add_cube(context): + bpy.ops.mesh.primitive_cube_add() + + @staticmethod + def add_sphere(context): + bpy.ops.mesh.primitive_uv_sphere_add() + + + @staticmethod + def cameraLightAction(context): + #bpy.context.scene.camera object and set its properties such as focal_length, sensor_width, and sensor_height. + bpy.context.scene.camera.location = 0.0,0.0,0.7 + bpy.context.scene.camera.rotation_mode = 'ZXY' + bpy.context.scene.camera.rotation_euler = degToRad(90),degToRad(0),degToRad(0) + #bpy.ops.object.lens_distort + + light = bpy.data.objects['Light'] + light.location.x = 0.0 + light.location.y = -5.0 + light.location.z = 1.0 + + @staticmethod + def takePicture(self,context): + print("takePicture called") + + targetPath = bpy.context.scene.datasetPath + + import os + if (checkIfPathExists(targetPath)): + os.system("rm %s/blender_face_dataset_*.jpg" % (targetPath)) + os.system("rm %s/blender_*_face_dataset_*.svg" % (targetPath)) + else: + print("Cannot take picture, given path %s does not exist" % (targetPath)) + return; + + bpy.context.scene.frame_set(0) # Always revert to first frame on dataset generation + #------------------------------------------------------------- + dumpSVG = bpy.context.scene.dumpSVG + dumpPNG = bpy.context.scene.dumpPNG + dump2D = bpy.context.scene.dump2D + dump3D = bpy.context.scene.dump3D + dumpSpecificVertices = bpy.context.scene.dumpSpecificVertices + #------------------------------------------------------------- + printSelectedVertex() + #------------------------------------------------------------- + csvVertexWhitelist=dict() + if(dumpSpecificVertices): + skinnedObjectName = bpy.context.scene.mnetTarget + for obj in bpy.data.objects[skinnedObjectName].children: + if (checkIfPathExists("%s/vertexWhitelist_%s.csv"%(targetPath,obj.name))): + csvVertexWhitelist[obj.name] = readCSVFile("%s/vertexWhitelist_%s.csv"%(targetPath,obj.name)) + else: + print("Could not find %s/vertexWhitelist_%s.csv "%(targetPath,obj.name)) + #------------------------------------------------------------- + if (dump2D or dumpSVG): + write_csv_2d_data_all_objects(baseDirectory=targetPath,fID=0,csvFile=dump2D,svgFile=dumpSVG,verticeCSVWhitelistForAllObjects=csvVertexWhitelist) + if(dump3D): + write_vertex_csv_3d_data(filename="/home/ammar/",fID=0) + #------------------------------------------------------------- + self.cameraLightAction(context=context) + #------------------------------------------------------------- + bpy.context.scene.render.image_settings.file_format='JPEG' + bpy.context.scene.render.filepath = "/home/ammar/test.jpg" + bpy.ops.render.render(write_still = True) + bpy.data.images['Render Result'].save_render + + @staticmethod + def generateRandomDataset(self,context,useCSV=False,useFFMPEG=False): + randomFramesNumber = bpy.context.scene.randomFramesNumber + print("generateRandomDataset called ",randomFramesNumber) + self.cameraLightAction(context=context) + + targetPath = bpy.context.scene.datasetPath + import time + startAt = time.time() + import os + if (checkIfPathExists(targetPath)): + os.system("rm %s/blender_face_dataset_*.jpg" % (targetPath)) + os.system("rm %s/blender_*_face_dataset_*.svg" % (targetPath)) + else: + print("Cannot generate random dataset, given path %s does not exist" % (targetPath)) + return; + + bpy.context.scene.frame_set(0) # Always revert to first frame on start of dataset generation + #--------------------------------------------------------------------------- + dumpSVG = bpy.context.scene.dumpSVG + dumpPNG = bpy.context.scene.dumpPNG + dump2D = bpy.context.scene.dump2D + dump3D = bpy.context.scene.dump3D + dumpSpecificVertices = bpy.context.scene.dumpSpecificVertices + #--------------------------------------------------------------------------- + csvVertexWhitelist=dict() + if(dumpSpecificVertices): + skinnedObjectName = bpy.context.scene.mnetTarget + for obj in bpy.data.objects[skinnedObjectName].children: + if (checkIfPathExists("%s/vertexWhitelist_%s.csv"%(targetPath,obj.name))): + csvVertexWhitelist[obj.name] = readCSVFile("%s/vertexWhitelist_%s.csv"%(targetPath,obj.name)) + else: + print("Could not find %s/vertexWhitelist_%s.csv "%(targetPath,obj.name)) + #print(csvVertexWhitelist) + #--------------------------------------------------------------------------- + wm = bpy.context.window_manager + #--------------------------------------------------------------------------- + armature_obj = bpy.context.scene.objects.get(bpy.context.scene.mnetSource) + target_obj = bpy.context.scene.objects.get(bpy.context.scene.mnetTarget) + #--------------------------------------------------------------------------- + + if armature_obj and target_obj: + armature_mod = target_obj.modifiers.new(name='Armature', type='ARMATURE') + #--------------------------------------------------------------------------- + if (useCSV): + #In this mode we will use the random poses found in the readDatasetCSVPath given by the user + csvFile = bpy.context.scene.readDatasetCSVPath + csvData = readCSVFile(csvFile) #,memPercentage=100 <- to test + randomFramesNumber = csvData["body"].shape[0] + print("Will now attempt to transmit ",randomFramesNumber," frames from ",csvFile) + wm.progress_begin(0,randomFramesNumber) + for fID in range(0,randomFramesNumber): + if (randomFramesNumber<10000) or (fID%1000==0): + wm.progress_update(fID) # Update mouse pointer progress in a conservative way to avoid X-Server error(?) + if (dumpPNG): + bpy.context.scene.frame_set(fID) # Set the current frame + # Set the joint values for the current frame + thisFaceConfig = self.retrieveFaceControls(self=self,context=context,csvData=csvData,fID=fID) + dumpBVHFile(thisFaceConfig,targetPath,fID) + if (dump2D or dumpSVG): + write_csv_2d_data_all_objects(baseDirectory=targetPath,fID=fID,csvFile=dump2D,svgFile=dumpSVG,verticeCSVWhitelistForAllObjects=csvVertexWhitelist) + if (dump3D): + write_vertex_csv_3d_data(filename="/home/ammar/",fID=0) + else: + wm.progress_begin(0,randomFramesNumber) + #In this more we will generate random poses + for fID in range(0,randomFramesNumber): + if (randomFramesNumber<10000) or (fID%1000==0): + wm.progress_update(fID) # Update mouse pointer progress in a conservative way to avoid X-Server error(?) + if (dumpPNG): + bpy.context.scene.frame_set(fID) # Set the current frame + # Set the joint values for the current frame + thisFaceConfig = self.retrieveFaceControls(self=self,context=context) + dumpBVHFile(thisFaceConfig,targetPath,fID) + if (dump2D or dumpSVG): + write_csv_2d_data_all_objects(baseDirectory=targetPath,fID=fID,csvFile=dump2D,svgFile=dumpSVG,verticeCSVWhitelistForAllObjects=csvVertexWhitelist) + if(dump3D): + write_vertex_csv_3d_data(filename="/home/ammar/",fID=0) + #--------------------------------------------------------------------------- + + if(dumpSpecificVertices): + combineCSVFiles("%s/2d_face_all.csv"%(targetPath),csvVertexWhitelist) + + #At this point we have added all new states to animation + #it has happened that after a lot of hours errors like #X Error of failed request: BadWindow (invalid Window parameter) Major opcode of failed request: 18 (X_ChangeProperty) + #might occur so let's save our blend file to make sure we can re-render if something goes wrong! + os.system("rm %s/faceRandomized.blend" % (targetPath)) + bpy.ops.wm.save_as_mainfile(filepath="%s/faceRandomized.blend" % (targetPath)) + + if (dumpPNG): + print("Will now attempt to render ",randomFramesNumber," frames ") + renderAsAnimation=True + #--------------------------------------------------------------------------- + if (renderAsAnimation): + wm.progress_end() + # Set the render engine and animation settings + #bpy.context.scene.render.engine = "CYCLES" + bpy.context.scene.render.image_settings.file_format='JPEG' + bpy.context.scene.render.filepath = "%s/blender_face_dataset_" % (targetPath) + bpy.context.scene.frame_start = 0 + bpy.context.scene.frame_end = randomFramesNumber-1 + + # Click the Render Animation button + bpy.ops.render.render('INVOKE_AREA',use_viewport = True, animation=True) + else: + #Then playback animation and save each frame as jpeg + for fID in range(0,randomFramesNumber): + bpy.context.scene.frame_set(fID) + wm.progress_update(fID) + bpy.context.view_layer.update() #function to update the view layer and trigger a redraw of the UI. + bpy.context.scene.render.image_settings.file_format='JPEG' + bpy.context.scene.render.filepath = "%s/blender_face_dataset_%04u.jpg" % (targetPath,fID) + bpy.ops.render.render(write_still = True) + bpy.data.images['Render Result'].save_render + if (fID%1000==0): + gc.collect() #Do garbage collection to help with memory leaks ? + wm.progress_end() + #--------------------------------------------------------------------------- + if (useFFMPEG): + print("Will attempt to execute : ") + print("ffmpeg -framerate 30 -i %s/blender_face_dataset_%%04d.jpg -s 1200x720 -y -r 30 -pix_fmt yuv420p -threads 8 %s/blender.mp4"% (targetPath,targetPath)) + os.system("ffmpeg -framerate 30 -i %s/blender_face_dataset_%%04d.jpg -s 1200x720 -y -r 30 -pix_fmt yuv420p -threads 8 %s/blender.mp4" % (targetPath,targetPath)) + #--------------------------------------------------------------------------- + endAt = time.time() + timeElapsed,timeUnit = timeDuration(startAt,endAt) + print("Time required to generate dataset was ",timeElapsed,timeUnit) + #--------------------------------------------------------------------------- + #ffmpeg -framerate 30 -i blender_face_dataset_%04d.jpg -s 1200x720 -y -r 30 -pix_fmt yuv420p -threads 8 livelastRun3DHiRes.mp4 + #ffmpeg -i /media/ammar/CVRL2/ammar/frames/ammarFaceFar.mp4-data/colorFrame_0_%05d.jpg -i /media/ammar/CVRL2/ammar/rendering/blender_face_dataset_%04d.jpg -filter_complex '[1:v]colorkey=0x464646:0.01:0.02[ckout];[0:v][ckout]overlay[out]' -map '[out]' output.mp4 + # or + #ffmpeg -i /media/ammar/games/ammarFaceFar.mp4-data/colorFrame_0_%05d.jpg -i /media/ammar/games/render/blender_face_dataset_%04d.jpg -filter_complex '[1:v]colorkey=0x464646:0.01:0.02[ckout];[0:v][ckout]overlay[out]' -map '[out]' output.mp4 + + @staticmethod + def setSkeleton(context,jointName,z,x,y): + setSkeletonRaw(jointName,z,x,y) + + @staticmethod + def retrieveConstantControls(self,context): + r = dict() + r["orbicularis03.R_Yrotation"]=172.0 + r["orbicularis04.R_Yrotation"]=172.0 + r["orbicularis03.L_Yrotation"]=-172.0 + r["orbicularis04.L_Yrotation"]=172.0 + r["levator06.L_Yrotation"]=-247.0 + r["levator06.R_Yrotation"]=247.0 + r["oris03.L_Xrotation"]=-40.0 + r["oris03.L_Yrotation"]=172.0 + r["oris07.L_Yrotation"]=172.0 + r["oris03.R_Xrotation"]=-40.0 + r["oris03.R_Yrotation"]=179.0 + r["oris07.R_Yrotation"]=172.0 + r["oris05_Xrotation"]=-35.0 + r["oris05_Yrotation"]=-176.0 + return r + + @staticmethod + def retrieveFaceControlsI(self,context): + scene = bpy.data.scenes[0] + #-------------------------------------------------------- + r = dict() + #-------------------------------------------------------- + r["hip_Xposition"] = randomize_property("Pos X") + r["hip_Yposition"] = randomize_property("Pos Y") + r["hip_Zposition"] = randomize_property("Depth") + #-------------------------------------------------------- + r["neck1_Zrotation"] = randomize_property("Neck Z") + r["neck1_Xrotation"] = randomize_property("Neck X") + r["neck1_Yrotation"] = randomize_property("Neck Y") + #-------------------------------------------------------- + r["eye.R_Zrotation"] = randomize_property("Eye Gaze L/R") + r["eye.R_Xrotation"] = randomize_property("Eye Gaze U/D") + r["eye.L_Zrotation"] = r["eye.R_Zrotation"] + r["eye.L_Xrotation"] = r["eye.R_Xrotation"] + #-------------------------------------------------------- + r["oculi01.R_Zrotation"] = randomize_property("R Eyebrow In U/D") + r["oculi01.L_Zrotation"] = randomize_property("L Eyebrow In U/D") + #-------------------------------------------------------- + r["orbicularis03.R_Xrotation"] = randomize_property("Eye Lid R U/D") + r["orbicularis04.R_Xrotation"] = -r["orbicularis03.R_Xrotation"] + r["orbicularis03.L_Xrotation"] = randomize_property("Eye Lid L U/D") + r["orbicularis04.L_Xrotation"] = -r["orbicularis03.L_Xrotation"] + blinkRand = random.uniform(0,1.0) + if (blinkRand<0.85): + r["orbicularis04.R_Xrotation"]=r["orbicularis04.L_Xrotation"] + #-------------------------------------------------------- + r["levator06.L_Xrotation"] = randomize_property("Nose L/R") + r["levator06.R_Xrotation"] = r["levator06.L_Xrotation"] + #-------------------------------------------------------- + r["levator03.L_Zrotation"] = randomize_property("Smile Active/Deactivated") + r["levator03.R_Zrotation"] = -r["levator03.L_Zrotation"] + #-------------------------------------------------------- + r["oris03.L_Zrotation"] = randomize_property("Mouth Top L") + r["oris07.L_Zrotation"] = min(randomize_property("Mouth Sides U/D"),0) + r["oris03.R_Zrotation"] = randomize_property("Mouth Top R") + r["oris07.R_Zrotation"] = r["oris07.L_Zrotation"] + #-------------------------------------------------------- + r["jaw_Xrotation"] = randomize_property("Mouth Open/Close") + r["jaw_Yrotation"] = randomize_property("Mouth L/R") + #-------------------------------------------------------- + r["oris04.L_Zrotation"] = randomize_property("Moustache L U/D") + r["oris04.R_Zrotation"] = -randomize_property("Moustache R U/D") + #-------------------------------------------------------- + r["oris06.L_Zrotation"] = randomize_property("Mouth Bot L") + r["oris06.R_Zrotation"] = -randomize_property("Mouth Bot R") + #-------------------------------------------------------- + return r + + @staticmethod + def retrieveFaceControlsFromCSV(self,context,csvData,fID): + scene = bpy.data.scenes[0] + #-------------------------------------------------------- + r = dict() + #-------------------------------------------------------- + r["hip_Xposition"] = resolveCSVRowColumn(csvData,"hip_Xposition",fID) + r["hip_Yposition"] = resolveCSVRowColumn(csvData,"hip_Yposition",fID) + r["hip_Zposition"] = resolveCSVRowColumn(csvData,"hip_Zposition",fID) + #-------------------------------------------------------- + r["neck1_Zrotation"] = resolveCSVRowColumn(csvData,"neck1_Zrotation",fID) + r["neck1_Xrotation"] = resolveCSVRowColumn(csvData,"neck1_Xrotation",fID) + r["neck1_Yrotation"] = resolveCSVRowColumn(csvData,"neck1_Yrotation",fID) + #-------------------------------------------------------- + r["eye.R_Zrotation"] = resolveCSVRowColumn(csvData,"eye.R_Zrotation",fID) + r["eye.R_Xrotation"] = resolveCSVRowColumn(csvData,"eye.R_Xrotation",fID) + r["eye.L_Zrotation"] = r["eye.R_Zrotation"] + r["eye.L_Xrotation"] = r["eye.R_Xrotation"] + #-------------------------------------------------------- + r["oculi01.R_Zrotation"] = resolveCSVRowColumn(csvData,"oculi01.R_Zrotation",fID) + r["oculi01.L_Zrotation"] = resolveCSVRowColumn(csvData,"oculi01.L_Zrotation",fID) + #-------------------------------------------------------- + r["orbicularis03.R_Xrotation"] = resolveCSVRowColumn(csvData,"orbicularis03.R_Xrotation",fID) + r["orbicularis04.R_Xrotation"] = -r["orbicularis03.R_Xrotation"] + r["orbicularis03.L_Xrotation"] = resolveCSVRowColumn(csvData,"orbicularis03.L_Xrotation",fID) + r["orbicularis04.L_Xrotation"] = -r["orbicularis03.L_Xrotation"] + #-------------------------------------------------------- + r["levator06.L_Xrotation"] = resolveCSVRowColumn(csvData,"levator06.L_Xrotation",fID) + r["levator06.R_Xrotation"] = r["levator06.L_Xrotation"] + #-------------------------------------------------------- + r["levator03.L_Zrotation"] = resolveCSVRowColumn(csvData,"levator03.L_Zrotation",fID) + r["levator03.R_Zrotation"] = -r["levator03.L_Zrotation"] + #-------------------------------------------------------- + r["oris03.L_Zrotation"] = resolveCSVRowColumn(csvData,"oris03.L_Zrotation",fID) + r["oris07.L_Zrotation"] = resolveCSVRowColumn(csvData,"oris07.L_Zrotation",fID) + r["oris03.R_Zrotation"] = resolveCSVRowColumn(csvData,"oris03.R_Zrotation",fID) + r["oris07.R_Zrotation"] = resolveCSVRowColumn(csvData,"oris07.R_Zrotation",fID) + #-------------------------------------------------------- + r["jaw_Xrotation"] = resolveCSVRowColumn(csvData,"jaw_Xrotation",fID) + r["jaw_Yrotation"] = resolveCSVRowColumn(csvData,"jaw_Yrotation",fID) + #-------------------------------------------------------- + r["oris04.L_Zrotation"] = resolveCSVRowColumn(csvData,"oris04.L_Zrotation",fID) + r["oris04.R_Zrotation"] = resolveCSVRowColumn(csvData,"oris04.R_Zrotation",fID) + #-------------------------------------------------------- + r["oris06.L_Zrotation"] = resolveCSVRowColumn(csvData,"oris06.L_Zrotation",fID) + r["oris06.R_Zrotation"] = resolveCSVRowColumn(csvData,"oris06.R_Zrotation",fID) + #-------------------------------------------------------- + return r + + + @staticmethod + def retrieveFaceControls(self,context,csvData=dict(),fID=0): + scene = bpy.data.scenes[0] + doIt=True + r = dict() + if (doIt): + #----------------------------------------------------------------- + if ("body" in csvData): + r.update(self.retrieveFaceControlsFromCSV(self=self,context=context,csvData=csvData,fID=fID)) + else: + r.update(self.retrieveFaceControlsI(self=self,context=context)) + #----------------------------------------------------------------- + scene['posX'] = r["hip_Xposition"] + scene['posY'] = r["hip_Yposition"] + scene['depth'] = r["hip_Zposition"] + bpy.context.scene.depth = scene['depth'] + #----------------------------------------------------------------- + scene['neck1Z'] = r["neck1_Zrotation"] + scene['neck1X'] = r["neck1_Xrotation"] + scene['neck1Y'] = r["neck1_Yrotation"] + bpy.context.scene.neckZ = scene['neck1Z'] + bpy.context.scene.neckX = scene['neck1X'] + bpy.context.scene.neckY = scene['neck1Y'] + #----------------------------------------------------------------- + scene['eyeLR'] = r["eye.R_Zrotation"] + scene['eyeUD'] = r["eye.R_Xrotation"] + bpy.context.scene.eyeLR = scene['eyeLR'] + bpy.context.scene.eyeUD = scene['eyeUD'] + eyeLR = bpy.context.scene.eyeLR + eyeUD = bpy.context.scene.eyeUD + #----------------------------------------------------------------- + scene['REyebrowInUD'] = -r["oculi01.R_Zrotation"] + scene['LEyebrowInUD'] = r["oculi01.L_Zrotation"] + bpy.context.scene.REyebrowInUD = scene['REyebrowInUD'] + bpy.context.scene.LEyebrowInUD = scene['LEyebrowInUD'] + REyebrowInUD = bpy.context.scene.REyebrowInUD + LEyebrowInUD = bpy.context.scene.LEyebrowInUD + #----------------------------------------------------------------- + scene['eyelidLUD'] = r["orbicularis04.L_Xrotation"] + scene['eyelidRUD'] = r["orbicularis04.R_Xrotation"] + bpy.context.scene.eyelidLUD = scene['eyelidLUD'] + bpy.context.scene.eyelidRUD = scene['eyelidRUD'] + eyelidRUD = bpy.context.scene.eyelidRUD + eyelidLUD = bpy.context.scene.eyelidLUD + #----------------------------------------------------------------- + scene['noseLR'] = r["levator06.L_Xrotation"] + bpy.context.scene.noseLR = scene['noseLR'] + noseLR = bpy.context.scene.noseLR + #----------------------------------------------------------------- + scene['smileAD'] = r["levator03.L_Zrotation"] + bpy.context.scene.smileAD = scene['smileAD'] + smileAD = bpy.context.scene.smileAD + #----------------------------------------------------------------- + scene['mouthTopL'] = r["oris03.L_Zrotation"] + scene['mouthTopR'] = -r["oris03.R_Zrotation"] + bpy.context.scene.mouthTopL = scene['mouthTopL'] + bpy.context.scene.mouthTopR = scene['mouthTopR'] + mouthTopL = bpy.context.scene.mouthTopL + mouthTopR = bpy.context.scene.mouthTopR + scene['mouthUD'] = r["oris07.L_Zrotation"] + bpy.context.scene.mouthUD = scene['mouthUD'] + mouthUD = bpy.context.scene.mouthUD + #----------------------------------------------------------------- + scene['mouthLR'] = r["jaw_Yrotation"] + bpy.context.scene.mouthLR = scene['mouthLR'] + mouthLR = bpy.context.scene.mouthLR + scene['mouthOC'] = r["jaw_Xrotation"] + bpy.context.scene.mouthOC = scene['mouthOC'] + mouthOC = bpy.context.scene.mouthOC + #----------------------------------------------------------------- + scene['moustacheLUD'] = r["oris04.L_Zrotation"] + scene['moustacheRUD'] = -r["oris04.R_Zrotation"] + bpy.context.scene.moustacheLUD = scene['moustacheLUD'] + bpy.context.scene.moustacheRUD = scene['moustacheRUD'] + moustacheLUD = bpy.context.scene.moustacheLUD + moustacheRUD = bpy.context.scene.moustacheRUD + #----------------------------------------------------------------- + scene['mouthBotL'] = r["oris06.L_Zrotation"] + scene['mouthBotR'] = -r["oris06.R_Zrotation"] + bpy.context.scene.mouthBotL = scene['mouthBotL'] + bpy.context.scene.mouthBotR = scene['mouthBotR'] + mouthBotL = bpy.context.scene.mouthBotL + mouthBotR = bpy.context.scene.mouthBotR + #----------------------------------------------------------------- + r.update(self.retrieveConstantControls(self=self,context=context)) + #----------------------------------------------------------------- + self.neckUpdate(self=self,context=context) + self.eyeGazeUpdate(self=self,context=context) + self.noseUpdate(self=self,context=context) + self.mouthUpdate(self=self,context=context) + return r + + + @staticmethod + def eyeGazeUpdate(self, context): + eyeLR = bpy.context.scene.eyeLR + eyeUD = bpy.context.scene.eyeUD #x #y #z + FaceBVHAnimation.setSkeleton(context,"eye.R",eyeUD,0,eyeLR) + FaceBVHAnimation.setSkeleton(context,"eye.L",eyeUD,0,eyeLR) + #----------------------------------------------------------------- + REyebrowInUD = bpy.context.scene.REyebrowInUD + LEyebrowInUD = bpy.context.scene.LEyebrowInUD #x #y #z + FaceBVHAnimation.setSkeleton(context,"oculi01.R",0,0,-REyebrowInUD) + FaceBVHAnimation.setSkeleton(context,"oculi01.L",0,0,LEyebrowInUD) + #----------------------------------------------------------------- + eyelidRUD = bpy.context.scene.eyelidRUD + eyelidLUD = bpy.context.scene.eyelidLUD #x #y #z + FaceBVHAnimation.setSkeleton(context,"orbicularis03.R",-eyelidRUD,172,0) + FaceBVHAnimation.setSkeleton(context,"orbicularis04.R",eyelidRUD,172,0) + FaceBVHAnimation.setSkeleton(context,"orbicularis03.L",-eyelidLUD,-172,0) + FaceBVHAnimation.setSkeleton(context,"orbicularis04.L",eyelidLUD,172,0) + + @staticmethod + def noseUpdate(self, context): + noseLR = bpy.context.scene.noseLR #x #y #z + FaceBVHAnimation.setSkeleton(context,"levator06.L",noseLR,-247,0) + FaceBVHAnimation.setSkeleton(context,"levator06.R",noseLR,+247,0) + + @staticmethod + def neckUpdate(self, context): + neckZ = bpy.context.scene.neckZ #x #y #z + neckX = bpy.context.scene.neckX #x #y #z + neckY = bpy.context.scene.neckY #x #y #z + FaceBVHAnimation.setSkeleton(context,"neck1",neckX,neckY,neckZ) + target_obj = bpy.context.scene.objects.get(bpy.context.scene.mnetTarget) + target_obj.location.x = 0.0 + target_obj.location.y = 0.0 + bpy.context.scene.depth + target_obj.location.z = 0.0 + target_obj.rotation_mode = 'ZXY' + target_obj.rotation_euler = (degToRad(0.0),degToRad(0.0),degToRad(0.0)) + setSkeletonPositionRaw("hip",bpy.context.scene.posX,bpy.context.scene.posY,bpy.context.scene.depth) + + @staticmethod + def mouthUpdate(self, context): + mouthTopL = bpy.context.scene.mouthTopL + mouthTopR = bpy.context.scene.mouthTopR + mouthUD = bpy.context.scene.mouthUD #x #y #z + FaceBVHAnimation.setSkeleton(context,"oris03.L",-40,172,mouthUD+mouthTopL) + FaceBVHAnimation.setSkeleton(context,"oris07.L",0,172,max(mouthUD,0)) + FaceBVHAnimation.setSkeleton(context,"oris03.R",-40,179,-mouthUD+mouthTopR) + FaceBVHAnimation.setSkeleton(context,"oris07.R",0,172,min(mouthUD,0)) + #----------------------------------------------------------------- + mouthOC = bpy.context.scene.mouthOC + mouthLR = bpy.context.scene.mouthLR + FaceBVHAnimation.setSkeleton(context,"jaw",mouthOC,mouthLR,0) + #----------------------------------------------------------------- + FaceBVHAnimation.setSkeleton(context,"oris05",-35,-176,0) + #----------------------------------------------------------------- + moustacheLUD = bpy.context.scene.moustacheLUD + moustacheRUD = bpy.context.scene.moustacheRUD #x #y #z + FaceBVHAnimation.setSkeleton(context,"oris04.L",0,0,moustacheLUD) + FaceBVHAnimation.setSkeleton(context,"oris04.R",0,0,moustacheRUD) + #----------------------------------------------------------------- + mouthBotL = bpy.context.scene.mouthBotL + mouthBotR = bpy.context.scene.mouthBotR #x #y #z + FaceBVHAnimation.setSkeleton(context,"oris06.L",0,0,mouthBotL) + FaceBVHAnimation.setSkeleton(context,"oris06.R",0,0,mouthBotR) + #----------------------------------------------------------------- + smileAD = bpy.context.scene.smileAD #x #y #z + FaceBVHAnimation.setSkeleton(context,"levator03.L",0,0,smileAD) + FaceBVHAnimation.setSkeleton(context,"levator03.R",0,0,-smileAD) + #----------------------------------------------------------------- + + @staticmethod + def copyFaceConstraints(context,doPosition=False,doRotation=True,doReverse=False): + FaceBVHAnimation.cameraLightAction(context=context) + context = bpy.context + scene = context.scene + #------------------------------------------------------- + associations = retrieveSkinToBVHAssotiationDict(doFace=True) + #------------------------------------------------------- + bvhObjectName = bpy.context.scene.mnetSource + skinnedObjectName = bpy.context.scene.mnetTarget + print("bvhObjectName",bvhObjectName) + print("skinnedObjectName",skinnedObjectName) + #------------------------------------------------------- + skinnedObject = scene.objects.get(skinnedObjectName) + bvhObject = scene.objects.get(bvhObjectName) + if (skinnedObject is not None) and (bvhObject is not None): + for skinnedBoneName in associations: + #------------------------------------------------ + bvhBoneName = associations[skinnedBoneName] + #------------------------------------------------ + skinnedBone = skinnedObject.pose.bones.get(skinnedBoneName) + bvhBone = bvhObject.pose.bones.get(bvhBoneName) + + if (doReverse): + # give it a copy rotation constraint + if (skinnedBone is not None) and (bvhBone is not None): + if (len(skinnedBone.constraints)>0): + for c in bvhBone.constraints: + bvhBone.constraints.remove(c) # Remove constraint + if (skinnedBoneName=="root") and (doPosition): + crc = bvhBone.constraints.new('COPY_LOCATION') + crc.target = skinnedObject + crc.subtarget = skinnedBoneName + elif (skinnedBoneName!="root") and (doRotation): + crc = bvhBone.constraints.new('COPY_ROTATION') + crc.target = skinnedObject + crc.subtarget = skinnedBoneName + else: + # give it a copy rotation constraint + if (skinnedBone is not None) and (bvhBone is not None): + if (len(skinnedBone.constraints)>0): + for c in skinnedBone.constraints: + skinnedBone.constraints.remove(c) # Remove constraint + if (skinnedBoneName=="root") and (doPosition): + crc = skinnedBone.constraints.new('COPY_LOCATION') + crc.target = bvhObject + crc.subtarget = bvhBoneName + elif (skinnedBoneName!="root") and (doRotation): + crc = skinnedBone.constraints.new('COPY_ROTATION') + crc.target = bvhObject + crc.subtarget = bvhBoneName + #------------------------------------------------------- + + def execute(self, context): + if self.action == 'LOADCSV': + self.generateRandomDataset(self=self,context=context,useCSV=True) + elif self.action == 'RENDERCSV': + dumpSVG = bpy.context.scene.dumpSVG; bpy.context.scene.dumpSVG = False + dumpPNG = bpy.context.scene.dumpPNG; bpy.context.scene.dumpPNG = True + dump2D = bpy.context.scene.dump2D; bpy.context.scene.dump2D = False + dump3D = bpy.context.scene.dump3D; bpy.context.scene.dump3D = False + dumpSpecificVertices = bpy.context.scene.dumpSpecificVertices; bpy.context.scene.dumpSpecificVertices = False + self.generateRandomDataset(self=self,context=context,useCSV=True,useFFMPEG=True) + elif self.action == 'RANDOM': + self.generateRandomDataset(self=self,context=context) + elif self.action == 'LINKBVH': + self.copyFaceConstraints(context=context,doPosition=True) + self.eyeGazeUpdate(self=self,context=context) + self.noseUpdate(self=self,context=context) + self.neckUpdate(self=self,context=context) + self.mouthUpdate(self=self,context=context) + elif self.action == 'REVERSELINKBVH': + self.copyFaceConstraints(context=context,doReverse=True) + elif self.action == 'PHOTO': + self.takePicture(self=self,context=context) + elif self.action == 'OPENMOUTH': + self.setSkeleton(context,"jaw",20,0,0) + elif self.action == 'CLOSEMOUTH': + self.setSkeleton(context,"jaw",0,0,0) + elif self.action == 'OPENEYES': + self.setSkeleton(context,"orbicularis03.R",0,172,0) + self.setSkeleton(context,"orbicularis04.R",0,172,0) + self.setSkeleton(context,"orbicularis03.L",0,172,0) + self.setSkeleton(context,"orbicularis04.L",0,172,0) + self.eyeGazeUpdate(self=self,context=context) + elif self.action == 'CLOSEEYES': + self.setSkeleton(context,"orbicularis03.R",-15,149,0) + self.setSkeleton(context,"orbicularis04.R",15,172,0) + self.setSkeleton(context,"orbicularis03.L",-15,193,0) + self.setSkeleton(context,"orbicularis04.L",15,172,0) + return {'FINISHED'} + +classes = (FaceBVHAnimationPanel,FaceBVHAnimation) + +def register(): + for cls in classes: + bpy.utils.register_class(cls) + + bpy.types.Scene.datasetPath = bpy.props.StringProperty(name="Dataset Path", default="~/", subtype="DIR_PATH") + bpy.types.Scene.readDatasetCSVPath = bpy.props.StringProperty(name="Dataset Path", default="~/", subtype="FILE_PATH") + bpy.types.Scene.mnetSource = bpy.props.StringProperty(name="Source BVH", default="Select Armature Object") + bpy.types.Scene.mnetTarget = bpy.props.StringProperty(name="Target Obj", default="Select Skinned Object") + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.zoomSVG = bpy.props.BoolProperty(name="Zoom SVG", default=False) + bpy.types.Scene.dumpSVG = bpy.props.BoolProperty(name="Dump SVG", default=False) + bpy.types.Scene.dumpPNG = bpy.props.BoolProperty(name="Dump PNG", default=True) + bpy.types.Scene.dumpSpecificVertices = bpy.props.BoolProperty(name="Only Dump 2D/3D for Specific Vertices", default=True) + bpy.types.Scene.dump2D = bpy.props.BoolProperty(name="Dump 2D CSV", default=True) + bpy.types.Scene.dump3D = bpy.props.BoolProperty(name="Dump 3D CSV", default=False) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.neckZ = bpy.props.FloatProperty(name="Neck Z", default=0.0, min=-20.0, max=20.0, update=FaceBVHAnimation.neckUpdate) + bpy.types.Scene.neckX = bpy.props.FloatProperty(name="Neck X", default=0.0, min=-20.0, max=20.0, update=FaceBVHAnimation.neckUpdate) + bpy.types.Scene.neckY = bpy.props.FloatProperty(name="Neck Y", default=0.0, min=-30.0, max=30.0, update=FaceBVHAnimation.neckUpdate) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.posX = bpy.props.FloatProperty(name="Pos X", default=0.0, min=-0.24, max=0.24, update=FaceBVHAnimation.neckUpdate) + bpy.types.Scene.posY = bpy.props.FloatProperty(name="Pos Y", default=0.0, min=-0.1, max=0.1, update=FaceBVHAnimation.neckUpdate) + bpy.types.Scene.depth = bpy.props.FloatProperty(name="Depth", default=-1.0, min=-2.4, max=-1.0, update=FaceBVHAnimation.neckUpdate) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.eyeLR = bpy.props.FloatProperty(name="Eye Gaze L/R", default=0.0, min=-45.36, max=45.36, update=FaceBVHAnimation.eyeGazeUpdate) + bpy.types.Scene.eyeUD = bpy.props.FloatProperty(name="Eye Gaze U/D", default=0.0, min=-10.0, max=16.0, update=FaceBVHAnimation.eyeGazeUpdate) + bpy.types.Scene.eyelidLUD = bpy.props.FloatProperty(name="Eye Lid L U/D", default=0.0, min=-15.0, max=15.0, update=FaceBVHAnimation.eyeGazeUpdate) + bpy.types.Scene.eyelidRUD = bpy.props.FloatProperty(name="Eye Lid R U/D", default=0.0, min=-15.0, max=15.0, update=FaceBVHAnimation.eyeGazeUpdate) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.noseLR = bpy.props.FloatProperty(name="Nose L/R", default=0.0, min=-9.0, max=9.0, update=FaceBVHAnimation.noseUpdate) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.mouthUD = bpy.props.FloatProperty(name="Mouth Sides U/D", default=0.0, min=-30.0, max=0.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.mouthLR = bpy.props.FloatProperty(name="Mouth L/R", default=0.0, min=-15.0, max=15.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.mouthOC = bpy.props.FloatProperty(name="Mouth Open/Close", default=0.0, min=-4.0, max=20.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.moustacheLUD = bpy.props.FloatProperty(name="Moustache L U/D", default=0.0, min=-30.0, max=0.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.moustacheRUD = bpy.props.FloatProperty(name="Moustache R U/D", default=0.0, min=0.0, max=30.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.mouthTopL = bpy.props.FloatProperty(name="Mouth Top L", default=0.0, min=-30.0, max=30.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.mouthTopR = bpy.props.FloatProperty(name="Mouth Top R", default=0.0, min=-30.0, max=30.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.mouthBotL = bpy.props.FloatProperty(name="Mouth Bot L", default=0.0, min=-30.0, max=30.0, update=FaceBVHAnimation.mouthUpdate) + bpy.types.Scene.mouthBotR = bpy.props.FloatProperty(name="Mouth Bot R", default=0.0, min=-30.0, max=30.0, update=FaceBVHAnimation.mouthUpdate) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.smileAD = bpy.props.FloatProperty(name="Smile Active/Deactivated", default=0.0, min=-8.0, max=9.0, update=FaceBVHAnimation.mouthUpdate) + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.randomFramesNumber = bpy.props.IntProperty(name="Random Frames", default=0, min=0, max=200000) #, update=FaceBVHAnimation.generateRandomDataset + #-------------------------------------------------------------------------------------------------------------------------------------------- + bpy.types.Scene.REyebrowInUD = bpy.props.FloatProperty(name="R Eyebrow In U/D", default=0.0, min=-20.0, max=20.0, update=FaceBVHAnimation.eyeGazeUpdate) + bpy.types.Scene.LEyebrowInUD = bpy.props.FloatProperty(name="L Eyebrow In U/D", default=0.0, min=-20.0, max=20.0, update=FaceBVHAnimation.eyeGazeUpdate) + +def unregister(): + for cls in classes: + bpy.utils.unregister_class(cls) + + del bpy.types.Scene.datasetPath + del bpy.types.Scene.readDatasetCSVPath + del bpy.types.Scene.mnetSource + del bpy.types.Scene.mnetTarget + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.zoomSVG + del bpy.types.Scene.dumpSVG + del bpy.types.Scene.dumpPNG + del bpy.types.Scene.dumpSpecificVertices + del bpy.types.Scene.dump2D + del bpy.types.Scene.dump3D + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.neck1Z + del bpy.types.Scene.neck1X + del bpy.types.Scene.neck1Y + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.posX + del bpy.types.Scene.posY + del bpy.types.Scene.depth + del bpy.types.Scene.eyeLR + del bpy.types.Scene.eyeUD + del bpy.types.Scene.eyelidLUD + del bpy.types.Scene.eyelidRUD + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.noseLR + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.mouthUD + del bpy.types.Scene.mouthLR + del bpy.types.Scene.mouthOC + del bpy.types.Scene.moustacheLUD + del bpy.types.Scene.moustacheRUD + del bpy.types.Scene.mouthTopL + del bpy.types.Scene.mouthTopR + del bpy.types.Scene.mouthBotL + del bpy.types.Scene.mouthBotR + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.smileAD + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.randomFramesNumber + #-------------------------------------------------------------------------------------------------------------------------------------------- + del bpy.types.Scene.REyebrowInUD + del bpy.types.Scene.LEyebrowInUD + +if __name__ == "__main__": + register() + # Get a list of all add-ons that are currently activated + activated_addons = [addon.module for addon in bpy.context.preferences.addons if addon] + + haveMPFB = False + # Print the name of each activated add-on + for addon in activated_addons: + print(addon) + if (addon=="mpfb"): + haveMPFB = True + print("We already have MPFB!") + + if (not haveMPFB): + import os + print("Receiving a fresh copy of MPFB!") + current_directory = os.getcwd() + print("Working from ",current_directory," directory") + os.system("wget http://download.tuxfamily.org/makehuman/plugins/mpfb2-latest.zip") + print(" Downloaded mpfb2-latest.zip and will now auto-install it for your convinience ") + bpy.ops.preferences.addon_install(filepath='%s/mpfb2-latest.zip' % os.getcwd()) + bpy.ops.preferences.addon_enable(module='mpfb') + bpy.ops.wm.save_userpref() + + # Get the path to the user preferences file + prefs_file = bpy.utils.user_resource('CONFIG') #bpy.context.preferences.filepath + + # Get the directory that contains the preferences file + prefs_dir = os.path.dirname(prefs_file) + + print(" Also installing the makehuman system assets!") + os.system("cd %s/mpfb/data && wget http://files.makehumancommunity.org/asset_packs/makehuman_system_assets/makehuman_system_assets_cc0.zip && unzip makehuman_system_assets_cc0.zip && rm makehuman_system_assets_cc0.zip" % prefs_dir) diff --git a/animation/MocapNET-kasisnu/src/python/blender/blender_mocapnet.py b/animation/MocapNET-kasisnu/src/python/blender/blender_mocapnet.py new file mode 100755 index 0000000..4b9f9b5 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/blender_mocapnet.py @@ -0,0 +1,395 @@ +#Written by Ammar Qammaz 2022-2023 +#This is a Blender Python script that upon loaded can facilitate animating a skinned model created by +#the MakeHuman plugin for Blender ( http://static.makehumancommunity.org/mpfb.html ) +mnetPluginVersion=float(0.02) + +import bpy +from bpy.props import EnumProperty + + +def retrieveSkinToBVHAssotiationDict(doBody=True,doHands=True,doFeet=True,doFace=False): + r = dict() + if (doBody): + r["root"]="Hip" #This should be hip not Hip but for some reason (ZYX rotation) there is a discrepancy + r["spine03"]="abdomen" + r["spine04"]="chest" + #--------------------------- + r["shoulder01.L"]="lCollar" + r["upperarm01.L"]="lShldr" + r["lowerarm01.L"]="lForeArm" + r["wrist.L"]="lHand" + #--------------------------- + r["shoulder01.R"]="rCollar" + r["upperarm01.R"]="rShldr" + r["lowerarm01.R"]="rForeArm" + r["wrist.R"]="rHand" + #--------------------------- + + if (doHands): + #--------------------------- + # L Hand + #--------------------------- + r["finger1-1.L"]="lthumb" + r["finger1-2.L"]="finger1-2.l" + r["finger1-3.L"]="finger1-3.l" + #--------------------------- + r["metacarpal1.L"]="metacarpal1.l" + r["finger2-1.L"]="finger2-1.l" + r["finger2-2.L"]="finger2-2.l" + r["finger2-3.L"]="finger2-3.l" + #--------------------------- + r["metacarpal2.L"]="metacarpal2.l" + r["finger3-1.L"]="finger3-1.l" + r["finger3-2.L"]="finger3-2.l" + r["finger3-3.L"]="finger3-3.l" + #--------------------------- + r["metacarpal3.L"]="metacarpal3.l" + r["finger4-1.L"]="finger4-1.l" + r["finger4-2.L"]="finger4-2.l" + r["finger4-3.L"]="finger4-3.l" + #--------------------------- + r["metacarpal4.L"]="metacarpal4.l" + r["finger5-1.L"]="finger5-1.l" + r["finger5-2.L"]="finger5-2.l" + r["finger5-3.L"]="finger5-3.l" + #--------------------------- + # R Hand + #--------------------------- + r["finger1-1.R"]="rthumb" + r["finger1-2.R"]="finger1-2.r" + r["finger1-3.R"]="finger1-3.r" + #--------------------------- + r["metacarpal1.R"]="metacarpal1.r" + r["finger2-1.R"]="finger2-1.r" + r["finger2-2.R"]="finger2-2.r" + r["finger2-3.R"]="finger2-3.r" + #--------------------------- + r["metacarpal2.R"]="metacarpal2.r" + r["finger3-1.R"]="finger3-1.r" + r["finger3-2.R"]="finger3-2.r" + r["finger3-3.R"]="finger3-3.r" + #--------------------------- + r["metacarpal3.R"]="metacarpal3.r" + r["finger4-1.R"]="finger4-1.r" + r["finger4-2.R"]="finger4-2.r" + r["finger4-3.R"]="finger4-3.r" + #--------------------------- + r["metacarpal4.R"]="metacarpal4.r" + r["finger5-1.R"]="finger5-1.r" + r["finger5-2.R"]="finger5-2.r" + r["finger5-3.R"]="finger5-3.r" + #--------------------------- + + if (doFeet): + r["upperleg02.L"]="lThigh" + r["lowerleg01.L"]="lShin" + r["foot.L"]="lFoot" + #--------------------------- + r["upperleg02.R"]="rThigh" + r["lowerleg01.R"]="rShin" + r["foot.R"]="rFoot" + + #--------------------------- + # L Foot + #--------------------------- + r["toe1-1.L"]="toe1-1.L" + r["toe1-2.L"]="toe1-2.L" + for toe in range(2,6): + for part in range(1,4): + r["toe%u-%u.L"%(toe,part)]="toe%u-%u.L"%(toe,part) + #--------------------------- + + #--------------------------- + # R Foot + #--------------------------- + r["toe1-1.R"]="toe1-1.R" + r["toe1-2.R"]="toe1-2.R" + for toe in range(2,6): + for part in range(1,4): + r["toe%u-%u.R"%(toe,part)]="toe%u-%u.R"%(toe,part) + #--------------------------- + + + #--------------------------- + # Face + #--------------------------- + #--------------------------- + if (doFace): + r["neck01"]="neck1" + r["head"]="head" + r["__jaw"]="__jaw" + r["jaw"]="jaw" + r["special04"]="special04" + r["oris02"]="oris02" + r["oris01"]="oris01" + r["oris06.L"]="oris06.l" + r["oris07.L"]="oris07.l" + r["oris06.R"]="oris06.r" + r["oris07.R"]="oris07.r" + r["tongue00"]="tongue00" + r["tongue01"]="tongue01" + r["tongue02"]="tongue02" + r["tongue03"]="tongue03" + r["__tongue04"]="__tongue04" + r["tongue04"]="tongue04" + r["tongue07.L"]="tongue07.l" + r["tongue07.R"]="tongue07.r" + r["tongue06.L"]="tongue06.l" + r["tongue06.R"]="tongue06.r" + r["tongue05.L"]="tongue05.l" + r["tongue05.R"]="tongue05.r" + r["__levator02.L"]="__levator02.l" + r["levator02.L"]="levator02.l" + r["levator03.L"]="levator03.l" + r["levator04.L"]="levator04.l" + r["levator05.L"]="levator05.l" + r["__levator02.R"]="__levator02.r" + r["levator02.R"]="levator02.r" + r["levator03.R"]="levator03.r" + r["levator04.R"]="levator04.r" + r["levator05.R"]="levator05.r" + r["__special01"]="__special01" + r["special01"]="special01" + r["oris04.L"]="oris04.l" + r["oris03.L"]="oris03.l" + r["oris04.R"]="oris04.r" + r["oris03.R"]="oris03.r" + r["oris06"]="oris06" + r["oris05"]="oris05" + r["__special03"]="__special03" + r["special03"]="special03" + r["__levator06.L"]="__levator06.l" + r["levator06.L"]="levator06.l" + r["__levator06.R"]="__levator06.r" + r["levator06.R"]="levator06.r" + r["special06.L"]="special06.l" + r["special05.L"]="special05.l" + r["eye.L"]="eye.l" + r["orbicularis03.L"]="orbicularis03.l" + r["orbicularis04.L"]="orbicularis04.l" + r["special06.R"]="special06.r" + r["special05.R"]="special05.r" + r["eye.R"]="eye.r" + r["orbicularis03.R"]="orbicularis03.r" + r["orbicularis04.R"]="orbicularis04.r" + r["__temporalis01.L"]="__temporalis01.l" + r["temporalis01.L"]="temporalis01.l" + r["oculi02.L"]="oculi02.l" + r["oculi01.L"]="oculi01.l" + r["__temporalis01.R"]="__temporalis01.r" + r["temporalis01.R"]="temporalis01.r" + r["oculi02.R"]="oculi02.r" + r["oculi01.R"]="oculi01.r" + r["__temporalis02.L"]="__temporalis02.l" + r["temporalis02.L"]="temporalis02.l" + r["risorius02.L"]="risorius02.l" + r["risorius03.L"]="risorius03.l" + r["__temporalis02.R"]="__temporalis02.r" + r["temporalis02.R"]="temporalis02.r" + r["risorius02.R"]="risorius02.r" + r["risorius03.R"]="risorius03.r" + + + return r + + + +class MocapNETBVHAnimationPanel(bpy.types.Panel): + """Creates a Panel in the Object properties window""" + bl_label = "MocapNET BVH Animation Helper" + bl_idname = "OBJECT_PT_mocapnet_panel" + bl_space_type = 'PROPERTIES' + bl_region_type = 'WINDOW' + bl_context = "object" + + def draw(self, context): + context = bpy.context + scene = context.scene + layout = self.layout + + + obj = context.object + + #layout = layout.split(factor=0.96, align=True) + #------------------------------------------------------------------ + #------------------------------------------------------------------ + row = layout.row() + row.label(text="MocapNET BVH Animation Helper v%0.2f" % mnetPluginVersion, icon='WORLD_DATA') + #------------------------------------------------------------------ + row = layout.row() + row.label(text="BVH file to use as source: ") + row = layout.row() + row.prop_search(scene, "mnetSource", scene, "objects", icon='ARMATURE_DATA') + #------------------------------------------------------------------ + row = layout.row() + row.label(text="Skinned Body to use as target: ") + row = layout.row() + row.prop_search(scene, "mnetTarget", scene, "objects", icon='OUTLINER_OB_ARMATURE') + #------------------------------------------------------------------ + row = layout.row() + row.label(text="Parts of MocapNET BVH file to link: ") + row = layout.row() + row.operator("mocapnet.mocapnet_op",text='Automatic').action='LINK' + row.operator("mocapnet.mocapnet_op",text='Upperbody').action='LINKUP' + row.operator("mocapnet.mocapnet_op",text='Face').action='LINKFACE' + row = layout.row() + row.label(text="Positional Component : ") + row = layout.row() + row.operator("mocapnet.mocapnet_op",text='Link Position').action='LINKPOS' + + +class MocapNETBVHAnimation(bpy.types.Operator): + """Creates a Panel in the Object properties window""" + bl_label = "MocapNET BVH Animation" + bl_idname = "mocapnet.mocapnet_op" + bl_description = 'MocapNET operation control' + bl_options = {'REGISTER', 'UNDO'} + + action: EnumProperty( + items=[ + ('LINK', 'Link MocapNET to Skinned Model', 'Link MocapNET to Skinned Model'), + ('LINKUP', 'Link MocapNET to Upper Body Only', 'Link MocapNET to Upper Body Only'), + ('LINKFACE', 'Link MocapNET to Face', 'Link MocapNET to Face'), + ('LINKPOS', 'Link MocapNET positional component', 'Link MocapNET positional component') + ] + ) + + @staticmethod + def add_cube(context): + bpy.ops.mesh.primitive_cube_add() + + @staticmethod + def add_sphere(context): + bpy.ops.mesh.primitive_uv_sphere_add() + + @staticmethod + def copyPosition(context,doBody=True,doHands=True,doFeet=True,doFace=False): + context = bpy.context + scene = context.scene + #------------------------------------------------------- + #associations = retrieveSkinToBVHAssotiationDict(doBody=doBody,doHands=doHands,doFeet=doFeet,doFace=doFace) + #------------------------------------------------------- + bvhObjectName = bpy.context.scene.mnetSource + skinnedObjectName = bpy.context.scene.mnetTarget + print("bvhObjectName",bvhObjectName) + print("skinnedObjectName",skinnedObjectName) + #------------------------------------------------------- + skinnedObject = scene.objects.get(skinnedObjectName) + bvhObject = scene.objects.get(bvhObjectName) + if (skinnedObject is not None) and (bvhObject is not None): + skinnedBoneName = "root" + bvhBoneName = "hip" + #------------------------------------------------ + #bvhBoneName = associations[skinnedBoneName] + #------------------------------------------------ + skinnedBone = skinnedObject.pose.bones.get(skinnedBoneName) + bvhBone = bvhObject.pose.bones.get(bvhBoneName) + print("pos associate ",skinnedBoneName," -> ",bvhBoneName) + # give it a copy rotation constraint + if (skinnedBone is not None) and (bvhBone is not None): + if (skinnedBoneName=="root"): + crc = skinnedBone.constraints.new('COPY_LOCATION') + crc.target = bvhObject + crc.subtarget = bvhBoneName + print("DONE ",skinnedBoneName) + + + + @staticmethod + def copySkeletonConstraints(context,doBody=True,doHands=True,doFeet=True,doFace=False,doPosition=False,doRotation=True): + context = bpy.context + scene = context.scene + #------------------------------------------------------- + associations = retrieveSkinToBVHAssotiationDict(doBody=doBody,doHands=doHands,doFeet=doFeet,doFace=doFace) + #------------------------------------------------------- + bvhObjectName = bpy.context.scene.mnetSource + skinnedObjectName = bpy.context.scene.mnetTarget + print("bvhObjectName",bvhObjectName) + print("skinnedObjectName",skinnedObjectName) + #------------------------------------------------------- + skinnedObject = scene.objects.get(skinnedObjectName) + bvhObject = scene.objects.get(bvhObjectName) + if (skinnedObject is not None) and (bvhObject is not None): + for skinnedBoneName in associations: + #------------------------------------------------ + bvhBoneName = associations[skinnedBoneName] + #------------------------------------------------ + skinnedBone = skinnedObject.pose.bones.get(skinnedBoneName) + bvhBone = bvhObject.pose.bones.get(bvhBoneName) + # give it a copy rotation constraint + if (skinnedBone is not None) and (bvhBone is not None): + if (len(skinnedBone.constraints)>0): + for c in skinnedBone.constraints: + skinnedBone.constraints.remove(c) # Remove constraint + if (skinnedBoneName=="root") and (doPosition): + crc = skinnedBone.constraints.new('COPY_LOCATION') + crc.target = bvhObject + crc.subtarget = bvhBoneName + if (doRotation): + crc = skinnedBone.constraints.new('COPY_ROTATION') + crc.target = bvhObject + crc.subtarget = bvhBoneName + + def execute(self, context): + if self.action == 'LINK': + self.copySkeletonConstraints(context=context,doBody=True,doHands=True,doFeet=True,doFace=False) + elif self.action == 'LINKUP': + self.copySkeletonConstraints(context=context,doBody=True,doHands=True,doFeet=False,doFace=False) + elif self.action == 'LINKFACE': + self.copySkeletonConstraints(context=context,doBody=False,doHands=False,doFeet=False,doFace=True) + elif self.action == 'LINKPOS': + self.copyPosition(context=context,doBody=True,doHands=False,doFeet=False,doFace=False) + return {'FINISHED'} + + +classes = (MocapNETBVHAnimationPanel, + MocapNETBVHAnimation) + +def register(): + for cls in classes: + bpy.utils.register_class(cls) + bpy.types.Scene.mnetSource = bpy.props.StringProperty(name="Source BVH", default="Select BVH Object") + bpy.types.Scene.mnetTarget = bpy.props.StringProperty(name="Target Obj", default="Select Skinned Object") + + + +def unregister(): + for cls in classes: + bpy.utils.unregister_class(cls) + del bpy.types.Scene.mnetSource + del bpy.types.Scene.mnetTarget + +if __name__ == "__main__": + register() + + # Get a list of all add-ons that are currently activated + activated_addons = [addon.module for addon in bpy.context.preferences.addons if addon] + + haveMPFB = False + # Print the name of each activated add-on + for addon in activated_addons: + print(addon) + if (addon=="mpfb"): + haveMPFB = True + print("We already have MPFB!") + + if (not haveMPFB): + import os + print("Receiving a fresh copy of MPFB!") + current_directory = os.getcwd() + print("Working from ",current_directory," directory") + os.system("wget http://download.tuxfamily.org/makehuman/plugins/mpfb2-latest.zip") + print(" Downloaded mpfb2-latest.zip and will now auto-install it for your convinience ") + bpy.ops.preferences.addon_install(filepath='%s/mpfb2-latest.zip' % os.getcwd()) + bpy.ops.preferences.addon_enable(module='mpfb') + bpy.ops.wm.save_userpref() + + # Get the path to the user preferences file + prefs_file = bpy.utils.user_resource('CONFIG') #bpy.context.preferences.filepath + + # Get the directory that contains the preferences file + prefs_dir = os.path.dirname(prefs_file) + + print(" Also installing the makehuman system assets!") + os.system("cd %s/mpfb/data && wget http://files.makehumancommunity.org/asset_packs/makehuman_system_assets/makehuman_system_assets_cc0.zip && unzip makehuman_system_assets_cc0.zip && rm makehuman_system_assets_cc0.zip" % prefs_dir) + diff --git a/animation/MocapNET-kasisnu/src/python/blender/downloadAndInstallBlender.sh b/animation/MocapNET-kasisnu/src/python/blender/downloadAndInstallBlender.sh new file mode 100755 index 0000000..d0fa4fe --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/downloadAndInstallBlender.sh @@ -0,0 +1,37 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +BLENDER="blender-3.4.1-linux-x64" + +if [[ -d "$BLENDER" ]] +then + echo "Blender seems to exist on your filesystem." +else + echo "Will get a copy of blender." + #https://www.blender.org/download/release/Blender3.4/blender-3.4.1-linux-x64.tar.xz/ + wget https://ftp.halifax.rwth-aachen.de/blender/release/Blender3.4/$BLENDER.tar.xz + tar -xf $BLENDER.tar.xz +fi + + +#This now happens from inside the blender_mocapnet.py script in the main function +#-------------------------------------------------------------------------------- +#git clone https://github.com/makehumancommunity/mpfb2 +#mkdir -p ~/.config/blender/3.4/scripts/addons/ +#cd ~/.config/blender/3.4/scripts/addons/ +#ln -s $DIR/mpfb2/src/mpfb +#OR +#wget http://download.tuxfamily.org/makehuman/plugins/mpfb2-latest.zip +#wget http://files.makehumancommunity.org/asset_packs/makehuman_system_assets/makehuman_system_assets_cc0.zip +#-------------------------------------------------------------------------------- + + +xdg-open "https://www.youtube.com/watch?v=ooLRUS5j4AI"& +cd "$DIR" + +$BLENDER/blender -y --python blender_mocapnet.py + + +exit 0 diff --git a/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/label.tag b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/label.tag new file mode 100644 index 0000000..9271a15 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/label.tag @@ -0,0 +1 @@ +face diff --git a/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.body.csv b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.body.csv new file mode 100644 index 0000000..315c54e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.body.csv @@ -0,0 +1,2 @@ +head_reye_2,head_reye_5,head_leye_1,head_leye_4,head_nostrills_2,head_chin,head_outmouth_0,head_outmouth_3,head_outmouth_6,head_outmouth_9 +4865,36,11483,6820,297,5171,402,466,7162,492 diff --git a/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv new file mode 100644 index 0000000..38de554 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv @@ -0,0 +1,2 @@ +head_reyebrow_2,head_reyebrow_4,head_leyebrow_2,head_leyebrow_4 +102,121,40,59 diff --git a/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.high-poly.csv b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.high-poly.csv new file mode 100644 index 0000000..9a5e314 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/faceWhiteLists/vertexWhitelist_newgirl.high-poly.csv @@ -0,0 +1,2 @@ +head_reye,head_leye +1023,485 diff --git a/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/label.tag b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/label.tag new file mode 100644 index 0000000..9271a15 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/label.tag @@ -0,0 +1 @@ +face diff --git a/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.body.csv b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.body.csv new file mode 100644 index 0000000..16396c3 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.body.csv @@ -0,0 +1,2 @@ +head_reye_0,head_reye_1,head_reye_2,head_reye_3,head_reye_4,head_reye_5,head_leye_0,head_leye_1,head_leye_2,head_leye_3,head_leye_4,head_leye_5,head_nosebone_0,head_nosebone_1,head_nosebone_2,head_nosebone_3,head_nostrills_0,head_nostrills_1,head_nostrills_2,head_nostrills_3,head_nostrills_4,head_rchin_0,head_rchin_1,head_rchin_2,head_rchin_3,head_rchin_4,head_rchin_5,head_rchin_6,head_rchin_7,head_chin,head_lchin_7,head_lchin_6,head_lchin_5,head_lchin_4,head_lchin_3,head_lchin_2,head_lchin_1,head_lchin_0,head_outmouth_0,head_outmouth_1,head_outmouth_2,head_outmouth_3,head_outmouth_4,head_outmouth_5,head_outmouth_6,head_outmouth_7,head_outmouth_8,head_outmouth_9,head_outmouth_10,head_outmouth_11,head_inmouth_0,head_inmouth_1,head_inmouth_2,head_inmouth_3,head_inmouth_4,head_inmouth_5,head_inmouth_6,head_inmouth_7 +4854,4849,4865,67,47,36,6851,11483,11467,11472,6820,13368,136,135,5063,5134,5095,295,297,7062,11710,256,5181,5176,5153,5299,5227,5172,5166,5171,11779,11835,11903,11904,11767,11929,11796,7025,402,448,460,466,7219,7208,7162,7233,7245,492,490,478,432,704,468,7429,7192,7279,494,534 diff --git a/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv new file mode 100644 index 0000000..cbc1cb2 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv @@ -0,0 +1,2 @@ +head_reyebrow_0,head_reyebrow_1,head_reyebrow_2,head_reyebrow_3,head_reyebrow_4,head_leyebrow_4,head_leyebrow_3,head_leyebrow_2,head_leyebrow_1,head_leyebrow_0 +96,93,102,111,121,59,49,40,31,57 diff --git a/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.high-poly.csv b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.high-poly.csv new file mode 100644 index 0000000..9a5e314 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/fullfaceWhiteLists/vertexWhitelist_newgirl.high-poly.csv @@ -0,0 +1,2 @@ +head_reye,head_leye +1023,485 diff --git a/animation/MocapNET-kasisnu/src/python/blender/headerWithHeadAndOneMotion.bvh b/animation/MocapNET-kasisnu/src/python/blender/headerWithHeadAndOneMotion.bvh new file mode 100644 index 0000000..5009ccf --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/headerWithHeadAndOneMotion.bvh @@ -0,0 +1,1022 @@ +HIERARCHY +ROOT hip +{ + OFFSET 0 0 0 + CHANNELS 6 Xposition Yposition Zposition Zrotation Yrotation Xrotation + JOINT abdomen + { + OFFSET 0 20.6881 -0.73152 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT chest + { + OFFSET 0 11.7043 -0.48768 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT neck + { + OFFSET 0 22.1894 -2.19456 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT neck1 + { + OFFSET 0.000000 5.364170 1.574630 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT head + { + OFFSET 0.000000 5.364141 1.574630 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT __jaw + { + OFFSET 0.000000 13.604700 -0.502080 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT jaw + { + OFFSET 0.000000 -13.499860 2.500710 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT special04 + { + OFFSET -0.000000 -6.835370 4.375500 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT oris02 + { + OFFSET 0.000000 1.711150 2.820850 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT oris01 + { + OFFSET -0.000000 0.972390 0.845650 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.000000 1.162291 0.607091 + } + } + } + JOINT oris06.l + { + OFFSET 0.000000 1.711150 2.820850 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT oris07.l + { + OFFSET 1.168850 0.445180 0.506110 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.450611 1.195178 0.204519 + } + } + } + JOINT oris06.r + { + OFFSET 0.000000 1.711150 2.820850 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT oris07.r + { + OFFSET -1.168850 0.445180 0.506110 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET -0.450611 1.195173 0.204519 + } + } + } + } + JOINT tongue00 + { + OFFSET -0.000000 -6.835370 4.375500 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT tongue01 + { + OFFSET 0.000000 3.973650 -3.762340 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT tongue02 + { + OFFSET 0.000000 0.429760 2.924710 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT tongue03 + { + OFFSET 0.000000 0.018530 2.059010 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT __tongue04 + { + OFFSET 0.000000 -0.440240 0.838860 + CHANNELS 3 Zrotation Xrotation Yrotation + JOINT tongue04 + { + OFFSET 0.000000 0.000000 0.000000 + CHANNELS 3 Zrotation Xrotation Yrotation + End Site + { + OFFSET 0.000000 -0.440230 0.838860 + } + } + } + JOINT 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 diff --git a/animation/MocapNET-kasisnu/src/python/blender/mouthWhiteLists/label.tag b/animation/MocapNET-kasisnu/src/python/blender/mouthWhiteLists/label.tag new file mode 100644 index 0000000..bbdb241 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/mouthWhiteLists/label.tag @@ -0,0 +1 @@ +mouth diff --git a/animation/MocapNET-kasisnu/src/python/blender/mouthWhiteLists/vertexWhitelist_newgirl.body.csv b/animation/MocapNET-kasisnu/src/python/blender/mouthWhiteLists/vertexWhitelist_newgirl.body.csv new file mode 100644 index 0000000..f1e57fe --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/mouthWhiteLists/vertexWhitelist_newgirl.body.csv @@ -0,0 +1,2 @@ +head_nostrills_2,head_chin,head_outmouth_0,head_outmouth_1,head_outmouth_2,head_outmouth_3,head_outmouth_4,head_outmouth_5,head_outmouth_6,head_outmouth_7,head_outmouth_8,head_outmouth_9,head_outmouth_10,head_outmouth_11,head_inmouth_0,head_inmouth_1,head_inmouth_2,head_inmouth_3,head_inmouth_4,head_inmouth_5,head_inmouth_6,head_inmouth_7 +297,5171,402,448,460,466,7219,7208,7162,7233,7245,492,490,478,432,704,468,7429,7192,7279,494,534 diff --git a/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/label.tag b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/label.tag new file mode 100644 index 0000000..45dea56 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/label.tag @@ -0,0 +1 @@ +reye diff --git a/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.body.csv b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.body.csv new file mode 100644 index 0000000..c4f6a6e --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.body.csv @@ -0,0 +1,2 @@ +head_reye_0,head_reye_1,head_reye_2,head_reye_3,head_reye_4,head_reye_5,head_nostrills_2,head_rchin_0,head_chin +4854,4849,4865,67,47,36,297,256,5171 diff --git a/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv new file mode 100644 index 0000000..3991b22 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.eyebrow002.csv @@ -0,0 +1,2 @@ +head_reyebrow_0,head_reyebrow_1,head_reyebrow_2,head_reyebrow_3,head_reyebrow_4 +96,93,102,111,121 diff --git a/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.high-poly.csv b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.high-poly.csv new file mode 100644 index 0000000..7454ec8 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/blender/reyeWhiteLists/vertexWhitelist_newgirl.high-poly.csv @@ -0,0 +1,2 @@ +head_reye +764 diff --git a/animation/MocapNET-kasisnu/src/python/compareUtility/aT.sh b/animation/MocapNET-kasisnu/src/python/compareUtility/aT.sh new file mode 100755 index 0000000..30f6692 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/compareUtility/aT.sh @@ -0,0 +1,62 @@ +#!/bin/bash +#Written by Ammar Qammaz a.k.a AmmarkoV - 2020 +# This bash script uses gnuplot and R so make sure to : +# sudo apt-get install gnuplot r-base + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +ORIG_DIR=`pwd` + +function getStatistics +{ + #use R to generate statistics + #sudo apt-get install r-base + R -q -e "x <- read.csv('$1', header = F); summary(x); sd(x[ , 1])" > $2 + cat $1 | wc -l >> $2 +} + + +IN="$1" +OUTPUT_IMAGE="AllDistancesFrequency_$2.png" +echo "plotAllJointsDistanceFrequency.sh $IN $OUTPUT_IMAGE" + +getStatistics $IN $IN-statsraw.txt +cat $IN-statsraw.txt | grep -E 'Min|Qu|Median|Mean|Max' > $IN-stats.txt +STATS=`cat $IN-stats.txt` +MEAN=`cat $IN-stats.txt | grep "Mean" | cut -d':' -f2 ` +MEDIAN=`cat $IN-stats.txt | grep "Median" | cut -d':' -f2 ` +MAXIMUM=`cat $IN-stats.txt | grep "Max" | cut -d':' -f2 ` +PLACETEXT=`cat $IN-stats.txt | grep "3rd" | cut -d':' -f2 ` + +LOW_LIMIT="50"; +LIMIT="150"; +NUMBER_OF_RECORDS=`wc -l $1 | cut -d' ' -f1` + +GNUPLOT_CMD="set terminal png; \ + set output \"$OUTPUT_IMAGE\"; set yrange [0:1];\ + set title \"Frequency precision diagram $IN \";\ + set xlabel \"Distance Of Joints(mm)\"; \ + set ylabel \"Frequency Of Value\"; \ + set arrow from $MEAN, graph 0 to $MEAN, graph 1 nohead; \ + set label \"Mean Value of $MEAN mm \" at $MEAN,0.40; \ + set arrow from $MEDIAN, graph 0 to $MEDIAN, graph 1 nohead; \ + set label \"Median Value of $MEDIAN mm \" at $MEDIAN,0.30; \ + set label \"$STATS\" at $PLACETEXT,0.85; \ + binwidth=3;\ + bin(x,width)=width*floor(x/width);\ + plot [0:] '$IN' using (bin(\$1,binwidth)):(1.0) smooth cnorm t 'smooth cumulative'" + + +echo "WE WILL RUN " +echo "gnuplot -e \"$GNUPLOT_CMD\"" +echo " " +echo " " + + +gnuplot -e "$GNUPLOT_CMD" + +rm $IN-statsraw.txt +rm $IN-stats.txt + +exit 0 diff --git a/animation/MocapNET-kasisnu/src/python/compareUtility/align2DPoints.py b/animation/MocapNET-kasisnu/src/python/compareUtility/align2DPoints.py new file mode 100644 index 0000000..bb496aa --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/compareUtility/align2DPoints.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 +#Written by Ammar Qammaz a.k.a AmmarkoV - 2020 + + +import h5py +import numpy as np +import csv +import os +import sys + +from scipy.spatial import procrustes +from scipy.linalg import orthogonal_procrustes + + +#Taken from https://github.com/una-dinosauria/3d-pose-baseline/blob/master/src/procrustes.py +def compute_similarity_transform(X, Y, compute_optimal_scale=False): + """ + A port of MATLAB's `procrustes` function to Numpy. + Adapted from http://stackoverflow.com/a/18927641/1884420 + Args + X: array NxM of targets, with N number of points and M point dimensionality + Y: array NxM of inputs + compute_optimal_scale: whether we compute optimal scale or force it to be 1 + Returns: + d: squared error after transformation + Z: transformed Y + T: computed rotation + b: scaling + c: translation + """ + + muX = X.mean(0) + muY = Y.mean(0) + + X0 = X - muX + Y0 = Y - muY + + ssX = (X0**2.).sum() + ssY = (Y0**2.).sum() + + # centred Frobenius norm + normX = np.sqrt(ssX) + normY = np.sqrt(ssY) + + # scale to equal (unit) norm + X0 = X0 / normX + Y0 = Y0 / normY + + # optimum rotation matrix of Y + A = np.dot(X0.T, Y0) + U,s,Vt = np.linalg.svd(A,full_matrices=False) + V = Vt.T + T = np.dot(V, U.T) + + # Make sure we have a rotation + detT = np.linalg.det(T) + V[:,-1] *= np.sign( detT ) + s[-1] *= np.sign( detT ) + T = np.dot(V, U.T) + + traceTA = s.sum() + + if compute_optimal_scale: # Compute optimum scaling of Y. + b = traceTA * normX / normY + d = 1 - traceTA**2 + Z = normX*traceTA*np.dot(Y0, T) + muX + else: # If no scaling allowed + b = 1 + d = 1 + ssY/ssX - 2 * traceTA * normY / normX + Z = normY*np.dot(Y0, T) + muX + + c = muX - b*np.dot(muY, T) + + return d, Z, T, b, c + + + + +def pointListsReturnAvgDistance(A,B): + numberOfPoints=A.shape[0] + if (A.shape[0]!=B.shape[0]): + print("Error comparing point lists of different length") + return inf + + distance=0.0 + for i in range(0,numberOfPoints): + #--------- + xA=A[i][0] + yA=A[i][1] + zA=A[i][2] + #--------- + xB=B[i][0] + yB=B[i][1] + zB=B[i][2] + #--------- + xAB=xA-xB + yAB=yA-yB + zAB=zA-zB + + #Pythagorean theorem for 3 dimensions + #distance = squareRoot( xAB^2 + yAB^2 + zAB^2 ) + distance+=np.sqrt( (xAB*xAB) + (yAB*yAB) + (zAB*zAB) ) + return distance/numberOfPoints + + + + + diff --git a/animation/MocapNET-kasisnu/src/python/compareUtility/compareUtility.py b/animation/MocapNET-kasisnu/src/python/compareUtility/compareUtility.py new file mode 100644 index 0000000..96b2a5d --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/compareUtility/compareUtility.py @@ -0,0 +1,622 @@ +#!/usr/bin/env python3 +#Written by Ammar Qammaz a.k.a AmmarkoV - 2020 + +from align2DPoints import pointListsReturnAvgDistance,compute_similarity_transform +from drawPointClouds import findJointID,get3DDistance,setupDrawing,drawLimbDimensions,drawLimbError,drawFrameError,drawAfterEndOfComparison +from writeCSVResults import writeCSVFileResults,appendRAWResultsForGNUplot + +import numpy as np +import gc +import os +import sys +import csv +import time +import array + +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.animation as animation +from mpl_toolkits.mplot3d import axes3d, Axes3D + +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + +#I have added a seperate list with the joints we want to compare +#to avoid the weird parent lists when you remove one joint +JOINTS_TO_COMPARE=list() +#JOINTS_TO_COMPARE.append('Nose') #0 +JOINTS_TO_COMPARE.append('Neck') #1 +JOINTS_TO_COMPARE.append('RShoulder') #2 +JOINTS_TO_COMPARE.append('RElbow') #3 +JOINTS_TO_COMPARE.append('RWrist') #4 +JOINTS_TO_COMPARE.append('LShoulder') #5 +JOINTS_TO_COMPARE.append('LElbow') #6 +JOINTS_TO_COMPARE.append('LWrist') #7 +JOINTS_TO_COMPARE.append('MidHip') #8 +JOINTS_TO_COMPARE.append('RHip') #9 +JOINTS_TO_COMPARE.append('RKnee') #10 +JOINTS_TO_COMPARE.append('RAnkle') #11 +JOINTS_TO_COMPARE.append('LHip') #12 +JOINTS_TO_COMPARE.append('LKnee') #13 +JOINTS_TO_COMPARE.append('LAnkle') #14 + + +#These are our labels they have to match what we want to process +#If you don't want something to be part of the measurment remove it from here +#as well as from the JOINT_PARENTS list +#--------------------------------------------------------------------- +JOINT_LABELS=list() +JOINT_LABELS.append('Nose') #0 +JOINT_LABELS.append('Neck') #1 +JOINT_LABELS.append('RShoulder') #2 +JOINT_LABELS.append('RElbow') #3 +JOINT_LABELS.append('RWrist') #4 +JOINT_LABELS.append('LShoulder') #5 +JOINT_LABELS.append('LElbow') #6 +JOINT_LABELS.append('LWrist') #7 +JOINT_LABELS.append('MidHip') #8 +JOINT_LABELS.append('RHip') #9 +JOINT_LABELS.append('RKnee') #10 +JOINT_LABELS.append('RAnkle') #11 +JOINT_LABELS.append('LHip') #12 +JOINT_LABELS.append('LKnee') #13 +JOINT_LABELS.append('LAnkle') #14 +JOINT_LABELS.append('REye') #15 +JOINT_LABELS.append('LEye') #16 +JOINT_LABELS.append('REar') #17 +JOINT_LABELS.append('LEar') #18 +JOINT_LABELS.append('LBigToe') #19 +JOINT_LABELS.append('LSmallToe') #20 +JOINT_LABELS.append('LHeel') #21 +JOINT_LABELS.append('RBigToe') #22 +JOINT_LABELS.append('RSmallToe') #23 +JOINT_LABELS.append('RHeel') #24 + + + +#These are our parents they have to match JOINT_LABELS +#If you don't want something to be part of the measurment remove it from here +#as well as from the JOINT_LABELS list +#--------------------------------------------------------------------- +JOINT_PARENTS=list() +JOINT_PARENTS.append(findJointID("Neck",JOINT_LABELS)) #0 Parent of Nose is Neck +JOINT_PARENTS.append(findJointID("MidHip",JOINT_LABELS)) #1 Parent of Neck is MidHip +JOINT_PARENTS.append(findJointID("Neck",JOINT_LABELS)) #2 Parent of RShoulder is Neck +JOINT_PARENTS.append(findJointID("RShoulder",JOINT_LABELS)) #3 Parent of RElbow is RShoulder +JOINT_PARENTS.append(findJointID("RElbow",JOINT_LABELS)) #4 etc ... +JOINT_PARENTS.append(findJointID("Neck",JOINT_LABELS)) #5 +JOINT_PARENTS.append(findJointID("LShoulder",JOINT_LABELS)) #6 +JOINT_PARENTS.append(findJointID("LElbow",JOINT_LABELS)) #7 +JOINT_PARENTS.append(findJointID("MidHip",JOINT_LABELS)) #8 +JOINT_PARENTS.append(findJointID("MidHip",JOINT_LABELS)) #9 +JOINT_PARENTS.append(findJointID("RHip",JOINT_LABELS)) #10 +JOINT_PARENTS.append(findJointID("RKnee",JOINT_LABELS)) #11 +JOINT_PARENTS.append(findJointID("MidHip",JOINT_LABELS)) #12 +JOINT_PARENTS.append(findJointID("LHip",JOINT_LABELS)) #13 +JOINT_PARENTS.append(findJointID("LKnee",JOINT_LABELS)) #14 +JOINT_PARENTS.append(findJointID("Nose",JOINT_LABELS)) #15 +JOINT_PARENTS.append(findJointID("Nose",JOINT_LABELS)) #16 +JOINT_PARENTS.append(findJointID("Nose",JOINT_LABELS)) #17 +JOINT_PARENTS.append(findJointID("Nose",JOINT_LABELS)) #18 +JOINT_PARENTS.append(findJointID("LAnkle",JOINT_LABELS)) #19 +JOINT_PARENTS.append(findJointID("LAnkle",JOINT_LABELS)) #20 +JOINT_PARENTS.append(findJointID("LAnkle",JOINT_LABELS)) #21 +JOINT_PARENTS.append(findJointID("RAnkle",JOINT_LABELS)) #22 +JOINT_PARENTS.append(findJointID("RAnkle",JOINT_LABELS)) #23 +JOINT_PARENTS.append(findJointID("RAnkle",JOINT_LABELS)) #24 + + +#These are our labels that based on JOINT_PARENTS auto-create the appropriate labels +#--------------------------------------------------------------------- +JOINT_PARENT_LABELS=list() +for i in range(0,len(JOINT_LABELS)): + JOINT_PARENT_LABELS.append(JOINT_LABELS[JOINT_PARENTS[i]]) +#--------------------------------------------------------------------- + + +def checkIfFileExists(filename): + return os.path.isfile(filename) + +def convert_bytes(num): + """ + this function will convert bytes to MB.... GB... etc + """ + step_unit = 1000.0 #1024 bad the size + + for x in ['bytes', 'KB', 'MB', 'GB', 'TB']: + if num < step_unit: + return "%3.1f %s" % (num, x) + num /= step_unit + +def getNumberOfLines(filename): + print("Counting number of lines in file ",filename) + with open(filename) as f: + return sum(1 for line in f) + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +def readCSVFile(filename,memPercentage,csvDelimiter,useHalfFloats,groupOutput): + print("CSV file :",filename,"..\n") + + if (not checkIfFileExists(filename)): + print( bcolors.WARNING + "Input file "+filename+" does not exist, cannot read ground truth.." + bcolors.ENDC) + print("Current Directory was "+os.getcwd()) + sys.exit(0) + start = time.time() + + dtypeSelected=np.dtype(np.float32) + dtypeSelectedByteSize=int(dtypeSelected.itemsize) + if (useHalfFloats): + dtypeSelected=np.dtype(np.float16) + dtypeSelectedByteSize=int(dtypeSelected.itemsize) + + progress=0.0 + sampleNumber=0 + receivedHeader=0 + inputNumberOfColumns=0 + + inputLabels=list() + + #------------------------------------------------------------------------------------------------- + numberOfSamplesInput=getNumberOfLines(filename)-2 + print(" Input file has ",numberOfSamplesInput," training samples\n") + #------------------------------------------------------------------------------------------------- + + + numberOfSamples = numberOfSamplesInput + numberOfSamplesLimit=int(numberOfSamples*memPercentage) + #------------------------------------------------------------------------------------------------- + if (memPercentage==0.0): + print("readGroundTruthFile was asked to occupy 0 memory so this probably means we just want one record") + numberOfSamplesLimit=2 + if (memPercentage>1.0): + print("Memory Limit will be interpreted as a raw value..") + numberOfSamplesLimit=int(memPercentage) + #------------------------------------------------------------------------------------------------- + + + thisInput = array.array('f') + #--------------------------------- + + fi = open(filename, "r") + readerIn = csv.reader( fi , delimiter=csvDelimiter, skipinitialspace=True) + for rowIn in readerIn: + #------------------------------------------------------ + if (not receivedHeader): #use header to get labels + #------------------------------------------------------ + inputNumberOfColumns=len(rowIn) + + #Make sure CSV files that end with delimiter are correctly handled.. + if (inputNumberOfColumns>0): + if (rowIn[inputNumberOfColumns-1]==''): + inputNumberOfColumns=inputNumberOfColumns-1 + #----------------------------------------------------- + inputLabels = list(rowIn[i] for i in range(0,inputNumberOfColumns) ) + print("Number of Input elements : ",len(inputLabels)) + #------------------------------------------------------ + + if (memPercentage==0): + print("Will only return labels\n") + return {'labels':inputLabels}; + + + #i=0 + #print("class Input(Enum):") + #for label in inputLabels: + # print(" ",label," = ",i," #",int(i/3)) + # print(" ",label,"=",int(i/3)) + # i=i+1 + + #--------------------------------- + # Allocate Lists + #--------------------------------- + for i in range(inputNumberOfColumns): + thisInput.append(0.0) + #--------------------------------- + + + #--------------------------------- + # Allocate Numpy Arrays + #--------------------------------- + inputSize=0 + startCompressed=0 + + inputSize=inputSize+inputNumberOfColumns + startCompressed=inputNumberOfColumns + + npInputBytesize=0+numberOfSamplesLimit * inputSize * dtypeSelectedByteSize + print(" Input file on disk has a shape of [",numberOfSamples,",",inputSize,"]") + print(" Input we will read has a shape of [",numberOfSamplesLimit,",",inputSize,"]") + print(" Input will occupy ",convert_bytes(npInputBytesize)," of RAM\n") + npInput = np.full([numberOfSamplesLimit,inputSize],fill_value=0,dtype=dtypeSelected,order='C') + #---------------------------------------------------------------------------------------------------------- + receivedHeader=1 + #sys.exit(0) + else: + #------------------------------------------- + # First convert our string INPUT to floats + #------------------------------------------- + for i in range(inputNumberOfColumns): + thisInput[i]=float(rowIn[i]) + #------------------------------------------- + for num in range(0,inputNumberOfColumns): + npInput[sampleNumber,num]=float(thisInput[num]); + #------------------------------------------- + sampleNumber=sampleNumber+1 + + if (numberOfSamples>0): + progress=sampleNumber/numberOfSamplesLimit + + if (sampleNumber%1000==0) : + progressString = "%0.2f"%float(100*progress) + print("\rReading from disk (",sampleNumber,") - ",progressString," % \r", end="", flush=True) + + if (numberOfSamplesLimit<=sampleNumber): + print("\rStopping reading file to obey memory limit given by parameter --mem ",memPercentage,"\n") + break + #------------------------------------------- + fi.close() + del readerIn + gc.collect() + + + print("\n read, Samples: ",sampleNumber,", was expecting ",numberOfSamples," samples\n") + print(npInput.shape) + + totalNumberOfBytes=npInput.nbytes; + totalNumberOfGigaBytes=totalNumberOfBytes/1073741824; + print("Size Occupied by data = ",totalNumberOfGigaBytes," GB \n") + + end = time.time() + print("Time elapsed : ",(end-start)/60," mins") + #--------------------------------------------------------------------- + + if (groupOutput==0): + #New better dictionary + output = dict() + for i in range(0,len(inputLabels)): + lowerCaseName = inputLabels[i].lower() + #print("Joint ",lowerCaseName) + output[lowerCaseName]=list() + for frameID in range(0,len(npInput)): + output[lowerCaseName].append(npInput[frameID][i]) + return output + else: + #This is the old dictionary way (better for tensorflow training) + return {'label':inputLabels, 'body':npInput }; + + + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- + + +#Main() program +#------------------------------------- +averageErrorDistances=list() +averageMotionEstimationDistancesBetweenFrames=list() + + +#These are labeling infos +#Used to correctly pack the results +#------------------------- +subject="S?" +action="Unknown" +subaction="?" +camera="?" +actionLabel="?" +addedPixelNoise=0 + +#These are configuration parameters +#------------------------------------------------------------------- +everyNFrames=0 # Run on every frame of the CSV files by default +drawPlot=0 # Don't Draw plot by default +doProcrustes=1 # Do procrustes analysis +procrustesScale=1 # Let procrustes also scale the point clouds +ground="h36.csv" # A default h36.csv filename +groundDelim=',' # By default the h36.csv has a ',' delimiter +output="out.csv" # A default output filename +outputDelim=';' # Damien's output has ';' delimiters, mine has ',' +ourScaleX=1.0 +ourScaleY=1.0 +ourScaleZ=1.0 + + +#Modifiers on comparisons.. +#------------------------------------------------------------------------------------------------ +if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--ourscale"): + ourScaleX=float(sys.argv[i+1]) + ourScaleY=float(sys.argv[i+2]) + ourScaleZ=float(sys.argv[i+3]) + if (sys.argv[i]=="--draw"): + drawPlot=1 + if (sys.argv[i]=="--from"): + output=sys.argv[i+1] + outputDelim=sys.argv[i+2] + if (sys.argv[i]=="--ground"): + ground=sys.argv[i+1] + groundDelim=sys.argv[i+2] + if (sys.argv[i]=="--noprocrustes"): + doProcrustes=0 + if (sys.argv[i]=="--every"): + everyNFrames=int(sys.argv[i+1]) + if (sys.argv[i]=="--info"): + subject=sys.argv[i+1] + action=sys.argv[i+2] + camera=sys.argv[i+3] + addedPixelNoise=float(sys.argv[i+4]) + print("\n Infos Set ",sys.argv[i+1]) + actionLabel=action + s=actionLabel.split("-") + subaction=s[1] + action=s[0] + action=action.replace('/','') + subaction=subaction.replace('/','') + actionLabel=actionLabel.replace('/','') + camera=camera.replace('/','') + + print("Subject ",subject," Action ",action," Subaction ",subaction," Camera ",camera) +#------------------------------------------------------------------------------------------------ + +if (drawPlot): + print("Using matplotlib:",matplotlib.__version__) + # === Plot and animate === + fig = plt.figure() + fig.set_size_inches(19.2, 10.8, forward=True) + + ax = fig.add_subplot(2, 2, 1, projection='3d') + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95) + + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + ax.view_init(90, 90) + + + +out=readCSVFile(output,1.0,outputDelim,0,0) +ground=readCSVFile(ground,1.0,groundDelim,0,0) + +print("Output number of keys : %u " % len(out.keys()) ) +print("GroundTruth number of keys : %u " % len(ground.keys()) ) + +if ( len(out.keys()) != len(ground.keys()) ): + print(bcolors.WARNING,"Inconsistent keys",bcolors.ENDC) + #sys.exit(0) + + +totalError=0.0 +totalSamples=0 +numberOfJointsToCompare = len(JOINTS_TO_COMPARE) +numberOfFrames = len(out['3dx_nose']) + + +#----------------------- +alljointDistances=list() +#----------------------- +jointDistance=list() +for jointID in range(0,numberOfJointsToCompare): + jointDistance.append(list()) +#----------------------- + + +#------------------------------------------------------------------------------------------------ +if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--frames"): + numberOfFrames=int(sys.argv[i+1]) +#------------------------------------------------------------------------------------------------ + + + + + +#We want to loop over every frame that we loaded.. +for frameID in range(0,numberOfFrames): + #In many papers they run the comparison ever N frames + #So depending on this setting you can actually execute + #the comparison computation everyNFrames + #----------------------------------------------------- + if ( (everyNFrames==0) or (frameID%everyNFrames==0) ): + executeComputations=1 + else: + executeComputations=0 + #----------------------------------------------------- + + if (executeComputations==1): + ourPointCloud=list() + h36PointCloud=list() + xlineStart=list() + xlineEnd=list() + ylineStart=list() + ylineEnd=list() + zlineStart=list() + zlineEnd=list() + + #Align midhip to make sure we start from a sane position + offsetX=(ourScaleX*out['3dx_midhip'][frameID])-ground['3dx_midhip'][frameID] + offsetY=(ourScaleY*out['3dy_midhip'][frameID])-ground['3dy_midhip'][frameID] + offsetZ=(ourScaleZ*out['3dz_midhip'][frameID])-ground['3dz_midhip'][frameID] + + for jointIDUnresolved in range(0,numberOfJointsToCompare): + #------------------------------------------------------------------ + #Our Joints to compare list has a different indexing + jointID = findJointID(JOINTS_TO_COMPARE[jointIDUnresolved],JOINT_LABELS) + if (jointID==-1): + print(bcolors.WARNING,"Failed to resolve joint: ",JOINTS_TO_COMPARE[jointIDUnresolved],bcolors.ENDC) + sys.exit(0) + #----------------------------------------------------------- + jointKey = JOINT_LABELS[jointID].lower() + parentKey = JOINT_PARENT_LABELS[jointID].lower() + #print("Frame %u / Key %s ( prev %s ) "% (frameID,jointKey,parentKey) ) + + #------------------------------------------------------------------ + #We select the joint and push it to the point cloud for this particular frame + xM=(ourScaleX*out['3dx_%s' % jointKey][frameID])-offsetX + yM=(ourScaleY*out['3dy_%s' % jointKey][frameID])-offsetY + zM=(ourScaleZ*out['3dz_%s' % jointKey][frameID])-offsetZ + ourPointCloud.append([xM,yM,zM]) + + #These lineStarts/lineEnds and parentKeys help draw lines connecting + #the dots in skeletons + xParentM=(ourScaleX*out['3dx_%s' % parentKey][frameID])-offsetX + yParentM=(ourScaleY*out['3dy_%s' % parentKey][frameID])-offsetY + zParentM=(ourScaleZ*out['3dz_%s' % parentKey][frameID])-offsetZ + xlineStart.append(xM) + ylineStart.append(yM) + zlineStart.append(zM) + xlineEnd.append(xParentM) + ylineEnd.append(yParentM) + zlineEnd.append(zParentM) + #------------------------------------------------------------------ + #We select the h36 joint and push it to the point cloud for this particular frame + xH=ground['3dx_%s' % jointKey][frameID] + yH=ground['3dy_%s' % jointKey][frameID] + zH=ground['3dz_%s' % jointKey][frameID] + h36PointCloud.append([xH,yH,zH]) + + #These lineStarts/lineEnds and parentKeys help draw lines connecting + #the dots in skeletons + xParentH=ground['3dx_%s' % parentKey][frameID] + yParentH=ground['3dy_%s' % parentKey][frameID] + zParentH=ground['3dz_%s' % parentKey][frameID] + xlineStart.append(xH) + ylineStart.append(yH) + zlineStart.append(zH) + xlineEnd.append(xParentH) + ylineEnd.append(yParentH) + zlineEnd.append(zParentH) + #------------------------------------------------------------------ + + #------------------------------------------------------------------ + + #We package our lists in numpy to be able to easily manipulate them + #------------------------------------------------------------------ + np_h36PointCloud = np.asarray(h36PointCloud,dtype=np.float32) + np_ourPointCloud = np.asarray(ourPointCloud,dtype=np.float32) + + + #This is the main comparison after using procrustes and transforming the pointcloud + #to align it or when just doing plain old euclidean distance + #-------------------------------------------------------------------------------- + if (doProcrustes): + d, Z, T, b, c = compute_similarity_transform(np_h36PointCloud,np_ourPointCloud,compute_optimal_scale=procrustesScale) + #disparity=np.sqrt(d) #d: squared error after transformation + #print("compute_similarity_transform : ",disparity) + + #Our point cloud is brought to the same translation and rotation as h36 point cloud + np_ourPointCloud = (b*np_ourPointCloud.dot(T))+c + disparity = pointListsReturnAvgDistance(np_ourPointCloud,np_h36PointCloud) + else: + disparity = pointListsReturnAvgDistance(np_ourPointCloud,np_h36PointCloud) + #-------------------------------------------------------------------------------- + + #We want to calculate Mean Per Joint Position Error (MPJPE) + #to do so we have to calculate the position error of each of the joints in our point cloud + #sum it up and then divide it through the number of samples + for jID in range(0,numberOfJointsToCompare): + #------------------------------------------------------------------ + #We use the np_ourPointCloud and np_h36PointCloud so that if procrustes analysis is enabled it will be used.. + perJointDisparity=get3DDistance( + np_ourPointCloud[jID][0],np_ourPointCloud[jID][1],np_ourPointCloud[jID][2], + np_h36PointCloud[jID][0],np_h36PointCloud[jID][1],np_h36PointCloud[jID][2] + ) + totalError+=perJointDisparity + totalSamples+=1 + #We also keep every sample on a list to do an analysis in the end + alljointDistances.append(perJointDisparity) + jointDistance[jID].append(perJointDisparity) + #------------------------------------------------------------------ + + print("Frame %u / Disparity %f " % (frameID,disparity)) + averageErrorDistances.append(disparity) + + #Draw the plot that summarizes what is happening if --draw is given + if (drawPlot): + drawAfterEndOfComparison( + JOINTS_TO_COMPARE, + JOINT_LABELS, + JOINT_PARENTS, + JOINT_PARENT_LABELS, + plt,ax,ax2,ax3,ax4, + xlineStart,xlineEnd, + ylineStart,ylineEnd, + zlineStart,zlineEnd, + np_ourPointCloud, + np_h36PointCloud, + frameID, + numberOfFrames, + everyNFrames, + disparity, + averageErrorDistances, + averageMotionEstimationDistancesBetweenFrames + ) + +#Since this always appends results be sure to remove the gnuplot.raw +#file before starting a new session.. +appendRAWResultsForGNUplot("gnuplot.raw",alljointDistances) + +#Final results we want is Mean Per Joint Position Error (MPJPE) so we rely on the perJointDisparities etc.. +#The result unit depends on the input to the script but it should be in millimeters for MocapNET output +#to change units you can use the --ourscale X Y Z commandline parameter, however millimeters of accuracy +#is the norm for 3D pose estimation work.. + +print("\nMean Per Joint Error for ",totalSamples," samples is ",totalError/totalSamples) +median = np.median(alljointDistances) +mean = np.mean(alljointDistances) +average = np.average(alljointDistances) +std = np.std(alljointDistances) +var = np.var(alljointDistances) +print("Mean is ",mean," Std is ",std," Var is ",var) + + + +writeHeader=0 +if not os.path.exists("results.csv"): + writeHeader=1 +writeCSVFileResults( + JOINTS_TO_COMPARE, + "results.csv", + writeHeader, + alljointDistances, + jointDistance, + numberOfJointsToCompare, + subject, + action, + subaction, + camera, + actionLabel, + addedPixelNoise + ) + + + + +if (drawPlot): + os.system("ffmpeg -framerate 25 -i p%05d.png -s 1920x1080 -y -r 30 -pix_fmt yuv420p -threads 8 lastcomp.mp4 && rm ./p*.png") # +sys.exit(0) + diff --git a/animation/MocapNET-kasisnu/src/python/compareUtility/drawPointClouds.py b/animation/MocapNET-kasisnu/src/python/compareUtility/drawPointClouds.py new file mode 100644 index 0000000..1a3908c --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/compareUtility/drawPointClouds.py @@ -0,0 +1,233 @@ +#!/usr/bin/env python3 +#Written by Ammar Qammaz a.k.a AmmarkoV - 2020 + +import numpy as np + +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.animation as animation + +from mpl_toolkits.mplot3d import axes3d, Axes3D +print("Using matplotlib:",matplotlib.__version__) + + +def pointListReturnXYZListForScatterPlot(A): + numberOfPoints=A.shape[0] + xs=list() + ys=list() + zs=list() + for i in range(0,numberOfPoints): + xs.append(A[i][0]) + ys.append(A[i][1]) + zs.append(A[i][2]) + return xs,ys,zs + + +def setupDrawing(fig,ax,ax2,ax3,ax4): + # === Plot and animate === + fig = plt.figure() + fig.set_size_inches(19.2, 10.8, forward=True) + + ax = fig.add_subplot(2, 2, 1, projection='3d') + ax2 = fig.add_subplot(2, 2, 2) + ax3 = fig.add_subplot(2, 2, 3) + ax4 = fig.add_subplot(2, 2, 4) + fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95) + + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + ax.view_init(90, 90) + + +def get3DDistance(jX,jY,jZ,pX,pY,pZ): + return np.sqrt( ((jX-pX)*(jX-pX)) + ((jY-pY)*(jY-pY)) + ((jZ-pZ)*(jZ-pZ)) ) + +def findJointID(jointName,labels): + for i in range(0,len(labels)): + if (jointName==labels[i]): + return i + print("Cannot find joint ",jointName," !") + return -1 + + +#----------------------------------------------------------------------------------------------------------------------- +def drawLimbDimensions(JOINTS_TO_COMPARE,JOINT_LABELS,JOINT_PARENTS,JOINT_PARENT_LABELS,h36mPoints,ourPoints,numberOfJoints,ax2): + labels = list() + h36m_distances = list() + mnet_distances = list() + + for jointID in range(0,len(JOINTS_TO_COMPARE)): + #--------------------------------------- + jointIDGlobal = findJointID(JOINTS_TO_COMPARE[jointID],JOINT_LABELS) + jointParentID=findJointID(JOINT_PARENT_LABELS[JOINT_PARENTS[jointIDGlobal]],JOINTS_TO_COMPARE) + #--------------------------------------- + if (jointParentID!=-1) and (jointIDGlobal!=-1): + labels.append(JOINT_LABELS[jointIDGlobal]) + jX=h36mPoints[jointID][0] + jY=h36mPoints[jointID][1] + jZ=h36mPoints[jointID][2] + pX=h36mPoints[jointParentID][0] + pY=h36mPoints[jointParentID][1] + pZ=h36mPoints[jointParentID][2] + distances=get3DDistance(jX,jY,jZ,pX,pY,pZ) + h36m_distances.append(distances) + #--------------------------------------- + jX=ourPoints[jointID][0] + jY=ourPoints[jointID][1] + jZ=ourPoints[jointID][2] + pX=ourPoints[jointParentID][0] + pY=ourPoints[jointParentID][1] + pZ=ourPoints[jointParentID][2] + distances=get3DDistance(jX,jY,jZ,pX,pY,pZ) + mnet_distances.append(distances) + #--------------------------------------- + + #------------------------------------------------- + x = np.arange(len(labels)) # the label locations + width = 0.35 # the width of the bars + + rects1 = ax2.bar(x - width/2, h36m_distances, width, label='H36M') + rects2 = ax2.bar(x + width/2, mnet_distances, width, label='Our Method') + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax2.set_ylim(auto=False,bottom=0,top=800) + ax2.set_ylabel('Limb dimensions in millimeters') + ax2.set_title('Comparison of H36M limb dimensions') + ax2.set_xticks(x) + ax2.set_xticklabels(labels, rotation=45, rotation_mode="anchor") + ax2.legend() + + #autolabel(rects1) + #autolabel(rects2) +#----------------------------------------------------------------------------------------------------------------------- + + + + +#----------------------------------------------------------------------------------------------------------------------- +def drawLimbError(JOINTS_TO_COMPARE,JOINT_LABELS,JOINT_PARENTS,JOINT_PARENT_LABELS,h36mPoints,ourPoints,numberOfJoints,ax3): + labels = list() + error_distances = list() + for jointID in range(0,len(JOINTS_TO_COMPARE)): + #--------------------------------------- + jointIDGlobal = findJointID(JOINTS_TO_COMPARE[jointID],JOINT_LABELS) + #--------------------------------------- + if (jointIDGlobal!=-1): + labels.append(JOINT_LABELS[jointIDGlobal]) + jX=h36mPoints[jointID][0] + jY=h36mPoints[jointID][1] + jZ=h36mPoints[jointID][2] + pX=ourPoints[jointID][0] + pY=ourPoints[jointID][1] + pZ=ourPoints[jointID][2] + distances=get3DDistance(jX,jY,jZ,pX,pY,pZ) + error_distances.append(distances) + #--------------------------------------- + + #------------------------------------------------- + x = np.arange(len(labels)) # the label locations + width = 0.35 # the width of the bars + + rects1 = ax3.bar(x - width/2, error_distances, width, label='Error in millimeters') + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax3.set_ylim(auto=False,bottom=0,top=250) + ax3.set_ylabel('Error in millimeters') + ax3.set_title('Comparison of 3D estimation error') + ax3.set_xticks(x) + ax3.set_xticklabels(labels, rotation=45, rotation_mode="anchor") + ax3.legend() +#----------------------------------------------------------------------------------------------------------------------- + + + +#----------------------------------------------------------------------------------------------------------------------- +def drawFrameError(averageErrorDistances,averageMotionEstimationDistancesBetweenFrames,ax4): + + #minimum=np.min(averageErrorDistances) + #ax4.plot((0,len(averageErrorDistances)), (minimum,minimum),label='Minimum Error') + + #maximum=np.max(averageErrorDistances) + #ax4.plot((0,len(averageErrorDistances)), (maximum,maximum),label='Maximum Error') + + #median=np.median(averageErrorDistances) + #ax4.plot((0,len(averageErrorDistances)), (median,median),label='Median Error (%0.2f mm)'% median) + + average=np.average(averageErrorDistances) + ax4.plot((0,len(averageErrorDistances)), (average,average),label='Average of average errors (%0.2f mm)' % average) + + ax4.plot(averageErrorDistances, label='Our method average error in millimeters') + ax4.plot(averageMotionEstimationDistancesBetweenFrames, label='Distance of average joint from previous frame') + + #ax4.set_ylim(auto=False,bottom=0,top=250) + ax4.set_xlabel('Experiment frame number') + ax4.set_ylabel('Millimeters') + ax4.set_title('Average 3D estimation error per frame') + #ax4.set_xticklabels(labels, rotation=45, rotation_mode="anchor") + ax4.legend() +#----------------------------------------------------------------------------------------------------------------------- + + + + + + +def drawAfterEndOfComparison( + JOINTS_TO_COMPARE, + JOINT_LABELS, + JOINT_PARENTS, + JOINT_PARENT_LABELS, + plt,ax,ax2,ax3,ax4, + xlineStart,xlineEnd, + ylineStart,ylineEnd, + zlineStart,zlineEnd, + ourPointCloud, + h36PointCloud, + frameID, + numberOfFrames, + everyNFrames, + disparity, + averageErrorDistances, + averageMotionEstimationDistancesBetweenFrames + ): + plt.cla() + ax.cla() + ax2.cla() + ax3.cla() + ax4.cla() + + #Print Skeletons and their connected lines.. + #for i in range(0,len(xlineStart)): + # ax.plot([xlineStart[i],xlineEnd[i]],[ylineStart[i],ylineEnd[i]],zs=[zlineStart[i],zlineEnd[i]]) + #-------------------------------------------- + xs, ys, zs = pointListReturnXYZListForScatterPlot(ourPointCloud) + ax.scatter(xs, ys, zs) + xs, ys, zs = pointListReturnXYZListForScatterPlot(h36PointCloud) + ax.scatter(xs, ys, zs) + + #Secondary plots for limb lengths etc.. + numberOfJoints = len(ourPointCloud) + drawLimbDimensions(JOINTS_TO_COMPARE,JOINT_LABELS,JOINT_PARENTS,JOINT_PARENT_LABELS,h36PointCloud,ourPointCloud,numberOfJoints,ax2) + drawLimbError(JOINTS_TO_COMPARE,JOINT_LABELS,JOINT_PARENTS,JOINT_PARENT_LABELS,h36PointCloud,ourPointCloud,numberOfJoints,ax3) + drawFrameError(averageErrorDistances,averageMotionEstimationDistancesBetweenFrames,ax4) + + #------------------------- + ax.text2D(0.05, 0.95, "Frame %u/%u - Increment %u - Procrustes Average Error %0.2f mm"%(frameID,numberOfFrames,everyNFrames,disparity) , transform=ax.transAxes) + #ax.text2D(0.05, 0.05, "RHand %0.2f mm / LHand %0.2f mm / RFoot %0.2f mm / LFoot %0.2f mm "%(currentDistances[4],currentDistances[7],currentDistances[11],currentDistances[14]) , transform=ax.transAxes) + #------------------------- + ax.set_xlim(auto=False,left=-600,right=300) + ax.set_ylim(auto=False,bottom=-1600,top=200) + ax.set_zlim(auto=False,bottom=2000,top=6000) + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + #------------------------- + plt.show(block=False) + plt.savefig('p%05u.png'%frameID) + #fig.canvas.draw() + plt.pause(0.001) + + + + diff --git a/animation/MocapNET-kasisnu/src/python/compareUtility/writeCSVResults.py b/animation/MocapNET-kasisnu/src/python/compareUtility/writeCSVResults.py new file mode 100644 index 0000000..6c1f977 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/compareUtility/writeCSVResults.py @@ -0,0 +1,110 @@ +#!/usr/bin/env python3 +#Written by Ammar Qammaz a.k.a AmmarkoV - 2020 + +import numpy as np + +#----------------------------------------------------------------------------------------------------------------------- +def writeCSVFileResults( + JOINT_LABELS, + outputFile, + addHeader, + globalJointDistances, + perJointDistance, + numberOfJoints, + subject, + action, + subaction, + camera, + actionLabel, + addedPixelNoise + ): + #Write header---------------- + if (addHeader): + file = open(outputFile,"w") + file.write("Subject,") + file.write("Action,") + file.write("ActionLabel,") + file.write("Subaction,") + file.write("Camera,") + file.write("Noise,") + file.write("Global_Median,") + #file.write("Global_Mean,") + file.write("Global_Average,") + file.write("Global_Std,") + file.write("Global_Var,") + for jointID in range(0,numberOfJoints): + file.write("%s_Median,"%JOINT_LABELS[jointID]) + #file.write("%s_Mean,"%JOINT_LABELS[jointID]) + file.write("%s_Average,"%JOINT_LABELS[jointID]) + file.write("%s_Std,"%JOINT_LABELS[jointID]) + file.write("%s_Var"%JOINT_LABELS[jointID]) + if (jointID!=numberOfJoints-1): + file.write(",") + else: + file.write("\n") + else: + file = open(outputFile,"a") + #---------------------------- + + median=np.median(globalJointDistances) + mean=np.mean(globalJointDistances) + average=np.average(globalJointDistances) + std=np.std(globalJointDistances) + var=np.var(globalJointDistances) + print("\nGlobal Median:",median," Average:",average," Std:",std,"Var:",var) #file.write(subject) + file.write(subject) + file.write(",") + file.write(action) + file.write(",") + file.write(actionLabel) + file.write(",") + file.write(subaction) + file.write(",") + file.write(camera) + file.write(",") + file.write(str(addedPixelNoise)) + file.write(",") + file.write(str(median)) + file.write(",") + #file.write(str(mean)) + #file.write(",") + file.write(str(average)) + file.write(",") + file.write(str(std)) + file.write(",") + file.write(str(var)) + file.write(",") + + for jointID in range(0,numberOfJoints): + median=np.median(perJointDistance[jointID]) + mean=np.mean(perJointDistance[jointID]) + average=np.average(perJointDistance[jointID]) + std=np.std(perJointDistance[jointID]) + var=np.var(perJointDistance[jointID]) + print("Joint ",JOINT_LABELS[jointID]," Median:",median," Mean:",mean," Average:",average," Std:",std,"Var:",var) + file.write(str(median)) + file.write(",") + #file.write(str(mean)) + #file.write(",") + file.write(str(average)) + file.write(",") + file.write(str(std)) + file.write(",") + file.write(str(var)) + if (jointID!=numberOfJoints-1): + file.write(",") + else: + file.write("\n") + + file.close() +#-------------------------------------------- + + + +#----------------------------------------------------------------------------------------------------------------------- +def appendRAWResultsForGNUplot(outputFile,globalJointDistances): + fileHandler = open(outputFile, "a") + for measurement in globalJointDistances: + fileHandler.write(str(measurement)) + fileHandler.write("\n") + fileHandler.close() diff --git a/animation/MocapNET-kasisnu/src/python/ctypes/build.sh b/animation/MocapNET-kasisnu/src/python/ctypes/build.sh new file mode 100755 index 0000000..241d34f --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/ctypes/build.sh @@ -0,0 +1,11 @@ +#!/bin/bash + +#gcc -c -fPIC c.c -o c.o +#gcc c.o -shared -o libcalci.so + +gcc -shared -o libcalci.so -fPIC c.c + +python3 p.py + + +exit 0 diff --git a/animation/MocapNET-kasisnu/src/python/ctypes/c.c b/animation/MocapNET-kasisnu/src/python/ctypes/c.c new file mode 100644 index 0000000..ee873af --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/ctypes/c.c @@ -0,0 +1,30 @@ +#include +#include +#include "c.h" + +void connect() +{ + printf("Connected to C extension...\n"); +} + +//return random value in range of 0-50 +int randNum() +{ + int nRand = rand() % 50; + return nRand; +} + +//add two number and return value +int addNum(int a, int b) +{ + int nAdd = a + b; + return nAdd; +} + +int printFloatList(float * l,int lSize) +{ + for (int i=0; i0.0) and (lV>0.0): + mnetPose2D["2DX_neck"]=(rX+lX)/2 + mnetPose2D["2DY_neck"]=(rY+lY)/2 + mnetPose2D["visible_neck"]=(rV+lV)/2 + #--------------------------------------------------- + + if ("2DX_rhip" in mnetPose2D) and ("2DY_rhip" in mnetPose2D) and ("visible_rhip" in mnetPose2D) and ("2DX_lhip" in mnetPose2D) and ("2DY_lhip" in mnetPose2D) and ("visible_lhip" in mnetPose2D) : + #--------------------------------------------- + rX = float(mnetPose2D["2DX_rhip"]) + rY = float(mnetPose2D["2DY_rhip"]) + rV = float(mnetPose2D["visible_rhip"]) + #--------------------------------------------- + lX = float(mnetPose2D["2DX_lhip"]) + lY = float(mnetPose2D["2DY_lhip"]) + lV = float(mnetPose2D["visible_lhip"]) + #--------------------------------------------- + if (rV>0.0) and (lV>0.0): + mnetPose2D["2DX_hip"]=(rX+lX)/2 + mnetPose2D["2DY_hip"]=(rY+lY)/2 + mnetPose2D["visible_hip"]=(rV+lV)/2 + #--------------------------------------------------- + return mnetPose2D + + + + + + +#MocapNET list of expected inputs +#frameNumber,skeletonID,totalSkeletons,2DX_head,2DY_head,visible_head,2DX_neck,2DY_neck,visible_neck,2DX_rshoulder,2DY_rshoulder,visible_rshoulder,2DX_relbow,2DY_relbow,visible_relbow,2DX_rhand,2DY_rhand,visible_rhand,2DX_lshoulder,2DY_lshoulder,visible_lshoulder,2DX_lelbow,2DY_lelbow,visible_lelbow,2DX_lhand,2DY_lhand,visible_lhand,2DX_hip,2DY_hip,visible_hip,2DX_rhip,2DY_rhip,visible_rhip,2DX_rknee,2DY_rknee,visible_rknee,2DX_rfoot,2DY_rfoot,visible_rfoot,2DX_lhip,2DY_lhip,visible_lhip,2DX_lknee,2DY_lknee,visible_lknee,2DX_lfoot,2DY_lfoot,visible_lfoot,2DX_endsite_eye.r,2DY_endsite_eye.r,visible_endsite_eye.r,2DX_endsite_eye.l,2DY_endsite_eye.l,visible_endsite_eye.l,2DX_rear,2DY_rear,visible_rear,2DX_lear,2DY_lear,visible_lear,2DX_endsite_toe1-2.l,2DY_endsite_toe1-2.l,visible_endsite_toe1-2.l,2DX_endsite_toe5-3.l,2DY_endsite_toe5-3.l,visible_endsite_toe5-3.l,2DX_lheel,2DY_lheel,visible_lheel,2DX_endsite_toe1-2.r,2DY_endsite_toe1-2.r,visible_endsite_toe1-2.r,2DX_endsite_toe5-3.r,2DY_endsite_toe5-3.r,visible_endsite_toe5-3.r,2DX_rheel,2DY_rheel,visible_rheel,2DX_bkg,2DY_bkg,visible_bkg, + + +def getHolisticBodyNameList(): + bn=list() + #--------------------------------------------------- + bn.append("head") #0 - nose + bn.append("head_leye_0") #1 - left_eye_inner + bn.append("endsite_eye.l") #2 - left_eye + bn.append("head_leye_3") #3 - left_eye_outer + bn.append("head_reye_3") #4 - right_eye_inner + bn.append("endsite_eye.r") #5 - right_eye + bn.append("head_reye_0") #6 - right_eye_outer + bn.append("lear") #7 - left_ear + bn.append("rear") #8 - right_ear + bn.append("head_outmouth_0") #9 - mouth_left + bn.append("head_outmouth_6") #10 - mouth_right + bn.append("lshoulder") #11 - left_shoulder + bn.append("rshoulder") #12 - right_shoulder + bn.append("lelbow") #13 - left_elbow + bn.append("relbow") #14 - right_elbow + bn.append("lhand") #15 - left_wrist + bn.append("rhand") #16 - right_wrist + bn.append("left_hand_pinky_4") #17 - left_pinky + bn.append("right_hand_pinky_4")#18 - right_pinky + bn.append("left_hand_index_4") #19 - left_index + bn.append("right_hand_index_4")#20 - right_index + bn.append("left_hand_thumb_4") #21 - left_thumb + bn.append("right_hand_thumb_4")#22 - right_thumb + bn.append("lhip") #23 - left_hip + bn.append("rhip") #24 - right_hip + bn.append("lknee") #25 - left_knee + bn.append("rknee") #26 - right_knee + bn.append("lfoot") #27 - left_ankle + bn.append("rfoot") #28 - right_ankle + bn.append("lheel") #29 - left_heel + bn.append("rheel") #30 - right_heel + bn.append("endsite_toe1-2.l") #31 - left_foot_index + bn.append("endsite_toe1-2.r") #32 - right_foot_index + return bn +#--------------------------------------------------- + + + + +#2DX_lhand,2DY_lhand,visible_lhand,2DX_lthumb,2DY_lthumb,visible_lthumb,2DX_finger1-2.l,2DY_finger1-2.l,visible_finger1-2.l,2DX_finger1-3.l,2DY_finger1-3.l,visible_finger1-3.l,2DX_endsite_finger1-3.l,2DY_endsite_finger1-3.l,visible_endsite_finger1-3.l,2DX_finger2-1.l,2DY_finger2-1.l,visible_finger2-1.l,2DX_finger2-2.l,2DY_finger2-2.l,visible_finger2-2.l,2DX_finger2-3.l,2DY_finger2-3.l,visible_finger2-3.l,2DX_endsite_finger2-3.l,2DY_endsite_finger2-3.l,visible_endsite_finger2-3.l,2DX_finger3-1.l,2DY_finger3-1.l,visible_finger3-1.l,2DX_finger3-2.l,2DY_finger3-2.l,visible_finger3-2.l,2DX_finger3-3.l,2DY_finger3-3.l,visible_finger3-3.l,2DX_endsite_finger3-3.l,2DY_endsite_finger3-3.l,visible_endsite_finger3-3.l,2DX_finger4-1.l,2DY_finger4-1.l,visible_finger4-1.l,2DX_finger4-2.l,2DY_finger4-2.l,visible_finger4-2.l,2DX_finger4-3.l,2DY_finger4-3.l,visible_finger4-3.l,2DX_endsite_finger4-3.l,2DY_endsite_finger4-3.l,visible_endsite_finger4-3.l,2DX_finger5-1.l,2DY_finger5-1.l,visible_finger5-1.l,2DX_finger5-2.l,2DY_finger5-2.l,visible_finger5-2.l,2DX_finger5-3.l,2DY_finger5-3.l,visible_finger5-3.l,2DX_endsite_finger5-3.l,2DY_endsite_finger5-3.l,visible_endsite_finger5-3.l +def getHolisticLHandNameList(): + bn=list() + #--------------------------------------------------- + bn.append("lhand") #0 - wrist + bn.append("lthumb") #1 - thumb_cmc + bn.append("finger1-2.l") #2 - thumb_mcp + bn.append("finger1-3.l") #3 - thumb_ip + bn.append("endsite_finger1-3.l") #4 - thumb_tip + bn.append("finger2-1.l") #5 - index_finger_mcp + bn.append("finger2-2.l") #6 - index_finger_pip + bn.append("finger2-3.l") #7 - index_finger_dip + bn.append("endsite_finger2-3.l") #8 - index_finger_tip + bn.append("finger3-1.l") #9 - middle_finger_mcp + bn.append("finger3-2.l") #10 - middle_finger_pip + bn.append("finger3-3.l") #11 - middle_finger_dip + bn.append("endsite_finger3-3.l") #12 - middle_finger_tip + bn.append("finger4-1.l") #13 - ring_finger_mcp + bn.append("finger4-2.l") #14 - ring_finger_pip + bn.append("finger4-3.l") #15 - ring_finger_dip + bn.append("endsite_finger4-3.l") #16 - ring_tip + bn.append("finger5-1.l") #17 - pinky_mcp + bn.append("finger5-2.l") #18 - pinky_pip + bn.append("finger5-3.l") #19 - pinky_dip + bn.append("endsite_finger5-3.l") #20 - pinky_tip + return bn +#--------------------------------------------------- + + + + +#2DX_rhand,2DY_rhand,visible_rhand,2DX_rthumb,2DY_rthumb,visible_rthumb,2DX_finger1-2.r,2DY_finger1-2.r,visible_finger1-2.r,2DX_finger1-3.r,2DY_finger1-3.r,visible_finger1-3.r,2DX_endsite_finger1-3.r,2DY_endsite_finger1-3.r,visible_endsite_finger1-3.r,2DX_finger2-1.r,2DY_finger2-1.r,visible_finger2-1.r,2DX_finger2-2.r,2DY_finger2-2.r,visible_finger2-2.r,2DX_finger2-3.r,2DY_finger2-3.r,visible_finger2-3.r,2DX_endsite_finger2-3.r,2DY_endsite_finger2-3.r,visible_endsite_finger2-3.r,2DX_finger3-1.r,2DY_finger3-1.r,visible_finger3-1.r,2DX_finger3-2.r,2DY_finger3-2.r,visible_finger3-2.r,2DX_finger3-3.r,2DY_finger3-3.r,visible_finger3-3.r,2DX_endsite_finger3-3.r,2DY_endsite_finger3-3.r,visible_endsite_finger3-3.r,2DX_finger4-1.r,2DY_finger4-1.r,visible_finger4-1.r,2DX_finger4-2.r,2DY_finger4-2.r,visible_finger4-2.r,2DX_finger4-3.r,2DY_finger4-3.r,visible_finger4-3.r,2DX_endsite_finger4-3.r,2DY_endsite_finger4-3.r,visible_endsite_finger4-3.r,2DX_finger5-1.r,2DY_finger5-1.r,visible_finger5-1.r,2DX_finger5-2.r,2DY_finger5-2.r,visible_finger5-2.r,2DX_finger5-3.r,2DY_finger5-3.r,visible_finger5-3.r,2DX_endsite_finger5-3.r,2DY_endsite_finger5-3.r,visible_endsite_finger5-3.r +def getHolisticRHandNameList(): + bn=list() + #--------------------------------------------------- + bn.append("rhand") #0 - wrist + bn.append("rthumb") #1 - thumb_cmc + bn.append("finger1-2.r") #2 - thumb_mcp + bn.append("finger1-3.r") #3 - thumb_ip + bn.append("endsite_finger1-3.r") #4 - thumb_tip + bn.append("finger2-1.r") #5 - index_finger_mcp + bn.append("finger2-2.r") #6 - index_finger_pip + bn.append("finger2-3.r") #7 - index_finger_dip + bn.append("endsite_finger2-3.r") #8 - index_finger_tip + bn.append("finger3-1.r") #9 - middle_finger_mcp + bn.append("finger3-2.r") #10 - middle_finger_pip + bn.append("finger3-3.r") #11 - middle_finger_dip + bn.append("endsite_finger3-3.r") #12 - middle_finger_tip + bn.append("finger4-1.r") #13 - ring_finger_mcp + bn.append("finger4-2.r") #14 - ring_finger_pip + bn.append("finger4-3.r") #15 - ring_finger_dip + bn.append("endsite_finger4-3.r") #16 - ring_tip + bn.append("finger5-1.r") #17 - pinky_mcp + bn.append("finger5-2.r") #18 - pinky_pip + bn.append("finger5-3.r") #19 - pinky_dip + bn.append("endsite_finger5-3.r") #20 - pinky_tip + return bn +#--------------------------------------------------- + + + +#2DX_head_rchin_0,2DY_head_rchin_0,visible_head_rchin_0,2DX_head_rchin_1,2DY_head_rchin_1,visible_head_rchin_1,2DX_head_rchin_2,2DY_head_rchin_2,visible_head_rchin_2,2DX_head_rchin_3,2DY_head_rchin_3,visible_head_rchin_3,2DX_head_rchin_4,2DY_head_rchin_4,visible_head_rchin_4,2DX_head_rchin_5,2DY_head_rchin_5,visible_head_rchin_5,2DX_head_rchin_6,2DY_head_rchin_6,visible_head_rchin_6,2DX_head_rchin_7,2DY_head_rchin_7,visible_head_rchin_7,2DX_head_chin,2DY_head_chin,visible_head_chin,2DX_head_lchin_7,2DY_head_lchin_7,visible_head_lchin_7,2DX_head_lchin_6,2DY_head_lchin_6,visible_head_lchin_6,2DX_head_lchin_5,2DY_head_lchin_5,visible_head_lchin_5,2DX_head_lchin_4,2DY_head_lchin_4,visible_head_lchin_4,2DX_head_lchin_3,2DY_head_lchin_3,visible_head_lchin_3,2DX_head_lchin_2,2DY_head_lchin_2,visible_head_lchin_2,2DX_head_lchin_1,2DY_head_lchin_1,visible_head_lchin_1,2DX_head_lchin_0,2DY_head_lchin_0,visible_head_lchin_0,2DX_head_reyebrow_0,2DY_head_reyebrow_0,visible_head_reyebrow_0,2DX_head_reyebrow_1,2DY_head_reyebrow_1,visible_head_reyebrow_1,2DX_head_reyebrow_2,2DY_head_reyebrow_2,visible_head_reyebrow_2,2DX_head_reyebrow_3,2DY_head_reyebrow_3,visible_head_reyebrow_3,2DX_head_reyebrow_4,2DY_head_reyebrow_4,visible_head_reyebrow_4,2DX_head_leyebrow_4,2DY_head_leyebrow_4,visible_head_leyebrow_4,2DX_head_leyebrow_3,2DY_head_leyebrow_3,visible_head_leyebrow_3,2DX_head_leyebrow_2,2DY_head_leyebrow_2,visible_head_leyebrow_2,2DX_head_leyebrow_1,2DY_head_leyebrow_1,visible_head_leyebrow_1,2DX_head_leyebrow_0,2DY_head_leyebrow_0,visible_head_leyebrow_0,2DX_head_nosebone_0,2DY_head_nosebone_0,visible_head_nosebone_0,2DX_head_nosebone_1,2DY_head_nosebone_1,visible_head_nosebone_1,2DX_head_nosebone_2,2DY_head_nosebone_2,visible_head_nosebone_2,2DX_head_nosebone_3,2DY_head_nosebone_3,visible_head_nosebone_3,2DX_head_nostrills_0,2DY_head_nostrills_0,visible_head_nostrills_0,2DX_head_nostrills_1,2DY_head_nostrills_1,visible_head_nostrills_1,2DX_head_nostrills_2,2DY_head_nostrills_2,visible_head_nostrills_2,2DX_head_nostrills_3,2DY_head_nostrills_3,visible_head_nostrills_3,2DX_head_nostrills_4,2DY_head_nostrills_4,visible_head_nostrills_4,2DX_head_reye_0,2DY_head_reye_0,visible_head_reye_0,2DX_head_reye_1,2DY_head_reye_1,visible_head_reye_1,2DX_head_reye_2,2DY_head_reye_2,visible_head_reye_2,2DX_head_reye_3,2DY_head_reye_3,visible_head_reye_3,2DX_head_reye_4,2DY_head_reye_4,visible_head_reye_4,2DX_head_reye_5,2DY_head_reye_5,visible_head_reye_5,2DX_head_leye_0,2DY_head_leye_0,visible_head_leye_0,2DX_head_leye_1,2DY_head_leye_1,visible_head_leye_1,2DX_head_leye_2,2DY_head_leye_2,visible_head_leye_2,2DX_head_leye_3,2DY_head_leye_3,visible_head_leye_3,2DX_head_leye_4,2DY_head_leye_4,visible_head_leye_4,2DX_head_leye_5,2DY_head_leye_5,visible_head_leye_5,2DX_head_outmouth_0,2DY_head_outmouth_0,visible_head_outmouth_0,2DX_head_outmouth_1,2DY_head_outmouth_1,visible_head_outmouth_1,2DX_head_outmouth_2,2DY_head_outmouth_2,visible_head_outmouth_2,2DX_head_outmouth_3,2DY_head_outmouth_3,visible_head_outmouth_3,2DX_head_outmouth_4,2DY_head_outmouth_4,visible_head_outmouth_4,2DX_head_outmouth_5,2DY_head_outmouth_5,visible_head_outmouth_5,2DX_head_outmouth_6,2DY_head_outmouth_6,visible_head_outmouth_6,2DX_head_outmouth_7,2DY_head_outmouth_7,visible_head_outmouth_7,2DX_head_outmouth_8,2DY_head_outmouth_8,visible_head_outmouth_8,2DX_head_outmouth_9,2DY_head_outmouth_9,visible_head_outmouth_9,2DX_head_outmouth_10,2DY_head_outmouth_10,visible_head_outmouth_10,2DX_head_outmouth_11,2DY_head_outmouth_11,visible_head_outmouth_11,2DX_head_inmouth_0,2DY_head_inmouth_0,visible_head_inmouth_0,2DX_head_inmouth_1,2DY_head_inmouth_1,visible_head_inmouth_1,2DX_head_inmouth_2,2DY_head_inmouth_2,visible_head_inmouth_2,2DX_head_inmouth_3,2DY_head_inmouth_3,visible_head_inmouth_3,2DX_head_inmouth_4,2DY_head_inmouth_4,visible_head_inmouth_4,2DX_head_inmouth_5,2DY_head_inmouth_5,visible_head_inmouth_5,2DX_head_inmouth_6,2DY_head_inmouth_6,visible_head_inmouth_6,2DX_head_inmouth_7,2DY_head_inmouth_7,visible_head_inmouth_7,2DX_head_reye,2DY_head_reye,visible_head_reye,2DX_head_leye,2DY_head_leye,visible_head_leye +def getHolisticFaceNameList(): + bn=list() + #--------------------------------------------------- + bn.append("head_outmouth_3") #0 - + bn.append("head_nosebone_3") #1 - + bn.append("head_nostrills_2") #2 - + bn.append("") #3 - + bn.append("") #4 - + bn.append("head_nosebone_2") #5 - + bn.append("head_nosebone_1") #6 - + bn.append("") #7 - + bn.append("") #8 - + bn.append("") #9 - + bn.append("") #10 - + bn.append("") #11 - + bn.append("head_inmouth_2") #12 - + bn.append("") #13 - + bn.append("") #14 - + bn.append("head_inmouth_6") #15 - + bn.append("") #16 - + bn.append("head_outmouth_9") #17 - + bn.append("") #18 - + bn.append("") #19 - + bn.append("") #20 - + bn.append("") #21 - + bn.append("") #22 - + bn.append("") #23 - + bn.append("") #24 - + bn.append("") #25 - + bn.append("") #26 - + bn.append("head_outmouth_2") #27 - + bn.append("") #28 - + bn.append("") #29 - + bn.append("") #30 - + bn.append("") #31 - + bn.append("") #32 - + bn.append("head_reye_0") #33 - + bn.append("") #34 - + bn.append("") #35 - + bn.append("") #36 - + bn.append("") #37 - + bn.append("") #38 - + bn.append("") #39 - + bn.append("head_outmouth_1") #40 - + bn.append("head_inmouth_1") #41 - + bn.append("") #42 - + bn.append("") #43 - + bn.append("") #44 - + bn.append("") #45 - + bn.append("") #46 - + bn.append("") #47 - + bn.append("") #48 - + bn.append("") #49 - + bn.append("") #50 - + bn.append("") #51 - + bn.append("") #52 - + bn.append("") #53 - + bn.append("") #54 - + bn.append("head_reyebrow_4") #55 - + bn.append("") #56 - + bn.append("") #57 - + bn.append("") #58 - + bn.append("") #59 - + bn.append("") #60 - + bn.append("head_outmouth_0") #61 - + bn.append("") #62 - + bn.append("head_reyebrow_1") #63 - + bn.append("") #64 - + bn.append("") #65 - + bn.append("head_reyebrow_3") #66 - + bn.append("") #67 - + bn.append("") #68 - + bn.append("") #69 - + bn.append("head_reyebrow_0") #70 - + bn.append("") #71 - + bn.append("") #72 - + bn.append("") #73 - + bn.append("") #74 - + bn.append("") #75 - + bn.append("") #76 - + bn.append("") #77 - + bn.append("head_inmouth_0") #78 - + bn.append("") #79 - + bn.append("") #80 - + bn.append("") #81 - + bn.append("") #82 - + bn.append("") #83 - + bn.append("head_outmouth_10") #84 - + bn.append("") #85 - + bn.append("") #86 - + bn.append("") #87 - + bn.append("") #88 - + bn.append("") #89 - + bn.append("") #90 - + bn.append("head_outmouth_11") #91 - + bn.append("") #92 - + bn.append("") #93 - + bn.append("") #94 - + bn.append("") #95 - + bn.append("") #96 - + bn.append("head_nostrills_1") #97 - + bn.append("head_nostrills_0") #98 - + bn.append("") #99 - + bn.append("") #100 - + bn.append("") #101 - + bn.append("") #102 - + bn.append("") #103 - + bn.append("") #104 - + bn.append("head_reyebrow_2") #105 - + bn.append("") #106 - + bn.append("") #107 - + bn.append("") #108 - + bn.append("") #109 - + bn.append("") #110 - + bn.append("") #111 - + bn.append("") #112 - + bn.append("") #113 - + bn.append("") #114 - + bn.append("") #115 - + bn.append("head_rchin_1") #116 - + bn.append("") #117 - + bn.append("") #118 - + bn.append("") #119 - + bn.append("") #120 - + bn.append("") #121 - + bn.append("") #122 - + bn.append("") #123 - + bn.append("") #124 - + bn.append("") #125 - + bn.append("") #126 - + bn.append("") #127 - + bn.append("") #128 - + bn.append("") #129 - + bn.append("") #130 - + bn.append("") #131 - + bn.append("") #132 - + bn.append("head_reye_3") #133 - + bn.append("") #134 - + bn.append("") #135 - + bn.append("") #136 - + bn.append("") #137 - + bn.append("") #138 - + bn.append("") #139 - + bn.append("") #140 - + bn.append("") #141 - + bn.append("") #142 - + bn.append("head_rchin_0") #143 - + bn.append("head_reye_4") #144 - + bn.append("") #145 - + bn.append("") #146 - + bn.append("head_rchin_2") #147 - + bn.append("head_rchin_7") #148 - + bn.append("") #149 - + bn.append("") #150 - + bn.append("") #151 - + bn.append("head_chin") #152 - + bn.append("head_reye_4") #153 - + bn.append("") #154 - + bn.append("") #155 - + bn.append("") #156 - + bn.append("") #157 - + bn.append("head_reye_2") #158 - + bn.append("") #159 - + bn.append("head_reye_1") #160 - + bn.append("") #161 - + bn.append("") #162 - + bn.append("") #163 - + bn.append("") #164 - + bn.append("") #165 - + bn.append("") #166 - + bn.append("") #167 - + bn.append("head_nosebone_0") #168 - + bn.append("") #169 - + bn.append("head_rchin_5") #170 - + bn.append("") #171 - + bn.append("") #172 - + bn.append("") #173 - + bn.append("") #174 - + bn.append("") #175 - + bn.append("head_rchin_6") #176 - + bn.append("") #177 - + bn.append("") #178 - + bn.append("head_inmouth_7") #179 - + bn.append("") #180 - + bn.append("") #181 - + bn.append("") #182 - + bn.append("") #183 - + bn.append("") #184 - + bn.append("") #185 - + bn.append("") #186 - + bn.append("") #187 - + bn.append("") #188 - + bn.append("") #189 - + bn.append("") #190 - + bn.append("") #191 - + bn.append("head_rchin_3") #192 - + bn.append("") #193 - + bn.append("") #194 - + bn.append("") #195 - + bn.append("") #196 - + bn.append("head_nosebone_2") #197 - + bn.append("") #198 - + bn.append("") #199 - + bn.append("") #200 - + bn.append("") #201 - + bn.append("") #202 - + bn.append("") #203 - + bn.append("") #204 - + bn.append("") #205 - + bn.append("") #206 - + bn.append("") #207 - + bn.append("") #208 - + bn.append("") #209 - + bn.append("head_rchin_4") #210 - + bn.append("") #211 - + bn.append("") #212 - + bn.append("") #213 - + bn.append("") #214 - + bn.append("") #215 - + bn.append("") #216 - + bn.append("") #217 - + bn.append("") #218 - + bn.append("") #219 - + bn.append("") #220 - + bn.append("") #221 - + bn.append("") #222 - + bn.append("") #223 - + bn.append("") #224 - + bn.append("") #225 - + bn.append("") #226 - + bn.append("") #227 - + bn.append("") #228 - + bn.append("") #229 - + bn.append("") #230 - + bn.append("") #231 - + bn.append("") #232 - + bn.append("") #233 - + bn.append("") #234 - + bn.append("") #235 - + bn.append("") #236 - + bn.append("") #237 - + bn.append("") #238 - + bn.append("") #239 - + bn.append("") #240 - + bn.append("") #241 - + bn.append("") #242 - + bn.append("") #243 - + bn.append("") #244 - + bn.append("") #245 - + bn.append("") #246 - + bn.append("") #247 - + bn.append("") #248 - + bn.append("") #249 - + bn.append("") #250 - + bn.append("") #251 - + bn.append("") #252 - + bn.append("head_leye_4") #253 - + bn.append("") #254 - + bn.append("") #255 - + bn.append("head_leye_5") #256 - + bn.append("") #257 - + bn.append("") #258 - + bn.append("") #259 - + bn.append("") #260 - + bn.append("") #261 - + bn.append("") #262 - + bn.append("head_leye_3") #263 - + bn.append("") #264 - + bn.append("head_lchin_0") #265 - + bn.append("") #266 - + bn.append("head_outmouth_4") #267 - + bn.append("") #268 - + bn.append("") #269 - + bn.append("") #270 - + bn.append("head_inmouth_3") #271 - + bn.append("") #272 - + bn.append("") #273 - + bn.append("") #274 - + bn.append("") #275 - + bn.append("head_leyebrow_0") #276 - + bn.append("") #277 - + bn.append("") #278 - + bn.append("") #279 - + bn.append("") #280 - + bn.append("") #281 - + bn.append("") #282 - + bn.append("") #283 - + bn.append("") #284 - + bn.append("head_leyebrow_4") #285 - + bn.append("") #286 - + bn.append("") #287 - + bn.append("") #288 - + bn.append("") #289 - + bn.append("") #290 - + bn.append("head_outmouth_6") #291 - + bn.append("") #292 - + bn.append("head_leyebrow_1") #293 - + bn.append("") #294 - + bn.append("") #295 - + bn.append("head_leyebrow_3") #296 - + bn.append("") #297 - + bn.append("") #298 - + bn.append("") #299 - + bn.append("") #300 - + bn.append("") #301 - + bn.append("") #302 - + bn.append("") #303 - + bn.append("") #304 - + bn.append("") #305 - + bn.append("") #306 - + bn.append("") #307 - + bn.append("head_inmouth_4") #308 - + bn.append("") #309 - + bn.append("") #310 - + bn.append("") #311 - + bn.append("") #312 - + bn.append("") #313 - + bn.append("head_outmouth_8") #314 - + bn.append("") #315 - + bn.append("") #316 - + bn.append("") #317 - + bn.append("") #318 - + bn.append("") #319 - + bn.append("") #320 - + bn.append("") #321 - + bn.append("") #322 - + bn.append("") #323 - + bn.append("") #324 - + bn.append("") #325 - + bn.append("head_nostrills_3") #326 - + bn.append("head_nostrills_4") #327 - + bn.append("") #328 - + bn.append("") #329 - + bn.append("") #330 - + bn.append("") #331 - + bn.append("") #332 - + bn.append("") #333 - + bn.append("head_leyebrow_2") #334 - + bn.append("") #335 - + bn.append("") #336 - + bn.append("") #337 - + bn.append("") #338 - + bn.append("") #339 - + bn.append("") #340 - + bn.append("") #341 - + bn.append("") #342 - + bn.append("") #343 - + bn.append("") #344 - + bn.append("") #345 - + bn.append("Head_LChin_1") #346 - + bn.append("") #347 - + bn.append("") #348 - + bn.append("") #349 - + bn.append("") #350 - + bn.append("") #351 - + bn.append("") #352 - + bn.append("") #353 - + bn.append("") #354 - + bn.append("") #355 - + bn.append("") #356 - + bn.append("") #357 - + bn.append("") #358 - + bn.append("") #359 - + bn.append("") #360 - + bn.append("") #361 - + bn.append("head_leye_0") #362 - + bn.append("") #363 - + bn.append("") #364 - + bn.append("") #365 - + bn.append("") #366 - + bn.append("") #367 - + bn.append("") #368 - + bn.append("") #369 - + bn.append("") #370 - + bn.append("") #371 - + bn.append("") #372 - + bn.append("") #373 - + bn.append("") #374 - + bn.append("") #375 - + bn.append("head_lchin_2") #376 - + bn.append("head_lchin_7") #377 - + bn.append("") #378 - + bn.append("") #379 - + bn.append("") #380 - + bn.append("") #381 - + bn.append("") #382 - + bn.append("") #383 - + bn.append("head_leye_1") #384 - + bn.append("") #385 - + bn.append("head_leye_2") #386 - + bn.append("") #387 - + bn.append("") #388 - + bn.append("") #389 - + bn.append("") #390 - + bn.append("") #391 - + bn.append("") #392 - + bn.append("") #393 - + bn.append("") #394 - + bn.append("head_lchin_5") #395 - + bn.append("") #396 - + bn.append("") #397 - + bn.append("") #398 - + bn.append("") #399 - + bn.append("head_lchin_6") #400 - + bn.append("") #401 - + bn.append("") #402 - + bn.append("head_inmouth_5") #403 - + bn.append("") #404 - + bn.append("head_outmouth_7") #405 - + bn.append("") #406 - + bn.append("") #407 - + bn.append("") #408 - + bn.append("") #409 - + bn.append("") #410 - + bn.append("") #411 - + bn.append("") #412 - + bn.append("") #413 - + bn.append("") #414 - + bn.append("") #415 - + bn.append("head_lchin_3") #416 - + bn.append("") #417 - + bn.append("") #418 - + bn.append("") #419 - + bn.append("") #420 - + bn.append("") #421 - + bn.append("") #422 - + bn.append("") #423 - + bn.append("") #424 - + bn.append("") #425 - + bn.append("") #426 - + bn.append("") #427 - + bn.append("") #428 - + bn.append("") #429 - + bn.append("head_lchin_4") #430 - + bn.append("") #431 - + bn.append("") #432 - + bn.append("") #433 - + bn.append("") #434 - + bn.append("") #435 - + bn.append("") #436 - + bn.append("") #437 - + bn.append("") #438 - + bn.append("") #439 - + bn.append("") #440 - + bn.append("") #441 - + bn.append("") #442 - + bn.append("") #443 - + bn.append("") #444 - + bn.append("") #445 - + bn.append("") #446 - + bn.append("") #447 - + bn.append("") #448 - + bn.append("") #449 - + bn.append("") #450 - + bn.append("") #451 - + bn.append("") #452 - + bn.append("") #453 - + bn.append("") #454 - + bn.append("") #455 - + bn.append("") #456 - + bn.append("") #457 - + bn.append("") #458 - + bn.append("") #459 - + bn.append("") #460 - + bn.append("") #461 - + bn.append("") #462 - + bn.append("") #463 - + bn.append("") #464 - + bn.append("") #465 - + bn.append("") #466 - + bn.append("") #467 - + bn.append("") #468 - + bn.append("") #469 - + bn.append("") #470 - + bn.append("") #471 - + bn.append("") #472 - + bn.append("") #473 - + bn.append("") #474 - + bn.append("") #475 - + bn.append("") #476 - + bn.append("") #477 - + bn.append("") #478 - + bn.append("") #479 - + bn.append("") #480 - + bn.append("") #481 - + bn.append("") #482 - + bn.append("") #483 - + bn.append("") #484 - + bn.append("") #485 - + bn.append("") #486 - + return bn +#--------------------------------------------------- + + + + + + + diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/holisticWebcam.py b/animation/MocapNET-kasisnu/src/python/mediapipe/holisticWebcam.py new file mode 100755 index 0000000..4fb1008 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mediapipe/holisticWebcam.py @@ -0,0 +1,59 @@ +#!/usr/bin/python3 + +import cv2 +import mediapipe as mp +import time + +mp_drawing = mp.solutions.drawing_utils +mp_holistic = mp.solutions.holistic + +# For webcam input: +cap = cv2.VideoCapture(0) + +with mp_holistic.Holistic(static_image_mode=True) as holistic: + while cap.isOpened(): + success, image = cap.read() + if not success: + print("Ignoring empty camera frame.") + # If loading a video, use 'break' instead of 'continue'. + continue + + start = time.time() + + # Flip the image horizontally for a later selfie-view display, and convert + # the BGR image to RGB. + image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) + # To improve performance, optionally mark the image as not writeable to + # pass by reference. + image.flags.writeable = False + results = holistic.process(image) + + # Draw the hand annotations on the image. + image.flags.writeable = True + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + end = time.time() + # Time elapsed + seconds = end - start + # Calculate frames per second + fps = 1 / seconds + print("\r Framerate : ",round(fps,2)," fps \r", end="", flush=True) + + annotated_image = image.copy() + #Compensate for name mediapipe change.. + try: + mp_drawing.draw_landmarks(annotated_image, results.face_landmarks , mp_holistic.FACEMESH_TESSELATION) #This used to be called FACE_CONNECTIONS + except: + mp_drawing.draw_landmarks(annotated_image, results.face_landmarks , mp_holistic.FACE_CONNECTIONS) #This used to be called FACE_CONNECTIONS + + mp_drawing.draw_landmarks(annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) + mp_drawing.draw_landmarks(annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) + # Use mp_holistic.UPPER_BODY_POSE_CONNECTIONS for drawing below when + # upper_body_only is set to True. + mp_drawing.draw_landmarks(annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) + + cv2.imshow('MediaPipe Holistic', annotated_image) + if cv2.waitKey(5) & 0xFF == 27: + break +cap.release() + diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipe.jpeg b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipe.jpeg new file mode 100644 index 0000000..f032e0c Binary files /dev/null and b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipe.jpeg differ diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipeHolistic2CSV.py b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipeHolistic2CSV.py new file mode 100755 index 0000000..b13345f --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipeHolistic2CSV.py @@ -0,0 +1,232 @@ +#!/usr/bin/python3 +import cv2 +import mediapipe as mp +import time +import os +import sys + +from tools import checkIfFileExists,createDirectory + +#Get drawing/holistic +mp_drawing = mp.solutions.drawing_utils +mp_holistic = mp.solutions.holistic + +#I have added a seperate list with the joints +from holisticPartNames import getHolisticBodyNameList, getHolisticFaceNameList, processPoseLandmarks, guessLandmarks +MEDIAPIPE_POSE_LANDMARK_NAMES=getHolisticBodyNameList() +MEDIAPIPE_FACE_LANDMARK_NAMES=getHolisticFaceNameList() + +#In an attempt to reduce the upkeep of this codebase as much as possible +#the python code parses directly the C defines to get the order of joints +#sorry about that but I am just one person maintaining all of this code :P +#this also means you need to run the python script from the root directory of the repository! +from C_Parser import readCListFromFile +headNames = readCListFromFile("src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp","HeadNames[]") +leftHandNames = readCListFromFile("src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp","COCOLeftHandNames[]") +rightHandNames = readCListFromFile("src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp","COCORightHandNames[]") +body25BodyNames = readCListFromFile("src/MocapNET2/MocapNETLib2/IO/commonSkeleton.hpp","Body25BodyNames[]") +#--------------------------------------------------------------------------- + + +def appendListOf2DXY(fo,listToUse): + firstElement=0 + for element in listToUse: + if (element.find("End of")==-1 ): + + if (firstElement==0): + firstElement=1 + else: + fo.write(",") + elementLowercased=element.lower() + fo.write("2DX_") + fo.write(elementLowercased) + fo.write(",2DY_") + fo.write(elementLowercased) + fo.write(",visible_") + fo.write(elementLowercased) + +def appendValuesOfListOf2DXY(fo,listToUse,values): + itemNumber=0 + for element in listToUse: + if (element.find("End of")==-1 ): + + thisLandmarkName = listToUse[itemNumber].lower() + labelX = "2DX_"+thisLandmarkName + labelY = "2DY_"+thisLandmarkName + labelV = "visible_"+thisLandmarkName + + #-------------------------------- + fo.write(",") + if labelX in values: + fo.write(str(values[labelX])) + else: + fo.write("0.0") + #-------------------------------- + fo.write(",") + if labelY in values: + fo.write(str(values[labelY])) + else: + fo.write("0.0") + #-------------------------------- + fo.write(",") + if labelV in values: + fo.write(str(values[labelV])) + else: + fo.write("0.0") + #-------------------------------- + itemNumber=itemNumber+1 + +def appendZerosForListOf2DXY(fo,listToUse): + for element in listToUse: + if (element.find("End of")==-1 ): + elementLowercased=element.lower() + fo.write(",0,0,0") + + + +def drawListNumbers(image,lst): + font = cv2.FONT_HERSHEY_SIMPLEX + org = (50, 50) + fontScale = 1 + color = (255, 0, 0) + thickness = 2 + cv2.putText(image, 'Parsing dataset through MediaPipe Holistic', org, font, fontScale, color, thickness, cv2.LINE_AA) + itemNumber=0 + #for item in lst['landmark']: + if lst is not None: + for item in lst.landmark: + org = ( int(item.x * image.shape[1]) , int(item.y * image.shape[0]) ) + cv2.putText(image, '%u'%(itemNumber), org, font, fontScale, color, thickness, cv2.LINE_AA) + itemNumber = itemNumber +1 + + + +def convertStreamToMocapNETCSV(): + videoFilePath ="shuffle.webm" + outputDatasetPath="frames/shuffle.webm" + + if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--from"): + videoFilePath=sys.argv[i+1] + if (sys.argv[i]=="-o"): + outputDatasetPath=sys.argv[i+1] + #Strip last character of output path if it is a / + if ( outputDatasetPath[len(outputDatasetPath)-1] == '/' ): + outputDatasetPath = outputDatasetPath[:-1] + + + #We append an -mpdata to make sure this is different from the OpenPose output + outputDatasetPath=outputDatasetPath+"-mpdata" + + #Make sure output path exists.. + createDirectory(outputDatasetPath) + + fo = open("%s/2dJoints_mediapipe.csv" % (outputDatasetPath), "w") + + #//Generate CSV header.. + fo.write("frameNumber,skeletonID,totalSkeletons,") + appendListOf2DXY(fo,body25BodyNames) + fo.write(",") + appendListOf2DXY(fo,leftHandNames) + fo.write(",") + appendListOf2DXY(fo,rightHandNames) + fo.write(",") + appendListOf2DXY(fo,headNames) + fo.write("\n") + + maxFailedFrames = 100 + failedFrames = 0 + frameNumber = 0 + # For webcam input: + cap = cv2.VideoCapture(videoFilePath) + + with mp_holistic.Holistic(static_image_mode=True) as holistic: + while cap.isOpened(): + success, image = cap.read() + if not success: + print("\rIgnoring empty camera frame. ",failedFrames,"/",maxFailedFrames,"\r", end="", flush=True) + failedFrames = failedFrames + 1 + # If loading a video, use 'break' instead of 'continue'. + if (failedFrames>100): + break + else: + failedFrames = 0 + + cv2.imwrite("%s/colorFrame_0_%05u.jpg"%(outputDatasetPath,frameNumber), image) + start = time.time() + + # Flip the image horizontally for a later selfie-view display, and convert + # the BGR image to RGB. + image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB) + # To improve performance, optionally mark the image as not writeable to + # pass by reference. + image.flags.writeable = False + results = holistic.process(image) + + # Draw the hand annotations on the image. + image.flags.writeable = True + image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR) + + end = time.time() + # Time elapsed + seconds = end - start + # Calculate frames per second + fps = 1 / seconds + print("\r Frame : ",frameNumber," | ",round(fps,2)," fps \r", end="", flush=True) + + annotated_image = image.copy() + #Compensate for name mediapipe change.. + try: + mp_drawing.draw_landmarks(annotated_image, results.face_landmarks , mp_holistic.FACEMESH_TESSELATION) #This used to be called FACE_CONNECTIONS + except: + mp_drawing.draw_landmarks(annotated_image, results.face_landmarks , mp_holistic.FACE_CONNECTIONS) #This used to be called FACE_CONNECTIONS + + mp_drawing.draw_landmarks(annotated_image, results.left_hand_landmarks, mp_holistic.HAND_CONNECTIONS) + mp_drawing.draw_landmarks(annotated_image, results.right_hand_landmarks, mp_holistic.HAND_CONNECTIONS) + # Use mp_holistic.UPPER_BODY_POSE_CONNECTIONS for drawing below when upper_body_only is set to True. + mp_drawing.draw_landmarks(annotated_image, results.pose_landmarks, mp_holistic.POSE_CONNECTIONS) + + #Draw visualization of 2D joints! + drawListNumbers(annotated_image,results.pose_landmarks) + + mnetPose2D = dict() + #------------------------------------------------------------------------------------ + processPoseLandmarks(mnetPose2D,MEDIAPIPE_POSE_LANDMARK_NAMES,results.pose_landmarks) + processPoseLandmarks(mnetPose2D,leftHandNames ,results.left_hand_landmarks) + processPoseLandmarks(mnetPose2D,rightHandNames ,results.right_hand_landmarks) + processPoseLandmarks(mnetPose2D,MEDIAPIPE_FACE_LANDMARK_NAMES,results.face_landmarks) + #------------------------------------------------------------------------------------ + guessLandmarks(mnetPose2D) #Some landmarks ( neck, hip need to be guessed by others ) + + skeletonID = 0 + totalSkeletons = 1 + #Write this CSV record ------------------------------------- + fo.write("%u,%u,%u,"%(frameNumber,skeletonID,totalSkeletons)) + appendValuesOfListOf2DXY(fo,body25BodyNames,mnetPose2D) + appendValuesOfListOf2DXY(fo,leftHandNames ,mnetPose2D) + appendValuesOfListOf2DXY(fo,rightHandNames ,mnetPose2D) + appendValuesOfListOf2DXY(fo,headNames ,mnetPose2D) + fo.write("\n") + #----------------------------------------------------------- + + #Done with frame ------------------------------------- + frameNumber = frameNumber + 1 + + + #Show visualization frame ------------------------------------- + cv2.imshow('MediaPipe Holistic', annotated_image) + if cv2.waitKey(1) & 0xFF == 27: + break + + cap.release() + fo.close() + + print("Done dumping to CSV using mediapipe") + print(" try running with MocapNET using : ") + print(" ./MocapNET2CSV --from %s/2dJoints_mediapipe.csv --show 3 --hands" % (outputDatasetPath)) + + +if __name__ == '__main__': + convertStreamToMocapNETCSV() diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipehand.png b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipehand.png new file mode 100644 index 0000000..f13746a Binary files /dev/null and b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipehand.png differ diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipehead.png b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipehead.png new file mode 100644 index 0000000..ae2d668 Binary files /dev/null and b/animation/MocapNET-kasisnu/src/python/mediapipe/mediapipehead.png differ diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/setup.sh b/animation/MocapNET-kasisnu/src/python/mediapipe/setup.sh new file mode 100755 index 0000000..e1e7c0b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mediapipe/setup.sh @@ -0,0 +1,12 @@ +python3 -m venv mp_env +source mp_env/bin/activate + +#For RPI4 +#sudo apt install libxcb-shm0 libcdio-paranoia-dev libsdl2-2.0-0 libxv1 libtheora0 libva-drm2 libva-x11-2 libvdpau1 libharfbuzz0b libbluray2 libatlas-base-dev libhdf5-103 libgtk-3-0 libdc1394-22 libopenexr23 +#pip install mediapipe-rpi4 opencv-python + +#For Regular x86_64 +python3 -m pip install mediapipe opencv-python +python3 holisticWebcam.py + + diff --git a/animation/MocapNET-kasisnu/src/python/mediapipe/tools.py b/animation/MocapNET-kasisnu/src/python/mediapipe/tools.py new file mode 100644 index 0000000..d5af7fe --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mediapipe/tools.py @@ -0,0 +1,8 @@ +import os + +def checkIfFileExists(filename): + return os.path.isfile(filename) + +def createDirectory(path): + if not os.path.exists(path): + os.makedirs(path) diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/DNNModel.py b/animation/MocapNET-kasisnu/src/python/mnet4/DNNModel.py new file mode 100755 index 0000000..4f2bf22 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/DNNModel.py @@ -0,0 +1,1196 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +import sys +import os +import time + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras.layers import concatenate, Add, Input, Dense, GlobalMaxPooling1D, GlobalAveragePooling1D, Flatten, Reshape, AlphaDropout, Dropout, Lambda, MaxPooling1D, MaxPooling2D, Conv2D, ZeroPadding1D +from tensorflow.keras.models import Model,Sequential, model_from_json +from tensorflow.keras.utils import plot_model + +from tensorflow.keras import layers +from tensorflow.keras import activations + +from tools import bcolors,createDirectory,tensorflowFriendlyModelName + +import math +import numpy as np + +#Defaults, will get overridden by setupDNNModelsUsingJSONConfiguration +useLambdas = 0 +numberOfLayers = 12 +dropoutRate = 0.15 #Global dropout rate +learningRate = 0.00025 #0.00045 #0.00025=MocapNET2019 +useModuloMetric = 0 +useQuadMetric = 0 +useSquaredMetric = 1 + +# ------------------------------------------------------------ +#https://github.com/cpuimage/HardMish +def hard_mish(x): + return tf.minimum(2., tf.nn.relu(x + 2.)) * 0.5 * x +# ------------------------------------------------------------ + +#https://stackoverflow.com/questions/46355068/keras-loss-function-for-360-degree-prediction + +# y in radians +#def mean_squared_error_360(y_true, y_pred): +# yTrueRads=tf.math.scalar_mul(0.017453292519943295,y_true) +# yPredRads=tf.math.scalar_mul(0.017453292519943295,y_pred) + +# return tf.reduce_mean(tf.math.square(tf.math.scalar_mul(57.295779513,tf.atan2(tf.sin(yTrueRads - yPredRads), tf.cos(yTrueRads - yPredRads))))) + #return tf.math.scalar_mul(57.295779513,tf.reduce_mean(tf.abs(tf.atan2(tf.sin(yTrueRads - yPredRads), tf.cos(yTrueRads - yPredRads))))) + +#def rmse_360(y_true, y_pred): +# return K.sqrt(mean_squared_error_360(y_true, y_pred)) + +def testMyObjective(): + X = tf.compat.v1.placeholder("float32", name="input") + Y = tf.compat.v1.placeholder("float32", name="input") + OUT = tf.abs(tf.subtract(tf.math.floormod(tf.add(X,180),360),tf.math.floormod(tf.add(Y,180),360))) + with tf.compat.v1.Session() as sess: + for x in range (-360,360): + for y in range (-360,360): + print("x=",x," y=",y," val=",sess.run(OUT, feed_dict={X:x,Y:y})) + sys.exit(0) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def mean_quad_error(yTrue,yPred): + #reduce_mean reduce_sum + return tf.reduce_mean(input_tensor=tf.math.square(tf.math.square(tf.subtract(yTrue,yPred)))) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def mean_squared_error_modulo_360(yTrue,yPred): + #reduce_mean reduce_sum + return tf.reduce_mean(input_tensor=tf.math.square(tf.abs(tf.subtract(tf.math.floormod(tf.add(yTrue,180),360),tf.math.floormod(tf.add(yPred,180),360))))) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def average_error_modulo_360(yTrue,yPred): + return tf.reduce_mean(input_tensor=tf.abs(tf.subtract(tf.math.floormod(tf.add(yTrue,180),360),tf.math.floormod(tf.add(yPred,180),360)))) + + +#=================================================================================================================================================================================== + + + + +#SWISH - https://arxiv.org/abs/1710.05941 +#MISH - https://arxiv.org/vc/arxiv/papers/1908/1908.08681v1.pdf / https://github.com/cpuimage/HardMish +#https://krutikabapat.github.io/Swish-Vs-Mish-Latest-Activation-Functions/ + +#------------------------------------------------------------- +#------------------------------------------------------------- +theActivationMethod='selu' # hard_mish 'selu' 'swish' +initializer = tf.keras.initializers.LecunNormal() #'lecun_normal' +#------------------------------------------------------------- +#------------------------------------------------------------- +def startProfiling(): + print(bcolors.WARNING,"Starting Tensorflow Profiling (this run will be slower than usual)..\n",bcolors.ENDC) + os.system("rm -rf profiling") + tf.profiler.experimental.start('profiling') +#------------------------------------------------------------- +def stopProfiling(): + print(bcolors.WARNING,"Stopping Tensorflow Profiling..\n",bcolors.ENDC) + tf.profiler.experimental.stop() +#------------------------------------------------------------- +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== + + + +def getActivationRandomization(configuration): + global theActivationMethod + theActivationMethod=configuration['activationFunction'] + global initializer + theRandomizationMethod=configuration['weightRandomizationFunction'] + #---------------------------------------------------------------------- + print(bcolors.OKGREEN,"Activation/Randomization set to ",theActivationMethod," -> ",theRandomizationMethod," ",bcolors.ENDC) + #---------------------------------------------------------------------- + thisSeed = 0 + if (configuration["setConstantSeedForReproducibleTraining"]==0): + import random + seed = random.randint(0,1024) + print("Setting random seed to ",seed,"! ") + + if (theRandomizationMethod=="auto"): + if (theActivationMethod=='selu'): + initializer = tf.keras.initializers.LecunNormal(seed=thisSeed) + print("Automatic resolution SeLU -> LeCun Normal") + elif (theActivationMethod=='swish'): + #Draws samples from a uniform distribution within [-limit, limit], where limit = sqrt(6 / fan_in) (fan_in is the number of input units in the weight tensor). + initializer=tf.keras.initializers.HeUniform(seed=thisSeed) #https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html + print("Automatic resolution SWISH -> He Uniform") + elif (theRandomizationMethod=="glorot_uniform"): #Xavier + initializer=tf.keras.initializers.GlorotUniform(seed=thisSeed) + elif (theRandomizationMethod=="lecun_normal"): + initializer=tf.keras.initializers.LecunNormal(seed=thisSeed) + elif (theRandomizationMethod=="he_uniform"): #Kaiming + initializer=tf.keras.initializers.HeUniform(seed=thisSeed) + else: + print(bcolors.FAIL,"Please add ",theActivationMethod,"/",theRandomizationMethod," to getActivationRandomization",bcolors.ENDC) + sys.exit(1) + + + + + +def setupDNNModelsUsingJSONConfiguration(configuration): + #Copy settings from configuration json file + #--------------------------------------------------------------- + global numberOfLayers + numberOfLayers=configuration['neuralNetworkDepth'] + #--------------------------------------------------------------- + #New code that handles activation/randomization + getActivationRandomization(configuration) + #--------------------------------------------------------------- + global dropoutRate + dropoutRate=configuration['dropoutRate'] + global learningRate + learningRate=configuration['learningRate'] #0.00045 #0.00025=MocapNET2019 + global useQuadMetric + useQuadMetric=configuration['useQuadLoss'] + global useSquaredMetric + useSquaredMetric=configuration['useSquaredLoss'] + #--------------------------------------------------------------- + print("Configuration setting numberOfLayers to ",numberOfLayers) + print("Configuration setting activationMethod to ",theActivationMethod) + print("Configuration setting dropoutRate to ",dropoutRate) + print("Configuration setting learningRate to ",learningRate) + print("Configuration setting useQuadMetric to ",useQuadMetric) + #--------------------------------------------------------------- +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def saveProgressEnded(message): + file = open("progress.txt","w") + file.write(str(message)) + file.write("\n") +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def saveLastCompletedJob(lastCompletedJob,currentJob,lastJob,approxTime): + file = open("progress.txt","w") + file.write(str(lastCompletedJob)) + file.write("\n") + file.write(str(currentJob)) + file.write("/") + file.write(str(lastJob)) + file.write("\n") + file.write("TimeApprox:") + file.write(str(approxTime)) + file.write(" minutes") + file.write("\n") +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def saveConfiguration(path,model,modelName,modelParameters,numberOfEpochs,batchSize,trainInput,trainOutput,outputType,history,startAt,endAt,median,mean,std,var): + finalPath = ("%s/%s") % (path,modelName) + createDirectory(path) + createDirectory(finalPath) + file = open("%s/configuration.txt" % finalPath,"w") + file.write("Number of Model Parameters:") + file.write(str(modelParameters)) + file.write("\nNumber of Epochs:") + file.write(str(numberOfEpochs)) + file.write("\nBatch Size:") + file.write(str(batchSize)) + #-------------------------------- + file.write("\nTrain Input Number of elements:") + file.write(str(len(trainInput))) + file.write("\nTrain Input: ") + for item in trainInput: + file.write("%s " % item) + #-------------------------------- + + + #file.write("\nModel:") + #file.write(model.summary()) #TypeError: write() argument must be str, not None + #file.write("\n") + + + #-------------------------------- + file.write("\nTest Output Number of elements:") + file.write(str(len(trainOutput))) + file.write("\nTest Output: ") + for item in trainOutput: + file.write("%s " % item) + #-------------------------------- + file.write("\n\nSpecific Output: ") + file.write(trainOutput[outputType]) + file.write("\n") + + + file.write("\n\nOutput Statistics for ") + title_string=" %s : Median=%0.2f,Mean=%0.2f,Std=%0.2f,Var=%0.2f" % (modelName,median,mean,std,var) + file.write(title_string) + file.write("\n") + + try: + file.write("\nTraining History Loss: ") + file.write(str(history.history['loss'])) + file.write("\n") + except: + print("Tried to save loss but no such history element was found..") + + try: + file.write("\nTraining History MAE: ") + file.write(str(history.history['mean_absolute_error'])) + file.write("\n") + except: + print("Tried to save mean absolute error but no such history element was found..") + + #file.write("\nTraining History MAE: ") + #file.write(str(history.history['mean_absolute_error'])) + #file.write("\n") + + #file.write("\nTraining History Accuracy: ") + #file.write(str(history.history['acc'])) + #file.write("\n") + + #file.write("\nTesting History Loss: ") + #file.write(str(history.history['val_loss'])) + #file.write("\n") + + #file.write("\nTesting History MAE: ") + #file.write(str(history.history['val_mean_absolute_error'])) + #file.write("\n") + + #file.write("\nTesting History Accuracy: ") + #file.write(str(history.history['val_acc'])) + #file.write("\n") + + file.write("\nDuration: ") + file.write(str((endAt-startAt)/60)) + file.write(" mins\n") + file.close() +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def loadNewModel(path): + #loaded_model = tf.saved_model.load(path) + #loaded_model.compile(loss='mse', optimizer='rmsprop', metrics=['mae', 'acc']) + #return loaded_model + return tf.keras.models.load_model(path) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +""" +Load the model to the supplied path (first try TF, then JSON/H5) +""" +def loadModel(path,filename): + if (os.path.isfile('%s/saved_model.pb' % (path))): + print(bcolors.OKGREEN,"Loading TF Model %s/saved_model.pb from disk " % (path),bcolors.ENDC) + loaded_model = tf.keras.models.load_model(path,custom_objects={'mean_quad_error':mean_quad_error}) + return loaded_model + elif (os.path.isfile('%s/%s.json' % (path,filename))) and (os.path.isfile('%s/%s.h5' % (path,filename))): + print(bcolors.FAIL,"File %s/%s.json does not exist\n",bcolors.ENDC) + json_file = open('%s/%s.json' % (path,filename),'r') + loaded_model_json = json_file.read() + json_file.close() + loaded_model = model_from_json(loaded_model_json) + loaded_model.load_weights("%s/%s.h5" % (path,filename)) + print(bcolors.OKGREEN,"Success ",bcolors.ENDC) + loaded_model.compile(loss='mse', optimizer='rmsprop', metrics=['mae', 'acc']) + print("Loading Model %s/%s from disk" % (path,filename)) + return loaded_model + else: + print(bcolors.FAIL,"Could not find model %s \n" % path,bcolors.ENDC) + sys.exit(1) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +""" +Save the model to the supplied path +""" +def saveModel(path,model,name="model"): + createDirectory(path) + # serialize model to JSON + model_json = model.to_json() + with open("%s/%s.json" % (path,name) , "w") as json_file: + json_file.write(model_json) + json_file.close() + # serialize weights to HDF5 + model.save_weights("%s/%s.h5" % (path,name)) + print("Saved model to disk at %s/%s.(h5/json)" % (path,name)) + #24/5/23: TF 2.12.0 : emmits you must feed a value for placeholder tensor 'inputs' with dtype float and shape [?,76] + model.save(path, save_format='tf') #save directory.. + print("Saved model to disk at %s/saved_model.pb (TF)" % (path)) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +""" +Counts the number of trainable parameters in a TensorFlow model +""" +def countModelParameters(model): + total_parameters = 0 + for variable in model.trainable_variables: + shape = variable.shape + variable_parametes = 1 + for dim in shape: + variable_parametes *= dim + total_parameters += variable_parametes + return total_parameters +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +# define autoencoder model +def newAutoencoderModel(modelName,inputDimension,shrinkRatio): + # create model + model = Sequential(name=modelName) #----------------------------------------------------------------------------------------------------------------------------------- + model.add(Dense(inputDimension, input_dim=inputDimension, activation='relu' , name='encoder')) + model.add(Dense(int(inputDimension/shrinkRatio), kernel_initializer='normal', activation='relu')) + model.add(Dense(inputDimension, activation='sigmoid' , name='decoder')) #----------------------------------------------------------------------------------------------------------------------------------- + # Compile model + model.compile(optimizer='adadelta',loss='binary_crossentropy', metrics=['accuracy']) + #model.compile(optimizer='rmsprop', loss='mse', metrics=['mae']) + model.summary() + return model + + +#---------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------- +# LAMBDA LAYERS +#---------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------- +#Found in https://github.com/keras-team/keras/issues/890 +#by https://github.com/marc-moreaux +def sliceL(dimension, start, end): + # Crops (or slices) a Tensor on a given dimension from start to end + # example : to crop tensor x[:, :, 5:10] + # call slice(2, 5, 10) as you want to crop on the second dimension + def func(x): + if dimension == 0: + return x[start: end] + if dimension == 1: + return x[:, start: end] + if dimension == 2: + return x[:, :, start: end] + if dimension == 3: + return x[:, :, :, start: end] + if dimension == 4: + return x[:, :, :, :, start: end] + return Lambda(func, name='Slice_from_%u_to_%u_in_%u-D'%(start,end,dimension)) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def modL(): + def func(x): + return (360+x)%360 + return Lambda(func, name='360_plus_x_modulo_360') +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def tag(kind="layer",name="mnet",index=0,number=0): + return "%s_%u_%s_%u"%(name,index,kind,number) + + +#---------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------- +# FIRST STAGE +#---------------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------------- +def newCategorizeOneHotModel(modelName,inputDimension,nonNSDMInputSize,numberOfChannelsPerNSDMElement,outputSize,networkCompression): + print('newCategorizeOneHotModel has input ',inputDimension,' elements and output of ',outputSize,' elements e:',tf.keras.backend.epsilon()) + print('Learning Rate is 0.00001, Dropout Rate is ',dropoutRate,' ') + modelName = tensorflowFriendlyModelName(modelName) + print("Model renamed to ",modelName," to make sure it doesn't call tensorflow problems ") + + inputs = Input(shape=(inputDimension,)) + + #positionalInput=nonNSDMInputSize + #differentiatedInput=inputDimension-positionalInput + #splitInput = sliceL(1,positionalInput,inputDimension)(inputs) + #inputDimension = inputDimension-positionalInput + + #initializer = tf.keras.initializers.lecun_normal(seed=0) + + outputArrayIndex=0 + + doInputSplit = 0 + selectedInput = inputs + + if (doInputSplit): + positionalInput=nonNSDMInputSize + differentiatedInput=inputDimension-positionalInput + splitInput = sliceL(1,positionalInput,inputDimension)(inputs) + inputDimension = inputDimension-positionalInput + selectedInput = splitInput + + # a layer instance is callable on a tensor, and returns a tensor + #goldenRatio=1.61803398875 // was 2 3 4 5 6 + #----------------------------------------------------------------------------------------------------------------------------------- + #Input | 320 + + #Shorthand names that fit in screen + act = theActivationMethod + kinit = initializer + indim = inputDimension + l = networkCompression + + # Layer 1 + thisLayerRatio=2 + layerNumber=1 + xA = Dense(int(indim/(thisLayerRatio*l)), input_shape=(inputDimension,) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(selectedInput)#(inputs) + xA = Dropout(0.2, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xA) + + # Layer 2 + thisLayerRatio=2 + layerNumber=layerNumber+1 + xB = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xA) + xB = Dropout(0.3, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xB) + + # Layer 3 + thisLayerRatio=3 + layerNumber=layerNumber+1 + xC = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xB) + xC = Dropout(0.3, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xC) + + # Layer 4 + thisLayerRatio=4 + layerNumber=layerNumber+1 + xD = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xC) + xD = Dropout(0.4, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xD) + sBD = Dense(int(indim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xB) + xD = Add()([xD,sBD]) # main + skip + + # Layer 5 + thisLayerRatio=5 + layerNumber=layerNumber+1 + xE = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xD) + xE = Dropout(0.4, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xE) + + # Layer 6 + thisLayerRatio=6 + layerNumber=layerNumber+1 + xF = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xE) + xF = Dropout(0.4, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xF) + + # Layer 7 + thisLayerRatio=7 + layerNumber=layerNumber+1 + xG = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xF) + xG = Dropout(0.4, name='classifier_%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xG) + sDG = Dense(int(indim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xD) + xG = Add()([xG,sDG]) # main + skip + + # Layer 8 + thisLayerRatio=8 + layerNumber=layerNumber+1 + xH = Dense(int(indim/(thisLayerRatio*l)) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xG) + #xH = Dropout(0.4)(xH) + + # Layer 9 + layerNumber=layerNumber+1 + xOut = Dense(int(outputSize) , kernel_initializer=kinit, activation=act, name='classifier_%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber) )(xH) + + #----------------------------------------------------------------------------------------------------------------------------------- + predictions = tf.keras.layers.Dense(int(outputSize),name='Category' , kernel_initializer='normal', activation='softmax')(xOut) + #----------------------------------------------------------------------------------------------------------------------------------- + + # This creates a model that includes + # the Input layer and three Dense layers + model = Model(name=modelName, inputs=inputs, outputs=predictions) + + #from tf.keras.metrics import categorical_accuracy + #model.compile(optimizer='adam', loss='binary_crossentropy', metrics=[categorical_accuracy]) + + #special slower learning.. + #For 3.8M samples 0.00001 is a good value.. + if (optimizer=="adam"): + activeOptimizer=tf.keras.optimizers.Adam(learning_rate=learningRate,epsilon=tf.keras.backend.epsilon()) + else: + activeOptimizer=tf.keras.optimizers.RMSprop(learning_rate=learningRate, rho=0.9, epsilon=tf.keras.backend.epsilon()) # epsilon=1e-6, lr=0.00025 is the old value + #------------------------------------------------------------------------------------------------------- + model.compile(optimizer=activeOptimizer, loss='categorical_crossentropy', metrics=['accuracy']) + #------------------------------------------------------------------------------------------------------- + model.summary() + #plot_model(model, to_file='modelXYZStage.png') + return model +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +""" + The regular MocapNETv3 Densely Connected Encoder Along With its Skip Connections +""" +def newDeepRotModel(configuration,modelName,outputArrayIndex,inputDimension,nonNSDMInputSize,numberOfChannelsPerNSDMElement,outputSize,networkCompression,probabilisticMode=0,quantize=False,optimizer="rmsprop"): + #return convDeepRotModel(modelName,outputArrayIndex,inputDimension,nonNSDMInputSize,numberOfChannelsPerNSDMElement,outputSize,networkCompression) + + dropoutRate = float(configuration["dropoutRate"]) + print('newXYZROTModel with skip connections has input ',inputDimension,' elements , compression λ=',networkCompression,' and output of ',outputSize,' elements') + print('Learning Rate is ',learningRate,', Dropout Rate is ',dropoutRate,' ') + print('Use Quad Loss is ',useQuadMetric,', Use Modulo Loss is ',useModuloMetric,' ') + print('Use Squared Loss is ',useSquaredMetric) + modelName = tensorflowFriendlyModelName(modelName) + print("Model renamed to ",modelName," to make sure it doesn't call tensorflow problems ") + + inputs = Input(shape=(inputDimension,)) + + doInputSplit = 0 + selectedInput = inputs + + if (doInputSplit): + positionalInput=nonNSDMInputSize + differentiatedInput=inputDimension-positionalInput + splitInput = sliceL(1,positionalInput,inputDimension)(inputs) + inputDimension = inputDimension-positionalInput + selectedInput = splitInput + + # a layer instance is callable on a tensor, and returns a tensor + #goldenRatio=1.61803398875 // was 2 3 4 5 6 + #----------------------------------------------------------------------------------------------------------------------------------- + #Input | 320 | 463 + + #Shorthand names that fit in screen + act = theActivationMethod + kinit = initializer + inptdim = inputDimension + + if (numberOfLayers==0): + print("Garbage configuration with 0 layers") + sys.exit(1) + + + # Layer 1 | 2 |160 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=1): + layerNumber=1 + thisLayerRatio=2.2 #BMVC21 this was 2.2 + xA = Dense(int(inptdim/(thisLayerRatio*networkCompression)), input_shape=(inptdim,) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(selectedInput) + if (dropoutRate>0.0): + xA = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xA) #0.2 vs dropoutRate + if (numberOfLayers==1): + xOut = xA + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 2 | 3 | 106 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=2): + layerNumber=layerNumber+1 + thisLayerRatio=3.0 #BMVC21 this was 3.0 + xB = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xA) + if (dropoutRate>0.0): + xB = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xB) #0.3 vs dropoutRate + if (numberOfLayers==2): + xOut = xB + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + + # Layer 3 | 4 | 80 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=3): + layerNumber=layerNumber+1 + thisLayerRatio=3.2 #BMVC21 this was 3.2 + sAC = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xA) + if (dropoutRate>0.0): + sAC = Dropout(dropoutRate, name='%s_%u_rdropout_%u'%(modelName,outputArrayIndex,layerNumber))(sAC) #0.4 vs dropoutRate + xC = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xB) + if (dropoutRate>0.0): + xC = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xC) #0.4 vs dropoutRate + xC = Add(name='%s_%u_add_%u'%(modelName,outputArrayIndex,layerNumber))([xC,sAC]) # main + skip + if (numberOfLayers==3): + xOut = xC + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 4 | 5 | 64 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=4): + layerNumber=layerNumber+1 + thisLayerRatio=3.4 #BMVC21 this was 3.4 + xD = Dense(int(inptdim/(thisLayerRatio*networkCompression)), kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xC) + if (dropoutRate>0.0): + xD = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xD) + if (numberOfLayers==4): + xOut = xD + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 5 | 6 | 53 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=5): + layerNumber=layerNumber+1 + thisLayerRatio=3.5 #BMVC21 this was 3.5 + sCE = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xC) + if (dropoutRate>0.0): + sCE = Dropout(dropoutRate, name='%s_%u_rdropout_%u'%(modelName,outputArrayIndex,layerNumber))(sCE) + xE = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xD) + if (dropoutRate>0.0): + xE = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xE) + xE = Add(name='%s_%u_add_%u'%(modelName,outputArrayIndex,layerNumber))([xE,sCE]) # main + skip + if (numberOfLayers==5): + xOut = xE + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 6 | 8 | 106 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=6): + layerNumber=layerNumber+1 + thisLayerRatio=3.8 #BMVC21 this was 3.8 + xF = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xE) + if (dropoutRate>0.0): + xF = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xF) + if (numberOfLayers==6): + xOut = xF + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 7 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=7): + layerNumber=layerNumber+1 + thisLayerRatio=4.2 #BMVC21 this was 4.2 + sEG = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xE) + if (dropoutRate>0.0): + sEG = Dropout(dropoutRate, name='%s_%u_rdropout_%u'%(modelName,outputArrayIndex,layerNumber))(sEG) + xG = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xF) + if (dropoutRate>0.0): + xG = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xG) + xG = Add(name='%s_%u_add_%u'%(modelName,outputArrayIndex,layerNumber))([xG,sEG]) # main + skip + if (numberOfLayers==7): + xOut = xG + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 8 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=8): + layerNumber=layerNumber+1 + thisLayerRatio=5 #BMVC21 this was 5 + xH = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xG) + if (dropoutRate>0.0): + xH = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xH) + if (numberOfLayers==8): + xOut = xH + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 9 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=9): + layerNumber=layerNumber+1 + thisLayerRatio=6 #BMVC21 this was 6 + sGI = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xG) + #if (dropoutRate>0.0): + # sGI = Dropout(dropoutRate, name='%s_%u_rdropout_%u'%(modelName,outputArrayIndex,layerNumber))(sGI) + xI = Dense(int(inptdim/(thisLayerRatio*networkCompression)) , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xH) + #if (dropoutRate>0.0): #After experiment 269A, dropout this late seems like a bad idea + # xI = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xI) + xI = Add(name='%s_%u_add_%u'%(modelName,outputArrayIndex,layerNumber))([xI,sGI]) # main + skip + if (numberOfLayers==9): + xOut = xI + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 10 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=10): + layerNumber=layerNumber+1 + preLastDimension = 60 #Pre probabilities was 32 + #Last Dimension before max pooling + xPreLast = Dense(preLastDimension , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xI) + #if (dropoutRate>0.0): #After experiment 269A, dropout this late seems like a bad idea + # xPreLast = Dropout(dropoutRate, name='%s_%u_dropout_%u'%(modelName,outputArrayIndex,layerNumber))(xPreLast) + if (numberOfLayers==10): + xOut = xPreLast + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 11 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=11): + layerNumber=layerNumber+1 + lastDimension = 60 #Pre probabilities was 16 + #Last Dimension before max pooling + xLast = Dense(lastDimension , kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u'%(modelName,outputArrayIndex,layerNumber))(xPreLast) + + #Lets pick the maximum response as our final output + #reshaped = Reshape([lastDimension,1])(xLast) + #flat = GlobalMaxPooling1D() (reshaped) + #flat = GlobalAveragePooling1D() (reshaped) + flat = xLast + if (numberOfLayers==11): + xOut = flat + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + # Layer 12 + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (numberOfLayers>=12): + layerNumber=layerNumber+1 #last activation no longer linear 'linear' + sEOut= Dense(int(outputSize), kernel_initializer=kinit, activation=act, name='%s_%u_residual_%u'%(modelName,outputArrayIndex,layerNumber))(xE) + xOut = Dense(int(outputSize), kernel_initializer=kinit, activation=act, name='%s_%u_layer_%u' %(modelName,outputArrayIndex,layerNumber))(flat) + xOut = Add(name='%s_%u_add_%u'%(modelName,outputArrayIndex,layerNumber))([xOut,sEOut]) # main + skip + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + + if (probabilisticMode): + #----------------------------------------------------------------------------------------------------------------------------------- + predictions = tf.keras.layers.Dense(int(outputSize), name='output_rotation_%u_%s'%(outputArrayIndex,modelName), kernel_initializer='normal', activation='softmax')(xOut) + #----------------------------------------------------------------------------------------------------------------------------------- + else: + #And also have a weight to scale it. + predictions = tf.keras.layers.Dense(int(outputSize),kernel_initializer='normal', activation='linear', name='output_rotation_%u_%s'%(outputArrayIndex,modelName))(xOut) + #----------------------------------------------------------------------------------------------------------------------------------- + + + # the Input layer and three Dense layers + model = Model(name=modelName, inputs=inputs, outputs=predictions) + #model.compile(optimizer='adam', loss='mse', metrics=['mae']) + if (optimizer=="adam"): + activeOptimizer=tf.keras.optimizers.Adam(learning_rate=learningRate,epsilon=tf.keras.backend.epsilon()) + else: + activeOptimizer=tf.keras.optimizers.RMSprop(learning_rate=learningRate, rho=0.9, epsilon=tf.keras.backend.epsilon()) # epsilon=1e-6, lr=0.00025 is the old value + #------------------------------------------------------------------------------------------------------- + + if (quantize): + #Careful not all activations are quantization aware + print("Will attempt to quantize model..!") + try: + import tensorflow_model_optimization as tfmot + quantize_model = tfmot.quantization.keras.quantize_model + model = quantize_model(model) + except: + print("Could not quantize model, you are using a non quantization aware activation function..!") + print("https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/python/core/quantization/keras/quantize_aware_activation.py") + + if (probabilisticMode): + model.compile(optimizer=activeOptimizer, loss='categorical_crossentropy', metrics=['accuracy']) + elif (useQuadMetric): + model.compile(optimizer=activeOptimizer, loss=mean_quad_error, metrics=['mae'] ) #Penalize really bad output.. + elif (useSquaredMetric): + model.compile(optimizer=activeOptimizer, loss='mse', metrics=['mae']) + elif (useModuloMetric): + model.compile(optimizer=activeOptimizer, loss=mean_squared_error_modulo_360, metrics=[average_error_modulo_360] ) #With 360 metrics=[rmse_360] + else: + model.compile(optimizer=activeOptimizer, loss='mae', metrics=['mae']) #Without 360 + + model.summary() + #plot_model(model, to_file='modelXYZStage.png') + return model +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +""" + A dummy model made to pad outputs and keep signature compatibility between different + NN ensembles +""" +def newTrivialModel(modelName,outputArrayIndex,inputDimension,outputSize): + modelName = tensorflowFriendlyModelName(modelName) + print("Model renamed to ",modelName," to make sure it doesn't call tensorflow problems ") + inputs = Input(shape=(inputDimension,)) + trivialInitializer = tf.keras.initializers.Zeros() + + #The trivial model is basically dead and useless and we want to simplify it as much as possible + #so that it occupies as little space in our network as possible + + #There are two ways to do this, first is by a tf split and the second by doing a Lambda function that ignores most inputs + #----------------------------------------------------------------------------------------------------------------------------------- + if (useLambdas==0): + ignoreInput = tf.split(inputs,inputDimension,num=inputDimension,axis=1,name='ignore_layer_for_%u_%s'%(outputArrayIndex,modelName)) #split inputs to single elements + splitInput = ignoreInput[0] #try to do same thing without lambdas + else: + #partOfInputToKeep=int(inputDimension) + partOfInputToKeep=1 + splitInput = sliceL(1,0,partOfInputToKeep)(inputs) + #----------------------------------------------------------------------------------------------------------------------------------- + + #Now connect our single input with a mock layer with one set of weights so that it can learn to send zeros out :P + #----------------------------------------------------------------------------------------------------------------------------------- + xOut = Dense(int(outputSize), kernel_initializer=trivialInitializer , activation='linear', name='mock_layer_for_%u_%s'%(outputArrayIndex,modelName) )(splitInput) + predictions = tf.keras.layers.Dense(int(outputSize), name='output_trivial_%u_%s'%(outputArrayIndex,modelName) )(xOut) + #----------------------------------------------------------------------------------------------------------------------------------- + + # This creates a model that includes + model = Model(name=modelName, inputs=inputs, outputs=predictions) + model.compile(optimizer='rmsprop', loss='mse', metrics=['mae']) + + #Do not emmit a summary for trivial models + #model.summary() + print("Not emitting a summary for trivial model ",modelName," with input dim ",inputDimension," and output ",outputSize) + return model +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def combineModels(configuration,directory,outputFilename,modelInputSize,modelOutputSize,modelPaths,startModel,endModel,label,lpdnnPadding=0,doPNGPlot=0,optimizer="rmsprop",skipTrivialModels=False): + inputLabel = "input_%s" % label + outputLabel = "result_%s" % label + + if (lpdnnPadding!=0): + print(bcolors.WARNING) + print("Using LPDNN Padding.. ") + print(bcolors.ENDC) + #Not setting batch_size=1 will result in a [?,..] shape which breaks things in the case of BonsApps/LPDNN + rawInput = Input(shape=(1,1,modelInputSize,), batch_size=1, name=inputLabel) + rawInput.set_shape((1,1,1,modelInputSize)) + singleInput = Reshape((modelInputSize,), input_shape=(1,1,1,modelInputSize,) , name='reshaped_'+inputLabel)(rawInput) + else: + print(bcolors.OKGREEN) + print("Using No Input Padding.. ") + print(bcolors.ENDC) + rawInput = Input(shape=(modelInputSize,), batch_size=1, name=inputLabel) + singleInput = rawInput + + print(bcolors.OKGREEN) + print("Combining model Input:%s / Output:%s / modelInputSize:(1,%u) / modelOutputSize:(1,%u) / Models Combined:(%u->%u) "%(inputLabel,outputLabel,modelInputSize,modelOutputSize,startModel,endModel)) + print("Input Shape is : ") + print(singleInput.get_shape()) + print(bcolors.ENDC) + + #Initialize our RMSProp optimizer + #---------------------------------------------------------------------------------------------------------------------------------------- + if (optimizer=="adam"): + activeOptimizer=tf.keras.optimizers.Adam(learning_rate=learningRate,epsilon=tf.keras.backend.epsilon()) + else: + activeOptimizer=tf.keras.optimizers.RMSprop(learning_rate=learningRate, rho=0.9, epsilon=tf.keras.backend.epsilon()) # epsilon=1e-6, lr=0.00025 is the old value + #------------------------------------------------------------------------------------------------------- + + #Start combining models + #inModelList = list() # empty list + #outModelList = list() # empty list + allModelList = list() # empty list + includedOutputs = list() # empty list + selectedColumns = list() # empty list + selectedColumnsCount = 0 + cumulativeTime = 0.0 + + from tools import getConfigurationJointIsDeclaredInHierarchy,getConfigurationJointPriority + + #if ((startModel==0) and (endModel==0)): + # print("THIS LOOKS LIKE THE SPECIAL CASE OF MERGING ONE THING WITH ITSELF.. :S") + # endModel=1 + + for modelNumber in range(startModel,endModel): + jointIsFormallyDeclared = getConfigurationJointIsDeclaredInHierarchy(configuration,modelPaths[modelNumber]) + jointPriority = getConfigurationJointPriority(configuration,modelPaths[modelNumber]) + if (skipTrivialModels) and (jointPriority==0): + print(bcolors.FAIL,"Skipping Models IS BUGGY BE CAREFUL ",bcolors.ENDC) + print(bcolors.WARNING,"Skipping Model %u/%u %s/%s!" % (modelNumber,endModel,directory,modelPaths[modelNumber]),bcolors.ENDC) + selectedColumns.append(0) + else: + start = time.time() + #-------------------------------------------------------------------------------------------------------- + print("Loading Model %u/%u %s/%s from disk" % (modelNumber,endModel,directory,modelPaths[modelNumber])) + #-------------------------------------------------------------------------------------------------------- + loaded_model = loadModel("%s/%s/"% (directory,modelPaths[modelNumber]),"model") + allModelList.append(loaded_model(singleInput)) + #-------------------------------------------------------------------------------------------------------- + #print("Inputs : ",allModelList[selectedColumnsCount].inputs) + #print("Outputs : ",allModelList[selectedColumnsCount].outputs) + #for layer in loaded_model.layers: + # if str(layer.name).find("input_")==0: + # print("Input layer is : ",layer.name) + # inModelList.append(layer) + # if str(layer.name).find("output_")==0: + # print("Output layer is : ",layer.name) + # outModelList.append(layer) + #-------------------------------------------------------------------------------------------------------- + includedOutputs.append(modelPaths[modelNumber]) + selectedColumns.append(1) + selectedColumnsCount = selectedColumnsCount + 1 + #-------------------------------------------------------------------------------------------------------- + end = time.time() + thisTime = (end-start) + cumulativeTime+=thisTime + #-------------------------------------------------------------------------------------------------------- + print(bcolors.OKGREEN,"loaded %u/%u @ %0.2f secs / Total %0.2f secs" % (modelNumber,endModel,thisTime,cumulativeTime),bcolors.ENDC) + #----------------------------------------------------------------------------------------------------------------------------------------- + print("Merging ",len(allModelList)," Models..") + if (len(allModelList)>1): + out = concatenate(allModelList[:],name=outputLabel) + elif (len(allModelList)==1): + #One model does not need concatenations + out = allModelList[0] + else: + print("combineModels: No Model found!") + raise ValueError('combineModels: No Model found.') + + mergedModel = Model(inputs=rawInput, outputs=out, name=configuration['OutputDirectory']) + mergedModel.compile(optimizer=activeOptimizer,loss='mse',metrics=['mae', 'acc']) #,jit_compile=True #<- this may cause trouble on non-XLA builds? + + print("Merged Model summary : ") + mergedModel.summary() + + if (doPNGPlot): + try: + tf.keras.utils.plot_model(mergedModel, expand_nested=True) + except: + print("Please install pydot for network graph plot!") + os.system('touch model.png') #<- just make a foo png + + print("Done merging model and saved it to disk") + return mergedModel,includedOutputs,selectedColumns +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def combineAsSingleModel(path,inputFilename,modelInputSize,modelOutputSize,outputFilename,label): + inputLabel = "input_%s" % label + outputLabel = "result_%s" % label + + #We basically want to rename a single model the same way as if we had combined it with others.. + loaded_model = loadModel(path,"%s/model" % inputFilename) + + print("Using TensorFlow ",tf.__version__) + print("Original Single Model was : ") + loaded_model.summary() + + doSimpleWay=0 + + if (doSimpleWay): + loaded_model.layers[0].name=inputLabel + loaded_model.layers[-1].name=outputLabel + loaded_model.compile(optimizer='rmsprop', loss='mse', metrics=['mae', 'acc']) + saveModel("%s/"%path,loaded_model) + print("Merged Single/Combined Model summary : ") + loaded_model.summary() + else: + dummyNetwork = newTrivialModel("DummyPadding",666,modelInputSize,modelOutputSize) + + print("Single model input will have ",modelInputSize," size") + singleInput = Input(shape=(modelInputSize,), name=inputLabel) + outModelList = list() # empty list + outModelList.append(loaded_model(singleInput)) + outModelList.append(dummyNetwork(singleInput)) + out = concatenate(outModelList[:],name=outputLabel) + mergedModel = Model(singleInput,out) + mergedModel.compile(optimizer='rmsprop', loss='mse', metrics=['mae', 'acc']) + saveModel("%s/"%path,mergedModel) + print("Merged Single/Combined Model summary : ") + mergedModel.summary() + + os.system('mv %s/model.h5 %s/%s.h5'%(path,path,outputFilename)) + os.system('mv %s/model.json %s/%s.json'%(path,path,outputFilename)) + return mergedModel +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def visualizeLayer(layerNumber,layerSize,hasSkip=False,dropout=0.3): + print("%02u"%layerNumber,end="") + if (hasSkip) : + print(" HAS SKIP ",end="") + else: + print(" ",end="") + for p in range(0,int(layerSize/10)): + print("█",end="") + print(layerSize," Dropout= %0.2f"%dropout) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def autobuilderBB(inputSize,outputSize,modelName="mnet",outputArrayIndex=0,depth=12,lambdaF=1.0,skip=True,dropOutStart=0.3,dropoutStopNLayersBeforeEnd=4): + print("Auto Builder for ",label," Input ",inputSize," and Output ",outputSize) + print("Depth=",depth," λ=",lambdaF," skip=",skip) + step = int((inputSize*lambdaF)/depth) + dropout = dropOutStart + dropStep = dropOutStart/depth + currentSize = inputSize + for layer in range(0,depth): + if (dropout<0.01): + dropout=0 + elif (layer <= depth-dropoutStopNLayersBeforeEnd): + dropout=0 + + visualizeLayer(layer,currentSize,skip!=0,dropout=dropout) + dropout = dropout - dropStep + currentSize = currentSize - step + + currentSize = outputSize + visualizeLayer(depth,currentSize,dropout=0) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def autobuilder(inputSize,outputSize,modelName="mnet",outputArrayIndex=0,depth=12,lambdaF=1.0,skip=True,dropOutStart=0.3,dropoutStopNLayersBeforeEnd=4,probabilisticMode=0,quantize=False,optimizer="rmsprop"): + #------------------------------------------------------------------------------------ + #------------------------------------------------------------------------------------ + print("Auto Builder for ",modelName," Input ",inputSize," and Output ",outputSize) + print("Depth=",depth," λ=",lambdaF," skip=",skip) + modelName = tensorflowFriendlyModelName(modelName) + print("Model renamed to ",modelName," to make sure it doesn't call tensorflow problems ") + #------------------------------------------------------------------------------------ + #------------------------------------------------------------------------------------ + inputs = Input(shape=(inputSize,)) + act = theActivationMethod + kinit = initializer + layers = list() + layerSizes = list() + layerNumber = int() + totalNumberOfLayers = 0 + #------------------------------------------------------------------------------------ + step = int((inputSize*lambdaF)/depth) + stepDecay = True + dropout = dropOutStart + dropStep = dropOutStart/depth + currentSize = inputSize + previousSize = inputSize + previousLayer = inputs + stop = False + #----------------------------------------------------------------------------------------------------------------------------------- + addLayers = list() + skipLayers = list() + print("Will now try to add skip layers to ",totalNumberOfLayers," layers") + #----------------------------------------------------------------------------------------------------------------------------------- + for layerNumber in range(0,depth): + if (currentSize<=outputSize): + stop = True + currentSize = outputSize + print("λ compression is too aggressive network max depth will be ",layerNumber) + if (dropout<0.01): + dropout=0 + visualizeLayer(layerNumber,currentSize,skip,dropout=dropout) + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + layers.append(Dense(int(currentSize),input_shape=(previousSize,), kernel_initializer=kinit, activation=act, name=tag("layer",modelName,outputArrayIndex,layerNumber))(previousLayer)) + layerSizes.append(int(currentSize)) + previousLayer = layers[len(layers)-1] + if (dropout!=0) and (layerNumber <= depth-dropoutStopNLayersBeforeEnd): + Dropout(dropout,name=tag("dropout",modelName,outputArrayIndex,layerNumber))(layers[len(layers)-1]) + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + if (skip): + for fromLayerMinusOne in range(0,layerNumber): + toSize = layerSizes[len(layerSizes)-1] + toLayer = len(layerSizes)-1 + if (fromLayerMinusOne==0): + fromSize = inputSize + skipLayers.append(Dense(int(toSize),input_shape=(inputSize,),kernel_initializer=kinit, activation=act, name=tag("skip_in_to_%u"%(toLayer),modelName,outputArrayIndex,fromLayerMinusOne))(inputs)) + elif (fromLayerMinusOne>0): + fromLayer = fromLayerMinusOne-1 + fromSize = layerSizes[fromLayer] + skipLayers.append(Dense(int(toSize),kernel_initializer=kinit, activation=act, name=tag("skip_%u_to_%u"%(fromLayer,toLayer),modelName,outputArrayIndex,fromLayer))(layers[fromLayer])) + #----------------------------------------------------------------------------------------------------- + if (dropout!=0) and (layerNumber<= depth - dropoutStopNLayersBeforeEnd): + Dropout(dropout,name=tag("skip_dropout_to_%u"%(toLayer),modelName,outputArrayIndex,layerNumber))(layers[len(layers)-1]) + #----------------------------------------------------------------------------------------------------- + #print("Add Skip ",fromLayer,"->",toLayer) + layers[toLayer] = Add(name=tag("add_%u_to_%u"%(fromLayerMinusOne,toLayer),modelName,outputArrayIndex,fromLayerMinusOne))([skipLayers[len(skipLayers)-1],layers[toLayer]]) + #------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ + dropout = dropout - dropStep + currentSize = currentSize - step + if (currentSize < 30): + dropout = 0 + if (stepDecay): + step = max(depth , int(3/6 * step)) + totalNumberOfLayers = totalNumberOfLayers + 1 + if (stop): + break + #----------------------------------------------------------------------------------------------------------------------------------- + currentSize = outputSize + visualizeLayer(depth,currentSize,dropout=0) + #----------------------------------------------------------------------------------------------------------------------------------- + predictions = tf.keras.layers.Dense(int(outputSize),kernel_initializer='normal', activation='linear', name='output_%u_%s'%(outputArrayIndex,modelName))(layers[len(layers)-1]) + #----------------------------------------------------------------------------------------------------------------------------------- + + # the Input layer and three Dense layers + model = Model(name=modelName, inputs=inputs, outputs=predictions) + #------------------------------------------------------------------------------------------------------- + if (optimizer=="adam"): + activeOptimizer=tf.keras.optimizers.Adam(learning_rate=learningRate,epsilon=tf.keras.backend.epsilon()) + else: + activeOptimizer=tf.keras.optimizers.RMSprop(learning_rate=learningRate, rho=0.9, epsilon=tf.keras.backend.epsilon()) # epsilon=1e-6, lr=0.00025 is the old value + #------------------------------------------------------------------------------------------------------- + + + #----------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------- + if (quantize): + #Careful not all activations are quantization aware + print("Will attempt to quantize model..!") + try: + import tensorflow_model_optimization as tfmot + quantize_model = tfmot.quantization.keras.quantize_model + model = quantize_model(model) + except: + print("Could not quantize model, you are using a non quantization aware activation function..!") + print("https://github.com/tensorflow/model-optimization/blob/master/tensorflow_model_optimization/python/core/quantization/keras/quantize_aware_activation.py") + #----------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------- + if (probabilisticMode): + model.compile(optimizer=activeOptimizer, loss='categorical_crossentropy', metrics=['accuracy']) + elif (useQuadMetric): + model.compile(optimizer=activeOptimizer, loss=mean_quad_error, metrics=['mae'] ) #Penalize really bad output.. + elif (useSquaredMetric): + model.compile(optimizer=activeOptimizer, loss='mse', metrics=['mae']) + elif (useModuloMetric): + model.compile(optimizer=activeOptimizer, loss=mean_squared_error_modulo_360, metrics=[average_error_modulo_360] ) #With 360 metrics=[rmse_360] + else: + model.compile(optimizer=activeOptimizer, loss='mae', metrics=['mae']) #Without 360 + #----------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------- + model.summary() + return model +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +def newEncoderModelSelector(configuration,modelName,outputArrayIndex,inputDimension,nonNSDMInputSize,numberOfChannelsPerNSDMElement,outputSize,networkCompression,probabilisticMode=0,quantize=False,optimizer="rmsprop"): + if ('autobuilder' in configuration) and (configuration['autobuilder']==1): + depth = int(configuration['neuralNetworkDepth']) + lambdaF = float(configuration['lamda']) + skip = (int(configuration['skipConnections']) == 1) + dropout = float(configuration['dropoutRate']) + return autobuilder( + inputSize=inputDimension, + outputSize=outputSize, + modelName=modelName, + outputArrayIndex=outputArrayIndex, + depth=depth, + lambdaF=lambdaF, + skip=skip, + dropOutStart=dropout, + probabilisticMode=probabilisticMode, + quantize=quantize, + optimizer=optimizer + ) + else: + return newDeepRotModel( + configuration, + modelName, + outputArrayIndex, + inputDimension, + nonNSDMInputSize, + numberOfChannelsPerNSDMElement, + outputSize, + networkCompression, + probabilisticMode=probabilisticMode, + quantize=quantize, + optimizer=optimizer + ) +# return convDeepRotModel(modelName,outputArrayIndex,inputDimension,nonNSDMInputSize,numberOfChannelsPerNSDMElement,outputSize,networkCompression,probabilisticMode=probabilisticMode,quantize=quantize) +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +#=================================================================================================================================================================================== +if __name__ == '__main__': + print("DNNModel.py is a library and cannot be run on it's own") + #------------ + inputSize = 160 + outputSize = 1 + label = "mnet" + depth = 12 + lambdaF = 1.5 + skip = True + + if (len(sys.argv)<=1): + print("Please supply arguments like : ") #33 inputs are 2D + print(bcolors.OKGREEN,"python3 DNNModel.py --inputs 160 --lambda 1.0 --depth 12 --outputs 1 ",bcolors.ENDC) + + if (len(sys.argv)>1): + print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--inputs"): + inputSize =int(sys.argv[i+1]) + if (sys.argv[i]=="--lambda"): + lambdaF =float(sys.argv[i+1]) + if (sys.argv[i]=="--depth"): + depth =int(sys.argv[i+1]) + if (sys.argv[i]=="--outputs"): + outputSize =int(sys.argv[i+1]) + + model = autobuilder(inputSize,outputSize,modelName=label,depth=depth,lambdaF=lambdaF,skip=skip) + try: + tf.keras.utils.plot_model(model,show_shapes=False,rankdir='LR',expand_nested=True) + except: + print("Please install pydot for network graph plot!") + os.system('touch model.png') #<- just make a foo png + #------------ + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/DNNOptimize.py b/animation/MocapNET-kasisnu/src/python/mnet4/DNNOptimize.py new file mode 100755 index 0000000..bef8fca --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/DNNOptimize.py @@ -0,0 +1,177 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" +#-------------------------------------------------------------- +#-------------------------------------------------------------- +#-------------------------------------------------------------- + +def pruneModel(model,trainIn,trainOut): + print("Will now attempt to optimize model..!") + import tensorflow as tf + import tensorflow_model_optimization as tfmot + initial_sparsity = 0.0 + final_sparsity = 0.75 + begin_step = 1000 + end_step = 5000 + pruning_params = { + 'pruning_schedule': tfmot.sparsity.keras.PolynomialDecay( + initial_sparsity=initial_sparsity, + final_sparsity=final_sparsity, + begin_step=begin_step, + end_step=end_step + ), + 'BatchNormalization': [] + } + model = tfmot.sparsity.keras.prune_low_magnitude(model, **pruning_params) + pruning_callback = tfmot.sparsity.keras.UpdatePruningStep() + + model.compile(optimizer='rmsprop', loss='mse', metrics=['mae', 'acc']) + + model.fit( + trainIn, + trainOut, + epochs=200, + batch_size=1024, + callbacks= pruning_callback, + verbose=1 + ) + return model + +#-------------------------------------------------------------- +#-------------------------------------------------------------- +#-------------------------------------------------------------- + +def clusterModel(model,configuration,trainIn,trainOut): + #https://blog.tensorflow.org/2020/08/tensorflow-model-optimization-toolkit-weight-clustering-api.html + print("Will now attempt to cluster model..!") + import tensorflow as tf + import tensorflow_model_optimization as tfmot + + rmsprop=tf.keras.optimizers.RMSprop(learning_rate=configuration['learningRate'], rho=0.9, epsilon=tf.keras.backend.epsilon()) + model.compile( + optimizer=rmsprop, + loss='mse', + metrics=['mae', 'acc'] + ) + + cluster_weights = tfmot.clustering.keras.cluster_weights + clustering_params = { + 'number_of_clusters': 32, + 'cluster_centroids_init': tfmot.clustering.keras.CentroidInitialization.LINEAR + } + clustered_model = cluster_weights(model, **clustering_params) + clustered_model.compile( + optimizer=rmsprop, + loss='mse', + metrics=['mae', 'acc'] + ) + clustered_model.fit( + trainIn, + trainOut, + epochs=200, + batch_size=1024, + verbose=1 + ) + + + # Prepare model for serving by removing training-only variables. + return tfmot.clustering.keras.strip_clustering(clustered_model) + +#-------------------------------------------------------------- +#-------------------------------------------------------------- +#-------------------------------------------------------------- + +def quantizeModel(model,trainIn,trainOut): + print("Will now attempt to quantize model..!") + import tensorflow as tf + import tensorflow_model_optimization as tfmot + + quantize_model = tfmot.quantization.keras.quantize_model + + # q_aware stands for for quantization aware. + q_aware_model = quantize_model(model) + + # `quantize_model` requires a recompile. + q_aware_model.compile(optimizer='rmsprop', loss='mse', metrics=['mae', 'acc']) + + q_aware_model.fit( + trainIn, + trainOut, + epochs=1, + batch_size=500, + verbose=1, + validation_split=0.1 + ) + + q_aware_model.summary() + return q_aware_model + +#-------------------------------------------------------------- +#-------------------------------------------------------------- +#-------------------------------------------------------------- + +def convertToTensorRT(model,trainIn=0,precision="fp32"): + try: + import os + import sys + import tensorflow as tf + from tensorflow.python.compiler.tensorrt import trt_convert as trt + print("Convert Model to TensorRT / ",precision) + #----------------------------------------------------------------- + os.system("rm -rf tensorRTIntermediateTFModel/") + os.system("rm -rf tensorRTIntermediateTRTModel/") + #----------------------------------------------------------------- + model.save("tensorRTIntermediateTFModel", save_format='tf') #save directory.. + + if (precision=="fp32"): + precision_mode='FP32' + elif (precision=="fp16"): + precision_mode='FP16' + elif (precision=="int8"): + precision_mode='INT8' + else: + print("Unknown precision setting ",precision) + return model + + + # https://www.tensorflow.org/api_docs/python/tf/experimental/tensorrt/Converter + # https://docs.nvidia.com/deeplearning/frameworks/tf-trt-user-guide/index.html#usage-example + print("\nConverting to TensorRT/Tensorflow model") + converter = trt.TrtGraphConverterV2( input_saved_model_dir="tensorRTIntermediateTFModel", precision_mode=precision_mode ) + + #with trt.Builder(TRT_LOGGER) as builder, builder.create_network(network_creation_flag) as network, trt.OnnxParser(network, TRT_LOGGER) as parser, builder.create_builder_config() as config: + # profile = builder.create_optimization_profile() + # profile.set_shape("input_1", (1, 224, 224, 3), (1, 224, 224, 3), (1, 224, 224, 3)) + # config.add_optimization_profile(profile) + # engine = builder.build_engine(network, config) + + + print("\nconverter.convert") + if (trainIn!=0): + converter.convert(calibration_input_fn=trainIn) + converter.build(input_fn=trainIn) + else: + converter.convert() + + + print("\nconverter.save") + converter.save("tensorRTIntermediateTRTModel") + model = tf.keras.models.load_model("tensorRTIntermediateTRTModel") + + os.system("rm -rf tensorRTIntermediateTFModel/") + os.system("rm -rf tensorRTIntermediateTRTModel/") + except: + print("Error while performing TensorRT conversion") + + return model + +#-------------------------------------------------------------- +#-------------------------------------------------------------- +#-------------------------------------------------------------- +if __name__ == '__main__': + print("DNNOptimize.py is a library and cannot be run on it's own") + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/DNNTraining.py b/animation/MocapNET-kasisnu/src/python/mnet4/DNNTraining.py new file mode 100755 index 0000000..864166a --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/DNNTraining.py @@ -0,0 +1,738 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + + +import os +import sys +import gc +import time +import json + + +import tensorflow as tf +from tensorflow import keras + +#from tensorflow.keras.backend.tensorflow_backend import set_session + +#from tensorflow.keras import backend as K +from tensorflow.keras.layers import Input, Dense +from tensorflow.keras.models import Model +from tensorflow.keras.models import Sequential +from tensorflow.keras.models import model_from_json +from tensorflow.keras.utils import plot_model + +import tensorflow.keras.callbacks +import numpy as np +from numba import njit #Test +from tools import bcolors,checkIfFileExists + +def printTrainingVersion(): + print(""" +███╗ ███╗ ██████╗ ██████╗ █████╗ ██████╗ ███╗ ██╗███████╗████████╗ +████╗ ████║██╔═══██╗██╔════╝██╔══██╗██╔══██╗████╗ ██║██╔════╝╚══██╔══╝ +██╔████╔██║██║ ██║██║ ███████║██████╔╝██╔██╗ ██║█████╗ ██║ +██║╚██╔╝██║██║ ██║██║ ██╔══██║██╔═══╝ ██║╚██╗██║██╔══╝ ██║ +██║ ╚═╝ ██║╚██████╔╝╚██████╗██║ ██║██║ ██║ ╚████║███████╗ ██║ +╚═╝ ╚═╝ ╚═════╝ ╚═════╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═══╝╚══════╝ ╚═╝ + """) + #----------------------------- + #BMVC 21 paper was submitted with Keras 2.2.4/Tensorflow 1.14/Numpy 1.16.2 + #----------------------------- + print("") + print("Tensorflow version : ",tf.__version__) + #print("Keras version : ",keras.__version__) <- no longer available in TF-2.13 + print("Numpy version : ",np.__version__) + #----------------------------- + from tensorflow.python.platform import build_info as tf_build_info + print("TF/CUDA version : ",tf_build_info.build_info['cuda_version']) + print("TF/CUDNN version : ",tf_build_info.build_info['cudnn_version']) + #----------------------------- + print("") + #----------------------------- + +def forceCPU(): + print(bcolors.WARNING,"User selected to use CPU mode",bcolors.ENDC) + os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 + os.environ["CUDA_VISIBLE_DEVICES"] = "" + +def limitGPUMemory(): + print("Limiting GPU memory usage for multiple instances..\n") + config = tf.compat.v1.ConfigProto() + config.gpu_options.per_process_gpu_memory_fraction = 0.2 + config.gpu_options.visible_device_list = "0" + set_session(tf.compat.v1.Session(config=config)) + +def setTensorflowBackendToHalfFloats(): + dtype='float16' + + #WARNING:tensorflow:Mixed precision compatibility check (mixed_float16): WARNING + #Your GPU may run slowly with dtype policy mixed_float16 because it does not have compute capability + #of at least 7.0. Your GPU: Quadro P6000, compute capability 6.1 + + from tensorflow.keras.mixed_precision import experimental as mixed_precision + policy = mixed_precision.Policy('mixed_float16') + mixed_precision.set_policy(policy) + + tf.keras.backend.set_floatx(dtype) + # default is 1e-7 which is too small for float16. Without adjusting the epsilon, we will get NaN predictions because of divide by zero problems + tf.keras.backend.set_epsilon(1e-4) + + print('Compute dtype: %s' % policy.compute_dtype) + print('Variable dtype: %s' % policy.variable_dtype) + print("\nUsing Half-Floats for training ") + + +def getRSquared(groundTruth, neuralNetworkOutput): + """ + Computes the R-squared metric. + https://en.wikipedia.org/wiki/Coefficient_of_determination + + Parameters: + -- groundTruth (numpy.ndarray): 1D array representing the ground truth. + -- neuralNetworkOutput (numpy.ndarray): 1D array representing the neural network output. + + Returns: + -- float: R-squared metric. + """ + + # Compute the mean of the ground truth + mean_ground_truth = np.mean(groundTruth) + + # Compute the sum of squares of the residuals + ss_residuals = np.sum((groundTruth - neuralNetworkOutput) ** 2) + + # Compute the sum of squares of the total variation + ss_total = np.sum((groundTruth - mean_ground_truth) ** 2) + + # Compute the R-squared metric + r_squared = 1 - (ss_residuals / ss_total) + + return r_squared + + + +@njit +def getRSquaredInPlace(groundTruth,mean_ground_truth,N,neuralNetworkOutput): + """ + Computes the R-squared metric. + https://en.wikipedia.org/wiki/Coefficient_of_determination + + Parameters: + -- groundTruth (numpy.ndarray): 1D array representing the ground truth. + -- neuralNetworkOutput (numpy.ndarray): 1D array representing the neural network output. + + Returns: + -- float: R-squared metric. + """ + + #N = groundTruth.shape[0] + if N == 0: + return np.NAN + + # Compute the mean of the ground truth + #mean_ground_truth = np.mean(groundTruth) + + ss_residuals = np.float32(0.0) + ss_total = np.float32(0.0) + + for i in range(N): + #-------------------------------------- + gt = np.float32(groundTruth[i][0]) #groundTruth is a tensorflow array so each element is also an array -> thus we need [0] + nn_out = np.float32(neuralNetworkOutput[i][0]) #neuralNetworkOutput is a tensorflow array so each element is also an array -> thus we need [0] + #-------------------------------------- + delta = gt - nn_out + ss_residuals += delta ** 2 + #-------------------------------------- + delta = gt - mean_ground_truth + ss_total += delta ** 2 + + # Compute the R-squared metric + r_squared = np.float32(1 - (ss_residuals / ss_total)) + + return r_squared + + + + +def getRSquaredForNeuralNetwork(mocapNETNetwork,networkInput,groundtruthOutput): + import time + startAt = time.time() + #----------------------------------------------------- + try: + print("Extracting R² : Getting neural network response..") + predictions = mocapNETNetwork.predict(networkInput) + #Since the groundtruth is very large and the machines I use don't have + #enough RAM this is an in place R^2 calculation to make sure the computer doesn't enter a page swapping + #loop that makes training grind to a halt..! + print("Extracting R² : Calculating..") + + mean_ground_truth = np.float32(np.mean(groundtruthOutput)) + #print("groundtruthOutput: ",groundtruthOutput) + rSQ = getRSquaredInPlace(groundtruthOutput,mean_ground_truth,groundtruthOutput.shape[0],predictions) + + #This is faster (if you have spare memory..) + #rSQ = getRSquared(groundtruthOutput,predictions) + del predictions + #except: + except Exception as e: + print("Failed to extract R² ..") + print("Error was : ",e) + rSQ = np.NAN + #----------------------------------------------------- + #----------------------------------------------------- + endAt = time.time() + print("R² = ",rSQ," it took ",(endAt-startAt)/60," mins to compute") + return rSQ + + +def logTrainingResults( + filename, + configuration, + outputType, + outputName, + history, + metrics + ): + if (outputType==0): + numberOfModelParametersStr="?" + if 'modelParameters' in configuration: + numberOfModelParametersStr="%u" % int(configuration['modelParameters']) + + #First record - Start of File + inputProcessing="" + if (configuration['eNSRM']): + inputProcessing=inputProcessing+" eNSRM" + if ('NSRMNormalizeAngles' in configuration) and (configuration['NSRMNormalizeAngles']): + if (configuration['NSRMNormalizeAngles']==1): + inputProcessing=inputProcessing+" NSRMNormalizeAngles" #Half + elif (configuration['NSRMNormalizeAngles']==2): + inputProcessing=inputProcessing+" NSRMNormalizeAnglesFull" + else: + inputProcessing=inputProcessing+" NSRMNormalizeAnglesUNKNOWN" + if (configuration['EDM']): + inputProcessing=inputProcessing+" EDM" + if (configuration['eigenPoses']): + inputProcessing=inputProcessing+" eigenPoses" + if (configuration['autobuilder']): + inputProcessing=inputProcessing+" autobuilder" + if ( (configuration['decompositionType']!="") and (configuration['doPCA']!="") and ( int(configuration['PCADimensionsKept'])>0 ) ): + inputProcessing=inputProcessing+" %s(%u)"%(configuration['decompositionType'],configuration['PCADimensionsKept']) + if (configuration['PCAAlsoKeepRawData']): + inputProcessing=inputProcessing+"+PCA_RawData" + inputProcessing=inputProcessing+" act("+configuration['activationFunction']+")" + inputProcessing=inputProcessing+" rand("+configuration['weightRandomizationFunction']+")" + inputProcessing=inputProcessing+" "+configuration['optimizer'] + if (configuration['balanced2DInputs']): + inputProcessing=inputProcessing+" balanced2DInputs" + + if (configuration['useQuadLoss']!=0): + inputProcessing=inputProcessing+" quadLoss" + + if (configuration['setConstantSeedForReproducibleTraining']!=1): + inputProcessing=inputProcessing+" random seed" + + if (configuration['include2DInputVisibilityFlags']==0): + inputProcessing=inputProcessing+" novis2Dflag" + + experimentDescription="" + experimentDescription=experimentDescription+"group %u"%(configuration["groupOutputs"]) + + if (configuration['useRadians']): + experimentDescription=experimentDescription+" radians" + #-------------------------------------------------------------------- + if ('outputValueDistribution' in configuration): + if (configuration['outputValueDistribution']=='balanced'): + experimentDescription=experimentDescription+" balanced offset" + elif (configuration['outputValueDistribution']=='positive'): + experimentDescription=experimentDescription+" positive offset" + elif (configuration['outputValueDistribution']=='negative'): + experimentDescription=experimentDescription+" negative offset" + #-------------------------------------------------------------------- + if ('outputNormalizationStrategy' in configuration): + experimentDescription=experimentDescription+" outputNormalizationStrategy="+configuration['outputNormalizationStrategy'] + #-------------------------------------------------------------------- + if (float(configuration['outputMultiplier'])!=1.0): + experimentDescription=experimentDescription+" outputScaling=%0.2f" % float(configuration['outputMultiplier']) + experimentDescription=experimentDescription+" NNDepth=%u"%configuration['neuralNetworkDepth'] + + #FIRST! RECORD! + with open(filename, 'w') as f: + f.write(""" + + + + Training summary of experiment %s for %s + + + """%(configuration['label'],configuration['hierarchyPartName'])) + + #Add the hostname.. + import socket + f.write("%s
\n"%socket.gethostname()) + + + f.write(""" + + + + + + + + + + + + + + + + + + + + + + + +
DatePartBatchSizeEpochsλRememberOcclusionsInput ProcessingHard Mining
%s%s%u%u/%u/%u%0.2fc %u/p %u%u%s
+ """% ( + configuration['date'], + configuration['hierarchyPartName'], + configuration['defaultBatchSize'], + configuration["defaultNumberOfEpochs"],configuration["highNumberOfEpochs"],configuration["veryHighNumberOfEpochs"], + configuration["lamda"], + configuration["rememberConsecutiveWeights"],configuration["rememberWeights"], + configuration["ignoreOcclusions"]==0, + inputProcessing + ) + ) + + f.write(""" + + + + + + + + + + + + + + + + + + + +
SerialStartEndTraining SamplesModel SizeDropout/L.RDescription
%s%u%u%u%s%0.2f/%0.5f%s
+ + + + + + + + + + + + """% ( + configuration['label'], + configuration['startPosition'], + configuration['endPosition'], + configuration['trainingSamples'], + numberOfModelParametersStr, + configuration['dropoutRate'], + configuration['learningRate'], + experimentDescription + ) + ) + f.close() + #---------------------------------- + + try: + lowestLossAchievedAt = 0 + if ("loss" in history.history) and ("mae" in history.history): + # Initialize variables to store the initial and lowest loss + initial_loss = history.history['loss'][0] + lowest_loss = initial_loss + + initial_mae = history.history['mae'][0] + lowest_mae = initial_mae + + # Iterate through the history object + count = 0 + for i, loss in enumerate(history.history['loss']): + # Update the lowest loss if a lower value is found + if loss < lowest_loss: + lowest_loss = loss + lowestLossAchievedAt = count + count = count + 1 + + lowest_mae = history.history['mae'][lowestLossAchievedAt] + + #By default don't scale + #---------------------------------------------------------------------------- + outputMinimumValue = 0.0 + outputMaximumValue = 0.0 + outputOffsetValue = 0.0 + outputScalarValues = 1.0 + outputScalarFractionValues = 1.0 + #---------------------------------------------------------------------------- + for v in range(0,len(configuration["outputOffsetLabels"])): + if (outputName.lower()==configuration["outputOffsetLabels"][v].lower()): + #we found a rule! + outputMinimumValue = float(configuration["outputOffsetMinima"][v]) + outputMaximumValue = float(configuration["outputOffsetMaxima"][v]) + outputOffsetValue = float(configuration["outputOffsetValues"][v]) + outputScalarValues = float(configuration["outputScalarValues"][v]) + outputScalarFractionValues = float(configuration["outputScalarFractionValues"][v]) + #---------------------------------------------------------------------------- + initialVAL = "" + lowestVAL = "" + if ("val_loss" in history.history) and ("val_mae" in history.history): + initial_VALloss = history.history['val_loss'][0] + lowest_VALloss = history.history['val_loss'][lowestLossAchievedAt] + initial_VALmae = history.history['val_mae'][0] + lowest_VALmae = history.history['val_mae'][lowestLossAchievedAt] + initial_VALmae = initial_VALmae * outputScalarFractionValues + lowest_VALmae = lowest_VALmae * outputScalarFractionValues + #-------------------------------------- + initialVAL = " - " + if ("test_rsquared_start" in metrics): + initialVAL = " - R² %0.2f/"%(metrics["test_rsquared_start"]) + initialVAL = initialVAL + "%0.4f/%0.4f" % (initial_VALloss,initial_VALmae) + #-------------------------------------- + lowestVAL = " - " + if ("test_rsquared_end" in metrics): + lowestVAL = " - R² %0.2f/"%(metrics["test_rsquared_end"]) + lowestVAL = lowestVAL + "%0.4f/%0.4f" % (lowest_VALloss,lowest_VALmae) + + #Every output now gets scaled always + initial_mae = initial_mae * outputScalarFractionValues + lowest_mae = lowest_mae * outputScalarFractionValues + + with open(filename, 'a') as f: + f.write("") + #------------------------------------------------ + #Start Loss + f.write("") + #------------------------------------------------ + #End Loss + f.write("") + #------------------------------------------------ + + #Training Epochs ---------------------------- + f.write("") + #Min/Max ---------------------------- + f.write("") + #Offset ----------------------------- + f.write("") + #Scalar ----------------------------- + f.write("") + f.write("") + #------------------------------------------------ + f.write("") + f.close() + except: + print("An exception occurred in logTrainingResults") + + + +def regularTraining(tensorboardLabel,mocapNETNetwork,numberOfEpochs,batchSize,earlyStoppingPatience,trainInput,trainOutput,testInput,testOutput,minD,modelIsTrivial=False,haveTestSet=False,useHalfFloats=False): + #We use a checkpoint system to return best model.. + if (os.path.isfile("bestW.hdf5")): + print(bcolors.WARNING,"Found a forgotten checkpoint file, erasing it to avoid trouble",bcolors.ENDC) + os.system('rm bestW.hdf5') + + #whatToMonitor='mean_absolute_error' + #minimumDelta=0.05 + whatToMonitor='loss' + + metrics = dict() + + if (useHalfFloats): + print("Early stopping will use MAE instead of loss due to half floats..") + whatToMonitor='mae' + minimumDelta=minD + + #To see use : + #tensorboard --logdir step0_upperbody_all/tensorboard --bind_all + #------------------------------------------------------------------------ + tensorboard = keras.callbacks.TensorBoard(log_dir=tensorboardLabel,histogram_freq=1) + #------------------------------------------------------------------------ + earlystopper = keras.callbacks.EarlyStopping( + monitor=whatToMonitor, + min_delta=minimumDelta, + patience=earlyStoppingPatience, + verbose=1, + mode='auto' + ) + #------------------------------------------------------------------------- + checkpointer = keras.callbacks.ModelCheckpoint( + filepath="bestW.hdf5", + monitor=whatToMonitor, + mode='min', + verbose=1, + save_freq='epoch', + save_best_only=True, + save_weights_only=True + ) + #------------------------------------------------------------------------ + callbacks = [ + earlystopper, + checkpointer, + tensorboard + ] + #------------------------------------------------------------------------ + + print("regularTraining begins -> BatchSize=%u / NumberOfEpochs=%u "%(batchSize,numberOfEpochs)) + #------------------------------------------------------------------------ + if (haveTestSet==False): + #Train without any test data ( probably to conserve memory ) + if (not modelIsTrivial): + metrics["train_rsquared_start"] = getRSquaredForNeuralNetwork(mocapNETNetwork,trainInput,trainOutput) + history = mocapNETNetwork.fit( + trainInput,trainOutput, + epochs=numberOfEpochs, + batch_size=batchSize, + shuffle=True, + #steps_per_epoch=5, #<- Use more steps per epoch? + callbacks=callbacks + ) + if (not modelIsTrivial): + metrics["train_rsquared_end"] = getRSquaredForNeuralNetwork(mocapNETNetwork,trainInput,trainOutput) + else: + #Train with test data + if (not modelIsTrivial): + metrics["train_rsquared_start"] = getRSquaredForNeuralNetwork(mocapNETNetwork,trainInput,trainOutput) + metrics["test_rsquared_start"] = getRSquaredForNeuralNetwork(mocapNETNetwork,testInput,testOutput) + history = mocapNETNetwork.fit( + trainInput,trainOutput, + epochs=numberOfEpochs, + batch_size=batchSize, + shuffle=True, + validation_data=(testInput,testOutput), + #validation_split=0.2, + callbacks=callbacks + ) + if (not modelIsTrivial): + metrics["train_rsquared_end"] = getRSquaredForNeuralNetwork(mocapNETNetwork,trainInput,trainOutput) + metrics["test_rsquared_end"] = getRSquaredForNeuralNetwork(mocapNETNetwork,testInput,testOutput) + #------------------------------------------------------------------------ + if (checkIfFileExists('bestW.hdf5')): + print("We use the best possible model from our ModelCheckpoint") + mocapNETNetwork.load_weights('bestW.hdf5') + os.system('rm bestW.hdf5') #Get rid of the checkpoint as soon as we are done reading it + else: + print("No bestW.hdf5 file found, just using last iteration..") + #------------------------------------------------------------------------ + return history,mocapNETNetwork,metrics +#------------------------------------------------------------------------------------------------------------------------------------------------- + +def compareTrainingOutputs(groundTruth,neuralOutput): + if (len(groundTruth)!=len(neuralOutput)): + print("Outputs have a different size") + return np.nan + + values = len(groundTruth) + + losses = (groundTruth[:]-neuralOutput[:])**2 + return losses +#---------------------------------------------------- + +def appendHistory(baseHistory,historyToAppend): + #print("baseHistory -> ",type(baseHistory)) + #print("baseHistory Contents -> ",baseHistory) + #print("historyToAppend -> ",type(historyToAppend)) + #print("historyToAppend Contents -> ",historyToAppend) + #print("baseHistory.history -> ",type(baseHistory.history)) + #print("historyToAppend.history -> ",type(historyToAppend.history)) + #----------------------------------------------------------------------------------------------------------- + if ("mae" in baseHistory.history) and ("mae" in historyToAppend.history): + baseHistory.history['mae'] = baseHistory.history['mae'] + historyToAppend.history['mae'] + if ("loss" in baseHistory.history) and ("loss" in historyToAppend.history): + baseHistory.history['loss'] = baseHistory.history['loss'] + historyToAppend.history['loss'] + if ("val_loss" in baseHistory.history) and ("val_loss" in historyToAppend.history): + baseHistory.history['val_loss'] = baseHistory.history['val_loss'] + historyToAppend.history['val_loss'] + if ("val_mae" in baseHistory.history) and ("val_mae" in historyToAppend.history): + baseHistory.history['val_mae'] = baseHistory.history['val_mae'] + historyToAppend.history['val_mae'] + #----------------------------------------------------------------------------------------------------------- + return baseHistory + + +def getLossManually(mocapNETNetwork,trainInput,trainOutput): + predictions = mocapNETNetwork.predict(trainInput) + losses = compareTrainingOutputs(predictions,trainOutput) + #print(losses) + print("Calculating Loss Statistics :") + #-------------------------- + maximum = np.max(losses) + minimum = np.min(losses) + median = np.median(losses) + mean = np.mean(losses) + from tools import calculateStandardDeviationInPlaceKnowingMean,convertStandardDeviationToVariance + std = calculateStandardDeviationInPlaceKnowingMean(losses,mean) #np.std(losses) + var = convertStandardDeviationToVariance(std) #np.var(losses) + #-------------------------- + titleString="Min=%0.2f,Max=%0.2f,Median=%0.2f,Mean=%0.2f,Std=%0.2f,Var=%0.2f" % (minimum,maximum,median,mean,std,var) + print(bcolors.WARNING," %s " % titleString,bcolors.ENDC) + + #difficultyRating = 1.5 + difficultyRating = 5.0 # 4.0 1.5 2.8 + badScoreAbove = mean + difficultyRating * np.sqrt(std) + print("Threshold for difficult poses is ",badScoreAbove) + + difficultPosesIndexes=list() + numberOfTrainingSamples = len(trainInput) + for z in range(0,numberOfTrainingSamples): + if (np.any(losses[z]>badScoreAbove)): + difficultPosesIndexes.append(z) + del losses + + return difficultPosesIndexes,mean,std + + + +def onlineHardExampleMiningTraining(tensorboardLabel,mocapNETNetwork,numberOfEpochs,numberOfHardEpochs,numberOfNormalEpochsAfterHard,batchSize,earlyStoppingPatience,trainInput,trainOutput,testInput,testOutput,minD,modelIsTrivial=False,haveTestSet=False,useHalfFloats=False): + #First do a regular training.. + halfEpochs=int(numberOfEpochs/2) + if (numberOfEpochs==1): + halfEpochs=numberOfEpochs + print(" Online Hard Example Mining Training session .. ") + + metrics = dict() + if (not modelIsTrivial): + metrics["train_rsquared_start"] = getRSquaredForNeuralNetwork(mocapNETNetwork,trainInput,trainOutput) + + dtypeSelected=np.float32 + if (useHalfFloats): + dtypeSelected=np.float16 + totalHistory,mocapNETNetwork = regularTraining(tensorboardLabel,mocapNETNetwork,halfEpochs,batchSize,earlyStoppingPatience,trainInput,trainOutput,testInput,testOutput,minD,haveTestSet=haveTestSet,useHalfFloats=useHalfFloats) + + if (numberOfEpochs==1): + print(" Trivial output will not mine etc. ") + return totalHistory + + bestTrainingMAE=0.0 + successfulUpdates=0 + + REGULAR_EPOCHS_AFTER_HARD=numberOfNormalEpochsAfterHard + HARD_EPOCHS=numberOfHardEpochs + + miningEpochs = int(halfEpochs/2) + + print("Getting initial state of network") + difficultPosesIndexes,mean,std = getLossManually(mocapNETNetwork,trainInput,trainOutput) + + print("Backing up model with mae ",mean) + mocapNETNetwork.save_weights("modelBackup.h5") + bestTrainingMAE=mean + + for i in range(0,miningEpochs): + #------------------------------------------------------------------------------------------------------------------------------------------------------- + #------------------------------------------------------------------------------------------------------------------------------------------------------- + #------------------------------------------------------------------------------------------------------------------------------------------------------- + numberOfTrainingSamples = len(trainInput) + inputSize = len (trainInput[1]) + outputSize = len (trainOutput[1]) + + ratioOfDatasetThatIsDifficult = float ( len(difficultPosesIndexes) / numberOfTrainingSamples ) + + #If more than 10% of the dataset is difficult + if (ratioOfDatasetThatIsDifficult>0.5): + print(bcolors.FAIL," More than half of the dataset is hard (%u samples) so we train on all samples as hard %u/%u" % (len(difficultPosesIndexes),i,miningEpochs),bcolors.ENDC) + regularHistory,mocapNETNetwork = regularTraining(tensorboardLabel,mocapNETNetwork,HARD_EPOCHS,batchSize,5,trainInput,trainOutput,testInput,testOutput,minD,haveTestSet=haveTestSet,useHalfFloats=useHalfFloats) #difficultInput,difficultOutput + totalHistory = appendHistory(totalHistory,regularHistory) + elif (ratioOfDatasetThatIsDifficult>0.1): + difficultInput = np.full([len(difficultPosesIndexes),inputSize],fill_value=0,dtype=dtypeSelected,order='C') + difficultOutput = np.full([len(difficultPosesIndexes),outputSize],fill_value=0,dtype=dtypeSelected,order='C') + for z in range(0,len(difficultPosesIndexes)): + for field in range(0,inputSize): + difficultInput[z,field]=trainInput[difficultPosesIndexes[z],field] + for field in range(0,outputSize): + difficultOutput[z,field]=trainOutput[difficultPosesIndexes[z],field] + + print(bcolors.OKBLUE," Will now train on %u difficult samples %u/%u" % (len(difficultPosesIndexes),i,miningEpochs),bcolors.ENDC) + newHistory,mocapNETNetwork = regularTraining(tensorboardLabel,mocapNETNetwork,HARD_EPOCHS,batchSize,5,difficultInput,difficultOutput,testInput,testOutput,minD,haveTestSet=haveTestSet,useHalfFloats=useHalfFloats) + totalHistory = appendHistory(totalHistory,newHistory) + del difficultInput + del difficultOutput + + if (meanj): + #----------------------------------------------------------------- + labelJ = getCompositeLabel( + rules['NSDM'][j]['joint'], + rules['NSDM'][j]['halfWayFromThisAnd'], + rules['NSDM'][j]['xOffset'], + rules['NSDM'][j]['yOffset'], + rules['NSDM'][j]['isVirtual'] + ) + #----------------------------------------------------------------- + result.append("EDM-%sY-%sY-Distance"%(labelI,labelJ)) + + #print("EDM matrix will look like this ",result) + return result; + + +def createEDMUsingRules(rules,thisInput): + result=list() + #----------------------------------------------------------------------------------------------------- + if (len(thisInput)==0): + print("createNSDMUsingRules called with no input") + return result + + if (not rules['inputJointMap'].checkJointListDimensions(thisInput)): + print("createNSDMUsingRules called with incorrect input size ") + return thisInput + #----------------------------------------------------------------------------------------------------- + numberOfNSDMRules=len(rules['NSDM']) + for i in range(0,numberOfNSDMRules): + iX,iY,iVisibility,iInvalidPoint = getCompositePoint(rules,i,thisInput) + for j in range(0,numberOfNSDMRules): + if (i>j): + # Ensure that each distance is computed only once since the EDM is a symmetric matrix. + #--------------------------------------------------------------------------- + jX,jY,jVisibility,jInvalidPoint = getCompositePoint(rules,j,thisInput) + if (iInvalidPoint or jInvalidPoint): #Changed to or 17/5/23 <- Why was this AND and not OR ? also C++ EDM.h code + result.append(np.float32(0.0)) + else: + result.append(getJoint2DDistancePoints(iX,iY,jX,jY)) + #--------------------------------------------------------------------------- + return result + + + + +if __name__ == '__main__': + print("EDM.py is a library it cannot be run standalone") diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/EigenPoses.py b/animation/MocapNET-kasisnu/src/python/mnet4/EigenPoses.py new file mode 100755 index 0000000..3338575 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/EigenPoses.py @@ -0,0 +1,68 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +import math +import sys +from enum import Enum +from NSDM import getCompositeLabel,getCompositePoint,getJoint2DDistancePoints + + +def EigenPoseLabels(rules): + result=list() + if ('eigenPoseData' in rules) and ('eigenPoses' in rules) and (int(rules['eigenPoses'])==1): + numberOfEigenPoseRules=len(rules['eigenPoseData']['in']) + print("EigenPoses Rules Number ",numberOfEigenPoseRules) + + for i in range(0,numberOfEigenPoseRules): + #----------------------------------------------------------------- + result.append("EigenPose-%u"%(i)) + #----------------------------------------------------------------- + return result; + + +def computeVectorSimilarity_SAD(vec1,vec2): + loss=float(0.0) + if (len(vec1)==len(vec2)): + #If we are here the vectors have the same size + for i in range(0,len(vec1)): + loss = loss + abs(vec1[i]-vec2[i]) + return loss + +def computeVectorSimilarity_MAD(vec1,vec2): + loss=float(0.0) + if (len(vec1)==len(vec2)): + #If we are here the vectors have the same size + for i in range(0,len(vec1)): + loss = loss + abs(vec1[i]-vec2[i]) + return loss/len(vec1) + +def computeVectorSimilarity(vec1,vec2,mode="MAD"): + if (len(vec1)!=len(vec2)): + print("EigenPoses.py: Asked for similarity on Vectors with Lengths",len(vec1)," vs ",len(vec2)) + sys.exit(0) + return float("nan") + #If we are here the vectors have the same size + if (mode=="SAD"): + return computeVectorSimilarity_SAD(vec1,vec2) + if (mode=="MAD"): + return computeVectorSimilarity_MAD(vec1,vec2) + +def createEigenPosesUsingRules(rules,thisInput): + result=list() + if ('eigenPoseData' in rules) and ('eigenPoses' in rules) and (int(rules['eigenPoses'])==1): + numberOfEigenPoseRules=len(rules['eigenPoseData']['in']) + for i in range(0,numberOfEigenPoseRules): + result.append(computeVectorSimilarity(thisInput,rules['eigenPoseData']['in'][i])) + return result + + + + + +if __name__ == '__main__': + print("EigenPoses.py is a library it cannot be run standalone") diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/MocapNET.py b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNET.py new file mode 100755 index 0000000..5a1fb18 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNET.py @@ -0,0 +1,1035 @@ +#!/usr/bin/python3 +#test +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +#------------------------------------------------------------------------------------------- +from readCSV import parseConfiguration,parseConfigurationInputJointMap,transformNetworkInput,initializeDecompositionForExecutionEngine,readGroundTruthFile,readCSVFile,parseOutputNormalization +from NSDM import NSDMLabels,createNSDMUsingRules,inputIsEnoughToCreateNSDM,performNSRMAlignment +from EDM import EDMLabels,createEDMUsingRules +from tools import bcolors,checkIfFileExists,readListFromFile,convertListToLowerCase,secondsToHz,getEntryIndexInList,eprint +#------------------------------------------------------------------------------------------- +from BVH.bvhConverter import BVH +#------------------------------------------------------------------------------------------- +from principleComponentAnalysis import PCA +#------------------------------------------------------------------------------------------- + +MOCAPNET_VERSION="4.0" + +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +import time +import os +import numpy as np +#------------------------------------------------------------------------------------------- +class MocapNETEnsembleCombination(): + def __init__(self): + self.ensembleNameList = list() + self.ensemblePathList = list() + def addEnsemble(self,name:str,path:str): + self.ensembleNameList.append(name) + self.ensemblePathList.append(path) +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +def checkIfAllListedElementsExistInDict(theList,theDict): + for element in theList: + if not element in theDict: + return False + return True +#------------------------------------------------------------------------------------------- +def checkIfAnyListedElementsExistsInString(theList,theString): + #-------------------------- + if (len(theList)==0): + return False + #-------------------------- + for element in theList: + if element in theString: + return True + return False +#------------------------------------------------------------------------------------------- +def flipHorizontalInput(inputList): + for k in inputList.keys(): + if ("2dx_" in k): + inputList[k]=1.0-inputList[k] + return inputList +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +def getSymmetricLEyeOutputs(): + #I AM NOT AT ALL SURE THE FOLLOWING ARE CORRECT.. + bn=dict() + #--------------------------------------------------- + #These are the actual useful outputs.. that REye has.. + bn["hip_xposition"] = (0.0,"") #0 #ignored + bn["hip_yposition"] = (0.0,"") #1 #ignored + bn["hip_zposition"] = (0.0,"") #2 #ignored + bn["neck1_zrotation"] = (0.0,"") #3 #ignored + bn["neck1_xrotation"] = (0.0,"") #4 #ignored + bn["neck1_yrotation"] = (0.0,"") #5 #ignored + bn["eye.r_zrotation"] = (1.0,"eye.l_zrotation") # + bn["eye.r_xrotation"] = (1.0,"eye.l_xrotation") # + bn["oculi01.r_zrotation"] = (1.0,"oculi01.l_zrotation") # + bn["orbicularis03.r_xrotation"] = (1.0,"orbicularis03.l_xrotation") # + bn["jaw_xrotation"] = (0.0,"") # + bn["jaw_yrotation"] = (0.0,"") # + #--------------------------------------------------- + #The rest should all be ignored ? + #--------------------------------------------------- + bn["oculi01.l_zrotation"] = (0.0,"") #ignored + bn["eye.l_zrotation"] = (0.0,"") # ignored + bn["eye.l_xrotation"] = (0.0,"") # ignored + bn["orbicularis04.r_xrotation"] = (0.0,"") #ignored + bn["orbicularis03.r_yrotation"] = (0.0,"") #ignored + bn["orbicularis04.r_yrotation"] = (0.0,"") #ignored + + bn["levator06.l_xrotation"] = (0.0,"") #ignored + bn["levator06.r_xrotation"] = (0.0,"") #ignored + bn["levator03.l_zrotation"] = (0.0,"") #ignored + bn["levator03.r_zrotation"] = (0.0,"") #ignored + + bn["oris03.l_zrotation"] = (0.0,"") #ignored + bn["oris03.r_zrotation"] = (0.0,"") #ignored + bn["oris07.l_zrotation"] = (0.0,"") #ignored + bn["oris07.r_zrotation"] = (0.0,"") #ignored + + bn["oris04.l_zrotation"] = (0.0,"") #ignored + bn["oris04.r_zrotation"] = (0.0,"") #ignored + bn["oris06.l_zrotation"] = (0.0,"") #ignored + bn["oris06.r_zrotation"] = (0.0,"") #ignored + + bn["orbicularis03.r_yrotation"] = (0.0,"") #ignored + bn["orbicularis04.r_yrotation"] = (0.0,"") #ignored + bn["orbicularis03.l_yrotation"] = (0.0,"") #ignored + bn["orbicularis04.l_yrotation"] = (0.0,"") #ignored + + bn["orbicularis03.l_xrotation"] = (0.0,"") # ignored + bn["orbicularis04.l_xrotation"] = (0.0,"") # ignored + + bn["levator06.l_yrotation"] = (0.0,"") #ignored + bn["levator06.r_yrotation"] = (0.0,"") #ignored + + bn["oris03.l_xrotation"] = (0.0,"") #ignored + bn["oris03.l_yrotation"] = (0.0,"") #ignored + bn["oris07.l_yrotation"] = (0.0,"") #ignored + bn["oris03.r_xrotation"] = (0.0,"") #ignored + bn["oris03.r_yrotation"] = (0.0,"") #ignored + bn["oris07.r_yrotation"] = (0.0,"") #ignored + + bn["oris05_xrotation"] = (0.0,"") #ignored + bn["oris05_yrotation"] = (0.0,"") #ignored + return bn +#--------------------------------------------------- +def getSymmetricLEyeNameList(): + bn=dict() + #--------------------------------------------------- + bn["head_reye_0"] = "head_leye_3" #0 + bn["head_reye_1"] = "head_leye_2" #1 + bn["head_reye_2"] = "head_leye_1" #2 + bn["head_reye_3"] = "head_leye_0" #3 + bn["head_reye_4"] = "head_leye_5" #4 + bn["head_reye_5"] = "head_leye_4" #5 + bn["head_reyebrow_0"] = "head_leyebrow_0" #6 + bn["head_reyebrow_1"] = "head_leyebrow_1" #7 + bn["head_reyebrow_2"] = "head_leyebrow_2" #8 + bn["head_reyebrow_3"] = "head_leyebrow_3" #9 + bn["head_reyebrow_4"] = "head_leyebrow_4" #10 + bn["head_reye"] = "head_leye" #11 + bn["head_rchin_0"] = "head_lchin_0" #12 + bn["head_nostrills_2"]= "head_nostrills_2" #13 + bn["head_chin"] = "head_chin" #14 + return bn +#--------------------------------------------------- +#--------------------------------------------------- +#--------------------------------------------------- + + +def getSymmetricLHandOutputs(): + bn=dict() + #--------------------------------------------------- + bn["lhand_xposition"] = (-1.0,"rhand_xposition") #0 + bn["lhand_yposition"] = (1.0,"rhand_yposition") #1 + bn["lhand_zposition"] = (1.0,"rhand_zposition") #2 + #-------------------------------------------------------------------- + #Flip Quaternion During Symmetric output Calculations + bn["lhand_wrotation"] = (-1.0,"rhand_wrotation") #3 {-w,z,y,x} + bn["lhand_xrotation"] = ( 1.0,"rhand_zrotation") #4 + bn["lhand_yrotation"] = ( 1.0,"rhand_yrotation") #5 + bn["lhand_zrotation"] = ( 1.0,"rhand_xrotation") #6 + #-------------------------------------------------------------------- + bn["finger2-1.l_zrotation"] = (-1.0,"finger2-1.r_zrotation") #7 + bn["finger2-1.l_xrotation"] = (-1.0,"finger2-1.r_xrotation") #8 + bn["finger2-1.l_yrotation"] = (-1.0,"finger2-1.r_yrotation") #9 + bn["finger2-2.l_zrotation"] = (-1.0,"finger2-2.r_zrotation") #10 + bn["finger2-2.l_xrotation"] = (-1.0,"finger2-2.r_xrotation") #11 + bn["finger2-2.l_yrotation"] = (-1.0,"finger2-2.r_yrotation") #12 + bn["finger2-3.l_zrotation"] = (-1.0,"finger2-3.r_zrotation") #13 + bn["finger2-3.l_xrotation"] = (-1.0,"finger2-3.r_xrotation") #14 + bn["finger2-3.l_yrotation"] = (-1.0,"finger2-3.r_yrotation") #15 + #-------------------------------------------------------------------- + bn["finger3-1.l_zrotation"] = (-1.0,"finger3-1.r_zrotation") #16 + bn["finger3-1.l_xrotation"] = (-1.0,"finger3-1.r_xrotation") #17 + bn["finger3-1.l_yrotation"] = (-1.0,"finger3-1.r_yrotation") #18 + bn["finger3-2.l_zrotation"] = (-1.0,"finger3-2.r_zrotation") #19 + bn["finger3-2.l_xrotation"] = (-1.0,"finger3-2.r_xrotation") #20 + bn["finger3-2.l_yrotation"] = (-1.0,"finger3-2.r_yrotation") #21 + bn["finger3-3.l_zrotation"] = (-1.0,"finger3-3.r_zrotation") #22 + bn["finger3-3.l_xrotation"] = (-1.0,"finger3-3.r_xrotation") #23 + bn["finger3-3.l_yrotation"] = (-1.0,"finger3-3.r_yrotation") #24 + #-------------------------------------------------------------------- + bn["finger4-1.l_zrotation"] = (-1.0,"finger4-1.r_zrotation") #25 + bn["finger4-1.l_xrotation"] = (-1.0,"finger4-1.r_xrotation") #26 + bn["finger4-1.l_yrotation"] = (-1.0,"finger4-1.r_yrotation") #27 + bn["finger4-2.l_zrotation"] = (-1.0,"finger4-2.r_zrotation") #28 + bn["finger4-2.l_xrotation"] = (-1.0,"finger4-2.r_xrotation") #29 + bn["finger4-2.l_yrotation"] = (-1.0,"finger4-2.r_yrotation") #30 + bn["finger4-3.l_zrotation"] = (-1.0,"finger4-3.r_zrotation") #31 + bn["finger4-3.l_xrotation"] = (-1.0,"finger4-3.r_xrotation") #32 + bn["finger4-3.l_yrotation"] = (-1.0,"finger4-3.r_yrotation") #33 + #-------------------------------------------------------------------- + bn["finger5-1.l_zrotation"] = (-1.0,"finger5-1.r_zrotation") #34 + bn["finger5-1.l_xrotation"] = (-1.0,"finger5-1.r_xrotation") #35 + bn["finger5-1.l_yrotation"] = (-1.0,"finger5-1.r_yrotation") #36 + bn["finger5-2.l_zrotation"] = (-1.0,"finger5-2.r_zrotation") #37 + bn["finger5-2.l_xrotation"] = (-1.0,"finger5-2.r_xrotation") #38 + bn["finger5-2.l_yrotation"] = (-1.0,"finger5-2.r_yrotation") #39 + bn["finger5-3.l_zrotation"] = (-1.0,"finger5-3.r_zrotation") #40 + bn["finger5-3.l_xrotation"] = (-1.0,"finger5-3.r_xrotation") #41 + bn["finger5-3.l_yrotation"] = (-1.0,"finger5-3.r_yrotation") #42 + #-------------------------------------------------------------------- + bn["lthumbBase_zrotation"] = (-1.0,"rthumbBase_zrotation") #43 +? + bn["lthumbBase_xrotation"] = (-1.0,"rthumbBase_xrotation") #44 +? + bn["lthumbBase_yrotation"] = (-1.0,"rthumbBase_yrotation") #45 + bn["lthumb_zrotation"] = (-1.0,"rthumb_zrotation") #46 + bn["lthumb_xrotation"] = (-1.0,"rthumb_xrotation") #47 + bn["lthumb_yrotation"] = (-1.0,"rthumb_yrotation") #48 + bn["finger1-2.l_zrotation"] = (-1.0,"finger1-2.r_zrotation") #49 + bn["finger1-2.l_xrotation"] = (-1.0,"finger1-2.r_xrotation") #50 + bn["finger1-2.l_yrotation"] = (-1.0,"finger1-2.r_yrotation") #51 + bn["finger1-3.l_zrotation"] = (-1.0,"finger1-3.r_zrotation") #52 + bn["finger1-3.l_xrotation"] = (-1.0,"finger1-3.r_xrotation") #53 + bn["finger1-3.l_yrotation"] = (-1.0,"finger1-3.r_yrotation") #54 + return bn +#--------------------------------------------------- +def getSymmetricLHandNameList(): + bn=dict() + #--------------------------------------------------- + #--------------------------------------------------- + bn["lhand"] = "rhand" #0 - wrist + bn["lthumb"] = "rthumb" #1 - thumb_cmc + bn["lthumbbase"] = "rthumbbase" #1 ? - thumb_cmc + bn["finger1-2.l"] = "finger1-2.r" #2 - thumb_mcp + bn["finger1-3.l"] = "finger1-3.r" #3 - thumb_ip + bn["endsite_finger1-3.l"] = "endsite_finger1-3.r" #4 - thumb_tip + bn["finger2-1.l"] = "finger2-1.r" #5 - index_finger_mcp + bn["finger2-2.l"] = "finger2-2.r" #6 - index_finger_pip + bn["finger2-3.l"] = "finger2-3.r" #7 - index_finger_dip + bn["endsite_finger2-3.l"] = "endsite_finger2-3.r" #8 - index_finger_tip + bn["finger3-1.l"] = "finger3-1.r" #9 - middle_finger_mcp + bn["finger3-2.l"] = "finger3-2.r" #10 - middle_finger_pip + bn["finger3-3.l"] = "finger3-3.r" #11 - middle_finger_dip + bn["endsite_finger3-3.l"] = "endsite_finger3-3.r" #12 - middle_finger_tip + bn["finger4-1.l"] = "finger4-1.r" #13 - ring_finger_mcp + bn["finger4-2.l"] = "finger4-2.r" #14 - ring_finger_pip + bn["finger4-3.l"] = "finger4-3.r" #15 - ring_finger_dip + bn["endsite_finger4-3.l"] = "endsite_finger4-3.r" #16 - ring_tip + bn["finger5-1.l"] = "finger5-1.r" #17 - pinky_mcp + bn["finger5-2.l"] = "finger5-2.r" #18 - pinky_pip + bn["finger5-3.l"] = "finger5-3.r" #19 - pinky_dip + bn["endsite_finger5-3.l"] = "endsite_finger5-3.r" #20 - pinky_tip + return bn +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- + + + +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +class SimulatedMirroredEnsemble(): + def __init__(self, + mirroredModel, + mirroringName, + symmetricNames = list(), + outputOperationsNeeded = list() + ): + self.mirroredModel = mirroredModel + self.partName = mirroringName + self.inputReadyForTF = np.empty([2, 1]) + self.NSRM = np.empty([2, 1]) + self.leftToRightNames = symmetricNames + self.mirroringName = mirroringName + self.outputOperationsNeeded = outputOperationsNeeded + self.serial = mirroredModel.serial + self.outputBVH = dict() + self.outputBVHMinima = dict() + self.outputBVHMaxima = dict() + #------------------------------- + self.simulated = True + #------------------------------- + self.output = dict() + self.outputMinimumValue = dict() + self.outputMaximumValue = dict() + #------------------------------- + self.directInputFlips = dict() + self.flippedInputFlips = dict() + + for key in self.mirroredModel.inputs: + s = key.split("_",1) + if (len(s)>0): + originalName = s[1] + if originalName in self.leftToRightNames: + flippedName = self.leftToRightNames[originalName] + flippedXKey = "2dx_%s" % flippedName + flippedYKey = "2dy_%s" % flippedName + flippedVisibleKey = "visible_%s" % flippedName + self.flippedInputFlips["2dx_%s" % originalName] = flippedXKey #<- this needs flip + self.directInputFlips["2dy_%s" % originalName] = flippedYKey #<- this we copy directly + self.directInputFlips["visible_%s" % originalName] = flippedVisibleKey #<- this we copy directly + + print("\n\n\nInputs that need to be subtracted from one : ",self.flippedInputFlips) + print("\n\n\nInputs that need to be just copied : ",self.directInputFlips) + + + def getModel(self): + return self.mirroredModel.model + + def getModelFlops(self): + return 0 + + def getModelParameters(self): + return 0 + + def test(self): + return 1 + + def prepareInput( + self, + input2D :dict, + configuration : dict + ): + self.inputReadyForTF = self.mirroredModel.predict(input2D=input2D) + self.NSRM = self.mirroredModel.NSRM + return self.inputReadyForTF + + def predict(self,input2D :dict): + #Replicating : https://github.com/FORTH-ModelBasedTracker/MocapNET/blob/mnet3/src/MocapNET2/MocapNETLib2/solutionParts/rightHandSym.cpp + #------------------------------------------------------------ + import copy + flippedInput2D = dict() + #------------------------------------------------------------ + doInputFlips = True # Debug switch should alawys be set to True + doOutputFlips = True # Debug switch should alawys be set to True + #------------------------------------------------------------ + if (doInputFlips): #Do Input flips! + #for key in input2D.keys(): + for keyR in self.mirroredModel.inputs: #<- fix right hand working only if left hand is visible + key = keyR.lower() + if (key in self.directInputFlips): + originalName = key + flippedName = self.directInputFlips[originalName] + if (flippedName in input2D): + flippedInput2D[originalName] = float(input2D[flippedName]) + elif (key in self.flippedInputFlips): + originalName = key + flippedName = self.flippedInputFlips[originalName] + if (flippedName in input2D): + if (float(input2D[flippedName])!=0.0): #<- This should be a check on the visibility channel + flippedInput2D[originalName] = 1.0 - float(input2D[flippedName]) + + #---------------------------------------------------------------------------- + leftHandinputReadyForTF = copy.deepcopy(self.mirroredModel.inputReadyForTF) + leftHandinputNSRM = copy.deepcopy(self.mirroredModel.NSRM) + # =========================================================================== + originalName = self.mirroredModel.partName + self.mirroredModel.partName = self.partName + #--- + self.mirroredOutput = self.mirroredModel.predict(input2D=flippedInput2D) + #--- + self.mirroredModel.partName = originalName + #print("flipped yield ",self.mirroredOutput) #Debug + # =========================================================================== + self.inputReadyForTF = copy.deepcopy(self.mirroredModel.inputReadyForTF) + self.NSRM = copy.deepcopy(self.mirroredModel.NSRM) + #---------------------------------------------------------------------------- + if (doOutputFlips): #Do Output Flips! + for originalKeyRaw in self.mirroredOutput: + originalKey = originalKeyRaw.lower() + #print("OUTPUT 3D FOUND ",originalKey," ",end="") #Debug + if (originalKey in self.outputOperationsNeeded): + flippedKey = self.outputOperationsNeeded[originalKey][1] + flippedFactor = self.outputOperationsNeeded[originalKey][0] + if (flippedFactor!=0.0) and (flippedKey!=""): + #print("USED ",flippedKey) #Debug + self.output[flippedKey] = flippedFactor * float(self.mirroredOutput[originalKey]) + #else: #Debug + # print("IGNORED ",flippedKey) #Debug + + else: + eprint("SYMMETRY: THIS SHOULD NOT HAPPEN / NO RULE FOR ",originalKey,flippedKey) + #------------------------------------------------------------ + #Restore left hand + self.mirroredModel.inputReadyForTF = copy.deepcopy(leftHandinputReadyForTF) + self.mirroredModel.NSRM = copy.deepcopy(leftHandinputNSRM) + #------------------------------------------------------------ + return self.output +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- + + + +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------- +class MocapNET(): + def __init__(self, + #------------------------------------------------- + bvhFilePath:str = "BVH/headerWithHeadAndOneMotion.bvh", + disablePCACode = 0, + disableSmoothingCode = 0, + doPerformanceProfiling = False, + doHCDPostProcessing = 1, + hcdLearningRate = 0.01, + hcdEpochs = 30, + hcdIterations = 15, + langevinDynamics = 0.0, + bvhScale = 1.0, + addNoise = 0.0, + multiThreaded = False, + smoothingSampling = 30.0, + smoothingCutoff = 5.0, + + bvhLibraryPath:str = "BVH/libBVHConverter.so", + smootherLibraryPath:str = "Smooth/libSmoothing.so", + #------------------------------------------------- + engine:str = "onnx", + ensembleToLoad: MocapNETEnsembleCombination = MocapNETEnsembleCombination(), + #------------------------------------------------- + record = False + #------------------------------------------------- + ): + #------------------------------------------------------------------------------- + self.record = record + self.inputHistory = list() + self.history = list() + self.outputHistory = list() + self.ensemble = dict() + #------------------------------------------------------------------------------- + #First initialize the engine.. + self.engine = engine + if (engine=="tensorflow") or (engine=="tf"): + from MocapNETTensorflow import MocapNETTensorflow + self.engineContext = MocapNETTensorflow() + elif (engine=="tflite"): + from MocapNETTFLite import MocapNETTFLite + self.engineContext = MocapNETTFLite() + elif (engine=="onnx"): + from MocapNETONNX import MocapNETONNX + self.engineContext = MocapNETONNX() + else: + print("selectMocapNETClassBasedOnEngine: Unknown engine ",engine) + sys.exit(1) + #------------------------------------------------------------------------------- + for ensembleID in range(0,len(ensembleToLoad.ensembleNameList)): + ensembleName = ensembleToLoad.ensembleNameList[ensembleID] + partName = "%s_all" % ensembleName + ensemblePath = ensembleToLoad.ensemblePathList[ensembleID] + if (ensemblePath!="symmetric"): + print(bcolors.OKGREEN,"Loading ",ensembleName,"..",bcolors.ENDC) + configurationPath = "%s/%s_configuration.json" % (ensemblePath,ensembleName) + if (engine=="tensorflow") or (engine=="tf"): + from MocapNETTensorflow import MocapNETTensorflowSubProblem + modelPath = "%s/" % (ensemblePath) + self.ensemble[ensembleName] = MocapNETTensorflowSubProblem( + context = self.engineContext, + configPath = configurationPath, + modelPath = modelPath, + partName = partName, + completelyDisablePCACode = disablePCACode + ) + elif (engine=="tflite"): + from MocapNETTFLite import MocapNETTFLiteSubProblem + modelPath = "%s/model.tflite" % (ensemblePath) + self.ensemble[ensembleName] = MocapNETTFLiteSubProblem( + context = self.engineContext, + configPath = configurationPath, + modelPath = modelPath, + partName = partName, + completelyDisablePCACode = disablePCACode + ) + elif (engine=="onnx"): + from MocapNETONNX import MocapNETONNXSubProblem + modelPath = "%s/model.onnx" % (ensemblePath) + self.ensemble[ensembleName] = MocapNETONNXSubProblem( + context = self.engineContext, + configPath = configurationPath, + modelPath = modelPath, + partName = partName, + completelyDisablePCACode = disablePCACode + ) + elif (ensemblePath=="symmetric"): + #If we are handling a lhand we get an rhand for free :P + if (ensembleName=="rhand"): + self.ensemble["rhand"] = SimulatedMirroredEnsemble( + mirroredModel = self.ensemble["lhand"], + mirroringName = "rhand", + symmetricNames = getSymmetricLHandNameList(), + outputOperationsNeeded = getSymmetricLHandOutputs() + ) + #If we are handling a reye we get an leye for free :P + if (ensembleName=="leye"): + self.ensemble["leye"] = SimulatedMirroredEnsemble( + mirroredModel = self.ensemble["reye"], + mirroringName = "leye", + symmetricNames = getSymmetricLEyeNameList(), + outputOperationsNeeded = getSymmetricLEyeOutputs() + ) + #------------------------------------------------------------------------------- + print(bcolors.OKGREEN,"Combined network has ",self.getModelParameters()," parameters..",bcolors.ENDC) + #------------------------------------------------------------------------------- + #------------------------------------------------------------------------------- + print(bcolors.OKGREEN,"Loading C/Python libraries..",bcolors.ENDC) + self.multiThreaded = multiThreaded + self.doFineTuning = doHCDPostProcessing + self.addNoise = addNoise + self.smoothingSampling = smoothingSampling + self.smoothingCutoff = smoothingCutoff + self.bvhScale = bvhScale + self.lastMAEErrorInPixels = 0.0 + if (disableSmoothingCode==1): + self.smoothingSampling = 0.0 + self.smoothingCutoff = 0.0 + self.hcdLearningRate = hcdLearningRate + self.hcdEpochs = hcdEpochs + self.hcdIterations = hcdIterations + #------------------------------------------------------------------------------- + self.langevinDynamics = langevinDynamics + self.bvhFilePath = bvhFilePath + self.bvh = BVH(bvhPath = bvhFilePath,libraryPath = bvhLibraryPath) + self.bvh.scale(self.bvhScale) + self.bvhJointList = convertListToLowerCase(self.bvh.getJointList()) + self.bvhJointParentList = self.bvh.getJointParentList() + #------------------------------------------------------------------------------- + self.incompleteUpperbodyInput = 1 + self.incompleteLowerbodyInput = 1 + #------------------------------------------------------------------------------- + self.framesProcessed = 0 + self.currentPrediction = dict() + self.previousPrediction = dict() + self.input2D = dict() + self.output = dict() + self.output2D = dict() + self.outputBVH = dict() + self.outputBVHMinima = dict() + self.outputBVHMaxima = dict() + self.output3D = dict() + + self.perfHistorySize = 30 + #------------------------------------------------------------------------------- + self.history_hz_2DEst = [] + self.hz_2DEst = 0.0 + self.history_hz_NN = [] + self.hz_NN = 0.0 + self.history_hz_HCD = [] + self.hz_HCD = 0.0 + self.history_hz_Vis = [] + self.hz_Vis = 0.0 + #------------------------------------------------------------------------------- + + + #------------------------------------------------------------------------------- + print("Caching networks : ") + self.test() + print(bcolors.OKGREEN,"MocapNET ready for use! ",bcolors.ENDC) + #------------------------------------------------------------------------------- + from tools import checkVersion + checkVersion(MOCAPNET_VERSION) + + def recordBVH(self,val:bool): + self.record=val + return True + + def hasEnsemble(self,name): + if (name in self.ensemble): + return True + else: + return False + + + def getUpperBodyModel(self): + return self.ensemble["upperbody"].getModel() + + def getLowerBodyModel(self): + return self.ensemble["lowerbody"].getModel() + + def getModelFlops(self): + total = 0.0 + for k in self.ensemble.keys(): + total = total + self.ensemble[k].getModelFlops() + return total + + def getModelParameters(self): + total = 0 + for k in self.ensemble.keys(): + total = total + self.ensemble[k].getModelParameters() + return total + + def getEnsembleSerials(self): + description = "" + from datetime import datetime, date, time, timezone + #description = datetime.now().strftime("%Y-%m-%d %H:%M:%S ") + description = datetime.now().strftime("%Y-%m-%d ") + for k in self.ensemble.keys(): + description = description + k + ":" + self.ensemble[k].serial + " " + return description + + + def test(self): + #------------------------------------------- + for k in self.ensemble.keys(): + print("Testing loaded ",k," model ") + self.ensemble[k].test() + #------------------------------------------- + + + def enforceBanlistOnOutput(self,output): + #Banlist ------------------------------------- + if "abdomen_zrotation" in output.keys(): + output["abdomen_zrotation"]=0.0 + if "abdomen_xrotation" in output.keys(): + output["abdomen_xrotation"]=0.0 + if "abdomen_yrotation" in output.keys(): + output["abdomen_yrotation"]=0.0 + #-------------------------------------------- + if "chest_zrotation" in output.keys(): + output["chest_zrotation"]=0.0 + if "chest_xrotation" in output.keys(): + output["chest_xrotation"]=0.0 + if "chest_yrotation" in output.keys(): + output["chest_yrotation"]=0.0 + #-------------------------------------------- + return output + + + def countMissingItemsPercentage(self, inputReadyForMocapNET ): + return 0.0 + + + def perturbInput(self,input2D :dict): + import random + for i in range(5): + print(bcolors.FAIL,"NOISE SCHEDULE IS ACTIVE AND SYNTHETIC NOISE IS ADDED (",self.addNoise,") ..",bcolors.ENDC) + peturbedInput2D = input2D + for coordLabel in peturbedInput2D.keys(): + #print(coordLabel) + coordLabelL = coordLabel.lower() + if ("2dx_" in coordLabelL) or ("2dy_" in coordLabelL): + if (peturbedInput2D[coordLabel]>0.0): + perturbation = random.uniform(float(-self.addNoise/2.0),float(self.addNoise/2.0)) + #print("Perturbing ",peturbedInput2D[coordLabel]," with ",perturbation," -> ",end="") + peturbedInput2D[coordLabel]=peturbedInput2D[coordLabel] + perturbation + #print(" ",peturbedInput2D[coordLabel]) + + return peturbedInput2D + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that maybe [it is listed in self.inputs and self.outputs]) + """ + def predict(self,input2D :dict): + start = time.time() + #-------------------------------------------------------------------------------------- + if (self.addNoise>0.0): + input2D = self.perturbInput(input2D) + #-------------------------------------------------------------------------------------- + self.input2D = input2D + self.output = dict() + self.outputBVHMinima = dict() + self.outputBVHMaxima = dict() + #-------------------------------------------------------------------------------------- + if (self.record): + self.inputHistory.append(input2D) + #-------------------------------------------------------------------------------------- + + #print("INPUT2D : ",input2D) + for k in self.ensemble.keys(): + thisEnsemble = self.ensemble[k] + #--------------------------------------------------------------- + ensembleOutput = thisEnsemble.predict(input2D) + #print("Ensemble ",thisEnsemble.partName," : ",ensembleOutput) #Debug + #--------------------------------------------------------------- + self.output.update(ensembleOutput) + if (not thisEnsemble.simulated): + self.outputBVHMinima.update(thisEnsemble.outputMinimumValue) + self.outputBVHMaxima.update(thisEnsemble.outputMaximumValue) + #--------------------------------------------------------------- + + self.output = self.enforceBanlistOnOutput(self.output) + + self.framesProcessed = self.framesProcessed + 1 + #-------------------------------------------------------------------------------------- + end = time.time() # Time elapsed + self.hz_NN = secondsToHz(end - start) + #--------------------------------------------------------------- + self.history_hz_NN.append(self.hz_NN) + if (len(self.history_hz_NN)>self.perfHistorySize): + self.history_hz_NN.pop(0) #Keep mnet history on limits + #--------------------------------------------------------------- + + + #If we want record, record the raw BVH prediction + #print("RECORD : ",self.output) + if (self.record): + self.outputHistory.append(self.output) #This does not have HCD improvement.. + + #print("\r MocapNET Wrapper NeuralNetwork Framerate : ",round(self.hz_NN,2)," fps \r", end="", flush=True) + #print("\n", end="", flush=True) + + return self.output + + + def printStatus(self): + import sys + sys.stdout.write("\rFrame "+str(self.framesProcessed)+"|"+self.engine+"|MPJPE "+str(round(self.bvh.lastMAEErrorInPixels,1))+" px|2D NN:"+str(round(self.hz_2DEst,1))+"Hz|MocapNET:"+str(round(self.hz_NN,1))+"Hz|HCD:"+str(round(self.hz_HCD,1))+"Hz ") + sys.stdout.flush() + + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that maybe [it is listed in self.inputs and self.outputs]) + """ + def predictMultiThreaded(self,input2D :dict): + #---------------------------------- + if (self.multiThreaded): + start = time.time() + #-------------------------------------------------------------------------------------- + if (self.addNoise>0.0): + input2D = self.perturbInput(input2D) + #-------------------------------------------------------------------------------------- + self.input2D = input2D + self.output = dict() + self.outputBVHMinima = dict() + self.outputBVHMaxima = dict() + #-------------------------------------------------------------------------------------- + if (self.record): + self.inputHistory.append(input2D) + #-------------------------------------------------------------------------------------- + + #Create and handle thread initialization if needed.. + self.threads = [] + import threading + for k in self.ensemble.keys(): + #--------------------------------------------------------------- + thisEnsemble = self.ensemble[k] + self.threads.append(threading.Thread(target=thisEnsemble.predict, args=(self.input2D,)) ) + #--------------------------------------------------------------- + #--------------------------------------- + for thread in self.threads: + thread.start() + #--------------------------------------- + #Parallel execution here.. + #--------------------------------------- + for thread in self.threads: + thread.join() + #--------------------------------------- + for k in self.ensemble.keys(): + thisEnsemble = self.ensemble[k] + self.output.update(thisEnsemble.output) + if (k!="rhand") and (k!="lhand") and (k!="leye") : #/Why ? + self.outputBVHMinima.update(thisEnsemble.outputMinimumValue) + self.outputBVHMaxima.update(thisEnsemble.outputMaximumValue) + #--------------------------------------------------------------- + self.output = self.enforceBanlistOnOutput(self.output) + self.framesProcessed = self.framesProcessed + 1 + end = time.time() # Time elapsed + self.hz_NN = secondsToHz(end - start) + #--------------------------------------------------------------- + self.history_hz_NN.append(self.hz_NN) + if (len(self.history_hz_NN)>self.perfHistorySize): + self.history_hz_NN.pop(0) #Keep mnet history on limits + #--------------------------------------------------------------- + + #If we want record, record the raw BVH prediction + #print("RECORD MT : ",self.output) + if (self.record): + self.outputHistory.append(self.output) #This does not have HCD improvement.. + + + #print("\r MocapNET MultiThreaded NeuralNetwork Framerate : ",round(self.hz_NN,2)," fps \r", end="", flush=True) + #print("\n", end="", flush=True) + else: + print("Fallback to single threaded code..") + self.predict(input2D) + return self.output + + + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that maybe [it is listed in self.inputs and self.outputs]) + """ + def fineTune(self,input2D :dict, NNOutput:dict): + if (self.bvh.modify(NNOutput)): + self.bvh.processFrame(0) #only have 1 frame ID + + if (self.hcdIterations>0) and (self.doFineTuning==1): + #print(bcolors.OKGREEN,"Running HCD..",bcolors.ENDC) + self.bvh.fineTuneToMatch("body", + input2D, + frameID = 0, + iterations = self.hcdIterations, + epochs = self.hcdEpochs, + lr = self.hcdLearningRate, + fSampling = self.smoothingSampling, + fCutoff = self.smoothingCutoff) + self.bvh.processFrame(0) #This should now be updated with the IK fine tuned prediction..! + + + def predict3DJoints(self,input2D :dict,runNN:bool=True,runHCD:bool=True): + #Extract a BVH dict of BVH motion fields + if ((runNN) or (len(self.previousPrediction)==0)): + if (self.multiThreaded): + rawBVHPrediction = self.predictMultiThreaded(input2D) #If multithreading is disabled this fallbacks to single threaded.. + else: + rawBVHPrediction = self.predict(input2D) + self.previousPrediction = rawBVHPrediction + else: + rawBVHPrediction = self.previousPrediction + + #This spams a lot.. + #print("Predictions from ensemble keys : ",rawBVHPrediction.keys()) + + # Deal with 3D Mode + #-------------------------------------------------------------------- + if ("upperbody" in self.ensemble) and ("lowerbody" in self.ensemble): + if ('outputMode' in self.ensemble["upperbody"].configuration) and ('outputMode' in self.ensemble["lowerbody"].configuration): + #Running on a recent build with bvh/3d output mode switching + if (self.ensemble["upperbody"].configuration['outputMode']=='3d') and (self.ensemble["lowerbody"].configuration['outputMode']=='3d'): + self.output3D = capitalizeCoordinateTags(self.output) + print(bcolors.OKGREEN,"DIRECT 3D RECOVERY!",bcolors.ENDC) + #print(self.output3D) + return self.output3D + #-------------------------------------------------------------------- + + + + #Modify our BVH armature with the new BVH values + if (self.bvh.modify(rawBVHPrediction)): + + #Remember BVH Pose + self.outputBVH = rawBVHPrediction + + #Render to 2D/3D + self.bvh.processFrame(0) #only have 1 frame ID <- we load our raw prediction + + fineTuningPasses = 0 + if (self.hcdIterations>0) and (self.doFineTuning==1) and (runHCD): + #print(bcolors.OKGREEN,"Running HCD..",bcolors.ENDC) + start = time.time() + if ("upperbody" in self.ensemble) or ("lowerbody" in self.ensemble): + self.bvh.fineTuneToMatch( + "body", + input2D, + frameID = 0, + iterations = self.hcdIterations, + epochs = self.hcdEpochs, + lr = self.hcdLearningRate, + fSampling = self.smoothingSampling, + fCutoff = self.smoothingCutoff, + langevinDynamics = self.langevinDynamics + ) + fineTuningPasses = fineTuningPasses + 1 + self.lastMAEErrorInPixels = self.bvh.lastMAEErrorInPixels + if ("lhand" in self.ensemble): + self.bvh.fineTuneToMatch( + "lhand", + input2D, + frameID = 0, + iterations = self.hcdIterations, + epochs = self.hcdEpochs, + lr = self.hcdLearningRate, + fSampling = self.smoothingSampling, + fCutoff = self.smoothingCutoff, + langevinDynamics = self.langevinDynamics + ) + fineTuningPasses = fineTuningPasses + 1 + if ("rhand" in self.ensemble): + self.bvh.fineTuneToMatch( + "rhand", + input2D, + frameID = 0, + iterations = self.hcdIterations, + epochs = self.hcdEpochs, + lr = self.hcdLearningRate, + fSampling = self.smoothingSampling, + fCutoff = self.smoothingCutoff, + langevinDynamics = self.langevinDynamics + ) + fineTuningPasses = fineTuningPasses + 1 + + #-------------------------------------------------------------------------------------- + self.bvh.smooth(frameID=0,fSampling = self.smoothingSampling,fCutoff = self.smoothingCutoff) + #self.bvh.processFrame(0) # <- this is now done internally to simplify code.. This should now be updated with the IK fine tuned prediction..! + #-------------------------------------------------------------------------------------- + end = time.time() + # Time elapsed + seconds = end - start + if (seconds==0.0): + seconds=1.0 + # Calculate frames per second + self.hz_HCD = 1 / seconds + #------------------------------------------------------------- + self.history_hz_HCD.append(self.hz_HCD) + if (len(self.history_hz_HCD)>self.perfHistorySize): + self.history_hz_HCD.pop(0) #Keep mnet history on limits + #------------------------------------------------------------- + #if (fineTuningPasses>0): + # print("MocapNET HCD Fine tuning Framerate : ",round(self.hz_HCD,2)," fps \n", end="", flush=True) + + + + #This block prevents(?) an endless loop of zeros.. + if ( self.bvh.lastMAEErrorInPixels<0.001 ): + print(bcolors.FAIL,"RESET SKELETON (",self.bvh.lastMAEErrorInPixels,") ",bcolors.ENDC) + #rawBVHPrediction["hip_XPosition"]=0.0 + #rawBVHPrediction["hip_YPosition"]=0.0 + #rawBVHPrediction["hip_ZPosition"]=-200.0 + #self.bvh.modify(rawBVHPrediction) + self.bvh.setMotionValueOfFrame(0,2,-200.0) + self.bvh.processFrame(0) #only have 1 frame ID <- we load our raw prediction + self.bvh.lastMAEErrorInPixels = 1000.0 + #print("input2D:",input2D) + #print("rawBVHPrediction:",rawBVHPrediction) + + + + + #If we want record the file + if (self.record): + self.history.append(self.bvh.getAllMotionValuesOfFrame(0)) + + #Retreive 2D/3D Values + self.output2D = dict() + self.output3D = dict() + for jointID in range(0,self.bvh.numberOfJoints): + #------------------------------------------- + jointName = self.bvh.getJointName(jointID).lower() + #------------------------------------------- + x3D,y3D,z3D = self.bvh.getJoint3D(jointID) + self.output3D["3DX_"+jointName]=float(x3D) + self.output3D["3DY_"+jointName]=float(y3D) + self.output3D["3DZ_"+jointName]=float(z3D) + #------------------------------------------- + x2D,y2D = self.bvh.getJoint2D(jointID) + self.output2D["2DX_"+jointName]=float(x2D) + self.output2D["2DY_"+jointName]=float(y2D) + #------------------------------------------- + else: + print(bcolors.FAIL,"We where unable to process the BVH output",bcolors.ENDC) + + + return self.output3D + + def __del__(self): + print(' ') + if (self.record): + print("Write BVH Output!") + self.bvh.saveBVHFileFromList("out.bvh",self.history) + + from tools import saveCSVFileFromListOfDicts + print("Write BVH Output in CSV format!") + saveCSVFileFromListOfDicts("out.csv",self.outputHistory) + print("Write 2D Input!") + saveCSVFileFromListOfDicts("in.csv",self.inputHistory) + else: + print("Did not record output due to --live mode") + + print('Thank you for using MocapNET!') + print('https://github.com/FORTH-ModelBasedTracker/MocapNET') + + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +def easyMocapNETConstructor( + engine = "onnx", + doProfiling = False, + doHCDPostProcessing = 1, + hcdLearningRate = 0.01, + hcdEpochs = 30, + hcdIterations = 15, + smoothingSampling = 30.0, + smoothingCutoff = 5.0, + multiThreaded = False, + bvhScale = 1.0, + doBody = True, + doUpperbody = False, #<- These get auto activated if doBody=True + doLowerbody = False, #<- These get auto activated if doBody=True + doFace = False, + doREye = False, + doMouth = False, + doHands = False, + doSymmetries = True, + addNoise = 0.0 + ): + combo = MocapNETEnsembleCombination() + #-------------------------------------------------------------- + if (doFace): + combo.addEnsemble("face","step1_face_all/") + if (doMouth): + combo.addEnsemble("mouth","step1_mouth_all/") + if (doREye): + combo.addEnsemble("reye","step1_reye_all/") + if(doSymmetries): + combo.addEnsemble("leye","symmetric") #leye will get initialized automatically + if (doHands): + combo.addEnsemble("lhand","step1_lhand_all/") + if(doSymmetries): + combo.addEnsemble("rhand","symmetric") #rhand will get initialized automatically + if (doBody or doLowerbody) : + combo.addEnsemble("lowerbody","step1_lowerbody_all/") + if (doBody or doUpperbody) : + combo.addEnsemble("upperbody","step1_upperbody_all/") + #-------------------------------------------------------------- + mnet = MocapNET( + doPerformanceProfiling = doProfiling, + doHCDPostProcessing = doHCDPostProcessing, + hcdLearningRate = hcdLearningRate, + hcdEpochs = hcdEpochs, + hcdIterations = hcdIterations, + multiThreaded = multiThreaded, + bvhScale = bvhScale, + engine = engine, + ensembleToLoad = combo, + addNoise = addNoise, + smoothingSampling = smoothingSampling, + smoothingCutoff = smoothingCutoff + ) + return mnet +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- + +if __name__ == '__main__': + mnet = MocapNET( + configUpperBodyPath = "step1_upperbody_all/upperbody_configuration.json", + modelUpperBodyPath="step1_upperbody_all", + configLowerBodyPath = "step1_lowerbody_all/lowerbody_configuration.json", + modelLowerBodyPath="step1_lowerbody_all", + bvhFilePath="BVH/headerWithHeadAndOneMotion.bvh" + ) + + mnet.test() + print("Survived Test!") diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETONNX.py b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETONNX.py new file mode 100755 index 0000000..3b333cf --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETONNX.py @@ -0,0 +1,469 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +import onnxruntime as ort +import onnx +import os +import sys +import time + +#------------------------------------------------------------------------------------------- +from readCSV import parseConfiguration,parseConfigurationInputJointMap,transformNetworkInput,initializeDecompositionForExecutionEngine,readGroundTruthFile,readCSVFile,parseOutputNormalization +from NSDM import NSDMLabels,createNSDMUsingRules,inputIsEnoughToCreateNSDM,performNSRMAlignment +from EDM import EDMLabels,createEDMUsingRules +from tools import bcolors,eprint,checkIfFileExists,readListFromFile,convertListToLowerCase,secondsToHz,capitalizeCoordinateTags,getEntryIndexInList,parseSerialNumberFromSummary +#------------------------------------------------------------------------------------------- +from BVH.bvhConverter import BVH +#------------------------------------------------------------------------------------------- +#from Smooth.smoothing import Smooth +#------------------------------------------------------------------------------------------- +from principleComponentAnalysis import PCA +#------------------------------------------------------------------------------------------- + +import numpy as np + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class MocapNETONNXSubProblem(): + def __init__(self, + context, + configPath:str, + modelPath:str, + partName:str, + completelyDisablePCACode = 0 + ): + #self.options = context.sess_options + self.options = ort.SessionOptions() + #------------------------------------------------------------------------------- + self.useOutputLimits = True #Careful, this should always be on! + self.partName = partName + self.configPath = configPath + self.configuration = parseConfiguration(configPath) + self.part = self.configuration["OutputDirectory"] + self.inputName = "input_all" + self.modelPath = modelPath + self.modelDirectory = os.path.dirname(self.modelPath) + self.frameNumber = 0 + #------------------------------------------------------------------------------- + onnxModelForCheck = onnx.load(modelPath) + onnx.checker.check_model(onnxModelForCheck) + print("ONNX devices available : ", ort.get_device()) + providers = ['CPUExecutionProvider'] + #providers = ['CUDAExecutionProvider'] + self.model = ort.InferenceSession(modelPath, providers=providers, sess_options=self.options) + for i in range(0,len(self.model.get_inputs())): + print("ONNX INPUTS ",self.model.get_inputs()[i].name) + self.inputName = self.model.get_inputs()[i].name + + self.model_input_name = self.model.get_inputs() + #------------------------------------------------------------------------------- + self.inputsWithNSRM = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkInputs.list")) + self.inputs = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkJoints.list")) + self.outputs = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkOutputs.list")) + self.configuration = parseConfigurationInputJointMap(self.configuration,self.inputs) + self.serial = parseSerialNumberFromSummary(self.modelDirectory+"/summary.html") + #------------------------------------------------------------------------------- + self.inputReadyForTF = np.empty([2, 1]) + self.NSRM = np.empty([2, 2]) + #------------------------------------------------------------------------------- + self.emptyList = [0.0] * len(self.inputsWithNSRM) + self.emptyInput = np.asarray([self.emptyList],dtype=np.float32) + self.emptyList = [0.0] * len(self.outputs) + self.emptyOutput = np.asarray([self.emptyList],dtype=np.float32) + #------------------------------------------------------------------------------- + self.outputScalars = [1.0] * len(self.outputs) + self.outputOffsets = [0.0] * len(self.outputs) + self.outputMinima = [-6000.0] * len(self.outputs) #huge limit that essentially doesn't limit anything + self.outputMaxima = [6000.0] * len(self.outputs) #huge limit that essentially doesn't limit anything + #------------------------------------------------------------------------------- + self.outputOffsets = parseOutputNormalization(self.modelDirectory,"/outputOffsets.csv",self.outputs,self.outputOffsets) + self.outputScalars = parseOutputNormalization(self.modelDirectory,"/outputScalarsFraction.csv",self.outputs,self.outputScalars) + self.outputMinima = parseOutputNormalization(self.modelDirectory,"/outputMinima.csv",self.outputs,self.outputMinima) + self.outputMaxima = parseOutputNormalization(self.modelDirectory,"/outputMaxima.csv",self.outputs,self.outputMaxima) + #------------------------------------------------------------------------------- + if (self.outputs[0]=="depth"): + self.outputs[0]="hip_zposition" + #------------------------------------------------------------------------------- + print("Output Mapping :") + for jointID in range(0,len(self.outputs)): + #self.outputScalars[jointID] = 1 / float(self.outputScalars[jointID]) + #print(" - Output ",self.outputs[jointID]," limits [",self.outputMinima[jointID],",",self.outputMaxima[jointID],"] scalar ",self.outputScalars[jointID]," offset ",self.outputOffsets[jointID]) + print("Out %s|Min %0.2f|Max %0.2f|Scalar %0.2f|Offset %0.2f"%(self.outputs[jointID],self.outputMinima[jointID],self.outputMaxima[jointID],self.outputScalars[jointID],self.outputOffsets[jointID])) + #------------------------------------------------------------------------------- + self.incompleteInput = 1 + #------------------------------------------------------------------------------- + self.simulated = False + #------------------------------------------------------------------------------- + self.output = dict() + self.outputMinimumValue = dict() + self.outputMaximumValue = dict() + #------------------------------------------------------------------------------- + self.disablePCACode = completelyDisablePCACode + if (not self.disablePCACode): + self.decompositionEngine = initializeDecompositionForExecutionEngine(self.configuration,self.modelDirectory,self.partName,disablePCACode=self.disablePCACode) + #------------------------------------------------------------------------------- + #The default compatibility setting is the BMVC2019 2channel NSDM, however nowadays we use NSRM + numberOfChannelsPerNSDMElement=2 + if (self.configuration['NSDMAlsoUseAlignmentAngles']==1): + numberOfChannelsPerNSDMElement=1 + print("Number of Channels Per NSDM element ",numberOfChannelsPerNSDMElement) + #------------------------------------------------------------------------------- + if ("eigenPoses" in self.configuration): + if (self.configuration['eigenPoses']==1): + self.configuration['eigenPoseData'] = readGroundTruthFile( + self.configuration, + "Eigenposes", + "%s/2d_%s_eigenposes.csv" % (os.path.dirname(self.modelPath),self.partName), + "%s/%s_%s_eigenposes.csv" % (os.path.dirname(self.modelPath),self.configuration['outputMode'],self.partName), #configuration['outputMode'] is either bvh or 3d + 1.0, + numberOfChannelsPerNSDMElement, + int(self.configuration['useRadians']),#useRadians, + 0,#useHalfFloats + externalDecomposition=self.decompositionEngine + ) + #------------------------------------------------------------------------------- + print("\n\n") + print("Inputs :",self.inputs) + print("Outputs :",self.outputs) + #------------------------------------------------------------------------------- + + def getModel(self): + return self.model + + def getModelFlops(self): + print("ONNX has no flops calculator") + return 0 + + def getModelParameters(self): + print("ONNX has no model parameters calculator") + return 0 + + def test(self): + #------------------------------------------- + thisInputONNX = { self.inputName : self.emptyInput} + output_names_onnx = [otp.name for otp in self.model.get_outputs()] + predictions = self.model.run(output_names_onnx,thisInputONNX)[0][0] + #------------------------------------------- + return 1 + + def prepareInput(self,input2D :dict,configuration : dict): + from readCSV import prepareInputG + thisFullInput, self.NSRM, thisInput, angleToRotate, missingRatio = prepareInputG(input2D,configuration,self.inputs,self.inputsWithNSRM,self.part,self.decompositionEngine,self.disablePCACode) + #appendCSVToFile(self.inputName+".csv",thisFullInput,fID=self.frameNumber) # <----------------- + inputReadyForTF = np.asarray([thisFullInput],dtype=np.float32) + return inputReadyForTF,missingRatio + + def logProbabilisticOutput(self,outputFromNN,resolution=60,increment=6.0,numberOfJoints=30): + xs=list() + #---------------------------------- + value = -180.0 + inc = increment + for r in range(0,resolution): + xs.append(value) + value=value+inc + #---------------------------------- + + ys=list() + #---------------------------------- + for j in range(0,numberOfJoints): + rs=list() + for r in range(0,resolution): + rs.append(outputFromNN[(j*resolution) + r]) + ys.append(rs) + #---------------------------------- + + # Importing packages + import matplotlib.pyplot as plt + + #plt.figure(figsize=(80,80)) + plt.clf() + plt.title("Output Distributions %s "%(self.partName)) + + # Define data values + #print("Should plot %u lines"%numberOfJoints) + for j in range(0,numberOfJoints): + plt.plot(xs, ys[j], label='%s (#%u)' %(self.outputs[j],j)) + #print("Plot %u"%j) + + plt.legend() + #plt.show() #<-This blocks + plt.draw() + plt.pause(0.01) + + + + + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that may be [it is listed in self.inputs and self.outputs]) + """ + def castProbabilisticOutputToDiscreteOutput(self, outputFromNN): + if ('probabilisticOutput' in self.configuration) and (self.configuration['probabilisticOutput']==1): + print(bcolors.OKGREEN,"DOING PROBABILISTIC OUTPUT",bcolors.ENDC) + + #Resolution incrementation + inc = 10.0 + #------------------------- + minV = -180.0 + maxV = 180.0 + resolution = 0 #<- gets automatically calculated as a function of inc.. + #------------------------- + i = minV + while(ibestValue): + bestValue = outputFromNN[(j*resolution + r)] + bestChoice = value + value=value+inc + pickedOutput.append(bestChoice) + + #pickedOutput[0] = 0 + #pickedOutput[1] = 0 + pickedOutput[2] = -160 + pickedOutput[3] = 0 + pickedOutput[4] = 0 + pickedOutput[5] = 0 + return pickedOutput + return outputFromNN + + + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that maybe [it is listed in self.inputs and self.outputs]) + """ + def predict(self, input2D:dict): + #print("Predict ",self.partName) + self.inputReadyForTF,missingRatio = self.prepareInput(input2D,self.configuration) + + if (missingRatio>0.3): + eprint("Not running ",self.partName," due to missing joints ")#,input2D) + return self.output + + + #Turns out on some decompositions like FastICA there are a lot of zeros! + #----------------------------------------------- + #Save cycles by not executing an empty data blob + #----------------------------------------------- + self.incompleteInput = 0 #<- This needs to be set to 0 to mark input is received..! + + #Cast and then run input through MocapNET + thisInputONNX = { self.inputName : self.inputReadyForTF } + output_names_onnx = [otp.name for otp in self.model.get_outputs()] + predictions = self.model.run(output_names_onnx,thisInputONNX)[0][0] + #predictions = self.model(self.inputReadyForTF,training=False) + + #PROBABILISTIC MODE + if ('probabilisticOutput' in self.configuration) and (self.configuration['probabilisticOutput']==1): + predictions = self.castProbabilisticOutputToDiscreteOutput(predictions) + + self.output = dict() + if (len(predictions)!=len(self.outputs)): + print(bcolors.FAIL,"Something bad happened.. the network regressed a different number of parameters (",len(predictions),") than what we expected (",len(self.outputs),") ",bcolors.ENDC) + raise IOError + #Go on with it + return self.output + + #Values to list.. + outputValueList = list() + + for i in range (len(self.outputs)): + outputValueList.append(float(predictions[i])) + + #============================================================================================================== + # THIS SHOULD BE COMMON IN TENSORFLOW/TF-LITE/ONNX + #============================================================================================================== + #Gather our numpy array output in the form of a labeled dictionary + if (self.useOutputLimits): + #Take into account output offsets/scaling + for i in range (len(self.outputs)): + #This should be the exact oposite of the operation in readCSV.py line 550 + recoveredValue = (float(outputValueList[i]) * float(self.outputScalars[i])) + float(self.outputOffsets[i]) + #--------------------------------------------------------------- + if (recoveredValue > self.outputMaxima[i]): + recoveredValue = self.outputMaxima[i] + if (recoveredValue < self.outputMinima[i]): + recoveredValue = self.outputMinima[i] + #--------------------------------------------------------------- + element = self.outputs[i] + self.output[element] = recoveredValue + self.outputMinimumValue[element] = float(self.outputMinima[i]) + self.outputMaximumValue[element] = float(self.outputMaxima[i]) + #--------------------------------------------------------------- + else: + #Not using limits + for i in range (len(self.outputs)): + element = self.outputs[i] + self.output[element] = float(outputValueList[i]) + self.outputMinimumValue[element] = float(self.outputMinima[i]) + self.outputMaximumValue[element] = float(self.outputMaxima[i]) + #============================================================================================================== + #============================================================================================================== + + + + self.frameNumber = self.frameNumber + 1 + return self.output +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class PoseNETONNX(): + def __init__( + self, + modelPath:str="movenet/model.onnx", + targetWidth = 192, + targetHeight = 192, + trainingWidth = 1920, + trainingHeight = 1080, + ): + #Tensorflow attempt to be reasonable + #------------------------------------------ + from holisticPartNames import getPoseNETBodyNameList + self.jointNames = getPoseNETBodyNameList() + #------------------------------------------ + import onnxruntime as ort + import onnx + onnxModelForCheck = onnx.load(modelPath) + onnx.checker.check_model(onnxModelForCheck) + print("ONNX devices available : ", ort.get_device()) + providers = ['CPUExecutionProvider'] + #providers = ['CUDAExecutionProvider'] + self.options = ort.SessionOptions() + self.model = ort.InferenceSession(modelPath, providers=providers, sess_options=self.options) + for i in range(0,len(self.model.get_inputs())): + print("ONNX INPUTS ",self.model.get_inputs()[i].name) + self.inputName = self.model.get_inputs()[i].name + + self.model_input_name = self.model.get_inputs() + #------------------------------------------ + self.output = dict() + self.hz = 0.0 + self.targetWidth = targetWidth + self.targetHeight = targetHeight + self.trainingWidth = trainingWidth + self.trainingHeight = trainingHeight + #------------------------------------------ + + def get2DOutput(self): + return self.output + + def convertImageToMocapNETInput(self,image,doFlipX=False,threshold=0.05): + import tensorflow as tf + import numpy as np + import time + import cv2 + sourceWidth = image.shape[1] + sourceHeight = image.shape[0] + currentAspectRatio=self.targetWidth/self.targetHeight + trainedAspectRatio=self.trainingWidth/self.trainingHeight + #Do resize on OpenCV end + #---------------------------------------------------------------- + from tools import img_resizeWithCrop,normalizedCoordinatesAdaptForVerticalImage,normalizedCoordinatesAdaptToResizedCrop + imageTransformed = img_resizeWithCrop(image,self.targetWidth,self.targetHeight) + #imageTransformed = img_resizeWithPadding(image,self.targetWidth,self.targetHeight) + imageTransformed = cv2.cvtColor(imageTransformed,cv2.COLOR_BGR2RGB) + #---------------------------------------------------------------- + + #Hand image to Tensorflow + #---------------------------------------------------------------- + imageONNX = np.expand_dims(imageTransformed, axis=0) + #------------------------------------------------------------------- + + + start = time.time() + #------------------------------------------------------------------- + #------------------------------------------------------------------- + thisInputONNX = { self.inputName : imageONNX.astype('int32')} + #Run input through MocapNET + output_names_onnx = [otp.name for otp in self.model.get_outputs()] + keypoints_with_scores = self.model.run(output_names_onnx,thisInputONNX)[0][0] + predictions = keypoints_with_scores[0] + #------------------------------------------------------------------- + #------------------------------------------------------------------- + seconds = time.time() - start + self.hz = 1 / (seconds+0.0001) + #print("MoveNET ONNX Framerate : ",round(self.hz,2)," fps ") + + + currentAspectRatio=sourceWidth/sourceHeight #We "change" aspect ratio by restoring points + for pointID in range(0,len(predictions)): + #Joints have y,x,acc order + nX,nY = normalizedCoordinatesAdaptToResizedCrop(sourceWidth,sourceHeight,self.targetWidth,self.targetHeight,predictions[pointID][1],predictions[pointID][0]) + nX,nY = normalizedCoordinatesAdaptForVerticalImage(sourceWidth,sourceHeight,self.trainingWidth,self.trainingHeight,nX,nY) + predictions[pointID][1]=nX + predictions[pointID][0]=nY + + from holisticPartNames import processPoseNETLandmarks + self.output = processPoseNETLandmarks(self.jointNames,predictions,currentAspectRatio,trainedAspectRatio,threshold=threshold,doFlipX=doFlipX) + #---------------------------------------------------------------- + from MocapNETVisualization import drawPoseNETLandmarks + self.image = drawPoseNETLandmarks(predictions,image,threshold=threshold,jointLabels=self.jointNames) + #---------------------------------------------------------------- + + return self.output,image +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class MocapNETONNX(): + def __init__(self ): + print(""" + ██████╗ ███╗ ██╗███╗ ██╗██╗ ██╗ +██╔═══██╗████╗ ██║████╗ ██║╚██╗██╔╝ +██║ ██║██╔██╗ ██║██╔██╗ ██║ ╚███╔╝ +██║ ██║██║╚██╗██║██║╚██╗██║ ██╔██╗ +╚██████╔╝██║ ╚████║██║ ╚████║██╔╝ ██╗ + ╚═════╝ ╚═╝ ╚═══╝╚═╝ ╚═══╝╚═╝ ╚═╝""") + self.sess_options = ort.SessionOptions() + self.sess_options.log_severity_level = 3 #<- log_level + self.sess_options.intra_op_num_threads = 4 + #self.sess_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL + self.sess_options.execution_mode = ort.ExecutionMode.ORT_PARALLEL + self.sess_options.inter_op_num_threads = 4 + self.sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL + + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +if __name__ == '__main__': + mnet = MocapNETONNX() + print("Survived Test!") +#------------------------------------------------------------------------------------------------------------------------------------------------------- diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETTFLite.py b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETTFLite.py new file mode 100755 index 0000000..258ce66 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETTFLite.py @@ -0,0 +1,419 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +import tensorflow as tf +import os +import sys +import time + +#------------------------------------------------------------------------------------------- +from readCSV import parseConfiguration,parseConfigurationInputJointMap,transformNetworkInput,initializeDecompositionForExecutionEngine,readGroundTruthFile,readCSVFile,parseOutputNormalization +from NSDM import NSDMLabels,createNSDMUsingRules,inputIsEnoughToCreateNSDM,performNSRMAlignment +from EDM import EDMLabels,createEDMUsingRules +from tools import bcolors,checkIfFileExists,readListFromFile,convertListToLowerCase,secondsToHz,getEntryIndexInList,parseSerialNumberFromSummary +#------------------------------------------------------------------------------------------- +from BVH.bvhConverter import BVH +#------------------------------------------------------------------------------------------- +#from Smooth.smoothing import Smooth +#------------------------------------------------------------------------------------------- +from principleComponentAnalysis import PCA +#------------------------------------------------------------------------------------------- + +import numpy as np + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class MocapNETTFLiteSubProblem(): + def __init__(self, + context, + configPath:str, + modelPath:str, + partName:str, + numberOfThreads = 4, + completelyDisablePCACode = 0 + ): + #------------------------------------------------------------------------------- + self.useOutputLimits = True #Careful, this should always be on! + self.partName = partName + self.configPath = configPath + self.configuration = parseConfiguration(configPath) + self.part = self.configuration["OutputDirectory"] + self.inputName = "input_all" + self.modelPath = modelPath + self.modelDirectory= os.path.dirname(self.modelPath) + self.frameNumber = 0 + #------------------------------------------------------------------------------- + #The default compatibility setting is the BMVC2019 2channel NSDM, however nowadays we use NSRM + numberOfChannelsPerNSDMElement=2 + if (self.configuration['NSDMAlsoUseAlignmentAngles']==1): + numberOfChannelsPerNSDMElement=1 + print("Number of Channels Per NSDM element ",numberOfChannelsPerNSDMElement) + if ("eigenPoses" in self.configuration): + if (self.configuration['eigenPoses']==1): + self.configuration['eigenPoseData'] = readGroundTruthFile( + self.configuration, + "Eigenposes", + "%s/2d_%s_eigenposes.csv" % (os.path.dirname(self.modelPath),self.partName ), + "%s/%s_%s_eigenposes.csv" % (os.path.dirname(self.modelPath),self.configuration['outputMode'],self.partName), #configuration['outputMode'] is either bvh or 3d + 1.0, + numberOfChannelsPerNSDMElement, + 0,#useRadians, + 0,#useHalfFloats + externalDecomposition=self.decompositionEngine + ) + #------------------------------------------------------------------------------- + self.model = tf.lite.Interpreter( + model_path=self.modelPath, + num_threads=numberOfThreads + ) + + self.model.allocate_tensors() + self.input_details = self.model.get_input_details() + self.output_details = self.model.get_output_details() + + # check the type of the input tensor + self.floating_model = self.input_details[0]['dtype'] == np.float32 + #------------------------------------------------------------------------------- + self.inputsWithNSRM = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkInputs.list")) + self.inputs = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkJoints.list")) + self.outputs = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkOutputs.list")) + self.configuration = parseConfigurationInputJointMap(self.configuration,self.inputs) + self.serial = parseSerialNumberFromSummary(self.modelDirectory+"/summary.html") + #------------------------------------------------------------------------------- + self.inputReadyForTF = np.empty([2, 1]) + self.NSRM = np.empty([2, 2]) + #------------------------------------------------------------------------------- + self.outputScalars = [1.0] * len(self.outputs) + self.outputOffsets = [0.0] * len(self.outputs) + self.outputMinima = [-6000.0] * len(self.outputs) #huge limit that essentially doesn't limit anything + self.outputMaxima = [6000.0] * len(self.outputs) #huge limit that essentially doesn't limit anything + #------------------------------------------------------------------------------- + self.outputOffsets = parseOutputNormalization(self.modelDirectory,"/outputOffsets.csv",self.outputs,self.outputOffsets) + #self.outputScalars = parseOutputNormalization(self.modelDirectory,"/outputScalars.csv",self.outputs,self.outputScalars) + #for jointID in range(0,len(self.outputs)): + # self.outputScalars[jointID] = 1 / float(self.outputScalars[jointID]) + self.outputScalars = parseOutputNormalization(self.modelDirectory,"/outputScalarsFraction.csv",self.outputs,self.outputScalars) + self.outputMinima = parseOutputNormalization(self.modelDirectory,"/outputMinima.csv",self.outputs,self.outputMinima) + self.outputMaxima = parseOutputNormalization(self.modelDirectory,"/outputMaxima.csv",self.outputs,self.outputMaxima) + #------------------------------------------------------------------------------- + if (self.outputs[0]=="depth"): + self.outputs[0]="hip_zposition" + #------------------------------------------------------------------------------- + print("Output Mapping :") + for jointID in range(0,len(self.outputs)): + #self.outputScalars[jointID] = 1 / float(self.outputScalars[jointID]) + #print("Output ",self.outputs[jointID]," min ",self.outputMinima[jointID]," max ",self.outputMaxima[jointID]," scalar ",self.outputScalars[jointID]," offset ",self.outputOffsets[jointID]) + print("Out %s|Min %0.2f|Max %0.2f|Scalar %0.2f|Offset %0.2f"%(self.outputs[jointID],self.outputMinima[jointID],self.outputMaxima[jointID],self.outputScalars[jointID],self.outputOffsets[jointID])) + #------------------------------------------------------------------------------- + self.incompleteInput = 1 + #------------------------------------------------------------------------------- + self.simulated = False + #------------------------------------------------------------------------------- + self.output = dict() + self.outputMinimumValue = dict() + self.outputMaximumValue = dict() + #------------------------------------------------------------------------------- + self.disablePCACode = completelyDisablePCACode + if (not self.disablePCACode): + self.decompositionEngine = initializeDecompositionForExecutionEngine(self.configuration,self.modelDirectory,self.partName,disablePCACode=self.disablePCACode) + #------------------------------------------------------------------------------- + print("\n\n") + print("Model Dir :",self.modelDirectory) + print("Inputs :",self.inputs) + print("Outputs :",self.outputs) + #------------------------------------------------------------------------------- + + def getModel(self): + return self.model + + def getModelFlops(self): + print("TF-Lite has no flops calculator") + return 0 + + def getModelParameters(self): + model = self.model + #concrete_func = model.signatures["serving_default"] + #print( concrete_func.inputs[0] ) + #print( concrete_func.inputs[0].shape ) + #inputShape = str(concrete_func.inputs[0].shape) + #inputShape = inputShape.strip("() ") + #inputShape = inputShape.replace(",", "x") + #inputShape = inputShape.replace("None", "1") + #inputShape = inputShape.strip(' ') + #print("Input Shape is : ",inputShape) + #------------------------------------------ + totalParameters = 0 + try: + trainableParams = np.sum([np.prod(v.get_shape()) for v in model.trainable_weights]) + totalParameters = int(totalParameters + nonTrainableParams) + except: + print("Could not get model trainable parameters for TF-Lite model..!") + + try: + nonTrainableParams = np.sum([np.prod(v.get_shape()) for v in model.non_trainable_weights]) + totalParameters = int(totalParameters + nonTrainableParams) + except: + print("Could not get model non-trainable parameters for TF-Lite model..!") + + + return totalParameters + + + def test(self): + #------------------------------------------- + emptyList = [0.0] * len(self.inputsWithNSRM) + emptyInput =np.asarray([emptyList],dtype=np.float32) + #------------------------------------------- + #print("Running zeros ") + self.model.set_tensor(self.input_details[0]['index'],emptyInput) + self.model.invoke() + predictions = self.model.get_tensor(self.output_details[0]['index']) + #------------------------------------------- + return 1 + + def prepareInput(self,input2D :dict,configuration : dict): + from readCSV import prepareInputG + thisFullInput, self.NSRM, thisInput, angleToRotate, missingRatio = prepareInputG(input2D,configuration,self.inputs,self.inputsWithNSRM,self.part,self.decompositionEngine,self.disablePCACode) + + inputReadyForTF = np.asarray([thisFullInput],dtype=np.float32) + return inputReadyForTF,missingRatio + + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that maybe [it is listed in self.inputs and self.outputs]) + """ + def predict(self,input2D :dict): + + self.inputReadyForTF,missingRatio = self.prepareInput(input2D,self.configuration) + + if (missingRatio>0.3): + print("Not running ",self.partName," due to missing joints ") + return self.output + + + #Turns out on some decompositions like FastICA there are a lot of zeros! + #----------------------------------------------- + #Save cycles by not executing an empty data blob + #----------------------------------------------- + self.incompleteInput = 0 #<- This needs to be set to 0 to mark input is received..! + #totalData = 1 + #zeroData = 0 + #for element in self.inputReadyForTF[0].tolist(): + # #print("ELEMENT ",element) + # totalData = totalData + 1 + # if ( (element<0.005) and (element>-0.005) ): + # zeroData=zeroData + 1 + #missingRatio = zeroData/totalData + #print("Missing Ratio : ",missingRatio) + #if (missingRatio>0.4): + # print(bcolors.FAIL,"Not executing NN with empty data ",bcolors.ENDC) + # #Reset armature..! + # for k in self.output.keys(): + # self.output[k]=0.0 + # self.incompleteInput = 1 + # return self.output + #----------------------------------------------- + + self.model.set_tensor(self.input_details[0]['index'],self.inputReadyForTF) + self.model.invoke() + predictions = self.model.get_tensor(self.output_details[0]['index']) + + self.output = dict() + if (len(predictions[0])!=len(self.outputs)): + print(bcolors.FAIL,"Something bad happened.. the ",self.partName," network regressed a different number of parameters (",len(predictions[0]),") than what we expected (",len(self.outputs),") ",bcolors.ENDC) + raise IOError + return self.output + + + #Values to list.. + outputValueList = list() + + for i in range (len(self.outputs)): + outputValueList.append(float(predictions[0][i])) + + #============================================================================================================== + # THIS SHOULD BE COMMON IN TENSORFLOW/TF-LITE/ONNX + #============================================================================================================== + #Gather our numpy array output in the form of a labeled dictionary + if (self.useOutputLimits): + #Take into account output offsets/scaling + for i in range (len(self.outputs)): + #This should be the exact oposite of the operation in readCSV.py line 550 + recoveredValue = (float(outputValueList[i]) * float(self.outputScalars[i])) + float(self.outputOffsets[i]) + #--------------------------------------------------------------- + if (recoveredValue > self.outputMaxima[i]): + recoveredValue = self.outputMaxima[i] + if (recoveredValue < self.outputMinima[i]): + recoveredValue = self.outputMinima[i] + #--------------------------------------------------------------- + element = self.outputs[i] + self.output[element] = recoveredValue + self.outputMinimumValue[element] = float(self.outputMinima[i]) + self.outputMaximumValue[element] = float(self.outputMaxima[i]) + #--------------------------------------------------------------- + else: + #Not using limits + for i in range (len(self.outputs)): + element = self.outputs[i] + self.output[element] = float(outputValueList[i]) + self.outputMinimumValue[element] = float(self.outputMinima[i]) + self.outputMaximumValue[element] = float(self.outputMaxima[i]) + #============================================================================================================== + #============================================================================================================== + + self.frameNumber = self.frameNumber + 1 + return self.output + + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class PoseNETTFLite(): + def __init__( + self, + modelPath:str="movenet/lite-model_movenet_singlepose_lightning_tflite_int8_4.tflite", + targetWidth = 192, + targetHeight = 192, + numberOfThreads = 4, + trainingWidth = 1920, + trainingHeight = 1080, + ): + #Tensorflow attempt to be reasonable + #------------------------------------------ + from holisticPartNames import getPoseNETBodyNameList + self.jointNames = getPoseNETBodyNameList() + #------------------------------------------ + import tensorflow as tf + # Initialize the TFLite interpreter + self.interpreter = tf.lite.Interpreter(model_path=modelPath,num_threads=numberOfThreads) + self.interpreter.allocate_tensors() + #------------------------------------------ + self.output = dict() + self.hz = 0.0 + self.targetWidth = targetWidth + self.targetHeight = targetHeight + self.trainingWidth = trainingWidth + self.trainingHeight = trainingHeight + #------------------------------------------ + + def get2DOutput(self): + return self.output + + def convertImageToMocapNETInput(self,image,doFlipX=False,threshold=0.05): + import tensorflow as tf + import numpy as np + import time + import cv2 + sourceWidth = image.shape[1] + sourceHeight = image.shape[0] + currentAspectRatio=self.targetWidth/self.targetHeight + trainedAspectRatio=self.trainingWidth/self.trainingHeight + + #Do resize on OpenCV end + #---------------------------------------------------------------- + from tools import img_resizeWithCrop,normalizedCoordinatesAdaptForVerticalImage,normalizedCoordinatesAdaptToResizedCrop + imageTransformed = img_resizeWithCrop(image,self.targetWidth,self.targetHeight) + #imageTransformed = img_resizeWithPadding(image,self.targetWidth,self.targetHeight) + imageTransformed = cv2.cvtColor(imageTransformed,cv2.COLOR_BGR2RGB) + #---------------------------------------------------------------- + + #Prepare image for Tensorflow + #---------------------------------------------------------------- + imageTF = np.expand_dims(imageTransformed, axis=0).astype('int32') + #---------------------------------------------------------------- + + # TF Lite format expects tensor type of float32. + input_image = tf.cast(imageTF, dtype=tf.uint8) # tf.float32 + input_details = self.interpreter.get_input_details() + output_details = self.interpreter.get_output_details() + #------------------------------------------------------------------- + + + start = time.time() + #------------------------------------------------------------------- + #------------------------------------------------------------------- + self.interpreter.set_tensor(input_details[0]['index'], input_image.numpy()) + self.interpreter.invoke() + + keypoints_with_scores = self.interpreter.get_tensor(output_details[0]['index']) # Output is a [1, 1, 17, 3] numpy array. + predictions = keypoints_with_scores[0][0] + #------------------------------------------------------------------- + #------------------------------------------------------------------- + seconds = time.time() - start + self.hz = 1 / (seconds+0.0001) + #print("MoveNET TFLite Framerate : ",round(self.hz,2)," fps ") + + + currentAspectRatio=sourceWidth/sourceHeight #We "change" aspect ratio by restoring points + for pointID in range(0,len(predictions)): + #Joints have y,x,acc order + nX,nY = normalizedCoordinatesAdaptToResizedCrop(sourceWidth,sourceHeight,self.targetWidth,self.targetHeight,predictions[pointID][1],predictions[pointID][0]) + nX,nY = normalizedCoordinatesAdaptForVerticalImage(sourceWidth,sourceHeight,self.trainingWidth,self.trainingHeight,nX,nY) + predictions[pointID][1]=nX + predictions[pointID][0]=nY + + from holisticPartNames import processPoseNETLandmarks + self.output = processPoseNETLandmarks(self.jointNames,predictions,currentAspectRatio,trainedAspectRatio,threshold=threshold,doFlipX=doFlipX) + #------------------------------------------------ + from MocapNETVisualization import drawPoseNETLandmarks + self.image = drawPoseNETLandmarks(predictions,image,threshold=threshold,jointLabels=self.jointNames) + #------------------------------------------------ + + return self.output,image +#---------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------- +#---------------------------------------------------------------------------------------------------------------------------- + + + + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- + +class MocapNETTFLite(): + def __init__(self,): + print(""" +████████╗███████╗ ██╗ ██╗████████╗███████╗ +╚══██╔══╝██╔════╝ ██║ ██║╚══██╔══╝██╔════╝ + ██║ █████╗█████╗██║ ██║ ██║ █████╗ + ██║ ██╔══╝╚════╝██║ ██║ ██║ ██╔══╝ + ██║ ██║ ███████╗██║ ██║ ███████╗ + ╚═╝ ╚═╝ ╚══════╝╚═╝ ╚═╝ ╚══════╝""") + #------------------------------------------------------------------------------- + #do nothing :P + #------------------------------------------------------------------------------- + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +if __name__ == '__main__': + mnet = MocapNETTFLite() + print("Survived Test!") +#------------------------------------------------------------------------------------------------------------------------------------------------------- diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETTensorflow.py b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETTensorflow.py new file mode 100755 index 0000000..4b15c03 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETTensorflow.py @@ -0,0 +1,530 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +#------------------------------------------------------------------------------------------- +from readCSV import parseConfiguration,parseConfigurationInputJointMap,transformNetworkInput,initializeDecompositionForExecutionEngine,readGroundTruthFile,readCSVFile,parseOutputNormalization +from NSDM import NSDMLabels,createNSDMUsingRules,inputIsEnoughToCreateNSDM,performNSRMAlignment +from EDM import EDMLabels,createEDMUsingRules +from tools import bcolors,checkIfFileExists,readListFromFile,convertListToLowerCase,secondsToHz,getEntryIndexInList,parseSerialNumberFromSummary +#------------------------------------------------------------------------------------------- +from BVH.bvhConverter import BVH +#------------------------------------------------------------------------------------------- +#from Smooth.smoothing import Smooth +#------------------------------------------------------------------------------------------- +from principleComponentAnalysis import PCA +#------------------------------------------------------------------------------------------- + +import time +import os +import numpy as np + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class MocapNETTensorflowSubProblem(): + def __init__(self, + context, + configPath:str, + modelPath:str, + partName:str, + device:str="/device:GPU:0", + doPerformanceProfiling = False, + tensorboard = 0, + completelyDisablePCACode = 0 + ): + #------------------------------------------------------------------------------- + self.useOutputLimits = True #Careful, this should always be on! + self.partName = partName + self.configPath = configPath + from readCSV import parseConfiguration + self.configuration = parseConfiguration(configPath) + self.part = partName#self.configuration["OutputDirectory"] + self.modelPath = modelPath + self.modelDirectory= os.path.dirname(self.modelPath) + from DNNModel import loadNewModel + self.model = loadNewModel(modelPath) + self.device = device + self.frameNumber = 0 + #------------------------------------------------------------------------------- + #The default compatibility setting is the BMVC2019 2channel NSDM, however nowadays we use NSRM + numberOfChannelsPerNSDMElement=2 + if (self.configuration['NSDMAlsoUseAlignmentAngles']==1): + numberOfChannelsPerNSDMElement=1 + print("Number of Channels Per NSDM element ",numberOfChannelsPerNSDMElement) + if ("eigenPoses" in self.configuration): + if (self.configuration['eigenPoses']==1): + self.configuration['eigenPoseData'] = readGroundTruthFile( + self.configuration, + "Eigenposes", + "%s/2d_%s_eigenposes.csv" % (os.path.dirname(self.modelPath),self.partName ), + "%s/%s_%s_eigenposes.csv" % (os.path.dirname(self.modelPath),self.configuration['outputMode'],self.partName), #configuration['outputMode'] is either bvh or 3d + 1.0, + numberOfChannelsPerNSDMElement, + 0,#useRadians, + 0,#useHalfFloats + externalDecomposition=self.decompositionEngine + ) + #------------------------------------------------------------------------------- + import tensorflow as tf + rmsprop=tf.keras.optimizers.RMSprop(learning_rate=0.002, rho=0.9, epsilon=tf.keras.backend.epsilon()) + self.model.compile( + optimizer=rmsprop, + loss='mse', + metrics=['mae', 'acc'], + jit_compile=True #<- this may cause trouble on non-XLA builds? + ) + self.modelKeras = self.model + #print("MocapNET Model for ",partName," has the following signatures ",self.model.signatures) + self.model = self.model.signatures['serving_default'] + + self.profile = doPerformanceProfiling + self.tensorboard = tensorboard + #------------------------------------------------------------------------------- + self.inputsWithNSRM = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkInputs.list")) + self.inputs = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkJoints.list")) + self.outputs = convertListToLowerCase(readListFromFile(self.modelDirectory+"/neuralNetworkOutputs.list")) + self.configuration = parseConfigurationInputJointMap(self.configuration,self.inputs) + self.serial = parseSerialNumberFromSummary(self.modelDirectory+"/summary.html") + #------------------------------------------------------------------------------- + self.inputReadyForTF = np.empty([2, 1]) + self.NSRM = np.empty([2, 2]) + #------------------------------------------------------------------------------- + self.outputScalars = [1.0] * len(self.outputs) + self.outputOffsets = [0.0] * len(self.outputs) + self.outputMinima = [-6000.0] * len(self.outputs) #huge limit that essentially doesn't limit anything + self.outputMaxima = [6000.0] * len(self.outputs) #huge limit that essentially doesn't limit anything + #------------------------------------------------------------------------------- + self.outputOffsets = parseOutputNormalization(self.modelDirectory,"/outputOffsets.csv",self.outputs,self.outputOffsets) + #self.outputScalars = parseOutputNormalization(self.modelDirectory,"/outputScalars.csv",self.outputs,self.outputScalars) + #for jointID in range(0,len(self.outputs)): + # self.outputScalars[jointID] = 1 / float(self.outputScalars[jointID]) + self.outputScalars = parseOutputNormalization(self.modelDirectory,"/outputScalarsFraction.csv",self.outputs,self.outputScalars) + self.outputMinima = parseOutputNormalization(self.modelDirectory,"/outputMinima.csv",self.outputs,self.outputMinima) + self.outputMaxima = parseOutputNormalization(self.modelDirectory,"/outputMaxima.csv",self.outputs,self.outputMaxima) + #------------------------------------------------------------------------------- + if (self.outputs[0]=="depth"): + self.outputs[0]="hip_zposition" + #------------------------------------------------------------------------------- + print("Output Mapping :") + for jointID in range(0,len(self.outputs)): + #self.outputScalars[jointID] = 1 / float(self.outputScalars[jointID]) + #print("Output ",self.outputs[jointID]," min ",self.outputMinima[jointID]," max ",self.outputMaxima[jointID]," scalar ",self.outputScalars[jointID]," offset ",self.outputOffsets[jointID]) + print("Out %s|Min %0.2f|Max %0.2f|Scalar %0.2f|Offset %0.2f"%(self.outputs[jointID],self.outputMinima[jointID],self.outputMaxima[jointID],self.outputScalars[jointID],self.outputOffsets[jointID])) + #------------------------------------------------------------------------------- + self.networkInputList = [0.0] * len(self.inputsWithNSRM) + self.networkInput = np.asarray([self.networkInputList],dtype=np.float32) + #------------------------------------------------------------------------------- + self.incompleteInput = 1 + #------------------------------------------------------------------------------- + self.simulated = False + #------------------------------------------------------------------------------- + self.output = dict() + self.outputMinimumValue = dict() + self.outputMaximumValue = dict() + #------------------------------------------------------------------------------- + self.disablePCACode = completelyDisablePCACode + if (not self.disablePCACode): + self.decompositionEngine = initializeDecompositionForExecutionEngine(self.configuration,self.modelDirectory,self.partName,disablePCACode=self.disablePCACode) + #------------------------------------------------------------------------------- + print("\n\n") + print("Inputs :",self.inputs) + print("Outputs :",self.outputs) + #------------------------------------------------------------------------------- + + def getModel(self): + return self.modelKeras + + def getModelFlops(self): + import tensorflow as tf + from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2_as_graph + model = self.modelKeras + concrete = tf.function(lambda inputs: model(inputs)) + concrete_func = concrete.get_concrete_function( + [tf.TensorSpec([1, *inputs.shape[1:]]) for inputs in model.inputs]) + frozen_func, graph_def = convert_variables_to_constants_v2_as_graph(concrete_func) + with tf.Graph().as_default() as graph: + tf.graph_util.import_graph_def(graph_def, name='') + run_meta = tf.compat.v1.RunMetadata() + opts = tf.compat.v1.profiler.ProfileOptionBuilder.float_operation() + flops = tf.compat.v1.profiler.profile(graph=graph, run_meta=run_meta, cmd="op", options=opts) + + totalFLOPS = int(flops.total_float_ops) + return totalFLOPS + return 0 + + def getModelParameters(self): + model = self.modelKeras + #------------------------------------------------------------------------------------------ + trainableParams = np.sum([np.prod(v.get_shape()) for v in model.trainable_weights]) + nonTrainableParams = np.sum([np.prod(v.get_shape()) for v in model.non_trainable_weights]) + totalParameters = int(trainableParams + nonTrainableParams) + return totalParameters + + def test(self): + #------------------------------------------- + emptyList = [0.0] * len(self.inputsWithNSRM) + emptyInput =np.asarray([emptyList],dtype=np.float32) + #------------------------------------------- + #print(bcolors.FAIL,"Running zeros ",bcolors.ENDC) + import tensorflow as tf + outputs = self.model(tf.cast(emptyInput,dtype=tf.float32)) #,training=False + #print("Outputs : ",outputs) + #print("Output Keys", outputs.keys()) + outKey = list(outputs.keys())[0] + outputsRaw = outputs[outKey] + predictions = outputsRaw + + #predictions = self.model(emptyInput,training=False) + #print("MocapNET result = ",predictions) + #------------------------------------------- + return 1 + + def prepareInput(self,input2D :dict,configuration : dict): + from readCSV import prepareInputG + thisFullInput, self.NSRM, thisInput, angleToRotate, missingRatio = prepareInputG(input2D,configuration,self.inputs,self.inputsWithNSRM,self.part,self.decompositionEngine,self.disablePCACode) + + #i=0 + #for value in thisFullInput: + # self.networkInput[i]=value + # i=i+1 + #import tensorflow as tf + #self.networkInput = tf.convert_to_tensor(self.networkInputNumpy) + self.networkInput = np.asarray([thisFullInput],dtype=np.float32) + return self.networkInput,missingRatio + #----------------------------- + #inputReadyForTF = np.asarray([thisFullInput],dtype=np.float32) + #return inputReadyForTF + + + """ + Convert a dictionary of 2D inputs to MocapNET output + (Whatever that maybe [it is listed in self.inputs and self.outputs]) + """ + def predict(self,input2D :dict): + + #This call works @ 400Hz + #-------------------------------------------------------------------------------------- + self.inputReadyForTF,missingRatio = self.prepareInput(input2D,self.configuration) + + + if (missingRatio>0.3): + print("Not running ",self.partName," due to missing joints ") + return self.output + + #Turns out on some decompositions like FastICA there are a lot of zeros! + #----------------------------------------------- + #Save cycles by not executing an empty data blob + #----------------------------------------------- + self.incompleteInput = 0 #<- This needs to be set to 0 to mark input is received..! + #totalData = 1 + #zeroData = 0 + #for element in self.inputReadyForTF[0].tolist(): + # #print("ELEMENT ",element) + # totalData = totalData + 1 + # if ( (element<0.005) and (element>-0.005) ): + # zeroData=zeroData + 1 + #missingRatio = zeroData/totalData + #print("Missing Ratio : ",missingRatio) + #if (missingRatio>0.4): + # print(bcolors.FAIL,"Not executing NN with empty data ",bcolors.ENDC) + # #Reset armature..! + # for k in self.output.keys(): + # self.output[k]=0.0 + # + # self.incompleteInput = 1 + # return self.output + #-------------------------------------------------------------------------------------- + predictions = list() + self.output = dict() + + + #-------------------------------------------------------------------------------------- + if (self.profile): + #Run input through MocapNET and Profile code (slower) + print(bcolors.WARNING,"WARNING: Profiling NN enabled, execution will be slower",bcolors.ENDC) + predictions = self.modelKeras.predict(self.inputReadyForTF,callbacks = [self.tensorboard]) + else: + #As stated in https://github.com/keras-team/keras/blob/v2.8.0/keras/engine/training.py#L1825-L2012 : + # and https://keras.io/getting_started/faq/#whats-the-difference-between-model-methods-predict-and-call + import tensorflow as tf + with tf.device(self.device): + #with tf.device('/device:CPU:0'): + inferenceOutputs = self.model(tf.cast(self.inputReadyForTF,dtype=tf.float32)) # ,training=False We shouldn't run predict to get as fast results as possible + #print("Outputs : ",outputs) + #print("Output Keys", outputs.keys()) + outKey = list(inferenceOutputs.keys())[0] + outputsRaw = inferenceOutputs[outKey] + #outputsRaw = outputs['result_all'] + predictions = outputsRaw + #-------------------------------------------------------------------------------------- + + + if (len(predictions)>0): + if(len(predictions[0])!=len(self.outputs)): + print(bcolors.FAIL,"Something bad happened.. the network regressed a different number of parameters than what we expected",bcolors.ENDC) + raise IOError + return self.output + + + #Values to list.. + outputValueList = list() + + for i in range (len(self.outputs)): + outputValueList.append(float(predictions[0][i])) + + #============================================================================================================== + # THIS SHOULD BE COMMON IN TENSORFLOW/TF-LITE/ONNX + #============================================================================================================== + #Gather our numpy array output in the form of a labeled dictionary + if (self.useOutputLimits): + #Take into account output offsets/scaling + for i in range (len(self.outputs)): + #This should be the exact oposite of the operation in readCSV.py line 550 + recoveredValue = (float(outputValueList[i]) * float(self.outputScalars[i])) + float(self.outputOffsets[i]) + #--------------------------------------------------------------- + if (recoveredValue > self.outputMaxima[i]): + recoveredValue = self.outputMaxima[i] + if (recoveredValue < self.outputMinima[i]): + recoveredValue = self.outputMinima[i] + #--------------------------------------------------------------- + element = self.outputs[i] + self.output[element] = recoveredValue + self.outputMinimumValue[element] = float(self.outputMinima[i]) + self.outputMaximumValue[element] = float(self.outputMaxima[i]) + #--------------------------------------------------------------- + else: + #Not using limits + for i in range (len(self.outputs)): + element = self.outputs[i] + self.output[element] = float(outputValueList[i]) + self.outputMinimumValue[element] = float(self.outputMinima[i]) + self.outputMaximumValue[element] = float(self.outputMaxima[i]) + #============================================================================================================== + #============================================================================================================== + + self.frameNumber = self.frameNumber + 1 + return self.output + + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +class PoseNET(): + def __init__( + self, + modelPath:str="movenet/", + targetWidth = 192, + targetHeight = 192, + trainingWidth = 1920, + trainingHeight = 1080, + ): + #Tensorflow attempt to be reasonable + #------------------------------------------ + from holisticPartNames import getPoseNETBodyNameList + self.jointNames = getPoseNETBodyNameList() + import tensorflow as tf + self.model = tf.saved_model.load(modelPath) + self.movenet = self.model.signatures['serving_default'] + #------------------------------------------ + self.output = dict() + self.hz = 0.0 + self.targetWidth = targetWidth + self.targetHeight = targetHeight + self.trainingWidth = trainingWidth + self.trainingHeight = trainingHeight + #------------------------------------------ + def get2DOutput(self): + return self.output + + def convertImageToMocapNETInput(self,image,doFlipX=False,threshold=0.05): + import tensorflow as tf + import numpy as np + import time + import cv2 + sourceWidth = image.shape[1] + sourceHeight = image.shape[0] + currentAspectRatio=self.targetWidth/self.targetHeight + trainedAspectRatio=self.trainingWidth/self.trainingHeight + #Do resize on OpenCV end + #---------------------------------------------------------------- + from tools import img_resizeWithCrop,normalizedCoordinatesAdaptForVerticalImage,normalizedCoordinatesAdaptToResizedCrop + imageTransformed = img_resizeWithCrop(image,self.targetWidth,self.targetHeight) + #imageTransformed = img_resizeWithPadding(image,self.targetWidth,self.targetHeight) + imageTransformed = cv2.cvtColor(imageTransformed,cv2.COLOR_BGR2RGB) + #---------------------------------------------------------------- + + #Prepare image for Tensorflow + #---------------------------------------------------------------- + imageTF = np.expand_dims(imageTransformed, axis=0).astype('int32') + #---------------------------------------------------------------- + + + start = time.time() + # TF Lite format expects tensor type of float32. + #------------------------------------------------------------------- + #------------------------------------------------------------------- + outputs = self.movenet(tf.cast(imageTF, dtype=tf.int32)) + keypoints_with_scores = outputs['output_0'] + predictionsRaw = keypoints_with_scores[0][0] + #------------------------------------------------------------------- + #------------------------------------------------------------------- + seconds = time.time() - start + self.hz = 1 / (seconds+0.0001) + #print("MoveNET Framerate : ",round(self.hz,2)," fps ") + + + currentAspectRatio=sourceWidth/sourceHeight #We "change" aspect ratio by restoring points + predictions=list() + for pointID in range(0,len(predictionsRaw)): + #Joints have y,x,acc order + thisPoint = list() + thisPoint.append(float(predictionsRaw[pointID][0])) #y + thisPoint.append(float(predictionsRaw[pointID][1])) #x + thisPoint.append(float(predictionsRaw[pointID][2])) #score + nX,nY = normalizedCoordinatesAdaptToResizedCrop(sourceWidth,sourceHeight,self.targetWidth,self.targetHeight,thisPoint[1],thisPoint[0]) + nX,nY = normalizedCoordinatesAdaptForVerticalImage(sourceWidth,sourceHeight,self.trainingWidth,self.trainingHeight,nX,nY) + thisPoint[0]= nY #Just update coords + thisPoint[1]= nX #Just update coords + predictions.append(thisPoint) + #------------------------------------------------------------------- + + from holisticPartNames import processPoseNETLandmarks + self.output = processPoseNETLandmarks(self.jointNames,predictions,currentAspectRatio,trainedAspectRatio,threshold=threshold,doFlipX=doFlipX) + + from MocapNETVisualization import drawPoseNETLandmarks + self.image = drawPoseNETLandmarks(predictions,image,threshold=threshold,jointLabels=self.jointNames) + #------------------------------------------------ + + return self.output,image +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- + +def get_available_devices(): + from tensorflow.python.client import device_lib + local_device_protos = device_lib.list_local_devices() + return [x.name for x in local_device_protos if x.device_type == 'GPU' or x.device_type == 'CPU'] + +class MocapNETTensorflow(): + def __init__(self, + doPerformanceProfiling = False, + ): + print(""" +████████╗███████╗███╗ ██╗███████╗ ██████╗ ██████╗ ███████╗██╗ ██████╗ ██╗ ██╗ +╚══██╔══╝██╔════╝████╗ ██║██╔════╝██╔═══██╗██╔══██╗██╔════╝██║ ██╔═══██╗██║ ██║ + ██║ █████╗ ██╔██╗ ██║███████╗██║ ██║██████╔╝█████╗ ██║ ██║ ██║██║ █╗ ██║ + ██║ ██╔══╝ ██║╚██╗██║╚════██║██║ ██║██╔══██╗██╔══╝ ██║ ██║ ██║██║███╗██║ + ██║ ███████╗██║ ╚████║███████║╚██████╔╝██║ ██║██║ ███████╗╚██████╔╝╚███╔███╔╝ + ╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝ ╚═════╝ ╚═╝ ╚═╝╚═╝ ╚══════╝ ╚═════╝ ╚══╝╚══╝""") + self.doPerformanceProfiling = doPerformanceProfiling + + #Tensorflow attempt to be reasonable + #------------------------------------------ + import gc + gc.collect() #Do garbage collection before allocating TF stuff + import os + os.environ['TF_ENABLE_GPU_GARBAGE_COLLECTION']='false' + #Make sure CUDA cache is not disabled! + os.environ['CUDA_CACHE_DISABLE'] = '0' + #Try to presist cudnn + os.environ['TF_USE_CUDNN_BATCHNORM_SPATIAL_PERSISTENT'] = '1' + #Try to allocate as little memory as possible + os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' + #Use seperate threads so execution is not throttled by CPU + os.environ['TF_GPU_THREAD_MODE'] = 'gpu_private' + #0 = all messages are logged (default behavior) + #1 = INFO messages are not printed + #2 = INFO and WARNING messages are not printed + #3 = INFO, WARNING, and ERROR messages are not printed + os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' + #improve the stability of the auto-tuning process used to select the fastest convolution algorithms + os.environ['TF_AUTOTUNE_THRESHOLD'] = '1' + #------------------------------------------ + import tensorflow as tf + devices = get_available_devices() + print("Available Tensorflow devices are : ",devices) + self.device = '/device:CPU:0' + for device in devices: + if (device.find("GPU")!=-1): + self.device = device + print("Selecting device : ",self.device) + + #If enabled, an op will be placed on CPU if any of the following are true + #1 - there's no GPU implementation for the OP + #2 - no GPU devices are known or registered + #3 - need to co-locate with reftype input(s) which are from CPU + tf.config.set_soft_device_placement(True) + + #Only give the warning when not profiling otherwise we will get an error! + if (not doPerformanceProfiling): + tf.config.experimental.set_device_policy('explicit') + + try: + tf.config.run_functions_eagerly(True) + tf.config.experimental.set_synchronous_execution(False) + except: + #Invalid device or cannot modify virtual devices once initialized. + pass + + try: + physical_devices = tf.config.list_physical_devices('CPU') + tf.config.experimental.set_memory_growth(physical_devices[0], True) + physical_devices = tf.config.list_physical_devices('GPU') + tf.config.experimental.set_memory_growth(physical_devices[0], True) + except: + #Invalid device or cannot modify virtual devices once initialized. + pass + + try: + tf.config.threading.set_intra_op_parallelism_threads(8) + tf.config.threading.set_inter_op_parallelism_threads(8) + except: + #Most probably : RuntimeError: Intra op parallelism cannot be modified after initialization + pass + + if (doPerformanceProfiling): + import tensorflow as tf + self.tensorboard = tf.keras.callbacks.TensorBoard(log_dir = "profiling",histogram_freq = 1) + #tensorboard --bind_all --logdir profiling + from DNNModel import startProfiling + startProfiling() + else: + self.tensorboard=0 + + + def __del__(self): + if (self.doPerformanceProfiling): + from DNNModel import stopProfiling + stopProfiling() + print("To see profile results \nUse :\n tensorboard --logdir profiling ") + print('TFLite stopped.') + +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +#------------------------------------------------------------------------------------------------------------------------------------------------------- +if __name__ == '__main__': + mnet = MocapNETTensorflow() + print("Survived Test!") +#------------------------------------------------------------------------------------------------------------------------------------------------------- diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETVisualization.py b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETVisualization.py new file mode 100755 index 0000000..cd0f947 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/MocapNETVisualization.py @@ -0,0 +1,1017 @@ +#!/usr/bin/python3 +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +def getColor(i): + if i>107: + i = i % 108 + #--------------------- + if (i==0): + return (247,252,253) + elif (i==1): + return (224,236,244) + elif (i==2): + return (191,211,230) + elif (i==3): + return (158,188,218) + elif (i==4): + return (140,150,198) + elif (i==5): + return (140,107,177) + elif (i==6): + return (136,65,157) + elif (i==7): + return (129,15,124) + elif (i==8): + return (77,0,75) + elif (i==9): + return (247,252,253) + elif (i==10): + return (229,245,249) + elif (i==11): + return (204,236,230) + elif (i==12): + return (153,216,201) + elif (i==13): + return (102,194,164) + elif (i==14): + return (65,174,118) + elif (i==15): + return (35,139,69) + elif (i==16): + return (0,109,44) + elif (i==17): + return (0,68,27) + elif (i==18): + return (247,252,240) + elif (i==19): + return (224,243,219) + elif (i==20): + return (204,235,197) + elif (i==21): + return (168,221,181) + elif (i==22): + return (123,204,196) + elif (i==23): + return (78,179,211) + elif (i==24): + return (43,140,190) + elif (i==25): + return (8,104,172) + elif (i==26): + return (8,64,129) + elif (i==27): + return (255,247,236) + elif (i==28): + return (254,232,200) + elif (i==29): + return (253,212,158) + elif (i==30): + return (253,187,132) + elif (i==31): + return (252,141,89) + elif (i==32): + return (239,101,72) + elif (i==33): + return (215,48,31) + elif (i==34): + return (179,0,0) + elif (i==35): + return (127,0,0) + elif (i==36): + return (255,247,251) + elif (i==37): + return (236,231,242) + elif (i==38): + return (208,209,230) + elif (i==39): + return (166,189,219) + elif (i==40): + return (116,169,207) + elif (i==41): + return (54,144,192) + elif (i==42): + return (5,112,176) + elif (i==43): + return (4,90,141) + elif (i==44): + return (2,56,88) + elif (i==45): + return (255,247,251) + elif (i==46): + return (236,226,240) + elif (i==47): + return (208,209,230) + elif (i==48): + return (166,189,219) + elif (i==49): + return (103,169,207) + elif (i==50): + return (54,144,192) + elif (i==51): + return (2,129,138) + elif (i==52): + return (1,108,89) + elif (i==53): + return (1,70,54) + elif (i==54): + return (247,244,249) + elif (i==55): + return (231,225,239) + elif (i==56): + return (212,185,218) + elif (i==57): + return (201,148,199) + elif (i==58): + return (223,101,176) + elif (i==59): + return (231,41,138) + elif (i==60): + return (206,18,86) + elif (i==61): + return (152,0,67) + elif (i==62): + return (103,0,31) + elif (i==63): + return (255,247,243) + elif (i==64): + return (253,224,221) + elif (i==65): + return (252,197,192) + elif (i==66): + return (250,159,181) + elif (i==67): + return (247,104,161) + elif (i==68): + return (221,52,151) + elif (i==69): + return (174,1,126) + elif (i==70): + return (122,1,119) + elif (i==71): + return (73,0,106) + elif (i==72): + return (255,255,229) + elif (i==73): + return (247,252,185) + elif (i==74): + return (217,240,163) + elif (i==75): + return (173,221,142) + elif (i==76): + return (120,198,121) + elif (i==77): + return (65,171,93) + elif (i==78): + return (35,132,67) + elif (i==79): + return (0,104,55) + elif (i==80): + return (0,69,41) + elif (i==81): + return (255,255,217) + elif (i==82): + return (237,248,177) + elif (i==83): + return (199,233,180) + elif (i==84): + return (127,205,187) + elif (i==85): + return (65,182,196) + elif (i==86): + return (29,145,192) + elif (i==87): + return (34,94,168) + elif (i==88): + return (37,52,148) + elif (i==89): + return (8,29,88) + elif (i==90): + return (255,255,229) + elif (i==91): + return (255,247,188) + elif (i==92): + return (254,227,145) + elif (i==93): + return (254,196,79) + elif (i==94): + return (254,153,41) + elif (i==95): + return (236,112,20) + elif (i==96): + return (204,76,2) + elif (i==97): + return (153,52,4) + elif (i==98): + return (102,37,6) + elif (i==99): + return (255,255,204) + elif (i==100): + return (255,237,160) + elif (i==101): + return (254,217,118) + elif (i==102): + return (254,178,76) + elif (i==103): + return (253,141,60) + elif (i==104): + return (252,78,42) + elif (i==105): + return (227,26,28) + elif (i==106): + return (189,0,38) + elif (i==107): + return (128,0,38) + + return (255,255,255) + +def drawMissingInput(image): + import cv2 + width = image.shape[1] + height = image.shape[0] + color = (0,0,255) + cv2.line(image, pt1=(0,0), pt2=(width,height), color=color, thickness=12) + cv2.line(image, pt1=(0,0+height), pt2=(width,0), color=color, thickness=12) + font = cv2.FONT_HERSHEY_SIMPLEX + org = (int(width/2)-300,int(height/2)) + fontScale = 2 + color = (0,0,0) + thickness = 2 + message = 'Incomplete Input' + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (int(width/2)+2-300,int(height/2)+2) + color = (255,255,255) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + return image + + + +def drawPoseNETLandmarks(predictions,image,threshold=0.25,jointLabels=dict()): + import cv2 + sourceWidth = image.shape[1] + sourceHeight = image.shape[0] + width = image.shape[1] + height = image.shape[0] + #jointLabels = getPoseNETBodyNameList() # getBody25NameList() + jID = 0 + for joint in predictions: + #print("Joint ",joint) + y2D = int(joint[0]*sourceHeight) + x2D = int(joint[1]*sourceWidth) + vis2D = float(joint[2]) + color=(0,255,255) + if (threshold>vis2D): + color=(0,0,255) + + cv2.circle(image,(x2D,y2D),2,color) + + font = cv2.FONT_HERSHEY_SIMPLEX + org = (x2D,y2D) + fontScale = 0.4 + thickness = 1 + message = '%s|%0.4f' % (jointLabels[jID],vis2D) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + jID += 1 + return image + + + +def resolveXY(input2D,joint,width,height,flipX=False): + x2D=0 + y2D=0 + jointName2DX = "2dx_"+joint + jointName2DY = "2dy_"+joint + if ( jointName2DX in input2D ) and ( jointName2DY in input2D ): + if (flipX): + x2D = int((1.0-input2D[jointName2DX])*width) + else: + x2D = int(input2D[jointName2DX]*width) + y2D = int(input2D[jointName2DY]*height) + else: + print("Cannot resolve ",joint) + #print(joint," resolved to ",x2D,",",y2D) + return x2D,y2D + + + +def drawMocapNETInput(input2D,image,flipX=False,doLines=True): + import cv2 + if (type(image)==type(None)): + print("Invalid Image given, can't do anything with it") + return image + width = image.shape[1] + height = image.shape[0] + #print("Drawing output to ",width,"x",height," cvmat") + + if (doLines): + #Draw lines + #==================================================================== + t=8 + x1,y1=resolveXY(input2D,"rshoulder",width,height,flipX=flipX) + x2,y2=resolveXY(input2D,"relbow",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + x1,y1=resolveXY(input2D,"rhand",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + x1,y1=resolveXY(input2D,"rshoulder",width,height,flipX=flipX) + x2,y2=resolveXY(input2D,"neck",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + x2,y2=resolveXY(input2D,"hip",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + x1,y1=resolveXY(input2D,"rhip",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + x2,y2=resolveXY(input2D,"rknee",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + x1,y1=resolveXY(input2D,"rfoot",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,0), thickness=t) + + x1,y1=resolveXY(input2D,"lshoulder",width,height,flipX=flipX) + x2,y2=resolveXY(input2D,"lelbow",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + x1,y1=resolveXY(input2D,"lhand",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + x1,y1=resolveXY(input2D,"lshoulder",width,height,flipX=flipX) + x2,y2=resolveXY(input2D,"neck",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + x2,y2=resolveXY(input2D,"hip",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + x1,y1=resolveXY(input2D,"lhip",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + x2,y2=resolveXY(input2D,"lknee",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + x1,y1=resolveXY(input2D,"lfoot",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,0,255), thickness=t) + + x1,y1=resolveXY(input2D,"neck",width,height,flipX=flipX) + x2,y2=resolveXY(input2D,"head",width,height,flipX=flipX) + cv2.line(image, pt1=(x1,y1), pt2=(x2,y2), color=(0,255,255), thickness=t) + #==================================================================== + + + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.4 + thickness = 1 + color = (0,0,0) + + for jointRaw in input2D: + #print("Joint ",jointRaw) + jointSplit = jointRaw.lower().split("_",1) + if (len(jointSplit)>1): + joint = jointSplit[1].lower() + jointName2DX = "2dx_"+joint + jointName2DY = "2dy_"+joint + if ( jointName2DX in input2D ) and ( jointName2DY in input2D ): + if (flipX): + x2D = int((1.0-input2D[jointName2DX])*width) + else: + x2D = int(input2D[jointName2DX]*width) + y2D = int(input2D[jointName2DY]*height) + #print("IS Joint ",joint,x2D,y2D) + color=(0,255,255) + circleSize = 2 + if (len(joint)>0): + #We have a joint Name + if not 'head' in joint: + circleSize = 4 #body joints are bigger + + + if (len(joint)>1): + if (joint[len(joint)-2]=='.') and (joint[len(joint)-1]=='r'): #Right Joint + color=(0,255,0) #GREEN COLOR + if (joint[len(joint)-2]=='.') and (joint[len(joint)-1]=='l'): #Left Joint + color=(0,0,255) #RED COLOR + + if (joint[0]=='r'): #Right Joint + color=(0,255,0) #GREEN COLOR + elif (joint[0]=='l'): #Left Joint + color=(0,0,255) #RED COLOR + elif ("head_l" in joint): + color=(0,0,255) #RED COLOR + #image = cv2.putText(image, "%s" % (joint.replace("head_","")) , (x2D+2,y2D), font, fontScale, color, thickness, cv2.LINE_AA) + elif ("head_r" in joint): + color=(0,255,0) #GREEN COLOR + #image = cv2.putText(image, "%s" % (joint.replace("head_","")) , (x2D+2,y2D), font, fontScale, color, thickness, cv2.LINE_AA) + #else: + # image = cv2.putText(image, "%s" % (joint) , (x2D+2,y2D), font, fontScale+0.7, color, thickness, cv2.LINE_AA) + cv2.circle(image,(x2D,y2D),circleSize,color,cv2.FILLED) + #if ("lshoulder"==joint) or ("lelbow"==joint) or ("lhand"==joint): + # image = cv2.putText(image, "%s" % (joint) , (x2D+2,y2D), font, fontScale, color, thickness, cv2.LINE_AA) + if ("head_reye"==joint) or ("head_leye"==joint) or ("reye"==joint) or ("leye"==joint): + color=(255,0,0) + circleSize = 4 + image = cv2.putText(image, "%s" % (joint) , (x2D+2,y2D), font, fontScale, color, thickness, cv2.LINE_AA) + cv2.circle(image,(x2D,y2D),circleSize,color,cv2.FILLED) + + #Post Visualization score + #jointNameVis = "visible_"+joint + #image = cv2.putText(image, "%0.2f" % (input2D[jointNameVis]) , (x2D+2,y2D), font, fontScale, color, thickness, cv2.LINE_AA) + + #if ('__' in joint): #Print __temporalis joint + # image = cv2.putText(image, joint , (x2D+2,y2D), font, fontScale, color, thickness, cv2.LINE_AA) + return image + +def drawMocapNETOutput(mnet,image,xOffset=0): #set xOffset to -400 to make visualization more clean by seperating 2D/3D + import cv2 + if (type(image)==type(None)): + print("Invalid Image given, can't do anything with it") + return image + width = image.shape[1] + height = image.shape[0] + #print("Drawing output to ",width,"x",height," cvmat") + + jointID = 0 + for joint in mnet.bvhJointList: + #---------------------------------------------------------------------------------------------- + jointParentID = mnet.bvhJointParentList[jointID] + jointParent = mnet.bvhJointList[jointParentID] + #print("Joint ",joint) + #print("Joint Parent",jointParent) + + #Enforce Joint LowerCase + joint = joint.lower() + jointParent = mnet.bvhJointList[jointParentID].lower() + + doThisDraw = 1 + #---------------------------------------------------------------------------------------------- + jointName2DX = "2DX_"+joint + jointName2DY = "2DY_"+joint + if ( not jointName2DX in mnet.output2D ) or ( not jointName2DY in mnet.output2D ): + doThisDraw = 0 + elif (mnet.output2D[jointName2DX]==0.0) and (mnet.output2D[jointName2DY]==0.0): + doThisDraw = 0 + else: + xA2D = xOffset + int((1.0-mnet.output2D[jointName2DX])*width) + yA2D = int(mnet.output2D[jointName2DY]*height) + #---------------------------------------------------------------------------------------------- + jointParentName2DX = "2DX_"+jointParent + jointParentName2DY = "2DY_"+jointParent + if ( not jointParentName2DX in mnet.output2D ) or ( not jointParentName2DY in mnet.output2D ): + doThisDraw = 0 + elif (mnet.output2D[jointParentName2DX]==0.0) and (mnet.output2D[jointParentName2DY]==0.0): + doThisDraw = 0 + else: + xB2D = xOffset + int((1.0-mnet.output2D[jointParentName2DX])*width) + yB2D = int(mnet.output2D[jointParentName2DY]*height) + #---------------------------------------------------------------------------------------------- + if (doThisDraw): + color=(255,0,0) #BLUE COLOR + if (joint[0]=='l'): + color=(0,0,255) #RED COLOR + if (joint[0]=='r'): + color=(0,255,0) #GREEN COLOR + cv2.line(image, pt1=(xA2D,yA2D), pt2=(xB2D,yB2D), color=color, thickness=12) + #---------------------------------------------------------------------------------------------- + jointID = jointID + 1 + #---------------------------------------------------------------------------------------------- + + for joint in mnet.bvhJointList: + #print("Joint ",joint) + joint = joint.lower() + jointName2DX = "2DX_"+joint + jointName2DY = "2DY_"+joint + if ( jointName2DX in mnet.output2D ) and ( jointName2DY in mnet.output2D ): + if (mnet.output2D[jointName2DX]!=0.0) or (mnet.output2D[jointName2DY]!=0.0): + x2D = xOffset + int((1.0-mnet.output2D["2DX_"+joint])*width) + y2D = int(mnet.output2D["2DY_"+joint]*height) + color=(0,255,255) + cv2.circle(image,(x2D,y2D),2,color) + #---------------------------------------------------------------------------------------------- + return image + + + + +def drawDescriptor(name,elements,image,x,y,w,h): + #------------------------------------ + if (elements.shape[1]==0): + return image + #------------------------------------ + import cv2 + block = int(w / elements.shape[1]) + #------------------------------------ + if (block==0): + return image + #------------------------------------ + #print("WIDTH ",w," BLOCK",block," ELEMENTS ",elements.shape[1]) + eI = 0 + for xI in range(x,x+w-block,block): + xA2D=xI + yA2D=y + xB2D=xI+block + yB2D=y+h + #---------------------------------------------- + val = elements[0][eI] + #---------------------------------------------- + greenValue = 0.0 + blueValue = 0.0 + #---------------------------------------------- + if (val<0.0): + blueValue=abs(val) + else: + greenValue=val + #---------------------------------------------- + color=( + min(255,int(255.0 * blueValue)), + min(255,int(255.0 * greenValue)), + min(255,int(25.5 * greenValue)) + ) + #---------------------------------------------- + if (xA2D!=0.0) and (yA2D!=0.0) and (xB2D!=0.0) and (yB2D!=0.0): + cv2.line(image, pt1=(xA2D,yA2D), pt2=(xB2D,yB2D), color=color, thickness=12) + eI +=1 + eI = min(eI,elements.shape[1]-1) + #---------------------------------------------- + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.5 + thickness = 1 + org = (x+10,y+int(h/2)+5) + color = (0,0,0) + image = cv2.putText(image, name , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (x+8,y+int(h/2)+3) + color = (255,255,255) + image = cv2.putText(image, name , org, font, fontScale, color, thickness, cv2.LINE_AA) + + return image + + + +def printNSDM(nsdm): + import math + from tools import bcolors + NSRMDimension = int(math.sqrt(len(nsdm))) + eI = 0 + for yI in range(0,NSRMDimension): + for xI in range(0,NSRMDimension): + #------------------------- + val = nsdm[eI] + eI +=1 + if (val==0.0): + print(bcolors.FAIL,end=" ") + elif (val<0.0): + print(bcolors.OKBLUE,end="") + else: + print(bcolors.OKGREEN,end=" ") + print("%0.2f " % val,end="") + print(bcolors.ENDC,end="") + print(" ") + + +def drawNSRM(name,elements,image,x,y,w,h): + import cv2 + import math + #print("Draw NSRM with ",len(elements)," elements ") + NSRMDimension = int(math.sqrt(len(elements))) + blockX = int(w/NSRMDimension) + blockY = int(h/NSRMDimension) + + #print("WIDTH ",w," BLOCK",block," ELEMENTS ",elements.shape[1]) + if (NSRMDimension<4): + print("drawNSRM not drawing matrix with len(elements) = ",len(elements)) + return image + + eI = 0 + for yI in range(0,NSRMDimension): + for xI in range(0,NSRMDimension): + xA2D=x + xI*blockX + yA2D=y + yI*blockY + xB2D=xA2D+blockX + yB2D=yA2D+blockY + #------------------------- + val = elements[eI] + eI +=1 + #------------------------- + redValue = 0 + greenValue = 0 + blueValue = 0 + #------------------------- + if (val==0.0): + redValue = 1 + elif (val<0.0): + blueValue = abs(val)#/2 + else: + greenValue = val#/2 + #------------------------- + color=( + int(255.0 * blueValue), #B + int(255.0 * greenValue), #G + int(255.0 * redValue) #R + ) + #------------------------- + cv2.rectangle(image, pt1=(xA2D,yA2D), pt2=(xB2D,yB2D), color=color, thickness=-1) + #----------------------------------- + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.5 + thickness = 1 + org = (x,y-10) + color = (0,0,0) + image = cv2.putText(image, name , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (x-2,y-8) + color = (255,255,255) + image = cv2.putText(image, name , org, font, fontScale, color, thickness, cv2.LINE_AA) + + return image + + + + +def drawMAE2DError(name,mae,image,x,y,w,h): + if (mae<=0.0): + return + import cv2 + #----------------------------------------- + color = (123,123,123) + if (mae<127): + color = (0,255-(mae*2),0) #B G R + elif (mae<255): + color = (0,mae,mae) #B G R + else: + color = (0,0,min(255,mae-255)) #B G R + #----------------------------------------- + xA2D=x + yA2D=y + xB2D=x+w + yB2D=y+h + cv2.rectangle(image, pt1=(xA2D,yA2D), pt2=(xB2D,yB2D), color=color, thickness=-1) + #----------------------------------------- + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.4 + thickness = 1 + #----------------------------------------- + yOffset=15 + message = '%s ' % (name) + org = (x+2,y+2+yOffset) + color = (0,0,0) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (x,y+yOffset) + color = (255,255,255) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + #----------------------------------------- + message = '%0.2f' % (mae) + color = (0,0,0) + org = (x,y+20+yOffset) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + color = (255,255,255) + org = (x+2,y+22+yOffset) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + #----------------------------------------- + + +def calculateRelativeValue(y,h,value,minimum,maximum): + if (maximum==minimum): + return int(y + (h/2)) + #------------------------------------------------- + #TODO IMPROVE THIS! + vRange = (maximum - minimum) + return int( y + (h/2) - ( value / vRange ) * (h/2) ) + + +def drawMocapNETSinglePlot(history,plotNumber,itemName,image,x,y,w,h,minimumValue,maximumValue): + import cv2 + color=getColor(plotNumber) + if (minimumValue==maximumValue): + color = (40,40,40) + + cv2.line(image, pt1=(x,y+h), pt2=(x+w,y+h), color=color, thickness=1) + cv2.line(image, pt1=(x,y), pt2=(x,y+h), color=color, thickness=1) + + font = cv2.FONT_HERSHEY_SIMPLEX + org = (x,y) + fontScale = 0.3 + tColor = (123,123,123) + thickness = 1 + message = '%s #%u ' % (itemName,plotNumber) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + message = 'Max %0.2f ' % (maximumValue) + org = (x,y+10) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + message = 'Min %0.2f ' % (minimumValue) + org = (x,y+h+10) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + + + for frameID in range(1,len(history)): + #Old code + #previousValue = int(y+history[frameID-1][itemName] + h/2) + #nextValue = int(y+history[frameID][itemName] + h/2) + #------------------------------------------------------------------------------------- + previousValue = calculateRelativeValue(y,h,history[frameID-1][itemName],minimumValue,maximumValue) + nextValue = calculateRelativeValue(y,h,history[frameID][itemName],minimumValue,maximumValue) + #------------------------------------------------------------------------------------- + jointPointPrev = (int(x+ frameID-1), previousValue ) + jointPointNext = (int(x+ frameID), nextValue ) + #cv::Scalar usedColor = getColorFromIndex(joint); + if (itemName=="hip_yrotation"): + color=(0,0,255) + + cv2.line(image, pt1=jointPointPrev, pt2=jointPointNext, color=color, thickness=1) + + #old code + #org = (int(x+len(history)),int(y+history[len(history)-1][itemName] + h/2)) + org = (int(x+len(history)), calculateRelativeValue(y,h,history[len(history)-1][itemName],minimumValue,maximumValue) ) + message = '%0.2f' % (history[len(history)-1][itemName]) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) +#--------------------------------------------------------------------------------------------- + + +def drawMocapNETSinglePlotValueList(valueList,plotNumber,itemName,image,x,y,w,h,minimumValue,maximumValue): + import cv2 + import numpy as np + color=getColor(plotNumber) + if (minimumValue==maximumValue): + color = (40,40,40) #Dead plot + + listMaxValue = np.max(valueList) + if (listMaxValue>maximumValue): + maximumValue=listMaxValue*2 #Adapt to maximum + + #------------------------------------------------------------------ + cv2.line(image, pt1=(x,y+h), pt2=(x+w,y+h), color=color, thickness=1) + cv2.line(image, pt1=(x,y), pt2=(x,y+h), color=color, thickness=1) + + font = cv2.FONT_HERSHEY_SIMPLEX + org = (x,y) + fontScale = 0.3 + tColor = (123,123,123) + thickness = 1 + message = '%s #%u ' % (itemName,plotNumber) + image = cv2.putText(image, message , (x-1,y-1), font, fontScale, (0,0,0) , thickness, cv2.LINE_AA) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + message = 'Max %0.2f ' % (maximumValue) + org = (x,y+10) + image = cv2.putText(image, message , (x-1,y+10-1), font, fontScale, (0,0,0) , thickness, cv2.LINE_AA) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + message = 'Min %0.2f ' % (minimumValue) + org = (x,y+h+10) + image = cv2.putText(image, message , (x-1,y+h+10-1), font, fontScale, (0,0,0) , thickness, cv2.LINE_AA) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + + + for frameID in range(1,len(valueList)): + #Old code + #previousValue = int(y+history[frameID-1][itemName] + h/2) + #nextValue = int(y+history[frameID][itemName] + h/2) + #------------------------------------------------------------------------------------- + previousValue = calculateRelativeValue(y,h,valueList[frameID-1],minimumValue,maximumValue) + nextValue = calculateRelativeValue(y,h,valueList[frameID],minimumValue,maximumValue) + #------------------------------------------------------------------------------------- + jointPointPrev = (int(x+ frameID-1), previousValue ) + jointPointNext = (int(x+ frameID), nextValue ) + #cv::Scalar usedColor = getColorFromIndex(joint); + if (itemName=="hip_yrotation"): + color=(0,0,255) + + cv2.line(image, pt1=jointPointPrev, pt2=jointPointNext, color=color, thickness=1) + + #old code + #org = (int(x+len(valueList)),int(y+valueList[len(valueList)-1] + h/2)) + org = (int(x+len(valueList)), calculateRelativeValue(y,h,valueList[len(valueList)-1],minimumValue,maximumValue) ) + message = '%0.2f' % (valueList[len(valueList)-1]) + image = cv2.putText(image, message , org, font, fontScale, (0,0,0), thickness, cv2.LINE_AA) + + org = (1+int(x+len(valueList)), 1+calculateRelativeValue(y,h,valueList[len(valueList)-1],minimumValue,maximumValue) ) + image = cv2.putText(image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + +#--------------------------------------------------------------------------------------------- + + + +def drawMocapNETAllPlots(history,width,height,minimumLimits=dict(),maximumLimits=dict()): + import cv2 + import numpy as np +#------------------------------------------------- + imageForPlot = np.zeros([height,width,3],dtype=np.uint8) +#------------------------------------------------- + margin = 25 + x = 0 + y = margin + widthOfGraphs = 80 + heightOfGraphs = 80 +#------------------------------------------------- + plotNumber = 0 + if (len(history)>0): + for itemName in history[0].keys(): + minimumValue=-180.0 + maximumValue= 180.0 + if (itemName in minimumLimits) and (itemName in maximumLimits): + minimumValue=float(minimumLimits[itemName]) + maximumValue=float(maximumLimits[itemName]) + + if (minimumValue!=maximumValue): + drawMocapNETSinglePlot(history,plotNumber,itemName,imageForPlot,x,y,widthOfGraphs,heightOfGraphs,minimumValue,maximumValue) + plotNumber=plotNumber+1 + y = y + heightOfGraphs + margin + if (y + heightOfGraphs > height - heightOfGraphs): + y = margin + x = x + widthOfGraphs + margin + #cv2.imshow('Motion History',imageForPlot) + return imageForPlot +#------------------------------------------------- + + + +def drawMocapNETFrequencyPlots(history): + import numpy as np + import matplotlib.pyplot as plt +#------------------------------------------------- + if (len(history)>0): + for itemName in history[0].keys(): + output="freq_%s.png" % itemName + + data=list() + for frameID in range(1,len(history)): + data.append(float(history[frameID][itemName])) + plt.cla() + plt.hist(data, bins=250) + # Add labels and title + plt.xlabel('Value') + plt.ylabel('Frequency') + plt.title('Histogram of %s of %u values'%(itemName,len(history))) + # Save figure as PNG file + plt.savefig(output) + #fig.savefig(output) +#------------------------------------------------- + + + +def drawValueLineInRange(history, minimumValues, maximumValues, label, result_img, x, y, w, h): + # Calculate the position of the value within the specified range + + if (h>20) and (len(history)>0): + h = h-20 + lastItem = len(history)-1 + if (label in history[lastItem]) and (label in minimumValues) and (label in maximumValues) : + minimumValue = minimumValues[label] + maximumValue = maximumValues[label] + import cv2 + #print("Items to select ",history[lastItem].keys()) + #print("Items to select ",len(history[lastItem])) + value = history[lastItem][label] + #print("DRAW ",value," between ",minimumValue," and ",maximumValue) + normalized_value = (value - minimumValue) / (maximumValue - minimumValue) + x_pos = int(x + normalized_value * w) + + # Draw a horizontal line to represent the value + #print("cv2.line") + color = (255,255,255) + colorB = (123,123,123) + + cv2.putText(result_img, label, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) + cv2.putText(result_img, "%0.2f"%value, (x, y + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 1) + + cv2.line(result_img, (x, y+h), (x+w,y+h), colorB, 2) #Horizontal line + cv2.line(result_img, (x, y+5), (x,y+h), colorB, 2) #Vertical line start + cv2.line(result_img, (x+w,y+5), (x+w,y+h), colorB, 2) #Vertical line end + + #Draw labels l/r + cv2.putText(result_img, "%0.1f"% minimumValue , (x,y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 0.3, color, 1) + cv2.putText(result_img, "%0.1f"% maximumValue , (x+w,y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 0.3, color, 1) + + #Draw Arrow + cv2.line(result_img, (x_pos, y), (x_pos, y + h), color, 2) + cv2.line(result_img, (x_pos-10, y+h-10), (x_pos, y + h), color, 2) + cv2.line(result_img, (x_pos+10, y+h-10), (x_pos, y + h), color, 2) + else: + print("Failed visualizing ",label," in range [",minimumValue,",",maximumValue,"]") + + + +def drawMNETSerials(mnet,image,x,y): + import cv2 + #----------------------------------------- + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.4 + thickness = 1 + #----------------------------------------- + #print("MNET Serials ",mnet.getEnsembleSerials()) + #----------------------------------------- + color = (0,0,0) + org = (x+2,y+2) + image = cv2.putText(image, mnet.getEnsembleSerials() , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (x,y) + color = (255,255,255) + image = cv2.putText(image, mnet.getEnsembleSerials() , org, font, fontScale, color, thickness, cv2.LINE_AA) + #----------------------------------------- + + +def registerVisualizationTime(mnet,startTime): + import time + end = time.time() # Time elapsed + from tools import secondsToHz + mnet.hz_Vis = secondsToHz(end - startTime) + mnet.history_hz_Vis.append(mnet.hz_Vis) + if (len(mnet.history_hz_Vis)>mnet.perfHistorySize): + mnet.history_hz_Vis.pop(0) #Keep mnet history on limits + + +def visualizeMocapNETEnsemble(mnet,annotated_image,plotBVHChannels=0,bvhAnglesForPlotting=list(),economic=False,drawOutput=True): + import time + start = time.time() # Time elapsed + try: + #from MocapNETVisualization import drawMocapNETOutput,drawMocapNETAllPlots,drawMissingInput,drawDescriptor,drawNSRM,drawMAE2DError + #------------------------------------------------------------------------------------ + if (drawOutput): + if ("upperbody" in mnet.ensemble): + drawMocapNETOutput(mnet,annotated_image) #only draw 3D ouput if upperbody is loaded and working.. + + drawMocapNETInput(mnet.input2D,annotated_image,doLines=(drawOutput==False)) + if (economic): + registerVisualizationTime(mnet,start) + return annotated_image,annotated_image + #------------------------------------------------------------------------------------ + + width = annotated_image.shape[1] + height = annotated_image.shape[0] + + locY = 10 + if ("upperbody" in mnet.ensemble) and (mnet.ensemble["upperbody"].configuration["decompositionType"]!=""): + dcmp = mnet.ensemble["upperbody"].configuration["decompositionType"] + drawDescriptor("%s upperbody" % dcmp,mnet.ensemble["upperbody"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + if ("lowerbody" in mnet.ensemble) and (mnet.ensemble["lowerbody"].configuration["decompositionType"]!=""): + dcmp = mnet.ensemble["lowerbody"].configuration["decompositionType"] + drawDescriptor("%s lowerbody"% dcmp,mnet.ensemble["lowerbody"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + if ("face" in mnet.ensemble) and (mnet.ensemble["face"].configuration["decompositionType"]!=""): + dcmp = mnet.ensemble["face"].configuration["decompositionType"] + drawDescriptor("%s face"% dcmp,mnet.ensemble["face"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + if ("reye" in mnet.ensemble) and (mnet.ensemble["reye"].configuration["decompositionType"]!=""): + dcmp = mnet.ensemble["reye"].configuration["decompositionType"] + drawDescriptor("%s reye"% dcmp,mnet.ensemble["reye"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + dcmp = mnet.ensemble["leye"].configuration["decompositionType"] + drawDescriptor("%s leye"% dcmp,mnet.ensemble["leye"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + if ("mouth" in mnet.ensemble) and (mnet.ensemble["mouth"].configuration["decompositionType"]!=""): + dcmp = mnet.ensemble["mouth"].configuration["decompositionType"] + drawDescriptor("%s mouth"% dcmp,mnet.ensemble["mouth"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + if ("lhand" in mnet.ensemble) and (mnet.ensemble["lhand"].configuration["decompositionType"]!=""): + dcmp = mnet.ensemble["lhand"].configuration["decompositionType"] + drawDescriptor("%s lhand"% dcmp,mnet.ensemble["lhand"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + dcmp = mnet.ensemble["rhand"].configuration["decompositionType"] + drawDescriptor("%s rhand"% dcmp,mnet.ensemble["rhand"].inputReadyForTF,annotated_image,10,locY,annotated_image.shape[1]-20,5); locY+=15 + #-------------------------------------------------------------------------------------------------------------- + NSRM_Y = 120 + NSRM_Body_Y = NSRM_Y + if ("upperbody" in mnet.ensemble): + drawNSRM("NSRM Up",mnet.ensemble["upperbody"].NSRM,annotated_image,10,NSRM_Y,100,100); NSRM_Y+=130 + if ("lowerbody" in mnet.ensemble): + drawNSRM("NSRM Down",mnet.ensemble["lowerbody"].NSRM,annotated_image,120,NSRM_Body_Y,100,100);# NSRM_Y+=130 + if ("face" in mnet.ensemble): + drawNSRM("NSRM Face",mnet.ensemble["face"].NSRM,annotated_image,10,NSRM_Y,100,100); NSRM_Y+=130 + if ("leye" in mnet.ensemble): + drawNSRM("NSRM LEye",mnet.ensemble["leye"].NSRM,annotated_image,120,NSRM_Y,100,100); + if ("reye" in mnet.ensemble): + drawNSRM("NSRM REye",mnet.ensemble["reye"].NSRM,annotated_image,10,NSRM_Y,100,100); NSRM_Y+=130 + if ("mouth" in mnet.ensemble): + drawNSRM("NSRM Mouth",mnet.ensemble["mouth"].NSRM,annotated_image,10,NSRM_Y,100,100); NSRM_Y+=130 + if ("lhand" in mnet.ensemble): + drawNSRM("NSRM LHand",mnet.ensemble["lhand"].NSRM,annotated_image,10,NSRM_Y,100,100); + drawNSRM("NSRM RHand",mnet.ensemble["rhand"].NSRM,annotated_image,120,NSRM_Y,100,100); NSRM_Y+=130 + + + #drawValueLineInRange(value, minimumValue, maximumValue, label, result_img, x, y, w, h): + #These cause the "failed visualizing" error to be emitted from drawValueLineInRange in google collab (why though?) + drawValueLineInRange(bvhAnglesForPlotting,mnet.outputBVHMinima,mnet.outputBVHMaxima,"hip_xposition",annotated_image,10,NSRM_Y,100,50); NSRM_Y+=90 + drawValueLineInRange(bvhAnglesForPlotting,mnet.outputBVHMinima,mnet.outputBVHMaxima,"hip_yposition",annotated_image,10,NSRM_Y,100,50); NSRM_Y+=90 + drawValueLineInRange(bvhAnglesForPlotting,mnet.outputBVHMinima,mnet.outputBVHMaxima,"hip_zposition",annotated_image,10,NSRM_Y,100,50) + + #-------------------------------------------------------------------------------------------------------------- + drawMAE2DError("2D M.A.E.",mnet.lastMAEErrorInPixels,annotated_image,width-70,height-120,width-10,height-90) + #-------------------------------------------------------------------------------------------------------------- + + perfWidgetY = 120 + if (len(mnet.history_hz_2DEst)>0): + drawMocapNETSinglePlotValueList(mnet.history_hz_2DEst,1,"RGB->2D FPS",annotated_image,width-70,perfWidgetY,70,70,0.0,30.0) + perfWidgetY += 100 + + if (len(mnet.history_hz_NN)>0): + drawMocapNETSinglePlotValueList(mnet.history_hz_NN,1,"NN FPS",annotated_image,width-70,perfWidgetY,70,70,0.0,30.0) + perfWidgetY += 100 + + if (len(mnet.history_hz_HCD)>0): + drawMocapNETSinglePlotValueList(mnet.history_hz_HCD,1,"HCD FPS",annotated_image,width-70,perfWidgetY,70,70,0.0,30.0) + perfWidgetY += 100 + + if (len(mnet.history_hz_Vis)>0): + drawMocapNETSinglePlotValueList(mnet.history_hz_Vis,1,"Visualization",annotated_image,width-70,perfWidgetY,70,70,0.0,30.0) + perfWidgetY += 100 + + drawMNETSerials(mnet,annotated_image,10,30) + + + + #if (mnet.incompleteUpperbodyInput and mnet.incompleteLowerbodyInput): + # drawMissingInput(annotated_image) + if (plotBVHChannels==1): + plotImage = drawMocapNETAllPlots(bvhAnglesForPlotting,1920,920,minimumLimits=mnet.outputBVHMinima,maximumLimits=mnet.outputBVHMaxima) + registerVisualizationTime(mnet,start) + return annotated_image,plotImage + except Exception as e: + print("\n\n\n\nFAILED: Exception while visualizing : ",e,"\n\n\n\n") + #Fall-through + registerVisualizationTime(mnet,start) + return annotated_image,annotated_image + + + + +if __name__ == '__main__': + print("MocapNETVisualization.py is a library and cannot run standalone") diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/NSDM.py b/animation/MocapNET-kasisnu/src/python/mnet4/NSDM.py new file mode 100755 index 0000000..2c8efa0 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/NSDM.py @@ -0,0 +1,521 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +import numpy as np +from enum import Enum + +goFromDegreesToRad=np.float32(np.pi/180.0) +goFromRadToDegrees=np.float32(180.0/np.pi) + +def getJoint2DDistancePoints(aX,aY,bX,bY): + xDistance=np.float32(bX-aX) + yDistance=np.float32(bY-aY) + return np.float32(np.sqrt( (xDistance*xDistance) + (yDistance*yDistance) )) + +def getNumberOfSquaredCompressedSquaredJoints(): + countList=len(getBodyNoHandsList()) + numberOfSquaredCompressedJoints=countList*countList*2 + return numberOfSquaredCompressedJoints + + +def getAngleToAlignToZero(iX,iY,jX,jY,NSRMNormalizeAngles=0): + #--------------------------------------------- + if ( (iX==jX) and (iY==jY) ): + return np.float32(0.0) + #--------------------------------------------- + #We have points a, b and c and we want to calculate angle b + aX= iX*100 + aY= iY*100 + #--------------------------------------------- + bX= jX*100 + bY= jY*100 + #--------------------------------------------- + lengthBetweenAAndB = getJoint2DDistancePoints(aX,aY,bX,bY) + #--------------------------------------------- + cX= bX + cY= bY - lengthBetweenAAndB + #--------------------------------------------- + #fprintf(stderr,"We want to align A(%0.2f,%0.2f) to C(%0.2f,%0.2f) with pivot B(%0.2f,%0.2f)\n",aX,aY,cX,cY,bX,bY) + #fprintf(stderr,"length AB = %0.2f\n",lengthBetweenAAndB); + #fprintf(stderr,"bY = %0.2f\n",bY); + #fprintf(stderr,"cY = %0.2f = %0.2f - %0.2f\n",cY,bY,lengthBetweenAAndB); + #Calulate vector a->b + abX = bX - aX + abY = bY - aY + #calculate vector c->b + cbX = bX - cX + cbY = bY - cY + #--------------------------------------------- + dot = np.float32( (abX * cbX) + (abY * cbY) ) # dot product + cross = np.float32( (abX * cbY) - (abY * cbX) ) # cross product + #--------------------------------------------- + alpha = np.arctan2(cross,dot) #arctan2 returns a value in the range [-pi, pi] + #--------------------------------------------- + #fprintf(stderr,"Angle is %0.2f rad or %0.2f degrees \n",alpha,alpha*goFromRadToDegrees); + if (NSRMNormalizeAngles==2): + return np.float32( (2.0*alpha) / np.pi) # Normalize output in range [-1..1] + if (NSRMNormalizeAngles==1): + #This should actually be (2*alpha) / np.pi + #arctan(R) -> [ -pi/2 , pi/2] + return np.float32(alpha / np.pi) # Normalize output in range [-1/2..1/2] + else: + return np.float32(alpha) # Output in range [-pi..pi] + + +""" + This is the new function to make resolving 2D coordinates on a vector easier and safer (but slower :P) +""" +def getJoint2DXYV(rules,positions,jointName): + #------------------------------------------------------------------------------------------ + x = np.float32(0.0) + y = np.float32(0.0) + visibility = np.float32(0.0) + #------------------------------------------------------------------------------------------ + positionDataLength = len(positions) + if (positionDataLength==0): + print("getJoint2DXYV cannot resolve positions for joint ",jointName," with no vector data") + return x,y,visibility + + if (not rules['inputJointMap'].checkJointListDimensions(positions)): + print("getJoint2DXYV cannot resolve positions for joint ",jointName," input joint map has a different dimension size..") + return x,y,visibility + + + + #Get correct indexes for jointName + #-------------------------------------------------------- + jID_X = rules['inputJointMap'].getJointID_2DX(jointName) + jID_Y = rules['inputJointMap'].getJointID_2DY(jointName) + jID_Vis = rules['inputJointMap'].getJointID_Visibility(jointName) + #-------------------------------------------------------- + if ( (positionDataLength<=jID_X) or (positionDataLength<=jID_Y) or (positionDataLength<=jID_Vis) ): + print("getJoint2DXYV cannot get positions for joint ",jointName," with a vector data of only ",positionDataLength) + return x,y,visibility + #-------------------------------------------------------- + if ( (jID_X==-1) or (jID_Y==-1) or (jID_Vis==-1) ): + print("getJoint2DXYV could not resolve joint ",jointName," with vector data of ",positionDataLength) + return x,y,visibility + #-------------------------------------------------------- + #print("getJoint2DXYV(%s->%u/%u/%u) length %u"%(jointName,jID_X,jID_Y,jID_Vis,positionDataLength)) + #Pedantic behavior on missing data + if (positions[jID_X]==0.0) or (positions[jID_Y]==0.0) or (positions[jID_Vis]==0.0): + return x,y,visibility + #------------------------------------ + x = np.float32(positions[jID_X]) + y = np.float32(positions[jID_Y]) + visibility = np.float32(positions[jID_Vis]) + #------------------------------------ + return x,y,visibility + + + +def rotate2DPointsTest(cx,cy,jX,jY,angleToRotateInRadians): + #----------------------------------------------- + s = np.float32( np.sin(angleToRotateInRadians) ) + c = np.float32( np.cos(angleToRotateInRadians) ) + #----------------------------------------------- + jX = np.float32(jX - cx) + jY = np.float32(jY - cy) + #----------------------------------------------- + xnew = np.float32( (jX * c) - (jY * s) ) + ynew = np.float32( (jX * s) + (jY * c) ) + #----------------------------------------------- + return xnew + cx , ynew + cy + + +def rotate2DPointsBasedOnJointAsCenter(rules,positions,angleToRotateInRadians,jointNameCenter): + if (len(positions)==0): + print("rotate2DPointsBasedOnJointAsCenter cannot work without input.. \n") + return positions + + s = np.float32( np.sin(angleToRotateInRadians) ) + c = np.float32( np.cos(angleToRotateInRadians) ) + + #-------------------------------------------------------- + cx,cy,cVisibility = getJoint2DXYV(rules,positions,jointNameCenter) + #-------------------------------------------------------- + + if (cVisibility==0.0): + print("rotate2DPointsBasedOnJointAsCenter: cannot work without pivot joint .. \n") + return positions + + + result = positions + + for jID in range(0,int(len(rules['NSDM'])) ): + #-------------------------------------------------------- + jointName=rules['NSDM'][jID]['joint'] + jX,jY,jVisibility = getJoint2DXYV(rules,positions,jointName) + #-------------------------------------------------------- + #printf("Rotating point %0.2f,%0.2f using pivot %0.2f,%0.2f by %0.2f deg -> "%(jX,jY,cx,cy,angle)) + + #Translate point back to origin: + jX = np.float32(jX - cx) + jY = np.float32(jY - cy) + + #Rotate point + xnew = np.float32( (jX * c) - (jY * s) ) + ynew = np.float32( (jX * s) + (jY * c) ) + + #Translate point back: + jID_X = rules['inputJointMap'].getJointID_2DX(jointName) + jID_Y = rules['inputJointMap'].getJointID_2DY(jointName) + jID_Vis = rules['inputJointMap'].getJointID_Visibility(jointName) + #---------------------------------------------------------------- + result[jID_X] = np.float32(xnew + cx) + result[jID_Y] = np.float32(ynew + cy) + result[jID_Vis] = np.float32(jVisibility) + + #printf("%0.2f,%0.2f\n"%(result[jID*3+0],result[jID*3+1])); + + return result + + + + +def performNSRMAlignment(thisInput,configuration): + angleToRotateInRadians = 0.0 + NSRMUseAlignmentToPivot = configuration['eNSRM'] + NSRMNormalizeAngles = 0 + if ("NSRMNormalizeAngles" in configuration) and (configuration["NSRMNormalizeAngles"]==1): + NSRMNormalizeAngles = 1 + if (NSRMUseAlignmentToPivot==1): + pivotPoint = configuration['Alignment'][0]['jointStart'] + referencePoint = configuration['Alignment'][0]['jointEnd'] + #----------------------------------------------------------------------------------------------- + if (pivotPoint!=referencePoint): + pivotX,pivotY,pivotVisibility = getJoint2DXYV(configuration,thisInput,pivotPoint) + #-------------------------------------------------------------------------------------------- + referenceX,referenceY,referenceVisibility = getJoint2DXYV(configuration,thisInput,referencePoint) + #-------------------------------------------------------------------------------------------- + if ((pivotVisibility!=0) and (referenceVisibility!=0)): + angleToRotateInRadians = getAngleToAlignToZero(pivotX,pivotY,referenceX,referenceY,NSRMNormalizeAngles) + rotatedInput = rotate2DPointsBasedOnJointAsCenter(configuration,thisInput,angleToRotateInRadians,pivotPoint) + return angleToRotateInRadians,rotatedInput + else: + print("Pivot Point ",pivotPoint," and Reference Point ",referencePoint," are the same\n") + #----------------------------------------------------------------------------------------------- + return angleToRotateInRadians,thisInput + + + + +def getCompositeLabel(jointA,jointB,xOffset,yOffset,virtualPointType): + labelI=jointA + labelIX="" + labelIY="" + if (virtualPointType==1): + if (xOffset<0): + labelIX="minus" + elif (xOffset>0): + labelIX="plus" + #---------------------------------- + if (yOffset<0): + labelIY="minus" + elif (yOffset>0): + labelIY="plus" + #---------------------------------- + labelI="virtual_"+jointA+"_x_"+labelIX+str(xOffset).replace('.','_').replace('-','_')+"_y_"+labelIY+str(yOffset).replace('.','_').replace('-','_') + elif (virtualPointType==2): + #---------------------------------- + labelI="halfway_"+jointA+"_and_"+jointB + return labelI + + + + + + +def NSDMLabels(rules): + result=list() + + useXY=1 + useAngles=0 + if (rules['NSDMAlsoUseAlignmentAngles']==1): + useXY=0 + useAngles=1 + + numberOfNSDMRules=len(rules['NSDM']) + print("Rules Number ",numberOfNSDMRules) + + for i in range(0,numberOfNSDMRules): + for j in range(0,numberOfNSDMRules): + if (i==j): + if (i==0): + result.append("NSRM-angleUsedFor2DRotation_%u"%(i)) + else: + result.append("NSRM-scaleBetween_"+rules['NSDM'][i]['joint']+"_and_"+rules['NSDM'][i]['joint']) + else: + #----------------------------------------------------------------- + labelI = getCompositeLabel( + rules['NSDM'][i]['joint'], + rules['NSDM'][i]['halfWayFromThisAnd'], + rules['NSDM'][i]['xOffset'], + rules['NSDM'][i]['yOffset'], + rules['NSDM'][i]['isVirtual'] + ) + #----------------------------------------------------------------- + labelJ = getCompositeLabel( + rules['NSDM'][j]['joint'], + rules['NSDM'][j]['halfWayFromThisAnd'], + rules['NSDM'][j]['xOffset'], + rules['NSDM'][j]['yOffset'], + rules['NSDM'][j]['isVirtual'] + ) + #----------------------------------------------------------------- + if (useXY): + result.append("NSDM-%sX-%sX"%(labelI,labelJ)) + result.append("NSDM-%sY-%sY"%(labelI,labelJ)) + if (useAngles): + result.append("NSRM-%sY-%sY-Angle"%(labelI,labelJ)) + #----------------------------------------------------------------- + + #print("NSDM matrix will look like this ",result) + return result; + +def inputIsEnoughToCreateNSDM(rules,thisInput): + numberOfNSDMRules=len(rules['NSDM']) + numberOfJointIDs =len(thisInput) + for i in range(0,numberOfNSDMRules): + jointName=rules['NSDM'][i]['joint'] + if (not rules['inputJointMap'].getJointID_Exists(jointName)): + return False + return True + +def getListOfMissingNSRMJoints(rules,thisInput): + missingList=list() + numberOfNSDMRules=len(rules['NSDM']) + numberOfJointIDs =len(thisInput) + for i in range(0,numberOfNSDMRules): + jointName=rules['NSDM'][i]['joint'] + if (not rules['inputJointMap'].getJointID_Exists(jointName)): + missingList.append(jointName) + else: + iX,iY,iVisibility = getJoint2DXYV(rules,thisInput,jointName) + if (iVisibility==0.0): + missingList.append(jointName) + return missingList + + + +def getCompositePoint(rules,i,thisInput): + #----------------------------------------------------------- + if (len(thisInput)==0): + print("getCompositePoint called with no input for element ",i) + return np.float32(0.0),np.float32(0.0),np.float32(0.0),1 + #-------------------------------------------------------- + invalidPoint = 0 + jointName = rules['NSDM'][i]['joint'] + #----------------------------------------------------------- + iX,iY,iVisibility = getJoint2DXYV(rules,thisInput,jointName) + #----------------------------------------------------------- + if ( iX!=0.0 and iY!=0.0 and iVisibility!=0.0 ): + #--------------------------------------------------------------------------- + # Synthetic Points + #--------------------------------------------------------------------------- + if (rules['NSDM'][i]['isVirtual']==1): + iX=iX+rules['NSDM'][i]['xOffset'] + iY=iY+rules['NSDM'][i]['yOffset'] + elif (rules['NSDM'][i]['isVirtual']==2): + secondTargetJointName=rules['NSDM'][i]['halfWayFromThisAnd'] + secondTargetX,secondTargetY,secondTargetVisibility = getJoint2DXYV(rules,thisInput,secondTargetJointName) + if ((secondTargetX!=0.0) or (secondTargetY!=0.0)): + iX=np.float32((iX+secondTargetX)/2) + iY=np.float32((iY+secondTargetY)/2) + else: + invalidPoint = 1 + iX = np.float32(0.0) + iY = np.float32(0.0) + iVisibility = np.float32(0.0) + #--------------------------------------------------------------------------- + else: + #Added : Fixed bug! 11/5/2023 + #If either of X,Y is zero we treat the point as completely invisible + invalidPoint = 1 + iX = np.float32(0.0) + iY = np.float32(0.0) + iVisibility = np.float32(0.0) + #----------------------------------------------------------- + return iX,iY,iVisibility,invalidPoint + + + + +def createNSDMUsingRules(rules,thisInput,angleUsedToRotateInput): + result=list() + #----------------------------------------------------------------------------------------------------- + if (len(thisInput)==0): + print("createNSDMUsingRules called with no input") + return result + + + if (not rules['inputJointMap'].checkJointListDimensions(thisInput)): + print("createNSDMUsingRules called with incorrect input size ") + return thisInput + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + NSRMNormalizeAngles = 0 + if ("NSRMNormalizeAngles" in rules) and (rules["NSRMNormalizeAngles"]==1): + NSRMNormalizeAngles = 1 + + doNormalization = (rules['NSDMNormalizationMasterSwitch']==1) + useXY = True + useAngles = False + if (rules['eNSRM']==1) or (rules['NSDMAlsoUseAlignmentAngles']==1): + useXY = False + useAngles = True + doNormalization = False + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + + #----------------------------------------------------------------------------------------------------- + # ..Main NSRM parameters .. + #----------------------------------------------------------------------------------------------------- + numberOfNSDMRules=len(rules['NSDM']) + for i in range(0,numberOfNSDMRules): + #--------------------------------------------------------------------------- + iX,iY,iVisibility,iInvalidPoint = getCompositePoint(rules,i,thisInput) + #--------------------------------------------------------------------------- + for j in range(0,numberOfNSDMRules): + #--------------------------------------------------------------------------- + jX,jY,jVisibility,jInvalidPoint = getCompositePoint(rules,j,thisInput) + #--------------------------------------------------------------------------- + if (iInvalidPoint or jInvalidPoint): + #If any of the two joints is invalid, invalidate all output + if (useXY): + result.append(np.float32(0.0)) + result.append(np.float32(0.0)) + if (useAngles): + result.append(np.float32(0.0)) + else: + if (useXY): + result.append(np.float32(iX-jX)) + result.append(np.float32(iY-jY)) + if (useAngles): + result.append(getAngleToAlignToZero(iX,iY,jX,jY,NSRMNormalizeAngles)) + #--------------------------------------------------------------------------- + + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + # ..New eNSRM diagonal parameters .. + #----------------------------------------------------------------------------------------------------- + if (rules['eNSRM']==1): + elementID=0 + #------------------------------------------------------------ + iJointName=rules['NSDM'][0]['joint'] #Pivot point + iX,iY,iVisibility = getJoint2DXYV(rules,thisInput,iJointName) + #------------------------------------------------------------ + for j in range(0,numberOfNSDMRules): + elementID = j * numberOfNSDMRules + j # Calculate diagonal elementID based on j + #--------------------------------------------------------------- + jJointName = rules['NSDM'][j]['joint'] + jX,jY,jVisibility = getJoint2DXYV(rules,thisInput,jJointName) + #--------------------------------------------------------------- + if (jVisibility!=0.0) and (iVisibility!=0.0): + #Populate diagonal elements with distance from our pivot point + result[elementID] = getJoint2DDistancePoints(iX,iY,jX,jY) + else: + result[elementID] = np.float32(0.0) + #--------------------------------------------------------------- + #Overwrite first (null) element of NSRM matrix with the angle used to rotate the input + result[0]=np.float32(angleUsedToRotateInput); + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + + if (doNormalization): + #normalization is made for NSDM not xNSRM + #Normalizing results.. --------------------------------------- + numberOfNSDMScalingRules=len(rules['NormalizeNSDMBasedOn']) + if (numberOfNSDMScalingRules>0): + numberOfDistanceSamples=0 + sumOfDistanceSamples=0.0 + for i in range(0,numberOfNSDMScalingRules): + #------------------------------------------------------------------------ + iJointName=rules['NormalizeNSDMBasedOn'][i]['jointStart'] + iX,iY,iVisibility = getJoint2DXYV(rules,thisInput,iJointName) + #------------------------------------------------------------------------ + jJointName=rules['NormalizeNSDMBasedOn'][i]['jointEnd'] + jX,jY,jVisibility = getJoint2DXYV(rules,thisInput,jJointName) + #------------------------------------------------------------------------ + if (iJointName==jJointName): + print("Error: Normalization Rule ",i," points to same start/end joint ",iJointID," == ",jJointID) + distance = getJoint2DDistancePoints(iX,iY,jX,jY) + if (distance>0.0): + numberOfDistanceSamples=numberOfDistanceSamples+1 + sumOfDistanceSamples=sumOfDistanceSamples+distance + + #------------------------------------------------------------------------------------------------- + scaleDistance=1.0 + #------------------------------------------------------------------------------------------------- + if (numberOfDistanceSamples>0): + scaleDistance=sumOfDistanceSamples/numberOfDistanceSamples + #------------------------------------------------------------------------------------------------- + #print("NSDM Scale = ",scaleDistance," \n") + if (scaleDistance!=1.0): + for i in range(0,len(result)): + result[i]=np.float32(result[i]/scaleDistance) + #------------------------------------------------------------------------------------------------- + + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + #----------------------------------------------------------------------------------------------------- + if (useXY): + for i in range(0,len(result)): + if (result[i]!=0.0): + result[i]=np.float32(0.5+result[i]) + #------------------------------------------------------------------------------------------------- + #print(result) + #print("Result has ", len(result), " elements " ) + #print("Result should have ", int( 2 * len(bodyWithoutHands) * len(bodyWithoutHands) ), " elements " ) + return result; + + + +if __name__ == '__main__': + print("NSDM.py is a library it cannot be run standalone") + + sign = 1.0 #This is positive +1.0 + + iX=0.5; iY=0.5; jX=1.0; jY=1.0 + a = getAngleToAlignToZero(iX,iY,jX,jY) + print("Points A(%0.2f,%0.2f) -> B(%0.2f,%0.2f) => angle %0.4f" %(iX,iY,jX,jY,a)) + jX,jY = rotate2DPointsTest(iX,iY,jX,jY,sign * a) + print("Rotated it goes to -> B(%0.2f,%0.2f) => angle to align %0.4f" %(jX,jY,getAngleToAlignToZero(iX,iY,jX,jY))) + + iX=0.5; iY=0.5; jX=0.5; jY=1.0 + a = getAngleToAlignToZero(iX,iY,jX,jY) + print("Points A(%0.2f,%0.2f) -> B(%0.2f,%0.2f) => angle %0.4f" %(iX,iY,jX,jY,a)) + jX,jY = rotate2DPointsTest(iX,iY,jX,jY,sign * a) + print("Rotated it goes to -> B(%0.2f,%0.2f) => angle to align %0.4f" %(jX,jY,getAngleToAlignToZero(iX,iY,jX,jY))) + + iX=0.5; iY=0.5; jX=0.0; jY=1.0 + a = getAngleToAlignToZero(iX,iY,jX,jY) + print("Points A(%0.2f,%0.2f) -> B(%0.2f,%0.2f) => angle %0.4f" %(iX,iY,jX,jY,a)) + jX,jY = rotate2DPointsTest(iX,iY,jX,jY,sign * a) + print("Rotated it goes to -> B(%0.2f,%0.2f) => angle to align %0.4f" %(jX,jY,getAngleToAlignToZero(iX,iY,jX,jY))) + + iX=0.5; iY=0.5; jX=1.0; jY=0.5 + a = getAngleToAlignToZero(iX,iY,jX,jY) + print("Points A(%0.2f,%0.2f) -> B(%0.2f,%0.2f) => angle %0.4f" %(iX,iY,jX,jY,a)) + jX,jY = rotate2DPointsTest(iX,iY,jX,jY,sign * a) + print("Rotated it goes to -> B(%0.2f,%0.2f) => angle to align %0.4f" %(jX,jY,getAngleToAlignToZero(iX,iY,jX,jY))) + + iX=0.5; iY=0.5; jX=1.0; jY=0.0 + a = getAngleToAlignToZero(iX,iY,jX,jY) + print("Points A(%0.2f,%0.2f) -> B(%0.2f,%0.2f) => angle %0.4f" %(iX,iY,jX,jY,a)) + jX,jY = rotate2DPointsTest(iX,iY,jX,jY,sign * a) + print("Rotated it goes to -> B(%0.2f,%0.2f) => angle to align %0.4f" %(jX,jY,getAngleToAlignToZero(iX,iY,jX,jY))) + + iX=0.5; iY=0.5; jX=0.0; jY=0.0 + a = getAngleToAlignToZero(iX,iY,jX,jY) + print("Points A(%0.2f,%0.2f) -> B(%0.2f,%0.2f) => angle %0.4f" %(iX,iY,jX,jY,a)) + jX,jY = rotate2DPointsTest(iX,iY,jX,jY,sign * a) + print("Rotated it goes to -> B(%0.2f,%0.2f) => angle to align %0.4f" %(jX,jY,getAngleToAlignToZero(iX,iY,jX,jY))) + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/PoseNET.py b/animation/MocapNET-kasisnu/src/python/mnet4/PoseNET.py new file mode 100755 index 0000000..b3f7751 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/PoseNET.py @@ -0,0 +1,455 @@ +#!/usr/bin/python3 + +""" +Author : "Ammar Qammaz" +Copyright : "2022 Foundation of Research and Technology, Computer Science Department Greece, See license.txt" +License : "FORTH" +""" + +import os +from tools import secondsToHz,eprint + +#https://tfhub.dev/google/movenet/singlepose/lightning/4 +#https://tfhub.dev/google/lite-model/movenet/singlepose/lightning/tflite/int8/4 +#wget -q -O lite-model_movenet_singlepose_lightning_tflite_int8_4.tflite https://storage.googleapis.com/tfhub-lite-models/google/lite-model/movenet/singlepose/lightning/tflite/int8/4.tflite + +#zip movenet.zip movenet/* movenet/*/* + +trainingWidth = 1920 +trainingHeight = 1080 + +def getCaptureDeviceFromPath(videoFilePath,videoWidth,videoHeight): + import cv2 + #------------------------------------------ + if (videoFilePath=="esp"): + from espStream import ESP32CamStreamer + cap = ESP32CamStreamer() + elif (videoFilePath=="webcam"): + cap = cv2.VideoCapture(0) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, videoWidth) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, videoHeight) + elif (videoFilePath=="/dev/video0"): + cap = cv2.VideoCapture(0) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, videoWidth) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, videoHeight) + elif (videoFilePath=="/dev/video1"): + cap = cv2.VideoCapture(1) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, videoWidth) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, videoHeight) + elif (videoFilePath=="/dev/video2"): + cap = cv2.VideoCapture(2) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, videoWidth) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, videoHeight) + else: + from tools import checkIfPathIsDirectory + if (checkIfPathIsDirectory(videoFilePath) and (not "/dev/" in videoFilePath) ): + from folderStream import FolderStreamer + cap = FolderStreamer(path=videoFilePath,width=videoWidth,height=videoHeight) + mnet.bvh.configureRendererFromFile("%s/color.calib"%videoFilePath) + else: + cap = cv2.VideoCapture(videoFilePath) + return cap + + + +def runPoseNETSerial(): + #Parse command line arguments + #----------------------------------------- + import sys + import cv2 + import time + headless = False + economicVisualization= False + saveVideo = False + videoFilePath = "webcam" + videoWidth = 1280 + videoHeight = 720 + doProfiling = False + doFlipX = False + engine = "onnx" + doNNEveryNFrames = 1 # 3 + bvhScale = 1.0 + doHCDPostProcessing = 1 + hcdLearningRate = 0.001 + hcdEpochs = 15 + hcdIterations = 30 + smoothingSampling = 30.0 + smoothingCutoff = 5.0 + threshold = 0.05 + calibrationFile = "" + plotBVHChannels = False + bvhAnglesForPlotting = list() + bvhAllAnglesForPlotting = list() + + + if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--save"): + saveVideo=True + if (sys.argv[i]=="--nnsubsample"): + doNNEveryNFrames = int(sys.argv[i+1]) + if (sys.argv[i]=="--headless"): + headless = True + if (sys.argv[i]=="--flipx"): + doFlipX = True + if (sys.argv[i]=="--plot"): + plotBVHChannels=True + if (sys.argv[i]=="--nonn"): + doNNEveryNFrames = 1000 + if (sys.argv[i]=="--calib"): + calibrationFile = sys.argv[i+1] + if (sys.argv[i]=="--ik"): + hcdLearningRate = float(sys.argv[i+1]) + hcdEpochs = int(sys.argv[i+2]) + hcdIterations = int(sys.argv[i+3]) + if (sys.argv[i]=="--smooth"): + smoothingSampling = float(sys.argv[i+1]) + smoothingCutoff = float(sys.argv[i+2]) + if (sys.argv[i]=="--noik"): + doHCDPostProcessing = 0 + doNNEveryNFrames = 1 + if (sys.argv[i]=="--scale"): + bvhScale=float(sys.argv[i+1]) + if (sys.argv[i]=="--from"): + videoFilePath=sys.argv[i+1] + if (sys.argv[i]=="--engine"): + engine=sys.argv[i+1] + if (sys.argv[i]=="--plot"): + plotBVHChannels=True + + # For webcam input: + frameNumber = 0 + #------------------------------------------ + cap = getCaptureDeviceFromPath(videoFilePath,videoWidth,videoHeight) + + #----------------------------------------- + #python3 -m tf2onnx.convert --saved-model movenet --opset 14 --output movenet/model.onnx + #zip movenet.zip movenet/* movenet/*/* + #----------------------------------------- + + bvhAnglesForPlotting = list() + + # Initialize the PoseNET + if (engine=="tensorflow"): + from MocapNETTensorflow import PoseNET + poseNET = PoseNET(modelPath="movenet/",trainingWidth=trainingWidth,trainingHeight=trainingHeight) + elif (engine=="onnx"): + from MocapNETONNX import PoseNETONNX + poseNET = PoseNETONNX(modelPath="movenet/model.onnx",trainingWidth=trainingWidth,trainingHeight=trainingHeight) + elif (engine=="tflite"): + from MocapNETTFLite import PoseNETTFLite + poseNET = PoseNETTFLite(trainingWidth=trainingWidth,trainingHeight=trainingHeight) + else: + print("Unknown engine (",engine,") for MoveNET") + sys.exit(1) + + + #Select a MocapNET class from tensorflow/tensorrt/onnx/tf-lite engines + from MocapNET import easyMocapNETConstructor + mnet = easyMocapNETConstructor( + engine, + doProfiling = doProfiling, + doBody = False, #<- override whole body + doUpperbody = True, + doLowerbody = True, + doHCDPostProcessing = doHCDPostProcessing, + hcdLearningRate = hcdLearningRate, + hcdEpochs = hcdEpochs, + hcdIterations = hcdIterations, + smoothingSampling = smoothingSampling, + smoothingCutoff = smoothingCutoff, + doFace = False, + doREye = False, + doMouth = False, + doHands = False, + bvhScale=bvhScale + ) + + + if (calibrationFile!=""): + print("Enforcing Calibration file : ",calibrationFile) + mnet.bvh.configureRendererFromFile(calibrationFile) + + mnet.test() + + + #------------------------------------------------ + #------------------------------------------------ + #------------------------------------------------ + print("\n\n\n\nStarting MocapNET in Singlethreaded mode using BlazePose 2D Input") + print("Please wait until input processing finishes!") + while cap.isOpened(): + success, annotated_image = cap.read() + if not success: + print("Ignoring empty camera frame.") + break + # If loading a video, use 'break' instead of 'continue'. + #continue + #print(image.type) + + start = time.time() + #Our 2D Joint Estimation + #------------------------------------------------------------------------------------ + mocapNETInput,annotated_image = poseNET.convertImageToMocapNETInput(annotated_image,doFlipX=doFlipX,threshold=threshold) + #------------------------------------------------------------------------------------ + end = time.time() # Time elapsed + mnet.hz_2DEst = secondsToHz(end - start) + mnet.history_hz_2DEst.append(mnet.hz_2DEst) + if (len(mnet.history_hz_2DEst)>mnet.perfHistorySize): + mnet.history_hz_2DEst.pop(0) #Keep mnet history on limits + + + + #Our 3D Joint Estimation + #------------------------------------------------------------------------------------ + doNN = (frameNumber%doNNEveryNFrames)==0 + mocapNET3DOutput = mnet.predict3DJoints(mocapNETInput,runNN=doNN,runHCD=True) + mocapNETBVHOutput = mnet.outputBVH + bvhAnglesForPlotting.append(mocapNETBVHOutput) + bvhAllAnglesForPlotting.append(mocapNETBVHOutput) + if (len(bvhAnglesForPlotting)>100): + bvhAnglesForPlotting.pop(0) + #------------------------------------------------------------------------------------ + from MocapNETVisualization import visualizeMocapNETEnsemble + image,plotImage = visualizeMocapNETEnsemble(mnet,annotated_image,plotBVHChannels=plotBVHChannels,bvhAnglesForPlotting=bvhAnglesForPlotting,economic=economicVisualization) + #------------------------------------------------------------------------------------ + + + seconds = time.time() - start + fps = 1 / (seconds+0.0001) + #print("\r PoseNET+MocepNET aggregate Framerate : ",round(fps,2)," fps \r", end="", flush=True) + #print("\n", end="", flush=True) + + font = cv2.FONT_HERSHEY_SIMPLEX + org = (50, 50) + fontScale = 1 + color = (0,0,0) + thickness = 2 + + message = 'MNET4+ ST/%s/NN:%u/%0.2f fps (2DNN %0.2f/3DNN %0.2f/3DHCD %0.2f)' % (engine,doNN,fps,poseNET.hz,mnet.hz_NN,mnet.hz_HCD) + annotated_image = cv2.putText(annotated_image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (52, 52) + color = (255,255,255) + annotated_image = cv2.putText(annotated_image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + + #cv2.imwrite('mediapipe_%05u.jpg'%frameNumber, annotated_image) + frameNumber = frameNumber + 1 + + + if (saveVideo): + cv2.imwrite('colorFrame_0_%05u.jpg'%(frameNumber), annotated_image) + if (plotBVHChannels): + cv2.imwrite('plotFrame_0_%05u.jpg'%(frameNumber), plotImage) + + if not headless: + cv2.imshow('MocapNET 4 using PoseNET Holistic 2D Joints', annotated_image) + if (plotBVHChannels): + cv2.imshow('MocapNET 4 using PoseNET Holistic Motion History',plotImage) + + if cv2.waitKey(1) & 0xFF == 27: + break + + + + cap.release() + + if (saveVideo): # 1280x720 by default + os.system("ffmpeg -framerate 30 -i colorFrame_0_%%05d.jpg -s %ux%u -y -r 30 -pix_fmt yuv420p -threads 8 livelastRun3DHiRes.mp4 && rm colorFrame_0_*.jpg " % (videoWidth,videoHeight)) # + if (plotBVHChannels): + os.system("ffmpeg -framerate 30 -i plotFrame_0_%05d.jpg -s 1200x720 -y -r 30 -pix_fmt yuv420p -threads 8 livelastPlot3DHiRes.mp4 && rm plotFrame_0_*.jpg") + + + + + +def runPoseNETParallel(): + #Parse command line arguments + #----------------------------------------- + import sys + import cv2 + import time + import threading + + headless = False + videoFilePath = "webcam" + videoWidth = 1280 + videoHeight = 720 + saveVideo = False + plotBVHChannels = False + doProfiling = False + doFlipX = False + engine = "onnx" + doNNEveryNFrames = 1 + bvhScale = 1.0 + threshold = 0.05 + + + if (len(sys.argv)>1): + #print('Argument List:', str(sys.argv)) + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--headless"): + headless = True + if (sys.argv[i]=="--flipx"): + doFlipX = True + if (sys.argv[i]=="--plot"): + plotBVHChannels=True + if (sys.argv[i]=="--scale"): + bvhScale=float(sys.argv[i+1]) + if (sys.argv[i]=="--noplot"): + plotBVHChannels=0 + if (sys.argv[i]=="--from"): + videoFilePath=sys.argv[i+1] + if (sys.argv[i]=="--engine"): + engine=sys.argv[i+1] + if (sys.argv[i]=="--dump"): + global doFrameDumpingForTiles + doFrameDumpingForTiles=1 + + + # For webcam input: + #----------------------------------------- + frameNumber = 0 + cap = getCaptureDeviceFromPath(videoFilePath,videoWidth,videoHeight) + #----------------------------------------- + #python3 -m tf2onnx.convert --saved-model movenet --opset 14 --output movenet/model.onnx + #zip movenet.zip movenet/* movenet/*/* + #----------------------------------------- + + from MocapNETVisualization import drawMocapNETOutput,drawMocapNETAllPlots,drawMissingInput + #It is important for MocapNET to be the first to be initialized! + #So the tensorflow configuration will be set by it ( if tensorflow engine is selected ) + #Select a MocapNET class from tensorflow/tensorrt/onnx/tf-lite engines + from MocapNET import easyMocapNETConstructor + mnet = easyMocapNETConstructor(engine,doProfiling=doProfiling,bvhScale=bvhScale) + mnet.test() + + bvhAnglesForPlotting = list() + + + # Initialize the PoseNET + if (engine=="tensorflow"): + from MocapNETTensorflow import PoseNET + poseNET = PoseNET(modelPath="movenet/",trainingWidth=trainingWidth,trainingHeight=trainingHeight) + elif (engine=="onnx"): + from MocapNETONNX import PoseNETONNX + poseNET = PoseNETONNX(modelPath="movenet/model.onnx",trainingWidth=trainingWidth,trainingHeight=trainingHeight) + elif (engine=="tflite"): + from MocapNETTFLite import PoseNETTFLite + poseNET = PoseNETTFLite(trainingWidth=trainingWidth,trainingHeight=trainingHeight) + else: + print("Unknown engine (",engine,") for MoveNET") + sys.exit(1) + + + + if cap.isOpened(): + success, previous_image = cap.read() + if not success: + print("Could not grab first frame!.") + sys.exit(0) + mocapNETInput,previous_image = poseNET.convertImageToMocapNETInput(previous_image,doFlipX=doFlipX,threshold=threshold) + + #------------------------------------------------ + #------------------------------------------------ + #------------------------------------------------ + print("Starting MocapNET in Multithreaded mode using BlazePose 2D Input") + while cap.isOpened(): + success, next_image = cap.read() + if not success: + print("Ignoring empty camera frame.") + break + # If loading a video, use 'break' instead of 'continue'. + #continue + #print(image.type) + + start = time.time() + #Our 2D Joint Estimation AND 3D Joint Estimation happening in parallel + #------------------------------------------------------------------------------------ + doNN = (frameNumber%doNNEveryNFrames)==0 + t1 = threading.Thread(name='predict3DJoints', target=mnet.predict3DJoints, args=(mocapNETInput,),kwargs={'runNN': doNN , 'runHCD' : True}) + t2 = threading.Thread(name='convertImageToMocapNETInput', target=poseNET.convertImageToMocapNETInput, args=(next_image,)) + #------------------------------------------------------------------------------------ + t1.start() + t2.start() + # All threads running in parallel, now we wait + # ... + t1.join() + t2.join() + #------------------------------------------------------------------------------------ + mocapNET3DOutput = mnet.output3D + mocapNETBVHOutput = mnet.outputBVH + bvhAnglesForPlotting.append(mocapNETBVHOutput) + if (len(bvhAnglesForPlotting)>100): + bvhAnglesForPlotting.pop(0) + + #------------------------------------------------------------------------------------ + from MocapNETVisualization import visualizeMocapNETEnsemble + image,plotImage = visualizeMocapNETEnsemble(mnet,previous_image,plotBVHChannels=plotBVHChannels,bvhAnglesForPlotting=bvhAnglesForPlotting,economic=True) + #------------------------------------------------------------------------------------ + + mocapNETInput = poseNET.output + next_image = poseNET.image + #------------------------------------------------------------------------------------ + seconds = time.time() - start + fps = 1 / (seconds+0.0001) + #print("\r MoveNET+MocepNET MT aggregate Framerate : ",round(fps,2)," fps \r", end="", flush=True) + #print("\n", end="", flush=True) + + + frameNumber = frameNumber + 1 + + + #drawMocapNETOutput(mnet,previous_image) + font = cv2.FONT_HERSHEY_SIMPLEX + org = (50, 50) + fontScale = 1 + color = (0,0,0) + thickness = 2 + + message = 'MNET4+ MT/%s/NN:%u/%0.2f fps (2DNN %0.2f/3DNN %0.2f/3DHCD %0.2f)' % (engine,doNN,fps,poseNET.hz,mnet.hz_NN,mnet.hz_HCD) + previous_image = cv2.putText(previous_image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + org = (52, 52) + color = (255,255,255) + previous_image = cv2.putText(previous_image, message , org, font, fontScale, color, thickness, cv2.LINE_AA) + #------------------------------------------------------------------------------------------------------------ + + if (saveVideo): + cv2.imwrite('colorFrame_0_%05u.jpg'%(frameNumber), annotated_image) + if (plotBVHChannels): + cv2.imwrite('plotFrame_0_%05u.jpg'%(frameNumber), plotImage) + + if not headless: + cv2.imshow('MocapNET 4 using MoveNET Holistic 2D Joints', previous_image) + if (plotBVHChannels): + cv2.imshow('MocapNET 4 using MoveNET Holistic Motion History',plotImage) + + if cv2.waitKey(1) & 0xFF == 27: + break + + previous_image = next_image + + cap.release() + + if (saveVideo): # + # 1280x720 by default + os.system("ffmpeg -framerate 30 -i colorFrame_0_%%05d.jpg -s %ux%u -y -r 30 -pix_fmt yuv420p -threads 8 livelastRun3DHiRes.mp4 && rm colorFrame_0_*.jpg " % (videoWidth,videoHeight)) # + if (plotBVHChannels): + os.system("ffmpeg -framerate 30 -i plotFrame_0_%05d.jpg -s 1200x720 -y -r 30 -pix_fmt yuv420p -threads 8 livelastPlot3DHiRes.mp4 && rm plotFrame_0_*.jpg") + + + + + +if __name__ == '__main__': + doSerialRun = True + import sys + if (len(sys.argv)>1): + for i in range(0, len(sys.argv)): + if (sys.argv[i]=="--mt"): + doSerialRun = False + runPoseNETParallel() + + if (doSerialRun): + runPoseNETSerial() + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/PoseNETServer.py b/animation/MocapNET-kasisnu/src/python/mnet4/PoseNETServer.py new file mode 100755 index 0000000..eba5864 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/PoseNETServer.py @@ -0,0 +1,232 @@ +import argparse +import asyncio +import json +import logging +import os +import ssl +import uuid + +import cv2 +from aiohttp import web +from av import VideoFrame + +from aiortc import MediaStreamTrack, RTCPeerConnection, RTCSessionDescription +from aiortc.contrib.media import MediaBlackhole, MediaPlayer, MediaRecorder, MediaRelay + +ROOT = os.path.dirname(__file__) + +logger = logging.getLogger("pc") +pcs = set() +relay = MediaRelay() + +requests = 0 + +# Initialize the PoseNET +from PoseNET import PoseNET,PoseNETONNX +#poseNET = PoseNET(modelPath="movenet/") +poseNET = PoseNETONNX(modelPath="movenet/model.onnx") + +from MocapNETVisualization import drawMocapNETOutput,drawMocapNETAllPlots,drawMissingInput + +#Select a MocapNET class from tensorflow/tensorrt/onnx/tf-lite engines +doProfiling = False +engine = "onnx" +from MocapNET import easyMocapNETConstructor +mnet = easyMocapNETConstructor(engine,doProfiling=doProfiling) +mnet.test() + + +class VideoTransformTrack(MediaStreamTrack): + """ + A video stream track that transforms frames from an another track. + """ + + kind = "video" + + def __init__(self, track, transform): + super().__init__() # don't forget this! + self.track = track + self.transform = transform + + async def recv(self): + frame = await self.track.recv() + + if self.transform == "cartoon": + img = frame.to_ndarray(format="bgr24") + + # prepare color + img_color = cv2.pyrDown(cv2.pyrDown(img)) + for _ in range(6): + img_color = cv2.bilateralFilter(img_color, 9, 9, 7) + img_color = cv2.pyrUp(cv2.pyrUp(img_color)) + + # prepare edges + img_edges = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) + img_edges = cv2.adaptiveThreshold( + cv2.medianBlur(img_edges, 7), + 255, + cv2.ADAPTIVE_THRESH_MEAN_C, + cv2.THRESH_BINARY, + 9, + 2, + ) + img_edges = cv2.cvtColor(img_edges, cv2.COLOR_GRAY2RGB) + + # combine color and edges + img = cv2.bitwise_and(img_color, img_edges) + + # rebuild a VideoFrame, preserving timing information + new_frame = VideoFrame.from_ndarray(img, format="bgr24") + new_frame.pts = frame.pts + new_frame.time_base = frame.time_base + return new_frame + elif self.transform == "edges": + # perform edge detection + img = frame.to_ndarray(format="bgr24") + img = cv2.cvtColor(cv2.Canny(img, 100, 200), cv2.COLOR_GRAY2BGR) + + # rebuild a VideoFrame, preserving timing information + new_frame = VideoFrame.from_ndarray(img, format="bgr24") + new_frame.pts = frame.pts + new_frame.time_base = frame.time_base + return new_frame + elif self.transform == "mocapnet": + # rotate image + img = frame.to_ndarray(format="bgr24") + + mocapNETInput,annotated_image = poseNET.convertImageToMocapNETInput(img) + mocapNET3DOutput = mnet.predict3DJoints(mocapNETInput,runNN=1,runHCD=True) + drawMocapNETOutput(mnet,annotated_image) + + + # rebuild a VideoFrame, preserving timing information + new_frame = VideoFrame.from_ndarray(annotated_image, format="bgr24") + new_frame.pts = frame.pts + new_frame.time_base = frame.time_base + return new_frame + else: + return frame + + +async def index(request): + content = open(os.path.join(ROOT, "PoseNETServer.html"), "r").read() + return web.Response(content_type="text/html", text=content) + + +async def javascript(request): + content = open(os.path.join(ROOT, "client.js"), "r").read() + return web.Response(content_type="application/javascript", text=content) + + +async def offer(request): + global requests + requests = requests + 1 + params = await request.json() + offer = RTCSessionDescription(sdp=params["sdp"], type=params["type"]) + + pc = RTCPeerConnection() + pc_id = "PeerConnection(%s)" % uuid.uuid4() + pcs.add(pc) + + def log_info(msg, *args): + logger.info(pc_id + " " + msg, *args) + + log_info("Created for %s", request.remote) + + # prepare local media + player = MediaPlayer(os.path.join(ROOT, "demo-instruct.wav")) + if args.record_to: + recorder = MediaRecorder("%s_%u.mp4"%(args.record_to,requests)) + else: + recorder = MediaBlackhole() + + @pc.on("datachannel") + def on_datachannel(channel): + @channel.on("message") + def on_message(message): + if isinstance(message, str) and message.startswith("ping"): + channel.send("pong" + message[4:]) + + @pc.on("connectionstatechange") + async def on_connectionstatechange(): + log_info("Connection state is %s", pc.connectionState) + if pc.connectionState == "failed": + await pc.close() + pcs.discard(pc) + + @pc.on("track") + def on_track(track): + log_info("Track %s received", track.kind) + + if track.kind == "audio": + pc.addTrack(player.audio) + recorder.addTrack(track) + elif track.kind == "video": + pc.addTrack( + VideoTransformTrack( + relay.subscribe(track), transform=params["video_transform"] + ) + ) + if args.record_to: + recorder.addTrack(relay.subscribe(track)) + + @track.on("ended") + async def on_ended(): + log_info("Track %s ended", track.kind) + await recorder.stop() + + # handle offer + await pc.setRemoteDescription(offer) + await recorder.start() + + # send answer + answer = await pc.createAnswer() + await pc.setLocalDescription(answer) + + return web.Response( + content_type="application/json", + text=json.dumps( + {"sdp": pc.localDescription.sdp, "type": pc.localDescription.type} + ), + ) + + +async def on_shutdown(app): + # close peer connections + coros = [pc.close() for pc in pcs] + await asyncio.gather(*coros) + pcs.clear() + + + +if __name__ == "__main__": + #openssl req --new --newkey rsa:4096 -x509 -sha256 --nodes --keyout apache.key --out apache-certificate.crt + parser = argparse.ArgumentParser(description="WebRTC audio / video / data-channels demo") + parser.add_argument("--cert-file", help="SSL certificate file (for HTTPS)") + parser.add_argument("--key-file", help="SSL key file (for HTTPS)") + parser.add_argument("--host", default="0.0.0.0", help="Host for HTTP server (default: 0.0.0.0)") + parser.add_argument("--port", type=int, default=8080, help="Port for HTTP server (default: 8080)") + parser.add_argument("--record-to", help="Write received media to a file."), + parser.add_argument("--verbose", "-v", action="count") + args = parser.parse_args() + + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + else: + logging.basicConfig(level=logging.INFO) + + + ssl_context = ssl.SSLContext() + ssl_context.load_cert_chain("apache-certificate.crt","apache.key") + #if args.cert_file: + # ssl_context = ssl.SSLContext() + # ssl_context.load_cert_chain(args.cert_file, args.key_file) + #else: + # ssl_context = None + + app = web.Application() + app.on_shutdown.append(on_shutdown) + app.router.add_get("/", index) + app.router.add_get("/client.js", javascript) + app.router.add_post("/offer", offer) + web.run_app(app, access_log=None, host=args.host, port=args.port, ssl_context=ssl_context) diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/align2DPoints.py b/animation/MocapNET-kasisnu/src/python/mnet4/align2DPoints.py new file mode 100755 index 0000000..755c67b --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/align2DPoints.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +import h5py +import numpy as np +import csv +import os +import sys + +from scipy.spatial import procrustes +from scipy.linalg import orthogonal_procrustes + +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.animation as animation + +#Taken from https://github.com/una-dinosauria/3d-pose-baseline/blob/master/src/procrustes.py +def compute_similarity_transform(X, Y, compute_optimal_scale=False): + """ + A port of MATLAB's `procrustes` function to Numpy. + Adapted from http://stackoverflow.com/a/18927641/1884420 + Args + X: array NxM of targets, with N number of points and M point dimensionality + Y: array NxM of inputs + compute_optimal_scale: whether we compute optimal scale or force it to be 1 + Returns: + d: squared error after transformation + Z: transformed Y + T: computed rotation + b: scaling + c: translation + """ + + muX = X.mean(0) + muY = Y.mean(0) + + X0 = X - muX + Y0 = Y - muY + + ssX = (X0**2.).sum() + ssY = (Y0**2.).sum() + + # centred Frobenius norm + normX = np.sqrt(ssX) + normY = np.sqrt(ssY) + + # scale to equal (unit) norm + X0 = X0 / normX + Y0 = Y0 / normY + + # optimum rotation matrix of Y + A = np.dot(X0.T, Y0) + U,s,Vt = np.linalg.svd(A,full_matrices=False) + V = Vt.T + T = np.dot(V, U.T) + + # Make sure we have a rotation + detT = np.linalg.det(T) + V[:,-1] *= np.sign( detT ) + s[-1] *= np.sign( detT ) + T = np.dot(V, U.T) + + traceTA = s.sum() + + if compute_optimal_scale: # Compute optimum scaling of Y. + b = traceTA * normX / normY + d = 1 - traceTA**2 + Z = normX*traceTA*np.dot(Y0, T) + muX + else: # If no scaling allowed + b = 1 + d = 1 + ssY/ssX - 2 * traceTA * normY / normX + Z = normY*np.dot(Y0, T) + muX + + c = muX - b*np.dot(muY, T) + + return d, Z, T, b, c + + +def pointListReturnXYZListForScatterPlot(A): + numberOfPoints=A.shape[0] + xs=list() + ys=list() + zs=list() + for i in range(0,numberOfPoints): + xs.append(A[i][0]) + ys.append(A[i][1]) + zs.append(A[i][2]) + return xs,ys,zs + + + +def pointListsReturnAvgDistance(A,B): + numberOfPoints=A.shape[0] + if (A.shape[0]!=B.shape[0]): + print("Error comparing point lists of different length") + return inf + + distance=0 + for i in range(0,numberOfPoints): + #--------- + xA=A[i][0] + yA=A[i][1] + zA=A[i][2] + #--------- + xB=B[i][0] + yB=B[i][1] + zB=B[i][2] + #--------- + xAB=xA-xB + yAB=yA-yB + zAB=zA-zB + + #Pythagorean theorem for 3 dimensions + #distance = squareRoot( xAB^2 + yAB^2 + zAB^2 ) + distance+=np.sqrt(xAB*xAB+yAB*yAB+zAB*zAB) + return distance/numberOfPoints + + + + + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/align3DPoints.py b/animation/MocapNET-kasisnu/src/python/mnet4/align3DPoints.py new file mode 100755 index 0000000..5212750 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/align3DPoints.py @@ -0,0 +1,280 @@ +#!/usr/bin/env python3 +#Written by Ammar Qammaz a.k.a AmmarkoV - 2020 + +import h5py +import numpy as np +import csv +import os +import sys + + +class bcolors: + HEADER = '\033[95m' + OKBLUE = '\033[94m' + OKGREEN = '\033[92m' + WARNING = '\033[93m' + FAIL = '\033[91m' + ENDC = '\033[0m' + BOLD = '\033[1m' + UNDERLINE = '\033[4m' + + +#Taken from https://github.com/una-dinosauria/3d-pose-baseline/blob/master/src/procrustes.py +def compute_similarity_transform(X, Y, compute_optimal_scale=False): + """ + A port of MATLAB's `procrustes` function to Numpy. + Adapted from http://stackoverflow.com/a/18927641/1884420 + Args + X: array NxM of targets, with N number of points and M point dimensionality + Y: array NxM of inputs + compute_optimal_scale: whether we compute optimal scale or force it to be 1 + Returns: + d: squared error after transformation + Z: transformed Y + T: computed rotation + b: scaling + c: translation + """ + + #We create a normalized version of X and Y called X0,Y0 + #that is centered on 0 + muX = X.mean(0) + muY = Y.mean(0) + + X0 = X - muX + Y0 = Y - muY + + ssX = (X0**2.).sum() + ssY = (Y0**2.).sum() + + # centred Frobenius norm + normX = np.sqrt(ssX) + normY = np.sqrt(ssY) + + # scale to equal (unit) norm + X0 = X0 / normX + Y0 = Y0 / normY + + #For reference this is the SciPy version : + #https://github.com/scipy/scipy/blob/v1.9.3/scipy/spatial/_procrustes.py#L15-L130 + #https://github.com/scipy/scipy/blob/v1.9.3/scipy/linalg/_procrustes.py#L12-L89 + + # optimum rotation matrix of Y + A = np.dot(X0.T, Y0) + + #full_matrices => bool, optional + #If True (default), u and vh have the shapes (..., M, M) and (..., N, N), respectively. + #Otherwise, the shapes are (..., M, K) and (..., K, N), respectively, where K = min(M, N). + U,s,Vt = np.linalg.svd(A,full_matrices=False) + V = Vt.T + T = np.dot(V, U.T) + + # Make sure we have a rotation + detT = np.linalg.det(T) + V[:,-1] *= np.sign( detT ) + s[-1] *= np.sign( detT ) + T = np.dot(V, U.T) + #------------------------- + traceTA = s.sum() + #------------------------- + if compute_optimal_scale: # Compute optimum scaling of Y. + b = traceTA * normX / normY + d = 1 - traceTA**2 + Z = normX*traceTA*np.dot(Y0, T) + muX + else: # If no scaling allowed + b = 1 + d = 1 + ssY/ssX - 2 * traceTA * normY / normX + Z = normY*np.dot(Y0, T) + muX + #------------------------- + c = muX - b*np.dot(muY, T) + #------------------------- + return d, Z, T, b, c + + + + +""" +Calculate Area Under Curve (AUC) +""" +def AUC(values,minValue,maxValue): + underCurve=0 + samples=len(values) + for value in values: + if ((minValue<=value) and (value<=maxValue) ): + underCurve=underCurve+1 + return 100*(underCurve/samples) + + +""" +Pythagorean theorem, get the 3D distance between two 3D points given their X,Y,Z coordinates +""" +def get3DDistance(jX,jY,jZ,pX,pY,pZ): + return np.sqrt( ((jX-pX)*(jX-pX)) + ((jY-pY)*(jY-pY)) + ((jZ-pZ)*(jZ-pZ)) ) + + +""" +Given two lists of 3D points A,B calculate their average distance +""" +def calculateAverageDistanceOf3DPoints(A,B): + numberOfPoints=A.shape[0] + + if (numberOfPoints==0): + print(bcolors.FAIL,"calculateAverageDistanceOf3DPoints(A,B), A has no points!",bcolors.ENDC) + return np.inf + elif (A.shape[0]!=B.shape[0]): + print(bcolors.FAIL,"Error comparing point lists of different length",bcolors.ENDC) + return np.inf + + distance=0.0 + for i in range(0,numberOfPoints): + #--------- + xA=A[i][0] + yA=A[i][1] + zA=A[i][2] + #--------- + xB=B[i][0] + yB=B[i][1] + zB=B[i][2] + #---------------------------------------------- + distance += get3DDistance(xA,yA,zA,xB,yB,zB) + #---------------------------------------------- + + return distance/numberOfPoints + + +""" +Return the jointID of a jointName in a list of labels without being case sensitive +""" +def findJointID(jointName,labels): + jointNameStreamlined=jointName.lower().strip() + for i in range(0,len(labels)): + labelStreamlined=labels[i].lower().strip() + if (jointNameStreamlined==labelStreamlined): + return i + print(bcolors.FAIL,"Cannot find joint `%s` between %u labels !"%(jointNameStreamlined,len(labels)),bcolors.ENDC) + #print(labels) + return -1 + + + +def compareGroundTruthToPrediction(configuration,groundTruth,prediction,doProcrustes=1,allowProcrustesToChangeScale=1,jointsToCompare=list(),useSciKitImplementation=False): + #print("GroundTruth : ",groundTruth) + #print("Prediction : ",prediction) + + #Automatically fill joints to compare if it is empty with whatever currently + #exists in configuration + numberOfJoints = len(configuration["hierarchy"]) + if (len(jointsToCompare)==0): + for jID in range (0,numberOfJoints): + jointsToCompare.append(configuration["hierarchy"][jID]["joint"].lower()) + else: + numberOfJoints = len(jointsToCompare) + #-------------------------------------------------------------------------- + + #Initialize our variables + #------------------------------- + comparedJoints = list() + groundTruth3DPoints = list() + mnet3DPoints = list() + #------------------------------- + scale = 1.0 + outputScale = 10.0 # We go from Centimeters to Millimeters! + numberOfJointsToCompare = 0 + #------------------------------- + + for jointName in jointsToCompare: + jointName = jointName.lower() #Make double sure if supplied as argument + + labelX = '3DX_%s' % jointName + labelY = '3DY_%s' % jointName + labelZ = '3DZ_%s' % jointName + + if ( + ( labelX in groundTruth ) and + ( labelY in groundTruth ) and + ( labelZ in groundTruth ) and + ( labelX in prediction ) and + ( labelY in prediction ) and + ( labelZ in prediction ) + ): + comparedJoints.append(jointName) + numberOfJointsToCompare = numberOfJointsToCompare + 1 + #-------------------------------------------- + # Grab ground truth for point + #-------------------------------------------- + x3DGT=scale*groundTruth[labelX] + y3DGT=scale*groundTruth[labelY] + z3DGT=scale*groundTruth[labelZ] + #-------------------------------------------- + groundTruth3DPoints.append([x3DGT,y3DGT,z3DGT]) + #-------------------------------------------- + + #-------------------------------------------- + # Grab MocapNET point + #-------------------------------------------- + x3DMNET=scale*prediction[labelX] + y3DMNET=scale*prediction[labelY] + z3DMNET=scale*prediction[labelZ] + #-------------------------------------------- + mnet3DPoints.append([x3DMNET,y3DMNET,z3DMNET]) + #-------------------------------------------- + else: + print(bcolors.WARNING,"Joint ",jointName," was not found this will influence results ") + + if (numberOfJointsToCompare==0): + print(bcolors.FAIL,"No joints where found ..",bcolors.ENDC) + + #We package our lists in numpy to be able to easily manipulate them + #------------------------------------------------------------------ + np_GTPointCloud = np.asarray(groundTruth3DPoints,dtype=np.float32) + np_OURPointCloud = np.asarray(mnet3DPoints,dtype=np.float32) + #------------------------------------------------------------------ + + #This is the main comparison after using procrustes and transforming the pointcloud + #to align it or when just doing plain old euclidean distance + #-------------------------------------------------------------------------------- + if (useSciKitImplementation): + from scipy.spatial import procrustes + mtx1, mtx2, disparity = procrustes(np_GTPointCloud,np_OURPointCloud) + np_GTPointCloud=mtx1 + np_OURPointCloud=mtx2 + elif (doProcrustes): + d, Z, T, b, c = compute_similarity_transform(np_GTPointCloud,np_OURPointCloud,compute_optimal_scale=allowProcrustesToChangeScale) + #disparity=np.sqrt(d) #d: squared error after transformation + #print("compute_similarity_transform : ",disparity) + + #Our point cloud is brought to the same translation and rotation as h36 point cloud + np_OURPointCloud = (b*np_OURPointCloud.dot(T))+c + disparity = outputScale * calculateAverageDistanceOf3DPoints(np_OURPointCloud,np_GTPointCloud) + else: + disparity = outputScale * calculateAverageDistanceOf3DPoints(np_OURPointCloud,np_GTPointCloud) + #-------------------------------------------------------------------------------- + #Here for each element in ground truth we want to get the same point from prediction.. + + #We want to calculate Mean Per Joint Position Error (MPJPE) + #to do so we have to calculate the position error of each of the joints in our point cloud + #sum it up and then divide it through the number of samples + totalError = 0.0 + totalSamples = 0 + #alljointDistances = list() + jointDistance = dict() + for jID in range(0,numberOfJointsToCompare): + #------------------------------------------------------------------ + #We use the np_ourPointCloud and np_h36PointCloud so that if procrustes analysis is enabled it will be used.. + perJointDisparity= outputScale * get3DDistance( + np_OURPointCloud[jID][0],np_OURPointCloud[jID][1],np_OURPointCloud[jID][2], + np_GTPointCloud[jID][0] ,np_GTPointCloud[jID][1] ,np_GTPointCloud[jID][2] + ) + totalError+=perJointDisparity + totalSamples+=1 + #We also keep every sample on a list to do an analysis in the end + #alljointDistances.append(perJointDisparity) + jointDistance[comparedJoints[jID]]=perJointDisparity + #------------------------------------------------------------------ + + jointDistance["meanAverageError"] = disparity + #print("Frame %u / Disparity %f " % (frameID,disparity)) + return jointDistance + + + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/colabStream.py b/animation/MocapNET-kasisnu/src/python/mnet4/colabStream.py new file mode 100644 index 0000000..f16d693 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/colabStream.py @@ -0,0 +1,104 @@ +# +# based on: https://colab.research.google.com/notebooks/snippets/advanced_outputs.ipynb#scrollTo=2viqYx97hPMi +# + +from IPython.display import display, Javascript +from google.colab.output import eval_js +from base64 import b64decode, b64encode +import numpy as np +import cv2 + +def init_camera(): + """Create objects and functions in HTML/JavaScript to access local web camera""" + + js = Javascript(''' + + // global variables to use in both functions + var div = null; + var video = null; // \s*
JointR² / Start Loss / M.A.ER² / End Loss / M.A.ETr.EpochsMin/MaxOffsetScalarScalar Fr.
") + f.write(outputName) + f.write("") + if ("train_rsquared_start" in metrics): + f.write("R² %0.2f/"%(metrics["train_rsquared_start"])) + f.write("%0.4f/%0.4f%s"%(initial_loss,initial_mae,initialVAL)) + f.write("") + if ("train_rsquared_end" in metrics): + f.write("R² %0.2f/"%(metrics["train_rsquared_end"])) + f.write("%0.4f/%0.4f%s"%(lowest_loss,lowest_mae,lowestVAL)) + f.write("") + f.write("%u"%(lowestLossAchievedAt)) + f.write("") + f.write("%0.4f/%0.4f"%(outputMinimumValue,outputMaximumValue)) + f.write("") + f.write("%0.4f"%(outputOffsetValue)) + f.write("") + f.write("%0.4f"%(outputScalarValues)) + f.write("") + f.write("%0.4f"%(outputScalarFractionValues)) + f.write("
Description<\/td>\s*<\/tr>\s*
\s*([A-Za-z0-9]+)' + + # Search for the pattern in the HTML content + match = re.search(pattern, content, re.DOTALL) + + if match: + serial_number = match.group(1) + return serial_number + + return "?" + +def filterListOfStringsByRegex(string_list, regex_pattern): + import re + #Usage : + #input_list = ["apple", "banana", "cherry", "date", "elderberry"] + #pattern = r"^[a-c].*" # Matches strings starting with letters a, b, or c + #result = filterListOfStringsByRegex(input_list, pattern) + + matched_strings = [] + + for string in string_list: + if re.match(regex_pattern, string): + matched_strings.append(string) + + return matched_strings + + +def convertListOfRegexToListOfLists(master_string_list,regex_list): + string_list_output = [] + for regex_pattern in regex_list: + string_list_output.append(filterListOfStringsByRegex(master_string_list,regex_pattern)) + return string_list_output + + +""" +Check if an entry is part of a given list +""" +def getEntryIndexInList(theList,theEntry): + i=0 + for listItem in theList: + if(theEntry.lower()==listItem.lower()): + return i + i=i+1 + return -1 + + +""" +Check if an entry is in a sublist of our configuration +""" +def checkIfEntryIsInConfigurationKey(configuration,theKey,theEntry): + for listItem in configuration[theKey]: + if(theEntry==listItem): return 1 + return 0 + +""" +Check if a joint is declared in the configuration hierarchy +""" +def getConfigurationJointIsDeclaredInHierarchy(configuration,theEntry): + #------------------------------------------------------------------------------------------- + if (theEntry=="everything"): + print(bcolors.WARNING,"EVERYTHING.. is declared always.. ",bcolors.ENDC) + return 1 + #------------------------------------------------------------------------------------------- + try: + out = theEntry.split('_') + theEntry=out[0] + except: + print("getConfigurationJointIsDeclaredInHierarchy could not split ",theEntry) + #------------------------------------------------------------------------------------------- + if 'banned' in configuration: + for listItem in configuration['banned']: + if(theEntry.lower()==listItem['output'].lower()): + print(bcolors.WARNING," Joint ",theEntry," is declared in banlist! ",bcolors.ENDC) + return 1 + + if 'hierarchy' in configuration: + for listItem in configuration['hierarchy']: + #print("Check ",theEntry," vs ",listItem['joint']) + if(theEntry.lower()==listItem['joint'].lower()): + print("The Joint ",theEntry," is : declared in hierarchy") + return 1 + + print("Joint is not declared in hierarchy..") + #------------------------------------------------------------------------------------------- + return 0 + + +""" +Retrieve the configuration joint priority of a joint is declared in the configuration hierarchy +""" +def getConfigurationJointPriority(configuration,theEntry): + #------------------------------------------------------------------------------------------- + if (theEntry=="everything"): + print("EVERYTHING.. has a high priority always.. ") + return 1 + #------------------------------------------------------------------------------------------- + try: + out = theEntry.split('_') + theEntry=out[0] + except: + print("getConfigurationJointPriority could not split ",theEntry) + #------------------------------------------------------------------------------------------- + if 'outputMode' in configuration: + if (configuration['outputMode'] == "3d"): + print(bcolors.WARNING,"We treat all joints as terribly important in 3D point mode!",bcolors.ENDC) + return 1 + + if 'banned' in configuration: + for listItem in configuration['banned']: + if(theEntry==listItem['output']): + print(bcolors.WARNING," Joint ",theEntry," is in banlist! ",bcolors.ENDC) + return 0 + + if 'hierarchy' in configuration: + for listItem in configuration['hierarchy']: + if(theEntry==listItem['joint']): + print("The Importance of Joint ",theEntry," is : ",listItem['importance']) + return listItem['importance'] + + print("The Importance of Joint ",theEntry," is : 0 ") + #------------------------------------------------------------------------------------------- + return 0 + + +""" +Get the parent network from our configuration joint hierarchy +""" +def getParentNetwork(configuration,theEntry): + #------------------------------------------------------------------------------------------- + if (theEntry=="everything"): + print("EVERYTHING.. has no parent.. ") + return "none" + #------------------------------------------------------------------------------------------- + try: + out = theEntry.split('_') + theJoint=out[0] + theChannel=out[1] + except: + print("getParentNetwork could not split ",theEntry) + theJoint=theEntry + theChannel=0 + #------------------------------------------------------------------------------------------- + print("Checking the parent of Joint(",theJoint,")/Channel(",theChannel,")") + for listItem in configuration['hierarchy']: + if(theJoint==listItem['joint']): + if (listItem['inheritNetwork']=="none"): + return "none" + parentNetwork="%s_%s" % (listItem['inheritNetwork'],theChannel) + if (parentNetwork==theEntry): + return "none" + else: + return parentNetwork + #------------------------------------------------------------------------------------------- + return "none" + + +if __name__ == '__main__': + print("Tools.py is a library!") + splitTextBasedOnGroupNumber(3,"tmp.tmp","tmpF.tmp") + diff --git a/animation/MocapNET-kasisnu/src/python/mnet4/writeCSV.py b/animation/MocapNET-kasisnu/src/python/mnet4/writeCSV.py new file mode 100755 index 0000000..5494fd3 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/mnet4/writeCSV.py @@ -0,0 +1,33 @@ +#!/usr/bin/python3 +import numpy as np +import csv +import gc +import time +import array +import sys + + +def writeCSVFileHeader(filenameOutput,inputListLabels,inputStart,inputEnd): + inputNumber=0 + fileCSV = open(filenameOutput,"w") + for entry in inputListLabels[inputStart:inputEnd]: + #if (inputNumber>=inputStart) and (inputNumber=inputStart) and (inputNumber=inputStart) and (inputNumber=inputStart) and (inputNumber1.0): + print("Memory Limit will be interpreted as a raw value..") + numberOfSamplesLimit=int(memPercentage) + #------------------------------------------------------------------------------------------------- + + + thisInput = array.array('f') + #--------------------------------- + + fi = open(filename, "r") + readerIn = csv.reader( fi , delimiter=csvDelimiter, skipinitialspace=True) + for rowIn in readerIn: + #------------------------------------------------------ + if (not receivedHeader): #use header to get labels + #------------------------------------------------------ + inputNumberOfColumns=len(rowIn) + + #Make sure CSV files that end with delimiter are correctly handled.. + if (inputNumberOfColumns>0): + if (rowIn[inputNumberOfColumns-1]==''): + inputNumberOfColumns=inputNumberOfColumns-1 + #----------------------------------------------------- + inputLabels = list(rowIn[i] for i in range(0,inputNumberOfColumns) ) + print("Number of Input elements : ",len(inputLabels)) + #------------------------------------------------------ + + if (memPercentage==0): + print("Will only return labels\n") + return {'labels':inputLabels}; + + + #i=0 + #print("class Input(Enum):") + #for label in inputLabels: + # print(" ",label," = ",i," #",int(i/3)) + # print(" ",label,"=",int(i/3)) + # i=i+1 + + #--------------------------------- + # Allocate Lists + #--------------------------------- + for i in range(inputNumberOfColumns): + thisInput.append(0.0) + #--------------------------------- + + + #--------------------------------- + # Allocate Numpy Arrays + #--------------------------------- + inputSize=0 + startCompressed=0 + + inputSize=inputSize+inputNumberOfColumns + startCompressed=inputNumberOfColumns + + npInputBytesize=0+numberOfSamplesLimit * inputSize * dtypeSelectedByteSize + print(" Input file on disk has a shape of [",numberOfSamples,",",inputSize,"]") + print(" Input we will read has a shape of [",numberOfSamplesLimit,",",inputSize,"]") + print(" Input will occupy ",convert_bytes(npInputBytesize)," of RAM\n") + npInput = np.full([numberOfSamplesLimit,inputSize],fill_value=0,dtype=dtypeSelected,order='C') + #---------------------------------------------------------------------------------------------------------- + receivedHeader=1 + #sys.exit(0) + else: + #------------------------------------------- + # First convert our string INPUT to floats + #------------------------------------------- + for i in range(inputNumberOfColumns): + thisInput[i]=float(rowIn[i]) + #------------------------------------------- + for num in range(0,inputNumberOfColumns): + npInput[sampleNumber,num]=float(thisInput[num]); + #------------------------------------------- + sampleNumber=sampleNumber+1 + + if (numberOfSamples>0): + progress=sampleNumber/numberOfSamplesLimit + + if (sampleNumber%1000==0) : + progressString = "%0.2f"%float(100*progress) + print("\rReading from disk (",sampleNumber,") - ",progressString," % \r", end="", flush=True) + + if (numberOfSamplesLimit<=sampleNumber): + print("\rStopping reading file to obey memory limit given by parameter --mem ",memPercentage,"\n") + break + #------------------------------------------- + fi.close() + del readerIn + gc.collect() + + + print("\n read, Samples: ",sampleNumber,", was expecting ",numberOfSamples," samples\n") + print(npInput.shape) + + totalNumberOfBytes=npInput.nbytes; + totalNumberOfGigaBytes=totalNumberOfBytes/1073741824; + print("Size Occupied by data = ",totalNumberOfGigaBytes," GB \n") + + end = time.time() + print("Time elapsed : ",(end-start)/60," mins") + #--------------------------------------------------------------------- + + if (groupOutput==0): + #New better dictionary + output = dict() + for i in range(0,len(inputLabels)): + lowerCaseName = inputLabels[i].lower() + #print("Joint ",lowerCaseName) + output[lowerCaseName]=list() + for frameID in range(0,len(npInput)): + output[lowerCaseName].append(npInput[frameID][i]) + return output + else: + #This is the old dictionary way (better for tensorflow training) + return {'label':inputLabels, 'body':npInput }; + + + +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- +#--------------------------------------------------------------------------------------------------------------------------------------------- + + + +drawPlot=1 + +ground=readCSVFile("ground3D.csv",1.0,',',0,1) + + +if (drawPlot): + print("Using matplotlib:",matplotlib.__version__) + # === Plot and animate === + fig = plt.figure() + fig.set_size_inches(19.2, 10.8, forward=True) + + ax = fig.add_subplot(1, 2, 1, projection='3d') + ax2 = fig.add_subplot(1, 2, 2) + #ax3 = fig.add_subplot(2, 2, 3) + #ax4 = fig.add_subplot(2, 2, 4) + fig.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95) + + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + ax.view_init(90, 90) + + +print("Ground truth file has %u elements ",len(ground['body'])) + + +for i in range(0,len(ground['body'])): + if (drawPlot): + plt.cla() + ax.cla() + ax2.cla() + #ax3.cla() + #ax4.cla() + + + categories = np.array([0, + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20 + ]) + + color0=(0.0, 1.0, 1.0, 1.0) #Palm + color1=(0.6, 0.0, 0.0, 1.0) #Thumb + color2=(0.8, 0.0, 0.0, 1.0) #Finger 1.2 + color3=(1.0, 0.0, 0.0, 1.0) #Finger 1.3 + color4=(0.0, 1.0, 1.0, 1.0) #BaseOfFourFingers + color5=(0.0, 1.0, 0.0, 1.0) #Finger 2.3 + color6=(0.0, 0.8, 0.0, 1.0) #Finger 2.2 + color7=(0.0, 0.6, 0.0, 1.0) #Finger 2.1 + color8=(0.0, 0.4, 0.0, 1.0) #Metacarpal 1 + color9=(0.0, 0.0, 1.0, 1.0) #Finger 3.3 + color10=(0.0, 0.0, 0.8, 1.0) #Finger 3.2 + color11=(0.0, 0.0, 0.6, 1.0) #Finger 3.1 + color12=(0.0, 0.0, 0.4, 1.0) #Metacarpal 2 + color13=(0.0, 1.0, 1.0, 1.0) #Finger 4.3 + color14=(0.0, 0.8, 0.8, 1.0) #Finger 4.2 + color15=(0.0, 0.6, 0.6, 1.0) #Finger 4.1 + color16=(0.0, 0.4, 0.4, 1.0) #Metacarpal 3 + color17=(1.0, 1.0, 0.0, 1.0) #Finger 5.3 + color18=(0.8, 0.8, 0.0, 1.0) #Finger 5.2 + color19=(0.6, 0.6, 0.0, 1.0) #Finger 5.1 + color20=(0.4, 0.4, 0.0, 1.0) #Metacarpal 4 + + colormap = np.array([color0,color1,color2,color3,color4,color5,color6,color7,color8,color9,color10,color11,color12,color13,color14,color15,color16,color17,color18,color19,color20]) + + xs, ys, zs = pointListReturnXYZListForScatterPlot(ground['body'][i]) + + ax.scatter(xs, ys, zs, c=colormap[categories]) + img = plt.imread("images/im%u.png" % i) + ax2.imshow(img) + + #------------------------- + ax.set_xlim(auto=False,left=-600,right=300) + ax.set_ylim(auto=False,bottom=-1600,top=200) + ax.set_zlim(auto=False,bottom=2000,top=6000) + ax.set_xlabel('X Axis') + ax.set_ylabel('Y Axis') + ax.set_zlabel('Z Axis') + #------------------------- + plt.show(block=False) + #plt.savefig('p%05u.png'%i) + #fig.canvas.draw() + plt.pause(0.001) diff --git a/animation/MocapNET-kasisnu/src/python/vae_hands_3d/prepareVE.sh b/animation/MocapNET-kasisnu/src/python/vae_hands_3d/prepareVE.sh new file mode 100755 index 0000000..b679d28 --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/vae_hands_3d/prepareVE.sh @@ -0,0 +1,16 @@ +#!/bin/bash +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + + python3 -m venv venv + source venv/bin/activate + pip install tensorflow==1.3.0 + pip install numpy==1.14.5 + pip install scipy==1.1.0 + pip install matplotlib==1.5.3 + pip install torch==0.3.1 + pip install opencv-python==3.4.1.15 + + python evaluate_model.py + +exit 0 diff --git a/animation/MocapNET-kasisnu/src/python/vae_hands_3d/rename_files.sh b/animation/MocapNET-kasisnu/src/python/vae_hands_3d/rename_files.sh new file mode 100755 index 0000000..e0b3ffd --- /dev/null +++ b/animation/MocapNET-kasisnu/src/python/vae_hands_3d/rename_files.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + +HAND="r" +FILE="ground3D.csv" +#cp ground3D.csv $FILE + +sed -i "s/_0,/_${HAND}hand,/g" $FILE +sed -i "s/_1,/_${HAND}thumb,/g" $FILE +sed -i "s/_2,/_finger1.2.${HAND},/g" $FILE +sed -i "s/_3,/_finger1.3.${HAND},/g" $FILE +sed -i "s/_4,/_${HAND}baseOfFourFingers,/g" $FILE +sed -i "s/_5,/_finger2.3.${HAND},/g" $FILE +sed -i "s/_6,/_finger2.2.${HAND},/g" $FILE +sed -i "s/_7,/_finger2.1.${HAND},/g" $FILE +sed -i "s/_8,/_metacarpal1.${HAND},/g" $FILE +sed -i "s/_9,/_finger3.3.${HAND},/g" $FILE +sed -i "s/_10,/_finger3.2.${HAND},/g" $FILE +sed -i "s/_11,/_finger3.1.${HAND},/g" $FILE +sed -i "s/_12,/_metacarpal2.${HAND},/g" $FILE +sed -i "s/_13,/_finger4.3.${HAND},/g" $FILE +sed -i "s/_14,/_finger4.2.${HAND},/g" $FILE +sed -i "s/_15,/_finger4.1.${HAND},/g" $FILE +sed -i "s/_16,/_metacarpal3.${HAND},/g" $FILE +sed -i "s/_17,/_finger5.3.${HAND},/g" $FILE +sed -i "s/_18,/_finger5.2.${HAND},/g" $FILE +sed -i "s/_19,/_finger5.1.${HAND},/g" $FILE +sed -i "s/_20,/_metacarpal4.${HAND},/g" $FILE + + +exit 0 diff --git a/animation/MocapNET-kasisnu/update.sh b/animation/MocapNET-kasisnu/update.sh new file mode 100755 index 0000000..495f4e8 --- /dev/null +++ b/animation/MocapNET-kasisnu/update.sh @@ -0,0 +1,30 @@ +#!/bin/bash + +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" +cd "$DIR" + + +if [ -f dependencies/RGBDAcquisition/README.md ]; then +cd dependencies/RGBDAcquisition +git pull origin master +cd "$DIR" +else +echo "Could not find RGBDAcquisition, please rerun the initialize.sh script .." +fi + + + +if [ -f dependencies/AmmarServer/README.md ]; then +echo "AmmarServer appears to exist, updating it .." +cd dependencies/AmmarServer +git pull origin master +cd "$DIR" +fi + + +#Now sync rest of code +cd "$DIR" +git pull origin master + + +exit 0 diff --git a/animation/MocapNET/docker/Dockerfile b/animation/MocapNET/docker/Dockerfile index 1db7f88..395fe88 100644 --- a/animation/MocapNET/docker/Dockerfile +++ b/animation/MocapNET/docker/Dockerfile @@ -1,6 +1,6 @@ FROM tensorflow/tensorflow:latest-gpu -ARG user_id="user" +ARG user_id="1000" ARG root_psw="12345678" ARG user_psw="ok" ARG user_name=user