Add MocapNET

animation/MocapNET/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")