mirror of
https://github.com/storytold/storyteller-ml.git
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Justin John
95 lines
3.7 KiB
Markdown
95 lines
3.7 KiB
Markdown
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# MocapNET V4
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Welcome to the MocapNET V4 documentation! 🎉
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🔗 **Original Documentation**: Find the original docs at [README_ORIGINAL.md](./README_ORIGINAL.md).
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This snapshot from the `tag mnet4` branch is brought to you directly from the [official MocapNET repo](https://github.com/FORTH-ModelBasedTracker/MocapNET/tree/mnet4). Dive into the world of motion capture with ease!
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## 🚀 Getting Started: Developer Installation
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### For Linux Users
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To install MocapNET V4 on a Linux system:
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```sh
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cd MocapNET/tree/mnet4/docker
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bash build_and_deploy.sh
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```
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This script will build and run a Docker image, setting up the necessary environment for MocapNET V4.
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### For Windows Users
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For Windows users, using the Windows Subsystem for Linux (WSL) is recommended:
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1. **Enable WSL on Windows**:
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- Open PowerShell as Administrator and run:
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```powershell
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dism.exe /online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux /all /norestart
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```
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- Restart your computer when prompted.
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2. **Install a Linux Distribution from the Microsoft Store**:
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- Select and install your preferred Linux distribution (e.g., Ubuntu, Debian, Fedora).
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3. **Set Up Your Linux Distribution**:
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- Launch the Linux distribution and follow the on-screen instructions to set up your account.
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4. **Update and Upgrade Your Linux Distribution**:
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- Run: `sudo apt update && sudo apt upgrade`
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5. **Install Docker on WSL**:
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- Follow the Docker installation instructions for your Linux distribution.
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6. **Install MocapNET V4**:
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- Navigate to the `MocapNET/tree/mnet4/docker` directory.
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- Run the `build_and_deploy.sh` script.
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For a detailed guide on setting up WSL, watch this YouTube tutorial: [How to Set Up WSL](https://www.youtube.com/watch?v=1HzYU2_t3yc)
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## 🎬 How to Use
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### Quick Start with a Sample Video
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Get started by downloading a sample video:
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```sh
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wget http://ammar.gr/mocapnet/shuffle.webm -O shuffle.webm
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```
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### Run the Magic ✨
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Execute this command:
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```sh
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python3 -m mediapipeHolisticWebcamMocapNET --from shuffle.webm --ik 0.001 99 99 --smooth 60 10 --all --save --plot --headless 2
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```
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### Command-Line Options Explained
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- `--headless`: Skip the live video display. Ideal for headless servers or GUI-less environments.
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- `--live`: Launches a live demo. Functionalities may vary.
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- `--mt`: Enable multi-threading for better performance on multi-core CPUs.
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- `--calib [filename]`: Use a specific calibration file for camera adjustments.
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- `--frameskip [number]`: Skip a set number of frames to reduce load or speed up processing.
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- `--nnsubsample [number]`: Run the neural network at set intervals (e.g., every 2nd frame).
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- `--ik [float int int]`: Set parameters for inverse kinematics optimization.
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- `--smooth [float float]`: Apply smoothing to motion capture data.
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- `--noik`: Turn off inverse kinematics processing.
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- `--aspectCorrection [float]`: Adjust video or image aspect ratio.
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- `--noise [float]`: Add noise to input for testing under varied conditions.
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- `--size [int int]`: Define video/image dimensions.
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- `--scale [float]`: Scale the output motion data.
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- `--plot`: Plot BVH channels for skeletal animation visualization.
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- `--all`: Activate all processing features (body, eyes, mouth, hands).
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- `--nobody`: Disable body joint estimation.
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- `--face`: Enable face joint estimation.
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- `--eyes / --reye`: Estimate right eye joints.
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- `--mouth`: Enable mouth joint estimation.
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- `--hands`: Estimate hand joints.
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- `--save`: Save outputs (images, videos, data, logs).
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- `--engine [engine_name]`: Choose a specific neural network backend.
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- `--from [filepath]`: Specify input source (video file or live feed).
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- `--profile`: Activate performance profiling.
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## THIS README IS A W.I.P
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