# MocapNET V4 Documentation

Welcome to the MocapNET V4 documentation! 🎉 Immerse yourself in the world of advanced motion capture technology with ease.

🔗 **Find the Original Documentation** at [README_ORIGINAL.md](./README_ORIGINAL.md).

This version from the `tag mnet4` branch is sourced directly from the [official MocapNET repository](https://github.com/FORTH-ModelBasedTracker/MocapNET/tree/mnet4).

## 🚀 Getting Started: Developer Installation via docker (For Server Deployment Usage)

To install MocapNET V4 on a Linux system:

1. **Clone the MocapNET Repository**:
   ```sh
   git clone https://github.com/storytold/storyteller-ml/master/animation/MocapNET.git
   ```

2. **Deployment**:
```sh
cd MocapNET/docker
bash build_and_deploy.sh
```

This script will build and run a Docker image, setting up the necessary environment for MocapNET V4.

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### 🚀 Getting Started: Main Installation Process (Run locally)

For both Linux and Windows (via WSL), follow these steps:

1. **Install Dependencies**:
   ```sh
   sudo apt-get install git build-essential cmake libopencv-dev libjpeg-dev libpng-dev libglew-dev libpthread-stubs0-dev
   ```

2. **Clone the MocapNET Repository**:
   ```sh
   git clone https://github.com/storytold/storyteller-ml/master/animation/MocapNET.git
   ```

3. **Navigate to MocapNET Directory**:
   ```sh
   cd MocapNET
   ```

4. **Run the Initialization Script**:
   ```sh
   ./initialize.sh
   ```

This will set up MocapNET V4 on your system, including all necessary dependencies.

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### Additional Steps for Windows Users

For Windows users, using the Windows Subsystem for Linux (WSL) is recommended:

1. **Enable WSL on Windows**:
   - Open PowerShell as Administrator and execute:
     ```powershell
     dism.exe /online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux /all /norestart
     ```
   - Restart your computer when prompted.

2. **Install a Linux Distribution from the Microsoft Store**:
   - Choose and install your preferred Linux distribution (e.g., Ubuntu, Debian, Fedora).

3. **Set Up Your Linux Distribution**:
   - Launch the Linux distribution and complete the account setup.

4. **Update and Upgrade Your Linux Distribution**:
   - Run `sudo apt update && sudo apt upgrade` in the Linux terminal.

For a detailed WSL setup guide, view [How to Set Up WSL](https://www.youtube.com/watch?v=1HzYU2_t3yc) on YouTube.

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## 🎬 How to Use MocapNET V4

### Preparing for Usage

Before running MocapNET V4, navigate to the appropriate directory:

```sh
cd src/python/mnet4
```

### Quick Start with a Sample Video

Now you're ready to download and use a sample video:

1. **Download a Sample Video**:
   ```sh
   wget http://ammar.gr/mocapnet/shuffle.webm -O shuffle.webm
   ```

2. **Process the Video**:
   Execute the following command to run MocapNET on the downloaded video:
   ```sh
   python3 -m mediapipeHolisticWebcamMocapNET --from shuffle.webm --ik 0.001 99 99 --smooth 60 10 --all --save --plot --headless 2
   ```

### Convert Output BVH

For further processing, such as generating a BVH file without face and toes bones, execute:

```sh
./GroundTruthDumper --from dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/Motions/DAZFriendlyCGSPEED_ZXYAndHandsAxisBigHands.bvh --merge src/python/mnet4/out.bvh dependencies/RGBDAcquisition/opengl_acquisition_shared_library/opengl_depth_and_color_renderer/Motions//mergeDazFriendlyAndAddHead.profile --setPositionRotation 0 0 0 0 0 0 --bvh remade.bvh
```
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### Notes:

- The output bvh file will be stored inside this directory ``MocapNET/src/python/mnet4`` with the filename ``out.bvh``

- The converted bvh file will be stored in the root directory ``MocapNET`` with the filename ``remade.bvh``

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### Command-Line Options Explained

- `--headless`: Skip the live video display. Ideal for headless servers or GUI-less environments.
- `--live`: Launches a live demo. Functionalities may vary.
- `--mt`: Enable multi-threading for better performance on multi-core CPUs.
- `--calib [filename]`: Use a specific calibration file for camera adjustments.
- `--frameskip [number]`: Skip a set number of frames to reduce load or speed up processing.
- `--nnsubsample [number]`: Run the neural network at set intervals (e.g., every 2nd frame).
- `--ik [float int int]`: Set parameters for inverse kinematics optimization.
- `--smooth [float float]`: Apply smoothing to motion capture data.
- `--noik`: Turn off inverse kinematics processing.
- `--aspectCorrection [float]`: Adjust video or image aspect ratio.
- `--noise [float]`: Add noise to input for testing under varied conditions.
- `--size [int int]`: Define video/image dimensions.
- `--scale [float]`: Scale the output motion data.
- `--plot`: Plot BVH channels for skeletal animation visualization.
- `--all`: Activate all processing features (body, eyes, mouth, hands).
- `--nobody`: Disable body joint estimation.
- `--face`: Enable face joint estimation.
- `--eyes / --reye`: Estimate right eye joints.
- `--mouth`: Enable mouth joint estimation.
- `--hands`: Estimate hand joints.
- `--save`: Save outputs (images, videos, data, logs).
- `--engine [engine_name]`: Choose a specific neural network backend.
- `--from [filepath]`: Specify input source (video file or live feed).
- `--profile`: Activate performance profiling.

## THIS README IS A W.I.P