P-STMO for PD

pose estimation for PD

Prerequisite

1. Environment
   • Linux system
   • Python > 3.6 distribution
2. Dependencies
   • Packages
     • Pytorch > 1.0.0
     • torchsample
     • ffmpeg
     • tqdm
     • pillow
     • scipy
     • pandas
     • h5py
     • visdom
     • nibabel
     • opencv-python (install with pip)
     • matplotlib

Usage

0. place your video into ./input folder. (There are thre test videos there).

Testing

1. change the video_path in the ./videopose_PSTMO.py
2. Run it! You will find the rendered output video in the ./outputs folder.

Acknowledgement

The codes are based on P-STMO codes, see https://github.com/paTRICK-swk/P-STMO

The 2D pose to 3D pose and visualization part is from VideoPose3D.

Some of the "In the wild" script is adapted from the other fork.

The project structure and ./videopose.py running script is adapted from this repo

Future Work

The other feature will be added to improve accuracy in the future:

• more data to be tested
• Use PD data as our training data
• use the 3D joints data to drive the neuromuscular model
• Change 2D pose estimation method such as AlphaPose.
• Test HR-Net as 2d joints detector.
• Test LightTrack as pose tracker.