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Entity Relationship Diagram

PosePipe is a human pose estimation (HPE) pipeline designed to facilitate movement analysis from videos.
It uses DataJoint to manage relationships between algorithms, videos, and intermediate outputs.

Key features:

  • Modular wrappers for numerous state-of-the-art HPE algorithms
  • Structured video and data management via DataJoint
  • Output visualizations to easily compare and analyze results
  • Designed for clinical research movement analysis pipelines

Quick Start

  1. Install PosePipe
pip install pose_pipeline

Detailed installation instructions are provided to launch a DataJoint MySQL database and install OpenMMLab packages.

  1. Test the pipeline

Use the Getting Started Notebook to start running your videos through the pose estimation framework.

Recent Updates and Supported Algorithms

Developer Setup

VSCode is recommended for development.

Include the following in your .vscode/settings.json to enable consistent black formatting:

{
  "python.formatting.blackArgs": [
    "--line-length=120",
    "--include='*py'",
    "--exclude='*ipynb'",
    "--extend-exclude='.env'",
    "--extend-exclude='3rdparty/*'"
  ],
  "editor.rulers": [120]
}

Project Info


Citation

If you use this tool for research, please cite:

@misc{posepipe2024,
  author       = {R James Cotton},
  title        = {PosePipe: Open-Source Human Pose Estimation Pipeline for Clinical Research},
  year         = {2024},
  howpublished = {\url{https://github.com/IntelligentSensingAndRehabilitation/PosePipeline}}
}

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PosePipe is a human pose estimation (HPE) pipeline to facilitate home movement analysis from videos

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