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Realtime and stable tools for Skeleton-based action recognition

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RealTimeActionRecongtionTools

Real-time and Stable Skeleton-based models and tools

ReadMe will update.

  1. HRNet weights in BaiduPans

    links:https://pan.baidu.com/s/1kA5YnB6ufaDxGnYoOyvujg codes:0my1

    put it in simple-HRNet/weights for demo put it in preprocess_data/simple-HRNet/weights for generate your own skeleton-based dataset.

  2. Yolo weights in BaiduPans

    links: https://pan.baidu.com/s/1wWg1Zl35TaYKYNdXDkeaYw

    codes: d9vk

    put it in simple-HRNet/models/detectors/yolo/weights put it in preprocess_data/simple-HRNet/models/detectors/yolo/weights for generate your own skeleton-based dataset.

3、generate your own skeleton-based dataset:

  1. python 1video_to_image.py to get frames from videos
  2. split dun frames into 5 parts python split5.py 3. cd simple-HRNet,
    1. python process_by_hrnet1-5 to generate json files from frames using hrnet(parallel).
    2. python generate_label, python kinetic_gendata.py to generate kinetic format custom dataset

4、training MSG3D and get pytorch model

code here:https://github.com/kenziyuliu/MS-G3D

5、convert torch model to onnx model using python torch2onnx.py

6、using msg3d onnx models in Demo/onnx_models/ to classify

7、run demo2D.sh and result in Demo/result

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