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README.md

Realtime Multi-Person Pose Estimation

This is a keras version of Realtime Multi-Person Pose Estimation project

Introduction

Code repo for reproducing 2017 CVPR paper using keras.

Requirements

Linux/Ubuntu system if not, try on Google Colab. Works like charm.!

Contents

  1. [intro to GoogleColab] 1.Training 2.Testing

Google Colab

  • Go to google colab and create new ipynb file.
  • !git clone {this repository clone}
  • go to dataset folder
  • !wget http://images.cocodataset.org/zips/test2017.zip
  • !wget http://images.cocodataset.org/zips/val2017.zip
  • !wget http://images.cocodataset.org/zips/train2017.zip
  • enter these commands one by one. then unzip each of them using !unzip {filename}
  • create annotations folder and upload these files from the given link https://drive.google.com/open?id=1Oa4FKj4xwOz_44Psi059Y3-on4Ebz0uv
  • DONE! YOUR DATASET IS READY

Training steps

  • Go to the "training" folder cd ../../../training.
  • Optionally, you can set the number of processes used to generate samples in parallel dataset.py -> find the line df = PrefetchDataZMQ(df, nr_proc=4)
  • Run the command in terminal python train_pose.py You can change no.of epochs in train_pose.py file
  • This will take some time depending upon your pc/ no.of epochs

Testing steps

  • Convert caffe model to keras model or download already converted keras model https://www.dropbox.com/s/llpxd14is7gyj0z/model.h5
  • Run the notebook demo.ipynb.
  • python demo_image.py --image sample_images/ski.jpg to run the picture demo. Result will be stored in the file result.png. You can use any image file as an input.

Related repository

Thanks to our mentor Dr. Tapas Badal for continuos encouragement.

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