zhs1 / keras_realtime_multi-person_pose_estimation

Keras version of Realtime Multi-Person Pose Estimation project

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.

Results

 

Contents

1. Converting caffe model
2. Testing
3. Training

Require

1. Keras
2. Caffe - docker required if you would like to convert caffe model to keras model. You don't have to compile/install caffe on your local machine.

Converting Caffe model to Keras model

Authors of original implementation released already trained caffe model which you can use to extract weights data.

• Download caffe model cd model; sh get_caffe_model.sh
• Dump caffe layers to numpy data cd ..; docker run -v [absolute path to your keras_Realtime_Multi-Person_Pose_Estimation folder]:/workspace -it bvlc/caffe:cpu python dump_caffe_layers.py Note that docker accepts only absolute paths so you have to set the full path to the folder containing this project.
• Convert caffe model (from numpy data) to keras model python caffe_to_keras.py

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.
• python demo_camera.py to run the web demo.

Training steps

• Install gsutil curl https://sdk.cloud.google.com | bash. This is a really helpful tool for downloading large datasets.
• Download the data set (~25 GB) cd dataset; sh get_dataset.sh,
• Download COCO official toolbox in dataset/coco/ .
• cd coco/PythonAPI; sudo python setup.py install to install pycocotools.
• Run cd ../..; python train_pose.py to start training.

Related repository

• CVPR'16, Convolutional Pose Machines.
• CVPR'17, Realtime Multi-Person Pose Estimation.

Citation

Please cite the paper in your publications if it helps your research:

@InProceedings{cao2017realtime,
  title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017}
  }