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tf-openpose

Openpose from CMU implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU.

Original Repo(Caffe) : https://github.com/CMU-Perceptual-Computing-Lab/openpose

Features

[x] CMU's original network architecture and weights.

[] Post processing from network output.

[] Faster network variants using mobilenet, lcnn architecture.

[] ROS Support.

Install

You need dependencies below.

  • python3

  • tensorflow 1.3

  • opencv 3

  • protobuf

Models

Inference Time

Dataset Model Description Inference Time
1 core cpu
Coco cmu CMU's original version. Same network, same weights. 3.65s / img
Coco dsconv Same as the cmu version except for the depthwise separable convolution of mobilenet. 0.44s / img
Coco mobilenet
Coco lcnn

Training

CMU Perceptual Computing Lab has modified Caffe to provide data augmentation.

This includes

  • scale : 0.7 ~ 1.3

  • rotation : -40 ~ 40 degrees

  • flip

  • cropping

See : https://github.com/CMU-Perceptual-Computing-Lab/caffe_train

References

OpenPose

[1] https://github.com/CMU-Perceptual-Computing-Lab/openpose

[2] Training Codes : https://github.com/ZheC/Realtime_Multi-Person_Pose_Estimation

[3] Custom Caffe by Openpose : https://github.com/CMU-Perceptual-Computing-Lab/caffe_train

Mobilenet

[2] Pretrained model : https://github.com/tensorflow/models/blob/master/slim/nets/mobilenet_v1.md

Libraries

[1] Tensorpack : https://github.com/ppwwyyxx/tensorpack