tf-openpose

'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices.

You can even run this on your macbook with descent FPS!

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

CMU's Original Model on Macbook Pro 15"	Mobilenet Variant on Macbook Pro 15"	Mobilenet Variant on Jetson TK2
~0.6 FPS	~4.2 FPS @ 368x368	~10 FPS @ 368x368
2.8GHz Quad-core i7	2.8GHz Quad-core i7	Jetson TX2 Embedded Board

Implemented features are listed here : features

Install

Dependencies

You need dependencies below.

• python3

• tensorflow 1.3

• opencv3

• protobuf

• python3-tk

Install

$ git clone https://www.github.com/ildoonet/tf-openpose
$ cd tf-openpose
$ pip3 install -r requirements.txt

Models

• cmu

  • the model based VGG pretrained network which described in the original paper.
  • I converted Weights in Caffe format to use in tensorflow.
  • weight download

• dsconv

  • Same architecture as the cmu version except for
    the depthwise separable convolution of mobilenet.
  • I trained it using 'transfer learning', but it provides not-enough speed and accuracy.

• mobilenet

  • Based on the mobilenet paper, 12 convolutional layers are used as feature-extraction layers.
  • To improve on small person, minor modification on the architecture have been made.
  • Three models were learned according to network size parameters.
    • mobilenet
      • 368x368 : weight download
    • mobilenet_fast
    • mobilenet_accurate
  • I published models which is not the best ones, but you can test them before you trained a model from the scratch.

Inference Time

Macbook Pro - 3.1GHz i5 Dual Core

Dataset	Model	Inference Time
Coco	cmu	10.0s @ 368x368
Coco	dsconv	1.10s @ 368x368
Coco	mobilenet_accurate	0.40s @ 368x368
Coco	mobilenet	0.24s @ 368x368
Coco	mobilenet_fast	0.16s @ 368x368

Jetson TX2

On embedded GPU Board from Nvidia, Test results are as below.

Dataset	Model	Inference Time
Coco	cmu	OOM @ 368x368 5.5s @ 320x240
Coco	mobilenet_accurate	0.18s @ 368x368
Coco	mobilenet	0.10s @ 368x368
Coco	mobilenet_fast	0.07s @ 368x368

CMU's original model can not be executed due to 'out of memory' on '368x368' size.

Demo

Test Inference

You can test the inference feature with a single image.

$ python3 inference.py --model=mobilenet --imgpath=...

Then you will see the screen as below with pafmap, heatmap, result and etc.

Realtime Webcam

$ python3 realtime_webcam.py --camera=0 --model=mobilenet --zoom=1.0

Then you will see the realtime webcam screen with estimated poses as below. This Realtime Result was recored on macbook pro 13" with 3.1Ghz Dual-Core CPU.

Training

See : etcs/training.md

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

[4] Keras Openpose : https://github.com/michalfaber/keras_Realtime_Multi-Person_Pose_Estimation

Mobilenet

[1] Original Paper : https://arxiv.org/abs/1704.04861

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

Libraries

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

Tensorflow Tips

[1] Freeze graph : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py

[2] Optimize graph : https://codelabs.developers.google.com/codelabs/tensorflow-for-poets-2