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A fast stacked hourglass network for human pose estimation on OpenVino

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yuanyuanli85/Fast_Stacked_Hourglass_Network_OpenVino

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A fast stacked hourglass network for human pose estimation on OpenVino. Stacked hourglass network proposed byStacked Hourglass Networks for Human Pose Estimation is a very good network for single-person pose estimation regarding to speed and accuracy.This repo contains a demo to show how to depoly model trained by Keras. It converts a Keras model to IR and shows how to use the generated IR to do inference.Have fun with OpenVino!

Installation

  • Python3
  • Install OpenVino 2018 R5
  • Install python dependencies
keras==2.1.5scipy==1.2.0tensorflow==1.12.0opencv-python==3.4.3.18

[Keras] Convert pre-trained Keras models

Download pre-trained hourglass models

  • Download models from Google drive and save them tomodels. You are going to download two files, one is json file for network configuration while another is weight.
  • hg_s2_b1_mobile, inputs: 256x256x3, Channel Number: 256, pckh 78.86% @MPII.
  • hg_s2_b1_tiny, inputs:192x192x3, Channel Number: 128, pckh@75.11%MPII.

Convert keras models to tensorflow forzen pb

  • Convert keras models to tf frozen pb
python3 tools/keras_to_tfpb.py --input_model_json ./models/path/to/network/json --input_model_weights./models/path/to/network/weight/h5 --out_tfpb ./models/hg_s2_b1_tf.pb

Use OpenVino Model Optimizer to convert tf pb to IR.

  • For CPU, please use mobile versionhg_s2_b1_mobile and FP32
~/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py -w ./models/hg_s2_b1_tf.pb --input_shape [1,256,256,3] --data_type FP32 --output_dir ./models/ --model_name hg_s2_mobile
  • For NCS2(Myriad), please use tiny versionhg_s2_b1_tiny and FP16
~/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo_tf.py -w ./models/hg_s2_b1_tf.pb --input_shape [1,192,128,3] --data_type FP16 --output_dir ./models/ --model_name hg_s2_tiny
  • .xml and.bin will be generated.

[PyTorch] Convert pre-trained Onnx models

Download model trained by pytorch

Download themodel_best.onnx model from below table to fit your accuracy and speed requirements.hg_s2_b1_mobile_fpd model trained by using the knowledge distillation proposed by paperFast Human Pose Estimation. Details can be found inFast_Human_Pose_Estimation_Pytorch.

Modelin_resfeatrues# of WeightsHeadShoulderElbowWristHipKneeAnkleMeanLink
hg_s2_b12561286.73m95.7494.5187.6881.7087.8180.8876.8386.58GoogleDrive
hg_s2_b1_mobile2561282.31m95.8093.6185.5079.6386.1377.8273.6284.69GoogleDrive
hg_s2_b1_mobile_fpd2561282.31m95.6794.0786.3179.6886.0079.6775.5185.41GoogleDrive
hg_s2_b1_tiny1921282.31m94.9592.8784.5978.1984.6877.7073.0783.88GoogleDrive

Convert onnx to IR

Use model optimizer to convert onnx to IR. FP32 for CPU while FP16 for MYRIAD

~/intel/computer_vision_sdk/deployment_tools/model_optimizer/mo.py -w ./models/model_best.onnx --data_type FP32 --output_dir ./models/ --model_name hg_s2_mobile_onnx

Run demo

  • Run single image demo on CPU
cd srcpython3 stacked_hourglass.py -i ../models/sample.jpg -m ../models/hg_s2_mobile.xml -d CPU -l /path/to/cpu/extension/library
  • Run single image demo on NCS2(MYRIAD)
cd srcpython3 stacked_hourglass.py -i ../models/sample.jpg -m ../models/hg_s2_tiny.xml -d MYRIAD
  • Run Aysnc demo with camera input on CPU
cd srcpython3 stacked_hourglass_camera_async.py -i cam -m ../models/hg_s2_mobile.xml -d CPU -l /path/to/cpu/extension/library

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