多人人体姿态估计-基于Pytorch实现的实时多人形体姿态估计算法-附项目源码-优质项目实战.zip

## Introduction Multi Person PoseEstimation By PyTorch ## Results <p align="left"> <img src="./readme/result.gif", width="720"> </p> ## Require 1. [Pytorch](http://pytorch.org/) ## Demo - Download [converted pytorch model](https://www.dropbox.com/s/ae071mfm2qoyc8v/pose_model.pth?dl=0). - Compile the C++ postprocessing: `cd lib/pafprocess; sh make.sh` - `python demo/picture_demo.py` to run the picture demo. - `python demo/web_demo.py` to run the web demo. ## Evalute - `python evaluate/evaluation.py` to evaluate the model on coco val2017 dataset. - It should have `mAP 0.653` for the rtpose, previous rtpose have `mAP 0.577` because we do left and right flip for heatmap and PAF for the evaluation. c ### Main Results | model name| mAP | Inference Time | | :---------: | :---------: |:---------: | |[original rtpose] | 0.653 |-| Download link: [rtpose](https://www.dropbox.com/s/ae071mfm2qoyc8v/pose_model.pth?dl=0) ## Development environment The code is developed using python 3.6 on Ubuntu 18.04. NVIDIA GPUs are needed. The code is developed and tested using 4 1080ti GPU cards. Other platforms or GPU cards are not fully tested. ## Quick start ### 1. Preparation #### 1.1 Prepare the dataset - `cd training; bash getData.sh` to obtain the COCO 2017 images in `/data/root/coco/images/`, keypoints annotations in `/data/root/coco/annotations/`, make them look like this: ``` ${DATA_ROOT} |-- coco |-- annotations |-- person_keypoints_train2017.json |-- person_keypoints_val2017.json |-- images |-- train2017 |-- 000000000009.jpg |-- 000000000025.jpg |-- 000000000030.jpg |-- ... |-- val2017 |-- 000000000139.jpg |-- 000000000285.jpg |-- 000000000632.jpg |-- ... ``` ### 2. How to train the model - Modify the data directory in `train/train_VGG19.py` and `python train/train_VGG19.py` ### Network Architecture - testing architecture  - training architecture