基于C++实现计算机视觉的坐姿矫正系统（论文+源码）

<div align="center"> <img src=".github/Logo_main_black.png", width="300"> </div> ----------------- | |`Default Config` |`CUDA (+Python)` |`CPU (+Python)` |`OpenCL (+Python)`| `Debug` | `Unity` | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | **`Linux`** | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | | **`MacOS`** | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | | **`Windows`** | [](https://ci.appveyor.com/project/gineshidalgo99/openpose/branch/master) | | | | | <!-- Note: Currently using [travis-matrix-badges](https://github.com/bjfish/travis-matrix-badges) vs. traditional [](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) --> [**OpenPose**](https://github.com/CMU-Perceptual-Computing-Lab/openpose) represents the **first real-time multi-person system to jointly detect human body, hand, facial, and foot keypoints (in total 135 keypoints) on single images**. It is **authored by [Gines Hidalgo](https://www.gineshidalgo.com), [Zhe Cao](https://people.eecs.berkeley.edu/~zhecao), [Tomas Simon](http://www.cs.cmu.edu/~tsimon), [Shih-En Wei](https://scholar.google.com/citations?user=sFQD3k4AAAAJ&hl=en), [Hanbyul Joo](https://jhugestar.github.io), and [Yaser Sheikh](http://www.cs.cmu.edu/~yaser)**. Currently, it is being **maintained by [Gines Hidalgo](https://www.gineshidalgo.com) and [Yaadhav Raaj](https://www.raaj.tech)**. In addition, OpenPose would not be possible without the [**CMU Panoptic Studio dataset**](http://domedb.perception.cs.cmu.edu). We would also like to thank all the people who helped OpenPose in any way. The main contributors are listed in [doc/contributors.md](doc/contributors.md). <!-- The [original CVPR 2017 repo](https://github.com/ZheC/Multi-Person-Pose-Estimation) includes Matlab and Python versions, as well as the training code. The body pose estimation work is based on [the original ECCV 2016 demo](https://github.com/CMU-Perceptual-Computing-Lab/caffe_rtpose). --> <p align="center"> <img src="doc/media/pose_face_hands.gif", width="480"> <br> ^{Authors <a href="https://www.gineshidalgo.com" target="_blank">Gines Hidalgo</a> (left) and <a href="https://jhugestar.github.io" target="_blank">Hanbyul Joo</a> (right) in front of the <a href="http://domedb.perception.cs.cmu.edu" target="_blank">CMU Panoptic Studio</a>} </p> ## Features - **Functionality**: - **2D real-time multi-person keypoint detection**: - 15 or 18 or **25-keypoint body/foot keypoint estimation**. **Running time invariant to number of detected people**. - **6-keypoint foot keypoint estimation**. Integrated together with the 25-keypoint body/foot keypoint detector. - **2x21-keypoint hand keypoint estimation**. Currently, **running time depends** on **number of detected people**. - **70-keypoint face keypoint estimation**. Currently, **running time depends** on **number of detected people**. - **3D real-time single-person keypoint detection**: - 3-D triangulation from multiple single views. - Synchronization of Flir cameras handled. - Compatible with Flir/Point Grey cameras, but provided C++ demos to add your custom input. - **Calibration toolbox**: - Easy estimation of distortion, intrinsic, and extrinsic camera parameters. - **Single-person tracking** for further speed up or visual smoothing. - **Input**: Image, video, webcam, Flir/Point Grey and IP camera. Included C++ demos to add your custom input. - **Output**: Basic image + keypoint display/saving (PNG, JPG, AVI, ...), keypoint saving (JSON, XML, YML, ...), and/or keypoints as array class. - **OS**: Ubuntu (20, 18, 16, 14), Windows (10, 8), Mac OSX, Nvidia TX2. - **Training and datasets**: - [**OpenPose Training**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train). - [**Foot dataset website**](https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset/). - **Others**: - Available: command-line demo, C++ wrapper, and C++ API. - [**Python API**](doc/modules/python_module.md). - [**Unity Plugin**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin). - CUDA (Nvidia GPU), OpenCL (AMD GPU), and CPU-only (no GPU) versions. ## Latest Features - Sep 2019: [**Training code released**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_train)! - Jan 2019: [**Unity plugin released**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin)! - Jan 2019: [**Improved Python API**](doc/modules/python_module.md) released! Including body, face, hands, and all the functionality of the C++ API! - Dec 2018: [**Foot dataset released**](https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset) and [**new paper released**](https://arxiv.org/abs/1812.08008)! For further details, check [all released features](doc/released_features.md) and [release notes](doc/release_notes.md). ## Results ### Body and Foot Estimation <p align="center"> <img src="doc/media/dance_foot.gif", width="360"> <br> ^{Testing the <a href="https://www.youtube.com/watch?v=2DiQUX11YaY" target="_blank"><i>Crazy Uptown Funk flashmob in Sydney</i></a> video sequence with OpenPose} </p> ### 3-D Reconstruction Module (Body, Foot, Face, and Hands) <p align="center"> <img src="doc/media/openpose3d.gif", width="360"> <br> ^{Testing the 3D Reconstruction Module of OpenPose} </p> ### Body, Foot, Face, and Hands Estimation <p align="center"> <img src="doc/media/pose_face.gif", width="360"> <img src="doc/media/pose_hands.gif", width="360"> <br>