<div align="center">
<img src=".github/Logo_main_black.png", width="300">
</div>
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| **`Linux`** |
|-------------|
|[](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, and facial keypoints (in total 130 keypoints) on single images**.
<p align="center">
<img src="doc/media/pose_face_hands.gif", width="480">
</p>
**Functionality**:
- **Real-time multi-person keypoint detection**.
- 15 or **18-keypoint body estimation**. **Running time invariant to number of detected people**.
- **2x21-keypoint hand** estimation. Currently, **running time depends** on **number of detected people**.
- **70-keypoint face** estimation. Currently, **running time depends** on **number of detected people**.
- **Input**: Image, video, webcam, 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.
- Available: command-line demo, C++ wrapper, and C++ API.
- **OS**: Ubuntu (14, 16), Windows (8, 10), Nvidia TX2.
## Latest Features
- Mar 2018: [**CPU version**](doc/installation.md#cpu-version)!
- Mar 2018: Improved [**3-D keypoint reconstruction module**](doc/3d_reconstruction_demo.md) (from multiple camera views)!
- Sep 2017: [**CMake**](doc/installation.md) installer and **IP camera** support!
- Jul 2017: [**Windows portable binaries and demo**](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases)!
- Jul 2017: **Hands** released!
- Jun 2017: **Face** released!
For further details, check [all released features](doc/released_features.md) and [release notes](doc/release_notes.md).
## Contents
1. [Latest Features](#latest-features)
2. [Results](#results)
3. [Installation, Reinstallation and Uninstallation](#installation-reinstallation-and-uninstallation)
4. [Quick Start](#quick-start)
5. [Output](#output)
6. [Speeding Up OpenPose and Benchmark](#speeding-up-openpose-and-benchmark)
7. [Send Us Failure Cases and Feedback!](#send-us-failure-cases-and-feedback)
8. [Authors and Contributors](#authors-and-contributors)
9. [Citation](#citation)
10. [License](#license)
## Results
### 3-D Reconstruction Module
<p align="center">
<img src="doc/media/openpose3d.png", width="360">
</p>
### Body Estimation
<p align="center">
<img src="doc/media/dance.gif", width="360">
</p>
### Body, Face, and Hands Estimation
<p align="center">
<img src="doc/media/pose_face.gif", width="360">
</p>
### Body and Hands Estimation
<p align="center">
<img src="doc/media/pose_hands.gif", width="360">
</p>
## Installation, Reinstallation and Uninstallation
**Windows portable version**: Simply download and use the latest version from the [Releases](https://github.com/CMU-Perceptual-Computing-Lab/openpose/releases) section.
Otherwise, check [doc/installation.md](doc/installation.md) for instructions on how to build OpenPose from source.
## Quick Start
Most users do not need the [OpenPose C++ API](#openpose-c-api), but they can simply use the basic [Demo](#demo) and/or [OpenPose Wrapper](#openpose-wrapper).
- **Demo**: To easily process images/video/webcam and display/save the results. See [doc/demo_overview.md](doc/demo_overview.md). E.g. run OpenPose in a video with:
```
# Ubuntu
./build/examples/openpose/openpose.bin --video examples/media/video.avi
:: Windows - Portable Demo
bin\OpenPoseDemo.exe --video examples\media\video.avi
```
- **OpenPose Wrapper**: If you want to read a specific input, and/or add your custom post-processing function, and/or implement your own display/saving, check the `Wrapper` tutorial on [examples/tutorial_wrapper/](examples/tutorial_wrapper/). You can create your custom code on [examples/user_code/](examples/user_code/) and quickly compile it by using `make all` in the OpenPose folder (assuming Makefile installer).
- **OpenPose C++ API**: See [doc/library_introduction.md](doc/library_introduction.md).
- **Adding an extra module**: Check [doc/library_add_new_module.md](./doc/library_add_new_module.md).
- **Standalone face or hand detector**:
- **Face** keypoint detection **without body** keypoint detection: If you want to speed it up (but also reduce amount of detected faces), check the OpenCV-face-detector approach in [doc/standalone_face_or_hand_keypoint_detector.md](doc/standalone_face_or_hand_keypoint_detector.md).
- **Use your own face/hand detector**: You can use the hand and/or face keypoint detectors with your own face or hand detectors, rather than using the body detector. E.g. useful for camera views at which the hands are visible but not the body (OpenPose detector would fail). See [doc/standalone_face_or_hand_keypoint_detector.md](doc/standalone_face_or_hand_keypoint_detector.md).
- **Library dependencies**: OpenPose uses default Caffe and OpenCV, as well as any Caffe dependency. The demos additionally use GFlags. It could easily be ported to other deep learning frameworks (Tensorflow, Torch, ...). Feel free to make a pull request if you implement any of those!
## Output
Output (format, keypoint index ordering, etc.) in [doc/output.md](doc/output.md).
## Speeding Up OpenPose and Benchmark
Check the OpenPose Benchmark and some hints to speed up OpenPose on [doc/faq.md#speed-up-and-benchmark](doc/faq.md#speed-up-and-benchmark).
## Send Us Failure Cases and Feedback!
Our library is open source for research purposes, and we want to continuously improve it! So please, let us know if...
1. ... you find videos or images where OpenPose does not seems to work well. Feel free to send them to openposecmu@gmail.com (email only for failure cases!), we will use them to improve the quality of the algorithm!
2. ... you find any bug (in functionality or speed).
3. ... you added some functionality to some class or some new Worker<T> subclass which we might potentially incorporate.
4. ... you know how to speed up or improve any part of the library.
5. ... you have a request about possible functionality.
6. ... etc.
Just comment on GitHub or make a pull request and we will answer as soon as possible! Send us an email if you use the library to make a cool demo or YouTube video!
## Authors and Contributors
OpenPose is authored by [Gines Hidalgo](https://www.gineshidalgo.com/), [Zhe Cao](http://www.andrew.cmu.edu/user/zhecao), [Tomas Simon](http://www.cs.cmu.edu/~tsimon/), [Shih-En Wei](https://scholar.google.com/citations?user=sFQD3k4AAAAJ&hl=en), [Hanbyul Joo](http://www.cs.cmu.edu/~hanbyulj/), and [Yaser Sheikh](http://www.cs.cmu.edu/~yaser/). Currently, it is being maintained by [Gines Hidalgo](https://www.gineshidalgo.com/), [Bikramjot Hanzra](https://www.linkedin.com/in/bikz05), and [Yaadhav Raaj](https://www.linkedin.com/in/yaadhavraaj). 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).
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).
## Citation
Please cite these papers in your publications if it helps your research (the face keypoint detector was trained using the same procedure described in [Simon et al. 2017]):
@inproceedings{cao2017realtime,
author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
booktitle = {CVPR},
title = {Realtime Multi-Person 2D Pose Estimation us
【源代码】openpose-1.3.0.7z
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【源代码】openpose-1.3.0.7z （446个子文件） 
video.avi 1.33MB
getModels.bat 2KB getCaffe3rdparty.bat 613B getFreeglut.bat 612B getOpenCV.bat 598B getCaffe.bat 597B Cuda.cmake 12KB Utils.cmake 2KB FindCuDNN.cmake 2KB FindGFlags.cmake 2KB FindGlog.cmake 1KB FindCaffe.cmake 651B FindSpinnaker.cmake 433B COPYING 34KB spinnakerWrapper.cpp 37KB 2_user_synchronous.cpp 36KB 1_user_asynchronous_output.cpp 35KB 3_user_asynchronous.cpp 34KB openpose.cpp 32KB 1_custom_post_processing.cpp 31KB handExtractorCaffe.cpp 24KB gui3D.cpp 21KB opencl.cpp 21KB bodyPartConnectorBase.cpp 20KB resizeAndMergeBaseCL.cpp 20KB poseExtractorCaffe.cpp 19KB poseParameters.cpp 19KB faceExtractorCaffe.cpp 17KB fileStream.cpp 16KB keypoint.cpp 15KB array.cpp 14KB poseExtractor.cpp 14KB nmsBaseCL.cpp 13KB flagsToOpenPose.cpp 13KB producer.cpp 13KB 2_user_processing_function.cpp 12KB 4_user_input_processing_output_and_datum.cpp 11KB handDetector.cpp 11KB 3_user_input_processing_and_output.cpp 11KB 2_extract_pose_or_heatmat_from_image.cpp 10KB 1_openpose_read_and_display.cpp 10KB netCaffe.cpp 10KB wrapperAuxiliary.cpp 10KB fileSystem.cpp 10KB cameraParameterReader.cpp 10KB 1_extract_from_image.cpp 9KB datum.cpp 9KB poseTriangulation.cpp 9KB openCv.cpp 9KB gui.cpp 9KB poseGpuRenderer.cpp 9KB resizeAndMergeCaffe.cpp 9KB bodyPartConnectorCaffe.cpp 8KB nmsBase.cpp 8KB rectangle.cpp 8KB nmsCaffe.cpp 7KB errorAndLog.cpp 7KB imageDirectoryReader.cpp 7KB profiler.cpp 7KB faceDetector.cpp 7KB point.cpp 6KB resizeAndMergeBase.cpp 6KB gpuRenderer.cpp 6KB webcamReader.cpp 6KB guiInfoAdder.cpp 5KB maximumCaffe.cpp 5KB frameDisplayer.cpp 5KB flirReader.cpp 5KB videoReader.cpp 5KB scaleAndSizeExtractor.cpp 5KB handFromJsonTest.cpp 5KB string.cpp 4KB videoCaptureReader.cpp 4KB resizeTest.cpp 4KB jsonOfstream.cpp 4KB poseParametersRender.cpp 4KB keepTopNPeople.cpp 4KB cocoJsonSaver.cpp 4KB renderer.cpp 4KB cuda.cpp 4KB handGpuRenderer.cpp 4KB videoSaver.cpp 4KB handExtractor.cpp 3KB faceGpuRenderer.cpp 3KB faceExtractor.cpp 3KB maximumBase.cpp 3KB keypointScaler.cpp 3KB heatMapSaver.cpp 3KB poseCpuRenderer.cpp 2KB faceDetectorOpenCV.cpp 2KB wrapperStructPose.cpp 2KB cvMatToOpInput.cpp 2KB poseRenderer.cpp 2KB ipCameraReader.cpp 2KB cvMatToOpOutput.cpp 2KB imageSaver.cpp 2KB keypointSaver.cpp 1KB handDetectorFromTxt.cpp 1KB renderPose.cpp 1KB wrapperStructOutput.cpp 1KB共 446 条
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