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<img src=".github/Logo_main_black.png", width="300">
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| |`Default Config` |`CUDA (+Python)` |`CPU (+Python)` |`OpenCL (+Python)`| `Debug` | `Unity` |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| **`MacOS`** | [![Status](https://travis-matrix-badges.herokuapp.com/repos/CMU-Perceptual-Computing-Lab/openpose/branches/master/7)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | | [![Status](https://travis-matrix-badges.herokuapp.com/repos/CMU-Perceptual-Computing-Lab/openpose/branches/master/7)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [![Status](https://travis-matrix-badges.herokuapp.com/repos/CMU-Perceptual-Computing-Lab/openpose/branches/master/8)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [![Status](https://travis-matrix-badges.herokuapp.com/repos/CMU-Perceptual-Computing-Lab/openpose/branches/master/9)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [![Status](https://travis-matrix-badges.herokuapp.com/repos/CMU-Perceptual-Computing-Lab/openpose/branches/master/10)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) | [![Status](https://travis-matrix-badges.herokuapp.com/repos/CMU-Perceptual-Computing-Lab/openpose/branches/master/11)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose) |
| **`Windows`** | [![Status](https://ci.appveyor.com/api/projects/status/5leescxxdwen77kg/branch/master?svg=true)](https://ci.appveyor.com/project/gineshidalgo99/openpose/branch/master) | | | | |
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[**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>
<sup>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></sup>
</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>
<sup>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</sup>
</p>
### 3-D Reconstruction Module (Body, Foot, Face, and Hands)
<p align="center">
<img src="doc/media/openpose3d.gif", width="360">
<br>
<sup>Testing the 3D Reconstruction Module of OpenPose</sup>
</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>
没有合适的资源?快使用搜索试试~ 我知道了~
温馨提示
OpenPose代表了第一个在单幅图像上联合检测人体、手部、面部和足部关键点(共 135 个关键点)的实时多人系统。 特征 主要功能: 2D实时多人关键点检测: 15、18 或25 关键点身体/脚关键点估计,包括6 脚关键点。运行时对检测到的人数不变。 2x21-keypoint 手部关键点估计。运行时间取决于检测到的人数。有关运行时不变的替代方案,请参阅OpenPose 训练。 70-keypoint 人脸关键点估计。运行时间取决于检测到的人数。有关运行时不变的替代方案,请参阅OpenPose 训练。 3D实时单人关键点检测: 来自多个单一视图的 3D 三角测量。 已处理 Flir 相机的同步。 兼容 Flir/Point Grey 相机。 校准工具箱:失真、内在和外在相机参数的估计。 单人跟踪以进一步加速或视觉平滑。 输入:图像、视频、网络摄像头、Flir/Point Grey、IP 摄像头,并支持添加您自己的自定义输入源(例如深度摄像头)。 输出:基本图像+关键点显示/保存(PNG,JPG,AVI,...),关键点保存(JSON,XML,YML,...),关键点作为数组类,
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收起资源包目录
OpenPose 1.7.0(身体、面部、手和脚估计的实时多人关键点检测库) (573个子文件)
video.avi 1.33MB
getModels.bat 2KB
getSpinnaker.bat 623B
getCaffe3rdparty.bat 616B
getOpenCV.bat 616B
getFreeglut.bat 612B
getCaffe.bat 600B
Cuda.cmake 13KB
FindLIBIGL.cmake 7KB
FindCuDNN.cmake 2KB
Utils.cmake 2KB
FindGFlags.cmake 2KB
FindGlog.cmake 1KB
FindCaffe.cmake 651B
FindSpinnaker.cmake 433B
COPYING 34KB
cameraParameterEstimation.cpp 126KB
bodyPartConnectorBase.cpp 82KB
spinnakerWrapper.cpp 54KB
poseExtractorCaffe.cpp 40KB
poseParameters.cpp 32KB
keypoint.cpp 29KB
openpose_python.cpp 27KB
unityBinding.cpp 26KB
gridPatternFunctions.cpp 25KB
resizeAndMergeBaseCL.cpp 25KB
handExtractorCaffe.cpp 24KB
personTracker.cpp 24KB
opencl.cpp 24KB
arrayCpuGpu.cpp 24KB
gui3D.cpp 23KB
personIdExtractor.cpp 21KB
array.cpp 20KB
nmsBaseCL.cpp 20KB
producer.cpp 19KB
18_synchronous_custom_all_and_datum.cpp 17KB
cameraParameterReader.cpp 17KB
bodyPartConnectorCaffe.cpp 17KB
fileSystem.cpp 17KB
jointAngleEstimation.cpp 16KB
fileStream.cpp 16KB
pyramidalLK.cpp 16KB
faceExtractorCaffe.cpp 16KB
poseExtractorNet.cpp 16KB
13_asynchronous_custom_input_output_and_datum.cpp 16KB
poseTriangulationPrivate.cpp 15KB
datum.cpp 15KB
05_keypoints_from_images_multi_gpu.cpp 15KB
openCv.cpp 15KB
flagsToOpenPose.cpp 14KB
cocoJsonSaver.cpp 14KB
gui.cpp 13KB
poseTriangulation.cpp 13KB
bodyPartConnectorBaseCL.cpp 13KB
wrapperAuxiliary.cpp 13KB
12_asynchronous_custom_output.cpp 13KB
17_synchronous_custom_output.cpp 13KB
guiAdam.cpp 13KB
errorAndLog.cpp 13KB
08_heatmaps_from_image.cpp 12KB
netCaffe.cpp 12KB
09_keypoints_from_heatmaps.cpp 12KB
poseGpuRenderer.cpp 12KB
07_hand_from_image.cpp 12KB
2_thread_user_input_processing_output_and_datum.cpp 11KB
videoSaver.cpp 11KB
06_face_from_image.cpp 11KB
handDetector.cpp 11KB
11_asynchronous_custom_input_multi_camera.cpp 11KB
04_keypoints_from_images.cpp 10KB
14_synchronous_custom_input.cpp 10KB
10_asynchronous_custom_input.cpp 10KB
1_custom_post_processing.cpp 10KB
nmsCaffe.cpp 10KB
03_keypoints_from_image.cpp 10KB
16_synchronous_custom_postprocessing.cpp 10KB
15_synchronous_custom_preprocessing.cpp 9KB
clTest.cpp 9KB
webcamReader.cpp 9KB
resizeAndMergeCaffe.cpp 9KB
matrix.cpp 9KB
1_thread_user_processing_function.cpp 9KB
guiInfoAdder.cpp 8KB
openpose.cpp 8KB
poseExtractor.cpp 8KB
profiler.cpp 8KB
nmsBase.cpp 8KB
rectangle.cpp 8KB
cvMatToOpInput.cpp 7KB
calibration.cpp 7KB
videoCaptureReader.cpp 7KB
faceDetector.cpp 7KB
netOpenCv.cpp 6KB
cvMatToOpOutput.cpp 6KB
udpSender.cpp 6KB
string.cpp 6KB
point.cpp 6KB
keypointScaler.cpp 6KB
maximumCaffe.cpp 6KB
datumProducer.cpp 6KB
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