README.md

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| **`Linux`** |
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|[![Build Status](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose.svg?branch=master)](https://travis-ci.org/CMU-Perceptual-Computing-Lab/openpose)|

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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**. **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**.
- Inputs: Image, video, webcam, and IP camera. Included C++ demos to add your custom input.
- Outputs: Basic GUI display, keypoint saving (JSON, XML, YML, ...), and/or rendered image/video + keypoint saving (PNG, JPG, AVI, ...).
- Available: command-line demo, C++ wrapper, and C++ API.
- OS: Ubuntu, Windows, Nvidia TX2.



## Latest News
- Sep 2017: **CMake** installer and **IP camera** support!
- Jul 2017: [**Windows portable demo**](doc/installation.md#installation---demo)!
- Jul 2017: **Hands** released!
- Jun 2017: **Face** released!
- May 2017: **Windows** version!
- Apr 2017: **Body** released!
- Check all the [release notes](doc/release_notes.md).



## Contents
1. [Latest News](#latest-news)
2. [Results](#results)
3. [Installation, Reinstallation and Uninstallation](#installation-reinstallation-and-uninstallation)
4. [Quick Start](#quick-start)
    1. [Demo](#demo)
    2. [OpenPose Wrapper](#openpose-wrapper)
    3. [Adding An Extra Module](#adding-an-extra-module)
    4. [OpenPose C++ API](#openpose-c-api)
    5. [Standalone Face Or Hand Detector](#standalone-face-or-hand-detector)
    6. [Library Dependencies](#library-dependencies)
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
### Body Estimation
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    <img src="doc/media/dance.gif", width="360">
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### Body, Face, and Hands Estimation
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    <img src="doc/media/pose_face.gif", width="360">
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### Body and Hands Estimation
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    <img src="doc/media/pose_hands.gif", width="360">
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## Installation, Reinstallation and Uninstallation
See [doc/installation.md](doc/installation.md) for instructions on how to build from source or how to download our portable binaries.



## 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
Ideal to process images/video/webcam and display/save the results. Check [doc/demo_overview.md](doc/demo_overview.md).

### OpenPose Wrapper
Ideal if you want to read a specific input, and/or add your custom post-processing function, and/or implement your own display/saving. Take a look to the `Wrapper` tutorial on [examples/tutorial_wrapper/](examples/tutorial_wrapper/). You might create your custom code on [examples/user_code/](examples/user_code/) and compile it by using `make all` in the OpenPose folder.

### Adding An Extra Module
Learn how to easily add an extra module to OpenPose in [doc/library_add_new_module.md](./library_add_new_module.md).

### OpenPose C++ API
Your case if you want to use the C++ API. See [doc/library_introduction.md](doc/library_introduction.md).

### Standalone Face Or Hand Detector
If you do not need the body detector and want to speed up the face keypoint detection, you can use the OpenCV-based approach, see [doc/standalone_face_or_hand_keypoint_detector.md](doc/standalone_face_or_hand_keypoint_detector.md).

You can also use the OpenPose 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, so that the OpenPose detector would fail. See [doc/standalone_face_or_hand_keypoint_detector.md](doc/standalone_face_or_hand_keypoint_detector.md).

### Library Dependencies
OpenPose currently uses OpenCV and Caffe, 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/installation.md#faq](doc/installation.md#faq).



## 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/) and [Bikramjot Hanzra](https://www.linkedin.com/in/bikz05). 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 using Part Affinity Fields},
      year = {2017}
    }

    @inproceedings{simon2017hand,
      author = {Tomas Simon and Hanbyul Joo and Iain Matthews and Yaser Sheikh},
      booktitle = {CVPR},
      title = {Hand Keypoint Detection in Single Images using Multiview Bootstrapping},
      year = {2017}
    }

    @inproceedings{wei2016cpm,
      author = {Shih-En Wei and Varun Ramakrishna and Takeo Kanade and Yaser Sheikh},
      booktitle = {CVPR},
      title = {Convolutional pose machines},
      year = {2016}
    }



## License
OpenPose is freely available for free non-commercial use, and may be redistributed under these conditions. Please, see the [license](LICENSE) for further details. [Interested in a commercial license? Check this link](https://flintbox.com/public/project/47343/). For commercial queries, contact [Yaser Sheikh](http://www.cs.cmu.edu/~yaser/).