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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.

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Notes from Jaerock

This repository is a slightly modified version of the original. I assume that we are using a Conda environment.

Prerequisites

I assume that CUDA and cuDNN are already installed in your system.

• Install CMake GUI

  $ sudo apt install cmake-qt-gui

• Install protobuf-compiler

  $ sudo apt install protobuf-compiler 

Create a Conda environment

$ conda create -n openpose python=3.6

Then, activate it.

$ source activate openpose

Now we are in the new Conda environment, openpose.

Install Some Important Conda Packages for OpenPose

(openpose) $ conda install opencv
(openpose) $ conda install protobuf

Clone this repository

My OpenPose version is 1.3.0. I cloned it on June 14, 2018.

Build OpenPose with Caffe

Preparation

I assume that your system does not have Caffe installed.

• Start CMake GUI

  (openpose) $ cmake-gui

  Note that you must run this in the openpose environment.

• Source... must be your main openpose folder.

• Build... can be same as the Source. But it is strongly recommended to use /build after the source folder name.

• Click "Configure"

  • You are not supposed to see any errors.
  • This will automatically start downloading Caffe and datasets.

• If everything is OK, then click "Generate" to create necessary files to build the openpose.

Build

• Go to the build folder.

  (openpose) $ make -j`nproc`

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Work with 3D Pose Baseline

• Create folders using init.sh

With a live camera

• Run OpenPose. All 2D joints in each video frame will be saved as the JSON file format.

  (openpose) $ ./build/examples/openpose/openpose.bin --write_json json_output_live

• Run 3D Pose Baseline in a new Terminal and activate the Conda environment for the 3D Pose Baseline. In my case, the environment name is pose-baseline. Note that you can find my repository for the 3D Pose Baseline in my github.

(pose-baseline) $ JSON_DIR=../openpose/json_output_live
(pose-baseline) $ python src/openpose_3dpose_sandbox_realtime.py --camera_frame --residual --batch_norm --dropout 0.5 --max_norm --evaluateActionWise --use_sh --epochs 200 --load 4874200 --openpose $JSON_DIR --write_gif --gif_fps 30 --verbose 3 

With a video

• Run OpenPose with the options as follows. You may need to change the filename for your own video files.

  ./build/examples/openpose/openpose.bin --video examples/media/walking.ogv --write_json json_output

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Features

• Functionality:
  • 2D real-time multi-person keypoint detection:
    • 15 or 18-keypoint body keypoint estimation. Running time invariant to number of detected people.
    • 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 multi-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.
• 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 (14, 16), Windows (8, 10), Mac OSX, Nvidia TX2.
• Others:
  • Available: command-line demo, C++ wrapper, and C++ API.
  • CUDA (Nvidia GPU) and CPU versions.

Latest Features

• Jun 2018: Mac OSX version (CPU)!
• Mar 2018: CPU version!
• Mar 2018: Improved 3-D keypoint reconstruction module (from multiple camera views)!
• Sep 2017: CMake installer and IP camera support!
• Jul 2017: Windows portable binaries and demo!
• Jul 2017: Hands released!
• Jun 2017: Face released! For further details, check all released features and release notes.

Contents

1. Latest Features
2. Results
3. Installation, Reinstallation and Uninstallation
4. Quick Start
5. Output
6. Speeding Up OpenPose and Benchmark
7. Send Us Failure Cases and Feedback!
8. Authors and Contributors
9. Citation
10. License

Results

3-D Reconstruction Module

Body Estimation

Body, Face, and Hands Estimation

Body and Hands Estimation

Installation, Reinstallation and Uninstallation

Windows portable version: Simply download and use the latest version from the Releases section.

Otherwise, check doc/installation.md for instructions on how to build OpenPose from source.

Quick Start

Most users do not need the OpenPose C++ API, but they can simply use the basic Demo and/or OpenPose Wrapper.

• Demo: To easily process images/video/webcam and display/save the results. See 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

• Calibration toolbox: To easily calibrate your cameras for 3-D OpenPose or any other stereo vision task. See doc/calibration_demo.md.

• 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/. You can create your custom code on 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.

• Adding an extra module: Check 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.
  • 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.

• 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.

Speeding Up OpenPose and Benchmark

Check the OpenPose Benchmark as well as some hints to speed up and/or reduce the memory requirements for OpenPose on doc/faq.md#speed-up-memory-reduction-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 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, Zhe Cao, Tomas Simon, Shih-En Wei, Hanbyul Joo, and Yaser Sheikh. Currently, it is being maintained by Gines Hidalgo and Yaadhav Raaj. The original CVPR 2017 repo includes Matlab and Python versions, as well as the training code. The body pose estimation work is based on the original ECCV 2016 demo.

In addition, OpenPose would not be possible without the CMU Panoptic Studio dataset.

We would also like to thank all the people who helped OpenPose in any way. The main contributors are listed in 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 for further details. Interested in a commercial license? Check this link. For commercial queries, contact Yaser Sheikh.