## 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**. - **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 (14, 16), Windows (8, 10), Mac OSX, Nvidia TX2. - **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 versions. ## Latest Features - Jan 2018: [**Unity plugin released**](https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin)! - Jan 2018: [**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**](https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset) and [**new paper released**](https://arxiv.org/abs/1812.08008)! - Sep 2018: [**Experimental single-person tracker**](doc/quick_start.md#tracking) for further speed up or visual smoothing! - Jun 2018: [**Combined body-foot model released! 40% faster and 5% more accurate**](doc/installation.md)! - Jun 2018: [**OpenCL/AMD graphic card version**](doc/installation.md) released! - Jun 2018: [**Calibration toolbox**](doc/modules/calibration_module.md) released! For further details, check [all released features](doc/released_features.md) and [release notes](doc/release_notes.md). ## Results ### Body and Foot Estimation
### 3-D Reconstruction Module (Body, Foot, Face, and Hands)
### Body, Foot, Face, and Hands Estimation
### Unity Plugin
### Runtime Analysis Inference time comparison between the 3 available pose estimation libraries: OpenPose, Alpha-Pose (fast Pytorch version), and Mask R-CNN:
This analysis was performed using the same images for each algorithm and a batch size of 1. Each analysis was repeated 1000 times and then averaged. This was all performed on a system with a Nvidia 1080 Ti and CUDA 8. Megvii (Face++) and MSRA GitHub repositories were excluded because they only provide pose estimation results given a cropped person. However, they suffer the same problem than Alpha-Pose and Mask R-CNN, their runtimes grow linearly with the number of people. ## Contents 1. [Features](#features) 2. [Latest Features](#latest-features) 3. [Results](#results) 4. [Installation, Reinstallation and Uninstallation](#installation-reinstallation-and-uninstallation) 5. [Quick Start](#quick-start) 6. [Output](#output) 7. [Speeding Up OpenPose and Benchmark](#speeding-up-openpose-and-benchmark) 8. [Foot Dataset](#foot-dataset) 9. [Send Us Failure Cases and Feedback!](#send-us-failure-cases-and-feedback) 10. [Authors and Contributors](#authors-and-contributors) 11. [Citation](#citation) 12. [License](#license) ## 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++/Python API, but can simply use the OpenPose Demo: - **OpenPose 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 ``` - **Calibration toolbox**: To easily calibrate your cameras for 3-D OpenPose or any other stereo vision task. See [doc/modules/calibration_module.md](doc/modules/calibration_module.md). - **OpenPose C++ API**: 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 C++ API tutorial on [examples/tutorial_api_cpp/](examples/tutorial_api_cpp/) and [doc/library_introduction.md](doc/library_introduction.md). You can create your custom code on [examples/user_code/](examples/user_code/) and quickly compile it with CMake when compiling the whole OpenPose project. Quickly **add your custom code**: See [examples/user_code/README.md](examples/user_code/README.md) for further details. - **OpenPose Python API**: Analogously to the C++ API, find the tutorial for the Python API on [examples/tutorial_api_python/](examples/tutorial_api_python/). - **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). ## Output Output (format, keypoint index ordering, etc.) in [doc/output.md](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/speed_up_preserving_accuracy.md](doc/speed_up_preserving_accuracy.md). ## Foot Dataset Check the [foot dataset website](https://cmu-perceptual-computing-lab.github.io/foot_keypoint_dataset/) and new [OpenPose paper](https://arxiv.org/abs/1812.08008) for more information. ## 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