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- Ubuntu 14 and 16.
- Windows 8 and 10.
- Nvidia Jetson TX2, installation instructions in doc/installation_jetson_tx2.md.
- OpenPose has also been used on Windows 7, Mac, CentOS, and Nvidia Jetson (TK1 and TX1) embedded systems. However, we do not officially support them at the moment.
- NVIDIA graphics card with at least 1.6 GB available (the
nvidia-smicommand checks the available GPU memory in Ubuntu). - At least 2 GB of free RAM memory.
- Highly recommended: cuDNN and a CPU with at least 8 cores.
Note: These requirements assume the default configuration (i.e. --net_resolution "656x368" and scale_number 1). You might need more (with a greater net resolution and/or number of scales) or less resources (with smaller net resolution and/or using the MPI and MPI_4 models).
The first step is to clone the OpenPose repository. It might be done with GitHub Desktop in Windows and from the terminal in Ubuntu:
git clone https://github.com/CMU-Perceptual-Computing-Lab/openposeOpenPose can be easily updated by clicking the synchronization button at the top-right part in GitHub Desktop in Windows, or by running git pull origin master in Ubuntu. After OpenPose has been updated, just run the Reinstallation section described below for your specific Operating System.
Recommended installation method, it is simpler and offers more customization settings. See doc/installation_cmake.md.
CUDA, cuDNN, OpenCV and Atlas must be already installed on your machine:
1. [CUDA](https://developer.nvidia.com/cuda-80-ga2-download-archive) must be installed. You should reboot your machine after installing CUDA.
2. [cuDNN](https://developer.nvidia.com/cudnn): Once you have downloaded it, just unzip it and copy (merge) the contents on the CUDA folder, e.g. `/usr/local/cuda-8.0/`. Note: We found OpenPose working ~10% faster with cuDNN 5.1 compared to cuDNN 6. Otherwise, check [Compiling without cuDNN](#compiling-without-cudnn).
3. OpenCV can be installed with `apt-get install libopencv-dev`. If you have compiled OpenCV 3 by your own, follow [Manual Compilation](#manual-compilation). After both Makefile.config files have been generated, edit them and uncomment the line `# OPENCV_VERSION := 3`. You might alternatively modify all `Makefile.config.UbuntuXX` files and then run the scripts in step 2.
4. In addition, OpenCV 3 does not incorporate the `opencv_contrib` module by default. Assuming you have OpenCV 3 compiled with the contrib module and you want to use it, append `opencv_contrib` at the end of the line `LIBRARIES += opencv_core opencv_highgui opencv_imgproc` in the `Makefile` file.
5. Atlas can be installed with `sudo apt-get install libatlas-base-dev`. Instead of Atlas, you can use OpenBLAS or Intel MKL by modifying the line `BLAS := atlas` in the same way as previosuly mentioned for the OpenCV version selection.
Build Caffe & the OpenPose library + download the required Caffe models for Ubuntu 14.04 or 16.04 (auto-detected for the script) and CUDA 8:
bash ./ubuntu/install_caffe_and_openpose_if_cuda8.shHighly important: This script only works with CUDA 8 and Ubuntu 14 or 16. Otherwise, see doc/installation_cmake.md or Installation - Manual Compilation.
Alternatively to the script installation, if you want to use CUDA 7, avoid using sh scripts, change some configuration labels (e.g. OpenCV version), etc., then:
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Install the Caffe prerequisites.
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Compile Caffe and OpenPose by running these lines:
### Install Caffe ### git submodule update --init --recursive cd 3rdparty/caffe/ # Select your desired Makefile file (run only one of the next 4 commands) cp Makefile.config.Ubuntu14_cuda7.example Makefile.config # Ubuntu 14, cuda 7 cp Makefile.config.Ubuntu14_cuda8.example Makefile.config # Ubuntu 14, cuda 8 cp Makefile.config.Ubuntu16_cuda7.example Makefile.config # Ubuntu 16, cuda 7 cp Makefile.config.Ubuntu16_cuda8.example Makefile.config # Ubuntu 16, cuda 8 # Change any custom flag from the resulting Makefile.config (e.g. OpenCV 3, Atlas/OpenBLAS/MKL, etc.) # Compile Caffe make all -j`nproc` && make distribute -j`nproc` ### Install OpenPose ### cd ../../models/ bash ./getModels.sh # It just downloads the Caffe trained models cd .. cp ubuntu/Makefile.example Makefile # Same file cp command as the one used for Caffe cp ubuntu/Makefile.config.Ubuntu14_cuda7.example Makefile.config # Change any custom flag from the resulting Makefile.config (e.g. OpenCV 3, Atlas/OpenBLAS/MKL, etc.) make all -j`nproc`NOTE: If you want to use your own Caffe distribution, follow the steps on Custom Caffe section and later re-compile the OpenPose library:
bash ./install_openpose_if_cuda8.shNote: These steps only need to be performed once. If you are interested in making changes to the OpenPose library, you can simply recompile it with:
make clean make all -j$(NUM_CORES)
Highly important: There are 2 Makefile.config.Ubuntu##.example analogous files, one in the main folder and one in 3rdparty/caffe/, corresponding to OpenPose and Caffe configuration files respectively. Any change must be done to both files (e.g. OpenCV 3 flag, Atlab/OpenBLAS/MKL flag, etc.). E.g. for CUDA 8 and Ubuntu16: 3rdparty/caffe/Makefile.config.Ubuntu16_cuda8.example and ubuntu/Makefile.config.Ubuntu16_cuda8.example.
If you updated some software that our library or 3rdparty use, or you simply want to reinstall it:
- Clean the OpenPose and Caffe compilation folders:
make clean && cd 3rdparty/caffe && make clean
- Repeat the Installation steps. You do not need to download the models again.
You just need to remove the OpenPose folder, by default called openpose/. E.g. rm -rf openpose/.
Q: Out of memory error - I get an error similar to: Check failed: error == cudaSuccess (2 vs. 0) out of memory.
A: Most probably cuDNN is not installed/enabled, the default Caffe model uses >12 GB of GPU memory, cuDNN reduces it to ~1.5 GB.
Q: Low speed - OpenPose is quite slow, is it normal? How can I speed it up?
A: Check the OpenPose Benchmark to discover the approximate speed of your graphics card. Some speed tips:
1. Use cuDNN 5.1 (cuDNN 6 is ~10% slower).
2. Reduce the `--net_resolution` (e.g. to 320x176) (lower accuracy).
3. For face, reduce the `--face_net_resolution`. The resolution 320x320 usually works pretty decently.
4. Use the `MPI_4_layers` model (lower accuracy and lower number of parts).
5. Change GPU rendering by CPU rendering to get approximately +0.5 FPS (`--render_pose 1`).
Q: Webcam is slow - Using a folder with images matches the speed FPS benchmarks, but the webcam has lower FPS. Note: often on Windows.
A: OpenCV has some issues with some camera drivers (specially on Windows). The first step should be to compile OpenCV by your own and re-compile OpenPose after that (following the Reinstallation section in Ubuntu or cleaning the project on Windows). If the speed is still slower, you can better debug it by running a webcam OpenCV example (e.g. this C++ example). If you are able to get the proper FPS with the OpenCV demo but OpenPose is still low, then let us know!
Q: Video and/or webcam are not working - Using a folder with images does work, but the video and/or the webcam do not. Note: often on Windows.
A: OpenCV has some issues with some camera drivers and video codecs (specially on Windows). Follow the same steps as the Webcam is slow question to test the webcam is working. After re-compiling OpenCV, you can also try this OpenCV example for video.
Q: I am getting an error of the type: munmap_chunk()/free/invalid pointer.
A: In order to run OpenCV 3.X and Caffe simultaneously, OpenCV must be compiled without WITH_GTK and with WITH_QT flags. On Ubuntu 16.04 the qt5 package is "qt5-default" and the OpenCV cmake option is WITH_QT.