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OpenPose ZED

This sample show how to simply use the ZED with OpenPose, the deep learning framework that detects the skeleton from a single 2D image. The 3D information provided by the ZED is used to place the joints in space. The output is a 3D view of the skeletons.

Installation

Openpose

This sample can be put in the folder examples/user_code/ OR preferably, compile and install openpose with the cmake and compile this anywhere

The installation process is very easy using cmake.

Clone the repository :

    git clone https://github.com/CMU-Perceptual-Computing-Lab/openpose/

Build and install it :

    cd openpose
    mkdir build
    cmake .. # This can take a while
    make -j8
    sudo make install

ZED SDK

The ZED SDK is also a requirement for this sample, download the ZED SDK and follows the instructions.

It requires ZED SDK 3.

Build the program

Open a terminal in the sample directory and execute the following command:

    mkdir build
    cd build
    cmake ..
    make -j8

We then need to make a symbolic link to the models folder to be able to loads it

    ln -s ~/path/to/openpose/models "$(pwd)"

A models folder should now be in the build folder

Run the program

  • Navigate to the build directory and launch the executable

  • Or open a terminal in the build directory and run the sample :

      ./zed_openpose -net_resolution 656x368

Options

Beyond the openpose option, several more were added, mainly:

Option Description
svo_path SVO file path to load instead of opening the ZED
ogl_ptcloud Boolean to show the point cloud in the OpenGL window
estimate_floor_plane Boolean to align the point cloud on the floor plane
opencv_display Enable the 2D View of OpenPose output
depth_display Display the depth map with OpenCV

Example :

    ./zed_openpose -net_resolution 320x240 -ogl_ptcloud true -svo_path ~/foo/bar.svo

Notes

  • This sample is a proof of concept and might not be robust to every situation, especially to detect the floor plane if the environment is cluttered.
  • This sample was only tested on Linux but should be easy to run on Windows.
  • This sample requires both Openpose and the ZED SDK which are heavily relying on the GPU.
  • Only the body keypoints are currently used, however we could imagine doing the same for hand and facial keypoints, though the precision required might be a limiting factor.