adujardin / zed-openpose

Real-time 3D multi-person detection made easy with OpenPose and the ZED

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.