Open Pose Person Detector

This is a person detector written on top of OpenPose. This detector is very accuracte compared to commonly used detectors like Haar detector, HOG Person Detector and Tensorflow Object Detection based detector.

Following image shows an example of detected people walking in an airport terminal. Small yellow crosses corresponds to the keypoints detected.

Prerequisites

• Compiled version of OpenPose
  • It is recommended to compile caffe shipped with OpenPose as well.
• Boost (apt install libboost-all-dev)
• CUDA 8 (Not tested on other versions)
• Numpy
• OpenCV (You will require opencv to be built when compiling OpenPose earlier. So follow the instructions given by OpenPose)
• Compiled version of pyboostcvconverter

Installation

Build content of this directory using CMake and move output file libOpenPersonDetectorAPI.so to project root. Use CMAKE-GUI to configure parameters of build process.

• Step 1: Download and Compile OpenPose Library. Obtain openpose_directory/build/lib/libopenpose.so

• Step 2: Clone this repo and navigate to root directory of this repo.

• Step 3: Using Terminal, Run cmake-gui ..

• Step 4: Define a suitable build directory. Click Configure.

• Step 5: If prompted, select GNU Compilers as Compiler Suite.

• Step 6: Provide required paths using UI configuration. Refer tool-tip texts for details. Usual paths/directories required for CMake:

  • Boost include directory /usr/include/boost
  • CUDA home /usr/local/cuda
  • Numpy include directory /usr/include/python3.5m
  • OpenCV Lib directory {$OpenCV Build Directory}/lib
  • OpenPose lib directory ${OpenPose build directory}/src/openpose
  • OpenPose Caffe Home Directory ${OpenPose project root}/3rdparty/caffe
  • OpenPose Caffe include Directory ${OpenPose project root}/3rdparty/caffe/include
  • OpenPose Caffe lib Directory ${OpenPose project root}/3rdparty/caffe/build/lib
  • OpenPose Include Dir ${OpenPose_root}/include
  • pyboostcvconverter include directory ${pyboostcvconverter_home}/include
  • pyboostcvconverter lib directory ${pyboostcvconverter_build_dir}
  • Python include directory /usr/include/python3.5
  • Python Lib Directory /usr/lib/x86_64-linux-gnu

• Step 7: Click Configure and Generate.

• Step 8: Navigate to build directory and run make.

• Step 9: Copy output file libOpenPersonDetectorAPI.so to Project Root of this repo

• Finally, you need to download the OpenPose models into ${REPO}/models directory similarly to OpenPose.

You may need to set the LD_LIBRARY_PATH to [OPENPOSE_CAFFE_LIB]:[CUDA_HOME]/lib64:[OPENPOSE_LIB]:[Python3.5_Path]/dist-packages.

Example

You can run the preview.py to see a demonstration.

python3 preview.py <path-to-video>

You can provide following additional options as well.

• --scale - Scale of the video
• --vdup - Vertical duplication of the video. This will process two frames at a time by connecting them vertically but sending through the detector only once.
• --hdup - Horizontal duplication. Similar to vertical duplication, this will join two frames horizontally before sending to detector.

API

This detector will output a list of detections where each detection is an object of the following class.

class PersonDetection:
    """
    Detection of a person
    """

    def __init__(self):
        self.tracked_points = {}  # Points detected by OP
        self.person_bound = None  # Boundary of person
        self.central_bound = None  # Boundary of central body of person (no hands and feet for X coordinate)
        self.upper_body_bound = None  # Boundary of upper body of person
        self.central_point = None  # Central point of person
        self.leg_point = None  # Average Feet point of person
        self.leg_count = None  # Number of detected feet
        self.estimated_leg_point = None  # Estimated feet point of person
        self.neck_hip_ankle_ratio = None
        self.neck_hip_knee_ratio = None
        self.head_direction = None
        self.head_direction_error = None
        self.hip_point = None
        self.elbow_point = None

Contributions

Contributions are more than welcome including improvements, bug fixes and adding new issues.

Notice

This library is written on top of OpenPose library which has its own License. Since we have not included OpenPose source code, we release our library with Apache licence. But this library users should be aware of OpenPose license when using it for commercial purposes.