Human Pose Estimation C++ Demo

This demo showcases the work of multi-person 2D pose estimation algorithm. The task is to predict a pose: body skeleton, which consists of keypoints and connections between them, for every person in an input video. The pose may contain up to 18 keypoints: ears, eyes, nose, neck, shoulders, elbows, wrists, hips, knees, and ankles. Some of potential use cases of the algorithm are action recognition and behavior understanding. You can use the following pre-trained model with the demo:

• human-pose-estimation-0001, which is a human pose estimation network, that produces two feature vectors. The algorithm uses these feature vectors to predict human poses.

For more information about the pre-trained model, refer to the model documentation.

The input frame height is scaled to model height, frame width is scaled to preserve initial aspect ratio and padded to multiple of 8.

Other demo objectives are:

• Video/Camera as inputs, via OpenCV*
• Visualization of all estimated poses

How It Works

On the start-up, the application reads command line parameters and loads human pose estimation model. Upon getting a frame from the OpenCV VideoCapture, the application executes human pose estimation algorithm and displays the results.

NOTE: By default, Open Model Zoo demos expect input with BGR channels order. If you trained your model to work with RGB order, you need to manually rearrange the default channels order in the demo application or reconvert your model using the Model Optimizer tool with --reverse_input_channels argument specified. For more information about the argument, refer to When to Reverse Input Channels section of Converting a Model Using General Conversion Parameters.

Running

Running the application with the -h option yields the following usage message:

InferenceEngine:
    API version ............ <version>
    Build .................. <number>
 
human_pose_estimation_demo [OPTION]
Options:
 
    -h                        Print a usage message.
    -at "<type>"              Required. Type of the network, either "ae" for
                              Associative Embedding or "openpose" for OpenPose.
    -i                        Required. An input to process. The input must be a single image, a folder of images, video file or camera id.
    -m "<path>"               Required. Path to an .xml file with a trained model.
    -o "<path>"               Optional. Name of output to save.
    -limit "<num>"            Optional. Number of frames to store in output. If 0 is set, all frames are stored.
    -tsize                    Optional. Target input size.
    -t                        Optional. Probability threshold for poses filtering.
      -l "<absolute_path>"    Required for CPU custom layers. Absolute path to a shared library with the kernel implementations.
          Or
      -c "<absolute_path>"    Required for GPU custom kernels. Absolute path to the .xml file with the kernel descriptions.
    -d "<device>"             Optional. Specify the target device to infer on (the list of available devices is shown below). Default value is CPU. Use "-d HETERO:<comma-separated_devices_list>" format to specify HETERO plugin. The demo will look for a suitable plugin for a specified device.
    -pc                       Optional. Enables per-layer performance report.
    -nireq "<integer>"        Optional. Number of infer requests. If this option is omitted, number of infer requests is determined automatically.
    -nthreads "<integer>"     Optional. Number of threads.
    -nstreams                 Optional. Number of streams to use for inference on the CPU or/and GPU in throughput mode (for HETERO and MULTI device cases use format <device1>:<nstreams1>,<device2>:<nstreams2> or just <nstreams>)
    -loop                     Optional. Enable reading the input in a loop.
    -no_show                  Optional. Do not show processed video.
    -u                        Optional. List of monitors to show initially.

Running the application with an empty list of options yields an error message.

To run the demo, you can use public or pre-trained models. To download the pre-trained models, use the OpenVINO Model Downloader. The list of models supported by the demo is in the models.lst file in the demo's directory.

NOTE: Before running the demo with a trained model, make sure the model is converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

For example, to do inference on a CPU, run the following command:

./human_pose_estimation_demo -i <path_to_video>/input_video.mp4 -m <path_to_model>/human-pose-estimation-0001.xml -d CPU -at openpose

Demo Output

The demo uses OpenCV to display the resulting frame with estimated poses and text report of FPS - frames per second performance for the human pose estimation demo.

NOTE: On VPU devices (Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs) this demo has been tested on the following Model Downloader available topologies: >* human-pose-estimation-0001 Other models may produce unexpected results on these devices.

See Also

• Using Open Model Zoo demos
• Model Optimizer
• Model Downloader