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Single Human Pose Estimation Demo (top-down pipeline)

This demo showcases top-down pipeline for human pose estimation on video or image. The task is to predict bboxes for every person on frame and then to predict a pose for every detected person. The pose may contain up to 17 keypoints: ears, eyes, nose, shoulders, elbows, wrists, hips, knees, and ankles.

How It Works

On the start-up, the application reads command line parameters and loads detection person model and single human pose estimation model. Upon getting a frame from the OpenCV VideoCapture, the demo executes top-down pipeline for this frame and displays the results.

Running

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

usage: single_human_pose_estimation_demo.py [-h] -m_od MODEL_OD -m_hpe MODEL_HPE
                                            -i INPUT [--loop] [-o OUTPUT]
                                            [-limit OUTPUT_LIMIT] [-d DEVICE]
                                            [--person_label PERSON_LABEL]
                                            [--no_show]
                                            [-u UTILIZATION_MONITORS]

optional arguments:
  -h, --help            Show this help message and exit.
  -m_od MODEL_OD, --model_od MODEL_OD
                        Required. Path to model of object detector in .xml format.
  -m_hpe MODEL_HPE, --model_hpe MODEL_HPE
                        Required. Path to model of human pose estimator in .xml format.
  -i INPUT, --input INPUT
                        Required. An input to process. The input must be a single image,
                        a folder of images, video file or camera id.
  --loop                Optional. Enable reading the input in a loop.
  -o OUTPUT, --output OUTPUT
                        Optional. Name of output to save.
  -limit OUTPUT_LIMIT, --output_limit OUTPUT_LIMIT
                        Optional. Number of frames to store in output.
                        If 0 is set, all frames are stored.
  -d DEVICE, --device DEVICE
                        Optional. Specify the target to infer on CPU or GPU.
  --person_label PERSON_LABEL
                        Optional. Label of class person for detector.
  --no_show             Optional. Do not display output.
  -u UTILIZATION_MONITORS, --utilization_monitors UTILIZATION_MONITORS
                        Optional. List of monitors to show initially.

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 models.lst.

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

python single_human_pose_estimation_demo.py --model_od <path_to_model>/mobilenet-ssd.xml --model_hpe <path_to_model>/single-human-pose-estimation-0001.xml --input <path_to_video>/back-passengers.avi

The demo uses OpenCV to display the resulting frame with estimated poses and reports performance in the following format: summary inference FPS (single human pose inference FPS / detector inference FPS).

See Also