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 the models.lst file in the demo's directory.
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
