This demo showcases Face Detection with RetinaFace. The task is to identify faces as axis-aligned boxes and their keypoints (facial landmarks) in an image.
How It Works
On the start-up, the application reads command-line parameters and loads a network to the Inference Engine. Upon getting a frame from the OpenCV VideoCapture, it performs inference and displays the results.
Running
Running the application with the -h option yields the following usage message:
usage: object_detection_demo_retinaface.py [-h] -m MODEL
[-i INPUT [INPUT ...]] [-d DEVICE]
[-pt_f FACE_PROB_THRESHOLD]
[-pt_m MASK_PROB_THRESHOLD]
[--no_show]
[-u UTILIZATION_MONITORS]
Options:
-h, --help Show this help message and exit.
-m MODEL, --model MODEL
Required. Path to an .xml file with a trained model.
-i INPUT [INPUT ...], --input INPUT [INPUT ...]
path to video or image/images
-d DEVICE, --device DEVICE
Optional. Specify the target device to infer on; CPU,
GPU, FPGA, HDDL or MYRIAD is acceptable. The demo will
look for a suitable plugin for device specified.
Default value is CPU
-pt_f FACE_PROB_THRESHOLD, --face_prob_threshold FACE_PROB_THRESHOLD
Optional. Probability threshold for face detections
filtering
-pt_m MASK_PROB_THRESHOLD, --mask_prob_threshold MASK_PROB_THRESHOLD
Optional. Probability threshold for mask detections
filtering
--no_show Optional. Don't show 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. You can download the pre-trained models with the OpenVINO Model Downloader or from https://download.01.org/opencv/.
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.
Demo Output
The demo uses OpenCV to display the resulting frame with detections and reports performance in the following format: summary inference FPS.
See Also
