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Multi-Channel Face Detection C++ Demo

This demo provides an inference pipeline for multi-channel face detection. The demo uses Face Detection network.

Other demo objectives are:

  • Up to 16 cameras as inputs, via OpenCV*
  • Visualization of detected faces from all channels on a single screen

How It Works

On startup, the application reads command line parameters and loads the specified networks. The Face Detection network is required.

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 the --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.

Preparing to Run

For demo input image or video files, refer to the section Media Files Available for Demos in the Open Model Zoo Demos Overview. The list of models supported by the demo is in <omz_dir>/demos/multi_channel_face_detection_demo/cpp/models.lst file. This file can be used as a parameter for Model Downloader and Converter to download and, if necessary, convert models to OpenVINO Inference Engine format (*.xml + *.bin).

An example of using the Model Downloader:

omz_downloader --list models.lst

An example of using the Model Converter:

omz_converter --list models.lst

Supported Models

  • face-detection-adas-0001
  • face-detection-retail-0004
  • face-detection-retail-0005
  • face-detection-retail-0044

NOTE: Refer to the tables Intel's Pre-Trained Models Device Support and Public Pre-Trained Models Device Support for the details on models inference support at different devices.

Running

Running the demo with the -h option yields a usage message.

To run the demo with the pre-trained face detection model on CPU, with one single camera, use the following command:

./multi_channel_face_detection_demo \
  -d CPU \
  -i 0 \
  -m <path_to_model>/face-detection-retail-0004.xml

To run the demo using two recorded video files, use the following command:

./multi_channel_face_detection_demo \
  -d CPU \
  -i <path_to_file>/file1,<path_to_file>/file2 \
  -m <path_to_model>/face-detection-retail-0004.xml

Video files will be processed simultaneously.

Input Video Sources

General parameter for input source is -i. You can run the demo on web cameras and video files simultaneously by specifying: -i <webcam_id0>,<webcam_id1>,<video_file1>,<video_file2> with paths to webcams and video files separated by a comma. To run the demo with a single input source (a web camera or a video file), but several channels, specify an additional parameter, duplicate_num, for example: -duplicate_num 4. You will see four channels. With several input sources, the -duplicate_num parameter will duplicate each of them.

Below are some examples of demo input specification:

-i <file1>,<file2>

To see all available web cameras, run the ls /dev/video* command. You will get output similar to the following:

user@user-PC:~ $ ls /dev/video*
/dev/video0 /dev/video1 /dev/video2

You can use -i option to connect all the three web cameras:

-i 0,1,2

To connect to IP cameras, use RTSP URIs:

-i rtsp://camera_address_1/,rtsp://camera_address_2/

Demo Output

The demo uses OpenCV to display the resulting frames with detections rendered as bounding boxes. The demo reports:

  • FPS: average rate of video frame processing (frames per second).
  • Latency: average time required to process one frame (from reading the frame to displaying the results).

You can also enable more detailed statistics in the output using the -show_stats option while running the demos. You can use these metrics to measure application-level performance.

See Also