Multi-Channel Face Detection Demo

This demo provides an inference pipeline for multi-channel face detection. The demo uses Face Detection network. The corresponding pre-trained model delivered with the product is face-detection-retail-0004, which is a primary detection network for finding faces.

For details on the models, please refer to the descriptions in the deployment_tools/intel_models folder of the OpenVINO™ toolkit installation directory.

Other demo objectives are:

How It Works

NOTE: Running the demo requires using at least one web camera attached to your machine.

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


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

./multi-channel-demo -h
multichannel_face_detection [OPTION]
-h Print a usage message.
-m "<path>" Required. Path to an .xml file with a trained face detection model.
-l "<absolute_path>" Required for MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the kernels impl.
-c "<absolute_path>" Required for clDNN (GPU)-targeted custom kernels. Absolute path to the xml file with the kernels desc.
-d "<device>" Specify the target device for Face Detection (CPU, GPU, FPGA, or MYRIAD). The demo will look for a suitable plugin for a specified device.
-nc Maximum number of processed camera inputs (web cams)
-bs Processing batch size, number of frames processed per infer request
-n_ir Number of infer requests
-n_iqs Frame queue size for input channels
-fps_sp FPS measurement sampling period. Duration between timepoints, msec
-n_sp Number of sampling periods
-pc Enables per-layer performance report.
-t Probability threshold for detections.
-no_show No show processed video.
-show_stats Enable statictics output
-duplicate_num Enable and specify number of channel additionally copied from real sources
-real_input_fps Disable input frames caching, for maximum throughput pipeline
-i Specify full path to input video files

For example, to run the demo with the pre-trained face detection model on FPGA with fallback on CPU, with one single camera, use the following command:

./multi-channel-demo -m <INSTALL_DIR>/deployment_tools/intel_models/face-detection-retail-0004/FP32/face-detection-retail-0004.xml
-l <demos_build_folder>/intel64/Release/lib/ -d HETERO:FPGA,CPU -nc 1

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

./multi-channel-sample -m <INSTALL_DIR>/deployment_tools/intel_models/face-detection-retail-0004/FP32/face-detection-retail-0004.xml
-l <samples_build_folder>/intel64/Release/lib/ -d HETERO:FPGA,CPU -i /path/to/file1 /path/to/file2

Video files will be processed repeatedly.

You can also run the demo on web cameras and video files simultaneously by specifing both parameters: -nc <number of cams> -i <video files sequentially, separated by space>. 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 3. You will see four channels: one real and three duplicated. With several input sources, the -duplicate_num parameter will duplicate each of them.

NOTE: Before running the sample with another 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 bunch of frames with detections rendered as bounding boxes. On the top of the screen, the demo reports throughput (in frames per second). If needed, it also reports more detailed statistics (use -show_stats option while running the demo to enable it).

See Also