Face Detection And Classification Sample (gst-launch command line)

This sample demonstrates face detection and classification pipeline constructed via gst-launch-1.0 command-line utility.

How It Works

The sample utilizes GStreamer command-line tool gst-launch-1.0 which can build and run GStreamer pipeline described in a string format. The string contains a list of GStreamer elements separated by exclamation mark !, each element may have properties specified in the format property = value.

This sample builds GStreamer pipeline of the following elements

  • filesrc or urisourcebin or v4l2src for input from file/URL/web-camera

  • decodebin for video decoding

  • videoconvert for converting video frame into different color formats

  • gvadetect for face detection based on OpenVINO™ Toolkit Inference Engine

  • gvaclassify inserted into pipeline three times for face classification on three DL models (age-gender, emotion, landmark points)

  • gvawatermark for bounding boxes and labels visualization

  • fpsdisplaysink for rendering output video into screen

    Note

    sync=false property in fpsdisplaysink element disables real-time synchronization so pipeline runs as fast as possible

The sample uses by default the following pre-trained models from OpenVINO™ Toolkit Open Model Zoo

  • face-detection-adas-0001 is primary detection network for finding faces

  • age-gender-recognition-retail-0013 age and gender estimation on detected faces

  • emotions-recognition-retail-0003 emotion estimation on detected faces

  • landmarks-regression-retail-0009 generates facial landmark points

Note

Before running samples (including this one), run script download_models.sh once (the script located in samples top folder) to download all models required for this and other samples.

The sample contains model_proc subfolder with .json files for each model with description of model input/output formats and post-processing rules for classification models.

Running

./face_detection_and_classification.sh [INPUT_VIDEO] [DEVICE] [SINK_ELEMENT]

The sample takes three command-line optional parameters:

  1. [INPUT_VIDEO] to specify input video file. The input could be

  • local video file

  • web camera device (ex. /dev/video0)

  • RTSP camera (URL starting with rtsp://) or other streaming source (ex URL starting with `http:// <http://>`__) If parameter is not specified, the sample by default streams video example from HTTPS link (utilizing urisourcebin element) so requires internet conection.

  1. [DEVICE] to specify device for detection and classification. Please refer to OpenVINO™ toolkit documentation for supported devices. https://docs.openvinotoolkit.org/latest/openvino_docs_IE_DG_supported_plugins_Supported_Devices.html You can find what devices are supported on your system by running following OpenVINO™ toolkit sample: https://docs.openvinotoolkit.org/latest/openvino_inference_engine_ie_bridges_python_sample_hello_query_device_README.html

  2. [SINK_ELEMENT] to choose between render mode and fps throughput mode:

    • display - render (default)

    • fps - FPS only

Sample Output

The sample

  • prints gst-launch-1.0 full command line into console

  • starts the command and either visualizes video with bounding boxes around detected faces, facial landmarks points and text with classification results (age/gender, emotion) for each detected face or prints out fps if you set SINK_ELEMENT = fps

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