gvapython Sample

This sample demonstrates gvapython element and ability to customize pipeline with application provided Python script for pre- or post-processing of inference operations. It typically used for interpretation of inference results and various application logic, especially if required in the middle of GStreamer pipeline.

How It Works

In this sample the gvapython element is used three times.

First time it's inserted after gvainference element running object detection. This demonstrates custom conversion of model output into list of bounding boxes. See file ssd_object_detection.py with conversion function coded in Python.

Second time it's inserted after gvainference element running object classification. This demonstrates custom conversion of model output into object attributes (age and gender in this example). See file age_gender_classification.py with conversion function coded in Python.

Third time it's inserted after previous gvapython to log ages into file. This demonstrates resource management in Python script. It opens log file in class constructor and closes it in destructor. Constructor will be called during pipeline initialization and destructor will be called on pipeline stop. See file age_logger.py. This is just an example of how to work with resources in Python script, for better logging please refer to Metadata Publishing Sample

Models

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

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.

Running

If Python requirements are not installed yet:

python3 -m pip install --upgrade pip
python3 -m pip install -r ../../../../requirements.txt
cd -

Run sample:

./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://) 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

See also