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.
In this sample the
gvapython element inserted into pipeline twice.
First time it inserted after
gvainference element running on object detection model, 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 inserted after
gvaclassify element running on object classification model, this demonstrates custom conversion model output into object attributes (age and gender in this example). See file
age_gender_classification.py with conversion function coded in Python.
The sample uses by default the following pre-trained models from OpenVINO™ Toolkit Open Model Zoo
NOTE: Before running samples (including this one), run script
download_models.shonce (the script located in
samplestop folder) to download all models required for this and other samples.
If Python requirements are not installed yet:
If command-line parameter not specified, the sample by default streams video example from HTTPS link (utilizing
urisourcebin element) so requires internet conection. The command-line parameter INPUT_VIDEO allows to change input video and supports
rtsp://) or other streaming source (ex URL starting with