yolov8_det_quantized.tflite
yolox_quantized.tflite
yolov8.json
yolox.json
Collects the video stream (source) and creates two copies of the source:
One stream is sent to the qtimetamux plugin to retain the video stream.
The other stream is sent to an ML inferencing pipeline.
Receives the video stream on its sinkpad.
Performs preprocessing:
Color conversion
Scaling down/up
Normalization on the stream data when the model expects the floating point values as input
Converts the video stream to a tensor stream on its source pad.
Loads the object detection model.
Modifies the graph for the chosen delegate.
Receives the tensor stream on its sinkpad.
Runs the inference and produces a tensor stream with the object detection results on its source pad.
Receives the inference tensors from object detection.
Converts the inference tensors on its sinkpad into formats such as video or text that the multimedia plugins can process later.
Applies the threshold to the chosen number of results.
Loads the corresponding modules for detection models.
Loads the YOLOv8 submodule.
Produces results as structures of text.
Sends them to the sinkpad of qtimetamux.
Receives video stream and text stream with bounding box results corresponding to the video stream on its sinkpads.
Produces GST buffers with contents of video stream from its sink pad.
Adds bounding boxes as GstVideoRegionOfInterest from data sinkpad to GST buffers meta (meta multiplexing) on its source pad.
Receives the multiplexed stream.
Overlays the bounding boxes on the VideoFrame using CL.
Produces GST buffers with overlays in its source pad.
Receives the video stream on its sinkpad.
Submits the video stream to Weston.
Weston renders the video stream and bounding boxes generated for the objects in that scene on a local display device.