run_pipeline.py

def run(self):
    pipeline = self.create_pipeline()
    # Pipeline defined, now the device is connected to
    with dai.Device(pipeline) as device:
        # Start pipeline
        device.startPipeline()

        # Set queues to be read from later
        self.high_quality_out = device.getOutputQueue(name="high_quality_out", maxSize=4, blocking=False)
        self.detection_out = device.getOutputQueue(name="detection_out", maxSize=4, blocking=False)

        # Create a separate thread which will read the final frames from a queue
        # and ingest them in a virtual webcam
        t = threading.Thread(target=self.pipe_to_virtual_webcam)
        t.start()

        # Start processing the detections frame by frame by reading from the 2 queues above
        final_frame = self.process_detections()

        # Add the final frame (with background replaced) to the queue for the virtual webcam to read from
        self.webcam_queue.put(cv2.cvtColor(final_frame, cv2.COLOR_BGR2RGB))

        # Wait for thread to finish
        t.join()
        self.webcam_queue.join()