define_pipeline.py

def create_pipeline(self):
        # Create pipeline and set version
        pipeline = dai.Pipeline()
        pipeline.setOpenVINOVersion(version=dai.OpenVINO.Version.VERSION_2021_3)

        # Camera specific settings
        cam = pipeline.createColorCamera()
        cam.setPreviewSize(*self.nn_shape)
        cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
        cam.setInterleaved(False)
        cam.setBoardSocket(dai.CameraBoardSocket.RGB)
        cam.setColorOrder(dai.ColorCameraProperties.ColorOrder.RGB)
        cam.setPreviewKeepAspectRatio(False)

        # Set camera high quality output
        cam_xout = pipeline.createXLinkOut()
        cam_xout.setStreamName("high_quality_out")
        cam_xout.input.setBlocking(False)

        # Define a neural network that will make predictions based on the source frames
        detection_nn = pipeline.createNeuralNetwork()
        detection_nn.setBlobPath(self.nn_path)
        detection_nn.input.setBlocking(False)

        # Create neural network output
        detection_nn_xout = pipeline.createXLinkOut()
        detection_nn_xout.setStreamName("detection_out")
        detection_nn_xout.input.setBlocking(False)

        # Link different inputs and outputs
        # Send the resized 'preview' stream to the nn, while the high quality output is sent to another queue
        cam.video.link(cam_xout.input)
        cam.preview.link(detection_nn.input)
        detection_nn.out.link(detection_nn_xout.input)
        
        return pipeline