text: #!/usr/bin/env python3
  
  from pathlib import Path
  import sys
  import cv2
  import depthai as dai
  import numpy as np
  
  # Get argument first
  nnPath = str((Path(__file__).parent / Path('models/mobilenet-ssd_openvino_2021.2_6shave.blob')).resolve().absolute())
  if len(sys.argv) > 1:
      nnPath = sys.argv[1]
  
  if not Path(nnPath).exists():
      import sys
      raise FileNotFoundError(f'Required file/s not found, please run "{sys.executable} install_requirements.py"')
  
  pipeline = dai.Pipeline()
  
  cam = pipeline.createColorCamera()
  cam.setBoardSocket(dai.CameraBoardSocket.RGB)
  cam.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
  
  videoEncoder = pipeline.createVideoEncoder()
  videoEncoder.setDefaultProfilePreset(1920, 1080, 30, dai.VideoEncoderProperties.Profile.H265_MAIN)
  cam.video.link(videoEncoder.input)
  
  videoOut = pipeline.createXLinkOut()
  videoOut.setStreamName('h265')
  videoEncoder.bitstream.link(videoOut.input)
  
  camRight = pipeline.createMonoCamera()
  camRight.setBoardSocket(dai.CameraBoardSocket.RIGHT)
  camRight.setResolution(dai.MonoCameraProperties.SensorResolution.THE_720_P)
  
  nn = pipeline.createMobileNetDetectionNetwork()
  nn.setConfidenceThreshold(0.5)
  nn.setBlobPath(nnPath)
  nn.setNumInferenceThreads(2)
  nn.input.setBlocking(False)
  
  manip = pipeline.createImageManip()
  manip.initialConfig.setResize(300, 300)
  # The NN model expects BGR input. By default ImageManip output type would be same as input (gray in this case)
  manip.initialConfig.setFrameType(dai.RawImgFrame.Type.BGR888p)
  camRight.out.link(manip.inputImage)
  manip.out.link(nn.input)
  
  xoutRight = pipeline.createXLinkOut()
  xoutRight.setStreamName("right")
  camRight.out.link(xoutRight.input)
  
  manipOut = pipeline.createXLinkOut()
  manipOut.setStreamName("manip")
  manip.out.link(manipOut.input)
  
  nnOut = pipeline.createXLinkOut()
  nnOut.setStreamName("nn")
  nn.out.link(nnOut.input)
  
  # MobilenetSSD label texts
  labelMap = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow",
              "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
  
  
  # Pipeline is defined, now we can connect to the device
  with dai.Device(pipeline) as device:
      # Start pipeline
      device.startPipeline()
  
      queue_size = 8
      qRight = device.getOutputQueue("right", queue_size)
      qManip = device.getOutputQueue("manip", queue_size)
      qDet = device.getOutputQueue("nn", queue_size)
      qRgbEnc = device.getOutputQueue('h265', maxSize=30, blocking=True)
  
      frame = None
      frameManip = None
      detections = []
      offsetX = (camRight.getResolutionWidth() - camRight.getResolutionHeight()) // 2
      croppedFrame = np.zeros((camRight.getResolutionHeight(), camRight.getResolutionHeight()))
  
      def frameNorm(frame, bbox):
          normVals = np.full(len(bbox), frame.shape[0])
          normVals[::2] = frame.shape[1]
          return (np.clip(np.array(bbox), 0, 1) * normVals).astype(int)
  
      videoFile = open('video.h265', 'wb')
      cv2.namedWindow("right", cv2.WINDOW_NORMAL)
      cv2.namedWindow("manip", cv2.WINDOW_NORMAL)
  
      while True:
          inRight = qRight.tryGet()
          inManip = qManip.tryGet()
          inDet = qDet.tryGet()
  
          while qRgbEnc.has():
              qRgbEnc.get().getData().tofile(videoFile)
  
          if inRight is not None:
              frame = inRight.getCvFrame()
  
          if inManip is not None:
              frameManip = inManip.getCvFrame()
  
          if inDet is not None:
              detections = inDet.detections
  
          if frame is not None:
              for detection in detections:
                  bbox = frameNorm(croppedFrame, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
                  bbox[::2] += offsetX
                  cv2.rectangle(frame, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
                  cv2.putText(frame, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
                  cv2.putText(frame, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
              cv2.imshow("right", frame)
  
          if frameManip is not None:
              for detection in detections:
                  bbox = frameNorm(frameManip, (detection.xmin, detection.ymin, detection.xmax, detection.ymax))
                  cv2.rectangle(frameManip, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
                  cv2.putText(frameManip, labelMap[detection.label], (bbox[0] + 10, bbox[1] + 20), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
                  cv2.putText(frameManip, f"{int(detection.confidence * 100)}%", (bbox[0] + 10, bbox[1] + 40), cv2.FONT_HERSHEY_TRIPLEX, 0.5, 255)
              cv2.imshow("manip", frameManip)
  
          if cv2.waitKey(1) == ord('q'):
              break
  
      videoFile.close()
  
      print("To view the encoded data, convert the stream file (.h265) into a video file (.mp4) using a command below:")
      print("ffmpeg -framerate 30 -i video.h265 -c copy video.mp4")
  