text: import airsim
  import cv2
  import numpy as np
  import torch
  from torchvision.utils import draw_bounding_boxes
  from torchvision import transforms
  
  model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
  
  '''
  Scene = 0, 
  DepthPlanar = 1, 
  DepthPerspective = 2,
  DepthVis = 3, 
  DisparityNormalized = 4,
  Segmentation = 5,
  SurfaceNormals = 6,
  Infrared = 7,
  OpticalFlow = 8,
  OpticalFlowVis = 9
  '''
  
  client = airsim.CarClient() # https://microsoft.github.io/AirSim/api_docs/html/_modules/airsim/client.html#CarClient
  client.confirmConnection()
  apiControl = False
  client.enableApiControl(apiControl)
  client.armDisarm(apiControl)
  car_controls = airsim.CarControls()
  
  while True:
  
      responses = client.simGetImages([airsim.ImageRequest(0, airsim.ImageType.Scene, False, False)])
      for response in responses:
          if response.pixels_as_float:
              print("Type %d, size %d" % (response.image_type, len(response.image_data_float)))
              airsim.write_pfm('py1.pfm', airsim.get_pfm_array(response))
          else:
              img1d = np.frombuffer(response.image_data_uint8, dtype=np.uint8)
              img = img1d.reshape(response.height, response.width, 3)
              
              results = model(img)
              res = results.xyxy[0]
              labels = results.pandas().xyxy[0]['name']
  
              boxes = []
              for ele in res:
                  boxes.append(list(ele[:4]))
  
              boxes = torch.tensor(boxes)
  
              img = transforms.ToTensor()(img.copy())
              img = img * 255
              img = img.type(torch.uint8)
              img = img.unsqueeze(0)
  
              drawn_boxes = draw_bounding_boxes(image=img[0], boxes= boxes, labels=results.pandas().xyxy[0]['name'], width=2, colors=['blue' for _ in range(len(labels))])
              
              tensor_to_pil = transforms.ToPILImage()(drawn_boxes.squeeze(0))
              pic = np.array(tensor_to_pil)
              
              cv2.imshow('detections', pic)
              cv2.waitKey(1)
  
          client.setCarControls(car_controls)
  
  