code: def yolo_detections_to_norfair_detections(
      yolo_detections, track_boxes
  ):
      norfair_detections = []
      boxes = []
      detections_as_xyxy = yolo_detections.xyxy[0]
      for detection_as_xyxy in detections_as_xyxy:
          detection_as_xyxy = detection_as_xyxy.cpu().numpy()
          bbox = np.array(
              [
                  [
                      detection_as_xyxy[0].item(),
                      detection_as_xyxy[1].item(),
                  ],
                  [
                      detection_as_xyxy[2].item(),
                      detection_as_xyxy[3].item(),
                  ],
              ]
          )
          boxes.append(bbox)
          if track_boxes:
              points = bbox
              scores = np.array(
                  [detection_as_xyxy[4], detection_as_xyxy[4]]
              )
          else:
              points = bbox.mean(axis=0, keepdims=True)
              scores = detection_as_xyxy[[4]]
  
          norfair_detections.append(
              Detection(
                  points=points,
                  scores=scores,
                  label=detection_as_xyxy[-1].item(),
              )
          )
  
      return norfair_detections, boxes
  
file_path: norfair/demos/camera_motion/src/demo.py
imports: import argparse
  from functools import partial
  import numpy
  import torch
  from norfair import AbsolutePaths, Detection, FixedCamera, Tracker, Video, draw_absolute_grid, draw_tracked_boxes
  from norfair.camera_motion import HomographyTransformationGetter, MotionEstimator, TranslationTransformationGetter
  from norfair.drawing import draw_tracked_objects
label_desc: Use image node_id 1 for calling a global function with example usage: yolo_detections_to_norfair_detections(yolo_detections, track_boxes) and returns: norfair_detections,  boxes