Untitled

def return_predictions(images_paths, model, classes=None):
  '''
  Create bounding box generator
  '''
  for image in images_paths:
      results = model.predict(image, classes=classes)
      for r in results:
        boxes = [box for box in r.boxes]
        names = [model.names[int(box.cls)] for box in r.boxes]
        yield boxes, names, image

def get_avg_depth(xyxy, depth_coords, depth_values):
  # depths_coords = dict(
  #     coords=depth_coords,
  # )
  # top left
  x_points, y_points = depth_coords
  xmin, ymin, xmax, ymax = xyxy

  filtered_points = np.logical_and.reduce((
        x_points >= xmin,
        x_points <= xmax,
        y_points >= ymin,
        y_points <= ymax
    ))
  avg_depth = np.mean(depth_values[filtered_points])
  return avg_depth