draw_bounding_boxes.py

import cv2

def draw_bounding_boxes(file_path, prediction_result):

  ## read image file from disk
  img = cv2.imread(file_path, cv2.IMREAD_COLOR)
  
  height = img.shape[0] # Image height
  width = img.shape[1] # Image width

  color = (255,0,0)
  
  for result in prediction_result.payload:

    score = result.image_object_detection.score * 100

    #exclude low confidence predictions
    if score > 80:

      text = f"{result.display_name} {score:.2f}%"

      # https://cloud.google.com/vision/automl/object-detection/docs/reference/rpc/google.cloud.automl.v1beta1

      x1 = result.image_object_detection.bounding_box.normalized_vertices[0].x
      # denormalize
      x1 = int(x1 * width )

      y1 = result.image_object_detection.bounding_box.normalized_vertices[0].y
      # denormalize
      y1 = int(y1 * height )

      x2 = result.image_object_detection.bounding_box.normalized_vertices[1].x
      # denormalize
      x2 = int(x2 * width )

      y2 = result.image_object_detection.bounding_box.normalized_vertices[1].y
      # denormalize
      y2 = int(y2 * height )

      print(f"Predicted label and score: {text}")
      print(f"Predicted x1, y1: {x1, y1}")
      print(f"Predicted x2, y2: {x2, y2}")
      
      cv2.rectangle(img, (x1,y1), (x2, y2), color, 2)

      # Add label with score
      cv2.putText(img, text, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)

  return img