dogorcat_detect_then_crop.py

for image_path in TEST_IMAGE_PATHS:
  image = Image.open(image_path)
  # the array based representation of the image will be used later in order to prepare the
  # result image with boxes and labels on it.
  image_np = load_image_into_numpy_array(image)
  # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
  image_np_expanded = np.expand_dims(image_np, axis=0)
  # Actual detection.
  output_dict = run_inference_for_single_image(image_np, detection_graph)
  # Visualization of the results of a detection.
  boxes = output_dict['detection_boxes']
  classes = output_dict['detection_classes']
  scores = output_dict['detection_scores']
  for i in range(boxes.shape[0]):
    if scores is None or scores[i] > 0.5:
      box = tuple(boxes[i].tolist())
      if classes[i] in category_index.keys():
        class_name = category_index[classes[i]]['name']
        if class_name == 'dog' or class_name == 'cat':
          ymin, xmin, ymax, xmax = box
          im_width, im_height = image.size
          (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                        ymin * im_height, ymax * im_height)
          cropped_image_np = image.crop((left, top, right, bottom))
          plt.figure(figsize=IMAGE_SIZE)
          plt.imshow(cropped_image_np)