module has no attribute 'return_coordinates'

[Teedies1102]

add this to the utils/visualization_utils.py

def return_coordinates(
    image,
    boxes,
    classes,
    scores,
    category_index,
    instance_masks=None,
    instance_boundaries=None,
    keypoints=None,
    use_normalized_coordinates=False,
    max_boxes_to_draw=20,
    min_score_thresh=.5,
    agnostic_mode=False,
    line_thickness=4,
    groundtruth_box_visualization_color='black',
    skip_scores=False,
    skip_labels=False):
  # Create a display string (and color) for every box location, group any boxes
  # that correspond to the same location.
  box_to_display_str_map = collections.defaultdict(list)
  box_to_color_map = collections.defaultdict(str)
  box_to_instance_masks_map = {}
  box_to_instance_boundaries_map = {}
  box_to_score_map = {}
  box_to_keypoints_map = collections.defaultdict(list)
  if not max_boxes_to_draw:
    max_boxes_to_draw = boxes.shape[0]
  for i in range(min(max_boxes_to_draw, boxes.shape[0])):
    if scores is None or scores[i] > min_score_thresh:
      box = tuple(boxes[i].tolist())
      if instance_masks is not None:
        box_to_instance_masks_map[box] = instance_masks[i]
      if instance_boundaries is not None:
        box_to_instance_boundaries_map[box] = instance_boundaries[i]
      if keypoints is not None:
        box_to_keypoints_map[box].extend(keypoints[i])
      if scores is None:
        box_to_color_map[box] = groundtruth_box_visualization_color
      else:
        display_str = ''
        if not skip_labels:
          if not agnostic_mode:
            if classes[i] in category_index.keys():
              class_name = category_index[classes[i]]['name']
            else:
              class_name = 'N/A'
            display_str = str(class_name)
        if not skip_scores:
          if not display_str:
            display_str = '{}%'.format(int(100*scores[i]))
          else:
            display_str = '{}: {}%'.format(display_str, int(100*scores[i]))
        box_to_display_str_map[box].append(display_str)
        box_to_score_map[box] = scores[i]
        if agnostic_mode:
          box_to_color_map[box] = 'DarkOrange'
        else:
          box_to_color_map[box] = STANDARD_COLORS[
              classes[i] % len(STANDARD_COLORS)]

  # Draw all boxes onto image.
  coordinates_list = []
  counter_for = 0
  for box, color in box_to_color_map.items():
    ymin, xmin, ymax, xmax = box
    height, width, channels = image.shape
    ymin = int(ymin*height)
    ymax = int(ymax*height)
    xmin = int(xmin*width)
    xmax = int(xmax*width)
    coordinates_list.append([ymin, ymax, xmin, xmax, (box_to_score_map[box]*100)])
    counter_for = counter_for + 1

  return coordinates_list

add this to Object_detection_dir.py

coordinates = vis_util.return_coordinates(
                        image,
                        np.squeeze(boxes),
                        np.squeeze(classes).astype(np.int32),
                        np.squeeze(scores),
                        category_index,
                        use_normalized_coordinates=True,
                        line_thickness=8,
                        min_score_thresh=0.80)

as well as this:

textfile = open("json/"+filename_string+".json", "a")
                    textfile.write(json.dumps(coordinates))
                    textfile.write("\n")

I think this should be all.

Originally posted by @psi43 in https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10/issues/69#issuecomment-410620593

Thanks for the code. Firstly, I do not have object_detection_dir nor Image, but i used jupyter notebook to write codes hence the code I had is as follow: img = cv2.imread(IMAGE_PATH) image_np = np.array(img)

input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32) detections = detect_fn(input_tensor)

num_detections = int(detections.pop('num_detections')) detections = {key: value[0, :num_detections].numpy() for key, value in detections.items()} detections['num_detections'] = num_detections

detection_classes should be ints.

detections['detection_classes'] = detections['detection_classes'].astype(np.int64)

label_id_offset = 1 image_np_with_detections = image_np.copy()

viz_utils.visualize_boxes_and_labels_on_image_array( image_np_with_detections, detections['detection_boxes'], detections['detection_classes']+label_id_offset, detections['detection_scores'], category_index, use_normalized_coordinates=True, max_boxes_to_draw=5, min_score_thresh=.8, agnostic_mode=False)

coordinates = viz_utils.return_coordinates( image, np.squeeze(boxes), np.squeeze(classes).astype(np.int32), np.squeeze(scores), category_index, use_normalized_coordinates=True, line_thickness=8, min_score_thresh=0.80)

textfile = open("json/"+filename_string+".json", "a") textfile.write(json.dumps(coordinates)) textfile.write("\n")

plt.imshow(cv2.cvtColor(image_np_with_detections, cv2.COLOR_BGR2RGB)) plt.show()

THE ISSUE is that when I run the code, there has no attribute called return_coordinates in the module visualization_utils.py. I had make sure that the function is indeed in the pyhton file, and that the name of the function is the same in both, but somehow it just return the same error. It would really helps if you have the time to solve this problem, but I know that you must be quite busy as there a lot of other people having same issues too.