Extracting coordinates instead of drawing a box

EDIT: Nevermind, got it to work!

Hey, first off, great tutorial, thank you so much.

I got it to run on ubuntu 16.04 as well with ease but I have a problem. I’m running on a CLI Ubuntu server, so instead of using an image as output, I’d just like to have the coordinates of the boxes.

I looked into the Object_detection_image.py and found where the boxes are being drawn, but it uses a function named visualize_boxes_and_labels_on_image_array to draw them. If I try to ouput the np.squeeze(boxes), it returns this:

[[0.5897823  0.35585764 0.87036747 0.5124078 ]
 [0.6508235  0.13419046 0.85757935 0.2114587 ]
 [0.64070517 0.14992228 0.8580698  0.23488007]
 ...
 [0.         0.         0.         0.        ]
 [0.         0.         0.         0.        ]
 [0.         0.         0.         0.        ]]

Is there a way to just get the coordinates from that?

Thank you for your time!

EDIT: Okay, I added a new function to the visualization_utils.py that returns the “ymin, ymax, xmin, xmax” variables, used in other functions of that file to draw the boxes. The problem is, they look like this: [[0.5897822976112366, 0.8703674674034119, 0.35585764050483704, 0.5124077796936035], [0.6508234739303589, 0.8575793504714966, 0.13419045507907867, 0.2114586979150772]] I was expecting coordinates. These seem like percentages.

EDIT: Okay, I got it to work.

[psi43]

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.

[PraveenNellihela]

This was very helpful. thank you so much. If anyone needs to access each coordinate separately change the 3rd last line in the newly added code to utils/visualization_utils.py which is

coordinates_list.append([ymin, ymax, xmin, xmax, (box_to_score_map[box]*100)])

into coordinates_list=[ymin, ymax, xmin, xmax, (box_to_score_map[box]*100)]

and you can access each ymin, ymax , xmin, xmax values separately using ymin=coordinate_list[0] etc. in your object detection file.