How to find bounding boxes coordinates in Tensorflow Object Detection API

Question

I'm using Tensorflow object detection API code. I trained my model and got great detection percentages. I have been trying to get the bounding boxes coordinates but it keeps on printing out a list of 100 bizarre arrays.

after a wide search online I found out what the numbers in the arrays meant (The bounding box coordinates are floats in [0.0, 1.0] relative to the width and height of the underlying image.) But still, my arrays are very different than the ones shown in examples online. Another weird thing is that I tested my module with a lot less than 100 images so how can there even be data of 100 bounding boxes coordinate.

The array I get;

 [[3.13721418e-01 4.65148419e-01 7.11575747e-01 6.85783863e-01]
 [9.78936195e-01 6.50490820e-03 9.97096300e-01 1.82596639e-01]
 [9.51383412e-01 0.00000000e+00 1.00000000e+00 3.88432704e-02]
 [9.85813320e-01 8.96016136e-02 9.97273505e-01 3.15960884e-01]
 [9.88873005e-01 2.13812709e-01 1.00000000e+00 4.14675951e-01]

 ......
 [4.42647263e-02 9.90755498e-01 2.57772505e-01 1.00000000e+00]
 [2.69711018e-05 5.21758199e-02 6.37509704e-01 6.62899792e-01]
 [0.00000000e+00 3.00989419e-01 9.92376506e-02 1.00000000e+00]
 [1.87531322e-01 2.66501214e-04 4.50700432e-01 1.23927500e-02]
 [9.36755657e-01 4.61095899e-01 9.92406607e-01 7.62619019e-01]]

The function that does the detection and gets the bounding boxes coordinates. output_dict['detection_boxes'] is where the array above is held.

def run_inference_for_single_image(image, graph):
  with graph.as_default():
    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[1], image.shape[2])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: image})

      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.int64)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]
  return output_dict

I expect the output to be regular x,y coordinates of the bounding boxes.

Danny Fang · Accepted Answer · 2019-05-13 19:28:30Z

The values in output_dict['detection_boxes'] are indeed in normalized format. By checking the values in the array you provided, those values are all between 0 and 1, therefore they are reasonable.

There are 100 boxes because the model always output the same number of bounding boxes. (It is equal to max_total_detections in the config file ). But not all of them are always meaningful, you need to filter some boxes out according to the confidence score, which is stored in output_dict['scores'].

To get regular bounding boxes. you can do as following:

boxes = np.squeeze(output_dict['detection_boxes'])
scores = np.squeeze(output_dict['detection_scores'])
#set a min thresh score, say 0.8
min_score_thresh = 0.8
bboxes = boxes[scores > min_score_thresh]

#get image size
im_width, im_height = image.size
final_box = []
for box in bboxes:
    ymin, xmin, ymax, xmax = box
    final_box.append([xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height])

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