Tensorflow Object detection API: Print detected class as output to terminal

Question

I have a simple question, but I can't figure out how to do it. I am using the TF Object detection API to detect images, it is working fine and given an image it will draw the bounding box with a label and confidence score of what class it thinks its detected. My question is how can I print the detected class (as a string) and the score to terminal i.e not just on the image but as an output to the terminal too.

Below is the code responsible for the image detection

with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    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)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      # Each box represents a part of the image where a particular object was detected.
      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      # Each score represent how level of confidence for each of the objects.
      # Score is shown on the result image, together with the class label.
      scores = detection_graph.get_tensor_by_name('detection_scores:0')
      classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      # Actual detection.
      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      # Visualization of the results of a detection.
      vis_util.visualize_boxes_and_labels_on_image_array(
          image_np,
          np.squeeze(boxes),
          np.squeeze(classes).astype(np.int32),
          np.squeeze(scores),
          category_index,
          use_normalized_coordinates=True,
          line_thickness=8, min_score_thresh=.2)
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)
      plt.show()

Thanks in advance, first post on Stack Overflow so please go easy on me

Answer 1

Well that's very easy. The classes are encrypted in the category_index which is a dict, so you could do something like this:

with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
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)
  image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
  # Each box represents a part of the image where a particular object was detected.
  boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
  # Each score represent how level of confidence for each of the objects.
  # Score is shown on the result image, together with the class label.
  scores = detection_graph.get_tensor_by_name('detection_scores:0')
  classes = detection_graph.get_tensor_by_name('detection_classes:0')
  num_detections = detection_graph.get_tensor_by_name('num_detections:0')
  # Actual detection.
  (boxes, scores, classes, num_detections) = sess.run(
      [boxes, scores, classes, num_detections],
      feed_dict={image_tensor: image_np_expanded})

  # Here output the category as string and score to terminal
  print([category_index.get(i) for i in classes[0]])
  print(scores)

Answer 2

Simply, go to the utils directory in object_detection folder and open the script visualization_utils.py. You will find a function namely visualize_boxes_and_labels_on_image_array, add a print command in the end of function to print the variable class_name(print(class_name)). Now run your code and see the magic.