Output score , class and id Extraction using TensorFlow object detection

Question

How can I extract the output scores for objects , object class ,object id detected in images , generated by the Tensorflow Model for Object Detection ?

I want to store all these details into individual variables so that later they can be stored in a database .

Using the same code as found in this link https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb

Please Help me out with the solution to this problem .

I've Tried

print(str(output_dict['detection_classes'][0] ) , ":" , str(output_dict['detection_scores'][0]))

This works and gives the object id and score for the class with the highest probability . But I want to extract the class name too and also the scores , Ids and names for all objects present in the image

Example of output : There are two dogs in the image . When I print out the result I get the id and score for the object with the highest probability[94% in this case] i want to print the object name too and also similar details for all other objects in the images

Answer 1

You may need some knowledge background about tensorflow object detection, short and quick solution here might be the way you expected :

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)
      image_np = load_image_into_numpy_array(image)
      image_np_expanded = np.expand_dims(image_np, axis=0)
      image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
      boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
      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)
      objects = []
      threshold = 0.5 # in order to get higher percentages you need to lower this number; usually at 0.01 you get 100% predicted objects
      for index, value in enumerate(classes[0]):
          object_dict = {}
          if scores[0, index] > threshold:
              object_dict[(category_index.get(value)).get('name').encode('utf8')] = \
                        scores[0, index]
              objects.append(object_dict)
      print (objects)
      print(len(np.where(scores[0] > threshold)[0])/num_detections[0])
      plt.figure(figsize=IMAGE_SIZE)
      plt.imshow(image_np)

Hope this helpful.