title: Counting detected objects with tensorflow API
body: <p>I'm using the tensorflow object detection tutorial and everything works great. I get the right number of detected objects. I'm having some issues trying to find a way to count the detected objects and print out the number. </p>
  
  <p>I've looked around the community and found that someone has done it with boxes.shape[0] however, I can't get it to work.</p>
  
  <p>I converted the tutorial to python and removed the calls to matplotlib. I don't need it to output the image with the boxes I just need it to print the number of detected objects.</p>
  
  <pre><code>import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow as tf
  import zipfile
  
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  
  
  
  
  # This is needed to display the images.
  #get_ipython().magic('matplotlib inline')
  
  # This is needed since the notebook is stored in the object_detection folder.
  sys.path.append("..")
  
  
  
  from utils import label_map_util
  
  from utils import visualization_utils as vis_util
  
  
  
  # What model to download.
  MODEL_NAME = 'test_inference_graph'
  
  # Path to frozen detection graph. This is the actual model  that is used for the object detection.
      PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
  
  # List of the strings that is used to add correct label for each box.
  PATH_TO_LABELS = os.path.join('training', 'object- detection.pbtxt')
  
  NUM_CLASSES = 1
  
  
  
  
  
  detection_graph = tf.Graph()
  with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
      serialized_graph = fid.read()
      od_graph_def.ParseFromString(serialized_graph)
      tf.import_graph_def(od_graph_def, name='')
  
  
  
  
  label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
  categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES,   use_display_name=True)
  category_index = label_map_util.create_category_index(categories)
  
  
  def load_image_into_numpy_array(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)
  
  
  
  # For the sake of simplicity we will use only 2 images:
  # image1.jpg
  # image2.jpg
  # If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
  PATH_TO_TEST_IMAGES_DIR = 'test_images'
  #TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'IMG_{}.PNG'.format(i)) for i in range(7464, 7483) ]
  TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'test-latest.jpg') ]
  
  # Size, in inches, of the output images.
  #IMAGE_SIZE = (20, 16)
  
  
  
  with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
      # Definite input and output Tensors for detection_graph
      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.
      detection_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.
      detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
      detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
      num_detections = detection_graph.get_tensor_by_name('num_detections:0')
      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)
        # Actual detection.
        (boxes, scores, classes, num) = sess.run(
            [detection_boxes, detection_scores, detection_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=1)
        plt.figure(figsize=IMAGE_SIZE)
        plt.imshow(image_np)
   '''
  ###Below always print 1
  #print(boxes.shape[0])
  taco = [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] &gt; 0.5]
  print(len(taco))
  </code></pre>