text: #!/usr/bin/env python
  
  PKG = 'object_detection'
  import roslib; roslib.load_manifest(PKG)
  import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow as tf
  import zipfile
  import label_map_util
  import visualization_utils as vis_util
  import numpy as np
  import cv2
  import os, rospkg
  
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  
  if tf.__version__ != '1.4.0':
      raise ImportError('Please upgrade your tensorflow installation to v1.4.0!')
  
  rp = rospkg.RosPack()
  SCRIPT_PATH = os.path.join(rp.get_path("object_detection"), "scripts")
  
  # What model to use.
  MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
  MODEL_PATH = os.path.join(SCRIPT_PATH, 'models', MODEL_NAME)
  
  # Path to frozen detection graph.
  # This is the actual model that is used for the object detection.
  PATH_TO_CKPT = os.path.join(MODEL_PATH, 'frozen_inference_graph.pb')
  
  # List of the strings that is used to add correct label for each box.
  PATH_TO_LABELS = os.path.join(SCRIPT_PATH, 'data', 'mscoco_label_map.pbtxt')
  
  NUM_CLASSES = 90
  
  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 = 'dataset/test_images'
  TEST_IMAGE_PATHS = [os.path.join(SCRIPT_PATH, PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3)]
  
  # Size, in inches, of the output images.
  IMAGE_SIZE = (12, 8)
  
  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')
          cap = cv2.VideoCapture(1)
          while(True):
              # Capture frame-by-frame
              ret, image_np = cap.read()
              # 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=4)
              # Display the resulting frame
              cv2.imshow('frame',image_np)
              if cv2.waitKey(1) & 0xFF == ord('q'):
                  break
          # When everything done, release the capture
          cap.release()
          cv2.destroyAllWindows()
  
  