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
  
  import cv2
  
  from webcamvideostream import *
  
  videoUrl = 1
  videoUrl = "/sharefolder/sdc/sdc-data/ossdc-simulator-TheCrew-PS4-30fps.mp4"
  
  webcam = False
  #webcam = True
  
  sct = None
  
  ret = True
  
  if webcam:
    cap = WebcamVideoStream(videoUrl,(1280,720),30)
    cap.start()
  else:
    cap = cv2.VideoCapture(videoUrl)
  
  # This is needed since the notebook is stored in the object_detection folder.
  sys.path.append("..")
  
  
  # ## Object detection imports
  # Here are the imports from the object detection module.
  
  # In[3]:
  
  from utils import label_map_util
  
  from utils import visualization_utils as vis_util
  
  
  # # Model preparation 
  
  # ## Variables
  # 
  # Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.  
  # 
  # By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
  
  # In[4]:
  
  # What model to download.
  MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
  MODEL_FILE = MODEL_NAME + '.tar.gz'
  DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
  
  # 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('data', 'mscoco_label_map.pbtxt')
  
  NUM_CLASSES = 90
  
  
  # ## Download Model
  
  # In[5]:
  '''
  opener = urllib.request.URLopener()
  opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
  tar_file = tarfile.open(MODEL_FILE)
  for file in tar_file.getmembers():
    file_name = os.path.basename(file.name)
    if 'frozen_inference_graph.pb' in file_name:
      tar_file.extract(file, os.getcwd())
  '''
  
  # ## Load a (frozen) Tensorflow model into memory.
  
  # In[6]:
  
  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='')
  
  
  # ## Loading label map
  # Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
  
  # In[7]:
  
  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)
  
  
  # ## Helper code
  
  # In[8]:
  
  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)
  
  
  # # Detection
  
  # In[9]:
  
  # 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, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
  
  # Size, in inches, of the output images.
  IMAGE_SIZE = (12, 8)
  
  # In[10]:
  
  from datetime import datetime
  
  precision = 10
  def getCurrentClock():
      #return time.clock()
      return datetime.now()
  
  frameCnt=0
  prevFrameCnt=0
  prevTime=getCurrentClock()
  
  with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
      while True:
        if sct is not None or webcam or cap.grab():
            if sct is not None:
                frame = numpy.asarray(sct.grab(mon))
            else:
                if webcam:
                    frame = cap.read()
                else:
                    flag, frame = cap.retrieve()    
            if not flag:
                    continue   
        image_np = frame
        # 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)
        
        frameCnt=frameCnt+1
        nowMicro = getCurrentClock()
        delta = (nowMicro-prevTime).total_seconds()
  
        if delta>=1.0:
            fpsValue = ((frameCnt-prevFrameCnt)/delta) 
            print("FPS = %3.2f, Frame = %6d" % (fpsValue, frameCnt))
            prevFrameCnt=frameCnt
  
        cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
        if cv2.waitKey(25) & 0xFF == ord('q'):
          cv2.destroyAllWindows()
          break
apis: numpy.squeeze tensorflow.gfile.GFile tensorflow.Graph numpy.expand_dims tensorflow.Session tensorflow.import_graph_def tensorflow.GraphDef