Real-Time object detector with Tensorflow and OpenCV

  import cv2
  from TensorFlow.models.research.object_detection.utils import label_map_util
  from TensorFlow.models.research.object_detection.utils import visualization_utils as vis_util

  import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow.compat.v1 as tf

  cap = cv2.VideoCapture(0)
  sys.path.append("..")

  MODEL_NAME = 'rfcn_resnet101_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
  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())

  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)
  keep_Going = True
  counter =0
  with detection_graph.as_default():
      with tf.Session(graph=detection_graph) as sess:
          while keep_Going:
              #print(counter=counter+1)
              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)
              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)

              cv2.imshow('object detection', cv2.resize(image_np, (800,600)))
              if cv2.waitKey(25) & 0xFF == ord('q'):
                  cv2.destroyAllWindows()
                  keep_Going = False