text: import tensorflow as tf
  import numpy as np
  from utils import label_map_util
  from utils import visualization_utils as vis_util
  from scipy.misc import imread, imsave
  import os
  
  MODEL_NAME = 'ssd_inception_v2_coco_2018_01_28'
  PATH_TO_CKPT = os.path.join(MODEL_NAME, 'frozen_inference_graph.pb')
  PATH_TO_LABELS = '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='')
          object_sess = tf.InteractiveSession()
          object_sess.run(tf.global_variables_initializer())
  
  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 detect_object(img,sess=object_sess):
      image_np_expanded = np.expand_dims(img, 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')
      (boxes, scores, classes, num_detections) = sess.run(
          [boxes, scores, classes, num_detections],
          feed_dict={image_tensor: image_np_expanded})
      return (boxes, scores, classes, num_detections)