code: import numpy as np
  import os
  import six.moves.urllib as urllib
  import sys
  import tarfile
  import tensorflow.compat.v1 as tf
  tf.disable_v2_behavior()
  import zipfile
  
  from collections import defaultdict
  from io import StringIO
  from matplotlib import pyplot as plt
  from PIL import Image
  
  from object_detection.utils import label_map_util
  
  from object_detection.utils import visualization_utils as vis_util
  
  # This is needed since the notebook is stored in the object_detection folder.
  sys.path.append("..")
  
  
  # script repurposed from sentdex's edits and TensorFlow's example script. Pretty messy as not all unnecessary
  # parts of the original have been removed
  
  
  
  
  # # 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.
  
  
  
  # What model to download.
  MODEL_NAME = 'trained_model'  # change to whatever folder has the new graph
  # MODEL_FILE = MODEL_NAME + '.tar.gz'   # these lines not needed as we are using our own model
  # 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('training', 'label.pbtxt')  # our labels are in training/object-detection.pbkt
  
  NUM_CLASSES = 3  # we only are using one class at the moment (mask at the time of edit)
  
  
  # ## Download Model
  
  
  # opener = urllib.request.URLopener()   # we don't need to download model since we have our own
  # 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.
  
  
  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)
  
  
  
  
  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'
  TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(0, 60)]  # adjust range for # of images in folder
  
  # Size, in inches, of the output images.
  IMAGE_SIZE = (12, 8)
  
  
  with detection_graph.as_default():
      with tf.Session(graph=detection_graph) as sess:
          i = 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)
              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)
  
              plt.figure(figsize=IMAGE_SIZE)
              plt.imshow(image_np)    # matplotlib is configured for command line only so we save the outputs instead
              plt.savefig("outputs/detection_output{}.png".format(i))  # create an outputs folder for the images to be saved
              i = i+1  # this was a quick fix for iteration, create a pull request if you'd like
  
apis: tensorflow.compat.v1.Session numpy.squeeze matplotlib.pyplot.figure tensorflow.compat.v1.Graph tensorflow.compat.v1.gfile.GFile tensorflow.compat.v1.import_graph_def matplotlib.pyplot.imshow numpy.expand_dims tensorflow.compat.v1.GraphDef tensorflow.compat.v1.disable_v2_behavior