diff --git a/Code-Sleep-Python/object_detection.py b/Code-Sleep-Python/object_detection.py
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+
+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
+cap = cv2.VideoCapture(0)
+sys.path.insert(0,'/anaconda3/lib/python3.6/site-packages/tensorflow/models/research/')
+from utils import label_map_util
+from utils import visualization_utils as vis_util
+MODEL_NAME = 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03'
+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)
+with detection_graph.as_default():
+  with tf.Session(graph=detection_graph) as sess:
+    while True:
+        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) == ord('q'):
+          cv2.destroyAllWindows()
+          break
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