diff --git a/face_detectio.py b/face_detectio.py
index 8a0b3a8..da2c5a3 100644
--- a/face_detectio.py
+++ b/face_detectio.py
@@ -35,7 +35,7 @@ while True:
         face_image = frame[top:bottom, left:right]
 
         # Blur the face image
-        face_image = cv2.GaussianBlur(face_image, (99, 99), 30)
+        face_image = cv2.GaussianBlur(face_image, (9, 9), 30)
 
         # Put the blurred face region back into the frame image
         frame[top:bottom, left:right] = face_image
diff --git a/motion_detector.py b/motion_detector.py
index a20cc76..2188fac 100644
--- a/motion_detector.py
+++ b/motion_detector.py
@@ -33,11 +33,9 @@ ap.add_argument("-amin", "--min-area", type=int, default=3000, help="minimum are
 ap.add_argument("-amax", "--max-area", type=int, default=10000, help="minimum area size")
 args = vars(ap.parse_args())
 
-time.sleep(5)
-
 # if the video argument is None, then we are reading from webcam
 args["video"] = "http://192.168.178.53:8000/stream.mjpg"
-#args["video"] = "./videos/example_02.mp4"
+args["video"] = "./videos/example_02.mp4"
 vs = cv2.VideoCapture(args["video"])
 counter = 0
 threashold = 50
diff --git a/tensor.py b/tensor.py
new file mode 100644
index 0000000..8da163c
--- /dev/null
+++ b/tensor.py
@@ -0,0 +1,89 @@
+# Code adapted from Tensorflow Object Detection Framework
+# https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
+# Tensorflow Object Detection Detector
+
+import numpy as np
+import tensorflow as tf
+import cv2
+import time
+import requests
+
+
+class DetectorAPI:
+    def __init__(self, path_to_ckpt):
+        self.path_to_ckpt = path_to_ckpt
+
+        self.detection_graph = tf.Graph()
+        with self.detection_graph.as_default():
+            od_graph_def = tf.GraphDef()
+            with tf.gfile.GFile(self.path_to_ckpt, 'rb') as fid:
+                serialized_graph = fid.read()
+                od_graph_def.ParseFromString(serialized_graph)
+                tf.import_graph_def(od_graph_def, name='')
+
+        self.default_graph = self.detection_graph.as_default()
+        self.sess = tf.Session(graph=self.detection_graph)
+
+        # Definite input and output Tensors for detection_graph
+        self.image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
+        # Each box represents a part of the image where a particular object was detected.
+        self.detection_boxes = self.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.
+        self.detection_scores = self.detection_graph.get_tensor_by_name('detection_scores:0')
+        self.detection_classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
+        self.num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
+
+    def processFrame(self, image):
+        # Expand dimensions since the trained_model expects images to have shape: [1, None, None, 3]
+        image_np_expanded = np.expand_dims(image, axis=0)
+        # Actual detection.
+        start_time = time.time()
+        (boxes, scores, classes, num) = self.sess.run(
+            [self.detection_boxes, self.detection_scores, self.detection_classes, self.num_detections],
+            feed_dict={self.image_tensor: image_np_expanded})
+        end_time = time.time()
+
+        print("Elapsed Time:", end_time-start_time)
+
+        im_height, im_width,_ = image.shape
+        boxes_list = [None for i in range(boxes.shape[1])]
+        for i in range(boxes.shape[1]):
+            boxes_list[i] = (int(boxes[0,i,0] * im_height),
+                        int(boxes[0,i,1]*im_width),
+                        int(boxes[0,i,2] * im_height),
+                        int(boxes[0,i,3]*im_width))
+
+        return boxes_list, scores[0].tolist(), [int(x) for x in classes[0].tolist()], int(num[0])
+
+    def close(self):
+        self.sess.close()
+        self.default_graph.close()
+
+if __name__ == "__main__":
+    model_path = "C:/Users/John/Desktop/ster_rcnn_inception_v2_coco_2018_01_28/ster_rcnn_inception_v2_coco_2018_01_28/ozen_inference_graph.pb"
+    odapi = DetectorAPI(path_to_ckpt=model_path)
+    threshold = 0.3
+    cap = cv2.VideoCapture("./videos/example_02.mp4")
+
+    while True:
+        r, img = cap.read()
+        img = cv2.resize(img, (720, 720))
+
+        boxes, scores, classes, num = odapi.processFrame(img)
+
+        # Visualization of the results of a detection.
+
+        for i in range(len(boxes)):
+            # Class 1 represents human
+            if classes[i] == 1 and scores[i] > threshold:
+                box = boxes[i]
+                cv2.rectangle(img,(box[1],box[0]),(box[3],box[2]),(255,0,0),2)
+                requests.get("http://192.168.178.53/play")
+            else:
+                requests.get("http://192.168.178.53/stop")
+
+        cv2.imshow("preview", img)
+        key = cv2.waitKey(1)
+        if key & 0xFF == ord('q'):
+            break