diff --git a/camera-test.py b/camera-test.py
deleted file mode 100644
index 3a33cf8..0000000
--- a/camera-test.py
+++ /dev/null
@@ -1,77 +0,0 @@
-# import numpy as np
-import cv2
-
-cap = cv2.VideoCapture('recordings/test1.webm')
-
-ret, frame1 = cap.read()
-ret, frame2 = cap.read()
-
-while(cap.isOpened()):
-    diff = cv2.absdiff(frame1, frame2)
-    gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
-    blur = cv2.GaussianBlur(gray, (5,5), 0)
-    _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
-    dilated = cv2.dilate(thresh, None, iterations=3)
-    contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-    for contour in contours:
-        (x, y, w, h) = cv2.boundingRect(contour)
-
-        if cv2.contourArea(contour) < 9000:
-            continue
-        cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
-        cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
-
-    cv2.imshow("feed", frame1)
-    frame1 = frame2
-    ret, frame2 = cap.read()
-
-    if cv2.waitKey(25) & 0xFF == ord('q'):
-        break
-
-# When everything done, release the capture
-cap.release()
-cv2.destroyAllWindows()
-
-
-
-
-# import numpy as np
-# import cv2
-
-# cap = cv2.VideoCapture('recordings/test1.webm')
-
-# while(cap.isOpened()):
-#     # Capture frame-by-frame
-#     ret, frame = cap.read()
-
-#     # Our operations on the frame come here
-#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-
-#     # Display the resulting frame
-#     cv2.imshow('frame',gray)
-#     if cv2.waitKey(25) & 0xFF == ord('q'):
-#         break
-
-# # When everything done, release the capture
-# cap.release()
-# cv2.destroyAllWindows()
-
-
-
-
-
-# from imageai.Detection import VideoObjectDetection
-# import os
-
-# execution_path = os.getcwd()
-
-# detector = VideoObjectDetection()
-# detector.setModelTypeAsYOLOv3()
-# detector.setModelPath( os.path.join(execution_path , "yolo.h5"))
-# detector.loadModel()
-
-# video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join( execution_path, "recordings/traffic-mini.mp4"),
-#                                 output_file_path=os.path.join(execution_path, "outputs/traffic_mini_detected_1")
-#                                 , frames_per_second=29, log_progress=True)
-# print(video_path)
diff --git a/project.py b/project.py
new file mode 100644
index 0000000..1a6aced
--- /dev/null
+++ b/project.py
@@ -0,0 +1,92 @@
+from pyimagesearch.centroidtracker import CentroidTracker
+from pyimagesearch.trackableobject import TrackableObject
+from imutils.video import VideoStream
+from imutils.video import FPS
+import numpy as np
+import argparse
+import imutils
+import time
+import dlib
+import cv2
+
+
+
+
+
+
+# import numpy as np
+# import cv2
+
+# cap = cv2.VideoCapture('recordings/test1.webm')
+
+# ret, frame1 = cap.read()
+# ret, frame2 = cap.read()
+
+# while(cap.isOpened()):
+#     diff = cv2.absdiff(frame1, frame2)
+#     gray = cv2.cvtColor(diff, cv2.COLOR_BGR2GRAY)
+#     blur = cv2.GaussianBlur(gray, (5,5), 0)
+#     _, thresh = cv2.threshold(blur, 20, 255, cv2.THRESH_BINARY)
+#     dilated = cv2.dilate(thresh, None, iterations=3)
+#     contours, _ = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+#     for contour in contours:
+#         (x, y, w, h) = cv2.boundingRect(contour)
+
+#         if cv2.contourArea(contour) < 9000:
+#             continue
+#         cv2.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
+#         cv2.putText(frame1, "Status: {}".format('Movement'), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
+
+#     cv2.imshow("feed", frame1)
+#     frame1 = frame2
+#     ret, frame2 = cap.read()
+
+#     if cv2.waitKey(25) & 0xFF == ord('q'):
+#         break
+
+# cap.release()
+# cv2.destroyAllWindows()
+
+
+
+
+# import numpy as np
+# import cv2
+
+# cap = cv2.VideoCapture('recordings/test1.webm')
+
+# while(cap.isOpened()):
+#     # Capture frame-by-frame
+#     ret, frame = cap.read()
+
+#     # Our operations on the frame come here
+#     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+#     # Display the resulting frame
+#     cv2.imshow('frame',gray)
+#     if cv2.waitKey(25) & 0xFF == ord('q'):
+#         break
+
+# # When everything done, release the capture
+# cap.release()
+# cv2.destroyAllWindows()
+
+
+
+
+
+# from imageai.Detection import VideoObjectDetection
+# import os
+
+# execution_path = os.getcwd()
+
+# detector = VideoObjectDetection()
+# detector.setModelTypeAsYOLOv3()
+# detector.setModelPath( os.path.join(execution_path , "yolo.h5"))
+# detector.loadModel()
+
+# video_path = detector.detectObjectsFromVideo(input_file_path=os.path.join( execution_path, "recordings/traffic-mini.mp4"),
+#                                 output_file_path=os.path.join(execution_path, "outputs/traffic_mini_detected_1")
+#                                 , frames_per_second=29, log_progress=True)
+# print(video_path)
diff --git a/trackableobject.py b/trackableobject.py
new file mode 100644
index 0000000..6f4137e
--- /dev/null
+++ b/trackableobject.py
@@ -0,0 +1,10 @@
+class TrackableObject:
+	def __init__(self, objectID, centroid):
+		# store the object ID, then initialize a list of centroids
+		# using the current centroid
+		self.objectID = objectID
+		self.centroids = [centroid]
+ 
+		# initialize a boolean used to indicate if the object has
+		# already been counted or not
+		self.counted = False
\ No newline at end of file