#Initializing dlib's HOG based facial landmark predictor.
print("[INFO] loading facial landmark predictor from dlib's library.")
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape-predictor"])
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right eye"]
print("[INFO] loading the webcam ...")
vs = VideoStream(src=0).start()
time.sleep(1.0)
while True:
#grab the FRAME from the threaded video stream file and resize it, and onvert it into grayscale.
frame = vs.fread()
frame = imutils.resize(frame, width = 550)
gray = cv2.cvtColor(frame, cv2.COLORBGR2GRAY)
#detect faces in the grayscale frame.
rects = detector(gray, 0)
for rect in rects:
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
#extract the left and the right eye coordinates, then calculating Eye Aspect Ratio
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
