text: from imutils import face_utils
  import datetime
  import imutils
  import time
  import dlib
  import cv2, math
  import numpy as np
  from imutils import face_utils, rotate_bound
  
  
  # initialize dlib's face detector (HOG-based) and then create
  # the facial landmark predictor
  print("[INFO] loading facial landmark predictor...")
  model = "filters/shape_predictor_68_face_landmarks.dat"
  detector = dlib.get_frontal_face_detector()
  predictor = dlib.shape_predictor(model) # link to model: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
  
  video_capture = cv2.VideoCapture(0,cv2.CAP_DSHOW)
  cv2.imshow('Video', np.empty((5,5),dtype=float))
  
  #points are tuples in the form (x,y)
  # returns angle between points in degrees
  def calculate_inclination(point1, point2):
      x1,x2,y1,y2 = point1[0], point2[0], point1[1], point2[1]
      incl = -180/math.pi*math.atan((float(y2-y1))/(x2-x1))
      return incl
  
  def calculate_boundbox(list_coordinates):
      x = min(list_coordinates[:,0])
      y = min(list_coordinates[:,1])
      w = max(list_coordinates[:,0]) - x
      h = max(list_coordinates[:,1]) - y
      return (x,y,w,h)
  
  def get_face_boundbox(points, face_part):
      if face_part == 1:
          (x,y,w,h) = calculate_boundbox(points[17:22]) #left eyebrow
      elif face_part == 2:
          (x,y,w,h) = calculate_boundbox(points[22:27]) #right eyebrow
      elif face_part == 3:
          (x,y,w,h) = calculate_boundbox(points[36:42]) #left eye
      elif face_part == 4:
          (x,y,w,h) = calculate_boundbox(points[42:48]) #right eye
      elif face_part == 5:
          (x,y,w,h) = calculate_boundbox(points[29:36]) #nose
      elif face_part == 6:
          (x,y,w,h) = calculate_boundbox(points[48:68]) #mouth
      return (x,y,w,h)
  
  while cv2.getWindowProperty('Video', 0) >= 0:
      # Capture frame-by-frame
      ret, frame = video_capture.read()
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
      # detect faces in the grayscale frame
      rects = detector(gray, 0)
  
      # loop over the face detections
      for rect in rects:
      	# determine the facial landmarks for the face region, then
      	# convert the facial landmark (x, y)-coordinates to a NumPy array
          shape = predictor(gray, rect)
  
          shape = face_utils.shape_to_np(shape)
  
          for i in range(1,7):
              (x,y,w,h) = get_face_boundbox(shape, i)
              cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 1)
  
          incl = calculate_inclination(shape[17], shape[26])
  
          img = cv2.imread("./sprites/doggy_ears.png")
          rows,cols = img.shape[0], img.shape[1]
          M = cv2.getRotationMatrix2D((cols/2,rows/2),incl,1)
          dst = cv2.warpAffine(img,M,(cols,rows))
          dst = rotate_bound(img, incl)
          cv2.imshow('sprite',dst)
  
          print ("Pixels distance points in mouth: ", shape[66][1] - shape[62][1])
  
          x,y, w, h = rect.left(), rect.top(), rect.width(), rect.height()
  
          cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 0, 255), 2)
  
          # loop over the (x, y)-coordinates for the facial landmarks
      	# and draw them on the image
          for (x, y) in shape:
              cv2.circle(frame, (x, y), 1, (0, 0, 255), -1)
  
      cv2.imshow("Frame", frame)
      key = cv2.waitKey(1) & 0xFF
      # if the `q` key was pressed, break from the loop
      if key == ord("q"):
      	break
  
  # When everything is done, release the capture
  video_capture.release()
  cv2.destroyAllWindows()
  