text: from imutils import face_utils
  import numpy as np
  import argparse
  import imutils
  import dlib
  import cv2
  
  
  ap = argparse.ArgumentParser()
  ap.add_argument("-p", "--shape-predictor", required=True,
  	help="path to facial landmark predictor")
  ap.add_argument("-i", "--image", required=True,
  	help="path to input image")
  args = vars(ap.parse_args())
  
  # initialize dlib's face detector (HOG-based) and then create
  # the facial landmark predictor
  detector = dlib.get_frontal_face_detector()
  predictor = dlib.shape_predictor(args["shape_predictor"])
  
  # load the input image, resize it, and convert it to grayscale
  image = cv2.imread(args["image"])
  image = imutils.resize(image, width=500)
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  
  # detect faces in the grayscale image
  rects = detector(gray, 1)
  
  # loop over the face detections
  for (i, rect) in enumerate(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)
  
      # convert dlib's rectangle to a OpenCV-style bounding box
      # [i.e., (x, y, w, h)], then draw the face bounding box
      (x, y, w, h) = face_utils.rect_to_bb(rect)
      cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
  
      # show the face number
      cv2.putText(image, "Face #{}".format(i + 1), (x - 10, y - 10),
                  cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
  
      # loop over the (x, y)-coordinates for the facial landmarks
      # and draw them on the image
      for (x, y) in shape:
          cv2.circle(image, (x, y), 1, (0, 0, 255), -1)
  
  # show the output image with the face detections + facial landmarks
  cv2.imshow("Output", image)
  cv2.waitKey(0)
  
  