code: # import the necessary packages
  from imutils import face_utils
  import numpy as np
  import argparse
  import imutils
  import dlib
  import cv2
  #python facial_landmarks.py --shape-predictor shape_predictor_68_face_landmarks.dat --image test1.jpg
  def rect_to_bb(rect):
  # take a bounding predicted by dlib and convert it
  # to the format (x, y, w, h) as we would normally do
  # with OpenCV
      x = rect.left()
      y = rect.top()
      w = rect.right() - x
      h = rect.bottom() - y
      # return a tuple of (x, y, w, h)
      return (x, y, w, h)
  
  def shape_to_np(shape, dtype="int"):
  # initialize the list of (x, y)-coordinates
      coords = np.zeros((68, 2), dtype=dtype)
  # loop over the 68 facial landmarks and convert them
  # to a 2-tuple of (x, y)-coordinates
      for i in range(0, 68):
          coords[i] = (shape.part(i).x, shape.part(i).y)
  # return the list of (x, y)-coordinates
      return coords
  
  # construct the argument parser and parse the arguments
  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"])
  
  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)
apis: numpy.zeros