text: # -*- coding: utf-8 -*-
  
  import PIL.Image
  import dlib
  import numpy as np
  from PIL import ImageFile
  
  ImageFile.LOAD_TRUNCATED_IMAGES = True
  
  face_detector = dlib.get_frontal_face_detector()
  
  predictor_68_point_model = 'models\shape_predictor_68_face_landmarks.dat'
  pose_predictor_68_point = dlib.shape_predictor(predictor_68_point_model)
  
  predictor_5_point_model = 'models\shape_predictor_5_face_landmarks.dat'
  pose_predictor_5_point = dlib.shape_predictor(predictor_5_point_model)
  
  cnn_face_detection_model = 'models\mmod_human_face_detector.dat'
  cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model)
  
  face_recognition_model = 'models\dlib_face_recognition_resnet_model_v1.dat'
  face_encoder = dlib.face_recognition_model_v1(face_recognition_model)
  
  
  def _rect_to_css(rect):
      """
      Convert a dlib 'rect' object to a plain tuple in (top, right, bottom, left) order
      :param rect: a dlib 'rect' object
      :return: a plain tuple representation of the rect in (top, right, bottom, left) order
      """
      return rect.top(), rect.right(), rect.bottom(), rect.left()
  
  
  def _css_to_rect(css):
      """
      Convert a tuple in (top, right, bottom, left) order to a dlib `rect` object
      :param css:  plain tuple representation of the rect in (top, right, bottom, left) order
      :return: a dlib `rect` object
      """
      return dlib.rectangle(css[3], css[0], css[1], css[2])
  
  
  def _trim_css_to_bounds(css, image_shape):
      """
      Make sure a tuple in (top, right, bottom, left) order is within the bounds of the image.
      :param css:  plain tuple representation of the rect in (top, right, bottom, left) order
      :param image_shape: numpy shape of the image array
      :return: a trimmed plain tuple representation of the rect in (top, right, bottom, left) order
      """
      return max(css[0], 0), min(css[1], image_shape[1]), min(css[2], image_shape[0]), max(css[3], 0)
  
  
  def face_distance(face_encodings, face_to_compare):
      """
      Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
      for each comparison face. The distance tells you how similar the faces are.
      :param face_encodings: List of face encodings to compare
      :param face_to_compare: A face encoding to compare against
      :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array
      """
      if len(face_encodings) == 0:
          return np.empty((0))
  
      return np.linalg.norm(face_encodings - face_to_compare, axis=1)
  
  
  def load_image_file(file, mode='RGB'):
      """
      Loads an image file (.jpg, .png, etc) into a numpy array
      :param file: image file name or file object to load
      :param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported.
      :return: image contents as numpy array
      """
      im = PIL.Image.open(file)
      if mode:
          im = im.convert(mode)
      return np.array(im)
  
  
  def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"):
      """
      Returns an array of bounding boxes of human faces in a image
      :param img: An image (as a numpy array)
      :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
      :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
                    deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
      :return: A list of dlib 'rect' objects of found face locations
      """
      if model == "cnn":
          return cnn_face_detector(img, number_of_times_to_upsample)
      else:
          return face_detector(img, number_of_times_to_upsample)
  
  
  def face_locations(img, number_of_times_to_upsample=1, model="hog"):
      """
      Returns an array of bounding boxes of human faces in a image
      :param img: An image (as a numpy array)
      :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
      :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
                    deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
      :return: A list of tuples of found face locations in css (top, right, bottom, left) order
      """
      if model == "cnn":
          return [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, "cnn")]
      else:
          return [_trim_css_to_bounds(_rect_to_css(face), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, model)]
  
  
  def _raw_face_locations_batched(images, number_of_times_to_upsample=1, batch_size=128):
      """
      Returns an 2d array of dlib rects of human faces in a image using the cnn face detector
      :param images: A list of images (each as a numpy array)
      :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
      :return: A list of dlib 'rect' objects of found face locations
      """
      return cnn_face_detector(images, number_of_times_to_upsample, batch_size=batch_size)
  
  
  def batch_face_locations(images, number_of_times_to_upsample=1, batch_size=128):
      """
      Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
      If you are using a GPU, this can give you much faster results since the GPU
      can process batches of images at once. If you aren't using a GPU, you don't need this function.
      :param images: A list of images (each as a numpy array)
      :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
      :param batch_size: How many images to include in each GPU processing batch.
      :return: A list of tuples of found face locations in css (top, right, bottom, left) order
      """
      def convert_cnn_detections_to_css(detections):
          return [_trim_css_to_bounds(_rect_to_css(face.rect), images[0].shape) for face in detections]
  
      raw_detections_batched = _raw_face_locations_batched(images, number_of_times_to_upsample, batch_size)
  
      return list(map(convert_cnn_detections_to_css, raw_detections_batched))
  
  
  def _raw_face_landmarks(face_image, face_locations=None, model="large"):
      if face_locations is None:
          face_locations = _raw_face_locations(face_image)
      # else:
      #     face_locations = [_css_to_rect(face_location) for face_location in face_locations]
  
      pose_predictor = pose_predictor_68_point
  
      if model == "small":
          pose_predictor = pose_predictor_5_point
  
      return [pose_predictor(face_image, face_location) for face_location in face_locations]
  
  
  def face_landmarks(face_image, face_locations=None, model="large"):
      """
      Given an image, returns a dict of face feature locations (eyes, nose, etc) for each face in the image
      :param face_image: image to search
      :param face_locations: Optionally provide a list of face locations to check.
      :param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster.
      :return: A list of dicts of face feature locations (eyes, nose, etc)
      """
      landmarks = _raw_face_landmarks(face_image, face_locations, model)
      landmarks_as_tuples = [[(p.x, p.y) for p in landmark.parts()] for landmark in landmarks]
  
      # For a definition of each point index, see https://cdn-images-1.medium.com/max/1600/1*AbEg31EgkbXSQehuNJBlWg.png
      if model == 'large':
          return [{
              "chin": points[0:17],
              "left_eyebrow": points[17:22],
              "right_eyebrow": points[22:27],
              "nose_bridge": points[27:31],
              "nose_tip": points[31:36],
              "left_eye": points[36:42],
              "right_eye": points[42:48],
              "top_lip": points[48:55] + [points[64]] + [points[63]] + [points[62]] + [points[61]] + [points[60]],
              "bottom_lip": points[54:60] + [points[48]] + [points[60]] + [points[67]] + [points[66]] + [points[65]] + [points[64]]
          } for points in landmarks_as_tuples]
      elif model == 'small':
          return [{
              "nose_tip": [points[4]],
              "left_eye": points[2:4],
              "right_eye": points[0:2],
          } for points in landmarks_as_tuples]
      else:
          raise ValueError("Invalid landmarks model type. Supported models are ['small', 'large'].")
  
  
  def face_encodings(face_image, known_face_locations=None, num_jitters=1, model="small"):
      """
      Given an image, return the 128-dimension face encoding for each face in the image.
      :param face_image: The image that contains one or more faces
      :param known_face_locations: Optional - the bounding boxes of each face if you already know them.
      :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
      :param model: Optional - which model to use. "large" or "small" (default) which only returns 5 points but is faster.
      :return: A list of 128-dimensional face encodings (one for each face in the image)
      """
      raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model)
      return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
  
  
  def compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6):
      """
      Compare a list of face encodings against a candidate encoding to see if they match.
      :param known_face_encodings: A list of known face encodings
      :param face_encoding_to_check: A single face encoding to compare against the list
      :param tolerance: How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance.
      :return: A list of True/False values indicating which known_face_encodings match the face encoding to check
      """
      return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)