class_docstr: This class contains all needed functions to work with faces in a frame
docstr: Get all facial landmarks in a face 
  
  Args:
      frame (cv2 image): the original frame. In RGB format.
      type (str): 5 or 68 facial landmarks
  
  Outputs:
      shape (array): facial landmarks' co-ords in format of of tuples (x,y)
docstr_tok: ["Get","all","facial","landmarks","in","a","face","Args",":","frame","(","cv2","image",")",":","the","original","frame",".","In","RGB","format",".","type","(","str",")",":","5","or","68","facial","landmarks","Outputs",":","shape","(","array",")",":","facial","landmarks","'","co-ords","in","format","of","of","tuples","(","x",",","y",")"]
code: def get_landmarks(self, frame, type):
      """
      Get all facial landmarks in a face
  
      Args:
          frame (cv2 image): the original frame. In RGB format.
          type (str): 5 or 68 facial landmarks
  
      Outputs:
          shape (array): facial landmarks' co-ords in format of of tuples (x,y)
      """
      if self.predictor is None:
          print("[INFO] load " + type + " facial landmarks model ...")
          self.predictor = dlib.shape_predictor(
              "shape_predictor_" + type + "_face_landmarks.dat"
          )
          print("[INFO] Load model - DONE!")
  
      if frame is None:
          return None, None
      # all face will be resized to a fix size, e.g width = 200
      # face = imutils.resize(face, width=200)
      # face must be gray
      gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
      rects = self.face_detection(frame)
  
      if len(rects) < 0 or len(rects) == 0:
          return None, None
  
      shape = self.predictor(gray, rects[0])
      shape = face_utils.shape_to_np(shape)
  
      # in shape, there are 68 pairs of (x, y) carrying coords of 68 points.
      # to draw landmarks, use: for (x, y) in shape: cv2.circle(image, (x, y), 1, (0, 0, 255), -1)
  
      return shape, rects
  
file_path: heart-rate-measurement-camera/face_utilities.py
imports: import cv2
  import numpy
  import dlib
  from imutils import face_utils
  import imutils
  from collections import OrderedDict
label_desc: Use image node_id 5 for calling the Face_utilities obj's underlying member method code with example usage: obj.get_landmarks(frame, type) and returns: shape,  rects, None,  None, None,  None