text: # import the necessary packages
  from imutils import face_utils
  import numpy as np
  import imutils
  import dlib
  import cv2
  
  
  # initialize dlib's face detector (HOG-based) and then create
  # the facial landmark predictor
  detector = dlib.get_frontal_face_detector()
  predictor = dlib.shape_predictor('./face_predictor.dat')
  
  # 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 landmark (x, y)-coordinates to a NumPy array
  	shape = predictor(gray, rect)
  	shape = face_utils.shape_to_np(shape)
  
  	# loop over the face parts individually
  	for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
  		# clone the original image so we can draw on it, then
  		# display the name of the face part on the image
  		clone = image.copy()
  		cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
  			0.7, (0, 0, 255), 2)
  
  		# loop over the subset of facial landmarks, drawing the
  		# specific face part
  		for (x, y) in shape[i:j]:
  			cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)
  		# extract the ROI of the face region as a separate image
  		(x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
  		roi = image[y:y + h, x:x + w]
  		roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)
  
  		# show the particular face part
  		cv2.imshow("ROI", roi)
  		cv2.imshow("Image", clone)
  		cv2.waitKey(0)
  
  	# visualize all facial landmarks with a transparent overlay
  	output = face_utils.visualize_facial_landmarks(image, shape)
  	cv2.imshow("Image", output)
  	cv2.waitKey(0)
  