dlib.shape_predictor(args[‘shape_predictor’])
ARGS [‘Shape_predictor’] location information
Return to the trained face 68 feature point detector

predictor(gray, rect)
Gray input 8 -bit grayscale or RGB image
RECT Start the location information of the boundary frame of the internal face detection
Return to the location of 68 feature points

import dlib
import cv2

img = cv2.imread("image.jpg")

gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

detector = dlib.get_frontal_face_detector() 

dets = detector(gray_img, 1)

# Use a model to build a feature extractor
predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat/shape_predictor_68_face_landmarks.dat')

for i, d in enumerate(dets):
 # Use Predictor to perform a key point for face to detect Shape as the result of returning
    shape = predictor(gray_img, d)
    
    for index, pt in enumerate(shape.parts()):
        print('Part {}: {}'.format(index, pt))
        pt_pos = (pt.x, pt.y)
        cv2.circle(img, pt_pos, 1, (255, 0, 0), 2)
        # Use CV2.Puttext to mark the serial number
        font = cv2.FONT_HERSHEY_SIMPLEX
        cv2.putText(img, str(index+1),pt_pos,font, 0.3, (0, 0, 255), 1, cv2.LINE_AA)
 
cv2.imshow('img', img)
k = cv2.waitKey()
cv2.destroyAllWindows()