Open source libraries have dlib

install ：https://pypi.org/simple/dlib/ , After downloading pip install

Model download ：Index of /files

1、dlib5 spot

Left and right canthus and middle of the person 5 spot .

import dlib
import numpy as np
import cv2
import time

predictor_path = '../model/shape_predictor_5_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

start_time = time.time()

frame = cv2.imread("../image/ab.jpg")
img = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
dets = detector(img, 1)

print(dets)

for index, det in enumerate(dets):
    right_top = (det.right(), det.top())
    left_bottom = (det.left(), det.bottom())
    #  Picture part detection frame 
    # cv2.rectangle(frame, right_top, left_bottom, (255, 0, 0), 2)
    print(right_top,left_bottom)

    #  Facial feature point detection 
    shape = predictor(img, det)
    landmarks = np.matrix([[p.x, p.y] for p in shape.parts()])
    for idx, point in enumerate(landmarks):
        # 68 The coordinates of point 
        pos = (point[0, 0], point[0, 1])
        cv2.circle(frame, pos, 5, (0, 0, 255), -1)
        cv2.putText(frame,str(idx),pos,cv2.FONT_HERSHEY_COMPLEX,0.6,(0,0,255),1)

cv2.imwrite('kp2.jpg',frame)
cv2.imshow('res',frame)
cv2.waitKey()

2、dlib68 spot

It basically contains the key points of the face area , But there are no key points in the forehead area and only the upper edge of the eyebrow .

import dlib
import numpy as np
import cv2
import time

predictor_path = '../model/shape_predictor_68_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

start_time = time.time()

frame = cv2.imread("../image/ab.jpg")
img = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
dets = detector(img, 1)

print(dets)

for index, det in enumerate(dets):
    right_top = (det.right(), det.top())
    left_bottom = (det.left(), det.bottom())
    #  Picture part detection frame 
    # cv2.rectangle(frame, right_top, left_bottom, (255, 0, 0), 2)
    print(right_top,left_bottom)

    #  Facial feature point detection 
    shape = predictor(img, det)
    landmarks = np.matrix([[p.x, p.y] for p in shape.parts()])
    for idx, point in enumerate(landmarks):
        # 68 The coordinates of point 
        pos = (point[0, 0], point[0, 1])
        cv2.circle(frame, pos, 5, (0, 0, 255), -1)
        cv2.putText(frame,str(idx),pos,cv2.FONT_HERSHEY_COMPLEX,0.6,(0,0,255),1)

cv2.imwrite('kp2.jpg',frame)
cv2.imshow('res',frame)
cv2.waitKey()

3、 dlib81 spot

Comparison 68 spot , More 13 Points are distributed in the forehead area , But the accuracy of the point is not too high .

import dlib
import numpy as np
import cv2
import time

predictor_path = '../model/shape_predictor_81_face_landmarks.dat'
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

start_time = time.time()

frame = cv2.imread("../image/ab.jpg")
img = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
dets = detector(img, 1)

print(dets)

for index, det in enumerate(dets):
    right_top = (det.right(), det.top())
    left_bottom = (det.left(), det.bottom())
    #  Picture part detection frame 
    # cv2.rectangle(frame, right_top, left_bottom, (255, 0, 0), 2)
    print(right_top,left_bottom)

    #  Facial feature point detection 
    shape = predictor(img, det)
    landmarks = np.matrix([[p.x, p.y] for p in shape.parts()])
    for idx, point in enumerate(landmarks):
        # 68 The coordinates of point 
        pos = (point[0, 0], point[0, 1])
        cv2.circle(frame, pos, 5, (0, 0, 255), -1)
        cv2.putText(frame,str(idx),pos,cv2.FONT_HERSHEY_COMPLEX,0.6,(0,0,255),1)

cv2.imwrite('kp2.jpg',frame)
cv2.imshow('res',frame)
cv2.waitKey()