这几天在做一个基于AU进行人脸识别的实验，在这过程中，需要提取AU指定部位。但是我又不想自己再训练一个网络，于是，就使用dlib和opencv提供的人脸关键点进行试验。

具体思路就是通过人脸关键点的坐标进行定位，并提取指定部分，这里就以提取额头部分为例：

#coding=utf-8
import numpy as np
import cv2
import dlib
from scipy.spatial import distance
import os
from imutils import face_utils

base_path="D:/CK_Emotion_DD/"
source_path=base_path+'cohn-kanade/'
to_path=base_path+'forehead/'

test_path=source_path+'/S010/001/S010_001_01594215.png'

shape_detector_path="D:/blink_detect/model/shape_predictor_68_face_landmarks.dat"

left_eye_up_point1=38-1
left_eye_up_point2=39-1
right_eye_up_point1=44-1
right_eye_up_point2=45-1

detector = dlib.get_frontal_face_detector()# 人脸检测器
predictor = dlib.shape_predictor(shape_detector_path)# 人脸特征点检测器

def catch_forehead(image_path,save_path):
    image = cv2.imread(image_path)
    cv2.imshow("1",image)
    cv2.waitKey()
    dets = detector(image,1)
    sp=image.shape
    h=sp[0]
    w=sp[1]
    print(h)
    for k, d in enumerate(dets):
        print("dets{}".format(d))
        print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
            k, d.left(), d.top(), d.right(), d.bottom()))
        shape = predictor(image, d)
        #print(shape.part(0).x,shape.part(0).y)
        l1=shape.part(left_eye_up_point1)
        l2=shape.part(left_eye_up_point2)
        r1=shape.part(right_eye_up_point1)
        r2=shape.part(right_eye_up_point2)
        y=min(l1.y,l2.y,r1.y,r2.y)
        print(y,h-d.top(),d.left(),w-d.right())
        forehead=image[d.top():y,d.left():d.right()]
        cv2.imshow('1',forehead)
        cv2.waitKey()
        cv2.imwrite(save_path,forehead)


catch_forehead(test_path,"d:/1.png")

提取效果如下