第二节课：人脸识别

步骤：定位、对齐、识别

1.使用openCV定位多个人脸

先确认路径，不正确用os.chdir()修改；

须找到cv2在电脑中的安装路径

import cv2
import os
import matplotlib.pyplot as plt#别名，缩短字符串

os.chdir(r'D:a learn sth大三上Python程序设计人脸识别')

def detect(filename):
    face_cascade=cv2.CascadeClassifier(r'D:anacondainstallfolderLibsite-packagescv2datahaarcascade_frontalface_default.xml')
#我在python的东西安装包在D:anacondainstallfolderLibsite-packages下

    img=cv2.imread(filename)#读取，查一下
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#将彩色的转换为灰度图片

    faces=face_cascade.detectMultiScale(gray,1.3,5)
    
    for(x,y,w,h)in faces:
        img=cv2.rectangle(img,(x,y),(x+w,y+w),(255,0,0),2)#用cv2函数画矩形；定义线的颜色（红，绿，蓝）
    plt.imshow(img)
    plt.axis('off')
    plt.show()
    
detect('yu.jpg')

2.人脸对齐

先安装 dlib,再装face_recognition（依赖于dlib）；

gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)改成灰度图；

import cv2
import dlib
import matplotlib.pyplot as plt

path="yu2.jpg"
img=cv2.imread(path)
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#改成灰度图

人脸分类器detector;

detector=dlib.get_frontal_face_detector()#人脸分类器

选用人脸检测器（5/68点），需要找到所在电脑中的路径

#68点人脸检测器
predictor=dlib.shape_predictor(r"D:anacondainstallfolderLibsite-packagesface_recognition_modelsmodelsshape_predictor_5_face_landmarks.dat")
#5点
#predictor=dlib.shape_predictor(r"D:anacondainstallfolderLibsite-packagesface_recognition_modelsmodelsshape_predictor_68_face_landmarks.dat")

dets=detector(gray,1)
for face in dets:
    shape=predictor(img,face)#寻找人脸的568个标记点；遍历所有点，打印出其坐标，并圈出来
    for pt in shape.parts():
        pt_pos=(pt.x,pt.y)
        img=cv2.circle(img,pt_pos,6,(255,0,0),1)#（255,0,0）是点的颜色
        
    plt.imshow(img)
    plt.axis('off')
    plt.show()

3.人脸识别

known_image；unknown_image；

import cv2
import face_recognition
import matplotlib.pyplot as plt

#known_image=cv2.imread('1.jpg')
known_image=face_recognition.load_image_file('1.jpg')

#unknown_image=cv2.imread('2.jpg')
unknown_image=face_recognition.load_image_file('2.jpg')

known_encoding；unknown_encoding

known_encoding=face_recognition.face_encodings(known_image)[0]
unknown_encoding=face_recognition.face_encodings(unknown_image)[0]

results=face_recognition.compare_faces([known_encoding],
                                     unknown_encoding,
                                     tolerance=0.4)
if results[0]==True:
    print("匹配成功，该未知图片与已有图片人脸可匹配！")
else:
    print("匹配失败！")
    
#print(known_encoding)
#print(unknown_encoding)
plt.imshow(known_image)
plt.axis('off')
plt.show()

plt.imshow(unknown_image)
plt.axis('off')
plt.show()

第二节课：人脸识别

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