本文使用Python实现了人脸识别，部分代码如下：

读取图片：

import cv2 as cv
img = cv.imread('face1.jpg')
cv.imshow('read_img',img)
cv.waitKey(0)
cv.destroyAllWindows()

灰度转换：

#读取图片
img = cv.imread('face1.jpg')
#灰度转换
gray_img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
#显示灰度图片
cv.imshow('gray',gray_img)
#保存灰度图片
cv.imwrite('gray_face1.jpg',gray_img)
cv.imshow('read_img',img)
cv.waitKey(0)
cv.destroyAllWindows()

人脸检测：

def face_detect_demo():
    gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
    face = face_detect.detectMultiScale(gary,1.01,5,0,(100,100),(300,300))
    for x,y,w,h in face:
        cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
    cv.imshow('result',img)

检测多个人脸：

#检测函数
def face_detect_demo():
    gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gary)
    for x,y,w,h in face:
        cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
    cv.imshow('result',img)

使用视频检测人脸：

#检测函数
def face_detect_demo(img):
    gary = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
    face_detect = cv.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml')
    face = face_detect.detectMultiScale(gary)
    for x,y,w,h in face:
        cv.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
    cv.imshow('result',img)

#读取摄像头
cap = cv.VideoCapture(0)
#循环
while True:
    flag,frame = cap.read()
    if not flag:
        break
    face_detect_demo(frame)
    if ord('q') == cv.waitKey(1):
        break
#释放内存
cv.destroyAllWindows()
#释放摄像头
cap.release()

数据集训练：

import os
import cv2
import sys
from PIL import Image
import numpy as np

def getImageAndLabels(path):
    facesSamples=[]
    ids=[]
    imagePaths=[os.path.join(path,f) for f in os.listdir(path)]
    #检测人脸
    face_detector = cv2.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
    print('数据排列：',imagePaths)
    for imagePath in imagePaths:
        PIL_img=Image.open(imagePath).convert('L')
        img_numpy=np.array(PIL_img,'uint8')
        faces = face_detector.detectMultiScale(img_numpy)
        id = int(os.path.split(imagePath)[1].split('.')[0])
        for x,y,w,h in faces:
            ids.append(id)
            facesSamples.append(img_numpy[y:y+h,x:x+w])
        print('id:', id)
    print('fs:', facesSamples)
    return facesSamples,ids

if __name__ == '__main__':
    path='./data/jm/'
    faces,ids=getImageAndLabels(path)
    recognizer=cv2.face.LBPHFaceRecognizer_create()
    recognizer.train(faces,np.array(ids))
    recognizer.write('trainer/trainer.yml')

人脸识别：

def face_detect_demo(img):
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)#转换为灰度
    face_detector=cv2.CascadeClassifier('D:/opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_alt2.xml')
    face=face_detector.detectMultiScale(gray,1.1,5,cv2.CASCADE_SCALE_IMAGE,(100,100),(300,300))
    for x,y,w,h in face:
        cv2.rectangle(img,(x,y),(x+w,y+h),color=(0,0,255),thickness=2)
        cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1)
        # 人脸识别
        ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
        if confidence > 80:
            global warningtime
            warningtime += 1
            if warningtime > 100:
               warning()
               warningtime = 0
            cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
        else:
            cv2.putText(img,str(names[ids-1]), (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 255, 0), 1)
    cv2.imshow('result',img)