1.引入库

代码如下（示例）：

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

2.读入数据

代码如下（示例）：

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

def train_img(path):
    #调用训练集
    face = cv.CascadeClassifier('haarcascade_frontalface_alt2.xml')
    #图片集
    picture = []
    #标签集
    ids = []
    #图片集合
    Imagepaths = [os.path.join(path, f) for f in os.listdir(path)]
    #循环每一张图片
    for Imagepath in Imagepaths:
        #提取这张图片的id号
        id = int(os.path.split(Imagepath)[1].split('.')[0])
        #将图片转灰
        PIL_img = Image.open(Imagepath).convert('L')
        #将图片转化为uint8
        img_numpy = np.array(PIL_img, 'uint8')
        #提取人脸
        faces = face.detectMultiScale(img_numpy)

        for x, y, w, h in faces:
            picture.append(img_numpy[y:y+h, x:x+w])
            ids.append(id)
    return picture, ids

path = 'D://ll//'
faces, ids = train_img(path)
recognizer = cv.face.LBPHFaceRecognizer_create()
recognizer.train(faces, np.array(ids))
recognizer.write('d:\\train.xml')

3.识别图片

import cv2 as cv
recognizer = cv.face.LBPHFaceRecognizer_create()
recognizer.read('d:\\train.xml')
def compare_img(img):
    face = cv.CascadeClassifier('haarcascade_frontalface_alt2.xml')
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    faces = face.detectMultiScale(gray)
    for x, y, w, h in faces:
        cv.rectangle(img,(x, y), (x+w, y+h), (0,255,0),2)
        id, picture = recognizer.predict(gray[y:y+h, x:x+w])
        print('图片-->',id,'相似度评分：',picture)

    cv.imshow('out', img)
img = cv.imread('D:\\33.jpg')
compare_img(img)
cv.waitKey(0)
cv.destroyAllWindows()

总结

大功告成