import os
import sys
from PIL import Image
import numpy as np
import cv2
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(r'E:\\anaconda1\\pkgs/libopencv-4.0.1-hbb9e17c_0\\Library/etc/haarcascades\\haarcascade_frontalface_alt2.xml')
# 打印数组imagePaths
print('数据排列：', imagePaths)
# 遍历列表中的图片
for imagePath in imagePaths:
# 打开图片,黑白化
PIL_img = Image.open(imagePath).convert('L')
# 将图像转换为数组，以黑白深浅
# PIL_img = cv2.resize(PIL_img, dsize=(400, 400))
img_numpy = np.array(PIL_img, 'uint8')
# 获取图片人脸特征
faces = face_detector.detectMultiScale(img_numpy)
# 获取每张图片的id和姓名
id = os.path.split(imagePath)[1].split('.')[0]
# 预防无面容照片
for x, y, w, h in faces:
ids.append(id)
facesSamples.append(img_numpy[y:y + h, x:x + w])
# 打印脸部特征和id
# print('fs:', facesSamples)
print('id:', id)
# print('fs:', facesSamples[id])
print('fs:', facesSamples)
# print('脸部例子：',facesSamples[0])
# print('身份信息：',ids[0])
return facesSamples, ids
if __name__ == '__main__':
# 图片路径
path = './data/'
# 获取图像数组和id标签数组和姓名
faces, ids = getImageAndLabels(path)
# 获取训练对象
recognizer = cv2.face.LBPHFaceRecognizer_create()
# recognizer.train(faces,names)#np.array(ids)
recognizer.train(faces, np.array(ids))
# 保存文件
recognizer.write('trainer/trainer3.yml')
看提示，是参数有问题， 在
recognizer.train(faces, np.array(ids))
前， 加一句print(len(faces),len(ids)) 看看输出是啥。
id = os.path.split(imagePath)[1].split('.')[0] 这里要是int类型
id = int(os.path.split(imagePath)[1].split('.')[0])