1 import os #遍历路径 2 import cv2 3 import sys 4 import numpy as np 5 from PIL import Image 6 def getImageAndLabels(path): 7 facesSamples = [] 8 ids = [] 9 imagePaths = [os.path.join(path,f) for f in os.listdir(path)] 10 # print(imagePaths) 11 # ------>>>如：'D:/百度云下载/train_data/data/jm\10.pgm' 12 # 检测人脸特征函数 13 face_detector = cv2.CascadeClassifier( 14 'D:/Opencv/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml') 15 # 遍历列表中的图片 16 for imagePath in imagePaths: 17 # 打开图片 18 PIL_img = Image.open(imagePath).convert('L') 19 # 将整张图像转换为数组 20 img_numpy = np.array(PIL_img,'uint8') 21 # print(img_numpy) 22 # 获取人脸 23 faces = face_detector.detectMultiScale(img_numpy) 24 # 获取每张图片的id 25 id = int(os.path.split(imagePath)[1].split('.')[0]) 26 # print(os.path.split(imagePath)) 27 # --->('D:/百度云下载/train_data/data/jm', '1.pgm') 28 # 人脸区域提取 29 for x,y,w,h in faces: 30 # 获得图像数组 31 facesSamples.append(img_numpy[y:y+h,x:x+w]) 32 # 获取标签数组 33 ids.append(id) 34 return facesSamples,ids 35 36 if __name__ == '__main__': 37 # 图片的路径 38 path = 'D:/百度云下载/train_data/data/jm' 39 # 获取图像数组和id标签数组 40 faces,ids = getImageAndLabels(path) 41 # 获取循环对象 42 recognizer = cv2.face.LBPHFaceRecognizer_create() 43 recognizer.train(faces,np.array(ids)) 44 # 保存文件 45 recognizer.write('D:/trainer/trainer.yml') 46
