args = vars(ap.parse_args()) print('[INFO] moving image to label folder.....') im = IM.MoveImageToLabel(dataPath=args['dataset']) im.makeFolder() im.move() print("[INFO] loading images...") imagePaths = [ x for x in list(paths.list_images(args['dataset'])) if x.split(os.path.sep)[-2] != 'jpg' ] classNames = [pt.split(os.path.sep)[-2] for pt in imagePaths] classNames = [str(x) for x in np.unique(classNames)] aap = AAP.AspectAwarePreprocesser(64, 64) iap = IAP.ImageToArrayPreprocess() sdl = SDL.SimpleDatasetLoader(preprocessors=[aap, iap]) (data, labels) = sdl.load(imagePaths, verbose=500) data = data.astype('float') / 255.0 (trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=43) trainY = LabelBinarizer().fit_transform(trainY) testY = LabelBinarizer().fit_transform(testY) print("[INFO] compiling model....")
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest') # 从磁盘中加载图片,并提取标签 print("[INFO] loading images...") imagePaths = list(paths.list_images(args['dataset'])) classNames = [pt.split(os.path.sep)[-2] for pt in imagePaths] classNames = [str(x) for x in np.unique(classNames)] # 初始化图像预处理 aap = AAP.AspectAwarePreprocesser(224, 224) iap = ITAP.ImageToArrayPreprocess() # 加载图像数据,并进行图像数据预处理 sdl = SDL.SimpleDatasetLoader(preprocessors=[aap, iap]) (data, labels) = sdl.load(imagePaths, verbose=500) data = data.astype("float") / 255.0 # 数据划分 (trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42) # 标签进行编码化处理 trainY = LabelBinarizer().fit_transform(trainY) testY = LabelBinarizer().fit_transform(testY)