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)