1 # -*- coding: utf-8 -*- 2 __author__ = ‘Administrator‘ 3  4 # import packages 5 from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img 6  7 datagen = ImageDataGenerator( 8         rotation_range=0.2, 9         width_shift_range=0.2,10         height_shift_range=0.2,11         shear_range=0.2,12         zoom_range=0.2,13         horizontal_flip=True,14         fill_mode=‘nearest‘)15 16 img = load_img(‘C:\Users\Administrator\Desktop\dataA\lena.jpg‘)  # this is a PIL image, please replace to your own file path17 x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)18 x = x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)19 20 # the .flow() command below generates batches of randomly transformed images21 # and saves the results to the `preview/` directory22 23 i = 024 for batch in datagen.flow(x,25                           batch_size=1,26                           save_to_dir=‘C:\Users\Administrator\Desktop\dataA\pre‘,#生成后的图像保存路径27                           save_prefix=‘lena‘,28                           save_format=‘jpg‘):29     i += 130     if i > 20:31         break  # otherwise the generator would loop indefinitely