深度学习中的Data Augmentation方法（转）基于keras

 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 path
17 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 images
21 # and saves the results to the `preview/` directory
22 
23 i = 0
24 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 += 1
30     if i > 20:
31         break  # otherwise the generator would loop indefinitely