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March 7, 2019 12:09
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This code helps to understand how data augmentation works in Keras.
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from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img | |
datagen = ImageDataGenerator( | |
rotation_range=40, | |
width_shift_range=0.2, | |
height_shift_range=0.2, | |
shear_range=0.2, | |
zoom_range=0.2, | |
horizontal_flip=True, | |
fill_mode='nearest') | |
img = load_img('DATA/TRAIN/CAT/cat.0.jpg') # this is a PIL image | |
x = img_to_array(img) # this is a Numpy array with shape (3, 150, 150) | |
x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150) | |
# the .flow() command below generates batches of randomly transformed images | |
# and saves the results to the `preview/` directory | |
i = 0 | |
for batch in datagen.flow(x, batch_size=1, | |
save_to_dir='preview', save_prefix='cat', save_format='jpeg'): | |
i += 1 | |
if i > 20: | |
break # otherwise the generator would loop indefinitely |
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