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image_processing.py
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image_processing.py
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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/cats/cat.0.jpg') # this is a PIL image
# this is a Numpy array with shape (3, 150, 150)
# 3-r,g,b; 150*150 2d matrix
x = img_to_array(img)
print "np array shape: " + str(x.shape)
x = x.reshape((1,) + x.shape) # this is a Numpy array with shape (1, 3, 150, 150)
print "np array shape: " + str(x.shape)
# 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