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"""Data Augmentation by numpy, scipy, threading and queue.

Alternatively, we can use TFRecord to preprocess data,

see `tutorial_cifar10_tfrecord.py` for more details.

"""

import time

import tensorlayer as tl

tl.logging.set_verbosity(tl.logging.DEBUG)

X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)

def distort_img(x):

x = tl.prepro.flip_axis(x, axis=1, is_random=True)

x = tl.prepro.crop(x, wrg=28, hrg=28, is_random=True)

return x

s = time.time()

results = tl.prepro.threading_data(X_train[0:100], distort_img)

print("took %.3fs" % (time.time() - s))

print(results.shape)

tl.vis.save_images(X_train[0:10], [1, 10], '_original.png')

tl.vis.save_images(results[0:10], [1, 10], '_distorted.png')