import numpy as np
import pickle
from PIL import Image
from tensorflow.examples.tutorials.mnist import input_data
#~ import scipy.io
#~ import glob
def resize_images(image_arrays, size=[28, 28]):
# convert float type to integer
image_arrays = (image_arrays * 255).astype('uint8')
resized_image_arrays = np.zeros([image_arrays.shape[0]]+size)
for i, image_array in enumerate(image_arrays):
image = Image.fromarray(image_array)
resized_image = image.resize(size=size, resample=Image.ANTIALIAS)
resized_image_arrays[i] = np.asarray(resized_image)
return np.expand_dims(resized_image_arrays, 3)
def save_pickle(data, path):
with open(path, 'wb') as f:
pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
print ('Saved %s..' %path)
def main():
mnist = input_data.read_data_sets(train_dir='mnist')
train = {'X': resize_images(mnist.train.images.reshape(-1, 28, 28)),
'y': mnist.train.labels}
test = {'X': resize_images(mnist.test.images.reshape(-1, 28, 28)),
'y': mnist.test.labels}
#~ train = {'X': mnist.train.images,
#~ 'y': mnist.train.labels}
#~ test = {'X': mnist.test.images,
#~ 'y': mnist.test.labels}
save_pickle(train, 'mnist/train.pkl')
save_pickle(test, 'mnist/test.pkl')
if __name__ == "__main__":
main()
