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preprocess_mnsit.py
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preprocess_mnsit.py
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from sklearn import datasets as ds
import numpy as np
import pickle
from PIL import Image
from tensorflow.examples.tutorials.mnist import input_data
def resize_images(image_arrays, size=[32, 32]):
# convert float type to integer
image_arrays = (image_arrays * 255).astype('uint8')
resized_image_arrays = np.zeros([image_arrays.shape[0]] + size + [3])
for i, image_array in enumerate(image_arrays):
image = Image.fromarray(image_array)
resized_image = image.resize(size=size, resample=Image.ANTIALIAS)
final_image = np.zeros(size+[3])
final_image[:,:,0] = final_image[:,:,1] = final_image[:,:,2] = resized_image
resized_image_arrays[i] = final_image
return resized_image_arrays
def resize_cover_images(image_arrays, size=[32, 32]):
ori_size = image_arrays.shape[1]
new_size = size[0]
s = (new_size-ori_size)//2
image_arrays = (image_arrays * 255).astype('uint8')
resized_image_arrays = np.zeros([image_arrays.shape[0]] + size + [3])
resized_image_arrays[:, s: s+ori_size, s: s+ori_size, 0] \
= resized_image_arrays[:, s: s+ori_size, s: s+ori_size, 1] \
= resized_image_arrays[:, s: s+ori_size, s: s+ori_size, 2] = image_arrays
return resized_image_arrays
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('MNIST_data')
train_x = list(mnist.train.images)
train_x.extend(list(mnist.validation.images))
train_x = np.array(train_x)
train_y = list(mnist.train.labels)
train_y.extend(list(mnist.validation.labels))
train_y = np.array(train_y)
test_x = mnist.test.images
test_y = mnist.test.labels
train = {'X': resize_images(train_x.reshape(-1, 28, 28)),
'y': train_y}
test = {'X': resize_images(test_x.reshape(-1, 28, 28)),
'y': test_y}
save_pickle(train, r'F:\mnist\train.pkl')
save_pickle(test, r'F:\mnist\test.pkl')
if __name__ == "__main__":
main()