Import modules



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%matplotlib inline



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import numpy as np
import matplotlib.pyplot as plt
import csv 
import os
from PIL import Image

Load MNIST Fashion data



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path = '/some/dir/'

with open(path + 'fashion-mnist_train.csv') as csvfile:
    clothing_reader = csv.reader(csvfile)
    next(clothing_reader)
    clothing_list=list(clothing_reader)



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clothing_list = [[int(j) for j in i] for i in clothing_list]



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len(clothing_list) #60,000 images for train
                   #10,000 images for validation









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60000



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# Sample to work off of
#clothing_list_sample = clothing_list[:10]

Create seperate class list



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classes = [[] for i in range(10)]

for i in clothing_list:
    for j in range(10):
        if int(i[0]) == j:
            classes[j].append(i[1:])
            break
        else:
            continue

Convert lists to numpy arrays



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for cl in classes:
    for idx,image in enumerate(cl):
        cl[idx] = np.reshape((np.array(image)),(28,28))

Plot sample images from each class

Class Description
0   T-shirt/top
1   Trouser
2   Pullover
3   Dress
4   Coat
5   Sandal
6   Shirt
7   Sneaker
8   Bag
9   Ankle boot



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for i,cl in enumerate(classes):
    plt.title('Image class is {}'.format(i))
    plt.imshow(cl[0], cmap='gray')
    plt.show()

Save each image to corresponding class directory



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for idx,cl in enumerate(classes):
    os.makedirs(path + 'mnist_fashion_train_png/class{}'.format(idx))
    for num,image in enumerate(cl):
        im = Image.fromarray(image.astype('uint8'))
        im = im.convert('L')
        im.save(path + 'mnist_fashion_train_png/class{idx}/img{num}.png'.format(idx=idx,num=num), 'PNG')



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