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1 import PIL
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2 import numpy, cPickle, gzip
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3 from numpy import *
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4 from utils import *
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5 #from logistic_sgd import load_data
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6
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7 def generate_img(source, n=1):
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8
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9
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10 #param : source = the path to the pickled data
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11 #param : n = the number of tiled image to create
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12
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13 #loading the dataset contaning images and labels (we gonna sample from the test set)
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14 f = gzip.open(source,'rb')
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15 train_set, valid_set, test_set = cPickle.load(f)
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16 test_set_x, test_set_y = test_set
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17 f.close()
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18
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19 max = test_set_x.shape[0]
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20
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21 # creating n images containing each 10 randomly pickes caraters from the test set
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22
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23 for i in range(n):
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24
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25 #picking randomly 10 images in the test set with their labels
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26 rng = numpy.random.RandomState(None)
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27 sample_idx = rng.randint(max)
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28 samples = numpy.array(test_set_x[sample_idx: sample_idx + 10 ])
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29 samples_labels = numpy.array(test_set_y[sample_idx: sample_idx + 10 ])
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30
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31
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32 #tiling images into a PIL images and saving it
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33 image = PIL.Image.fromarray(tile_raster_images( samples,
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34 img_shape = (28,28), tile_shape = (1,10),
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35 tile_spacing=(1,1)))
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36
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37 print ' ... is saving images'
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38 img_name = source + str(i)
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39 image.save(img_name+'.png')
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40
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41 #saving the corresponding labels : todo after testing if the saving works
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42 print '... is saving labels'
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43 numpy.savetxt(img_name+'.txt', samples_labels)
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44
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45 print n, 'images saved'
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46
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47 if __name__ == '__main__':
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48
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49 print '... is generating samples'
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50 generate_img('mnist.pkl.gz', n=5)
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51 print 'done'
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52
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