Mercurial > ift6266

annotate transformations/pipeline.py @ 11:dbc806d025a2

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Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Wed, 27 Jan 2010 19:14:37 -0500
parents faacc76d21c2
children f6b6c74bb82f
rev   line source
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faacc76d21c2 Basic new pipeline script for the images tranforms
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1 from __future__ import with_statement
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3 import sys, os
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4 import numpy
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5 import filetensor as ft
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6 import random
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8 BATCH_SIZE = 100
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10 #import <modules> and stuff them in mods below
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12 mods = []
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14 # DANGER: HIGH VOLTAGE -- DO NOT EDIT BELOW THIS LINE
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15 # -----------------------------------------------------------
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17 train_data = open('/data/lisa/data/nist/by_class/all/all_train_data.ft', 'rb')
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19 dim = tuple(ft._read_header(train_data)[3])
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21 res_data = numpy.empty(dim)
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23 all_settings = ['complexity']
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25 for mod in mods:
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26 all_settings += mod.get_settings_names()
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28 params = numpy.empty(((dim[0]/BATCH_SIZE)+1, len(all_settings)))
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30 for i in xrange(0, dim[0], BATCH_SIZE):
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31 train_data.seek(0)
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32 imgs = ft.read(train_data, slice(i, i+BATCH_SIZE))
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34 complexity = random.random()
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35 p = i/BATCH_SIZE
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36 j = 1
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37 for mod in mods:
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38 par = mod.regenerate_parameters(complexity)
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39 params[p, j:j+len(par)] = par
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40 j += len(par)
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42 for k in range(imgs.shape[0]):
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43 c = imgs[k]
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44 for mod in mods:
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45 c = mod.transform_image(c)
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46 res_data[i+k] = c
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48 with open(sys.argv[1], 'wb') as f:
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49 ft.write(f, res_data)
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51 numpy.save(sys.argv[2], params)