Mercurial > ift6266

annotate transformations/affine_transform.py @ 41:fdb0e0870fb4

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Beaucoup de modifications à pipeline.py pour généraliser et un début de visualisation, et créé un wrapper (run_pipeline.py) pour appeler avec GIMP. - Modifications à pipeline.py - Wrappé la boucle du pipeline dans une classe - Isolé le problème de itérer sur les batches et les complexités dans des itérateurs - Permet d'avoir des ordres compliqués de batch (plusieurs sources), de complexités - Maintenant regenerate_parameters() est appelé pour chaque image. - Command line arguments avec getopt(). On pourra rajouter des options ainsi. - run_pipeline.py - Le but est de permettre de passer des arguments. Pas facile (pas trouvé comment de façon simple) avec la command line pour appeler GIMP en mode batch. C'est un hack ici. - Le but ultime est de permettre de lancer les jobs sur les clusters avec dbidispatch en précisant les options (diff. pour chaque job) sur la ligne de commande.
author fsavard
date Wed, 03 Feb 2010 17:08:27 -0500
parents 17caecc92544
children 81b9567ec4ae
rev   line source
39
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1 #!/usr/bin/python
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2 # coding: utf-8
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3
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4 '''
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5 Simple implementation of random affine transformations based on the Python
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6 Imaging Module affine transformations.
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7
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8
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9 Author: Razvan Pascanu
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10 '''
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11
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12 import numpy, Image
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13
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15
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16 class AffineTransformation():
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17 def __init__( self, shape = (32,32), seed = None):
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18 self.shape = shape
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19 self.rng = numpy.random.RandomState(seed)
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20
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21 def transform(self,NIST_image):
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22
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23 im = Image.fromarray( \
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24 numpy.asarray(\
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25 NIST_image.reshape(self.shape), dtype='uint8'))
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26 # generate random affine transformation
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27 # a point (x',y') of the new image corresponds to (x,y) of the old
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28 # image where :
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29 # x' = params[0]*x + params[1]*y + params[2]
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30 # y' = params[3]*x + params[4]*y _ params[5]
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31
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32 # the ranges are set manually as to look acceptable
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33 params = self.rng.uniform(size = 6) -.5
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34 params[2] *= 8.
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35 params[5] *= 8.
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36 params[0] = 1. + params[0]*0.4
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37 params[3] = 0. + params[3]*0.4
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38 params[1] = 0 + params[1]*0.4
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39 params[4] = 1 + params[4]*0.4
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40
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41 print params
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42 nwim = im.transform( (32,32), Image.AFFINE, params)
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43 return numpy.asarray(nwim)
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44
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45
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46
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47 if __name__ =='__main__':
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48 print 'random test'
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49
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50 from pylearn.io import filetensor as ft
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51 import pylab
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52
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53 datapath = '/data/lisa/data/nist/by_class/'
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54
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55 f = open(datapath+'digits/digits_train_data.ft')
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56 d = ft.read(f)
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57 f.close()
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58
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59
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60 transformer = AffineTransformation()
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61 id = numpy.random.randint(30)
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62
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63 pylab.figure()
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64 pylab.imshow(d[id].reshape((32,32)))
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65 pylab.figure()
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66 pylab.imshow(transformer.transform(d[id]).reshape((32,32)))
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67
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68 pylab.show()
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69