Mercurial > ift6266

annotate transformations/affine_transform.py @ 115:b84a0d009af8

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changes on pipeline mecanism: we now sample a different complexity for each transformations, this because when we use the same sampled complexity for all the modules 1/8 of the time we are close to 0 and we obtain an image very close to the source, we now save a complexity for each module in the parameters array
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Wed, 17 Feb 2010 16:20:15 -0500
parents 859ebd7f8754
children 5e00ed18ae32
rev   line source
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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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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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16 class AffineTransformation():
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17 def __init__( self, complexity = .5):
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18 self.shape = (32,32)
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19 self.rng = numpy.random.RandomState()
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20 self.complexity = complexity
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21 params = self.rng.uniform(size=6) -.5
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22 self.a = 1. + params[0]*.4*complexity
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23 self.b = 0. + params[1]*.4*complexity
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24 self.c = params[2]*8.*complexity
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25 self.d = 0. + params[3]*.4*complexity
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26 self.e = 1. + params[4]*.4*complexity
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27 self.f = params[5]*8.*complexity
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30 def _get_current_parameters(self):
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31 return [self.a, self.b, self.c, self.d, self.e, self.f]
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32
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33 def get_settings_names(self):
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34 return ['a','b','c','d','e','f']
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35
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36 def regenerate_parameters(self, complexity):
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37 # generate random affine transformation
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38 # a point (x',y') of the new image corresponds to (x,y) of the old
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39 # image where :
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40 # x' = params[0]*x + params[1]*y + params[2]
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41 # y' = params[3]*x + params[4]*y _ params[5]
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42
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43 # the ranges are set manually as to look acceptable
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44
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45 self.complexity = complexity
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46 params = self.rng.uniform(size=6) -.5
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47 self.a = 1. + params[0]*.4*complexity
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48 self.b = 0. + params[1]*.4*complexity
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49 self.c = params[2]*8.*complexity
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50 self.d = 0. + params[3]*.4*complexity
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51 self.e = 1. + params[4]*.4*complexity
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52 self.f = params[5]*8.*complexity
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53 return self._get_current_parameters()
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58 def transform_image(self,NIST_image):
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59
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60 im = Image.fromarray( \
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61 numpy.asarray(\
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62 NIST_image.reshape(self.shape)*255.0, dtype='uint8'))
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63 nwim = im.transform( (32,32), Image.AFFINE, [self.a,self.b,self.c,self.d,self.e,self.f])
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64 return numpy.asarray(numpy.asarray(nwim)/255.0,dtype='float32')
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67
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68 if __name__ =='__main__':
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69 print 'random test'
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70
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71 from pylearn.io import filetensor as ft
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72 import pylab
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73
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74 datapath = '/data/lisa/data/nist/by_class/'
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75
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76 f = open(datapath+'digits/digits_train_data.ft')
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77 d = ft.read(f)
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78 f.close()
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81 transformer = AffineTransformation()
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82 id = numpy.random.randint(30)
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83
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84 pylab.figure()
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85 pylab.imshow(d[id].reshape((32,32)))
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86 pylab.figure()
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87 pylab.imshow(transformer.transform_image(d[id]).reshape((32,32)))
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89 pylab.show()
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90