Mercurial > ift6266

annotate data_generation/transformations/affine_transform.py @ 272:f6d9b6b89c2a

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ajouté : module de préparation de batches en fonction d'un ratio de classes
author Guillaume Sicard <guitch21@gmail.com>
date Mon, 22 Mar 2010 08:34:48 -0400
parents 1f5937e9e530
children
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.complexity = complexity
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20 params = numpy.random.uniform(size=6) -.5
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21 self.a = 1. + params[0]*.6*complexity
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22 self.b = 0. + params[1]*.6*complexity
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23 self.c = params[2]*8.*complexity
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24 self.d = 0. + params[3]*.6*complexity
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25 self.e = 1. + params[4]*.6*complexity
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26 self.f = params[5]*8.*complexity
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28
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29 def _get_current_parameters(self):
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30 return [self.a, self.b, self.c, self.d, self.e, self.f]
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31
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32 def get_settings_names(self):
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33 return ['a','b','c','d','e','f']
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34
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35 def regenerate_parameters(self, complexity):
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36 # generate random affine transformation
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37 # a point (x',y') of the new image corresponds to (x,y) of the old
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38 # image where :
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39 # x' = params[0]*x + params[1]*y + params[2]
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40 # y' = params[3]*x + params[4]*y _ params[5]
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41
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42 # the ranges are set manually as to look acceptable
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43
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44 self.complexity = complexity
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45 params = numpy.random.uniform(size=6) -.5
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46 self.a = 1. + params[0]*.8*complexity
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47 self.b = 0. + params[1]*.8*complexity
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48 self.c = params[2]*9.*complexity
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49 self.d = 0. + params[3]*.8*complexity
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50 self.e = 1. + params[4]*.8*complexity
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51 self.f = params[5]*9.*complexity
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52 return self._get_current_parameters()
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57 def transform_image(self,NIST_image):
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58
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59 im = Image.fromarray( \
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60 numpy.asarray(\
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61 NIST_image.reshape(self.shape)*255.0, dtype='uint8'))
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62 nwim = im.transform( (32,32), Image.AFFINE, [self.a,self.b,self.c,self.d,self.e,self.f])
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63 return numpy.asarray(numpy.asarray(nwim)/255.0,dtype='float32')
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67 if __name__ =='__main__':
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68 print 'random test'
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69
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70 from pylearn.io import filetensor as ft
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71 import pylab
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72
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73 datapath = '/data/lisa/data/nist/by_class/'
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74
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75 f = open(datapath+'digits/digits_train_data.ft')
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76 d = ft.read(f)
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77 f.close()
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80 transformer = AffineTransformation()
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81 id = numpy.random.randint(30)
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83 pylab.figure()
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84 pylab.imshow(d[id].reshape((32,32)))
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85 pylab.figure()
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86 pylab.imshow(transformer.transform_image(d[id]).reshape((32,32)))
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88 pylab.show()
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