Mercurial > ift6266

comparison transformations/BruitGauss.py @ 111:259439a4f9e7

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Changed a bit the code for BruitGauss.py for simplicity
author Xavier Glorot <glorotxa@iro.umontreal.ca>
date Tue, 16 Feb 2010 17:24:21 -0500
parents 313c249d638e
children fa35d9ecf664
comparison
equal deleted inserted replaced
110:93b4b84d86cf 111:259439a4f9e7
33 self.grandeurmax = 20 33 self.grandeurmax = 20
34 self.sigmamax = 6.0 34 self.sigmamax = 6.0
35 self.regenerate_parameters(complexity) 35 self.regenerate_parameters(complexity)
36 36
37 def get_settings_names(self): 37 def get_settings_names(self):
38 return ['nb_chng','sigma_gauss','grandeur','effectuer'] 38 return ['nb_chng','sigma_gauss','grandeur']
39 39
40 def regenerate_parameters(self, complexity): 40 def regenerate_parameters(self, complexity):
41 self.nb_chng=3+int(numpy.random.rand()*self.nb_chngmax*complexity) 41 self.effectuer =numpy.random.binomial(1,0.25) ##### On a 25% de faire un bruit #####
42
42 43
43 if float(complexity) > 0: 44 if self.effectuer and complexity > 0:
45 self.nb_chng=3+int(numpy.random.rand()*self.nb_chngmax*complexity)
44 self.sigma_gauss=2.0 + numpy.random.rand()*self.sigmamax*complexity 46 self.sigma_gauss=2.0 + numpy.random.rand()*self.sigmamax*complexity
45 self.grandeur=12+int(numpy.random.rand()*self.grandeurmax*complexity) 47 self.grandeur=12+int(numpy.random.rand()*self.grandeurmax*complexity)
46 self.effectuer =numpy.random.binomial(1,0.25) ##### On a 25% de faire un bruit ##### 48 #creation du noyau gaussien
49 self.gauss=numpy.zeros((self.grandeur,self.grandeur))
50 x0 = y0 = self.grandeur/2.0
51 for i in xrange(self.grandeur):
52 for j in xrange(self.grandeur):
53 self.gauss[i,j]=numpy.exp(-((i-x0)**2 + (j-y0)**2) / self.sigma_gauss**2)
54 #creation de la fenetre de moyennage
55 self.moy=numpy.zeros((self.grandeur,self.grandeur))
56 x0 = y0 = self.grandeur/2
57 for i in xrange(0,self.grandeur):
58 for j in xrange(0,self.grandeur):
59 self.moy[i,j]=((numpy.sqrt(2*(self.grandeur/2.0)**2) -\
60 numpy.sqrt(numpy.abs(i-self.grandeur/2.0)**2+numpy.abs(j-self.grandeur/2.0)**2))/numpy.sqrt((self.grandeur/2.0)**2))**5
47 else: 61 else:
48 self.effectuer = 0
49 self.sigma_gauss = 1 # eviter division par 0 62 self.sigma_gauss = 1 # eviter division par 0
50 self.grandeur=1 63 self.grandeur=1
51 #Un peu de paranoia ici, mais on ne sait jamais 64 self.nb_chng = 0
65 self.effectuer = 0
52 66
53 #creation du noyau gaussien
54 self.gauss=numpy.zeros((self.grandeur,self.grandeur))
55 x0 = y0 = self.grandeur/2.0
56 for i in xrange(self.grandeur):
57 for j in xrange(self.grandeur):
58 self.gauss[i,j]=numpy.exp(-((i-x0)**2 + (j-y0)**2) / self.sigma_gauss**2)
59 #creation de la fenetre de moyennage
60 self.moy=numpy.zeros((self.grandeur,self.grandeur))
61 x0 = y0 = self.grandeur/2
62 for i in xrange(0,self.grandeur):
63 for j in xrange(0,self.grandeur):
64 self.moy[i,j]=((numpy.sqrt(2*(self.grandeur/2.0)**2) - numpy.sqrt(numpy.abs(i-self.grandeur/2.0)**2+numpy.abs(j-self.grandeur/2.0)**2))/\
65 numpy.sqrt((self.grandeur/2.0)**2))**5
66
67 return self._get_current_parameters() 67 return self._get_current_parameters()
68 68
69 def _get_current_parameters(self): 69 def _get_current_parameters(self):
70 return [self.nb_chng,self.sigma_gauss,self.grandeur,self.effectuer] 70 return [self.nb_chng,self.sigma_gauss,self.grandeur]
71 71
72 72
73 def transform_image(self, image): 73 def transform_image(self, image):
74 if self.effectuer == 0: 74 if self.effectuer == 0:
75 return image 75 return image