Mercurial > ift6266

annotate data_generation/transformations/contrast.py @ 612:21d53fd07f6e

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reviews AISTATS
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Mon, 20 Dec 2010 11:54:35 -0500
parents 1f5937e9e530
children
rev   line source
27
0b9350998dbe Added a contrast.py script difining the Contrast transformation class
Xavier Glorot <glorotxa@iro.umontreal.ca>
parents:
diff changeset
1 #!/usr/bin/python
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2 # coding: utf-8
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3
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Xavier Glorot <glorotxa@iro.umontreal.ca>
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4 '''
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5 Simple implementation of random contrast. This always switch half the time the polarity.
31
7ef8aac2cdb5 comment change and useless import removed for contrast.py
Xavier Glorot <glorotxa@iro.umontreal.ca>
parents: 27
diff changeset
6 then it decides of a random contrast dependant of the complexity, the mean of the maximum and minimum
7ef8aac2cdb5 comment change and useless import removed for contrast.py
Xavier Glorot <glorotxa@iro.umontreal.ca>
parents: 27
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7 pixel value stays 0 (to avoid import bias change between exemples).
27
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8
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9 Author: Xavier Glorot
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Xavier Glorot <glorotxa@iro.umontreal.ca>
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10 '''
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11
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12 import numpy as N
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13 import copy
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14
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15
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Xavier Glorot <glorotxa@iro.umontreal.ca>
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16 class Contrast():
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17 def __init__(self,complexity = 1):
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18 #---------- private attributes
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19 self.__nx__ = 32 #xdim of the images
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20 self.__ny__ = 32 #ydim of the images
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21 self.__Pinvert__ = 0.5 #probability to switch polarity
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22 self.__mincontrast__ = 0.15
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23 self.__resolution__ = 256
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24 self.__rangecontrastres__ = self.__resolution__ - N.int(self.__mincontrast__*self.__resolution__)
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25 #------------------------------------------------
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26
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Xavier Glorot <glorotxa@iro.umontreal.ca>
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27 #---------- generation parameters
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28 self.regenerate_parameters(complexity)
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29 #------------------------------------------------
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30
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31 def _get_current_parameters(self):
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32 return [self.invert,self.contrast]
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33
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34 def get_settings_names(self):
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35 return ['invert','contrast']
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36
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37 def regenerate_parameters(self, complexity):
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38 self.invert = (N.random.uniform() < self.__Pinvert__)
0b9350998dbe Added a contrast.py script difining the Contrast transformation class
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39 self.contrast = self.__resolution__ - N.random.randint(1 + self.__rangecontrastres__ * complexity)
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40 return self._get_current_parameters()
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41
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42 def transform_1_image(self,image): #the real transformation method
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43 maxi = image.max()
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44 mini = image.min()
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45 if self.invert:
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46 newimage = 1 - (self.__resolution__- self.contrast) / (2 * float(self.__resolution__)) -\
0b9350998dbe Added a contrast.py script difining the Contrast transformation class
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47 (image - mini) / float(maxi - mini) * self.contrast / float(self.__resolution__)
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48 else:
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49 newimage = (self.__resolution__- self.contrast) / (2 * float(self.__resolution__)) +\
0b9350998dbe Added a contrast.py script difining the Contrast transformation class
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50 (image - mini) / float(maxi - mini) * self.contrast / float(self.__resolution__)
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51 if image.dtype == 'uint8':
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52 return N.asarray(newimage*255,dtype='uint8')
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53 else:
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54 return N.asarray(newimage,dtype=image.dtype)
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55
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56 def transform_image(self,image): #handling different format
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57 if image.shape == (self.__nx__,self.__ny__):
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58 return self.transform_1_image(image)
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59 if image.ndim == 3:
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60 newimage = copy.copy(image)
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61 for i in range(image.shape[0]):
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62 newimage[i,:,:] = self.transform_1_image(image[i,:,:])
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63 return newimage
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parents:
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64 if image.ndim == 2 and image.shape != (self.__nx__,self.__ny__):
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parents:
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65 newimage = N.reshape(image,(image.shape[0],self.__nx__,self.__ny__))
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parents:
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66 for i in range(image.shape[0]):
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67 newimage[i,:,:] = self.transform_1_image(newimage[i,:,:])
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68 return N.reshape(newimage,image.shape)
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69 if image.ndim == 1:
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parents:
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70 newimage = N.reshape(image,(self.__nx__,self.__ny__))
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parents:
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71 newimage = self.transform_1_image(newimage)
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72 return N.reshape(newimage,image.shape)
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73 assert False #should never go there
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74
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75
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76
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77
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78 #test on NIST (you need pylearn and access to NIST to do that)
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79
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80 if __name__ == '__main__':
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81
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82 from pylearn.io import filetensor as ft
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83 import copy
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84 import pygame
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85 import time
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parents:
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86 datapath = '/data/lisa/data/nist/by_class/'
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parents:
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87 f = open(datapath+'digits/digits_train_data.ft')
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88 d = ft.read(f)
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89
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90 pygame.surfarray.use_arraytype('numpy')
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91
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92 pygame.display.init()
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93 screen = pygame.display.set_mode((8*2*32,8*32),0,8)
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parents:
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94 anglcolorpalette=[(x,x,x) for x in xrange(0,256)]
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parents:
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95 screen.set_palette(anglcolorpalette)
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parents:
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96
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97 MyContrast = Contrast()
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98
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99 debut=time.time()
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100 MyContrast.transform_image(d)
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101 fin=time.time()
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102 print '------------------------------------------------'
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103 print d.shape[0],' images transformed in :', fin-debut, ' seconds'
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104 print '------------------------------------------------'
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105 print (fin-debut)/d.shape[0]*1000000,' microseconds per image'
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106 print '------------------------------------------------'
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107 print MyContrast.get_settings_names()
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108 print MyContrast._get_current_parameters()
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109 print MyContrast.regenerate_parameters(0)
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110 print MyContrast.regenerate_parameters(0.5)
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parents:
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111 print MyContrast.regenerate_parameters(1)
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parents:
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112 for i in range(10000):
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113 a=d[i,:]
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114 b=N.asarray(N.reshape(a,(32,32))).T
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115
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116 new=pygame.surfarray.make_surface(b)
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117 new=pygame.transform.scale2x(new)
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118 new=pygame.transform.scale2x(new)
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119 new=pygame.transform.scale2x(new)
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120 new.set_palette(anglcolorpalette)
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121 screen.blit(new,(0,0))
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122
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parents:
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123 print MyContrast.get_settings_names(), MyContrast.regenerate_parameters(1)
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parents:
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124 c=MyContrast.transform_image(a)
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parents:
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125 b=N.asarray(N.reshape(c,(32,32))).T
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parents:
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126
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127 new=pygame.surfarray.make_surface(b)
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128 new=pygame.transform.scale2x(new)
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parents:
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129 new=pygame.transform.scale2x(new)
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parents:
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130 new=pygame.transform.scale2x(new)
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parents:
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131 new.set_palette(anglcolorpalette)
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parents:
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132 screen.blit(new,(8*32,0))
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133
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134 pygame.display.update()
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135 raw_input('Press Enter')
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parents:
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136
0b9350998dbe Added a contrast.py script difining the Contrast transformation class
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137 pygame.display.quit()