Mercurial > ift6266

annotate data_generation/transformations/thick.py @ 358:31641a84e0ae

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Initial commit for the experimental setup of the denoising convolutional network
author humel
date Thu, 22 Apr 2010 00:49:42 -0400
parents 1f5937e9e530
children
rev   line source
11
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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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 thickness deformation using morphological
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6 operation of scipy.
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7 Only one morphological operation applied (dilation or erosion), the kernel is random
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8 out of a list of 12 symmetric kernels. (only 5 to be chosen for erosion because it can
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9 hurt the recognizability of the charater and 12 for dilation).
11
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10
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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11 Author: Xavier Glorot
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12
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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13 '''
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14
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15 import scipy.ndimage.morphology
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16 import numpy as N
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17
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18
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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19 class Thick():
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20 def __init__(self,complexity = 1):
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21 #---------- private attributes
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22 self.__nx__ = 32 #xdim of the images
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23 self.__ny__ = 32 #ydim of the images
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24 self.__erodemax__ = 5 #nb of index max of erode structuring elements
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25 self.__dilatemax__ = 9 #nb of index max of dilation structuring elements
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26 self.__structuring_elements__ = [N.asarray([[1,1]]),N.asarray([[1],[1]]),\
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27 N.asarray([[1,1],[1,1]]),N.asarray([[0,1,0],[1,1,1],[0,1,0]]),\
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28 N.asarray([[1,1,1],[1,1,1]]),N.asarray([[1,1],[1,1],[1,1]]),\
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29 N.asarray([[1,1,1],[1,1,1],[1,1,1]]),\
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30 N.asarray([[1,1,1,1],[1,1,1,1],[1,1,1,1]]),\
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31 N.asarray([[1,1,1],[1,1,1],[1,1,1],[1,1,1]]),\
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32 N.asarray([[0,0,1,0,0],[0,1,1,1,0],[1,1,1,1,1],[0,1,1,1,0],[0,0,1,0,0]]),\
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33 N.asarray([[1,1,1,1],[1,1,1,1]]),N.asarray([[1,1],[1,1],[1,1],[1,1]])]
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34 #------------------------------------------------
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35
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36 #---------- generation parameters
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37 self.regenerate_parameters(complexity)
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38 #------------------------------------------------
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39
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40 def _get_current_parameters(self):
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41 return [self.thick_param]
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42
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43 def get_settings_names(self):
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44 return ['thick_param']
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45
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46 def regenerate_parameters(self, complexity):
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47 self.erodenb = N.ceil(complexity * self.__erodemax__)
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48 self.dilatenb = N.ceil(complexity * self.__dilatemax__)
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49 self.Perode = self.erodenb / (self.dilatenb + self.erodenb + 1.0)
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50 self.Pdilate = self.dilatenb / (self.dilatenb + self.erodenb + 1.0)
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51 assert (self.Perode + self.Pdilate <= 1) & (self.Perode + self.Pdilate >= 0)
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52 assert (complexity >= 0) & (complexity <= 1)
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53 P = N.random.uniform()
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54 if P>1-(self.Pdilate+self.Perode):
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55 if P>1-(self.Pdilate+self.Perode)+self.Perode:
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56 self.meth = 1
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57 self.nb=N.random.randint(self.dilatenb)
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58 else:
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59 self.meth = -1
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60 self.nb=N.random.randint(self.erodenb)
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61 else:
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62 self.meth = 0
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63 self.nb = -1
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64 self.thick_param = self.meth*self.nb
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65 return self._get_current_parameters()
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66
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67 def transform_1_image(self,image): #the real transformation method
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68 if self.meth!=0:
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69 maxi = float(N.max(image))
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70 mini = float(N.min(image))
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71
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72 imagenorm=image/maxi
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73
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74 if self.meth==1:
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75 trans=scipy.ndimage.morphology.grey_dilation\
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76 (imagenorm,size=self.__structuring_elements__[self.nb].shape,structure=self.__structuring_elements__[self.nb])
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77 else:
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78 trans=scipy.ndimage.morphology.grey_erosion\
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79 (imagenorm,size=self.__structuring_elements__[self.nb].shape,structure=self.__structuring_elements__[self.nb])
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80
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81 #------renormalizing
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82 maxit = N.max(trans)
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83 minit = N.min(trans)
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84 trans= N.asarray((trans - (minit+mini)) / (maxit - (minit+mini)) * maxi,dtype=image.dtype)
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85 #--------
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86 return trans
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87 else:
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88 return image
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89
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90 def transform_image(self,image): #handling different format
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91 if image.shape == (self.__nx__,self.__ny__):
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92 return self.transform_1_image(image)
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93 if image.ndim == 3:
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94 newimage = copy.copy(image)
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95 for i in range(image.shape[0]):
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96 newimage[i,:,:] = self.transform_1_image(image[i,:,:])
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97 return newimage
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98 if image.ndim == 2 and image.shape != (self.__nx__,self.__ny__):
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99 newimage = N.reshape(image,(image.shape[0],self.__nx__,self.__ny__))
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100 for i in range(image.shape[0]):
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101 newimage[i,:,:] = self.transform_1_image(newimage[i,:,:])
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102 return N.reshape(newimage,image.shape)
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103 if image.ndim == 1:
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104 newimage = N.reshape(image,(self.__nx__,self.__ny__))
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105 newimage = self.transform_1_image(newimage)
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106 return N.reshape(newimage,image.shape)
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107 assert False #should never go there
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108
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109
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110
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111
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112 #test on NIST (you need pylearn and access to NIST to do that)
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113
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114 if __name__ == '__main__':
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115
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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116 from pylearn.io import filetensor as ft
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117 import copy
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118 import pygame
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119 import time
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120 datapath = '/data/lisa/data/nist/by_class/'
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121 f = open(datapath+'digits/digits_train_data.ft')
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122 d = ft.read(f)
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123
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124 pygame.surfarray.use_arraytype('numpy')
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125
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126 pygame.display.init()
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127 screen = pygame.display.set_mode((8*4*32,8*32),0,8)
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128 anglcolorpalette=[(x,x,x) for x in xrange(0,256)]
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129 screen.set_palette(anglcolorpalette)
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130
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131 MyThick = Thick()
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132
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133 #debut=time.time()
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134 #MyThick.transform_image(d)
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135 #fin=time.time()
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136 #print '------------------------------------------------'
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137 #print d.shape[0],' images transformed in :', fin-debut, ' seconds'
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138 #print '------------------------------------------------'
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139 #print (fin-debut)/d.shape[0]*1000000,' microseconds per image'
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140 #print '------------------------------------------------'
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141 #print MyThick.get_settings_names()
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142 #print MyThick._get_current_parameters()
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143 #print MyThick.regenerate_parameters(0)
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144 #print MyThick.regenerate_parameters(0.5)
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145 #print MyThick.regenerate_parameters(1)
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146 for i in range(10000):
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147 a=d[i,:]
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148 b=N.asarray(N.reshape(a,(32,32))).T
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149
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150 new=pygame.surfarray.make_surface(b)
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151 new=pygame.transform.scale2x(new)
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152 new=pygame.transform.scale2x(new)
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153 new=pygame.transform.scale2x(new)
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154 new.set_palette(anglcolorpalette)
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155 screen.blit(new,(0,0))
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156
23
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157 #max dilation
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158 MyThick.meth=1
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159 MyThick.nb=MyThick.__dilatemax__
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160 c=MyThick.transform_image(a)
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161 b=N.asarray(N.reshape(c,(32,32))).T
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162
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163 new=pygame.surfarray.make_surface(b)
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164 new=pygame.transform.scale2x(new)
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165 new=pygame.transform.scale2x(new)
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166 new=pygame.transform.scale2x(new)
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167 new.set_palette(anglcolorpalette)
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168 screen.blit(new,(8*32,0))
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169
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170 #max erosion
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171 MyThick.meth=-1
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172 MyThick.nb=MyThick.__erodemax__
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173 c=MyThick.transform_image(a)
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174 b=N.asarray(N.reshape(c,(32,32))).T
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175
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176 new=pygame.surfarray.make_surface(b)
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177 new=pygame.transform.scale2x(new)
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178 new=pygame.transform.scale2x(new)
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179 new=pygame.transform.scale2x(new)
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180 new.set_palette(anglcolorpalette)
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181 screen.blit(new,(8*2*32,0))
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182
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183 #random
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184 print MyThick.get_settings_names(), MyThick.regenerate_parameters(1)
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185 c=MyThick.transform_image(a)
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186 b=N.asarray(N.reshape(c,(32,32))).T
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187
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188 new=pygame.surfarray.make_surface(b)
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189 new=pygame.transform.scale2x(new)
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190 new=pygame.transform.scale2x(new)
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191 new=pygame.transform.scale2x(new)
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192 new.set_palette(anglcolorpalette)
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193 screen.blit(new,(8*3*32,0))
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194
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195 pygame.display.update()
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196 raw_input('Press Enter')
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197
17
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198 pygame.display.quit()