annotate transformations/thick.py @ 12:d511445f19da

appliquer filtres gimp sur des arrays numpy plutot que sur des fichiers et structure classe
author boulanni <nicolas_boulanger@hotmail.com>
date Wed, 27 Jan 2010 21:05:58 -0500
parents dbc806d025a2
children a25474d4d34f
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
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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4 '''
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5 Simple implementation of random thickness deformation using morphological
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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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 11 symmetric kernels.
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9
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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10 Author: Xavier Glorot
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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11
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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12 Usage:
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13 '''
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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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
dbc806d025a2 Added a thick.py script defining a Thick class transforming randomly the thickness of the characters
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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
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23 self.__ny__ = 32
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24 self.__erodemax__ = 4
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25 self.__dilatemax__ = 11
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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.erodenb = N.ceil(complexity * self.__erodemax__)
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38 self.dilatenb = N.ceil(complexity * self.__dilatemax__)
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39 self.Perode = self.erodenb / (self.dilatenb + self.erodenb + 1.0)
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40 self.Pdilate = self.dilatenb / (self.dilatenb + self.erodenb + 1.0)
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41 assert (self.Perode + self.Pdilate <= 1) & (self.Perode + self.Pdilate >= 0)
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42 assert (complexity >= 0) & (complexity <= 1)
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43 #------------------------------------------------
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44
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45 def _get_current_parameters(self):
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46 return [self.erodenb, self.dilatenb, self.Perode, self.Pdilate]
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47
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48 def get_settings_names(self):
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49 return ['erodenb','dilatenb','Perode','Pdilate']
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50
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51 def regenerate_parameters(self, complexity):
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52 self.erodenb = N.ceil(complexity * self.__erodemax__)
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53 self.dilatenb = N.ceil(complexity * self.__dilatemax__)
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54 self.Perode = self.erodenb / (self.dilatenb + self.erodenb + 1.0)
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55 self.Pdilate = self.dilatenb / (self.dilatenb + self.erodenb + 1.0)
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56 assert (self.Perode + self.Pdilate <= 1) & (self.Perode + self.Pdilate >= 0)
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57 assert (complexity >= 0) & (complexity <= 1)
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58 return self._get_current_parameters()
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59
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60 def transform_1_image(self,image,genparam_save = None):
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61 P = N.random.uniform()
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62
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63 if P>1-(self.Pdilate+self.Perode):
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64 maxi = float(N.max(image))
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65 mini = float(N.min(image))
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66
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67 if maxi>1.0:
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68 image=image/maxi
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69
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70 if P>1-(self.Pdilate+self.Perode)+self.Perode:
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71 nb=N.random.randint(self.dilatenb)
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72 trans=scipy.ndimage.morphology.grey_dilation\
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73 (image,size=self.__structuring_elements__[nb].shape,structure=self.__structuring_elements__[nb])
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74 meth = 'dilate'
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75 else:
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76 nb=N.random.randint(self.erodenb)
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77 trans=scipy.ndimage.morphology.grey_erosion\
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78 (image,size=self.__structuring_elements__[nb].shape,structure=self.__structuring_elements__[nb])
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79 meth = 'erode'
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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= numpy.asarray((trans - (minit+mini)) / (maxit - (minit+mini)) * maxi,dtype=image.dtype)
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85 #--------
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86 if genparam_save is not None:
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87 genparam_save.update({'Thick':{'meth':meth,'nb':nb}})
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88 return trans
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89 else:
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90 meth = 'nothing'
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91 nb = 0
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92 if genparam_save is not None:
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93 genparam_save.update({'Thick':{'meth':meth,'nb':nb}})
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94 return image
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95
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96 def transform_image(self,image,genparam_save = None):
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97 if image.ndim == 2:
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98 newimage = N.reshape(image,(image.shape[0],self.__nx__,self.__ny__))
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99 for i in range(image.shape[0]):
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100 if genparam_save is not None:
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101 newimage[i,:,:] = self.transform_1_image(newimage[i,:,:],genparam_save[i])
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102 else:
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103 newimage[i,:,:] = self.transform_1_image(newimage[i,:,:])
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104 return N.reshape(newimage,image.shape)
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105 else:
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106 newimage = N.reshape(image,(self.__nx__,self.__ny__))
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107 if genparam_save is not None:
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108 newimage = self.transform_1_image(newimage,genparam_save)
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109 else:
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110 newimage = self.transform_1_image(newimage)
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111 return N.reshape(newimage,image.shape)
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112
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113
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114
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115
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116 #test on NIST (you need pylearn and access to NIST to do that)
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117
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118 if __name__ == '__main__':
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119
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120 from pylearn.io import filetensor as ft
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121 import copy, numpy
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122 import pygame
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123 import time
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124 datapath = '/data/lisa/data/nist/by_class/'
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125 f = open(datapath+'digits/digits_train_data.ft')
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126 d = ft.read(f)
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127
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128 pygame.surfarray.use_arraytype('numpy')
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129
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130 pygame.display.init()
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131 screen = pygame.display.set_mode((8*2*32,8*32),0,8)
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132 anglcolorpalette=[(x,x,x) for x in xrange(0,256)]
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133 screen.set_palette(anglcolorpalette)
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134
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135 MyThick = Thick()
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136
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137 #debut=time.time()
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138 #MyThick.transform_image(d)
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139 #fin=time.time()
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140 #print '------------------------------------------------'
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141 #print d.shape[0],' images transformed in :', fin-debut, ' seconds'
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142 #print '------------------------------------------------'
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143 #print (fin-debut)/d.shape[0]*1000000,' microseconds per image'
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144 #print '------------------------------------------------'
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145 #print MyThick.get_settings_names()
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146 #print MyThick._get_current_parameters()
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147 #print MyThick.regenerate_parameters(0)
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148 #print MyThick.regenerate_parameters(0.5)
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149 #print MyThick.regenerate_parameters(1)
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150 for i in range(10000):
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151 a=d[i,:]
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152 b=N.asarray(N.reshape(a,(32,32))).T
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153
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154 new=pygame.surfarray.make_surface(b)
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155 new=pygame.transform.scale2x(new)
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156 new=pygame.transform.scale2x(new)
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157 new=pygame.transform.scale2x(new)
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158 new.set_palette(anglcolorpalette)
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159 screen.blit(new,(0,0))
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160
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161 dd={}
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162 c=MyThick.transform_image(a,dd)
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163 b=N.asarray(N.reshape(c,(32,32))).T
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164
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165 new=pygame.surfarray.make_surface(b)
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166 new=pygame.transform.scale2x(new)
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167 new=pygame.transform.scale2x(new)
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168 new=pygame.transform.scale2x(new)
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169 new.set_palette(anglcolorpalette)
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170 screen.blit(new,(8*32,0))
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171
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172 pygame.display.update()
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173 print dd
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174 raw_input('Press Enter')
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175
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176 pygame.display.quit()