Mercurial > ift6266

annotate transformations/add_background_image.py @ 123:999bf84ddec6

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Maintenant, plus grande probablite d'effectuer une occlusion. Proba= 0.3 maintenant
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Thu, 18 Feb 2010 11:10:53 -0500
parents 8aadb0f59a64
children
rev   line source
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1 #!/usr/bin/python
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2 # -*- coding: iso-8859-1 -*-
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4 '''
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5 Implementation of random background adding to a specific image
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6
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7 Author: Guillaume Sicard
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8 '''
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10 import sys, os, random
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11 import cPickle
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12 import Image, numpy
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14 class AddBackground():
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15 def __init__(self, threshold = 128, complexity = 1):
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16 self.h = 32
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17 self.w = 32
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18 self.threshold = 1;
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19 try: #in order to load locally if it is available
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20 self.bg_image_file = '/Tmp/image_net/'
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21 f=open(self.bg_image_file+'filelist.pkl')
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22 except:
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23 self.bg_image_file = '/data/lisa/data/ift6266h10/image_net/'
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24 f=open(self.bg_image_file+'filelist.pkl')
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25 self.image_files = cPickle.load(f)
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26 f.close()
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27 self.regenerate_parameters(complexity)
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29 def get_current_parameters(self):
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30 return [self.contrast]
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31 # get threshold value
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32 def get_settings_names(self):
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33 return ['contrast']
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34
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35 # no need, except for testmod.py
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36 def regenerate_parameters(self, complexity):
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37 self.contrast = 1-numpy.random.rand()*complexity
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38 return [self.contrast]
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40 # load an image
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41 def load_image(self,filename):
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42 image = Image.open(filename).convert('L')
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43 image = numpy.asarray(image)
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44 image = (image / 255.0).astype(numpy.float32)
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45 return image
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47 # save an image
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48 def save_image(self,array, filename):
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49 image = (array * 255.0).astype('int')
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50 image = Image.fromarray(image)
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51 if (filename != ''):
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52 image.save(filename)
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53 else:
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54 image.show()
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56 # make a random 32x32 crop of an image
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57 def rand_crop(self,image):
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58 i_w, i_h = image.shape
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59 x, y = random.randint(0, i_w - self.w), random.randint(0, i_h - self.h)
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60 return image[x:x + self.w, y:y + self.h]
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62 # select a random background image from "bg_image_file" and crops it
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63 def rand_bg_image(self,maximage):
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64 i = random.randint(0, len(self.image_files) - 1)
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66 image = self.load_image(self.bg_image_file + self.image_files[i])
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67 self.bg_image = self.rand_crop(image)
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68 maxbg = self.bg_image.max()
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69 self.bg_image = self.bg_image / maxbg * ( max(maximage - self.contrast,0.0) )
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71 # set "bg_image" as background to "image", based on a pixels threshold
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72 def set_bg(self,image):
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73 tensor = numpy.asarray([self.bg_image,image],dtype='float32')
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74 return tensor.max(0)
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75
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76 # transform an image file and return an array
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77 def transform_image_from_file(self, filename):
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78 self.rand_bg_image()
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79 image = self.load_image(filename)
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80 image = self.set_bg(image)
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81 return image
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83 # standard array to array transform
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84 def transform_image(self, image):
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85 self.rand_bg_image(image.max())
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86 image = self.set_bg(image)
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87 return image
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89 # test method
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90 def test(self,filename):
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91 import time
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93 sys.stdout.write('Starting addBackground test : loading image')
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94 sys.stdout.flush()
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95
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96 image = self.load_image(filename)
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98 t = 0
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99 n = 500
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100 for i in range(n):
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101 t0 = time.time()
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102 image2 = self.transform_image(image)
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103 t = ( i * t + (time.time() - t0) ) / (i + 1)
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104 sys.stdout.write('.')
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105 sys.stdout.flush()
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106
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107 print "Done!\nAverage time : " + str(1000 * t) + " ms"
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108
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109 if __name__ == '__main__':
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110
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111 myAddBackground = AddBackground()
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112 myAddBackground.test('./images/0-LiberationSans-Italic.ttf.jpg')