Mercurial > ift6266

annotate transformations/slant.py @ 41:fdb0e0870fb4

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Beaucoup de modifications à pipeline.py pour généraliser et un début de visualisation, et créé un wrapper (run_pipeline.py) pour appeler avec GIMP. - Modifications à pipeline.py - Wrappé la boucle du pipeline dans une classe - Isolé le problème de itérer sur les batches et les complexités dans des itérateurs - Permet d'avoir des ordres compliqués de batch (plusieurs sources), de complexités - Maintenant regenerate_parameters() est appelé pour chaque image. - Command line arguments avec getopt(). On pourra rajouter des options ainsi. - run_pipeline.py - Le but est de permettre de passer des arguments. Pas facile (pas trouvé comment de façon simple) avec la command line pour appeler GIMP en mode batch. C'est un hack ici. - Le but ultime est de permettre de lancer les jobs sur les clusters avec dbidispatch en précisant les options (diff. pour chaque job) sur la ligne de commande.
author fsavard
date Wed, 03 Feb 2010 17:08:27 -0500
parents 0f1337994716
children
rev   line source
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1 #!/usr/bin/python
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2 # coding: utf-8
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4 '''
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5 Author: Youssouf
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6
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7 this module add a slant effect to the image.
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8
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9 To obtain the slant effect, each row of the array is shifted proportionately by a step controlled by the complexity.
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11 '''
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12
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13 import numpy
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16 class Slant():
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17 def __init__(self, complexity=1):
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18 #---------- private attributes
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19 self.direction = 1
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20 self.angle = 0
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21
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22 #---------- generation parameters
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23 self.regenerate_parameters(complexity)
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24 #------------------------------------------------
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25
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26 def _get_current_parameters(self):
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27 return [self.angle, self.direction]
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28
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29 def get_settings_names(self):
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30 return ['angle', 'direction']
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32 def regenerate_parameters(self, complexity):
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33 self.angle = numpy.random.uniform(0.0, complexity)
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34 P = numpy.random.uniform()
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35 self.direction = 1;
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36 if P < 0.5:
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37 self.direction = -1;
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38 return self._get_current_parameters()
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41 def transform_image(self,image):
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42 if self.angle == 0:
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43 return image
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44
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45 ysize, xsize = image.shape
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46 slant = self.direction*self.angle
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47
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48 output = image.copy()
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49
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50 # shift all the rows
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51 for i in range(ysize):
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52 line = image[i]
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53 delta = round((i*slant)) % xsize
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54 line1 = line[:xsize-delta]
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55 line2 = line[xsize-delta:xsize]
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56
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57 output[i][delta:xsize] = line1
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58 output[i][0:delta] = line2
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61 #correction to center the image
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62 correction = (self.direction)*round(self.angle*ysize/2)
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63 correction = (xsize - correction) % xsize
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65 # center the region
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66 line1 = output[0:ysize,0:xsize-correction].copy()
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67 line2 = output[0:ysize,xsize-correction:xsize].copy()
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68 output[0:ysize,correction:xsize] = line1
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69 output[0:ysize,0:correction] = line2
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72 return output
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75 # Test function
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76 # Load an image in local and create several samples of the effect on the
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77 # original image with different parameter. All the samples are saved in a single image, the 1st image being the original.
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78
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79 def test_slant():
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80 import scipy
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81 img_name = "test_img/mnist_0.png"
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82 dest_img_name = "test_img/slanted.png"
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83 nb_samples = 10
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84 im = Image.open(img_name)
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85 im = im.convert("L")
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86 image = numpy.asarray(im)
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87
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88 image_final = image
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89 slant = Slant()
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90 for i in range(nb_samples):
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91 slant.regenerate_parameters(1)
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92 image_slant = slant.transform_image(image)
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93 image_final = scipy.hstack((image_final,image_slant))
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94
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95 im = Image.fromarray(image_final.astype('uint8'), "L")
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96 im.save(dest_img_name)
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97
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98 # Test
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99 if __name__ == '__main__':
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100 import sys, os, fnmatch
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101 import Image
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102
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103 test_slant()
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104