Mercurial > ift6266

annotate data_generation/transformations/slant.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
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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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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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71
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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