Mercurial > ift6266

annotate transformations/affine_transform.py @ 47:3bc75139654a

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Ajout d'un petit bruit gaussien sur chaque pixel de l'image.
author SylvainPL <sylvain.pannetier.lebeuf@umontreal.ca>
date Thu, 04 Feb 2010 10:32:07 -0500
parents 17caecc92544
children 81b9567ec4ae
rev   line source
39
17caecc92544 affine transformation using PIL
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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 affine transformations based on the Python
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6 Imaging Module affine transformations.
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7
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8
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9 Author: Razvan Pascanu
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10 '''
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11
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12 import numpy, Image
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13
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15
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16 class AffineTransformation():
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17 def __init__( self, shape = (32,32), seed = None):
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18 self.shape = shape
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19 self.rng = numpy.random.RandomState(seed)
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20
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21 def transform(self,NIST_image):
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22
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23 im = Image.fromarray( \
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24 numpy.asarray(\
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25 NIST_image.reshape(self.shape), dtype='uint8'))
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26 # generate random affine transformation
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27 # a point (x',y') of the new image corresponds to (x,y) of the old
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28 # image where :
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29 # x' = params[0]*x + params[1]*y + params[2]
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30 # y' = params[3]*x + params[4]*y _ params[5]
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31
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32 # the ranges are set manually as to look acceptable
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33 params = self.rng.uniform(size = 6) -.5
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34 params[2] *= 8.
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35 params[5] *= 8.
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36 params[0] = 1. + params[0]*0.4
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37 params[3] = 0. + params[3]*0.4
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38 params[1] = 0 + params[1]*0.4
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39 params[4] = 1 + params[4]*0.4
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40
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41 print params
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42 nwim = im.transform( (32,32), Image.AFFINE, params)
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43 return numpy.asarray(nwim)
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44
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45
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46
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47 if __name__ =='__main__':
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48 print 'random test'
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49
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50 from pylearn.io import filetensor as ft
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51 import pylab
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52
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53 datapath = '/data/lisa/data/nist/by_class/'
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54
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55 f = open(datapath+'digits/digits_train_data.ft')
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56 d = ft.read(f)
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57 f.close()
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58
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59
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60 transformer = AffineTransformation()
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61 id = numpy.random.randint(30)
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62
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63 pylab.figure()
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64 pylab.imshow(d[id].reshape((32,32)))
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65 pylab.figure()
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66 pylab.imshow(transformer.transform(d[id]).reshape((32,32)))
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67
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68 pylab.show()
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69