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comparison data_generation/transformations/affine_transform.py @ 167:1f5937e9e530

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More moves - transformations into data_generation, added "deep" folder
author Dumitru Erhan <dumitru.erhan@gmail.com>
date Fri, 26 Feb 2010 14:15:38 -0500
parents transformations/affine_transform.py@ce56e8ca960d
children
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166:17ae5a1a4dd1 167:1f5937e9e530
1 #!/usr/bin/python
2 # coding: utf-8
3
4 '''
5 Simple implementation of random affine transformations based on the Python
6 Imaging Module affine transformations.
7
8
9 Author: Razvan Pascanu
10 '''
11
12 import numpy, Image
13
14
15
16 class AffineTransformation():
17 def __init__( self, complexity = .5):
18 self.shape = (32,32)
19 self.complexity = complexity
20 params = numpy.random.uniform(size=6) -.5
21 self.a = 1. + params[0]*.6*complexity
22 self.b = 0. + params[1]*.6*complexity
23 self.c = params[2]*8.*complexity
24 self.d = 0. + params[3]*.6*complexity
25 self.e = 1. + params[4]*.6*complexity
26 self.f = params[5]*8.*complexity
27
28
29 def _get_current_parameters(self):
30 return [self.a, self.b, self.c, self.d, self.e, self.f]
31
32 def get_settings_names(self):
33 return ['a','b','c','d','e','f']
34
35 def regenerate_parameters(self, complexity):
36 # generate random affine transformation
37 # a point (x',y') of the new image corresponds to (x,y) of the old
38 # image where :
39 # x' = params[0]*x + params[1]*y + params[2]
40 # y' = params[3]*x + params[4]*y _ params[5]
41
42 # the ranges are set manually as to look acceptable
43
44 self.complexity = complexity
45 params = numpy.random.uniform(size=6) -.5
46 self.a = 1. + params[0]*.8*complexity
47 self.b = 0. + params[1]*.8*complexity
48 self.c = params[2]*9.*complexity
49 self.d = 0. + params[3]*.8*complexity
50 self.e = 1. + params[4]*.8*complexity
51 self.f = params[5]*9.*complexity
52 return self._get_current_parameters()
53
54
55
56
57 def transform_image(self,NIST_image):
58
59 im = Image.fromarray( \
60 numpy.asarray(\
61 NIST_image.reshape(self.shape)*255.0, dtype='uint8'))
62 nwim = im.transform( (32,32), Image.AFFINE, [self.a,self.b,self.c,self.d,self.e,self.f])
63 return numpy.asarray(numpy.asarray(nwim)/255.0,dtype='float32')
64
65
66
67 if __name__ =='__main__':
68 print 'random test'
69
70 from pylearn.io import filetensor as ft
71 import pylab
72
73 datapath = '/data/lisa/data/nist/by_class/'
74
75 f = open(datapath+'digits/digits_train_data.ft')
76 d = ft.read(f)
77 f.close()
78
79
80 transformer = AffineTransformation()
81 id = numpy.random.randint(30)
82
83 pylab.figure()
84 pylab.imshow(d[id].reshape((32,32)))
85 pylab.figure()
86 pylab.imshow(transformer.transform_image(d[id]).reshape((32,32)))
87
88 pylab.show()
89