Mercurial > ift6266
view data_generation/transformations/affine_transform.py @ 599:587674030c5a
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author | boulanni <nicolas_boulanger@hotmail.com> |
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date | Fri, 15 Oct 2010 14:14:06 -0400 |
parents | 1f5937e9e530 |
children |
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#!/usr/bin/python # coding: utf-8 ''' Simple implementation of random affine transformations based on the Python Imaging Module affine transformations. Author: Razvan Pascanu ''' import numpy, Image class AffineTransformation(): def __init__( self, complexity = .5): self.shape = (32,32) self.complexity = complexity params = numpy.random.uniform(size=6) -.5 self.a = 1. + params[0]*.6*complexity self.b = 0. + params[1]*.6*complexity self.c = params[2]*8.*complexity self.d = 0. + params[3]*.6*complexity self.e = 1. + params[4]*.6*complexity self.f = params[5]*8.*complexity def _get_current_parameters(self): return [self.a, self.b, self.c, self.d, self.e, self.f] def get_settings_names(self): return ['a','b','c','d','e','f'] def regenerate_parameters(self, complexity): # generate random affine transformation # a point (x',y') of the new image corresponds to (x,y) of the old # image where : # x' = params[0]*x + params[1]*y + params[2] # y' = params[3]*x + params[4]*y _ params[5] # the ranges are set manually as to look acceptable self.complexity = complexity params = numpy.random.uniform(size=6) -.5 self.a = 1. + params[0]*.8*complexity self.b = 0. + params[1]*.8*complexity self.c = params[2]*9.*complexity self.d = 0. + params[3]*.8*complexity self.e = 1. + params[4]*.8*complexity self.f = params[5]*9.*complexity return self._get_current_parameters() def transform_image(self,NIST_image): im = Image.fromarray( \ numpy.asarray(\ NIST_image.reshape(self.shape)*255.0, dtype='uint8')) nwim = im.transform( (32,32), Image.AFFINE, [self.a,self.b,self.c,self.d,self.e,self.f]) return numpy.asarray(numpy.asarray(nwim)/255.0,dtype='float32') if __name__ =='__main__': print 'random test' from pylearn.io import filetensor as ft import pylab datapath = '/data/lisa/data/nist/by_class/' f = open(datapath+'digits/digits_train_data.ft') d = ft.read(f) f.close() transformer = AffineTransformation() id = numpy.random.randint(30) pylab.figure() pylab.imshow(d[id].reshape((32,32))) pylab.figure() pylab.imshow(transformer.transform_image(d[id]).reshape((32,32))) pylab.show()