view transformations/DistorsionGauss.py @ 86:b3d76ebf2fac

Modifs pour interface parameters determined by complexity
author fsavard <francois.savard@polymtl.ca>
date Wed, 10 Feb 2010 17:50:58 -0500
parents aee278ebc827
children e352a98fcc0a
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#!/usr/bin/python
# coding: utf-8

'''
Ajout d'une composante aleatoire dans chaque pixel de l'image.
C'est une distorsion gaussienne de moyenne 0 et d'écart type complexity/10

Sylvain Pannetier Lebeuf dans le cadre de IFT6266, hiver 2010

'''

import numpy
import random

class DistorsionGauss():
    
    def __init__(self):
        self.ecart_type=0.1 #L'ecart type de la gaussienne
        
    def get_settings_names(self):
        return []
    
    def get_settings_name_determined_by_complexity(self):
        return ['ecart_type']

    def regenerate_parameters(self, complexity):
        self.ecart_type=float(complexity)/10
        return self._get_current_parameters()

    def _get_current_parameters(self):
        return []
    
    def get_parameters_determined_by_complexity(self, complexity):
        return [float(complexity)/10]
    
    def transform_image(self, image):
        image=image.reshape(1024,1)
        aleatoire=numpy.zeros((1024,1)).astype('float32')
        for i in xrange(0,1024):
            aleatoire[i]=float(random.gauss(0,self.ecart_type))
        image=image+aleatoire
        
        
        #Ramener tout entre 0 et 1. Ancienne facon de normaliser.
        #Resultats moins interessant je trouve.
##        if numpy.min(image) < 0:
##            image-=numpy.min(image)
##        if numpy.max(image) > 1:
##            image/=numpy.max(image)
            
        for i in xrange(0,1024):
            image[i]=min(1,max(0,image[i]))
            
        return image.reshape(32,32)


#---TESTS---

def _load_image():
    f = open('/home/sylvain/Dropbox/Msc/IFT6266/donnees/lower_test_data.ft')  #Le jeu de donnees est en local. 
    d = ft.read(f)
    w=numpy.asarray(d[random.randint(0,100)])
    return (w/255.0).astype('float')

def _test(complexite):
    img=_load_image()
    transfo = DistorsionGauss()
    pylab.imshow(img.reshape((32,32)))
    pylab.show()
    print transfo.get_settings_names()
    print transfo.regenerate_parameters(complexite)
    
    img_trans=transfo.transform_image(img)
    
    pylab.imshow(img_trans.reshape((32,32)))
    pylab.show()
    

if __name__ == '__main__':
    from pylearn.io import filetensor as ft
    import pylab
    for i in xrange(0,5):
        _test(0.5)