# HG changeset patch
# User Xavier Glorot <glorotxa@iro.umontreal.ca>
# Date 1264792210 18000
# Node ID 0b9350998dbe2e38fb58be5b98f9881b920b9b0a
# Parent  47e7202d4f190eaf91bfcf06e6fdfe2873313b46
Added a contrast.py script difining the Contrast transformation class

diff -r 47e7202d4f19 -r 0b9350998dbe transformations/contrast.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/transformations/contrast.py	Fri Jan 29 14:10:10 2010 -0500
@@ -0,0 +1,137 @@
+#!/usr/bin/python
+# coding: utf-8
+
+'''
+Simple implementation of random contrast. This always switch half the time the polarity.
+then it decide of a bias and of a contrast, both of them are dependant of the complexity.
+
+Author: Xavier Glorot
+'''
+
+import scipy.ndimage.morphology
+import numpy as N
+import copy
+
+
+class Contrast():
+    def __init__(self,complexity = 1):
+        #---------- private attributes
+        self.__nx__ = 32 #xdim of the images
+        self.__ny__ = 32 #ydim of the images
+        self.__Pinvert__ = 0.5 #probability to switch polarity
+        self.__mincontrast__ = 0.15
+        self.__resolution__ = 256
+        self.__rangecontrastres__ = self.__resolution__ - N.int(self.__mincontrast__*self.__resolution__)
+        #------------------------------------------------
+        
+        #---------- generation parameters
+        self.regenerate_parameters(complexity)
+        #------------------------------------------------
+    
+    def _get_current_parameters(self):
+        return [self.invert,self.contrast]
+    
+    def get_settings_names(self):
+        return ['invert','contrast']
+    
+    def regenerate_parameters(self, complexity):
+        self.invert = (N.random.uniform() < self.__Pinvert__)
+        self.contrast = self.__resolution__ - N.random.randint(1 + self.__rangecontrastres__ * complexity)
+        return self._get_current_parameters()
+    
+    def transform_1_image(self,image): #the real transformation method
+        maxi = image.max()
+        mini = image.min()
+        if self.invert:
+            newimage = 1 - (self.__resolution__- self.contrast) / (2 * float(self.__resolution__)) -\
+                        (image - mini) / float(maxi - mini) * self.contrast / float(self.__resolution__)
+        else:
+            newimage = (self.__resolution__- self.contrast) / (2 * float(self.__resolution__)) +\
+                        (image - mini) / float(maxi - mini) * self.contrast / float(self.__resolution__)
+        if image.dtype == 'uint8':
+            return N.asarray(newimage*255,dtype='uint8')
+        else:
+            return N.asarray(newimage,dtype=image.dtype)
+    
+    def transform_image(self,image): #handling different format
+        if image.shape == (self.__nx__,self.__ny__):
+            return self.transform_1_image(image)
+        if image.ndim == 3:
+            newimage = copy.copy(image)
+            for i in range(image.shape[0]):
+                newimage[i,:,:] = self.transform_1_image(image[i,:,:])
+            return newimage
+        if image.ndim == 2 and image.shape != (self.__nx__,self.__ny__):
+            newimage = N.reshape(image,(image.shape[0],self.__nx__,self.__ny__))
+            for i in range(image.shape[0]):
+                newimage[i,:,:] = self.transform_1_image(newimage[i,:,:])
+            return N.reshape(newimage,image.shape)
+        if image.ndim == 1:
+            newimage = N.reshape(image,(self.__nx__,self.__ny__))
+            newimage = self.transform_1_image(newimage)
+            return N.reshape(newimage,image.shape)
+        assert False #should never go there
+
+
+
+
+#test on NIST (you need pylearn and access to NIST to do that)
+
+if __name__ == '__main__':
+    
+    from pylearn.io import filetensor as ft
+    import copy
+    import pygame
+    import time
+    datapath = '/data/lisa/data/nist/by_class/'
+    f = open(datapath+'digits/digits_train_data.ft')
+    d = ft.read(f)
+    
+    pygame.surfarray.use_arraytype('numpy')
+    
+    pygame.display.init()
+    screen = pygame.display.set_mode((8*2*32,8*32),0,8)
+    anglcolorpalette=[(x,x,x) for x in xrange(0,256)]
+    screen.set_palette(anglcolorpalette)
+    
+    MyContrast = Contrast()
+    
+    debut=time.time()
+    MyContrast.transform_image(d)
+    fin=time.time()
+    print '------------------------------------------------'
+    print d.shape[0],' images transformed in :', fin-debut, ' seconds'
+    print '------------------------------------------------'
+    print (fin-debut)/d.shape[0]*1000000,' microseconds per image'
+    print '------------------------------------------------'
+    print MyContrast.get_settings_names()
+    print MyContrast._get_current_parameters()
+    print MyContrast.regenerate_parameters(0)
+    print MyContrast.regenerate_parameters(0.5)
+    print MyContrast.regenerate_parameters(1)
+    for i in range(10000):
+        a=d[i,:]
+        b=N.asarray(N.reshape(a,(32,32))).T
+        
+        new=pygame.surfarray.make_surface(b)
+        new=pygame.transform.scale2x(new)
+        new=pygame.transform.scale2x(new)
+        new=pygame.transform.scale2x(new)
+        new.set_palette(anglcolorpalette)
+        screen.blit(new,(0,0))
+        
+        print MyContrast.get_settings_names(), MyContrast.regenerate_parameters(1)
+        c=MyContrast.transform_image(a)
+        b=N.asarray(N.reshape(c,(32,32))).T
+        
+        new=pygame.surfarray.make_surface(b)
+        new=pygame.transform.scale2x(new)
+        new=pygame.transform.scale2x(new)
+        new=pygame.transform.scale2x(new)
+        new.set_palette(anglcolorpalette)
+        screen.blit(new,(8*32,0))
+        
+        pygame.display.update()
+        raw_input('Press Enter')
+    
+    pygame.display.quit()