--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/data_generation/transformations/thick.py	Fri Feb 26 14:15:38 2010 -0500
@@ -0,0 +1,198 @@
+#!/usr/bin/python
+# coding: utf-8
+
+'''
+Simple implementation of random thickness deformation using morphological
+operation of scipy.
+Only one morphological operation applied (dilation or erosion), the kernel is random
+out of a list of 12 symmetric kernels. (only 5 to be chosen for erosion because it can
+hurt the recognizability of the charater and 12 for dilation).
+
+Author: Xavier Glorot
+
+'''
+
+import scipy.ndimage.morphology
+import numpy as N
+
+
+class Thick():
+    def __init__(self,complexity = 1):
+        #---------- private attributes
+        self.__nx__ = 32 #xdim of the images
+        self.__ny__ = 32 #ydim of the images
+        self.__erodemax__ = 5 #nb of index max of erode structuring elements
+        self.__dilatemax__ = 9 #nb of index max of dilation structuring elements
+        self.__structuring_elements__ = [N.asarray([[1,1]]),N.asarray([[1],[1]]),\
+                                        N.asarray([[1,1],[1,1]]),N.asarray([[0,1,0],[1,1,1],[0,1,0]]),\
+                                        N.asarray([[1,1,1],[1,1,1]]),N.asarray([[1,1],[1,1],[1,1]]),\
+                                        N.asarray([[1,1,1],[1,1,1],[1,1,1]]),\
+                                        N.asarray([[1,1,1,1],[1,1,1,1],[1,1,1,1]]),\
+                                        N.asarray([[1,1,1],[1,1,1],[1,1,1],[1,1,1]]),\
+                                        N.asarray([[0,0,1,0,0],[0,1,1,1,0],[1,1,1,1,1],[0,1,1,1,0],[0,0,1,0,0]]),\
+                                        N.asarray([[1,1,1,1],[1,1,1,1]]),N.asarray([[1,1],[1,1],[1,1],[1,1]])]
+        #------------------------------------------------
+        
+        #---------- generation parameters
+        self.regenerate_parameters(complexity)
+        #------------------------------------------------
+    
+    def _get_current_parameters(self):
+        return [self.thick_param]
+    
+    def get_settings_names(self):
+        return ['thick_param']
+    
+    def regenerate_parameters(self, complexity):
+        self.erodenb = N.ceil(complexity * self.__erodemax__)
+        self.dilatenb = N.ceil(complexity * self.__dilatemax__)
+        self.Perode = self.erodenb / (self.dilatenb + self.erodenb + 1.0)
+        self.Pdilate = self.dilatenb / (self.dilatenb   + self.erodenb + 1.0)
+        assert (self.Perode + self.Pdilate <= 1) & (self.Perode + self.Pdilate >= 0)
+        assert (complexity >= 0) & (complexity <= 1)
+        P = N.random.uniform()
+        if P>1-(self.Pdilate+self.Perode):
+            if P>1-(self.Pdilate+self.Perode)+self.Perode:
+                self.meth = 1
+                self.nb=N.random.randint(self.dilatenb)
+            else:
+                self.meth = -1
+                self.nb=N.random.randint(self.erodenb)
+        else:
+            self.meth = 0
+            self.nb = -1
+        self.thick_param = self.meth*self.nb
+        return self._get_current_parameters()
+    
+    def transform_1_image(self,image): #the real transformation method
+        if self.meth!=0:
+            maxi = float(N.max(image))
+            mini = float(N.min(image))
+            
+            imagenorm=image/maxi
+            
+            if self.meth==1:
+                trans=scipy.ndimage.morphology.grey_dilation\
+                    (imagenorm,size=self.__structuring_elements__[self.nb].shape,structure=self.__structuring_elements__[self.nb])
+            else:
+                trans=scipy.ndimage.morphology.grey_erosion\
+                    (imagenorm,size=self.__structuring_elements__[self.nb].shape,structure=self.__structuring_elements__[self.nb])
+            
+            #------renormalizing
+            maxit = N.max(trans)
+            minit = N.min(trans)
+            trans= N.asarray((trans - (minit+mini)) / (maxit - (minit+mini)) * maxi,dtype=image.dtype)
+            #--------
+            return trans
+        else:
+            return image
+    
+    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*4*32,8*32),0,8)
+    anglcolorpalette=[(x,x,x) for x in xrange(0,256)]
+    screen.set_palette(anglcolorpalette)
+    
+    MyThick = Thick()
+    
+    #debut=time.time()
+    #MyThick.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 MyThick.get_settings_names()
+    #print MyThick._get_current_parameters()
+    #print MyThick.regenerate_parameters(0)
+    #print MyThick.regenerate_parameters(0.5)
+    #print MyThick.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))
+        
+        #max dilation
+        MyThick.meth=1
+        MyThick.nb=MyThick.__dilatemax__
+        c=MyThick.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))
+        
+        #max erosion
+        MyThick.meth=-1
+        MyThick.nb=MyThick.__erodemax__
+        c=MyThick.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*2*32,0))
+        
+        #random
+        print MyThick.get_settings_names(), MyThick.regenerate_parameters(1)
+        c=MyThick.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*3*32,0))
+        
+        pygame.display.update()
+        raw_input('Press Enter')
+    
+    pygame.display.quit()