--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/data_generation/transformations/affine_transform.py	Fri Feb 26 14:15:38 2010 -0500
@@ -0,0 +1,89 @@
+#!/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()
+