--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/transformations/affine_transform.py	Thu Feb 04 10:27:58 2010 -0500
@@ -0,0 +1,69 @@
+#!/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, shape = (32,32), seed = None):
+        self.shape = shape
+        self.rng = numpy.random.RandomState(seed)
+
+    def transform(self,NIST_image):
+    
+        im = Image.fromarray( \
+                numpy.asarray(\
+                       NIST_image.reshape(self.shape), dtype='uint8'))
+        # 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
+        params = self.rng.uniform(size = 6) -.5
+        params[2] *= 8.
+        params[5] *= 8.
+        params[0] = 1. + params[0]*0.4
+        params[3] = 0. + params[3]*0.4
+        params[1] = 0  + params[1]*0.4
+        params[4] = 1  + params[4]*0.4
+
+        print params
+        nwim = im.transform( (32,32), Image.AFFINE, params)
+        return numpy.asarray(nwim)
+
+
+
+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(d[id]).reshape((32,32)))
+
+    pylab.show()
+