Used native grayscale function instead of pylab.

src/LBP.py

-from pylab import imread, figure, show, imshow, zeros, axis
+#!/usr/bin/python
+import Image as im
 
-def to_grayscale(image):
-    """Turn a RGB image to a grayscale image."""
-    result = zeros(image.shape[:2])
-    
-    for x in xrange(len(image)):
-        for y in xrange(len(image[0])):
-            result[x][y] = image[x][y].sum() / 3
-            
-    return result
+def domainIterator(image):
+    """Iterate over the pixels of an image."""
+    for y in xrange(image.shape[0]):
+        for x in xrange(image.shape[1]):
+            yield y, x
 
 # Divide the examined window to cells (e.g. 16x16 pixels for each cell).
 
@@ -27,10 +24,5 @@ def to_grayscale(image):
 # Optionally normalize the histogram. Concatenate normalized histograms of all
 # cells. This gives the feature vector for the window.
 
-image = imread("../images/test.png")
-image = to_grayscale(image)
-
-figure()
-imshow(image, cmap='gray')
-axis('off')
-show()
+image = im.open("../images/test.png").convert('L')
+image.show()