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 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
 
-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
-        
 # Divide the examined window to cells (e.g. 16x16 pixels for each cell).
 
-# For each pixel in a cell, compare the pixel to each of its 8 neighbors 
-# (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the 
+# For each pixel in a cell, compare the pixel to each of its 8 neighbors
+# (on its left-top, left-middle, left-bottom, right-top, etc.). Follow the
 # pixels along a circle, i.e. clockwise or counter-clockwise.
-    
-# Where the center pixel's value is greater than the neighbor, write "1". 
-# Otherwise, write "0". This gives an 8-digit binary number (which is usually 
+
+# Where the center pixel's value is greater than the neighbor, write "1".
+# Otherwise, write "0". This gives an 8-digit binary number (which is usually
 # converted to decimal for convenience).
 
-# Compute the histogram, over the cell, of the frequency of each "number" 
-# occurring (i.e., each combination of which pixels are smaller and which are 
+# Compute the histogram, over the cell, of the frequency of each "number"
+# occurring (i.e., each combination of which pixels are smaller and which are
 # greater than the center).
-    
-# 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)
+# Optionally normalize the histogram. Concatenate normalized histograms of all
+# cells. This gives the feature vector for the window.
 
-figure()
-imshow(image, cmap='gray')
-axis('off')
-show()
+image = im.open("../images/test.png").convert('L')
+image.show()