pep8. Documenatation correction.

doc/examples/plot_multiblock_local_binary_pattern.py

@@ -10,11 +10,11 @@ The features are calculated similarly to local binary patterns (LBPs),
 except that summed blocks are used instead of individual pixel values.
 
 MB-LBP is an extension of LBP that can be computed on multiple scales
-in constant time using the integral image.
-9 equally-sized rectangles are used to compute a feature.
-For each rectangle, the sum of the pixel intensities is computed.
-Comparisons of these sums to that of the central rectangle determine
-the feature, similarly to LBP (See `LBP <plot_local_binary_pattern.html>`_).
+in constant time using the integral image. 9 equally-sized rectangles
+are used to compute a feature. For each rectangle, the sum of the pixel
+intensities is computed. Comparisons of these sums to that of the central
+rectangle determine the feature, similarly to LBP
+(See `LBP <plot_local_binary_pattern.html>`_).
 
 First, we generate an image to illustrate the functioning of MB-LBP:
 we take a (9, 9) rectangle and divide it into (3, 3) block,
@@ -25,6 +25,7 @@ upon which we then apply MB-LBP.
 from __future__ import print_function
 from skimage.feature import multiblock_local_binary_pattern
 import numpy as np
+from numpy.testing import assert_equal
 from skimage.transform import integral_image
 
 # Create test matrix where first and fifth
@@ -35,17 +36,16 @@ test_img[3:6, 3:6] = 1
 test_img[:3, :3] = 50
 test_img[6:, 6:] = 50
 
-# MB-LBP is filled in reverse order.
-# So the first and fifth bits from the end should
-# be filled.
+# First and fifth bits should be filled.
+# This correct value will be compared to
+# the computed one.
 correct_answer = 0b10001000
 
 int_img = integral_image(test_img)
 
 lbp_code = multiblock_local_binary_pattern(int_img, 0, 0, 3, 3)
 
-print(correct_answer)
-print(lbp_code)
+assert_equal(correct_answer, lbp_code)
 
 """
 Now let's apply the operator to a real image and see how the visualization works.
@@ -71,6 +71,6 @@ plt.imshow(img, interpolation='nearest')
 
 On the above plot we see the result of computing a MB-LBP and visualization
 of the computed feature. The rectangles that have less intensity than the central
-rectangle are marked in cyan. The ones that have bigger intensity values
+rectangle are marked in cyan. The ones that have higher intensity values
 are marked in white. The central rectangle is left untouched.
 """