Merge pull request #1536 from warmspringwinds/mb-lbp

FEAT: multi-block local binary patterns (MB-LBP)

doc/examples/plot_multiblock_local_binary_pattern.py

@@ -0,0 +1,73 @@
+"""
+===========================================================
+Multi-Block Local Binary Pattern for texture classification
+===========================================================
+
+This example shows how to compute multi-block local binary pattern (MB-LBP)
+features as well as how to visualize them.
+
+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>`_).
+
+First, we generate an image to illustrate the functioning of MB-LBP: consider
+a (9, 9) rectangle and divide it into (3, 3) block, upon which we then apply
+MB-LBP.
+
+"""
+from __future__ import print_function
+from skimage.feature import multiblock_lbp
+import numpy as np
+from numpy.testing import assert_equal
+from skimage.transform import integral_image
+
+# Create test matrix where first and fifth rectangles starting
+# from top left clockwise have greater value than the central one.
+test_img = np.zeros((9, 9), dtype='uint8')
+test_img[3:6, 3:6] = 1
+test_img[:3, :3] = 50
+test_img[6:, 6:] = 50
+
+# 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_lbp(int_img, 0, 0, 3, 3)
+
+assert_equal(correct_answer, lbp_code)
+
+"""
+Now let's apply the operator to a real image and see how the
+visualization works.
+"""
+from skimage import data
+from matplotlib import pyplot as plt
+from skimage.feature import draw_multiblock_lbp
+
+test_img = data.coins()
+
+int_img = integral_image(test_img)
+
+lbp_code = multiblock_lbp(int_img, 0, 0, 90, 90)
+
+img = draw_multiblock_lbp(test_img, 0, 0, 90, 90,
+                          lbp_code=lbp_code, alpha=0.5)
+
+
+plt.imshow(img, interpolation='nearest')
+
+"""
+.. image:: PLOT2RST.current_figure
+
+On the above plot we see the result of computing a MB-LBP and visualization of
+the computed feature. The rectangles that have less intensities' sum than the
+central rectangle are marked in cyan. The ones that have higher intensity
+values are marked in white. The central rectangle is left untouched.
+"""