Cython implementation of MB-LBP. Updated MB-LBP visualization without matplotlib.Examples to gallery were added. Tests are made more easily readable.

doc/examples/plot_multiblock_local_binary_pattern.py

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+"""
+===========================================================
+Multi-Block Local Binary Pattern for texture classification
+===========================================================
+
+In this example, we will see how to compute the multi-block
+local binary pattern at a specified image and how to visualize it.
+
+The features are calculated in a way similar to local binary
+patterns, except that block summed up pixel values
+rather than pixel values are used.
+
+`MB-LBP` is an extension of LBP that can be computed on any
+scales in a constant time using integral image. It consists of
+`9` equal-sized rectangles. They are used to compute a feature.
+Sum of pixels' intensity values in each of them are compared
+to the central rectangle and depending on comparison result,
+the feature descriptor is computed.
+
+We will start with a simple image that we will generate by our
+own to show how the `MB-LBP` works. We will create a `(9, 9)`
+rectangle with and divide it into `9` blocks. After this
+we will apply `MB-LBP` on it.
+
+
+"""
+from __future__ import print_function
+from skimage.feature import multiblock_local_binary_pattern
+import numpy as np
+from skimage.util import img_as_float
+from skimage.transform import integral_image
+
+# Create dummy matrix where first and fifth
+# rectangles have greater value than the central one
+# Therefore, the following bits should be 1.
+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
+
+# MB-LBP is filled in reverse order.
+# So the first and fifth bits from the end should
+# be filled.
+correct_answer = 0b10001000
+
+# The function accepts the float images.
+# Also it has to be C-contiguous.
+test_img = img_as_float(test_img)
+int_img = integral_image(test_img)
+
+lbp_code = multiblock_local_binary_pattern(int_img, 0, 0, 3, 3)
+
+print(lbp_code == correct_answer)
+
+"""
+Now let's apply the operator to a real image and see how the visualization works.
+"""
+
+from __future__ import print_function
+from skimage.feature import (multiblock_local_binary_pattern,
+                             visualize_multiblock_lbp)
+from skimage.util import img_as_float
+from skimage.transform import integral_image
+from skimage import data
+from matplotlib import pyplot as plt
+
+test_img = data.coins()
+
+test_img = img_as_float(test_img)
+int_img = integral_image(test_img)
+
+lbp_code = multiblock_local_binary_pattern(int_img, 0, 0, 90, 90)
+
+img = visualize_multiblock_lbp(test_img, 0, 0, 90, 90,
+                               lbp_code=lbp_code)
+
+
+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 intensity than the central
+rectangle are marked with cyan color. The ones that have bigger intensity values
+are marked with white color. The central rectangle is left untouched.
+"""