"""
===========================================================
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 `_).
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 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')
plt.show()
######################################################################
# 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.