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dan
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""
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===========================================================
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Multi-Block Local Binary Pattern for texture classification
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===========================================================
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This example shows how to compute multi-block local binary
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pattern (MB-LBP) features as well as how to visualize them.
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The features are calculated similarly to local binary patterns (LBPs),
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except that summed blocks are used instead of individual pixel values.
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MB-LBP is an extension of LBP that can be computed on multiple scales
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in constant time using the integral image. 9 equally-sized rectangles
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are used to compute a feature. For each rectangle, the sum of the pixel
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intensities is computed. Comparisons of these sums to that of the central
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rectangle determine the feature, similarly to LBP
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(See `LBP <plot_local_binary_pattern.html>`_).
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First, we generate an image to illustrate the functioning of MB-LBP:
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we take a (9, 9) rectangle and divide it into (3, 3) block,
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upon which we then apply MB-LBP.
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"""
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from __future__ import print_function
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from skimage.feature import multiblock_lbp
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import numpy as np
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from numpy.testing import assert_equal
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from skimage.transform import integral_image
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# Create test matrix where first and fifth rectangles starting
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# from top left clockwise have greater value than the central one.
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test_img = np.zeros((9, 9), dtype='uint8')
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test_img[3:6, 3:6] = 1
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test_img[:3, :3] = 50
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test_img[6:, 6:] = 50
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# First and fifth bits should be filled. This correct value will
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# be compared to the computed one.
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correct_answer = 0b10001000
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int_img = integral_image(test_img)
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lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3)
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assert_equal(correct_answer, lbp_code)
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"""
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Now let's apply the operator to a real image and see how the
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visualization works.
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"""
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from skimage import data
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from matplotlib import pyplot as plt
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from skimage.feature import draw_multiblock_lbp
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test_img = data.coins()
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int_img = integral_image(test_img)
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lbp_code = multiblock_lbp(int_img, 0, 0, 90, 90)
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img = draw_multiblock_lbp(test_img, 0, 0, 90, 90,
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lbp_code=lbp_code, alpha=0.5)
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plt.imshow(img, interpolation='nearest')
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"""
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.. image:: PLOT2RST.current_figure
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On the above plot we see the result of computing a MB-LBP and visualization of
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the computed feature. The rectangles that have less intensities' sum than the
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central rectangle are marked in cyan. The ones that have higher intensity
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values are marked in white. The central rectangle is left untouched.
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"""
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