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