mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
"""
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===============================================
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Local Binary Pattern for texture classification
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===============================================
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In this example, we will see how to classify textures based on LBP (Local Binary
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Pattern). The histogram of the LBP result is a good measure to classify
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textures. For simplicity the histogram distributions are then tested against
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each other using the Kullback-Leibler-Divergence.
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"""
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import os
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import glob
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import numpy as np
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import pylab
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import scipy.ndimage as nd
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import skimage.feature as ft
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from skimage.io import imread
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from skimage import data
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# settings for LBP
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METHOD = 'uniform'
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P = 16
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R = 2
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def kullback_leibler_divergence(p, q):
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p = np.asarray(p)
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q = np.asarray(q)
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filt = np.logical_and(p != 0, q != 0)
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return np.sum(p[filt] * np.log2(p[filt] / q[filt]))
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def match(refs, img):
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best_score = 10
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best_name = None
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lbp = ft.local_binary_pattern(img, P, R, METHOD)
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hist, _ = np.histogram(lbp, normed=True, bins=P + 2, range=(0, P + 2))
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for name, ref in refs.items():
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ref_hist, _ = np.histogram(ref, normed=True, bins=P + 2,
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range=(0, P + 2))
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score = kullback_leibler_divergence(hist, ref_hist)
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if score < best_score:
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best_score = score
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best_name = name
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return best_name
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brick = data.load('brick.png')
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grass = data.load('grass.png')
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wall = data.load('rough-wall.png')
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refs = {
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'brick': ft.local_binary_pattern(brick, P, R, METHOD),
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'grass': ft.local_binary_pattern(grass, P, R, METHOD),
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'wall': ft.local_binary_pattern(wall, P, R, METHOD)
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}
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print match(refs, nd.rotate(brick, angle=30, reshape=False))
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print match(refs, nd.rotate(brick, angle=70, reshape=False))
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print match(refs, nd.rotate(grass, angle=145, reshape=False))
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