mirror of
https://github.com/wassname/scikit-image.git
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Johannes Schönberger
90 lines
2.7 KiB
Python
90 lines
2.7 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
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Binary Pattern). This example uses different rotated textures, taken from
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http://sipi.usc.edu/database/database.php?volume=rotate.
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The histogram of the LBP result is a good measure to classify textures. For
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simplicity the histogram distributions are then tested against each other using
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the Kullback-Leibler-Divergence.
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Preparation
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===========
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First you need to download and extract the texture image set from
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http://sipi.usc.edu/database/database.php?volume=rotate. Make sure you change
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the path to the extracted images in the script (`IMAGE_FOLDER`). You must run
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the `dump_refs` function only once, so the computation is faster. Finally you
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can match any of the rotated images against the reference textures.
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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 skimage.feature as ft
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from skimage.io import imread
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IMAGE_FOLDER = 'images'
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REF_DUMP_FOLDER = 'refs'
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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 dump_refs(refs):
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os.mkdir(REF_DUMP_FOLDER)
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file_names = glob.glob('%s/*.000.tiff' % IMAGE_FOLDER)
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for file_name in file_names:
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name, _ = os.path.splitext(os.path.basename(file_name))
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lbp = ft.local_binary_pattern(ref, P, R, METHOD)
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np.save('refs/%s.npy' % name, lbp)
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def load_refs():
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file_names = glob.glob('refs/*.000.npy')
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refs = {}
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for file_name in file_names:
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name, _ = os.path.splitext(os.path.basename(file_name))
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refs[name] = np.load(file_name)
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return refs
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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, 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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# compute LBP for each reference image once and dump result, once run this
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# should be commented out
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dump_refs(refs)
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# match any rotated image against reference textures
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refs = load_refs()
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img = imread(os.path.join(IMAGE_FOLDER, 'grass.060.tiff'))
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print match(refs, img) # grass.000
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img = imread(os.path.join(IMAGE_FOLDER, 'brick.060.tiff'))
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print match(refs, img) # brick.000
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img = imread(os.path.join(IMAGE_FOLDER, 'bubbles.060.tiff'))
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print match(refs, img) # bubbles.000
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