From c9709ca22aa6b80eb76ad9687d9872ddd383eff7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20Sch=C3=B6nberger?= <hannesschoenberger@gmail.com>
Date: Fri, 3 Aug 2012 19:46:06 +0200
Subject: [PATCH] add example script for local binary pattern

---
 .../applications/plot_local_binary_pattern.py | 89 +++++++++++++++++++
 1 file changed, 89 insertions(+)
 create mode 100644 doc/examples/applications/plot_local_binary_pattern.py

diff --git a/doc/examples/applications/plot_local_binary_pattern.py b/doc/examples/applications/plot_local_binary_pattern.py
new file mode 100644
index 00000000..1e65d14f
--- /dev/null
+++ b/doc/examples/applications/plot_local_binary_pattern.py
@@ -0,0 +1,89 @@
+"""
+===============================================
+Local Binary Pattern for texture classification
+===============================================
+
+In this example, we will see how to classify textures based on LBP (Local
+Binary Pattern). This example uses different rotated textures, taken from
+http://sipi.usc.edu/database/database.php?volume=rotate.
+
+The histogram of the LBP result is a good measure to classify textures. For
+simplicity the histogram distributions are then tested against each other using
+the Kullback-Leibler-Divergence.
+
+Preparation
+===========
+
+First you need to download and extract the texture image set from
+http://sipi.usc.edu/database/database.php?volume=rotate. Make sure you change
+the path to the extracted images in the script (`IMAGE_FOLDER`). You must run
+the `dump_refs` function only once, so the computation is faster. Finally you
+can match any of the rotated images against the reference textures.
+"""
+
+import os
+import glob
+import numpy as np
+import pylab
+import skimage.feature as ft
+from skimage.io import imread
+
+
+IMAGE_FOLDER = 'images'
+REF_DUMP_FOLDER = 'refs'
+METHOD = 'uniform'
+P = 16
+R = 2
+
+
+def kullback_leibler_divergence(p, q):
+    p = np.asarray(p)
+    q = np.asarray(q)
+    filt = np.logical_and(p != 0, q != 0)
+    return np.sum(p[filt] * np.log2(p[filt] / q[filt]))
+
+
+def dump_refs(refs):
+    os.mkdir(REF_DUMP_FOLDER)
+    file_names = glob.glob('%s/*.000.tiff' % IMAGE_FOLDER)
+    for file_name in file_names:
+        name, _ = os.path.splitext(os.path.basename(file_name))
+        lbp = ft.local_binary_pattern(ref, P, R, METHOD)
+        np.save('refs/%s.npy' % name, lbp)
+
+
+def load_refs():
+    file_names = glob.glob('refs/*.000.npy')
+    refs = {}
+    for file_name in file_names:
+        name, _ = os.path.splitext(os.path.basename(file_name))
+        refs[name] = np.load(file_name)
+    return refs
+
+
+def match(refs, img):
+    best_score = 10
+    best_name = None
+    lbp = ft.local_binary_pattern(img, P, R, METHOD)
+    hist, _ = np.histogram(lbp, normed=True, bins=P+2, range=(0, P+2))
+    for name, ref in refs.items():
+        ref_hist, _ = np.histogram(ref, normed=True, bins=P+2, range=(0, P+2))
+        score = kullback_leibler_divergence(hist, ref_hist)
+        if score < best_score:
+            best_score = score
+            best_name = name
+    return best_name
+
+
+# compute LBP for each reference image once and dump result, once run this
+# should be commented out
+dump_refs(refs)
+
+# match any rotated image against reference textures
+refs = load_refs()
+img = imread(os.path.join(IMAGE_FOLDER, 'grass.060.tiff'))
+print match(refs, img) # grass.000
+img = imread(os.path.join(IMAGE_FOLDER, 'brick.060.tiff'))
+print match(refs, img) # brick.000
+img = imread(os.path.join(IMAGE_FOLDER, 'bubbles.060.tiff'))
+print match(refs, img) # bubbles.000