.. _example_plot_local_binary_pattern.py:


===============================================
Local Binary Pattern for texture classification
===============================================

In this example, we will see how to classify textures based on LBP (Local
Binary Pattern). LBP looks at points surrounding a central point and tests
whether the surrounding points are greater than or less than the central point
(i.e. gives a binary result).

Before trying out LBP on an image, it helps to look at a schematic of LBPs.
The below code is just used to plot the schematic.


.. code-block:: python

	from __future__ import print_function
	import numpy as np
	import matplotlib.pyplot as plt
	
	
	METHOD = 'uniform'
	plt.rcParams['font.size'] = 9
	
	
	def plot_circle(ax, center, radius, color):
	    circle = plt.Circle(center, radius, facecolor=color, edgecolor='0.5')
	    ax.add_patch(circle)
	
	
	def plot_lbp_model(ax, binary_values):
	    """Draw the schematic for a local binary pattern."""
	    # Geometry spec
	    theta = np.deg2rad(45)
	    R = 1
	    r = 0.15
	    w = 1.5
	    gray = '0.5'
	
	    # Draw the central pixel.
	    plot_circle(ax, (0, 0), radius=r, color=gray)
	    # Draw the surrounding pixels.
	    for i, facecolor in enumerate(binary_values):
	        x = R * np.cos(i * theta)
	        y = R * np.sin(i * theta)
	        plot_circle(ax, (x, y), radius=r, color=str(facecolor))
	
	    # Draw the pixel grid.
	    for x in np.linspace(-w, w, 4):
	        ax.axvline(x, color=gray)
	        ax.axhline(x, color=gray)
	
	    # Tweak the layout.
	    ax.axis('image')
	    ax.axis('off')
	    size = w + 0.2
	    ax.set_xlim(-size, size)
	    ax.set_ylim(-size, size)
	
	
	fig, axes = plt.subplots(ncols=5, figsize=(7, 2))
	
	titles = ['flat', 'flat', 'edge', 'corner', 'non-uniform']
	
	binary_patterns = [np.zeros(8),
	                   np.ones(8),
	                   np.hstack([np.ones(4), np.zeros(4)]),
	                   np.hstack([np.zeros(3), np.ones(5)]),
	                   [1, 0, 0, 1, 1, 1, 0, 0]]
	
	for ax, values, name in zip(axes, binary_patterns, titles):
	    plot_lbp_model(ax, values)
	    ax.set_title(name)
	
	

.. image:: images/plot_local_binary_pattern_1.png

The figure above shows example results with black (or white) representing
pixels that are less (or more) intense than the central pixel. When surrounding
pixels are all black or all white, then that image region is flat (i.e.
featureless). Groups of continuous black or white pixels are considered
"uniform" patterns that can be interpreted as corners or edges. If pixels
switch back-and-forth between black and white pixels, the pattern is considered
"non-uniform".

When using LBP to detect texture, you measure a collection of LBPs over an
image patch and look at the distribution of these LBPs. Lets apply LBP to
a brick texture.


.. code-block:: python

	
	from skimage.transform import rotate
	from skimage.feature import local_binary_pattern
	from skimage import data
	from skimage.color import label2rgb
	
	# settings for LBP
	radius = 3
	n_points = 8 * radius
	
	
	def overlay_labels(image, lbp, labels):
	    mask = np.logical_or.reduce([lbp == each for each in labels])
	    return label2rgb(mask, image=image, bg_label=0, alpha=0.5)
	
	
	def highlight_bars(bars, indexes):
	    for i in indexes:
	        bars[i].set_facecolor('r')
	
	
	image = data.load('brick.png')
	lbp = local_binary_pattern(image, n_points, radius, METHOD)
	
	def hist(ax, lbp):
	    n_bins = lbp.max() + 1
	    return ax.hist(lbp.ravel(), normed=True, bins=n_bins, range=(0, n_bins),
	                   facecolor='0.5')
	
	# plot histograms of LBP of textures
	fig, (ax_img, ax_hist) = plt.subplots(nrows=2, ncols=3, figsize=(9, 6))
	plt.gray()
	
	titles = ('edge', 'flat', 'corner')
	w = width = radius - 1
	edge_labels = range(n_points // 2 - w, n_points // 2 + w + 1)
	flat_labels = list(range(0, w + 1)) + list(range(n_points - w, n_points + 2))
	i_14 = n_points // 4            # 1/4th of the histogram
	i_34 = 3 * (n_points // 4)      # 3/4th of the histogram
	corner_labels = (list(range(i_14 - w, i_14 + w + 1)) +
	                 list(range(i_34 - w, i_34 + w + 1)))
	
	label_sets = (edge_labels, flat_labels, corner_labels)
	
	for ax, labels in zip(ax_img, label_sets):
	    ax.imshow(overlay_labels(image, lbp, labels))
	
	for ax, labels, name in zip(ax_hist, label_sets, titles):
	    counts, _, bars = hist(ax, lbp)
	    highlight_bars(bars, labels)
	    ax.set_ylim(ymax=np.max(counts[:-1]))
	    ax.set_xlim(xmax=n_points + 2)
	    ax.set_title(name)
	
	ax_hist[0].set_ylabel('Percentage')
	for ax in ax_img:
	    ax.axis('off')
	
	
	

.. image:: images/plot_local_binary_pattern_2.png

The above plot highlights flat, edge-like, and corner-like regions of the
image.

The histogram of the LBP result is a good measure to classify textures. Here,
we test the histogram distributions against each other using the
Kullback-Leibler-Divergence.


.. code-block:: python

	
	# settings for LBP
	radius = 2
	n_points = 8 * radius
	
	
	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 match(refs, img):
	    best_score = 10
	    best_name = None
	    lbp = local_binary_pattern(img, n_points, radius, METHOD)
	    n_bins = lbp.max() + 1
	    hist, _ = np.histogram(lbp, normed=True, bins=n_bins, range=(0, n_bins))
	    for name, ref in refs.items():
	        ref_hist, _ = np.histogram(ref, normed=True, bins=n_bins,
	                                   range=(0, n_bins))
	        score = kullback_leibler_divergence(hist, ref_hist)
	        if score < best_score:
	            best_score = score
	            best_name = name
	    return best_name
	
	
	brick = data.load('brick.png')
	grass = data.load('grass.png')
	wall = data.load('rough-wall.png')
	
	refs = {
	    'brick': local_binary_pattern(brick, n_points, radius, METHOD),
	    'grass': local_binary_pattern(grass, n_points, radius, METHOD),
	    'wall': local_binary_pattern(wall, n_points, radius, METHOD)
	}
	
	# classify rotated textures
	print('Rotated images matched against references using LBP:')
	print('original: brick, rotated: 30deg, match result: ',
	      match(refs, rotate(brick, angle=30, resize=False)))
	print('original: brick, rotated: 70deg, match result: ',
	      match(refs, rotate(brick, angle=70, resize=False)))
	print('original: grass, rotated: 145deg, match result: ',
	      match(refs, rotate(grass, angle=145, resize=False)))
	
	# plot histograms of LBP of textures
	fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(nrows=2, ncols=3,
	                                                       figsize=(9, 6))
	plt.gray()
	
	ax1.imshow(brick)
	ax1.axis('off')
	hist(ax4, refs['brick'])
	ax4.set_ylabel('Percentage')
	
	ax2.imshow(grass)
	ax2.axis('off')
	hist(ax5, refs['grass'])
	ax5.set_xlabel('Uniform LBP values')
	
	ax3.imshow(wall)
	ax3.axis('off')
	hist(ax6, refs['wall'])
	
	

.. image:: images/plot_local_binary_pattern_3.png


.. code-block:: python

	
	plt.show()
	

**Python source code:** :download:`download <plot_local_binary_pattern.py>`
(generated using ``skimage`` |version|)



**IPython Notebook:** :download:`download <./notebook/plot_local_binary_pattern.ipynb>`
(generated using ``skimage`` |version|)

