ENH: Added a gallery example of GLCM textures

doc/examples/plot_glcm.py

@@ -0,0 +1,87 @@
+"""
+=====================
+GLCM Texture Features
+=====================
+
+This module provides an example of texture classification using grey
+level co-occurance matrices (GLCMs). A GLCM is a histogram of 
+co-occuring greyscale values at a given offset over an image. 
+
+In this example, samples of two different textures are extracted from
+an image: grassy areas and sky areas. For each patch, a GLCM with 
+a horizontal offset of 5 is computed. Next, two features of the
+GLCM matrices are computed: dissimilarity and correlation. These are
+plotted to illustrate that the classes form clusters in feature space.
+
+In a typical classification problem, the final step (not included in 
+this example) would be to train a classifier, such as logistic 
+regression, to label image patches from new images. 
+
+"""
+
+import os
+from skimage.feature import compute_glcm, compute_glcm_prop
+from skimage.io import imread
+from skimage import data_dir
+import matplotlib.pyplot as plt
+
+PATCH_SIZE = 21
+
+# open the camera image
+image = imread(os.path.join(data_dir, 'camera.png'))
+if False:
+    plt.figure()
+    plt.imshow(image)
+    plt.show()
+    import sys
+    sys.exit()
+
+# select some patches from grassy areas of the image
+locations = [(474, 291), (440, 433), (466, 18), (462, 236)]
+grass_patches = []
+for loc in locations:
+    grass_patches.append(image[loc[0]:loc[0] + PATCH_SIZE, 
+                               loc[1]:loc[1] + PATCH_SIZE])
+
+# select some patches from sky areas of the image
+locations = [(54, 48), (21, 233), (90, 380), (195, 330)]
+sky_patches = []
+for loc in locations:
+    sky_patches.append(image[loc[0]:loc[0] + PATCH_SIZE, 
+                             loc[1]:loc[1] + PATCH_SIZE])
+
+# compute some GLCM properties each patch
+xs = []
+ys = []
+for i, patch in enumerate(grass_patches + sky_patches):
+    glcm = compute_glcm(patch, [5], [0], 256, symmetric=True, normed=True)
+    xs.append(compute_glcm_prop(glcm, 'dissimilarity')[0, 0])
+    ys.append(compute_glcm_prop(glcm, 'correlation')[0, 0])
+
+# display the image patches
+plt.figure(figsize=(8, 8))
+for i, patch in enumerate(grass_patches):
+    plt.subplot(3, len(grass_patches), i+1)
+    plt.imshow(patch, cmap=plt.cm.gray, interpolation='nearest', 
+               vmin=0, vmax=255)
+    plt.xlabel('Grass %d'%(i + 1))
+    
+for i, patch in enumerate(sky_patches):
+    plt.subplot(3, len(grass_patches), i+len(grass_patches)+1)
+    plt.imshow(patch, cmap=plt.cm.gray, interpolation='nearest', 
+               vmin=0, vmax=255)    
+    plt.xlabel('Sky %d'%(i + 1))
+
+# for each patch, plot (dissimilarity, correlation)
+plt.subplot(3, 1, 3)
+plt.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go', 
+         label='Grass')
+plt.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo', 
+         label='Sky')
+plt.xlabel('GLCM Dissimilarity')
+plt.ylabel('GLVM Correlation')
+plt.legend()
+
+# display the patches and plot
+plt.suptitle('Grey level co-occurance matrix features', fontsize=14)
+plt.show()