Added sections to gallery of examples

Modified travis_script.sh to account for the new structure of the gallery Added README.txt files in directories of gallery examples Fixed references to gallery images in user guide pages Fixed broken links
2026-07-11 19:19:59 +08:00 · 2015-12-13 20:40:02 +01:00
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+"""
+=====================
+GLCM Texture Features
+=====================
+
+This example illustrates texture classification using texture
+classification using grey level co-occurrence matrices (GLCMs).
+A GLCM is a histogram of co-occurring 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 matplotlib.pyplot as plt
+
+from skimage.feature import greycomatrix, greycoprops
+from skimage import data
+
+
+PATCH_SIZE = 21
+
+# open the camera image
+image = data.camera()
+
+# select some patches from grassy areas of the image
+grass_locations = [(474, 291), (440, 433), (466, 18), (462, 236)]
+grass_patches = []
+for loc in grass_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
+sky_locations = [(54, 48), (21, 233), (90, 380), (195, 330)]
+sky_patches = []
+for loc in sky_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 patch in (grass_patches + sky_patches):
+    glcm = greycomatrix(patch, [5], [0], 256, symmetric=True, normed=True)
+    xs.append(greycoprops(glcm, 'dissimilarity')[0, 0])
+    ys.append(greycoprops(glcm, 'correlation')[0, 0])
+
+# create the figure
+fig = plt.figure(figsize=(8, 8))
+
+# display original image with locations of patches
+ax = fig.add_subplot(3, 2, 1)
+ax.imshow(image, cmap=plt.cm.gray, interpolation='nearest',
+          vmin=0, vmax=255)
+for (y, x) in grass_locations:
+    ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
+for (y, x) in sky_locations:
+    ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
+ax.set_xlabel('Original Image')
+ax.set_xticks([])
+ax.set_yticks([])
+ax.axis('image')
+
+# for each patch, plot (dissimilarity, correlation)
+ax = fig.add_subplot(3, 2, 2)
+ax.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go',
+        label='Grass')
+ax.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo',
+        label='Sky')
+ax.set_xlabel('GLCM Dissimilarity')
+ax.set_ylabel('GLVM Correlation')
+ax.legend()
+
+# display the image patches
+for i, patch in enumerate(grass_patches):
+    ax = fig.add_subplot(3, len(grass_patches), len(grass_patches)*1 + i + 1)
+    ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
+              vmin=0, vmax=255)
+    ax.set_xlabel('Grass %d' % (i + 1))
+
+for i, patch in enumerate(sky_patches):
+    ax = fig.add_subplot(3, len(sky_patches), len(sky_patches)*2 + i + 1)
+    ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
+              vmin=0, vmax=255)
+    ax.set_xlabel('Sky %d' % (i + 1))
+
+
+# display the patches and plot
+fig.suptitle('Grey level co-occurrence matrix features', fontsize=14)
+plt.show()