mirror of
https://github.com/wassname/scikit-image.git
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emmanuelle
55f5103dd8
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
98 lines
3.2 KiB
Python
98 lines
3.2 KiB
Python
"""
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=====================
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GLCM Texture Features
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=====================
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This example illustrates texture classification using texture
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classification using grey level co-occurrence matrices (GLCMs).
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A GLCM is a histogram of co-occurring greyscale values at a given
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offset over an image.
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In this example, samples of two different textures are extracted from
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an image: grassy areas and sky areas. For each patch, a GLCM with
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a horizontal offset of 5 is computed. Next, two features of the
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GLCM matrices are computed: dissimilarity and correlation. These are
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plotted to illustrate that the classes form clusters in feature space.
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In a typical classification problem, the final step (not included in
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this example) would be to train a classifier, such as logistic
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regression, to label image patches from new images.
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"""
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import matplotlib.pyplot as plt
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from skimage.feature import greycomatrix, greycoprops
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from skimage import data
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PATCH_SIZE = 21
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# open the camera image
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image = data.camera()
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# select some patches from grassy areas of the image
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grass_locations = [(474, 291), (440, 433), (466, 18), (462, 236)]
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grass_patches = []
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for loc in grass_locations:
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grass_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
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loc[1]:loc[1] + PATCH_SIZE])
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# select some patches from sky areas of the image
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sky_locations = [(54, 48), (21, 233), (90, 380), (195, 330)]
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sky_patches = []
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for loc in sky_locations:
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sky_patches.append(image[loc[0]:loc[0] + PATCH_SIZE,
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loc[1]:loc[1] + PATCH_SIZE])
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# compute some GLCM properties each patch
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xs = []
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ys = []
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for patch in (grass_patches + sky_patches):
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glcm = greycomatrix(patch, [5], [0], 256, symmetric=True, normed=True)
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xs.append(greycoprops(glcm, 'dissimilarity')[0, 0])
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ys.append(greycoprops(glcm, 'correlation')[0, 0])
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# create the figure
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fig = plt.figure(figsize=(8, 8))
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# display original image with locations of patches
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ax = fig.add_subplot(3, 2, 1)
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ax.imshow(image, cmap=plt.cm.gray, interpolation='nearest',
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vmin=0, vmax=255)
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for (y, x) in grass_locations:
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ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
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for (y, x) in sky_locations:
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ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
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ax.set_xlabel('Original Image')
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ax.set_xticks([])
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ax.set_yticks([])
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ax.axis('image')
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# for each patch, plot (dissimilarity, correlation)
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ax = fig.add_subplot(3, 2, 2)
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ax.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go',
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label='Grass')
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ax.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo',
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label='Sky')
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ax.set_xlabel('GLCM Dissimilarity')
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ax.set_ylabel('GLVM Correlation')
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ax.legend()
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# display the image patches
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for i, patch in enumerate(grass_patches):
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ax = fig.add_subplot(3, len(grass_patches), len(grass_patches)*1 + i + 1)
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ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
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vmin=0, vmax=255)
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ax.set_xlabel('Grass %d' % (i + 1))
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for i, patch in enumerate(sky_patches):
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ax = fig.add_subplot(3, len(sky_patches), len(sky_patches)*2 + i + 1)
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ax.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
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vmin=0, vmax=255)
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ax.set_xlabel('Sky %d' % (i + 1))
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# display the patches and plot
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fig.suptitle('Grey level co-occurrence matrix features', fontsize=14)
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plt.show()
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