mirror of
https://github.com/wassname/scikit-image.git
synced 2026-06-28 03:34:59 +08:00
Stefan van der Walt
96 lines
3.2 KiB
Python
96 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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from skimage.feature import greycomatrix, greycoprops
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from skimage import data
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import matplotlib.pyplot as plt
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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 i, patch in enumerate(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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plt.figure(figsize=(8, 8))
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# display the image patches
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for i, patch in enumerate(grass_patches):
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plt.subplot(3, len(grass_patches), len(grass_patches) * 1 + i + 1)
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plt.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
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vmin=0, vmax=255)
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plt.xlabel('Grass %d' % (i + 1))
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for i, patch in enumerate(sky_patches):
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plt.subplot(3, len(grass_patches), len(grass_patches) * 2 + i + 1)
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plt.imshow(patch, cmap=plt.cm.gray, interpolation='nearest',
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vmin=0, vmax=255)
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plt.xlabel('Sky %d' % (i + 1))
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# display original image with locations of patches
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plt.subplot(3, 2, 1)
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plt.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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plt.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
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for (y, x) in sky_locations:
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plt.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
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plt.xlabel('Original Image')
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plt.xticks([])
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plt.yticks([])
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plt.axis('image')
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# for each patch, plot (dissimilarity, correlation)
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plt.subplot(3, 2, 2)
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plt.plot(xs[:len(grass_patches)], ys[:len(grass_patches)], 'go',
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label='Grass')
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plt.plot(xs[len(grass_patches):], ys[len(grass_patches):], 'bo',
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label='Sky')
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plt.xlabel('GLCM Dissimilarity')
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plt.ylabel('GLVM Correlation')
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plt.legend()
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# display the patches and plot
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plt.suptitle('Grey level co-occurrence matrix features', fontsize=14)
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plt.show()
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