57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
"""
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=========================================
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Segmenting the picture of Lena in regions
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=========================================
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This example uses :ref:`spectral_clustering` on a graph created from
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voxel-to-voxel difference on an image to break this image into multiple
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partly-homogenous regions.
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This procedure (spectral clustering on an image) is an efficient
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approximate solution for finding normalized graph cuts.
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"""
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print __doc__
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD
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import numpy as np
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import scipy as sp
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import pylab as pl
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from sklearn.feature_extraction import image
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from sklearn.cluster import spectral_clustering
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lena = sp.misc.lena()
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# Downsample the image by a factor of 4
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lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
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lena = lena[::2, ::2] + lena[1::2, ::2] + lena[::2, 1::2] + lena[1::2, 1::2]
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# Convert the image into a graph with the value of the gradient on the
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# edges.
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graph = image.img_to_graph(lena)
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# Take a decreasing function of the gradient: an exponential
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# The smaller beta is, the more independant the segmentation is of the
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# actual image. For beta=1, the segmentation is close to a voronoi
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beta = 5
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eps = 1e-6
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graph.data = np.exp(-beta * graph.data / lena.std()) + eps
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# Apply spectral clustering (this step goes much faster if you have pyamg
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# installed)
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N_REGIONS = 11
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labels = spectral_clustering(graph, k=N_REGIONS)
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labels = labels.reshape(lena.shape)
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###############################################################################
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# Visualize the resulting regions
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pl.figure(figsize=(5, 5))
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pl.imshow(lena, cmap=pl.cm.gray)
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for l in range(N_REGIONS):
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pl.contour(labels == l, contours=1,
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colors=[pl.cm.spectral(l / float(N_REGIONS)), ])
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pl.xticks(())
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pl.yticks(())
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pl.show()
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