75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
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"""
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=========================================================================
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A demo of structured Ward hierarchical clustering on a raccoon face image
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=========================================================================
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Compute the segmentation of a 2D image with Ward hierarchical
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clustering. The clustering is spatially constrained in order
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for each segmented region to be in one piece.
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"""
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# Author : Vincent Michel, 2010
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# Alexandre Gramfort, 2011
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# License: BSD 3 clause
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print(__doc__)
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import time as time
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import numpy as np
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import scipy as sp
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import matplotlib.pyplot as plt
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from sklearn.feature_extraction.image import grid_to_graph
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from sklearn.cluster import AgglomerativeClustering
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from sklearn.utils.testing import SkipTest
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from sklearn.utils.fixes import sp_version
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if sp_version < (0, 12):
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raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
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"thus does not include the scipy.misc.face() image.")
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###############################################################################
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# Generate data
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try:
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face = sp.face(gray=True)
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except AttributeError:
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# Newer versions of scipy have face in misc
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from scipy import misc
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face = misc.face(gray=True)
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# Resize it to 10% of the original size to speed up the processing
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face = sp.misc.imresize(face, 0.10) / 255.
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X = np.reshape(face, (-1, 1))
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###############################################################################
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# Define the structure A of the data. Pixels connected to their neighbors.
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connectivity = grid_to_graph(*face.shape)
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###############################################################################
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# Compute clustering
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print("Compute structured hierarchical clustering...")
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st = time.time()
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n_clusters = 15 # number of regions
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ward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',
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connectivity=connectivity)
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ward.fit(X)
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label = np.reshape(ward.labels_, face.shape)
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print("Elapsed time: ", time.time() - st)
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print("Number of pixels: ", label.size)
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print("Number of clusters: ", np.unique(label).size)
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###############################################################################
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# Plot the results on an image
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plt.figure(figsize=(5, 5))
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plt.imshow(face, cmap=plt.cm.gray)
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for l in range(n_clusters):
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plt.contour(label == l, contours=1,
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colors=[plt.cm.spectral(l / float(n_clusters)), ])
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plt.xticks(())
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plt.yticks(())
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plt.show()
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