81 lines
2.5 KiB
Python
81 lines
2.5 KiB
Python
"""
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===================================================
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Segmenting the picture of a raccoon face 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-homogeneous 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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There are two options to assign labels:
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* with 'kmeans' spectral clustering will cluster samples in the embedding space
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using a kmeans algorithm
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* whereas 'discrete' will iteratively search for the closest partition
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space to the embedding space.
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"""
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print(__doc__)
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>, Brian Cheung
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# License: BSD 3 clause
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import 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 import image
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from sklearn.cluster import spectral_clustering
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# load the raccoon face as a numpy array
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try: # SciPy >= 0.16 have face in misc
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from scipy.misc import face
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face = face(gray=True)
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except ImportError:
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face = sp.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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# 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(face)
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# Take a decreasing function of the gradient: an exponential
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# The smaller beta is, the more independent 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 / graph.data.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 = 25
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#############################################################################
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# Visualize the resulting regions
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for assign_labels in ('kmeans', 'discretize'):
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t0 = time.time()
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labels = spectral_clustering(graph, n_clusters=N_REGIONS,
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assign_labels=assign_labels, random_state=1)
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t1 = time.time()
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labels = labels.reshape(face.shape)
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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_REGIONS):
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plt.contour(labels == l, contours=1,
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colors=[plt.cm.spectral(l / float(N_REGIONS))])
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plt.xticks(())
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plt.yticks(())
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title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0))
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print(title)
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plt.title(title)
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plt.show()
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