File Content: plot_face_segmentation.py """ =================================================== Segmenting the picture of a raccoon face in regions =================================================== This example uses :ref:`spectral_clustering` on a graph created from voxel-to-voxel difference on an image to break this image into multiple partly-homogeneous regions. This procedure (spectral clustering on an image) is an efficient approximate solution for finding normalized graph cuts. There are two options to assign labels: * with 'kmeans' spectral clustering will cluster samples in the embedding space using a kmeans algorithm * whereas 'discrete' will iteratively search for the closest partition space to the embedding space. """ print(__doc__) # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>, Brian Cheung # License: BSD 3 clause import time import numpy as np import scipy as sp import matplotlib.pyplot as plt from sklearn.feature_extraction import image from sklearn.cluster import spectral_clustering from sklearn.utils.testing import SkipTest from sklearn.utils.fixes import sp_version if sp_version < (0, 12): raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and " "thus does not include the scipy.misc.face() image.") # load the raccoon face as a numpy array try: face = sp.face(gray=True) except AttributeError: # Newer versions of scipy have face in misc from scipy import misc face = misc.face(gray=True) # Resize it to 10% of the original size to speed up the processing face = sp.misc.imresize(face, 0.10) / 255. # Convert the image into a graph with the value of the gradient on the # edges. graph = image.img_to_graph(face) # Take a decreasing function of the gradient: an exponential # The smaller beta is, the more independent the segmentation is of the # actual image. For beta=1, the segmentation is close to a voronoi beta = 5 eps = 1e-6 graph.data = np.exp(-beta * graph.data / graph.data.std()) + eps # Apply spectral clustering (this step goes much faster if you have pyamg # installed) N_REGIONS = 25 ############################################################################# # Visualize the resulting regions for assign_labels in ('kmeans', 'discretize'): t0 = time.time() labels = spectral_clustering(graph, n_clusters=N_REGIONS, assign_labels=assign_labels, random_state=1) t1 = time.time() labels = labels.reshape(face.shape) plt.figure(figsize=(5, 5)) plt.imshow(face, cmap=plt.cm.gray) for l in range(N_REGIONS): plt.contour(labels == l, contours=1, colors=[plt.cm.spectral(l / float(N_REGIONS))]) plt.xticks(()) plt.yticks(()) title = 'Spectral clustering: %s, %.2fs' % (assign_labels, (t1 - t0)) print(title) plt.title(title) plt.show() Submit