.. _sphx_glr_auto_examples_cluster_plot_face_ward_segmentation.py: ========================================================================= A demo of structured Ward hierarchical clustering on a raccoon face image ========================================================================= Compute the segmentation of a 2D image with Ward hierarchical clustering. The clustering is spatially constrained in order for each segmented region to be in one piece. .. image:: /auto_examples/cluster/images/sphx_glr_plot_face_ward_segmentation_001.png :align: center .. rst-class:: sphx-glr-script-out Out:: Compute structured hierarchical clustering... Elapsed time: 0.6450910568237305 Number of pixels: 7752 Number of clusters: 15 | .. code-block:: python # Author : Vincent Michel, 2010 # Alexandre Gramfort, 2011 # License: BSD 3 clause print(__doc__) import time as time import numpy as np import scipy as sp import matplotlib.pyplot as plt from sklearn.feature_extraction.image import grid_to_graph from sklearn.cluster import AgglomerativeClustering # ############################################################################# # Generate data try: # SciPy >= 0.16 have face in misc from scipy.misc import face face = face(gray=True) except ImportError: face = sp.face(gray=True) # Resize it to 10% of the original size to speed up the processing face = sp.misc.imresize(face, 0.10) / 255. X = np.reshape(face, (-1, 1)) # ############################################################################# # Define the structure A of the data. Pixels connected to their neighbors. connectivity = grid_to_graph(*face.shape) # ############################################################################# # Compute clustering print("Compute structured hierarchical clustering...") st = time.time() n_clusters = 15 # number of regions ward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward', connectivity=connectivity) ward.fit(X) label = np.reshape(ward.labels_, face.shape) print("Elapsed time: ", time.time() - st) print("Number of pixels: ", label.size) print("Number of clusters: ", np.unique(label).size) # ############################################################################# # Plot the results on an image plt.figure(figsize=(5, 5)) plt.imshow(face, cmap=plt.cm.gray) for l in range(n_clusters): plt.contour(label == l, contours=1, colors=[plt.cm.nipy_spectral(l / float(n_clusters)), ]) plt.xticks(()) plt.yticks(()) plt.show() **Total running time of the script:** ( 0 minutes 0.958 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_face_ward_segmentation.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_face_ward_segmentation.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_