Note
Click here to download the full example code
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.
Out:
Compute structured hierarchical clustering... Elapsed time: 0.3571193218231201 Number of pixels: 4697 Number of clusters: 27
# Author : Vincent Michel, 2010 # Alexandre Gramfort, 2011 # License: BSD 3 clause print(__doc__) import time as time import numpy as np from scipy.ndimage.filters import gaussian_filter import matplotlib.pyplot as plt from skimage.data import coins from skimage.transform import rescale from sklearn.feature_extraction.image import grid_to_graph from sklearn.cluster import AgglomerativeClustering # ############################################################################# # Generate data orig_coins = coins() # Resize it to 20% of the original size to speed up the processing # Applying a Gaussian filter for smoothing prior to down-scaling # reduces aliasing artifacts. smoothened_coins = gaussian_filter(orig_coins, sigma=2) rescaled_coins = rescale(smoothened_coins, 0.2, mode="reflect") X = np.reshape(rescaled_coins, (-1, 1)) # ############################################################################# # Define the structure A of the data. Pixels connected to their neighbors. connectivity = grid_to_graph(*rescaled_coins.shape) # ############################################################################# # Compute clustering print("Compute structured hierarchical clustering...") st = time.time() n_clusters = 27 # number of regions ward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward', connectivity=connectivity) ward.fit(X) label = np.reshape(ward.labels_, rescaled_coins.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(rescaled_coins, cmap=plt.cm.gray) for l in range(n_clusters): plt.contour(label == l, colors=[plt.cm.nipy_spectral(l / float(n_clusters)), ]) plt.xticks(()) plt.yticks(()) plt.show()
Total running time of the script: ( 0 minutes 0.602 seconds)
Gallery generated by Sphinx-Gallery
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html