.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/cluster/plot_coin_ward_segmentation.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_cluster_plot_coin_ward_segmentation.py: ====================================================================== A demo of structured Ward hierarchical clustering on an image of coins ====================================================================== 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. .. GENERATED FROM PYTHON SOURCE LINES 10-78 .. image:: /auto_examples/cluster/images/sphx_glr_plot_coin_ward_segmentation_001.png :alt: plot coin ward segmentation :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Compute structured hierarchical clustering... Elapsed time: 0.2314894199371338 Number of pixels: 4697 Number of clusters: 27 | .. code-block:: default # 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 import skimage from skimage.data import coins from skimage.transform import rescale from sklearn.feature_extraction.image import grid_to_graph from sklearn.cluster import AgglomerativeClustering from sklearn.utils.fixes import parse_version # these were introduced in skimage-0.14 if parse_version(skimage.__version__) >= parse_version('0.14'): rescale_params = {'anti_aliasing': False, 'multichannel': False} else: rescale_params = {} # ############################################################################# # 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", **rescale_params) 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.599 seconds) .. _sphx_glr_download_auto_examples_cluster_plot_coin_ward_segmentation.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/cluster/plot_coin_ward_segmentation.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_coin_ward_segmentation.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_coin_ward_segmentation.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_