.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_coin_ward_segmentation.py>` to download the full example code
.. 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.



.. image:: /auto_examples/cluster/images/sphx_glr_plot_coin_ward_segmentation_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    /home/circleci/miniconda/envs/testenv/lib/python3.6/site-packages/skimage/transform/_warps.py:23: UserWarning: The default multichannel argument (None) is deprecated.  Please specify either True or False explicitly.  multichannel will default to False starting with release 0.16.
      warn('The default multichannel argument (None) is deprecated.  Please '
    Compute structured hierarchical clustering...
    Elapsed time:  0.21477413177490234
    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

    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()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.563 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_coin_ward_segmentation.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_coin_ward_segmentation.py <plot_coin_ward_segmentation.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_coin_ward_segmentation.ipynb <plot_coin_ward_segmentation.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_