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

    Click :ref:`here <sphx_glr_download_auto_examples_cluster_plot_coin_segmentation.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_cluster_plot_coin_segmentation.py:


================================================
Segmenting the picture of greek coins 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.


.. code-block:: default

    print(__doc__)

    # Author: Gael Varoquaux <gael.varoquaux@normalesup.org>, Brian Cheung
    # License: BSD 3 clause

    import 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 import image
    from sklearn.cluster import spectral_clustering


    # load the coins as a numpy array
    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")

    # Convert the image into a graph with the value of the gradient on the
    # edges.
    graph = image.img_to_graph(rescaled_coins)

    # 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 = 10
    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





.. 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 '



Visualize the resulting regions


.. code-block:: default


    for assign_labels in ('kmeans', 'discretize'):
        t0 = time.time()
        labels = spectral_clustering(graph, n_clusters=N_REGIONS,
                                     assign_labels=assign_labels, random_state=42)
        t1 = time.time()
        labels = labels.reshape(rescaled_coins.shape)

        plt.figure(figsize=(5, 5))
        plt.imshow(rescaled_coins, cmap=plt.cm.gray)
        for l in range(N_REGIONS):
            plt.contour(labels == l,
                        colors=[plt.cm.nipy_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()



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_001.png
            :class: sphx-glr-multi-img

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_coin_segmentation_002.png
            :class: sphx-glr-multi-img


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

 Out:

 .. code-block:: none

    Spectral clustering: kmeans, 5.67s
    Spectral clustering: discretize, 7.80s




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

   **Total running time of the script:** ( 0 minutes  14.510 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_coin_segmentation.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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