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

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

.. _sphx_glr_advanced_image_processing_auto_examples_plot_spectral_clustering.py:


Segmentation with spectral clustering
======================================

This example uses spectral clustering to do segmentation.



.. code-block:: python


    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.feature_extraction import image
    from sklearn.cluster import spectral_clustering








.. code-block:: python

    l = 100
    x, y = np.indices((l, l))

    center1 = (28, 24)
    center2 = (40, 50)
    center3 = (67, 58)
    center4 = (24, 70)

    radius1, radius2, radius3, radius4 = 16, 14, 15, 14

    circle1 = (x - center1[0])**2 + (y - center1[1])**2 < radius1**2
    circle2 = (x - center2[0])**2 + (y - center2[1])**2 < radius2**2
    circle3 = (x - center3[0])**2 + (y - center3[1])**2 < radius3**2
    circle4 = (x - center4[0])**2 + (y - center4[1])**2 < radius4**2







4 circles



.. code-block:: python

    img = circle1 + circle2 + circle3 + circle4
    mask = img.astype(bool)
    img = img.astype(float)

    img += 1 + 0.2*np.random.randn(*img.shape)

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

    # Take a decreasing function of the gradient: we take it weakly
    # dependant from the gradient the segmentation is close to a voronoi
    graph.data = np.exp(-graph.data / graph.data.std())

    # Force the solver to be arpack, since amg is numerically
    # unstable on this example
    labels = spectral_clustering(graph, n_clusters=4)
    label_im = -np.ones(mask.shape)
    label_im[mask] = labels

    plt.figure(figsize=(6, 3))
    plt.subplot(121)
    plt.imshow(img, cmap=plt.cm.nipy_spectral, interpolation='nearest')
    plt.axis('off')
    plt.subplot(122)
    plt.imshow(label_im, cmap=plt.cm.nipy_spectral, interpolation='nearest')
    plt.axis('off')

    plt.subplots_adjust(wspace=0, hspace=0., top=0.99, bottom=0.01, left=0.01, right=0.99)
    plt.show()



.. image:: /advanced/image_processing/auto_examples/images/sphx_glr_plot_spectral_clustering_001.png
    :class: sphx-glr-single-img




**Total running time of the script:** ( 0 minutes  0.241 seconds)


.. _sphx_glr_download_advanced_image_processing_auto_examples_plot_spectral_clustering.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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