.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "advanced/image_processing/auto_examples/plot_spectral_clustering.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <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.

.. GENERATED FROM PYTHON SOURCE LINES 7-14

.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

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








.. GENERATED FROM PYTHON SOURCE LINES 15-30

.. 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








.. GENERATED FROM PYTHON SOURCE LINES 31-32

4 circles

.. GENERATED FROM PYTHON SOURCE LINES 32-63

.. code-block:: Python

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

    rng = np.random.default_rng(27446968)
    img += 1 + 0.2 * rng.normal(size=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
    # dependent 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="nipy_spectral", interpolation="nearest")
    plt.axis("off")
    plt.subplot(122)
    plt.imshow(label_im, cmap="nipy_spectral", interpolation="nearest")
    plt.axis("off")

    plt.subplots_adjust(wspace=0, hspace=0.0, top=0.99, bottom=0.01, left=0.01, right=0.99)
    plt.show()



.. image-sg:: /advanced/image_processing/auto_examples/images/sphx_glr_plot_spectral_clustering_001.png
   :alt: plot spectral clustering
   :srcset: /advanced/image_processing/auto_examples/images/sphx_glr_plot_spectral_clustering_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.244 seconds)


.. _sphx_glr_download_advanced_image_processing_auto_examples_plot_spectral_clustering.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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

    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_spectral_clustering.zip <plot_spectral_clustering.zip>`


.. only:: html

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

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