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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_segmentation_plot_ncut.py>`
        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_segmentation_plot_ncut.py:


==============
Normalized Cut
==============

This example constructs a Region Adjacency Graph (RAG) and recursively performs
a Normalized Cut on it [1]_.

References
----------
.. [1] Shi, J.; Malik, J., "Normalized cuts and image segmentation",
       Pattern Analysis and Machine Intelligence,
       IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.

.. GENERATED FROM PYTHON SOURCE LINES 15-39



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_ncut_001.png
   :alt: plot ncut
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_ncut_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from skimage import data, segmentation, color
    from skimage import graph
    from matplotlib import pyplot as plt


    img = data.coffee()

    labels1 = segmentation.slic(img, compactness=30, n_segments=400, start_label=1)
    out1 = color.label2rgb(labels1, img, kind='avg', bg_label=0)

    g = graph.rag_mean_color(img, labels1, mode='similarity')
    labels2 = graph.cut_normalized(labels1, g)
    out2 = color.label2rgb(labels2, img, kind='avg', bg_label=0)

    fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8))

    ax[0].imshow(out1)
    ax[1].imshow(out2)

    for a in ax:
        a.axis('off')

    plt.tight_layout()


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

   **Total running time of the script:** (0 minutes 1.036 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_ncut.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-image/scikit-image/v0.24.0?filepath=notebooks/auto_examples/segmentation/plot_ncut.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. only:: html

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

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