.. _sphx_glr_auto_examples_cluster_plot_face_segmentation.py:


===================================================
Segmenting the picture of a raccoon face 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:: python

    print(__doc__)

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

    import time

    import numpy as np
    import scipy as sp
    import matplotlib.pyplot as plt

    from sklearn.feature_extraction import image
    from sklearn.cluster import spectral_clustering
    from sklearn.utils.testing import SkipTest
    from sklearn.utils.fixes import sp_version

    if sp_version < (0, 12):
        raise SkipTest("Skipping because SciPy version earlier than 0.12.0 and "
                       "thus does not include the scipy.misc.face() image.")


    # load the raccoon face as a numpy array
    try:
        face = sp.face(gray=True)
    except AttributeError:
        # Newer versions of scipy have face in misc
        from scipy import misc
        face = misc.face(gray=True)

    # Resize it to 10% of the original size to speed up the processing
    face = sp.misc.imresize(face, 0.10) / 255.

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

    # 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 = 5
    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







Visualize the resulting regions


.. code-block:: python


    for assign_labels in ('kmeans', 'discretize'):
        t0 = time.time()
        labels = spectral_clustering(graph, n_clusters=N_REGIONS,
                                     assign_labels=assign_labels, random_state=1)
        t1 = time.time()
        labels = labels.reshape(face.shape)

        plt.figure(figsize=(5, 5))
        plt.imshow(face, cmap=plt.cm.gray)
        for l in range(N_REGIONS):
            plt.contour(labels == l, contours=1,
                        colors=[plt.cm.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_face_segmentation_001.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_face_segmentation_002.png
            :scale: 47


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

 Out::

      Spectral clustering: kmeans, 6.28s
    Spectral clustering: discretize, 5.39s


**Total running time of the script:**
(0 minutes 12.710 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_face_segmentation.py <plot_face_segmentation.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_face_segmentation.ipynb <plot_face_segmentation.ipynb>`