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

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

.. _sphx_glr_auto_examples_cluster_plot_face_compress.py:


=========================================================
Vector Quantization Example
=========================================================

Face, a 1024 x 768 size image of a raccoon face,
is used here to illustrate how `k`-means is
used for vector quantization.




.. code-block:: python

    print(__doc__)


    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

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

    from sklearn import cluster


    try:  # SciPy >= 0.16 have face in misc
        from scipy.misc import face
        face = face(gray=True)
    except ImportError:
        face = sp.face(gray=True)

    n_clusters = 5
    np.random.seed(0)

    X = face.reshape((-1, 1))  # We need an (n_sample, n_feature) array
    k_means = cluster.KMeans(n_clusters=n_clusters, n_init=4)
    k_means.fit(X)
    values = k_means.cluster_centers_.squeeze()
    labels = k_means.labels_

    # create an array from labels and values
    face_compressed = np.choose(labels, values)
    face_compressed.shape = face.shape

    vmin = face.min()
    vmax = face.max()

    # original face
    plt.figure(1, figsize=(3, 2.2))
    plt.imshow(face, cmap=plt.cm.gray, vmin=vmin, vmax=256)

    # compressed face
    plt.figure(2, figsize=(3, 2.2))
    plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)

    # equal bins face
    regular_values = np.linspace(0, 256, n_clusters + 1)
    regular_labels = np.searchsorted(regular_values, face) - 1
    regular_values = .5 * (regular_values[1:] + regular_values[:-1])  # mean
    regular_face = np.choose(regular_labels.ravel(), regular_values, mode="clip")
    regular_face.shape = face.shape
    plt.figure(3, figsize=(3, 2.2))
    plt.imshow(regular_face, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)

    # histogram
    plt.figure(4, figsize=(3, 2.2))
    plt.clf()
    plt.axes([.01, .01, .98, .98])
    plt.hist(X, bins=256, color='.5', edgecolor='.5')
    plt.yticks(())
    plt.xticks(regular_values)
    values = np.sort(values)
    for center_1, center_2 in zip(values[:-1], values[1:]):
        plt.axvline(.5 * (center_1 + center_2), color='b')

    for center_1, center_2 in zip(regular_values[:-1], regular_values[1:]):
        plt.axvline(.5 * (center_1 + center_2), color='b', linestyle='--')

    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_face_compress.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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