.. _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.




.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_001.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_002.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_003.png
            :scale: 47

    *

      .. image:: /auto_examples/cluster/images/sphx_glr_plot_face_compress_004.png
            :scale: 47





.. 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
    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.")

    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)

    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 3.968 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

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