.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/features_detection/plot_gabors_from_astronaut.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_features_detection_plot_gabors_from_astronaut.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_features_detection_plot_gabors_from_astronaut.py:


============================================================
Gabors / Primary Visual Cortex "Simple Cells" from an Image
============================================================

How to build a (bio-plausible) *sparse* dictionary (or 'codebook', or
'filterbank') for e.g. image classification without any fancy math and
with just standard python scientific libraries?

Please find below a short answer ;-)

This simple example shows how to get Gabor-like filters [1]_ using just
a simple image. In our example, we use a photograph of the astronaut Eileen
Collins. Gabor filters are good approximations of the "Simple Cells" [2]_
receptive fields [3]_ found in the mammalian primary visual cortex (V1)
(for details, see e.g. the Nobel-prize winning work of Hubel & Wiesel done
in the 60s [4]_ [5]_).

Here we use McQueen's 'kmeans' algorithm [6]_, as a simple biologically
plausible hebbian-like learning rule and we apply it (a) to patches of
the original image (retinal projection), and (b) to patches of an
LGN-like [7]_ image using a simple difference of gaussians (DoG)
approximation.

Enjoy ;-) And keep in mind that getting Gabors on natural image patches
is not rocket science.

.. [1] https://en.wikipedia.org/wiki/Gabor_filter
.. [2] https://en.wikipedia.org/wiki/Simple_cell
.. [3] https://en.wikipedia.org/wiki/Receptive_field
.. [4] D. H. Hubel and T. N., Wiesel Receptive Fields of Single Neurones
       in the Cat's Striate Cortex, J. Physiol. pp. 574-591 (148) 1959
.. [5] D. H. Hubel and T. N., Wiesel Receptive Fields, Binocular
       Interaction, and Functional Architecture in the Cat's Visual Cortex,
       J. Physiol. 160 pp.  106-154 1962
.. [6] https://en.wikipedia.org/wiki/K-means_clustering
.. [7] https://en.wikipedia.org/wiki/Lateral_geniculate_nucleus

.. GENERATED FROM PYTHON SOURCE LINES 39-90



.. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_gabors_from_astronaut_001.png
   :alt: Image (original), K-means filterbank (codebook) on original image, Image (LGN-like DoG), K-means filterbank (codebook) on LGN-like DoG image
   :srcset: /auto_examples/features_detection/images/sphx_glr_plot_gabors_from_astronaut_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/runner/work/scikit-image/scikit-image/doc/examples/features_detection/plot_gabors_from_astronaut.py:57: UserWarning:

    One of the clusters is empty. Re-run kmeans with a different initialization.







|

.. code-block:: Python


    from scipy.cluster.vq import kmeans2
    from scipy import ndimage as ndi
    import matplotlib.pyplot as plt

    from skimage import data
    from skimage import color
    from skimage.util.shape import view_as_windows
    from skimage.util import montage

    patch_shape = 8, 8
    n_filters = 49

    astro = color.rgb2gray(data.astronaut())

    # -- filterbank1 on original image
    patches1 = view_as_windows(astro, patch_shape)
    patches1 = patches1.reshape(-1, patch_shape[0] * patch_shape[1])[::8]
    fb1, _ = kmeans2(patches1, n_filters, minit='points')
    fb1 = fb1.reshape((-1,) + patch_shape)
    fb1_montage = montage(fb1, rescale_intensity=True)

    # -- filterbank2 LGN-like image
    astro_dog = ndi.gaussian_filter(astro, 0.5) - ndi.gaussian_filter(astro, 1)
    patches2 = view_as_windows(astro_dog, patch_shape)
    patches2 = patches2.reshape(-1, patch_shape[0] * patch_shape[1])[::8]
    fb2, _ = kmeans2(patches2, n_filters, minit='points')
    fb2 = fb2.reshape((-1,) + patch_shape)
    fb2_montage = montage(fb2, rescale_intensity=True)

    # -- plotting
    fig, axes = plt.subplots(2, 2, figsize=(7, 6))
    ax = axes.ravel()

    ax[0].imshow(astro, cmap=plt.cm.gray)
    ax[0].set_title("Image (original)")

    ax[1].imshow(fb1_montage, cmap=plt.cm.gray)
    ax[1].set_title("K-means filterbank (codebook)\non original image")

    ax[2].imshow(astro_dog, cmap=plt.cm.gray)
    ax[2].set_title("Image (LGN-like DoG)")

    ax[3].imshow(fb2_montage, cmap=plt.cm.gray)
    ax[3].set_title("K-means filterbank (codebook)\non LGN-like DoG image")

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

    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.992 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_gabors_from_astronaut.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/v0.23.2?filepath=notebooks/auto_examples/features_detection/plot_gabors_from_astronaut.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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