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


===========================================================
Multi-Block Local Binary Pattern for texture classification
===========================================================

This example shows how to compute multi-block local binary pattern (MB-LBP)
features as well as how to visualize them.

The features are calculated similarly to local binary patterns (LBPs), except
that summed blocks are used instead of individual pixel values.

MB-LBP is an extension of LBP that can be computed on multiple scales in
constant time using the integral image. 9 equally-sized rectangles are used to
compute a feature. For each rectangle, the sum of the pixel intensities is
computed. Comparisons of these sums to that of the central rectangle determine
the feature, similarly to LBP (See `LBP <plot_local_binary_pattern.html>`_).

First, we generate an image to illustrate the functioning of MB-LBP: consider
a (9, 9) rectangle and divide it into (3, 3) block, upon which we then apply
MB-LBP.

.. GENERATED FROM PYTHON SOURCE LINES 23-46

.. code-block:: Python


    from skimage.feature import multiblock_lbp
    import numpy as np
    from numpy.testing import assert_equal
    from skimage.transform import integral_image

    # Create test matrix where first and fifth rectangles starting
    # from top left clockwise have greater value than the central one.
    test_img = np.zeros((9, 9), dtype='uint8')
    test_img[3:6, 3:6] = 1
    test_img[:3, :3] = 50
    test_img[6:, 6:] = 50

    # First and fifth bits should be filled. This correct value will
    #  be compared to the computed one.
    correct_answer = 0b10001000

    int_img = integral_image(test_img)

    lbp_code = multiblock_lbp(int_img, 0, 0, 3, 3)

    assert_equal(correct_answer, lbp_code)








.. GENERATED FROM PYTHON SOURCE LINES 47-49

Now let's apply the operator to a real image and see how the visualization
works.

.. GENERATED FROM PYTHON SOURCE LINES 49-67

.. code-block:: Python


    from skimage import data
    from matplotlib import pyplot as plt
    from skimage.feature import draw_multiblock_lbp

    test_img = data.coins()

    int_img = integral_image(test_img)

    lbp_code = multiblock_lbp(int_img, 0, 0, 90, 90)

    img = draw_multiblock_lbp(test_img, 0, 0, 90, 90, lbp_code=lbp_code, alpha=0.5)


    plt.imshow(img)

    plt.show()




.. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_multiblock_local_binary_pattern_001.png
   :alt: plot multiblock local binary pattern
   :srcset: /auto_examples/features_detection/images/sphx_glr_plot_multiblock_local_binary_pattern_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 68-73

On the above plot we see the result of computing a MB-LBP and visualization
of the computed feature. The rectangles that have less intensities' sum
than the central rectangle are marked in cyan. The ones that have higher
intensity values are marked in white. The central rectangle is left
untouched.


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

   **Total running time of the script:** (0 minutes 0.583 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_multiblock_local_binary_pattern.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_multiblock_local_binary_pattern.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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