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


=======
Entropy
=======

In information theory, information entropy is the log-base-2 of the number of
possible outcomes for a message.

For an image, local entropy is related to the complexity contained in a given
neighborhood, typically defined by a structuring element. The entropy filter can
detect subtle variations in the local gray level distribution.

In the first example, the image is composed of two surfaces with two slightly
different distributions. The image has a uniform random distribution in the
range [-15, +15] in the middle of the image and a uniform random distribution in
the range [-14, 14] at the image borders, both centered at a gray value of 128.
To detect the central square, we compute the local entropy measure using a
circular structuring element of a radius big enough to capture the local gray
level distribution. The second example shows how to detect texture in the camera
image using a smaller structuring element.

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Object detection
================

.. GENERATED FROM PYTHON SOURCE LINES 27-57

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from skimage import data
    from skimage.util import img_as_ubyte
    from skimage.filters.rank import entropy
    from skimage.morphology import disk

    rng = np.random.default_rng()

    noise_mask = np.full((128, 128), 28, dtype=np.uint8)
    noise_mask[32:-32, 32:-32] = 30

    noise = (noise_mask * rng.random(noise_mask.shape) - 0.5 * noise_mask).astype(np.uint8)
    img = noise + 128

    entr_img = entropy(img, disk(10))

    fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(10, 4))

    img0 = ax0.imshow(noise_mask, cmap='gray')
    ax0.set_title("Object")
    ax1.imshow(img, cmap='gray')
    ax1.set_title("Noisy image")
    ax2.imshow(entr_img, cmap='viridis')
    ax2.set_title("Local entropy")

    fig.tight_layout()




.. image-sg:: /auto_examples/filters/images/sphx_glr_plot_entropy_001.png
   :alt: Object, Noisy image, Local entropy
   :srcset: /auto_examples/filters/images/sphx_glr_plot_entropy_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 58-60

Texture detection
=================

.. GENERATED FROM PYTHON SOURCE LINES 60-78

.. code-block:: Python


    image = img_as_ubyte(data.camera())

    fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12, 4), sharex=True, sharey=True)

    img0 = ax0.imshow(image, cmap=plt.cm.gray)
    ax0.set_title("Image")
    ax0.axis("off")
    fig.colorbar(img0, ax=ax0)

    img1 = ax1.imshow(entropy(image, disk(5)), cmap='gray')
    ax1.set_title("Entropy")
    ax1.axis("off")
    fig.colorbar(img1, ax=ax1)

    fig.tight_layout()

    plt.show()



.. image-sg:: /auto_examples/filters/images/sphx_glr_plot_entropy_002.png
   :alt: Image, Entropy
   :srcset: /auto_examples/filters/images/sphx_glr_plot_entropy_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.229 seconds)


.. _sphx_glr_download_auto_examples_filters_plot_entropy.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/filters/plot_entropy.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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