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


============
Thresholding
============

Thresholding is used to create a binary image from a grayscale image [1]_.

.. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29

.. seealso::
    A more comprehensive presentation on
    :ref:`sphx_glr_auto_examples_applications_plot_thresholding_guide.py`

.. GENERATED FROM PYTHON SOURCE LINES 15-21

.. code-block:: default



    import matplotlib.pyplot as plt
    from skimage import data
    from skimage.filters import threshold_otsu








.. GENERATED FROM PYTHON SOURCE LINES 22-30

We illustrate how to apply one of these thresholding algorithms.
Otsu's method [2]_ calculates an "optimal" threshold (marked by a red line in the
histogram below) by maximizing the variance between two classes of pixels,
which are separated by the threshold. Equivalently, this threshold minimizes
the intra-class variance.

.. [2] https://en.wikipedia.org/wiki/Otsu's_method


.. GENERATED FROM PYTHON SOURCE LINES 30-56

.. code-block:: default


    image = data.camera()
    thresh = threshold_otsu(image)
    binary = image > thresh

    fig, axes = plt.subplots(ncols=3, figsize=(8, 2.5))
    ax = axes.ravel()
    ax[0] = plt.subplot(1, 3, 1)
    ax[1] = plt.subplot(1, 3, 2)
    ax[2] = plt.subplot(1, 3, 3, sharex=ax[0], sharey=ax[0])

    ax[0].imshow(image, cmap=plt.cm.gray)
    ax[0].set_title('Original')
    ax[0].axis('off')

    ax[1].hist(image.ravel(), bins=256)
    ax[1].set_title('Histogram')
    ax[1].axvline(thresh, color='r')

    ax[2].imshow(binary, cmap=plt.cm.gray)
    ax[2].set_title('Thresholded')
    ax[2].axis('off')

    plt.show()





.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_thresholding_001.png
   :alt: Original, Histogram, Thresholded
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_thresholding_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 57-64

If you are not familiar with the details of the different algorithms and the
underlying assumptions, it is often difficult to know which algorithm will give
the best results. Therefore, Scikit-image includes a function to evaluate
thresholding algorithms provided by the library. At a glance, you can select
the best algorithm for your data without a deep understanding of their
mechanisms.


.. GENERATED FROM PYTHON SOURCE LINES 64-71

.. code-block:: default


    from skimage.filters import try_all_threshold

    img = data.page()

    fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
    plt.show()



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_thresholding_002.png
   :alt: Original, Isodata, Li, Mean, Minimum, Otsu, Triangle, Yen
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_thresholding_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.622 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_thresholding.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/v0.22.x?filepath=notebooks/auto_examples/segmentation/plot_thresholding.ipynb
        :alt: Launch binder
        :width: 150 px



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

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

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

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


.. only:: html

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

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