.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_segmentation_plot_thresholding.py>` to download the full example code
.. 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.py`




.. code-block:: python

    ============
    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.py`

    """


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




.. code-block:: pytb

    Traceback (most recent call last):
      File "/build/skimage-Lp2Zl4/skimage-0.16.2/doc/examples/segmentation/plot_thresholding.py", line 1
        ============
        ^
    SyntaxError: invalid syntax




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




.. code-block:: python


    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()



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.




.. code-block:: python


    from skimage.filters import try_all_threshold

    img = data.page()

    # Here, we specify a radius for local thresholding algorithms.
    # If it is not specified, only global algorithms are called.
    fig, ax = try_all_threshold(img, figsize=(10, 8), verbose=False)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_thresholding.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :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.readthedocs.io>`_
