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


===============================
Markers for watershed transform
===============================

The watershed is a classical algorithm used for **segmentation**, that
is, for separating different objects in an image.

Here a marker image is built from the region of low gradient inside the image.
In a gradient image, the areas of high values provide barriers that help to
segment the image.
Using markers on the lower values will ensure that the segmented objects are
found.

See Wikipedia_ for more details on the algorithm.

.. _Wikipedia: https://en.wikipedia.org/wiki/Watershed_(image_processing)

.. GENERATED FROM PYTHON SOURCE LINES 20-70



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_marked_watershed_001.png
   :alt: Original, Local Gradient, Markers, Segmented
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_marked_watershed_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from scipy import ndimage as ndi
    import matplotlib.pyplot as plt

    from skimage.morphology import disk
    from skimage.segmentation import watershed
    from skimage import data
    from skimage.filters import rank
    from skimage.util import img_as_ubyte


    image = img_as_ubyte(data.eagle())

    # denoise image
    denoised = rank.median(image, disk(2))

    # find continuous region (low gradient -
    # where less than 10 for this image) --> markers
    # disk(5) is used here to get a more smooth image
    markers = rank.gradient(denoised, disk(5)) < 10
    markers = ndi.label(markers)[0]

    # local gradient (disk(2) is used to keep edges thin)
    gradient = rank.gradient(denoised, disk(2))

    # process the watershed
    labels = watershed(gradient, markers)

    # display results
    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8), sharex=True, sharey=True)
    ax = axes.ravel()

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

    ax[1].imshow(gradient, cmap=plt.cm.nipy_spectral)
    ax[1].set_title("Local Gradient")

    ax[2].imshow(markers, cmap=plt.cm.nipy_spectral)
    ax[2].set_title("Markers")

    ax[3].imshow(image, cmap=plt.cm.gray)
    ax[3].imshow(labels, cmap=plt.cm.nipy_spectral, alpha=0.5)
    ax[3].set_title("Segmented")

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

    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 4.435 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_marked_watershed.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/v0.24.0?filepath=notebooks/auto_examples/segmentation/plot_marked_watershed.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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