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


======================
Watershed segmentation
======================

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

Starting from user-defined markers, the watershed algorithm treats
pixels values as a local topography (elevation). The algorithm floods
basins from the markers until basins attributed to different markers
meet on watershed lines.  In many cases, markers are chosen as local
minima of the image, from which basins are flooded.

In the example below, two overlapping circles are to be separated. To
do so, one computes an image that is the distance to the
background. The maxima of this distance (i.e., the minima of the
opposite of the distance) are chosen as markers and the flooding of
basins from such markers separates the two circles along a watershed
line.

See Wikipedia_ for more details on the algorithm.

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

.. GENERATED FROM PYTHON SOURCE LINES 27-68



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_watershed_001.png
   :alt: Overlapping objects, Distances, Separated objects
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_watershed_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


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

    from skimage.segmentation import watershed
    from skimage.feature import peak_local_max


    # Generate an initial image with two overlapping circles
    x, y = np.indices((80, 80))
    x1, y1, x2, y2 = 28, 28, 44, 52
    r1, r2 = 16, 20
    mask_circle1 = (x - x1) ** 2 + (y - y1) ** 2 < r1**2
    mask_circle2 = (x - x2) ** 2 + (y - y2) ** 2 < r2**2
    image = np.logical_or(mask_circle1, mask_circle2)

    # Now we want to separate the two objects in image
    # Generate the markers as local maxima of the distance to the background
    distance = ndi.distance_transform_edt(image)
    coords = peak_local_max(distance, footprint=np.ones((3, 3)), labels=image)
    mask = np.zeros(distance.shape, dtype=bool)
    mask[tuple(coords.T)] = True
    markers, _ = ndi.label(mask)
    labels = watershed(-distance, markers, mask=image)

    fig, axes = plt.subplots(ncols=3, figsize=(9, 3), sharex=True, sharey=True)
    ax = axes.ravel()

    ax[0].imshow(image, cmap=plt.cm.gray)
    ax[0].set_title('Overlapping objects')
    ax[1].imshow(-distance, cmap=plt.cm.gray)
    ax[1].set_title('Distances')
    ax[2].imshow(labels, cmap=plt.cm.nipy_spectral)
    ax[2].set_title('Separated objects')

    for a in ax:
        a.set_axis_off()

    fig.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.158 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_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/v0.23.2?filepath=notebooks/auto_examples/segmentation/plot_watershed.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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