.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "packages/scikit-image/auto_examples/plot_segmentations.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_packages_scikit-image_auto_examples_plot_segmentations.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_packages_scikit-image_auto_examples_plot_segmentations.py:


Watershed and random walker for segmentation
============================================

This example compares two segmentation methods in order to separate two
connected disks: the watershed algorithm, and the random walker algorithm.

Both segmentation methods require seeds, that are pixels belonging
unambigusouly to a reagion. Here, local maxima of the distance map to the
background are used as seeds.

.. GENERATED FROM PYTHON SOURCE LINES 12-63



.. image-sg:: /packages/scikit-image/auto_examples/images/sphx_glr_plot_segmentations_001.png
   :alt: image, distance map, watershed segmentation, random walker segmentation
   :srcset: /packages/scikit-image/auto_examples/images/sphx_glr_plot_segmentations_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import numpy as np
    from skimage.segmentation import watershed
    from skimage.feature import peak_local_max
    from skimage import measure
    from skimage.segmentation import random_walker
    import matplotlib.pyplot as plt
    import scipy as sp

    # 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 = sp.ndimage.distance_transform_edt(image)
    peak_idx = peak_local_max(
        distance, footprint=np.ones((3, 3)), labels=image
    )
    peak_mask = np.zeros_like(distance, dtype=bool)
    peak_mask[tuple(peak_idx.T)] = True
    markers = measure.label(peak_mask)
    labels_ws = watershed(-distance, markers, mask=image)

    markers[~image] = -1
    labels_rw = random_walker(image, markers)

    plt.figure(figsize=(12, 3.5))
    plt.subplot(141)
    plt.imshow(image, cmap='gray', interpolation='nearest')
    plt.axis('off')
    plt.title('image')
    plt.subplot(142)
    plt.imshow(-distance, interpolation='nearest')
    plt.axis('off')
    plt.title('distance map')
    plt.subplot(143)
    plt.imshow(labels_ws, cmap='nipy_spectral', interpolation='nearest')
    plt.axis('off')
    plt.title('watershed segmentation')
    plt.subplot(144)
    plt.imshow(labels_rw, cmap='nipy_spectral', interpolation='nearest')
    plt.axis('off')
    plt.title('random walker segmentation')

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.146 seconds)


.. _sphx_glr_download_packages_scikit-image_auto_examples_plot_segmentations.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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