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


====================
Active Contour Model
====================

The active contour model is a method to fit open or closed splines to lines or
edges in an image [1]_. It works by minimising an energy that is in part
defined by the image and part by the spline's shape: length and smoothness. The
minimization is done implicitly in the shape energy and explicitly in the
image energy.

In the following two examples the active contour model is used (1) to segment
the face of a person from the rest of an image by fitting a closed curve
to the edges of the face and (2) to find the darkest curve between two fixed
points while obeying smoothness considerations. Typically it is a good idea to
smooth images a bit before analyzing, as done in the following examples.

We initialize a circle around the astronaut's face and use the default boundary
condition ``boundary_condition='periodic'`` to fit a closed curve. The default
parameters ``w_line=0, w_edge=1`` will make the curve search towards edges,
such as the boundaries of the face.

.. [1] *Snakes: Active contour models*. Kass, M.; Witkin, A.; Terzopoulos, D.
       International Journal of Computer Vision 1 (4): 321 (1988).
       :DOI:`10.1007/BF00133570`

.. GENERATED FROM PYTHON SOURCE LINES 27-56

.. code-block:: default


    import numpy as np
    import matplotlib.pyplot as plt
    from skimage.color import rgb2gray
    from skimage import data
    from skimage.filters import gaussian
    from skimage.segmentation import active_contour


    img = data.astronaut()
    img = rgb2gray(img)

    s = np.linspace(0, 2*np.pi, 400)
    r = 100 + 100*np.sin(s)
    c = 220 + 100*np.cos(s)
    init = np.array([r, c]).T

    snake = active_contour(gaussian(img, 3, preserve_range=False),
                           init, alpha=0.015, beta=10, gamma=0.001)

    fig, ax = plt.subplots(figsize=(7, 7))
    ax.imshow(img, cmap=plt.cm.gray)
    ax.plot(init[:, 1], init[:, 0], '--r', lw=3)
    ax.plot(snake[:, 1], snake[:, 0], '-b', lw=3)
    ax.set_xticks([]), ax.set_yticks([])
    ax.axis([0, img.shape[1], img.shape[0], 0])

    plt.show()




.. image-sg:: /auto_examples/edges/images/sphx_glr_plot_active_contours_001.png
   :alt: plot active contours
   :srcset: /auto_examples/edges/images/sphx_glr_plot_active_contours_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 57-61

Here we initialize a straight line between two points, `(5, 136)` and
`(424, 50)`, and require that the spline has its end points there by giving
the boundary condition `boundary_condition='fixed'`. We furthermore
make the algorithm search for dark lines by giving a negative `w_line` value.

.. GENERATED FROM PYTHON SOURCE LINES 61-80

.. code-block:: default


    img = data.text()

    r = np.linspace(136, 50, 100)
    c = np.linspace(5, 424, 100)
    init = np.array([r, c]).T

    snake = active_contour(gaussian(img, 1, preserve_range=False),
                           init, boundary_condition='fixed',
                           alpha=0.1, beta=1.0, w_line=-5, w_edge=0, gamma=0.1)

    fig, ax = plt.subplots(figsize=(9, 5))
    ax.imshow(img, cmap=plt.cm.gray)
    ax.plot(init[:, 1], init[:, 0], '--r', lw=3)
    ax.plot(snake[:, 1], snake[:, 0], '-b', lw=3)
    ax.set_xticks([]), ax.set_yticks([])
    ax.axis([0, img.shape[1], img.shape[0], 0])

    plt.show()



.. image-sg:: /auto_examples/edges/images/sphx_glr_plot_active_contours_002.png
   :alt: plot active contours
   :srcset: /auto_examples/edges/images/sphx_glr_plot_active_contours_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 2.154 seconds)


.. _sphx_glr_download_auto_examples_edges_plot_active_contours.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/edges/plot_active_contours.ipynb
        :alt: Launch binder
        :width: 150 px



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

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

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

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


.. only:: html

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

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