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


===============================
Extrema
===============================

We detect local maxima in a galaxy image. The image is corrupted by noise,
generating many local maxima. To keep only those maxima with sufficient
local contrast, we use h-maxima.

.. GENERATED FROM PYTHON SOURCE LINES 10-36

.. code-block:: Python


    import matplotlib.pyplot as plt

    from skimage.measure import label
    from skimage import data
    from skimage import color
    from skimage.morphology import extrema
    from skimage import exposure


    color_image = data.hubble_deep_field()

    # for illustration purposes, we work on a crop of the image.
    x_0 = 70
    y_0 = 354
    width = 100
    height = 100

    img = color.rgb2gray(color_image)[y_0 : (y_0 + height), x_0 : (x_0 + width)]

    # the rescaling is done only for visualization purpose.
    # the algorithms would work identically in an unscaled version of the
    # image. However, the parameter h needs to be adapted to the scale.
    img = exposure.rescale_intensity(img)









.. GENERATED FROM PYTHON SOURCE LINES 37-38

MAXIMA DETECTION

.. GENERATED FROM PYTHON SOURCE LINES 38-66

.. code-block:: Python


    # Maxima in the galaxy image are detected by mathematical morphology.
    # There is no a priori constraint on the density.

    # We find all local maxima
    local_maxima = extrema.local_maxima(img)
    label_maxima = label(local_maxima)
    overlay = color.label2rgb(
        label_maxima, img, alpha=0.7, bg_label=0, bg_color=None, colors=[(1, 0, 0)]
    )

    # We observed in the previous image, that there are many local maxima
    # that are caused by the noise in the image.
    # For this, we find all local maxima with a height of h.
    # This height is the gray level value by which we need to descent
    # in order to reach a higher maximum and it can be seen as a local
    # contrast measurement.
    # The value of h scales with the dynamic range of the image, i.e.
    # if we multiply the image with a constant, we need to multiply
    # the value of h with the same constant in order to achieve the same result.
    h = 0.05
    h_maxima = extrema.h_maxima(img, h)
    label_h_maxima = label(h_maxima)
    overlay_h = color.label2rgb(
        label_h_maxima, img, alpha=0.7, bg_label=0, bg_color=None, colors=[(1, 0, 0)]
    )









.. GENERATED FROM PYTHON SOURCE LINES 67-68

GRAPHICAL OUTPUT

.. GENERATED FROM PYTHON SOURCE LINES 68-84

.. code-block:: Python


    # a new figure with 3 subplots
    fig, ax = plt.subplots(1, 3, figsize=(15, 5))

    ax[0].imshow(img, cmap='gray')
    ax[0].set_title('Original image')
    ax[0].axis('off')

    ax[1].imshow(overlay)
    ax[1].set_title('Local Maxima')
    ax[1].axis('off')

    ax[2].imshow(overlay_h)
    ax[2].set_title(f'h maxima for h = {h:.2f}')
    ax[2].axis('off')
    plt.show()



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_extrema_001.png
   :alt: Original image, Local Maxima, h maxima for h = 0.05
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_extrema_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.251 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_extrema.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_extrema.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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