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


=========================
Filtering regional maxima
=========================

Here, we use morphological reconstruction to create a background image, which
we can subtract from the original image to isolate bright features (regional
maxima).

First we try reconstruction by dilation starting at the edges of the image. We
initialize a seed image to the minimum intensity of the image, and set its
border to be the pixel values in the original image. These maximal pixels will
get dilated in order to reconstruct the background image.

.. GENERATED FROM PYTHON SOURCE LINES 16-35

.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from scipy.ndimage import gaussian_filter
    from skimage import data
    from skimage import img_as_float
    from skimage.morphology import reconstruction

    # Convert to float: Important for subtraction later which won't work with uint8
    image = img_as_float(data.coins())
    image = gaussian_filter(image, 1)

    seed = np.copy(image)
    seed[1:-1, 1:-1] = image.min()
    mask = image

    dilated = reconstruction(seed, mask, method='dilation')








.. GENERATED FROM PYTHON SOURCE LINES 36-38

Subtracting the dilated image leaves an image with just the coins and a
flat, black background, as shown below.

.. GENERATED FROM PYTHON SOURCE LINES 38-57

.. code-block:: Python


    fig, (ax0, ax1, ax2) = plt.subplots(
        nrows=1, ncols=3, figsize=(8, 2.5), sharex=True, sharey=True
    )

    ax0.imshow(image, cmap='gray')
    ax0.set_title('original image')
    ax0.axis('off')

    ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray')
    ax1.set_title('dilated')
    ax1.axis('off')

    ax2.imshow(image - dilated, cmap='gray')
    ax2.set_title('image - dilated')
    ax2.axis('off')

    fig.tight_layout()




.. image-sg:: /auto_examples/color_exposure/images/sphx_glr_plot_regional_maxima_001.png
   :alt: original image, dilated, image - dilated
   :srcset: /auto_examples/color_exposure/images/sphx_glr_plot_regional_maxima_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 58-66

Although the features (i.e. the coins) are clearly isolated, the coins
surrounded by a bright background in the original image are dimmer in the
subtracted image. We can attempt to correct this using a different seed
image.

Instead of creating a seed image with maxima along the image border, we can
use the features of the image itself to seed the reconstruction process.
Here, the seed image is the original image minus a fixed value, ``h``.

.. GENERATED FROM PYTHON SOURCE LINES 66-72

.. code-block:: Python


    h = 0.4
    seed = image - h
    dilated = reconstruction(seed, mask, method='dilation')
    hdome = image - dilated








.. GENERATED FROM PYTHON SOURCE LINES 73-76

To get a feel for the reconstruction process, we plot the intensity of the
mask, seed, and dilated images along a slice of the image (indicated by red
line).

.. GENERATED FROM PYTHON SOURCE LINES 76-101

.. code-block:: Python


    fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 2.5))
    yslice = 197

    ax0.plot(mask[yslice], '0.5', label='mask')
    ax0.plot(seed[yslice], 'k', label='seed')
    ax0.plot(dilated[yslice], 'r', label='dilated')
    ax0.set_ylim(-0.2, 2)
    ax0.set_title('image slice')
    ax0.set_xticks([])
    ax0.legend()

    ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray')
    ax1.axhline(yslice, color='r', alpha=0.4)
    ax1.set_title('dilated')
    ax1.axis('off')

    ax2.imshow(hdome, cmap='gray')
    ax2.axhline(yslice, color='r', alpha=0.4)
    ax2.set_title('image - dilated')
    ax2.axis('off')

    fig.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/color_exposure/images/sphx_glr_plot_regional_maxima_002.png
   :alt: image slice, dilated, image - dilated
   :srcset: /auto_examples/color_exposure/images/sphx_glr_plot_regional_maxima_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 102-109

As you can see in the image slice, each coin is given a different baseline
intensity in the reconstructed image; this is because we used the local
intensity (shifted by ``h``) as a seed value. As a result, the coins in the
subtracted image have similar pixel intensities. The final result is known
as the h-dome of an image since this tends to isolate regional maxima of
height ``h``. This operation is particularly useful when your images are
unevenly illuminated.


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

   **Total running time of the script:** (0 minutes 0.726 seconds)


.. _sphx_glr_download_auto_examples_color_exposure_plot_regional_maxima.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/color_exposure/plot_regional_maxima.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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