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


===================
Label image regions
===================

This example shows how to segment an image with image labelling. The following
steps are applied:

1. Thresholding with automatic Otsu method
2. Close small holes with binary closing
3. Remove artifacts touching image border
4. Measure image regions to filter small objects

.. GENERATED FROM PYTHON SOURCE LINES 14-62



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_label_001.png
   :alt: plot label
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_label_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import matplotlib.patches as mpatches

    from skimage import data
    from skimage.filters import threshold_otsu
    from skimage.segmentation import clear_border
    from skimage.measure import label, regionprops
    from skimage.morphology import closing, square
    from skimage.color import label2rgb


    image = data.coins()[50:-50, 50:-50]

    # apply threshold
    thresh = threshold_otsu(image)
    bw = closing(image > thresh, square(3))

    # remove artifacts connected to image border
    cleared = clear_border(bw)

    # label image regions
    label_image = label(cleared)
    # to make the background transparent, pass the value of `bg_label`,
    # and leave `bg_color` as `None` and `kind` as `overlay`
    image_label_overlay = label2rgb(label_image, image=image, bg_label=0)

    fig, ax = plt.subplots(figsize=(10, 6))
    ax.imshow(image_label_overlay)

    for region in regionprops(label_image):
        # take regions with large enough areas
        if region.area >= 100:
            # draw rectangle around segmented coins
            minr, minc, maxr, maxc = region.bbox
            rect = mpatches.Rectangle(
                (minc, minr),
                maxc - minc,
                maxr - minr,
                fill=False,
                edgecolor='red',
                linewidth=2,
            )
            ax.add_patch(rect)

    ax.set_axis_off()
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.425 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_label.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_label.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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