
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/segmentation/plot_regionprops_table.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_regionprops_table.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_segmentation_plot_regionprops_table.py:


===================================================
Explore and visualize region properties with pandas
===================================================

This toy example shows how to compute the size of every labelled region in a
series of 10 images. We use 2D images and then 3D images. The blob-like
regions are generated synthetically. As the volume fraction (i.e., ratio of
pixels or voxels covered by the blobs) increases, the number of blobs
(regions) decreases, and the size (area or volume) of a single region can get
larger and larger. The area (size) values are available in a pandas-compatible
format, which makes for convenient data analysis and visualization.

Besides area, many other region properties are available.

.. GENERATED FROM PYTHON SOURCE LINES 17-28

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import seaborn as sns

    from skimage import data, measure


    fractions = np.linspace(0.05, 0.5, 10)








.. GENERATED FROM PYTHON SOURCE LINES 29-31

2D images
=========

.. GENERATED FROM PYTHON SOURCE LINES 31-65

.. code-block:: Python


    images = [data.binary_blobs(volume_fraction=f) for f in fractions]

    labeled_images = [measure.label(image) for image in images]

    properties = ['label', 'area']

    tables = [
        measure.regionprops_table(image, properties=properties) for image in labeled_images
    ]
    tables = [pd.DataFrame(table) for table in tables]

    for fraction, table in zip(fractions, tables):
        table['volume fraction'] = fraction

    areas = pd.concat(tables, axis=0)

    # Create custom grid of subplots
    grid = plt.GridSpec(2, 2)
    ax1 = plt.subplot(grid[0, 0])
    ax2 = plt.subplot(grid[0, 1])
    ax = plt.subplot(grid[1, :])
    # Show image with lowest volume fraction
    ax1.imshow(images[0], cmap='gray_r')
    ax1.set_axis_off()
    ax1.set_title(f'fraction {fractions[0]}')
    # Show image with highest volume fraction
    ax2.imshow(images[-1], cmap='gray_r')
    ax2.set_axis_off()
    ax2.set_title(f'fraction {fractions[-1]}')
    # Plot area vs volume fraction
    areas.plot(x='volume fraction', y='area', kind='scatter', ax=ax)
    plt.show()




.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_table_001.png
   :alt: fraction 0.05, fraction 0.5
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_table_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 66-71

In the scatterplot, many points seem to be overlapping at low area values.
To get a better sense of the distribution, we may want to add some 'jitter'
to the visualization. To this end, we use :obj:`seaborn.stripplot` (from
`seaborn library <https://seaborn.pydata.org/>`_ for statistical data visualization)
with argument ``jitter=True``.

.. GENERATED FROM PYTHON SOURCE LINES 71-78

.. code-block:: Python


    fig, ax = plt.subplots()
    sns.stripplot(x='volume fraction', y='area', data=areas, jitter=True, ax=ax)
    # Fix floating point rendering
    ax.set_xticklabels([f'{frac:.2f}' for frac in fractions])
    plt.show()




.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_table_002.png
   :alt: plot regionprops table
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_table_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /build/skimage-CpQ62b/skimage-0.26.0/doc/examples/segmentation/plot_regionprops_table.py:75: UserWarning:

    set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.





.. GENERATED FROM PYTHON SOURCE LINES 79-86

3D images
=========
Doing the same analysis in 3D, we find a much more dramatic behaviour: blobs
coalesce into a single, giant piece as the volume fraction crosses ~0.25.
This corresponds to the `percolation threshold
<https://en.wikipedia.org/wiki/Percolation_threshold>`_ in statistical
physics and graph theory.

.. GENERATED FROM PYTHON SOURCE LINES 86-109

.. code-block:: Python


    images = [data.binary_blobs(length=128, n_dim=3, volume_fraction=f) for f in fractions]

    labeled_images = [measure.label(image) for image in images]

    properties = ['label', 'area']

    tables = [
        measure.regionprops_table(image, properties=properties) for image in labeled_images
    ]
    tables = [pd.DataFrame(table) for table in tables]

    for fraction, table in zip(fractions, tables):
        table['volume fraction'] = fraction

    blob_volumes = pd.concat(tables, axis=0)

    fig, ax = plt.subplots()
    sns.stripplot(x='volume fraction', y='area', data=blob_volumes, jitter=True, ax=ax)
    ax.set_ylabel('blob size (3D)')
    # Fix floating point rendering
    ax.set_xticklabels([f'{frac:.2f}' for frac in fractions])
    plt.show()



.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_table_003.png
   :alt: plot regionprops table
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_table_003.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /build/skimage-CpQ62b/skimage-0.26.0/doc/examples/segmentation/plot_regionprops_table.py:107: UserWarning:

    set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.






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

   **Total running time of the script:** (0 minutes 4.612 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_regionprops_table.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_regionprops_table.zip <plot_regionprops_table.zip>`


.. only:: html

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

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