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


=========================
Measure region properties
=========================

This example shows how to measure properties of labelled image regions. We
first analyze an image with two ellipses. Below we show how to explore
interactively the properties of labelled objects.

.. GENERATED FROM PYTHON SOURCE LINES 10-33

.. code-block:: default

    import math
    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd

    from skimage.draw import ellipse
    from skimage.measure import label, regionprops, regionprops_table
    from skimage.transform import rotate


    image = np.zeros((600, 600))

    rr, cc = ellipse(300, 350, 100, 220)
    image[rr, cc] = 1

    image = rotate(image, angle=15, order=0)

    rr, cc = ellipse(100, 100, 60, 50)
    image[rr, cc] = 1

    label_img = label(image)
    regions = regionprops(label_img)








.. GENERATED FROM PYTHON SOURCE LINES 34-37

We use the :py:func:`skimage.measure.regionprops` result to draw certain
properties on each region. For example, in red, we plot the major and minor
axes of each ellipse.

.. GENERATED FROM PYTHON SOURCE LINES 37-61

.. code-block:: default


    fig, ax = plt.subplots()
    ax.imshow(image, cmap=plt.cm.gray)

    for props in regions:
        y0, x0 = props.centroid
        orientation = props.orientation
        x1 = x0 + math.cos(orientation) * 0.5 * props.axis_minor_length
        y1 = y0 - math.sin(orientation) * 0.5 * props.axis_minor_length
        x2 = x0 - math.sin(orientation) * 0.5 * props.axis_major_length
        y2 = y0 - math.cos(orientation) * 0.5 * props.axis_major_length

        ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)
        ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)
        ax.plot(x0, y0, '.g', markersize=15)

        minr, minc, maxr, maxc = props.bbox
        bx = (minc, maxc, maxc, minc, minc)
        by = (minr, minr, maxr, maxr, minr)
        ax.plot(bx, by, '-b', linewidth=2.5)

    ax.axis((0, 600, 600, 0))
    plt.show()




.. image-sg:: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_001.png
   :alt: plot regionprops
   :srcset: /auto_examples/segmentation/images/sphx_glr_plot_regionprops_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 62-67

We use the :py:func:`skimage.measure.regionprops_table` function to compute
(selected) properties for each region. Note that
``skimage.measure.regionprops_table`` actually computes the properties,
whereas ``skimage.measure.regionprops`` computes them when they come in use
(lazy evaluation).

.. GENERATED FROM PYTHON SOURCE LINES 67-73

.. code-block:: default


    props = regionprops_table(label_img, properties=('centroid',
                                                     'orientation',
                                                     'axis_major_length',
                                                     'axis_minor_length'))








.. GENERATED FROM PYTHON SOURCE LINES 74-77

We now display a table of these selected properties (one region per row),
the ``skimage.measure.regionprops_table`` result being a pandas-compatible
dict.

.. GENERATED FROM PYTHON SOURCE LINES 77-80

.. code-block:: default


    pd.DataFrame(props)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>centroid-0</th>
          <th>centroid-1</th>
          <th>orientation</th>
          <th>axis_major_length</th>
          <th>axis_minor_length</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>100.000000</td>
          <td>100.000000</td>
          <td>0.000000</td>
          <td>119.807049</td>
          <td>99.823995</td>
        </tr>
        <tr>
          <th>1</th>
          <td>286.914167</td>
          <td>348.412995</td>
          <td>-1.308966</td>
          <td>440.015503</td>
          <td>199.918850</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 81-85

It is also possible to explore interactively the properties of labelled
objects by visualizing them in the hover information of the labels.
This example uses plotly in order to display properties when
hovering over the objects.

.. GENERATED FROM PYTHON SOURCE LINES 85-120

.. code-block:: default


    import plotly
    import plotly.express as px
    import plotly.graph_objects as go
    from skimage import data, filters, measure, morphology

    img = data.coins()
    # Binary image, post-process the binary mask and compute labels
    threshold = filters.threshold_otsu(img)
    mask = img > threshold
    mask = morphology.remove_small_objects(mask, 50)
    mask = morphology.remove_small_holes(mask, 50)
    labels = measure.label(mask)

    fig = px.imshow(img, binary_string=True)
    fig.update_traces(hoverinfo='skip') # hover is only for label info

    props = measure.regionprops(labels, img)
    properties = ['area', 'eccentricity', 'perimeter', 'intensity_mean']

    # For each label, add a filled scatter trace for its contour,
    # and display the properties of the label in the hover of this trace.
    for index in range(1, labels.max()):
        label_i = props[index].label
        contour = measure.find_contours(labels == label_i, 0.5)[0]
        y, x = contour.T
        hoverinfo = ''
        for prop_name in properties:
            hoverinfo += f'<b>{prop_name}: {getattr(props[index], prop_name):.2f}</b><br>'
        fig.add_trace(go.Scatter(
            x=x, y=y, name=label_i,
            mode='lines', fill='toself', showlegend=False,
            hovertemplate=hoverinfo, hoveron='points+fills'))

    plotly.io.show(fig)



.. raw:: html
    :file: images/sphx_glr_plot_regionprops_002.html






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

   **Total running time of the script:** ( 0 minutes  0.998 seconds)


.. _sphx_glr_download_auto_examples_segmentation_plot_regionprops.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/v0.21.x?filepath=notebooks/auto_examples/segmentation/plot_regionprops.ipynb
        :alt: Launch binder
        :width: 150 px



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

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

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

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


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

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