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


=================
Template Matching
=================

We use template matching to identify the occurrence of an image patch
(in this case, a sub-image centered on a single coin). Here, we
return a single match (the exact same coin), so the maximum value in the
``match_template`` result corresponds to the coin location. The other coins
look similar, and thus have local maxima; if you expect multiple matches, you
should use a proper peak-finding function.

The ``match_template`` function uses fast, normalized cross-correlation [1]_
to find instances of the template in the image. Note that the peaks in the
output of ``match_template`` correspond to the origin (i.e. top-left corner) of
the template.

.. [1] J. P. Lewis, "Fast Normalized Cross-Correlation", Industrial Light and
       Magic.

.. GENERATED FROM PYTHON SOURCE LINES 22-62



.. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_template_001.png
   :alt: template, image, `match_template` result
   :srcset: /auto_examples/features_detection/images/sphx_glr_plot_template_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from skimage import data
    from skimage.feature import match_template


    image = data.coins()
    coin = image[170:220, 75:130]

    result = match_template(image, coin)
    ij = np.unravel_index(np.argmax(result), result.shape)
    x, y = ij[::-1]

    fig = plt.figure(figsize=(8, 3))
    ax1 = plt.subplot(1, 3, 1)
    ax2 = plt.subplot(1, 3, 2)
    ax3 = plt.subplot(1, 3, 3, sharex=ax2, sharey=ax2)

    ax1.imshow(coin, cmap=plt.cm.gray)
    ax1.set_axis_off()
    ax1.set_title('template')

    ax2.imshow(image, cmap=plt.cm.gray)
    ax2.set_axis_off()
    ax2.set_title('image')
    # highlight matched region
    hcoin, wcoin = coin.shape
    rect = plt.Rectangle((x, y), wcoin, hcoin, edgecolor='r', facecolor='none')
    ax2.add_patch(rect)

    ax3.imshow(result)
    ax3.set_axis_off()
    ax3.set_title('`match_template`\nresult')
    # highlight matched region
    ax3.autoscale(False)
    ax3.plot(x, y, 'o', markeredgecolor='r', markerfacecolor='none', markersize=10)

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.243 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_template.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/features_detection/plot_template.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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


.. only:: html

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

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