.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/features_detection/plot_sift.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_sift.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_features_detection_plot_sift.py:


==============================================
SIFT feature detector and descriptor extractor
==============================================

This example demonstrates the SIFT feature detection and its description
algorithm.

The scale-invariant feature transform (SIFT) [1]_ was published in 1999 and is
still one of the most popular feature detectors available, as its promises to
be "invariant to image scaling, translation, and rotation, and partially
in-variant to illumination changes and affine or 3D projection" [2]_. Its
biggest drawback is its runtime, that's said to be "at two orders of
magnitude" [3]_ slower than ORB, which makes it unsuitable for real-time
applications.

References
----------
.. [1] https://en.wikipedia.org/wiki/Scale-invariant_feature_transform

.. [2] D.G. Lowe. "Object recognition from local scale-invariant
       features", Proceedings of the Seventh IEEE International
       Conference on Computer Vision, 1999, vol.2, pp. 1150-1157.
       :DOI:`10.1109/ICCV.1999.790410`

.. [3] Ethan Rublee, Vincent Rabaud, Kurt Konolige and Gary Bradski
       "ORB: An efficient alternative to SIFT and SURF"
       http://www.gwylab.com/download/ORB_2012.pdf

.. GENERATED FROM PYTHON SOURCE LINES 30-89

.. code-block:: default

    import matplotlib.pyplot as plt

    from skimage import data
    from skimage import transform
    from skimage.color import rgb2gray
    from skimage.feature import match_descriptors, plot_matches, SIFT

    img1 = rgb2gray(data.astronaut())
    img2 = transform.rotate(img1, 180)
    tform = transform.AffineTransform(scale=(1.3, 1.1), rotation=0.5,
                                      translation=(0, -200))
    img3 = transform.warp(img1, tform)

    descriptor_extractor = SIFT()

    descriptor_extractor.detect_and_extract(img1)
    keypoints1 = descriptor_extractor.keypoints
    descriptors1 = descriptor_extractor.descriptors

    descriptor_extractor.detect_and_extract(img2)
    keypoints2 = descriptor_extractor.keypoints
    descriptors2 = descriptor_extractor.descriptors

    descriptor_extractor.detect_and_extract(img3)
    keypoints3 = descriptor_extractor.keypoints
    descriptors3 = descriptor_extractor.descriptors

    matches12 = match_descriptors(descriptors1, descriptors2, max_ratio=0.6,
                                  cross_check=True)
    matches13 = match_descriptors(descriptors1, descriptors3, max_ratio=0.6,
                                  cross_check=True)
    fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(11, 8))

    plt.gray()

    plot_matches(ax[0, 0], img1, img2, keypoints1, keypoints2, matches12)
    ax[0, 0].axis('off')
    ax[0, 0].set_title("Original Image vs. Flipped Image\n"
                       "(all keypoints and matches)")

    plot_matches(ax[1, 0], img1, img3, keypoints1, keypoints3, matches13)
    ax[1, 0].axis('off')
    ax[1, 0].set_title("Original Image vs. Transformed Image\n"
                       "(all keypoints and matches)")

    plot_matches(ax[0, 1], img1, img2, keypoints1, keypoints2, matches12[::15],
                 only_matches=True)
    ax[0, 1].axis('off')
    ax[0, 1].set_title("Original Image vs. Flipped Image\n"
                       "(subset of matches for visibility)")

    plot_matches(ax[1, 1], img1, img3, keypoints1, keypoints3, matches13[::15],
                 only_matches=True)
    ax[1, 1].axis('off')
    ax[1, 1].set_title("Original Image vs. Transformed Image\n"
                       "(subset of matches for visibility)")

    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_sift.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/main?filepath=notebooks/auto_examples/features_detection/plot_sift.ipynb
        :alt: Launch binder
        :width: 150 px



    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/features_detection/plot_sift.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. only:: html

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

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