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

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

.. _sphx_glr_auto_examples_features_detection_plot_brief.py:


=======================
BRIEF binary descriptor
=======================

This example demonstrates the BRIEF binary description algorithm. The descriptor
consists of relatively few bits and can be computed using a set of intensity
difference tests. The short binary descriptor results in low memory footprint
and very efficient matching based on the Hamming distance metric. BRIEF does not
provide rotation-invariance. Scale-invariance can be achieved by detecting and
extracting features at different scales.

.. GENERATED FROM PYTHON SOURCE LINES 14-82



.. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_brief_001.png
   :alt: Original Image vs. Transformed Image, Original Image vs. Transformed Image
   :srcset: /auto_examples/features_detection/images/sphx_glr_plot_brief_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    from skimage import data
    from skimage import transform
    from skimage.feature import (
        match_descriptors,
        corner_peaks,
        corner_harris,
        plot_matched_features,
        BRIEF,
    )
    from skimage.color import rgb2gray
    import matplotlib.pyplot as plt


    img1 = rgb2gray(data.astronaut())
    tform = transform.AffineTransform(scale=(1.2, 1.2), translation=(0, -100))
    img2 = transform.warp(img1, tform)
    img3 = transform.rotate(img1, 25)

    keypoints1 = corner_peaks(corner_harris(img1), min_distance=5, threshold_rel=0.1)
    keypoints2 = corner_peaks(corner_harris(img2), min_distance=5, threshold_rel=0.1)
    keypoints3 = corner_peaks(corner_harris(img3), min_distance=5, threshold_rel=0.1)

    extractor = BRIEF()

    extractor.extract(img1, keypoints1)
    keypoints1 = keypoints1[extractor.mask]
    descriptors1 = extractor.descriptors

    extractor.extract(img2, keypoints2)
    keypoints2 = keypoints2[extractor.mask]
    descriptors2 = extractor.descriptors

    extractor.extract(img3, keypoints3)
    keypoints3 = keypoints3[extractor.mask]
    descriptors3 = extractor.descriptors

    matches12 = match_descriptors(descriptors1, descriptors2, cross_check=True)
    matches13 = match_descriptors(descriptors1, descriptors3, cross_check=True)

    fig, ax = plt.subplots(nrows=2, ncols=1)

    plt.gray()

    plot_matched_features(
        img1,
        img2,
        keypoints0=keypoints1,
        keypoints1=keypoints2,
        matches=matches12,
        ax=ax[0],
    )
    ax[0].axis('off')
    ax[0].set_title("Original Image vs. Transformed Image")

    plot_matched_features(
        img1,
        img3,
        keypoints0=keypoints1,
        keypoints1=keypoints3,
        matches=matches13,
        ax=ax[1],
    )
    ax[1].axis('off')
    ax[1].set_title("Original Image vs. Transformed Image")


    plt.show()


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

   **Total running time of the script:** (0 minutes 0.588 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_brief.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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