.. DO NOT EDIT.
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.. "auto_examples/features_detection/plot_haar.py"
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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_features_detection_plot_haar.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_haar.py:


============================
Haar-like feature descriptor
============================

Haar-like features are simple digital image features that were introduced in a
real-time face detector [1]_. These features can be efficiently computed on any
scale in constant time, using an integral image [1]_. After that, a small
number of critical features is selected from this large set of potential
features (e.g., using AdaBoost learning algorithm as in [1]_). The following
example will show the mechanism to build this family of descriptors.

References
----------

.. [1] Viola, Paul, and Michael J. Jones. "Robust real-time face
       detection." International journal of computer vision 57.2
       (2004): 137-154.
       https://www.merl.com/publications/docs/TR2004-043.pdf
       :DOI:`10.1109/CVPR.2001.990517`

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.. code-block:: Python


    import numpy as np
    import matplotlib.pyplot as plt

    from skimage.feature import haar_like_feature_coord
    from skimage.feature import draw_haar_like_feature








.. GENERATED FROM PYTHON SOURCE LINES 31-36

Different types of Haar-like feature descriptors
##############################################################################
 The Haar-like feature descriptors come into 5 different types as illustrated
 in the figure below. The value of the descriptor is equal to the difference
 between the sum of intensity values in the green and the red one.

.. GENERATED FROM PYTHON SOURCE LINES 36-62

.. code-block:: Python


    images = [
        np.zeros((2, 2)),
        np.zeros((2, 2)),
        np.zeros((3, 3)),
        np.zeros((3, 3)),
        np.zeros((2, 2)),
    ]

    feature_types = ['type-2-x', 'type-2-y', 'type-3-x', 'type-3-y', 'type-4']

    fig, axs = plt.subplots(3, 2)
    for ax, img, feat_t in zip(np.ravel(axs), images, feature_types):
        coord, _ = haar_like_feature_coord(img.shape[0], img.shape[1], feat_t)
        haar_feature = draw_haar_like_feature(
            img, 0, 0, img.shape[0], img.shape[1], coord, max_n_features=1, rng=0
        )
        ax.imshow(haar_feature)
        ax.set_title(feat_t)
        ax.set_xticks([])
        ax.set_yticks([])

    fig.suptitle('The different Haar-like feature descriptors')
    plt.axis('off')
    plt.show()




.. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_haar_001.png
   :alt: The different Haar-like feature descriptors, type-2-x, type-2-y, type-3-x, type-3-y, type-4
   :srcset: /auto_examples/features_detection/images/sphx_glr_plot_haar_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 63-68

The value of the descriptor is equal to the difference between the sum of the
intensity values in the green rectangle and the red one. The red area is
subtracted to the sum of the pixel intensities of the green In practice, the
Haar-like features will be placed in all possible location of an image and a
feature value will be computed for each of these locations.


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

   **Total running time of the script:** (0 minutes 0.108 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_haar.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/v0.24.0?filepath=notebooks/auto_examples/features_detection/plot_haar.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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


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