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


=====================
GLCM Texture Features
=====================

This example illustrates texture classification using gray level
co-occurrence matrices (GLCMs) [1]_. A GLCM is a histogram of co-occurring
grayscale values at a given offset over an image.

In this example, samples of two different textures are extracted from
an image: grassy areas and sky areas. For each patch, a GLCM with
a horizontal offset of 5 (`distance=[5]` and `angles=[0]`) is computed.
Next, two features of the GLCM matrices are computed: dissimilarity and
correlation. These are plotted to illustrate that the classes form
clusters in feature space.
In a typical classification problem, the final step (not included in
this example) would be to train a classifier, such as logistic
regression, to label image patches from new images.

.. versionchanged:: 0.19
           `greymatrix` was renamed to `graymatrix` in 0.19.
.. versionchanged:: 0.19
           `greycoprops` was renamed to `graycoprops` in 0.19.

References
----------
.. [1] Haralick, RM.; Shanmugam, K.,
       "Textural features for image classification"
       IEEE Transactions on systems, man, and cybernetics 6 (1973): 610-621.
       :DOI:`10.1109/TSMC.1973.4309314`

.. GENERATED FROM PYTHON SOURCE LINES 32-109



.. image-sg:: /auto_examples/features_detection/images/sphx_glr_plot_glcm_001.png
   :alt: Grey level co-occurrence matrix features
   :srcset: /auto_examples/features_detection/images/sphx_glr_plot_glcm_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt

    from skimage.feature import graycomatrix, graycoprops
    from skimage import data


    PATCH_SIZE = 21

    # open the camera image
    image = data.camera()

    # select some patches from grassy areas of the image
    grass_locations = [(280, 454), (342, 223), (444, 192), (455, 455)]
    grass_patches = []
    for loc in grass_locations:
        grass_patches.append(
            image[loc[0] : loc[0] + PATCH_SIZE, loc[1] : loc[1] + PATCH_SIZE]
        )

    # select some patches from sky areas of the image
    sky_locations = [(38, 34), (139, 28), (37, 437), (145, 379)]
    sky_patches = []
    for loc in sky_locations:
        sky_patches.append(
            image[loc[0] : loc[0] + PATCH_SIZE, loc[1] : loc[1] + PATCH_SIZE]
        )

    # compute some GLCM properties each patch
    xs = []
    ys = []
    for patch in grass_patches + sky_patches:
        glcm = graycomatrix(
            patch, distances=[5], angles=[0], levels=256, symmetric=True, normed=True
        )
        xs.append(graycoprops(glcm, 'dissimilarity')[0, 0])
        ys.append(graycoprops(glcm, 'correlation')[0, 0])

    # create the figure
    fig = plt.figure(figsize=(8, 8))

    # display original image with locations of patches
    ax = fig.add_subplot(3, 2, 1)
    ax.imshow(image, cmap=plt.cm.gray, vmin=0, vmax=255)
    for y, x in grass_locations:
        ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'gs')
    for y, x in sky_locations:
        ax.plot(x + PATCH_SIZE / 2, y + PATCH_SIZE / 2, 'bs')
    ax.set_xlabel('Original Image')
    ax.set_xticks([])
    ax.set_yticks([])
    ax.axis('image')

    # for each patch, plot (dissimilarity, correlation)
    ax = fig.add_subplot(3, 2, 2)
    ax.plot(xs[: len(grass_patches)], ys[: len(grass_patches)], 'go', label='Grass')
    ax.plot(xs[len(grass_patches) :], ys[len(grass_patches) :], 'bo', label='Sky')
    ax.set_xlabel('GLCM Dissimilarity')
    ax.set_ylabel('GLCM Correlation')
    ax.legend()

    # display the image patches
    for i, patch in enumerate(grass_patches):
        ax = fig.add_subplot(3, len(grass_patches), len(grass_patches) * 1 + i + 1)
        ax.imshow(patch, cmap=plt.cm.gray, vmin=0, vmax=255)
        ax.set_xlabel(f"Grass {i + 1}")

    for i, patch in enumerate(sky_patches):
        ax = fig.add_subplot(3, len(sky_patches), len(sky_patches) * 2 + i + 1)
        ax.imshow(patch, cmap=plt.cm.gray, vmin=0, vmax=255)
        ax.set_xlabel(f"Sky {i + 1}")


    # display the patches and plot
    fig.suptitle('Grey level co-occurrence matrix features', fontsize=14, y=1.05)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.250 seconds)


.. _sphx_glr_download_auto_examples_features_detection_plot_glcm.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_glcm.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. only:: html

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

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