.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "advanced/image_processing/auto_examples/plot_GMM.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_advanced_image_processing_auto_examples_plot_GMM.py>`
        to download the full example code.

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

.. _sphx_glr_advanced_image_processing_auto_examples_plot_GMM.py:


Segmentation with Gaussian mixture models
=========================================

This example performs a Gaussian mixture model analysis of the image
histogram to find the right thresholds for separating foreground from
background.

.. GENERATED FROM PYTHON SOURCE LINES 10-55



.. image-sg:: /advanced/image_processing/auto_examples/images/sphx_glr_plot_GMM_001.png
   :alt: plot GMM
   :srcset: /advanced/image_processing/auto_examples/images/sphx_glr_plot_GMM_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import numpy as np
    import scipy as sp
    import matplotlib.pyplot as plt
    from sklearn.mixture import GaussianMixture

    rng = np.random.default_rng(27446968)
    n = 10
    l = 256
    im = np.zeros((l, l))
    points = l * rng.random((2, n**2))
    im[(points[0]).astype(int), (points[1]).astype(int)] = 1
    im = sp.ndimage.gaussian_filter(im, sigma=l / (4.0 * n))

    mask = (im > im.mean()).astype(float)


    img = mask + 0.3 * rng.normal(size=mask.shape)

    hist, bin_edges = np.histogram(img, bins=60)
    bin_centers = 0.5 * (bin_edges[:-1] + bin_edges[1:])

    classif = GaussianMixture(n_components=2)
    classif.fit(img.reshape((img.size, 1)))

    threshold = np.mean(classif.means_)
    binary_img = img > threshold


    plt.figure(figsize=(11, 4))

    plt.subplot(131)
    plt.imshow(img)
    plt.axis("off")
    plt.subplot(132)
    plt.plot(bin_centers, hist, lw=2)
    plt.axvline(0.5, color="r", ls="--", lw=2)
    plt.text(0.57, 0.8, "histogram", fontsize=20, transform=plt.gca().transAxes)
    plt.yticks([])
    plt.subplot(133)
    plt.imshow(binary_img, cmap="gray", interpolation="nearest")
    plt.axis("off")

    plt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.675 seconds)


.. _sphx_glr_download_advanced_image_processing_auto_examples_plot_GMM.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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