.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_otda_color_images.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_otda_color_images.py:


=============================
OT for image color adaptation
=============================

This example presents a way of transferring colors between two images
with Optimal Transport as introduced in [6]

[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).
Regularized discrete optimal transport.
SIAM Journal on Imaging Sciences, 7(3), 1853-1882.


.. code-block:: default


    # Authors: Remi Flamary <remi.flamary@unice.fr>
    #          Stanislas Chambon <stan.chambon@gmail.com>
    #
    # License: MIT License

    import numpy as np
    import matplotlib.pylab as pl
    import ot


    r = np.random.RandomState(42)


    def im2mat(I):
        """Converts an image to matrix (one pixel per line)"""
        return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))


    def mat2im(X, shape):
        """Converts back a matrix to an image"""
        return X.reshape(shape)


    def minmax(I):
        return np.clip(I, 0, 1)









Generate data
-------------


.. code-block:: default


    # Loading images
    I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256
    I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256

    X1 = im2mat(I1)
    X2 = im2mat(I2)

    # training samples
    nb = 1000
    idx1 = r.randint(X1.shape[0], size=(nb,))
    idx2 = r.randint(X2.shape[0], size=(nb,))

    Xs = X1[idx1, :]
    Xt = X2[idx2, :]









Plot original image
-------------------


.. code-block:: default


    pl.figure(1, figsize=(6.4, 3))

    pl.subplot(1, 2, 1)
    pl.imshow(I1)
    pl.axis('off')
    pl.title('Image 1')

    pl.subplot(1, 2, 2)
    pl.imshow(I2)
    pl.axis('off')
    pl.title('Image 2')





.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Text(0.5, 1.0, 'Image 2')



Scatter plot of colors
----------------------


.. code-block:: default


    pl.figure(2, figsize=(6.4, 3))

    pl.subplot(1, 2, 1)
    pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)
    pl.axis([0, 1, 0, 1])
    pl.xlabel('Red')
    pl.ylabel('Blue')
    pl.title('Image 1')

    pl.subplot(1, 2, 2)
    pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)
    pl.axis([0, 1, 0, 1])
    pl.xlabel('Red')
    pl.ylabel('Blue')
    pl.title('Image 2')
    pl.tight_layout()





.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_002.png
    :class: sphx-glr-single-img





Instantiate the different transport algorithms and fit them
-----------------------------------------------------------


.. code-block:: default


    # EMDTransport
    ot_emd = ot.da.EMDTransport()
    ot_emd.fit(Xs=Xs, Xt=Xt)

    # SinkhornTransport
    ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
    ot_sinkhorn.fit(Xs=Xs, Xt=Xt)

    # prediction between images (using out of sample prediction as in [6])
    transp_Xs_emd = ot_emd.transform(Xs=X1)
    transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)

    transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)
    transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)

    I1t = minmax(mat2im(transp_Xs_emd, I1.shape))
    I2t = minmax(mat2im(transp_Xt_emd, I2.shape))

    I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
    I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))









Plot new images
---------------


.. code-block:: default


    pl.figure(3, figsize=(8, 4))

    pl.subplot(2, 3, 1)
    pl.imshow(I1)
    pl.axis('off')
    pl.title('Image 1')

    pl.subplot(2, 3, 2)
    pl.imshow(I1t)
    pl.axis('off')
    pl.title('Image 1 Adapt')

    pl.subplot(2, 3, 3)
    pl.imshow(I1te)
    pl.axis('off')
    pl.title('Image 1 Adapt (reg)')

    pl.subplot(2, 3, 4)
    pl.imshow(I2)
    pl.axis('off')
    pl.title('Image 2')

    pl.subplot(2, 3, 5)
    pl.imshow(I2t)
    pl.axis('off')
    pl.title('Image 2 Adapt')

    pl.subplot(2, 3, 6)
    pl.imshow(I2te)
    pl.axis('off')
    pl.title('Image 2 Adapt (reg)')
    pl.tight_layout()

    pl.show()



.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_003.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /home/circleci/project/examples/plot_otda_color_images.py:164: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      pl.show()





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

   **Total running time of the script:** ( 2 minutes  8.164 seconds)


.. _sphx_glr_download_auto_examples_plot_otda_color_images.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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