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+
+
+.. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py:
+
+
+====================================================================================
+OT for domain adaptation with image color adaptation [6] with mapping estimation [8]
+====================================================================================
+
+[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized
+    discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),
+    1853-1882.
+[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
+    discrete optimal transport", Neural Information Processing Systems (NIPS),
+    2016.
+
+
+
+
+.. code-block:: python
+
+
+    # Authors: Remi Flamary <remi.flamary@unice.fr>
+    #          Stanislas Chambon <stan.chambon@gmail.com>
+    #
+    # License: MIT License
+
+    import numpy as np
+    from scipy import ndimage
+    import matplotlib.pylab as pl
+    import ot
+
+    r = np.random.RandomState(42)
+
+
+    def im2mat(I):
+        """Converts and 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:: python
+
+
+    # Loading images
+    I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256
+    I2 = ndimage.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, :]
+
+
+
+
+
+
+
+
+Domain adaptation for pixel distribution transfer
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    # EMDTransport
+    ot_emd = ot.da.EMDTransport()
+    ot_emd.fit(Xs=Xs, Xt=Xt)
+    transp_Xs_emd = ot_emd.transform(Xs=X1)
+    Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))
+
+    # SinkhornTransport
+    ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)
+    ot_sinkhorn.fit(Xs=Xs, Xt=Xt)
+    transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)
+    Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))
+
+    ot_mapping_linear = ot.da.MappingTransport(
+        mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)
+    ot_mapping_linear.fit(Xs=Xs, Xt=Xt)
+
+    X1tl = ot_mapping_linear.transform(Xs=X1)
+    Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))
+
+    ot_mapping_gaussian = ot.da.MappingTransport(
+        mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)
+    ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)
+
+    X1tn = ot_mapping_gaussian.transform(Xs=X1)  # use the estimated mapping
+    Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))
+
+
+
+
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out::
+
+    It.  |Loss        |Delta loss
+    --------------------------------
+        0|3.680514e+02|0.000000e+00
+        1|3.592359e+02|-2.395185e-02
+        2|3.590581e+02|-4.947749e-04
+        3|3.589663e+02|-2.556471e-04
+        4|3.589095e+02|-1.582289e-04
+        5|3.588707e+02|-1.081994e-04
+        6|3.588423e+02|-7.911661e-05
+        7|3.588206e+02|-6.055473e-05
+        8|3.588034e+02|-4.778202e-05
+        9|3.587895e+02|-3.886420e-05
+       10|3.587781e+02|-3.182249e-05
+       11|3.587684e+02|-2.695669e-05
+       12|3.587602e+02|-2.298642e-05
+       13|3.587530e+02|-1.993240e-05
+       14|3.587468e+02|-1.736014e-05
+       15|3.587413e+02|-1.518037e-05
+       16|3.587365e+02|-1.358038e-05
+       17|3.587321e+02|-1.215346e-05
+       18|3.587282e+02|-1.091639e-05
+       19|3.587278e+02|-9.877929e-07
+    It.  |Loss        |Delta loss
+    --------------------------------
+        0|3.784725e+02|0.000000e+00
+        1|3.646380e+02|-3.655332e-02
+        2|3.642858e+02|-9.660434e-04
+        3|3.641516e+02|-3.683776e-04
+        4|3.640785e+02|-2.008220e-04
+        5|3.640320e+02|-1.276966e-04
+        6|3.639999e+02|-8.796173e-05
+        7|3.639764e+02|-6.455658e-05
+        8|3.639583e+02|-4.976436e-05
+        9|3.639440e+02|-3.946556e-05
+       10|3.639322e+02|-3.222132e-05
+
+
+plot original images
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    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')
+    pl.tight_layout()
+
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_001.png
+    :align: center
+
+
+
+
+plot pixel values distribution
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    pl.figure(2, figsize=(6.4, 5))
+
+    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_mapping_colors_images_003.png
+    :align: center
+
+
+
+
+plot transformed images
+#############################################################################
+
+
+
+.. code-block:: python
+
+
+    pl.figure(2, figsize=(10, 5))
+
+    pl.subplot(2, 3, 1)
+    pl.imshow(I1)
+    pl.axis('off')
+    pl.title('Im. 1')
+
+    pl.subplot(2, 3, 4)
+    pl.imshow(I2)
+    pl.axis('off')
+    pl.title('Im. 2')
+
+    pl.subplot(2, 3, 2)
+    pl.imshow(Image_emd)
+    pl.axis('off')
+    pl.title('EmdTransport')
+
+    pl.subplot(2, 3, 5)
+    pl.imshow(Image_sinkhorn)
+    pl.axis('off')
+    pl.title('SinkhornTransport')
+
+    pl.subplot(2, 3, 3)
+    pl.imshow(Image_mapping_linear)
+    pl.axis('off')
+    pl.title('MappingTransport (linear)')
+
+    pl.subplot(2, 3, 6)
+    pl.imshow(Image_mapping_gaussian)
+    pl.axis('off')
+    pl.title('MappingTransport (gaussian)')
+    pl.tight_layout()
+
+    pl.show()
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_004.png
+    :align: center
+
+
+
+
+**Total running time of the script:** ( 2 minutes  45.618 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_otda_mapping_colors_images.py <plot_otda_mapping_colors_images.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb <plot_otda_mapping_colors_images.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_