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+
+
+.. _sphx_glr_auto_examples_plot_OTDA_color_images.py:
+
+
+========================================================
+OT for domain adaptation with image color adaptation [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.
+
+
+
+
+.. rst-class:: sphx-glr-horizontal
+
+
+    *
+
+      .. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_001.png
+            :scale: 47
+
+    *
+
+      .. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_002.png
+            :scale: 47
+
+
+
+
+
+.. code-block:: python
+
+
+    import numpy as np
+    import scipy.ndimage as spi
+    import matplotlib.pylab as pl
+    import ot
+
+
+    #%% Loading images
+
+    I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256
+    I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256
+
+    #%% Plot images
+
+    pl.figure(1)
+
+    pl.subplot(1,2,1)
+    pl.imshow(I1)
+    pl.title('Image 1')
+
+    pl.subplot(1,2,2)
+    pl.imshow(I2)
+    pl.title('Image 2')
+
+    pl.show()
+
+    #%% Image conversion and dataset generation
+
+    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)
+
+    X1=im2mat(I1)
+    X2=im2mat(I2)
+
+    # training samples
+    nb=1000
+    idx1=np.random.randint(X1.shape[0],size=(nb,))
+    idx2=np.random.randint(X2.shape[0],size=(nb,))
+
+    xs=X1[idx1,:]
+    xt=X2[idx2,:]
+
+    #%% Plot image distributions
+
+
+    pl.figure(2,(10,5))
+
+    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.imshow(I2)
+    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.show()
+
+
+
+    #%% domain adaptation between images
+
+    # LP problem
+    da_emd=ot.da.OTDA()     # init class
+    da_emd.fit(xs,xt)       # fit distributions
+
+
+    # sinkhorn regularization
+    lambd=1e-1
+    da_entrop=ot.da.OTDA_sinkhorn()
+    da_entrop.fit(xs,xt,reg=lambd)
+
+
+
+    #%% prediction between images (using out of sample prediction as in [6])
+
+    X1t=da_emd.predict(X1)
+    X2t=da_emd.predict(X2,-1)
+
+
+    X1te=da_entrop.predict(X1)
+    X2te=da_entrop.predict(X2,-1)
+
+
+    def minmax(I):
+        return np.minimum(np.maximum(I,0),1)
+
+    I1t=minmax(mat2im(X1t,I1.shape))
+    I2t=minmax(mat2im(X2t,I2.shape))
+
+    I1te=minmax(mat2im(X1te,I1.shape))
+    I2te=minmax(mat2im(X2te,I2.shape))
+
+    #%% plot all images
+
+    pl.figure(2,(10,8))
+
+    pl.subplot(2,3,1)
+
+    pl.imshow(I1)
+    pl.title('Image 1')
+
+    pl.subplot(2,3,2)
+    pl.imshow(I1t)
+    pl.title('Image 1 Adapt')
+
+
+    pl.subplot(2,3,3)
+    pl.imshow(I1te)
+    pl.title('Image 1 Adapt (reg)')
+
+    pl.subplot(2,3,4)
+
+    pl.imshow(I2)
+    pl.title('Image 2')
+
+    pl.subplot(2,3,5)
+    pl.imshow(I2t)
+    pl.title('Image 2 Adapt')
+
+
+    pl.subplot(2,3,6)
+    pl.imshow(I2te)
+    pl.title('Image 2 Adapt (reg)')
+
+    pl.show()
+
+**Total running time of the script:** ( 0 minutes  24.815 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_OTDA_color_images.py <plot_OTDA_color_images.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_OTDA_color_images.ipynb <plot_OTDA_color_images.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_