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diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst new file mode 100644 index 0000000..8394fb0 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -0,0 +1,305 @@ + + +.. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py: + + +===================================================== +OT for image color adaptation with mapping estimation +===================================================== + +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|3.680518e+02|0.000000e+00 + 1|3.592439e+02|-2.393116e-02 + 2|3.590632e+02|-5.030248e-04 + 3|3.589698e+02|-2.601358e-04 + 4|3.589118e+02|-1.614977e-04 + 5|3.588724e+02|-1.097608e-04 + 6|3.588436e+02|-8.035205e-05 + 7|3.588215e+02|-6.141923e-05 + 8|3.588042e+02|-4.832627e-05 + 9|3.587902e+02|-3.909574e-05 + 10|3.587786e+02|-3.225418e-05 + 11|3.587688e+02|-2.712592e-05 + 12|3.587605e+02|-2.314041e-05 + 13|3.587534e+02|-1.991287e-05 + 14|3.587471e+02|-1.744348e-05 + 15|3.587416e+02|-1.544523e-05 + 16|3.587367e+02|-1.364654e-05 + 17|3.587323e+02|-1.230435e-05 + 18|3.587284e+02|-1.093370e-05 + 19|3.587276e+02|-2.052728e-06 + It. |Loss |Delta loss + -------------------------------- + 0|3.784758e+02|0.000000e+00 + 1|3.646352e+02|-3.656911e-02 + 2|3.642861e+02|-9.574714e-04 + 3|3.641523e+02|-3.672061e-04 + 4|3.640788e+02|-2.020990e-04 + 5|3.640321e+02|-1.282701e-04 + 6|3.640002e+02|-8.751240e-05 + 7|3.639765e+02|-6.521203e-05 + 8|3.639582e+02|-5.007767e-05 + 9|3.639439e+02|-3.938917e-05 + 10|3.639323e+02|-3.187865e-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 52.212 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>`_ |