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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 deleted file mode 100644 index 2afdc8a..0000000 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ /dev/null @@ -1,310 +0,0 @@ - - -.. _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_sinkhorn.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|3.680534e+02|0.000000e+00 - 1|3.592501e+02|-2.391854e-02 - 2|3.590682e+02|-5.061555e-04 - 3|3.589745e+02|-2.610227e-04 - 4|3.589167e+02|-1.611644e-04 - 5|3.588768e+02|-1.109242e-04 - 6|3.588482e+02|-7.972733e-05 - 7|3.588261e+02|-6.166174e-05 - 8|3.588086e+02|-4.871697e-05 - 9|3.587946e+02|-3.919056e-05 - 10|3.587830e+02|-3.228124e-05 - 11|3.587731e+02|-2.744744e-05 - 12|3.587648e+02|-2.334451e-05 - 13|3.587576e+02|-1.995629e-05 - 14|3.587513e+02|-1.761058e-05 - 15|3.587457e+02|-1.542568e-05 - 16|3.587408e+02|-1.366315e-05 - 17|3.587365e+02|-1.221732e-05 - 18|3.587325e+02|-1.102488e-05 - 19|3.587303e+02|-6.062107e-06 - It. |Loss |Delta loss - -------------------------------- - 0|3.784871e+02|0.000000e+00 - 1|3.646491e+02|-3.656142e-02 - 2|3.642975e+02|-9.642655e-04 - 3|3.641626e+02|-3.702413e-04 - 4|3.640888e+02|-2.026301e-04 - 5|3.640419e+02|-1.289607e-04 - 6|3.640097e+02|-8.831646e-05 - 7|3.639861e+02|-6.487612e-05 - 8|3.639679e+02|-4.994063e-05 - 9|3.639536e+02|-3.941436e-05 - 10|3.639419e+02|-3.209753e-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:** ( 3 minutes 14.206 seconds) - - - -.. only :: html - - .. 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>` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |