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author | Gard Spreemann <gspr@nonempty.org> | 2020-01-20 14:07:53 +0100 |
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committer | Gard Spreemann <gspr@nonempty.org> | 2020-01-20 14:07:53 +0100 |
commit | bdfb24ff37ea777d6e266b145047cd4e281ebac3 (patch) | |
tree | 00cbac5f3dc25a4ee76164828abd72c1cbab37cc /docs/source/auto_examples/plot_otda_color_images.rst | |
parent | abc441b00f0fe2fa4ef0efc4e1aa67b27cca9a13 (diff) | |
parent | 5e70a77fbb2feec513f21c9ef65dcc535329ace6 (diff) |
Merge tag '0.6.0' into debian/sid
Diffstat (limited to 'docs/source/auto_examples/plot_otda_color_images.rst')
-rw-r--r-- | docs/source/auto_examples/plot_otda_color_images.rst | 262 |
1 files changed, 262 insertions, 0 deletions
diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst new file mode 100644 index 0000000..ab0406e --- /dev/null +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -0,0 +1,262 @@ + + +.. _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:: 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 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:: 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, :] + + + + + + + + +Plot original image +------------------- + + + +.. 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') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_001.png + :align: center + + + + +Scatter plot of colors +---------------------- + + + +.. code-block:: python + + + 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_003.png + :align: center + + + + +Instantiate the different transport algorithms and fit them +----------------------------------------------------------- + + + +.. code-block:: python + + + # 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:: python + + + 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_005.png + :align: center + + + + +**Total running time of the script:** ( 3 minutes 55.541 seconds) + + + +.. only :: html + + .. 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>` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |