.. _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 # Stanislas Chambon # # 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|3.680512e+02|0.000000e+00 1|3.592454e+02|-2.392562e-02 2|3.590671e+02|-4.960473e-04 3|3.589736e+02|-2.604894e-04 4|3.589161e+02|-1.602816e-04 5|3.588766e+02|-1.099971e-04 6|3.588476e+02|-8.084400e-05 7|3.588256e+02|-6.131161e-05 8|3.588083e+02|-4.807549e-05 9|3.587943e+02|-3.899414e-05 10|3.587827e+02|-3.245280e-05 11|3.587729e+02|-2.721256e-05 12|3.587646e+02|-2.316249e-05 13|3.587574e+02|-2.000192e-05 14|3.587512e+02|-1.748898e-05 15|3.587457e+02|-1.535131e-05 16|3.587408e+02|-1.366515e-05 17|3.587364e+02|-1.210563e-05 18|3.587325e+02|-1.097138e-05 19|3.587310e+02|-4.099596e-06 It. |Loss |Delta loss -------------------------------- 0|3.784805e+02|0.000000e+00 1|3.646476e+02|-3.654847e-02 2|3.642970e+02|-9.615381e-04 3|3.641622e+02|-3.699897e-04 4|3.640886e+02|-2.021154e-04 5|3.640419e+02|-1.280913e-04 6|3.640096e+02|-8.898145e-05 7|3.639858e+02|-6.514301e-05 8|3.639677e+02|-4.977195e-05 9|3.639534e+02|-3.936050e-05 10|3.639417e+02|-3.205223e-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 5.213 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_otda_mapping_colors_images.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_