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diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst b/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst new file mode 100644 index 0000000..60be3a4 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst @@ -0,0 +1,246 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_mapping_color_images.py: + + +==================================================================================== +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. + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_color_images_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_color_images_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|3.624802e+02|0.000000e+00 + 1|3.547180e+02|-2.141395e-02 + 2|3.545494e+02|-4.753955e-04 + 3|3.544646e+02|-2.391784e-04 + 4|3.544126e+02|-1.466280e-04 + 5|3.543775e+02|-9.921805e-05 + 6|3.543518e+02|-7.245828e-05 + 7|3.543323e+02|-5.491924e-05 + 8|3.543170e+02|-4.342401e-05 + 9|3.543046e+02|-3.472174e-05 + 10|3.542945e+02|-2.878681e-05 + 11|3.542859e+02|-2.417065e-05 + 12|3.542786e+02|-2.058131e-05 + 13|3.542723e+02|-1.768262e-05 + 14|3.542668e+02|-1.551616e-05 + 15|3.542620e+02|-1.371909e-05 + 16|3.542577e+02|-1.213326e-05 + 17|3.542538e+02|-1.085481e-05 + 18|3.542531e+02|-1.996006e-06 + It. |Loss |Delta loss + -------------------------------- + 0|3.555768e+02|0.000000e+00 + 1|3.510071e+02|-1.285164e-02 + 2|3.509110e+02|-2.736701e-04 + 3|3.508748e+02|-1.031476e-04 + 4|3.508506e+02|-6.910585e-05 + 5|3.508330e+02|-5.014608e-05 + 6|3.508195e+02|-3.839166e-05 + 7|3.508090e+02|-3.004218e-05 + 8|3.508005e+02|-2.417627e-05 + 9|3.507935e+02|-2.004621e-05 + 10|3.507876e+02|-1.681731e-05 + + + + +| + + +.. 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 + def minmax(I): + return np.minimum(np.maximum(I,0),1) + # LP problem + da_emd=ot.da.OTDA() # init class + da_emd.fit(xs,xt) # fit distributions + + X1t=da_emd.predict(X1) # out of sample + I1t=minmax(mat2im(X1t,I1.shape)) + + # sinkhorn regularization + lambd=1e-1 + da_entrop=ot.da.OTDA_sinkhorn() + da_entrop.fit(xs,xt,reg=lambd) + + X1te=da_entrop.predict(X1) + I1te=minmax(mat2im(X1te,I1.shape)) + + # linear mapping estimation + eta=1e-8 # quadratic regularization for regression + mu=1e0 # weight of the OT linear term + bias=True # estimate a bias + + ot_mapping=ot.da.OTDA_mapping_linear() + ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + + X1tl=ot_mapping.predict(X1) # use the estimated mapping + I1tl=minmax(mat2im(X1tl,I1.shape)) + + # nonlinear mapping estimation + eta=1e-2 # quadratic regularization for regression + mu=1e0 # weight of the OT linear term + bias=False # estimate a bias + sigma=1 # sigma bandwidth fot gaussian kernel + + + ot_mapping_kernel=ot.da.OTDA_mapping_kernel() + ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + + X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping + I1tn=minmax(mat2im(X1tn,I1.shape)) + #%% plot images + + + pl.figure(2,(10,8)) + + pl.subplot(2,3,1) + + pl.imshow(I1) + pl.title('Im. 1') + + pl.subplot(2,3,2) + + pl.imshow(I2) + pl.title('Im. 2') + + + pl.subplot(2,3,3) + pl.imshow(I1t) + pl.title('Im. 1 Interp LP') + + pl.subplot(2,3,4) + pl.imshow(I1te) + pl.title('Im. 1 Interp Entrop') + + + pl.subplot(2,3,5) + pl.imshow(I1tl) + pl.title('Im. 1 Linear mapping') + + pl.subplot(2,3,6) + pl.imshow(I1tn) + pl.title('Im. 1 nonlinear mapping') + + pl.show() + +**Total running time of the script:** ( 1 minutes 59.537 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_mapping_color_images.py <plot_OTDA_mapping_color_images.py>` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_mapping_color_images.ipynb <plot_OTDA_mapping_color_images.ipynb>` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ |