From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [MRG] Actually run sphinx-gallery (#146) * generate gallery * remove mock * add sklearn to requirermnt?txt for example * remove latex from fgw example * add networks for graph example * remove all * add requirement.txt rtd * rtd debug * update readme * eradthedoc with redirection * add conf rtd --- .../source/auto_examples/plot_otda_color_images.py | 164 --------------------- 1 file changed, 164 deletions(-) delete mode 100644 docs/source/auto_examples/plot_otda_color_images.py (limited to 'docs/source/auto_examples/plot_otda_color_images.py') diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py deleted file mode 100644 index d9cbd2b..0000000 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================= -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. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -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 -# ------------- - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.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 -# ------------------- - -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') - - -############################################################################## -# Scatter plot of colors -# ---------------------- - -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() - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# 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 -# --------------- - -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() -- cgit v1.2.3