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# -*- coding: utf-8 -*-
"""
=====================================================
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.
"""
# Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
# sphinx_gallery_thumbnail_number = 3
import numpy as np
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
# -------------
# 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, :]
##############################################################################
# Domain adaptation for pixel distribution transfer
# -------------------------------------------------
# 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))
##############################################################################
# Plot original images
# --------------------
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()
##############################################################################
# Plot pixel values distribution
# ------------------------------
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()
##############################################################################
# Plot transformed images
# -----------------------
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()
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