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# -*- 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 <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
# -------------
# 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
# -------------------
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()
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