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.. _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>`_
|
