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.. _sphx_glr_auto_examples_plot_OTDA_color_images.py:
========================================================
OT for domain adaptation with image color adaptation [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.
.. rst-class:: sphx-glr-horizontal
*
.. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_001.png
:scale: 47
*
.. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_002.png
:scale: 47
.. 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
# LP problem
da_emd=ot.da.OTDA() # init class
da_emd.fit(xs,xt) # fit distributions
# sinkhorn regularization
lambd=1e-1
da_entrop=ot.da.OTDA_sinkhorn()
da_entrop.fit(xs,xt,reg=lambd)
#%% prediction between images (using out of sample prediction as in [6])
X1t=da_emd.predict(X1)
X2t=da_emd.predict(X2,-1)
X1te=da_entrop.predict(X1)
X2te=da_entrop.predict(X2,-1)
def minmax(I):
return np.minimum(np.maximum(I,0),1)
I1t=minmax(mat2im(X1t,I1.shape))
I2t=minmax(mat2im(X2t,I2.shape))
I1te=minmax(mat2im(X1te,I1.shape))
I2te=minmax(mat2im(X2te,I2.shape))
#%% plot all images
pl.figure(2,(10,8))
pl.subplot(2,3,1)
pl.imshow(I1)
pl.title('Image 1')
pl.subplot(2,3,2)
pl.imshow(I1t)
pl.title('Image 1 Adapt')
pl.subplot(2,3,3)
pl.imshow(I1te)
pl.title('Image 1 Adapt (reg)')
pl.subplot(2,3,4)
pl.imshow(I2)
pl.title('Image 2')
pl.subplot(2,3,5)
pl.imshow(I2t)
pl.title('Image 2 Adapt')
pl.subplot(2,3,6)
pl.imshow(I2te)
pl.title('Image 2 Adapt (reg)')
pl.show()
**Total running time of the script:** ( 0 minutes 24.815 seconds)
.. container:: sphx-glr-footer
.. container:: sphx-glr-download
:download:`Download Python source code: plot_OTDA_color_images.py <plot_OTDA_color_images.py>`
.. container:: sphx-glr-download
:download:`Download Jupyter notebook: plot_OTDA_color_images.ipynb <plot_OTDA_color_images.ipynb>`
.. rst-class:: sphx-glr-signature
`Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
|
