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Diffstat (limited to 'examples/plot_OTDA_color_images.py')
-rw-r--r-- | examples/plot_OTDA_color_images.py | 145 |
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diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py new file mode 100644 index 0000000..7ddaa2b --- /dev/null +++ b/examples/plot_OTDA_color_images.py @@ -0,0 +1,145 @@ +# -*- coding: utf-8 -*- +""" +===================================================================================== +Demo of Optimal transport for domain adaptation with image color adaptation as in [6] +===================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +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()
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