From 3f46a247b174918e24e2e448b21de21bb0037b2c Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Wed, 2 Nov 2016 12:04:15 +0100
Subject: add image color adaptation demo

---
 data/autumn.jpg                    | Bin 0 -> 621005 bytes
 data/fallingwater.jpg              | Bin 0 -> 382775 bytes
 data/ocean_day.jpg                 | Bin 0 -> 76640 bytes
 data/ocean_sunset.jpg              | Bin 0 -> 131739 bytes
 data/woods.jpg                     | Bin 0 -> 387260 bytes
 examples/demo_OTDA_color_images.py | 119 +++++++++++++++++++++++++++++++++++++
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 create mode 100644 data/autumn.jpg
 create mode 100644 data/fallingwater.jpg
 create mode 100644 data/ocean_day.jpg
 create mode 100644 data/ocean_sunset.jpg
 create mode 100644 data/woods.jpg
 create mode 100644 examples/demo_OTDA_color_images.py

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diff --git a/data/ocean_day.jpg b/data/ocean_day.jpg
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diff --git a/data/ocean_sunset.jpg b/data/ocean_sunset.jpg
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diff --git a/data/woods.jpg b/data/woods.jpg
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diff --git a/examples/demo_OTDA_color_images.py b/examples/demo_OTDA_color_images.py
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+++ b/examples/demo_OTDA_color_images.py
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+# -*- 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,:]
+
+#%% 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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