From ab5918b2e2dc88a3520c059e6a79a6f81959381e Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Wed, 30 Aug 2017 17:02:59 +0200
Subject: add files and notebooks

---
 .../source/auto_examples/plot_otda_color_images.py | 165 +++++++++++++++++++++
 1 file changed, 165 insertions(+)
 create mode 100644 docs/source/auto_examples/plot_otda_color_images.py

(limited to 'docs/source/auto_examples/plot_otda_color_images.py')

diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py
new file mode 100644
index 0000000..46ad44b
--- /dev/null
+++ b/docs/source/auto_examples/plot_otda_color_images.py
@@ -0,0 +1,165 @@
+# -*- coding: utf-8 -*-
+"""
+========================================================
+OT for domain adaptation with image color adaptation [6]
+========================================================
+
+This example presents a way of transferring colors between two image
+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 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)
+
+
+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, :]
+
+
+##############################################################################
+# 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_emd.transform(Xs=X1)
+transp_Xt_sinkhorn = ot_emd.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 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()
+
+
+##############################################################################
+# 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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