1 files changed, 0 insertions, 165 deletions
diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py
deleted file mode 100644
index 62383a2..0000000
--- a/examples/plot_otda_color_images.py
+++ /dev/null
@@ -1,165 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-=============================
-OT for image color adaptation
-=============================
-
-This example presents a way of transferring colors between two images
-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 an 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, :]
-
-
-##############################################################################
-# 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()
-
-
-##############################################################################
-# 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_sinkhorn.transform(Xs=X1)
-transp_Xt_sinkhorn = ot_sinkhorn.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 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()