From 94bc743954bbcefda22ee6a28623164641debb79 Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Fri, 4 Nov 2016 16:09:27 +0100
Subject: new example for mapping

---
 examples/demo_OTDA_mapping_color_images.py | 157 +++++++++++++++++++++++++++++
 1 file changed, 157 insertions(+)
 create mode 100644 examples/demo_OTDA_mapping_color_images.py

diff --git a/examples/demo_OTDA_mapping_color_images.py b/examples/demo_OTDA_mapping_color_images.py
new file mode 100644
index 0000000..2744f6c
--- /dev/null
+++ b/examples/demo_OTDA_mapping_color_images.py
@@ -0,0 +1,157 @@
+# -*- coding: utf-8 -*-
+"""
+Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8]
+
+[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized
+    discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.
+[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for
+    discrete optimal transport", Neural Information Processing Systems (NIPS), 2016.
+
+
+"""
+
+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
+def minmax(I):
+    return np.minimum(np.maximum(I,0),1)
+# LP problem
+da_emd=ot.da.OTDA()     # init class
+da_emd.fit(xs,xt)       # fit distributions
+
+X1t=da_emd.predict(X1)  # out of sample
+I1t=minmax(mat2im(X1t,I1.shape))
+
+# sinkhorn regularization
+lambd=1e-1
+da_entrop=ot.da.OTDA_sinkhorn()
+da_entrop.fit(xs,xt,reg=lambd)
+
+X1te=da_entrop.predict(X1)
+I1te=minmax(mat2im(X1te,I1.shape))
+
+# linear mapping estimation
+eta=1e-8   # quadratic regularization for regression
+mu=1e0     # weight of the OT linear term
+bias=True  # estimate a bias
+
+ot_mapping=ot.da.OTDA_mapping_linear()
+ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)
+
+X1tl=ot_mapping.predict(X1) # use the estimated mapping
+I1tl=minmax(mat2im(X1tl,I1.shape))
+
+# nonlinear mapping estimation
+eta=1e-2   # quadratic regularization for regression
+mu=1e0     # weight of the OT linear term
+bias=False  # estimate a bias
+sigma=1    # sigma bandwidth fot gaussian kernel
+
+
+ot_mapping_kernel=ot.da.OTDA_mapping_kernel()
+ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)
+
+X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping
+I1tn=minmax(mat2im(X1tn,I1.shape))
+#%% plot images
+
+
+pl.figure(2,(10,8))
+
+pl.subplot(2,3,1)
+
+pl.imshow(I1)
+pl.title('Im. 1')
+
+pl.subplot(2,3,2)
+
+pl.imshow(I2)
+pl.title('Im. 2')
+
+
+pl.subplot(2,3,3)
+pl.imshow(I1t)
+pl.title('Im. 1 Interp LP')
+
+pl.subplot(2,3,4)
+pl.imshow(I1te)
+pl.title('Im. 1 Interp Entrop')
+
+
+pl.subplot(2,3,5)
+pl.imshow(I1tl)
+pl.title('Im. 1 Linear mapping')
+
+pl.subplot(2,3,6)
+pl.imshow(I1tn)
+pl.title('Im. 1 nonlinear mapping')
+
+pl.show()
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