From ae1ede4ed31973213b5945721b7b9fe8e4992a1c Mon Sep 17 00:00:00 2001
From: Rémi Flamary <remi.flamary@gmail.com>
Date: Mon, 24 Sep 2018 11:00:14 +0200
Subject: update doc+notebooks for convolutional

---
 .../auto_examples/plot_convolutional_barycenter.py | 92 ++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 docs/source/auto_examples/plot_convolutional_barycenter.py

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

diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.py b/docs/source/auto_examples/plot_convolutional_barycenter.py
new file mode 100644
index 0000000..e74db04
--- /dev/null
+++ b/docs/source/auto_examples/plot_convolutional_barycenter.py
@@ -0,0 +1,92 @@
+
+#%%
+# -*- coding: utf-8 -*-
+"""
+============================================
+Convolutional Wasserstein Barycenter example
+============================================
+
+This example is designed to illustrate how the Convolutional Wasserstein Barycenter
+function of POT works.
+"""
+
+# Author: Nicolas Courty <ncourty@irisa.fr>
+#
+# License: MIT License
+
+
+import numpy as np
+import pylab as pl
+import ot
+
+##############################################################################
+# Data preparation
+# ----------------
+#
+# The four distributions are constructed from 4 simple images
+
+
+f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]
+f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]
+f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]
+f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]
+
+A = []
+f1 = f1 / np.sum(f1)
+f2 = f2 / np.sum(f2)
+f3 = f3 / np.sum(f3)
+f4 = f4 / np.sum(f4)
+A.append(f1)
+A.append(f2)
+A.append(f3)
+A.append(f4)
+A = np.array(A)
+
+nb_images = 5
+
+# those are the four corners coordinates that will be interpolated by bilinear
+# interpolation
+v1 = np.array((1, 0, 0, 0))
+v2 = np.array((0, 1, 0, 0))
+v3 = np.array((0, 0, 1, 0))
+v4 = np.array((0, 0, 0, 1))
+
+
+##############################################################################
+# Barycenter computation and visualization
+# ----------------------------------------
+#
+
+pl.figure(figsize=(10, 10))
+pl.title('Convolutional Wasserstein Barycenters in POT')
+cm = 'Blues'
+# regularization parameter
+reg = 0.004
+for i in range(nb_images):
+    for j in range(nb_images):
+        pl.subplot(nb_images, nb_images, i * nb_images + j + 1)
+        tx = float(i) / (nb_images - 1)
+        ty = float(j) / (nb_images - 1)
+
+        # weights are constructed by bilinear interpolation
+        tmp1 = (1 - tx) * v1 + tx * v2
+        tmp2 = (1 - tx) * v3 + tx * v4
+        weights = (1 - ty) * tmp1 + ty * tmp2
+
+        if i == 0 and j == 0:
+            pl.imshow(f1, cmap=cm)
+            pl.axis('off')
+        elif i == 0 and j == (nb_images - 1):
+            pl.imshow(f3, cmap=cm)
+            pl.axis('off')
+        elif i == (nb_images - 1) and j == 0:
+            pl.imshow(f2, cmap=cm)
+            pl.axis('off')
+        elif i == (nb_images - 1) and j == (nb_images - 1):
+            pl.imshow(f4, cmap=cm)
+            pl.axis('off')
+        else:
+            # call to barycenter computation
+            pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)
+            pl.axis('off')
+pl.show()
-- 
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