From a54775103541ea37f54269de1ba1e1396a6d7b30 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:32:57 +0200 Subject: exmaples in sections --- examples/plot_convolutional_barycenter.py | 92 ------------------------------- 1 file changed, 92 deletions(-) delete mode 100644 examples/plot_convolutional_barycenter.py (limited to 'examples/plot_convolutional_barycenter.py') diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py deleted file mode 100644 index e74db04..0000000 --- a/examples/plot_convolutional_barycenter.py +++ /dev/null @@ -1,92 +0,0 @@ - -#%% -# -*- 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 -# -# 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() -- cgit v1.2.3