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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2023 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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// modification, are permitted provided that the following conditions are met:
//
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//
// Author: Sameer Agarwal (sameeragarwal@google.com)
// David Gallup (dgallup@google.com)
#include "ceres/canonical_views_clustering.h"
#include <unordered_map>
#include "ceres/graph.h"
#include "gtest/gtest.h"
namespace ceres::internal {
const int kVertexIds[] = {0, 1, 2, 3};
class CanonicalViewsTest : public ::testing::Test {
protected:
void SetUp() final {
// The graph structure is as follows.
//
// Vertex weights: 0 2 2 0
// V0-----V1-----V2-----V3
// Edge weights: 0.8 0.9 0.3
const double kVertexWeights[] = {0.0, 2.0, 2.0, -1.0};
for (int i = 0; i < 4; ++i) {
graph_.AddVertex(i, kVertexWeights[i]);
}
// Create self edges.
// CanonicalViews requires that every view "sees" itself.
for (int i = 0; i < 4; ++i) {
graph_.AddEdge(i, i, 1.0);
}
// Create three edges.
const double kEdgeWeights[] = {0.8, 0.9, 0.3};
for (int i = 0; i < 3; ++i) {
// The graph interface is directed, so remember to create both
// edges.
graph_.AddEdge(kVertexIds[i], kVertexIds[i + 1], kEdgeWeights[i]);
}
}
void ComputeClustering() {
ComputeCanonicalViewsClustering(options_, graph_, &centers_, &membership_);
}
WeightedGraph<int> graph_;
CanonicalViewsClusteringOptions options_;
std::vector<int> centers_;
std::unordered_map<int, int> membership_;
};
TEST_F(CanonicalViewsTest, ComputeCanonicalViewsTest) {
options_.min_views = 0;
options_.size_penalty_weight = 0.5;
options_.similarity_penalty_weight = 0.0;
options_.view_score_weight = 0.0;
ComputeClustering();
// 2 canonical views.
EXPECT_EQ(centers_.size(), 2);
EXPECT_EQ(centers_[0], kVertexIds[1]);
EXPECT_EQ(centers_[1], kVertexIds[3]);
// Check cluster membership.
EXPECT_EQ(FindOrDie(membership_, kVertexIds[0]), 0);
EXPECT_EQ(FindOrDie(membership_, kVertexIds[1]), 0);
EXPECT_EQ(FindOrDie(membership_, kVertexIds[2]), 0);
EXPECT_EQ(FindOrDie(membership_, kVertexIds[3]), 1);
}
// Increases size penalty so the second canonical view won't be
// chosen.
TEST_F(CanonicalViewsTest, SizePenaltyTest) {
options_.min_views = 0;
options_.size_penalty_weight = 2.0;
options_.similarity_penalty_weight = 0.0;
options_.view_score_weight = 0.0;
ComputeClustering();
// 1 canonical view.
EXPECT_EQ(centers_.size(), 1);
EXPECT_EQ(centers_[0], kVertexIds[1]);
}
// Increases view score weight so vertex 2 will be chosen.
TEST_F(CanonicalViewsTest, ViewScoreTest) {
options_.min_views = 0;
options_.size_penalty_weight = 0.5;
options_.similarity_penalty_weight = 0.0;
options_.view_score_weight = 1.0;
ComputeClustering();
// 2 canonical views.
EXPECT_EQ(centers_.size(), 2);
EXPECT_EQ(centers_[0], kVertexIds[1]);
EXPECT_EQ(centers_[1], kVertexIds[2]);
}
// Increases similarity penalty so vertex 2 won't be chosen despite
// it's view score.
TEST_F(CanonicalViewsTest, SimilarityPenaltyTest) {
options_.min_views = 0;
options_.size_penalty_weight = 0.5;
options_.similarity_penalty_weight = 3.0;
options_.view_score_weight = 1.0;
ComputeClustering();
// 2 canonical views.
EXPECT_EQ(centers_.size(), 1);
EXPECT_EQ(centers_[0], kVertexIds[1]);
}
} // namespace ceres::internal