1 // Ceres Solver - A fast non-linear least squares minimizer
2 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
3 // http://code.google.com/p/ceres-solver/
4 //
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6 // modification, are permitted provided that the following conditions are met:
7 //
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9 // this list of conditions and the following disclaimer.
10 // * Redistributions in binary form must reproduce the above copyright notice,
11 // this list of conditions and the following disclaimer in the documentation
12 // and/or other materials provided with the distribution.
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14 // used to endorse or promote products derived from this software without
15 // specific prior written permission.
16 //
17 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
18 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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20 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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24 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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26 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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28 //
29 // Author: Sameer Agarwal (sameeragarwal@google.com)
30 // David Gallup (dgallup@google.com)
31
32 #ifndef CERES_NO_SUITESPARSE
33
34 #include "ceres/canonical_views_clustering.h"
35
36 #include "ceres/collections_port.h"
37 #include "ceres/graph.h"
38 #include "gtest/gtest.h"
39
40 namespace ceres {
41 namespace internal {
42
43 const int kVertexIds[] = {0, 1, 2, 3};
44 class CanonicalViewsTest : public ::testing::Test {
45 protected:
SetUp()46 virtual void SetUp() {
47 // The graph structure is as follows.
48 //
49 // Vertex weights: 0 2 2 0
50 // V0-----V1-----V2-----V3
51 // Edge weights: 0.8 0.9 0.3
52 const double kVertexWeights[] = {0.0, 2.0, 2.0, -1.0};
53 for (int i = 0; i < 4; ++i) {
54 graph_.AddVertex(i, kVertexWeights[i]);
55 }
56 // Create self edges.
57 // CanonicalViews requires that every view "sees" itself.
58 for (int i = 0; i < 4; ++i) {
59 graph_.AddEdge(i, i, 1.0);
60 }
61
62 // Create three edges.
63 const double kEdgeWeights[] = {0.8, 0.9, 0.3};
64 for (int i = 0; i < 3; ++i) {
65 // The graph interface is directed, so remember to create both
66 // edges.
67 graph_.AddEdge(kVertexIds[i], kVertexIds[i + 1], kEdgeWeights[i]);
68 }
69 }
70
ComputeClustering()71 void ComputeClustering() {
72 ComputeCanonicalViewsClustering(graph_, options_, ¢ers_, &membership_);
73 }
74
75 Graph<int> graph_;
76
77 CanonicalViewsClusteringOptions options_;
78 vector<int> centers_;
79 HashMap<int, int> membership_;
80 };
81
TEST_F(CanonicalViewsTest,ComputeCanonicalViewsTest)82 TEST_F(CanonicalViewsTest, ComputeCanonicalViewsTest) {
83 options_.min_views = 0;
84 options_.size_penalty_weight = 0.5;
85 options_.similarity_penalty_weight = 0.0;
86 options_.view_score_weight = 0.0;
87 ComputeClustering();
88
89 // 2 canonical views.
90 EXPECT_EQ(centers_.size(), 2);
91 EXPECT_EQ(centers_[0], kVertexIds[1]);
92 EXPECT_EQ(centers_[1], kVertexIds[3]);
93
94 // Check cluster membership.
95 EXPECT_EQ(FindOrDie(membership_, kVertexIds[0]), 0);
96 EXPECT_EQ(FindOrDie(membership_, kVertexIds[1]), 0);
97 EXPECT_EQ(FindOrDie(membership_, kVertexIds[2]), 0);
98 EXPECT_EQ(FindOrDie(membership_, kVertexIds[3]), 1);
99 }
100
101 // Increases size penalty so the second canonical view won't be
102 // chosen.
TEST_F(CanonicalViewsTest,SizePenaltyTest)103 TEST_F(CanonicalViewsTest, SizePenaltyTest) {
104 options_.min_views = 0;
105 options_.size_penalty_weight = 2.0;
106 options_.similarity_penalty_weight = 0.0;
107 options_.view_score_weight = 0.0;
108 ComputeClustering();
109
110 // 1 canonical view.
111 EXPECT_EQ(centers_.size(), 1);
112 EXPECT_EQ(centers_[0], kVertexIds[1]);
113 }
114
115
116 // Increases view score weight so vertex 2 will be chosen.
TEST_F(CanonicalViewsTest,ViewScoreTest)117 TEST_F(CanonicalViewsTest, ViewScoreTest) {
118 options_.min_views = 0;
119 options_.size_penalty_weight = 0.5;
120 options_.similarity_penalty_weight = 0.0;
121 options_.view_score_weight = 1.0;
122 ComputeClustering();
123
124 // 2 canonical views.
125 EXPECT_EQ(centers_.size(), 2);
126 EXPECT_EQ(centers_[0], kVertexIds[1]);
127 EXPECT_EQ(centers_[1], kVertexIds[2]);
128 }
129
130 // Increases similarity penalty so vertex 2 won't be chosen despite
131 // it's view score.
TEST_F(CanonicalViewsTest,SimilarityPenaltyTest)132 TEST_F(CanonicalViewsTest, SimilarityPenaltyTest) {
133 options_.min_views = 0;
134 options_.size_penalty_weight = 0.5;
135 options_.similarity_penalty_weight = 3.0;
136 options_.view_score_weight = 1.0;
137 ComputeClustering();
138
139 // 2 canonical views.
140 EXPECT_EQ(centers_.size(), 1);
141 EXPECT_EQ(centers_[0], kVertexIds[1]);
142 }
143
144 } // namespace internal
145 } // namespace ceres
146
147 #endif // CERES_NO_SUITESPARSE
148