vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

modules/sfm/test/test_robust.cpp

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+/*
+ * Software License Agreement (BSD License)
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+ *  Copyright (c) 2009, Willow Garage, Inc.
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+ *  Redistribution and use in source and binary forms, with or without
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+ */
+
+#include "test_precomp.hpp"
+#include "opencv2/sfm/robust.hpp"
+
+namespace opencv_test { namespace {
+
+TEST(Sfm_robust, fundamentalFromCorrespondences8PointRobust)
+{
+    double tolerance = 1e-8;
+    const int n = 16;
+    Mat_<double> x1(2,n);
+    x1 << 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5,
+          0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 5;
+
+    Mat_<double> x2 = x1.clone();
+    for (int i = 0; i < n; ++i)
+    {
+        x2(0,i) += i % 2;  // Multiple horizontal disparities.
+    }
+    x2(0,n - 1) = 10;
+    x2(1,n - 1) = 10;      // The outlier has vertical disparity.
+
+    Matx33d F;
+    vector<int> inliers;
+    fundamentalFromCorrespondences8PointRobust(x1, x2, 0.1, F, inliers);
+
+    // F should be 0, 0,  0,
+    //             0, 0, -1,
+    //             0, 1,  0
+    EXPECT_NEAR(0.0, F(0,0), tolerance);
+    EXPECT_NEAR(0.0, F(0,1), tolerance);
+    EXPECT_NEAR(0.0, F(0,2), tolerance);
+    EXPECT_NEAR(0.0, F(1,0), tolerance);
+    EXPECT_NEAR(0.0, F(1,1), tolerance);
+    EXPECT_NEAR(0.0, F(2,0), tolerance);
+    EXPECT_NEAR(0.0, F(2,2), tolerance);
+    EXPECT_NEAR(F(1,2), -F(2,1), tolerance);
+
+    EXPECT_EQ(n - 1, inliers.size());
+}
+
+
+TEST(Sfm_robust, fundamentalFromCorrespondences8PointRealisticNoOutliers)
+{
+    double tolerance = 1e-8;
+    TwoViewDataSet d;
+    generateTwoViewRandomScene(d);
+
+    Matx33d F_estimated;
+
+    vector<int> inliers;
+    fundamentalFromCorrespondences8PointRobust(d.x1, d.x2, 3.0, F_estimated, inliers);
+    EXPECT_EQ(d.x1.cols, inliers.size());
+
+    // Normalize.
+    Matx33d F_gt_norm, F_estimated_norm;
+    normalizeFundamental(d.F, F_gt_norm);
+    normalizeFundamental(F_estimated, F_estimated_norm);
+    EXPECT_MATRIX_NEAR(F_gt_norm, F_estimated_norm, tolerance);
+
+    // Check fundamental properties.
+    expectFundamentalProperties( F_estimated, d.x1, d.x2, tolerance);
+}
+
+
+TEST(Sfm_robust, fundamentalFromCorrespondences7PointRobust)
+{
+    double tolerance = 1e-8;
+    const int n = 16;
+    Mat_<double> x1(2,n);
+    x1 << 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5,
+          0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 5;
+
+    Mat_<double> x2 = x1.clone();
+    for (int i = 0; i < n; ++i)
+    {
+        x2(0,i) += i % 2;  // Multiple horizontal disparities.
+    }
+    x2(0,n - 1) = 10;
+    x2(1,n - 1) = 10;      // The outlier has vertical disparity.
+
+    Matx33d F;
+    vector<int> inliers;
+    fundamentalFromCorrespondences7PointRobust(x1, x2, 0.1, F, inliers);
+
+    // F should be 0, 0,  0,
+    //             0, 0, -1,
+    //             0, 1,  0
+    EXPECT_NEAR(0.0, F(0,0), tolerance);
+    EXPECT_NEAR(0.0, F(0,1), tolerance);
+    EXPECT_NEAR(0.0, F(0,2), tolerance);
+    EXPECT_NEAR(0.0, F(1,0), tolerance);
+    EXPECT_NEAR(0.0, F(1,1), tolerance);
+    EXPECT_NEAR(0.0, F(2,0), tolerance);
+    EXPECT_NEAR(0.0, F(2,2), tolerance);
+    EXPECT_NEAR(F(1,2), -F(2,1), tolerance);
+
+    EXPECT_EQ(n - 1, inliers.size());
+}
+
+
+TEST(Sfm_robust, fundamentalFromCorrespondences7PointRealisticNoOutliers)
+{
+    double tolerance = 1e-8;
+    TwoViewDataSet d;
+    generateTwoViewRandomScene(d);
+
+    Matx33d F_estimated;
+
+    vector<int> inliers;
+    fundamentalFromCorrespondences7PointRobust(d.x1, d.x2, 3.0, F_estimated, inliers);
+    EXPECT_EQ(d.x1.cols, inliers.size());
+
+    // Normalize.
+    Matx33d F_gt_norm, F_estimated_norm;
+    normalizeFundamental(d.F, F_gt_norm);
+    normalizeFundamental(F_estimated, F_estimated_norm);
+    EXPECT_MATRIX_NEAR(F_gt_norm, F_estimated_norm, tolerance);
+
+    // Check fundamental properties.
+    expectFundamentalProperties( F_estimated, d.x1, d.x2, tolerance);
+}
+
+}} // namespace