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Merge branch 4.x

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This commit is contained in:
Alexander Alekhin
2023-01-09 11:08:02 +00:00
parent 8a62b03761 2ab46d9553
commit 593a376566
880 changed files with 83958 additions and 9368 deletions
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modules/3d/test/test_usac.cpp
+52 -17
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@@ -299,8 +299,11 @@ TEST(usac_Fundamental, regression_19639)
EXPECT_TRUE(m.empty());
}
CV_ENUM(UsacMethod, USAC_DEFAULT, USAC_ACCURATE, USAC_PROSAC, USAC_FAST, USAC_MAGSAC)
typedef TestWithParam<UsacMethod> usac_Essential;
TEST(usac_Essential, accuracy) {
TEST_P(usac_Essential, accuracy) {
int method = GetParam();
std::vector<int> gt_inliers;
const int pts_size = 1500;
cv::RNG &rng = cv::theRNG();
@@ -312,26 +315,58 @@ TEST(usac_Essential, accuracy) {
int inl_size = generatePoints(rng, pts1, pts2, K1, K2, false /*two calib*/,
pts_size, TestSolver ::Fundam, inl_ratio, 0.01 /*noise std, works bad with high noise*/, gt_inliers);
const double conf = 0.99, thr = 1.;
for (auto flag : flags) {
cv::Mat mask, E;
try {
E = cv::findEssentialMat(pts1, pts2, K1, flag, conf, thr, 1000/*maxIters*/, mask);
} catch (cv::Exception &e) {
if (e.code != cv::Error::StsNotImplemented)
FAIL() << "Essential matrix estimation failed!\n";
else continue;
}
// calibrate points
cv::Mat cpts1_3d, cpts2_3d;
cv::vconcat(pts1, cv::Mat::ones(1, pts1.cols, pts1.type()), cpts1_3d);
cv::vconcat(pts2, cv::Mat::ones(1, pts2.cols, pts2.type()), cpts2_3d);
cpts1_3d = K1.inv() * cpts1_3d; cpts2_3d = K1.inv() * cpts2_3d;
checkInliersMask(TestSolver::Essen, inl_size, thr / ((K1.at<double>(0,0) + K1.at<double>(1,1)) / 2),
cpts1_3d.rowRange(0,2), cpts2_3d.rowRange(0,2), E, mask);
cv::Mat mask, E;
try {
E = cv::findEssentialMat(pts1, pts2, K1, method, conf, thr, 1000/*maxIters*/, mask);
} catch (cv::Exception &e) {
if (e.code != cv::Error::StsNotImplemented)
FAIL() << "Essential matrix estimation failed!\n";
else continue;
}
// calibrate points
cv::Mat cpts1_3d, cpts2_3d;
cv::vconcat(pts1, cv::Mat::ones(1, pts1.cols, pts1.type()), cpts1_3d);
cv::vconcat(pts2, cv::Mat::ones(1, pts2.cols, pts2.type()), cpts2_3d);
cpts1_3d = K1.inv() * cpts1_3d; cpts2_3d = K1.inv() * cpts2_3d;
checkInliersMask(TestSolver::Essen, inl_size, thr / ((K1.at<double>(0,0) + K1.at<double>(1,1)) / 2),
cpts1_3d.rowRange(0,2), cpts2_3d.rowRange(0,2), E, mask);
}
}
TEST_P(usac_Essential, maxiters) {
int method = GetParam();
cv::RNG &rng = cv::theRNG();
cv::Mat mask;
cv::Mat K1 = cv::Mat(cv::Matx33d(1, 0, 0,
0, 1, 0,
0, 0, 1.));
const double conf = 0.99, thr = 0.5;
int roll_results_sum = 0;
for (int iters = 0; iters < 10; iters++) {
cv::Mat E1, E2;
try {
cv::Mat pts1 = cv::Mat(2, 50, CV_64F);
cv::Mat pts2 = cv::Mat(2, 50, CV_64F);
rng.fill(pts1, cv::RNG::UNIFORM, 0.0, 1.0);
rng.fill(pts2, cv::RNG::UNIFORM, 0.0, 1.0);
E1 = cv::findEssentialMat(pts1, pts2, K1, method, conf, thr, 1, mask);
E2 = cv::findEssentialMat(pts1, pts2, K1, method, conf, thr, 1000, mask);
if (E1.dims != E2.dims) { continue; }
roll_results_sum += cv::norm(E1, E2, NORM_L1) != 0;
} catch (cv::Exception &e) {
if (e.code != cv::Error::StsNotImplemented)
FAIL() << "Essential matrix estimation failed!\n";
else continue;
}
EXPECT_NE(roll_results_sum, 0);
}
}
INSTANTIATE_TEST_CASE_P(Calib3d, usac_Essential, UsacMethod::all());
TEST(usac_P3P, accuracy) {
std::vector<int> gt_inliers;
const int pts_size = 3000;