700 lines
26 KiB
C++
700 lines
26 KiB
C++
#include "test_precomp.hpp"
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#include <fstream>
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#include <opencv2/ts/gpu_test.hpp>
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#include "../src/fisheye.hpp"
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class FisheyeTest : public ::testing::Test {
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protected:
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const static cv::Size imageSize;
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const static cv::Matx33d K;
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const static cv::Vec4d D;
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const static cv::Matx33d R;
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const static cv::Vec3d T;
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std::string datasets_repository_path;
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virtual void SetUp() {
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datasets_repository_path = combine(cvtest::TS::ptr()->get_data_path(), "cameracalibration/fisheye");
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}
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protected:
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std::string combine(const std::string& _item1, const std::string& _item2);
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std::string combine_format(const std::string& item1, const std::string& item2, ...);
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void readPoints(std::vector<std::vector<cv::Point3d> >& objectPoints,
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std::vector<std::vector<cv::Point2d> >& imagePoints,
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const std::string& path, const int n_images);
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void readExtrinsics(const std::string& file, cv::OutputArray _R, cv::OutputArray _T, cv::OutputArray _R1, cv::OutputArray _R2,
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cv::OutputArray _P1, cv::OutputArray _P2, cv::OutputArray _Q);
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cv::Mat mergeRectification(const cv::Mat& l, const cv::Mat& r);
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};
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////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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/// TESTS::
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TEST_F(FisheyeTest, projectPoints)
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{
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double cols = this->imageSize.width,
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rows = this->imageSize.height;
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const int N = 20;
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cv::Mat distorted0(1, N*N, CV_64FC2), undist1, undist2, distorted1, distorted2;
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undist2.create(distorted0.size(), CV_MAKETYPE(distorted0.depth(), 3));
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cv::Vec2d* pts = distorted0.ptr<cv::Vec2d>();
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cv::Vec2d c(this->K(0, 2), this->K(1, 2));
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for(int y = 0, k = 0; y < N; ++y)
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for(int x = 0; x < N; ++x)
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{
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cv::Vec2d point(x*cols/(N-1.f), y*rows/(N-1.f));
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pts[k++] = (point - c) * 0.85 + c;
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}
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cv::Fisheye::undistortPoints(distorted0, undist1, this->K, this->D);
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cv::Vec2d* u1 = undist1.ptr<cv::Vec2d>();
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cv::Vec3d* u2 = undist2.ptr<cv::Vec3d>();
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for(int i = 0; i < (int)distorted0.total(); ++i)
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u2[i] = cv::Vec3d(u1[i][0], u1[i][1], 1.0);
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cv::Fisheye::distortPoints(undist1, distorted1, this->K, this->D);
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cv::Fisheye::projectPoints(undist2, distorted2, cv::Vec3d::all(0), cv::Vec3d::all(0), this->K, this->D);
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EXPECT_MAT_NEAR(distorted0, distorted1, 1e-10);
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EXPECT_MAT_NEAR(distorted0, distorted2, 1e-10);
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}
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TEST_F(FisheyeTest, undistortImage)
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{
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cv::Matx33d K = this->K;
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cv::Mat D = cv::Mat(this->D);
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std::string file = combine(datasets_repository_path, "image000001.png");
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cv::Matx33d newK = K;
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cv::Mat distorted = cv::imread(file), undistorted;
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{
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newK(0, 0) = 100;
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newK(1, 1) = 100;
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cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/new_f_100.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/new_f_100.png"), undistorted));
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else
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EXPECT_MAT_NEAR(correct, undistorted, 1e-10);
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}
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{
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double balance = 1.0;
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cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
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cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/balance_1.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/balance_1.0.png"), undistorted));
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else
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EXPECT_MAT_NEAR(correct, undistorted, 1e-10);
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}
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{
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double balance = 0.0;
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cv::Fisheye::estimateNewCameraMatrixForUndistortRectify(K, D, distorted.size(), cv::noArray(), newK, balance);
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cv::Fisheye::undistortImage(distorted, undistorted, K, D, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "test_undistortImage/balance_0.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "test_undistortImage/balance_0.0.png"), undistorted));
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else
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EXPECT_MAT_NEAR(correct, undistorted, 1e-10);
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}
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}
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TEST_F(FisheyeTest, jacobians)
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{
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int n = 10;
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cv::Mat X(1, n, CV_64FC3);
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cv::Mat om(3, 1, CV_64F), T(3, 1, CV_64F);
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cv::Mat f(2, 1, CV_64F), c(2, 1, CV_64F);
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cv::Mat k(4, 1, CV_64F);
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double alpha;
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cv::RNG& r = cv::theRNG();
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r.fill(X, cv::RNG::NORMAL, 2, 1);
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X = cv::abs(X) * 10;
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r.fill(om, cv::RNG::NORMAL, 0, 1);
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om = cv::abs(om);
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r.fill(T, cv::RNG::NORMAL, 0, 1);
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T = cv::abs(T); T.at<double>(2) = 4; T *= 10;
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r.fill(f, cv::RNG::NORMAL, 0, 1);
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f = cv::abs(f) * 1000;
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r.fill(c, cv::RNG::NORMAL, 0, 1);
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c = cv::abs(c) * 1000;
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r.fill(k, cv::RNG::NORMAL, 0, 1);
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k*= 0.5;
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alpha = 0.01*r.gaussian(1);
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cv::Mat x1, x2, xpred;
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cv::Matx33d K(f.at<double>(0), alpha * f.at<double>(0), c.at<double>(0),
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0, f.at<double>(1), c.at<double>(1),
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0, 0, 1);
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cv::Mat jacobians;
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cv::Fisheye::projectPoints(X, x1, om, T, K, k, alpha, jacobians);
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//test on T:
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cv::Mat dT(3, 1, CV_64FC1);
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r.fill(dT, cv::RNG::NORMAL, 0, 1);
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dT *= 1e-9*cv::norm(T);
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cv::Mat T2 = T + dT;
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cv::Fisheye::projectPoints(X, x2, om, T2, K, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(11,14) * dT).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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//test on om:
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cv::Mat dom(3, 1, CV_64FC1);
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r.fill(dom, cv::RNG::NORMAL, 0, 1);
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dom *= 1e-9*cv::norm(om);
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cv::Mat om2 = om + dom;
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cv::Fisheye::projectPoints(X, x2, om2, T, K, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(8,11) * dom).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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//test on f:
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cv::Mat df(2, 1, CV_64FC1);
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r.fill(df, cv::RNG::NORMAL, 0, 1);
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df *= 1e-9*cv::norm(f);
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cv::Matx33d K2 = K + cv::Matx33d(df.at<double>(0), df.at<double>(0) * alpha, 0, 0, df.at<double>(1), 0, 0, 0, 0);
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cv::Fisheye::projectPoints(X, x2, om, T, K2, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(0,2) * df).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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//test on c:
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cv::Mat dc(2, 1, CV_64FC1);
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r.fill(dc, cv::RNG::NORMAL, 0, 1);
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dc *= 1e-9*cv::norm(c);
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K2 = K + cv::Matx33d(0, 0, dc.at<double>(0), 0, 0, dc.at<double>(1), 0, 0, 0);
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cv::Fisheye::projectPoints(X, x2, om, T, K2, k, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(2,4) * dc).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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//test on k:
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cv::Mat dk(4, 1, CV_64FC1);
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r.fill(dk, cv::RNG::NORMAL, 0, 1);
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dk *= 1e-9*cv::norm(k);
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cv::Mat k2 = k + dk;
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cv::Fisheye::projectPoints(X, x2, om, T, K, k2, alpha, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.colRange(4,8) * dk).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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//test on alpha:
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cv::Mat dalpha(1, 1, CV_64FC1);
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r.fill(dalpha, cv::RNG::NORMAL, 0, 1);
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dalpha *= 1e-9*cv::norm(f);
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double alpha2 = alpha + dalpha.at<double>(0);
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K2 = K + cv::Matx33d(0, f.at<double>(0) * dalpha.at<double>(0), 0, 0, 0, 0, 0, 0, 0);
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cv::Fisheye::projectPoints(X, x2, om, T, K, k, alpha2, cv::noArray());
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xpred = x1 + cv::Mat(jacobians.col(14) * dalpha).reshape(2, 1);
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CV_Assert (cv::norm(x2 - xpred) < 1e-10);
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}
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TEST_F(FisheyeTest, Calibration)
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{
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const int n_images = 34;
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std::vector<std::vector<cv::Point2d> > imagePoints;
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std::vector<std::vector<cv::Point3d> > objectPoints;
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readPoints(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images);
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int flag = 0;
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flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
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flag |= cv::Fisheye::CALIB_CHECK_COND;
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flag |= cv::Fisheye::CALIB_FIX_SKEW;
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cv::Matx33d K;
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cv::Vec4d D;
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cv::Fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
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cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
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EXPECT_MAT_NEAR(K, this->K, 1e-10);
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EXPECT_MAT_NEAR(D, this->D, 1e-10);
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}
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TEST_F(FisheyeTest, Homography)
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{
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const int n_images = 1;
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std::vector<std::vector<cv::Point2d> > imagePoints;
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std::vector<std::vector<cv::Point3d> > objectPoints;
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readPoints(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images);
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cv::internal::IntrinsicParams param;
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param.Init(cv::Vec2d(cv::max(imageSize.width, imageSize.height) / CV_PI, cv::max(imageSize.width, imageSize.height) / CV_PI),
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cv::Vec2d(imageSize.width / 2.0 - 0.5, imageSize.height / 2.0 - 0.5));
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cv::Mat _imagePoints (imagePoints[0]);
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cv::Mat _objectPoints(objectPoints[0]);
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cv::Mat imagePointsNormalized = NormalizePixels(_imagePoints, param).reshape(1).t();
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_objectPoints = _objectPoints.reshape(1).t();
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cv::Mat objectPointsMean, covObjectPoints;
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int Np = imagePointsNormalized.cols;
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cv::calcCovarMatrix(_objectPoints, covObjectPoints, objectPointsMean, CV_COVAR_NORMAL | CV_COVAR_COLS);
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cv::SVD svd(covObjectPoints);
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cv::Mat R(svd.vt);
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if (cv::norm(R(cv::Rect(2, 0, 1, 2))) < 1e-6)
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R = cv::Mat::eye(3,3, CV_64FC1);
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if (cv::determinant(R) < 0)
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R = -R;
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cv::Mat T = -R * objectPointsMean;
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cv::Mat X_new = R * _objectPoints + T * cv::Mat::ones(1, Np, CV_64FC1);
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cv::Mat H = cv::internal::ComputeHomography(imagePointsNormalized, X_new.rowRange(0, 2));
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cv::Mat M = cv::Mat::ones(3, X_new.cols, CV_64FC1);
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X_new.rowRange(0, 2).copyTo(M.rowRange(0, 2));
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cv::Mat mrep = H * M;
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cv::divide(mrep, cv::Mat::ones(3,1, CV_64FC1) * mrep.row(2).clone(), mrep);
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cv::Mat merr = (mrep.rowRange(0, 2) - imagePointsNormalized).t();
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cv::Vec2d std_err;
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cv::meanStdDev(merr.reshape(2), cv::noArray(), std_err);
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std_err *= sqrt((double)merr.reshape(2).total() / (merr.reshape(2).total() - 1));
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cv::Vec2d correct_std_err(0.00516740156010384, 0.00644205331553901);
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EXPECT_MAT_NEAR(std_err, correct_std_err, 1e-12);
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}
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TEST_F(FisheyeTest, EtimateUncertainties)
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{
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const int n_images = 34;
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std::vector<std::vector<cv::Point2d> > imagePoints;
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std::vector<std::vector<cv::Point3d> > objectPoints;
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readPoints(objectPoints, imagePoints, combine(datasets_repository_path, "calib-3_stereo_from_JY/left"), n_images);
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int flag = 0;
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flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
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flag |= cv::Fisheye::CALIB_CHECK_COND;
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flag |= cv::Fisheye::CALIB_FIX_SKEW;
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cv::Matx33d K;
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cv::Vec4d D;
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std::vector<cv::Vec3d> rvec;
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std::vector<cv::Vec3d> tvec;
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cv::Fisheye::calibrate(objectPoints, imagePoints, imageSize, K, D,
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rvec, tvec, flag, cv::TermCriteria(3, 20, 1e-6));
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cv::internal::IntrinsicParams param, errors;
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cv::Vec2d err_std;
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double thresh_cond = 1e6;
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int check_cond = 1;
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param.Init(cv::Vec2d(K(0,0), K(1,1)), cv::Vec2d(K(0,2), K(1, 2)), D);
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param.isEstimate = std::vector<int>(9, 1);
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param.isEstimate[4] = 0;
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errors.isEstimate = param.isEstimate;
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double rms;
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cv::internal::EstimateUncertainties(objectPoints, imagePoints, param, rvec, tvec,
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errors, err_std, thresh_cond, check_cond, rms);
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EXPECT_MAT_NEAR(errors.f, cv::Vec2d(1.29837104202046, 1.31565641071524), 1e-10);
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EXPECT_MAT_NEAR(errors.c, cv::Vec2d(0.890439368129246, 0.816096854937896), 1e-10);
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EXPECT_MAT_NEAR(errors.k, cv::Vec4d(0.00516248605191506, 0.0168181467500934, 0.0213118690274604, 0.00916010877545648), 1e-10);
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EXPECT_MAT_NEAR(err_std, cv::Vec2d(0.187475975266883, 0.185678953263995), 1e-10);
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CV_Assert(abs(rms - 0.263782587133546) < 1e-10);
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CV_Assert(errors.alpha == 0);
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}
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TEST_F(FisheyeTest, rectify)
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{
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const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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cv::Size calibration_size = this->imageSize, requested_size = calibration_size;
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cv::Matx33d K1 = this->K, K2 = K1;
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cv::Mat D1 = cv::Mat(this->D), D2 = D1;
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cv::Vec3d T = this->T;
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cv::Matx33d R = this->R;
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double balance = 0.0, fov_scale = 1.1;
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cv::Mat R1, R2, P1, P2, Q;
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cv::Fisheye::stereoRectify(K1, D1, K2, D2, calibration_size, R, T, R1, R2, P1, P2, Q,
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cv::CALIB_ZERO_DISPARITY, requested_size, balance, fov_scale);
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cv::Mat lmapx, lmapy, rmapx, rmapy;
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//rewrite for fisheye
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cv::Fisheye::initUndistortRectifyMap(K1, D1, R1, P1, requested_size, CV_32F, lmapx, lmapy);
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cv::Fisheye::initUndistortRectifyMap(K2, D2, R2, P2, requested_size, CV_32F, rmapx, rmapy);
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cv::Mat l, r, lundist, rundist;
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cv::VideoCapture lcap(combine(folder, "left/stereo_pair_%03d.jpg")),
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rcap(combine(folder, "right/stereo_pair_%03d.jpg"));
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for(int i = 0;; ++i)
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{
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lcap >> l; rcap >> r;
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if (l.empty() || r.empty())
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break;
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int ndisp = 128;
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cv::rectangle(l, cv::Rect(255, 0, 829, l.rows-1), CV_RGB(255, 0, 0));
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cv::rectangle(r, cv::Rect(255, 0, 829, l.rows-1), CV_RGB(255, 0, 0));
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cv::rectangle(r, cv::Rect(255-ndisp, 0, 829+ndisp ,l.rows-1), CV_RGB(255, 0, 0));
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cv::remap(l, lundist, lmapx, lmapy, cv::INTER_LINEAR);
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cv::remap(r, rundist, rmapx, rmapy, cv::INTER_LINEAR);
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cv::Mat rectification = mergeRectification(lundist, rundist);
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cv::Mat correct = cv::imread(combine_format(folder, "test_rectify/rectification_AB_%03d.png", i));
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if (correct.empty())
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cv::imwrite(combine_format(folder, "test_rectify/rectification_AB_%03d.png", i), rectification);
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else
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EXPECT_MAT_NEAR(correct, rectification, 1e-10);
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}
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}
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TEST_F(FisheyeTest, stereoCalibrate)
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{
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const int n_images = 34;
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const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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std::vector<std::vector<cv::Point2d> > leftPoints(n_images);
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std::vector<std::vector<cv::Point2d> > rightPoints(n_images);
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std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
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cv::FileStorage fs_left(combine(folder, "left.xml"), cv::FileStorage::READ);
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CV_Assert(fs_left.isOpened());
|
|
for(int i = 0; i < n_images; ++i)
|
|
fs_left[cv::format("image_%d", i )] >> leftPoints[i];
|
|
fs_left.release();
|
|
|
|
cv::FileStorage fs_right(combine(folder, "right.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs_right.isOpened());
|
|
for(int i = 0; i < n_images; ++i)
|
|
fs_right[cv::format("image_%d", i )] >> rightPoints[i];
|
|
fs_right.release();
|
|
|
|
cv::FileStorage fs_object(combine(folder, "object.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs_object.isOpened());
|
|
for(int i = 0; i < n_images; ++i)
|
|
fs_object[cv::format("image_%d", i )] >> objectPoints[i];
|
|
fs_object.release();
|
|
|
|
std::ofstream fs;
|
|
|
|
for (size_t i = 0; i < leftPoints.size(); i++)
|
|
{
|
|
std::string ss = combine(folder, "left");
|
|
ss = combine_format(ss, "%d", i);
|
|
fs.open(ss.c_str());
|
|
CV_Assert(fs.is_open());
|
|
for (size_t j = 0; j < leftPoints[i].size(); j++)
|
|
{
|
|
double x = leftPoints[i][j].x;
|
|
double y = leftPoints[i][j].y;
|
|
fs << std::setprecision(15) << x << "; " << y;
|
|
fs << std::endl;
|
|
}
|
|
fs.close();
|
|
}
|
|
|
|
for (size_t i = 0; i < rightPoints.size(); i++)
|
|
{
|
|
std::string ss = combine(folder, "right");
|
|
ss = combine_format(ss, "%d", i);
|
|
fs.open(ss.c_str());
|
|
CV_Assert(fs.is_open());
|
|
for (size_t j = 0; j < rightPoints[i].size(); j++)
|
|
{
|
|
double x = rightPoints[i][j].x;
|
|
double y = rightPoints[i][j].y;
|
|
fs << std::setprecision(15) << x << "; " << y;
|
|
fs << std::endl;
|
|
}
|
|
fs.close();
|
|
}
|
|
|
|
for (size_t i = 0; i < objectPoints.size(); i++)
|
|
{
|
|
std::string ss = combine(folder, "object");
|
|
ss = combine_format(ss, "%d", i);
|
|
fs.open(ss.c_str());
|
|
CV_Assert(fs.is_open());
|
|
for (size_t j = 0; j < objectPoints[i].size(); j++)
|
|
{
|
|
double x = objectPoints[i][j].x;
|
|
double y = objectPoints[i][j].y;
|
|
double z = objectPoints[i][j].z;
|
|
fs << std::setprecision(15) << x << "; " << y;
|
|
fs << std::setprecision(15) << "; " << z;
|
|
fs << std::endl;
|
|
}
|
|
fs.close();
|
|
}
|
|
|
|
cv::Matx33d K1, K2, R;
|
|
cv::Vec3d T;
|
|
cv::Vec4d D1, D2;
|
|
|
|
int flag = 0;
|
|
flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
|
|
flag |= cv::Fisheye::CALIB_CHECK_COND;
|
|
flag |= cv::Fisheye::CALIB_FIX_SKEW;
|
|
// flag |= cv::Fisheye::CALIB_FIX_INTRINSIC;
|
|
|
|
cv::Fisheye::stereoCalibrate(objectPoints, leftPoints, rightPoints,
|
|
K1, D1, K2, D2, imageSize, R, T, flag,
|
|
cv::TermCriteria(3, 12, 0));
|
|
|
|
cv::Matx33d R_correct( 0.9975587205950972, 0.06953016383322372, 0.006492709911733523,
|
|
-0.06956823121068059, 0.9975601387249519, 0.005833595226966235,
|
|
-0.006071257768382089, -0.006271040135405457, 0.9999619062167968);
|
|
cv::Vec3d T_correct(-0.099402724724121, 0.00270812139265413, 0.00129330292472699);
|
|
cv::Matx33d K1_correct (561.195925927249, 0, 621.282400272412,
|
|
0, 562.849402029712, 380.555455380889,
|
|
0, 0, 1);
|
|
|
|
cv::Matx33d K2_correct (560.395452535348, 0, 678.971652040359,
|
|
0, 561.90171021422, 380.401340535339,
|
|
0, 0, 1);
|
|
|
|
cv::Vec4d D1_correct (-7.44253716539556e-05, -0.00702662033932424, 0.00737569823650885, -0.00342230256441771);
|
|
cv::Vec4d D2_correct (-0.0130785435677431, 0.0284434505383497, -0.0360333869900506, 0.0144724062347222);
|
|
|
|
EXPECT_MAT_NEAR(R, R_correct, 1e-10);
|
|
EXPECT_MAT_NEAR(T, T_correct, 1e-10);
|
|
|
|
EXPECT_MAT_NEAR(K1, K1_correct, 1e-10);
|
|
EXPECT_MAT_NEAR(K2, K2_correct, 1e-10);
|
|
|
|
EXPECT_MAT_NEAR(D1, D1_correct, 1e-10);
|
|
EXPECT_MAT_NEAR(D2, D2_correct, 1e-10);
|
|
|
|
}
|
|
|
|
TEST_F(FisheyeTest, stereoCalibrateFixIntrinsic)
|
|
{
|
|
const int n_images = 34;
|
|
|
|
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
|
|
|
|
std::vector<std::vector<cv::Point2d> > leftPoints(n_images);
|
|
std::vector<std::vector<cv::Point2d> > rightPoints(n_images);
|
|
std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
|
|
|
|
cv::FileStorage fs_left(combine(folder, "left.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs_left.isOpened());
|
|
for(int i = 0; i < n_images; ++i)
|
|
fs_left[cv::format("image_%d", i )] >> leftPoints[i];
|
|
fs_left.release();
|
|
|
|
cv::FileStorage fs_right(combine(folder, "right.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs_right.isOpened());
|
|
for(int i = 0; i < n_images; ++i)
|
|
fs_right[cv::format("image_%d", i )] >> rightPoints[i];
|
|
fs_right.release();
|
|
|
|
cv::FileStorage fs_object(combine(folder, "object.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs_object.isOpened());
|
|
for(int i = 0; i < n_images; ++i)
|
|
fs_object[cv::format("image_%d", i )] >> objectPoints[i];
|
|
fs_object.release();
|
|
|
|
|
|
std::ofstream fs;
|
|
|
|
for (size_t i = 0; i < leftPoints.size(); i++)
|
|
{
|
|
std::string ss = combine(folder, "left");
|
|
ss = combine_format(ss, "%d", i);
|
|
fs.open(ss.c_str());
|
|
CV_Assert(fs.is_open());
|
|
for (size_t j = 0; j < leftPoints[i].size(); j++)
|
|
{
|
|
double x = leftPoints[i][j].x;
|
|
double y = leftPoints[i][j].y;
|
|
fs << std::setprecision(15) << x << "; " << y;
|
|
fs << std::endl;
|
|
}
|
|
fs.close();
|
|
}
|
|
|
|
for (size_t i = 0; i < rightPoints.size(); i++)
|
|
{
|
|
std::string ss = combine(folder, "right");
|
|
ss = combine_format(ss, "%d", i);
|
|
fs.open(ss.c_str());
|
|
CV_Assert(fs.is_open());
|
|
for (size_t j = 0; j < rightPoints[i].size(); j++)
|
|
{
|
|
double x = rightPoints[i][j].x;
|
|
double y = rightPoints[i][j].y;
|
|
fs << std::setprecision(15) << x << "; " << y;
|
|
fs << std::endl;
|
|
}
|
|
fs.close();
|
|
}
|
|
|
|
for (size_t i = 0; i < objectPoints.size(); i++)
|
|
{
|
|
std::string ss = combine(folder, "object");
|
|
ss = combine_format(ss, "%d", i);
|
|
fs.open(ss.c_str());
|
|
CV_Assert(fs.is_open());
|
|
for (size_t j = 0; j < objectPoints[i].size(); j++)
|
|
{
|
|
double x = objectPoints[i][j].x;
|
|
double y = objectPoints[i][j].y;
|
|
double z = objectPoints[i][j].z;
|
|
fs << std::setprecision(15) << x << "; " << y;
|
|
fs << std::setprecision(15) << "; " << z;
|
|
fs << std::endl;
|
|
}
|
|
fs.close();
|
|
}
|
|
|
|
cv::Matx33d R;
|
|
cv::Vec3d T;
|
|
|
|
int flag = 0;
|
|
flag |= cv::Fisheye::CALIB_RECOMPUTE_EXTRINSIC;
|
|
flag |= cv::Fisheye::CALIB_CHECK_COND;
|
|
flag |= cv::Fisheye::CALIB_FIX_SKEW;
|
|
flag |= cv::Fisheye::CALIB_FIX_INTRINSIC;
|
|
|
|
cv::Matx33d K1 (561.195925927249, 0, 621.282400272412,
|
|
0, 562.849402029712, 380.555455380889,
|
|
0, 0, 1);
|
|
|
|
cv::Matx33d K2 (560.395452535348, 0, 678.971652040359,
|
|
0, 561.90171021422, 380.401340535339,
|
|
0, 0, 1);
|
|
|
|
cv::Vec4d D1 (-7.44253716539556e-05, -0.00702662033932424, 0.00737569823650885, -0.00342230256441771);
|
|
cv::Vec4d D2 (-0.0130785435677431, 0.0284434505383497, -0.0360333869900506, 0.0144724062347222);
|
|
|
|
cv::Fisheye::stereoCalibrate(objectPoints, leftPoints, rightPoints,
|
|
K1, D1, K2, D2, imageSize, R, T, flag,
|
|
cv::TermCriteria(3, 12, 0));
|
|
|
|
cv::Matx33d R_correct( 0.9975587205950972, 0.06953016383322372, 0.006492709911733523,
|
|
-0.06956823121068059, 0.9975601387249519, 0.005833595226966235,
|
|
-0.006071257768382089, -0.006271040135405457, 0.9999619062167968);
|
|
cv::Vec3d T_correct(-0.099402724724121, 0.00270812139265413, 0.00129330292472699);
|
|
|
|
|
|
EXPECT_MAT_NEAR(R, R_correct, 1e-10);
|
|
EXPECT_MAT_NEAR(T, T_correct, 1e-10);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
|
|
/// FisheyeTest::
|
|
|
|
const cv::Size FisheyeTest::imageSize(1280, 800);
|
|
|
|
const cv::Matx33d FisheyeTest::K(558.478087865323, 0, 620.458515360843,
|
|
0, 560.506767351568, 381.939424848348,
|
|
0, 0, 1);
|
|
|
|
const cv::Vec4d FisheyeTest::D(-0.0014613319981768, -0.00329861110580401, 0.00605760088590183, -0.00374209380722371);
|
|
|
|
const cv::Matx33d FisheyeTest::R ( 9.9756700084424932e-01, 6.9698277640183867e-02, 1.4929569991321144e-03,
|
|
-6.9711825162322980e-02, 9.9748249845531767e-01, 1.2997180766418455e-02,
|
|
-5.8331736398316541e-04,-1.3069635393884985e-02, 9.9991441852366736e-01);
|
|
|
|
const cv::Vec3d FisheyeTest::T(-9.9217369356044638e-02, 3.1741831972356663e-03, 1.8551007952921010e-04);
|
|
|
|
std::string FisheyeTest::combine(const std::string& _item1, const std::string& _item2)
|
|
{
|
|
std::string item1 = _item1, item2 = _item2;
|
|
std::replace(item1.begin(), item1.end(), '\\', '/');
|
|
std::replace(item2.begin(), item2.end(), '\\', '/');
|
|
|
|
if (item1.empty())
|
|
return item2;
|
|
|
|
if (item2.empty())
|
|
return item1;
|
|
|
|
char last = item1[item1.size()-1];
|
|
return item1 + (last != '/' ? "/" : "") + item2;
|
|
}
|
|
|
|
std::string FisheyeTest::combine_format(const std::string& item1, const std::string& item2, ...)
|
|
{
|
|
std::string fmt = combine(item1, item2);
|
|
char buffer[1 << 16];
|
|
va_list args;
|
|
va_start( args, item2 );
|
|
vsprintf( buffer, fmt.c_str(), args );
|
|
va_end( args );
|
|
return std::string(buffer);
|
|
}
|
|
|
|
void FisheyeTest::readPoints(std::vector<std::vector<cv::Point3d> >& objectPoints,
|
|
std::vector<std::vector<cv::Point2d> >& imagePoints,
|
|
const std::string& path, const int n_images)
|
|
{
|
|
objectPoints.resize(n_images);
|
|
imagePoints.resize(n_images);
|
|
|
|
cv::FileStorage fs1(combine(path, "objectPoints.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs1.isOpened());
|
|
for (size_t i = 0; i < objectPoints.size(); ++i)
|
|
{
|
|
fs1[cv::format("image_%d", i)] >> objectPoints[i];
|
|
}
|
|
fs1.release();
|
|
|
|
cv::FileStorage fs2(combine(path, "imagePoints.xml"), cv::FileStorage::READ);
|
|
CV_Assert(fs2.isOpened());
|
|
for (size_t i = 0; i < imagePoints.size(); ++i)
|
|
{
|
|
fs2[cv::format("image_%d", i)] >> imagePoints[i];
|
|
}
|
|
fs2.release();
|
|
}
|
|
|
|
void FisheyeTest::readExtrinsics(const std::string& file, cv::OutputArray _R, cv::OutputArray _T, cv::OutputArray _R1, cv::OutputArray _R2,
|
|
cv::OutputArray _P1, cv::OutputArray _P2, cv::OutputArray _Q)
|
|
{
|
|
cv::FileStorage fs(file, cv::FileStorage::READ);
|
|
CV_Assert(fs.isOpened());
|
|
|
|
cv::Mat R, T, R1, R2, P1, P2, Q;
|
|
fs["R"] >> R; fs["T"] >> T; fs["R1"] >> R1; fs["R2"] >> R2; fs["P1"] >> P1; fs["P2"] >> P2; fs["Q"] >> Q;
|
|
if (_R.needed()) R.copyTo(_R); if(_T.needed()) T.copyTo(_T); if (_R1.needed()) R1.copyTo(_R1); if (_R2.needed()) R2.copyTo(_R2);
|
|
if(_P1.needed()) P1.copyTo(_P1); if(_P2.needed()) P2.copyTo(_P2); if(_Q.needed()) Q.copyTo(_Q);
|
|
}
|
|
|
|
cv::Mat FisheyeTest::mergeRectification(const cv::Mat& l, const cv::Mat& r)
|
|
{
|
|
CV_Assert(l.type() == r.type() && l.size() == r.size());
|
|
cv::Mat merged(l.rows, l.cols * 2, l.type());
|
|
cv::Mat lpart = merged.colRange(0, l.cols);
|
|
cv::Mat rpart = merged.colRange(l.cols, merged.cols);
|
|
l.copyTo(lpart);
|
|
r.copyTo(rpart);
|
|
|
|
for(int i = 0; i < l.rows; i+=20)
|
|
cv::line(merged, cv::Point(0, i), cv::Point(merged.cols, i), CV_RGB(0, 255, 0));
|
|
|
|
return merged;
|
|
}
|