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620 lines
24 KiB
C++
620 lines
24 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#include <opencv2/ts/cuda_test.hpp>
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#include "../src/fisheye.hpp"
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#include "opencv2/videoio.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(), "cv/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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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, DISABLED_undistortImage)
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{
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cv::Matx33d theK = this->K;
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cv::Mat theD = cv::Mat(this->D);
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std::string file = combine(datasets_repository_path, "/calib-3_stereo_from_JY/left/stereo_pair_014.jpg");
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cv::Matx33d newK = theK;
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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, theK, theD, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "new_f_100.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "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(theK, theD, distorted.size(), cv::noArray(), newK, balance);
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cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "balance_1.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "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(theK, theD, distorted.size(), cv::noArray(), newK, balance);
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cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
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cv::Mat correct = cv::imread(combine(datasets_repository_path, "balance_0.0.png"));
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if (correct.empty())
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CV_Assert(cv::imwrite(combine(datasets_repository_path, "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), theT(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;
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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(theT, cv::RNG::NORMAL, 0, 1);
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theT = cv::abs(theT); theT.at<double>(2) = 4; theT *= 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 theK(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, theT, theK, 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(theT);
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cv::Mat T2 = theT + dT;
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cv::fisheye::projectPoints(X, x2, om, T2, theK, 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, theT, theK, 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 = theK + 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, theT, 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 = theK + 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, theT, 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, theT, theK, 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 = theK + 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, theT, theK, 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(n_images);
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std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
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const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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cv::FileStorage fs_left(combine(folder, "left.xml"), cv::FileStorage::READ);
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CV_Assert(fs_left.isOpened());
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for(int i = 0; i < n_images; ++i)
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fs_left[cv::format("image_%d", i )] >> imagePoints[i];
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fs_left.release();
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cv::FileStorage fs_object(combine(folder, "object.xml"), cv::FileStorage::READ);
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CV_Assert(fs_object.isOpened());
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for(int i = 0; i < n_images; ++i)
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fs_object[cv::format("image_%d", i )] >> objectPoints[i];
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fs_object.release();
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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 theK;
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cv::Vec4d theD;
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cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, theK, theD,
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cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
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EXPECT_MAT_NEAR(theK, this->K, 1e-10);
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EXPECT_MAT_NEAR(theD, 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(n_images);
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std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
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const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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cv::FileStorage fs_left(combine(folder, "left.xml"), cv::FileStorage::READ);
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CV_Assert(fs_left.isOpened());
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for(int i = 0; i < n_images; ++i)
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fs_left[cv::format("image_%d", i )] >> imagePoints[i];
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fs_left.release();
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cv::FileStorage fs_object(combine(folder, "object.xml"), cv::FileStorage::READ);
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CV_Assert(fs_object.isOpened());
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for(int i = 0; i < n_images; ++i)
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fs_object[cv::format("image_%d", i )] >> objectPoints[i];
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fs_object.release();
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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 theR(svd.vt);
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if (cv::norm(theR(cv::Rect(2, 0, 1, 2))) < 1e-6)
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theR = cv::Mat::eye(3,3, CV_64FC1);
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if (cv::determinant(theR) < 0)
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theR = -theR;
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cv::Mat theT = -theR * objectPointsMean;
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cv::Mat X_new = theR * _objectPoints + theT * 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(n_images);
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std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
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const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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cv::FileStorage fs_left(combine(folder, "left.xml"), cv::FileStorage::READ);
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CV_Assert(fs_left.isOpened());
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for(int i = 0; i < n_images; ++i)
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fs_left[cv::format("image_%d", i )] >> imagePoints[i];
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fs_left.release();
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cv::FileStorage fs_object(combine(folder, "object.xml"), cv::FileStorage::READ);
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CV_Assert(fs_object.isOpened());
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for(int i = 0; i < n_images; ++i)
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fs_object[cv::format("image_%d", i )] >> objectPoints[i];
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fs_object.release();
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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 theK;
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cv::Vec4d theD;
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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, theK, theD,
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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(theK(0,0), theK(1,1)), cv::Vec2d(theK(0,2), theK(1, 2)), theD);
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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(fabs(rms - 0.263782587133546) < 1e-10);
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|
CV_Assert(errors.alpha == 0);
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|
}
|
|
|
|
#ifdef HAVE_TEGRA_OPTIMIZATION
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|
// not passing accuracy constrains
|
|
TEST_F(fisheyeTest, DISABLED_rectify)
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|
#else
|
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TEST_F(fisheyeTest, rectify)
|
|
#endif
|
|
{
|
|
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
|
|
|
|
cv::Size calibration_size = this->imageSize, requested_size = calibration_size;
|
|
cv::Matx33d K1 = this->K, K2 = K1;
|
|
cv::Mat D1 = cv::Mat(this->D), D2 = D1;
|
|
|
|
cv::Vec3d theT = this->T;
|
|
cv::Matx33d theR = this->R;
|
|
|
|
double balance = 0.0, fov_scale = 1.1;
|
|
cv::Mat R1, R2, P1, P2, Q;
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|
cv::fisheye::stereoRectify(K1, D1, K2, D2, calibration_size, theR, theT, R1, R2, P1, P2, Q,
|
|
cv::CALIB_ZERO_DISPARITY, requested_size, balance, fov_scale);
|
|
|
|
cv::Mat lmapx, lmapy, rmapx, rmapy;
|
|
//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);
|
|
|
|
cv::Mat l, r, lundist, rundist;
|
|
cv::VideoCapture lcap(combine(folder, "left/stereo_pair_%03d.jpg")),
|
|
rcap(combine(folder, "right/stereo_pair_%03d.jpg"));
|
|
|
|
for(int i = 0;; ++i)
|
|
{
|
|
lcap >> l; rcap >> r;
|
|
if (l.empty() || r.empty())
|
|
break;
|
|
|
|
int ndisp = 128;
|
|
cv::rectangle(l, cv::Rect(255, 0, 829, l.rows-1), cv::Scalar(0, 0, 255));
|
|
cv::rectangle(r, cv::Rect(255, 0, 829, l.rows-1), cv::Scalar(0, 0, 255));
|
|
cv::rectangle(r, cv::Rect(255-ndisp, 0, 829+ndisp ,l.rows-1), cv::Scalar(0, 0, 255));
|
|
cv::remap(l, lundist, lmapx, lmapy, cv::INTER_LINEAR);
|
|
cv::remap(r, rundist, rmapx, rmapy, cv::INTER_LINEAR);
|
|
|
|
cv::Mat rectification = mergeRectification(lundist, rundist);
|
|
|
|
cv::Mat correct = cv::imread(combine(datasets_repository_path, cv::format("rectification_AB_%03d.png", i)));
|
|
|
|
if (correct.empty())
|
|
cv::imwrite(combine(datasets_repository_path, cv::format("rectification_AB_%03d.png", i)), rectification);
|
|
else
|
|
EXPECT_MAT_NEAR(correct, rectification, 1e-10);
|
|
}
|
|
}
|
|
|
|
TEST_F(fisheyeTest, stereoCalibrate)
|
|
{
|
|
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();
|
|
|
|
cv::Matx33d K1, K2, theR;
|
|
cv::Vec3d theT;
|
|
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, theR, theT, 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(theR, R_correct, 1e-10);
|
|
EXPECT_MAT_NEAR(theT, 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();
|
|
|
|
cv::Matx33d theR;
|
|
cv::Vec3d theT;
|
|
|
|
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, theR, theT, 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(theR, R_correct, 1e-10);
|
|
EXPECT_MAT_NEAR(theT, 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;
|
|
}
|
|
|
|
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::Scalar(0, 255, 0));
|
|
|
|
return merged;
|
|
}
|