mirror/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2024-11-25 11:40:44 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity
106413a3ea
opencv/modules/calib3d/test/test_fisheye.cpp
Jeff McBride 562c15cbe9 Don't assume all images have the same number of calibration points 
Cast to (int) to avoid warnings

Add test for different points number case in fisheye calibration function
2016-08-02 17:21:17 +03:00

662 lines
26 KiB
C++
Raw Blame History

/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
#include <opencv2/ts/cuda_test.hpp>
#include "../src/fisheye.hpp"
#include "opencv2/videoio.hpp"
class fisheyeTest : public ::testing::Test {
protected:
const static cv::Size imageSize;
const static cv::Matx33d K;
const static cv::Vec4d D;
const static cv::Matx33d R;
const static cv::Vec3d T;
std::string datasets_repository_path;
virtual void SetUp() {
datasets_repository_path = combine(cvtest::TS::ptr()->get_data_path(), "cv/cameracalibration/fisheye");
}
protected:
std::string combine(const std::string& _item1, const std::string& _item2);
cv::Mat mergeRectification(const cv::Mat& l, const cv::Mat& r);
};
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// TESTS::
TEST_F(fisheyeTest, projectPoints)
{
double cols = this->imageSize.width,
rows = this->imageSize.height;
const int N = 20;
cv::Mat distorted0(1, N*N, CV_64FC2), undist1, undist2, distorted1, distorted2;
undist2.create(distorted0.size(), CV_MAKETYPE(distorted0.depth(), 3));
cv::Vec2d* pts = distorted0.ptr<cv::Vec2d>();
cv::Vec2d c(this->K(0, 2), this->K(1, 2));
for(int y = 0, k = 0; y < N; ++y)
for(int x = 0; x < N; ++x)
{
cv::Vec2d point(x*cols/(N-1.f), y*rows/(N-1.f));
pts[k++] = (point - c) * 0.85 + c;
}
cv::fisheye::undistortPoints(distorted0, undist1, this->K, this->D);
cv::Vec2d* u1 = undist1.ptr<cv::Vec2d>();
cv::Vec3d* u2 = undist2.ptr<cv::Vec3d>();
for(int i = 0; i < (int)distorted0.total(); ++i)
u2[i] = cv::Vec3d(u1[i][0], u1[i][1], 1.0);
cv::fisheye::distortPoints(undist1, distorted1, this->K, this->D);
cv::fisheye::projectPoints(undist2, distorted2, cv::Vec3d::all(0), cv::Vec3d::all(0), this->K, this->D);
EXPECT_MAT_NEAR(distorted0, distorted1, 1e-10);
EXPECT_MAT_NEAR(distorted0, distorted2, 1e-10);
}
TEST_F(fisheyeTest, DISABLED_undistortImage)
{
cv::Matx33d theK = this->K;
cv::Mat theD = cv::Mat(this->D);
std::string file = combine(datasets_repository_path, "/calib-3_stereo_from_JY/left/stereo_pair_014.jpg");
cv::Matx33d newK = theK;
cv::Mat distorted = cv::imread(file), undistorted;
{
newK(0, 0) = 100;
newK(1, 1) = 100;
cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
cv::Mat correct = cv::imread(combine(datasets_repository_path, "new_f_100.png"));
if (correct.empty())
CV_Assert(cv::imwrite(combine(datasets_repository_path, "new_f_100.png"), undistorted));
else
EXPECT_MAT_NEAR(correct, undistorted, 1e-10);
}
{
double balance = 1.0;
cv::fisheye::estimateNewCameraMatrixForUndistortRectify(theK, theD, distorted.size(), cv::noArray(), newK, balance);
cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
cv::Mat correct = cv::imread(combine(datasets_repository_path, "balance_1.0.png"));
if (correct.empty())
CV_Assert(cv::imwrite(combine(datasets_repository_path, "balance_1.0.png"), undistorted));
else
EXPECT_MAT_NEAR(correct, undistorted, 1e-10);
}
{
double balance = 0.0;
cv::fisheye::estimateNewCameraMatrixForUndistortRectify(theK, theD, distorted.size(), cv::noArray(), newK, balance);
cv::fisheye::undistortImage(distorted, undistorted, theK, theD, newK);
cv::Mat correct = cv::imread(combine(datasets_repository_path, "balance_0.0.png"));
if (correct.empty())
CV_Assert(cv::imwrite(combine(datasets_repository_path, "balance_0.0.png"), undistorted));
else
EXPECT_MAT_NEAR(correct, undistorted, 1e-10);
}
}
TEST_F(fisheyeTest, jacobians)
{
int n = 10;
cv::Mat X(1, n, CV_64FC3);
cv::Mat om(3, 1, CV_64F), theT(3, 1, CV_64F);
cv::Mat f(2, 1, CV_64F), c(2, 1, CV_64F);
cv::Mat k(4, 1, CV_64F);
double alpha;
cv::RNG r;
r.fill(X, cv::RNG::NORMAL, 2, 1);
X = cv::abs(X) * 10;
r.fill(om, cv::RNG::NORMAL, 0, 1);
om = cv::abs(om);
r.fill(theT, cv::RNG::NORMAL, 0, 1);
theT = cv::abs(theT); theT.at<double>(2) = 4; theT *= 10;
r.fill(f, cv::RNG::NORMAL, 0, 1);
f = cv::abs(f) * 1000;
r.fill(c, cv::RNG::NORMAL, 0, 1);
c = cv::abs(c) * 1000;
r.fill(k, cv::RNG::NORMAL, 0, 1);
k*= 0.5;
alpha = 0.01*r.gaussian(1);
cv::Mat x1, x2, xpred;
cv::Matx33d theK(f.at<double>(0), alpha * f.at<double>(0), c.at<double>(0),
0, f.at<double>(1), c.at<double>(1),
0, 0, 1);
cv::Mat jacobians;
cv::fisheye::projectPoints(X, x1, om, theT, theK, k, alpha, jacobians);
//test on T:
cv::Mat dT(3, 1, CV_64FC1);
r.fill(dT, cv::RNG::NORMAL, 0, 1);
dT *= 1e-9*cv::norm(theT);
cv::Mat T2 = theT + dT;
cv::fisheye::projectPoints(X, x2, om, T2, theK, k, alpha, cv::noArray());
xpred = x1 + cv::Mat(jacobians.colRange(11,14) * dT).reshape(2, 1);
CV_Assert (cv::norm(x2 - xpred) < 1e-10);
//test on om:
cv::Mat dom(3, 1, CV_64FC1);
r.fill(dom, cv::RNG::NORMAL, 0, 1);
dom *= 1e-9*cv::norm(om);
cv::Mat om2 = om + dom;
cv::fisheye::projectPoints(X, x2, om2, theT, theK, k, alpha, cv::noArray());
xpred = x1 + cv::Mat(jacobians.colRange(8,11) * dom).reshape(2, 1);
CV_Assert (cv::norm(x2 - xpred) < 1e-10);
//test on f:
cv::Mat df(2, 1, CV_64FC1);
r.fill(df, cv::RNG::NORMAL, 0, 1);
df *= 1e-9*cv::norm(f);
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);
cv::fisheye::projectPoints(X, x2, om, theT, K2, k, alpha, cv::noArray());
xpred = x1 + cv::Mat(jacobians.colRange(0,2) * df).reshape(2, 1);
CV_Assert (cv::norm(x2 - xpred) < 1e-10);
//test on c:
cv::Mat dc(2, 1, CV_64FC1);
r.fill(dc, cv::RNG::NORMAL, 0, 1);
dc *= 1e-9*cv::norm(c);
K2 = theK + cv::Matx33d(0, 0, dc.at<double>(0), 0, 0, dc.at<double>(1), 0, 0, 0);
cv::fisheye::projectPoints(X, x2, om, theT, K2, k, alpha, cv::noArray());
xpred = x1 + cv::Mat(jacobians.colRange(2,4) * dc).reshape(2, 1);
CV_Assert (cv::norm(x2 - xpred) < 1e-10);
//test on k:
cv::Mat dk(4, 1, CV_64FC1);
r.fill(dk, cv::RNG::NORMAL, 0, 1);
dk *= 1e-9*cv::norm(k);
cv::Mat k2 = k + dk;
cv::fisheye::projectPoints(X, x2, om, theT, theK, k2, alpha, cv::noArray());
xpred = x1 + cv::Mat(jacobians.colRange(4,8) * dk).reshape(2, 1);
CV_Assert (cv::norm(x2 - xpred) < 1e-10);
//test on alpha:
cv::Mat dalpha(1, 1, CV_64FC1);
r.fill(dalpha, cv::RNG::NORMAL, 0, 1);
dalpha *= 1e-9*cv::norm(f);
double alpha2 = alpha + dalpha.at<double>(0);
K2 = theK + cv::Matx33d(0, f.at<double>(0) * dalpha.at<double>(0), 0, 0, 0, 0, 0, 0, 0);
cv::fisheye::projectPoints(X, x2, om, theT, theK, k, alpha2, cv::noArray());
xpred = x1 + cv::Mat(jacobians.col(14) * dalpha).reshape(2, 1);
CV_Assert (cv::norm(x2 - xpred) < 1e-10);
}
TEST_F(fisheyeTest, Calibration)
{
const int n_images = 34;
std::vector<std::vector<cv::Point2d> > imagePoints(n_images);
std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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 )] >> imagePoints[i];
fs_left.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();
int flag = 0;
flag |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;
flag |= cv::fisheye::CALIB_CHECK_COND;
flag |= cv::fisheye::CALIB_FIX_SKEW;
cv::Matx33d theK;
cv::Vec4d theD;
cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, theK, theD,
cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
EXPECT_MAT_NEAR(theK, this->K, 1e-10);
EXPECT_MAT_NEAR(theD, this->D, 1e-10);
}
TEST_F(fisheyeTest, Homography)
{
const int n_images = 1;
std::vector<std::vector<cv::Point2d> > imagePoints(n_images);
std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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 )] >> imagePoints[i];
fs_left.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::internal::IntrinsicParams param;
param.Init(cv::Vec2d(cv::max(imageSize.width, imageSize.height) / CV_PI, cv::max(imageSize.width, imageSize.height) / CV_PI),
cv::Vec2d(imageSize.width / 2.0 - 0.5, imageSize.height / 2.0 - 0.5));
cv::Mat _imagePoints (imagePoints[0]);
cv::Mat _objectPoints(objectPoints[0]);
cv::Mat imagePointsNormalized = NormalizePixels(_imagePoints, param).reshape(1).t();
_objectPoints = _objectPoints.reshape(1).t();
cv::Mat objectPointsMean, covObjectPoints;
int Np = imagePointsNormalized.cols;
cv::calcCovarMatrix(_objectPoints, covObjectPoints, objectPointsMean, cv::COVAR_NORMAL | cv::COVAR_COLS);
cv::SVD svd(covObjectPoints);
cv::Mat theR(svd.vt);
if (cv::norm(theR(cv::Rect(2, 0, 1, 2))) < 1e-6)
theR = cv::Mat::eye(3,3, CV_64FC1);
if (cv::determinant(theR) < 0)
theR = -theR;
cv::Mat theT = -theR * objectPointsMean;
cv::Mat X_new = theR * _objectPoints + theT * cv::Mat::ones(1, Np, CV_64FC1);
cv::Mat H = cv::internal::ComputeHomography(imagePointsNormalized, X_new.rowRange(0, 2));
cv::Mat M = cv::Mat::ones(3, X_new.cols, CV_64FC1);
X_new.rowRange(0, 2).copyTo(M.rowRange(0, 2));
cv::Mat mrep = H * M;
cv::divide(mrep, cv::Mat::ones(3,1, CV_64FC1) * mrep.row(2).clone(), mrep);
cv::Mat merr = (mrep.rowRange(0, 2) - imagePointsNormalized).t();
cv::Vec2d std_err;
cv::meanStdDev(merr.reshape(2), cv::noArray(), std_err);
std_err *= sqrt((double)merr.reshape(2).total() / (merr.reshape(2).total() - 1));
cv::Vec2d correct_std_err(0.00516740156010384, 0.00644205331553901);
EXPECT_MAT_NEAR(std_err, correct_std_err, 1e-12);
}
TEST_F(fisheyeTest, EtimateUncertainties)
{
const int n_images = 34;
std::vector<std::vector<cv::Point2d> > imagePoints(n_images);
std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
const std::string folder =combine(datasets_repository_path, "calib-3_stereo_from_JY");
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 )] >> imagePoints[i];
fs_left.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();
int flag = 0;
flag |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;
flag |= cv::fisheye::CALIB_CHECK_COND;
flag |= cv::fisheye::CALIB_FIX_SKEW;
cv::Matx33d theK;
cv::Vec4d theD;
std::vector<cv::Vec3d> rvec;
std::vector<cv::Vec3d> tvec;
cv::fisheye::calibrate(objectPoints, imagePoints, imageSize, theK, theD,
rvec, tvec, flag, cv::TermCriteria(3, 20, 1e-6));
cv::internal::IntrinsicParams param, errors;
cv::Vec2d err_std;
double thresh_cond = 1e6;
int check_cond = 1;
param.Init(cv::Vec2d(theK(0,0), theK(1,1)), cv::Vec2d(theK(0,2), theK(1, 2)), theD);
param.isEstimate = std::vector<uchar>(9, 1);
param.isEstimate[4] = 0;
errors.isEstimate = param.isEstimate;
double rms;
cv::internal::EstimateUncertainties(objectPoints, imagePoints, param, rvec, tvec,
errors, err_std, thresh_cond, check_cond, rms);
EXPECT_MAT_NEAR(errors.f, cv::Vec2d(1.29837104202046, 1.31565641071524), 1e-10);
EXPECT_MAT_NEAR(errors.c, cv::Vec2d(0.890439368129246, 0.816096854937896), 1e-10);
EXPECT_MAT_NEAR(errors.k, cv::Vec4d(0.00516248605191506, 0.0168181467500934, 0.0213118690274604, 0.00916010877545648), 1e-10);
EXPECT_MAT_NEAR(err_std, cv::Vec2d(0.187475975266883, 0.185678953263995), 1e-10);
CV_Assert(fabs(rms - 0.263782587133546) < 1e-10);
CV_Assert(errors.alpha == 0);
}
#ifdef HAVE_TEGRA_OPTIMIZATION
// not passing accuracy constrains
TEST_F(fisheyeTest, DISABLED_rectify)
#else
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;
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
cv::fisheye::initUndistortRectifyMap(K1, D1, R1, P1, requested_size, CV_32F, lmapx, lmapy);
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);
}
TEST_F(fisheyeTest, CalibrationWithDifferentPointsNumber)
{
const int n_images = 2;
std::vector<std::vector<cv::Point2d> > imagePoints(n_images);
std::vector<std::vector<cv::Point3d> > objectPoints(n_images);
std::vector<cv::Point2d> imgPoints1(10);
std::vector<cv::Point2d> imgPoints2(15);
std::vector<cv::Point3d> objectPoints1(imgPoints1.size());
std::vector<cv::Point3d> objectPoints2(imgPoints2.size());
for (size_t i = 0; i < imgPoints1.size(); i++)
{
imgPoints1[i] = cv::Point2d((double)i, (double)i);
objectPoints1[i] = cv::Point3d((double)i, (double)i, 10.0);
}
for (size_t i = 0; i < imgPoints2.size(); i++)
{
imgPoints2[i] = cv::Point2d(i + 0.5, i + 0.5);
objectPoints2[i] = cv::Point3d(i + 0.5, i + 0.5, 10.0);
}
imagePoints[0] = imgPoints1;
imagePoints[1] = imgPoints2;
objectPoints[0] = objectPoints1;
objectPoints[1] = objectPoints2;
cv::Matx33d theK = cv::Matx33d::eye();
cv::Vec4d theD;
int flag = 0;
flag |= cv::fisheye::CALIB_RECOMPUTE_EXTRINSIC;
flag |= cv::fisheye::CALIB_USE_INTRINSIC_GUESS;
flag |= cv::fisheye::CALIB_FIX_SKEW;
cv::fisheye::calibrate(objectPoints, imagePoints, cv::Size(100, 100), theK, theD,
cv::noArray(), cv::noArray(), flag, cv::TermCriteria(3, 20, 1e-6));
}
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
/// 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;
}
Reference in New Issue View Git Blame Copy Permalink