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opencv/modules/calib3d/test/test_fisheye.cpp
Tomoaki Teshima 62c17dc3cf fix typo and align white spaces
2017-04-17 08:32:08 +09:00

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
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// For Open Source Computer Vision Library
//
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#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, EstimateUncertainties)
{
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;
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;
}
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