mirror of
https://github.com/opencv/opencv.git
synced 2024-11-27 20:50:25 +08:00
226 lines
7.6 KiB
C++
226 lines
7.6 KiB
C++
/*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, 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"
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
class CV_Affine3D_EstTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_Affine3D_EstTest();
|
|
~CV_Affine3D_EstTest();
|
|
protected:
|
|
void run(int);
|
|
|
|
bool test4Points();
|
|
bool testNPoints();
|
|
};
|
|
|
|
CV_Affine3D_EstTest::CV_Affine3D_EstTest()
|
|
{
|
|
}
|
|
CV_Affine3D_EstTest::~CV_Affine3D_EstTest() {}
|
|
|
|
|
|
float rngIn(float from, float to) { return from + (to-from) * (float)theRNG(); }
|
|
|
|
|
|
struct WrapAff
|
|
{
|
|
const double *F;
|
|
WrapAff(const Mat& aff) : F(aff.ptr<double>()) {}
|
|
Point3f operator()(const Point3f& p)
|
|
{
|
|
return Point3f( (float)(p.x * F[0] + p.y * F[1] + p.z * F[2] + F[3]),
|
|
(float)(p.x * F[4] + p.y * F[5] + p.z * F[6] + F[7]),
|
|
(float)(p.x * F[8] + p.y * F[9] + p.z * F[10] + F[11]) );
|
|
}
|
|
};
|
|
|
|
bool CV_Affine3D_EstTest::test4Points()
|
|
{
|
|
Mat aff(3, 4, CV_64F);
|
|
cv::randu(aff, Scalar(1), Scalar(3));
|
|
|
|
// setting points that are no in the same line
|
|
|
|
Mat fpts(1, 4, CV_32FC3);
|
|
Mat tpts(1, 4, CV_32FC3);
|
|
|
|
fpts.ptr<Point3f>()[0] = Point3f( rngIn(1,2), rngIn(1,2), rngIn(5, 6) );
|
|
fpts.ptr<Point3f>()[1] = Point3f( rngIn(3,4), rngIn(3,4), rngIn(5, 6) );
|
|
fpts.ptr<Point3f>()[2] = Point3f( rngIn(1,2), rngIn(3,4), rngIn(5, 6) );
|
|
fpts.ptr<Point3f>()[3] = Point3f( rngIn(3,4), rngIn(1,2), rngIn(5, 6) );
|
|
|
|
std::transform(fpts.ptr<Point3f>(), fpts.ptr<Point3f>() + 4, tpts.ptr<Point3f>(), WrapAff(aff));
|
|
|
|
Mat aff_est;
|
|
vector<uchar> outliers;
|
|
estimateAffine3D(fpts, tpts, aff_est, outliers);
|
|
|
|
const double thres = 1e-3;
|
|
if (cvtest::norm(aff_est, aff, NORM_INF) > thres)
|
|
{
|
|
//cout << cvtest::norm(aff_est, aff, NORM_INF) << endl;
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
struct Noise
|
|
{
|
|
float l;
|
|
Noise(float level) : l(level) {}
|
|
Point3f operator()(const Point3f& p)
|
|
{
|
|
RNG& rng = theRNG();
|
|
return Point3f( p.x + l * (float)rng, p.y + l * (float)rng, p.z + l * (float)rng);
|
|
}
|
|
};
|
|
|
|
bool CV_Affine3D_EstTest::testNPoints()
|
|
{
|
|
Mat aff(3, 4, CV_64F);
|
|
cv::randu(aff, Scalar(-2), Scalar(2));
|
|
|
|
// setting points that are no in the same line
|
|
|
|
const int n = 100;
|
|
const int m = 3*n/5;
|
|
const Point3f shift_outl = Point3f(15, 15, 15);
|
|
const float noise_level = 20.f;
|
|
|
|
Mat fpts(1, n, CV_32FC3);
|
|
Mat tpts(1, n, CV_32FC3);
|
|
|
|
randu(fpts, Scalar::all(0), Scalar::all(100));
|
|
std::transform(fpts.ptr<Point3f>(), fpts.ptr<Point3f>() + n, tpts.ptr<Point3f>(), WrapAff(aff));
|
|
|
|
/* adding noise*/
|
|
std::transform(tpts.ptr<Point3f>() + m, tpts.ptr<Point3f>() + n, tpts.ptr<Point3f>() + m,
|
|
[=] (const Point3f& pt) -> Point3f { return Noise(noise_level)(pt + shift_outl); });
|
|
|
|
Mat aff_est;
|
|
vector<uchar> outl;
|
|
int res = estimateAffine3D(fpts, tpts, aff_est, outl);
|
|
|
|
if (!res)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
return false;
|
|
}
|
|
|
|
const double thres = 1e-4;
|
|
if (cvtest::norm(aff_est, aff, NORM_INF) > thres)
|
|
{
|
|
cout << "aff est: " << aff_est << endl;
|
|
cout << "aff ref: " << aff << endl;
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
return false;
|
|
}
|
|
|
|
bool outl_good = std::count(outl.begin(), outl.end(), 1) == m &&
|
|
m == std::accumulate(outl.begin(), outl.begin() + m, 0);
|
|
|
|
if (!outl_good)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
|
|
void CV_Affine3D_EstTest::run( int /* start_from */)
|
|
{
|
|
cvtest::DefaultRngAuto dra;
|
|
|
|
if (!test4Points())
|
|
return;
|
|
|
|
if (!testNPoints())
|
|
return;
|
|
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
|
|
TEST(Calib3d_EstimateAffine3D, accuracy) { CV_Affine3D_EstTest test; test.safe_run(); }
|
|
|
|
TEST(Calib3d_EstimateAffine3D, regression_16007)
|
|
{
|
|
std::vector<cv::Point3f> m1, m2;
|
|
m1.push_back(Point3f(1.0f, 0.0f, 0.0f)); m2.push_back(Point3f(1.0f, 1.0f, 0.0f));
|
|
m1.push_back(Point3f(1.0f, 0.0f, 1.0f)); m2.push_back(Point3f(1.0f, 1.0f, 1.0f));
|
|
m1.push_back(Point3f(0.5f, 0.0f, 0.5f)); m2.push_back(Point3f(0.5f, 1.0f, 0.5f));
|
|
m1.push_back(Point3f(2.5f, 0.0f, 2.5f)); m2.push_back(Point3f(2.5f, 1.0f, 2.5f));
|
|
m1.push_back(Point3f(2.0f, 0.0f, 1.0f)); m2.push_back(Point3f(2.0f, 1.0f, 1.0f));
|
|
|
|
cv::Mat m3D, inl;
|
|
int res = cv::estimateAffine3D(m1, m2, m3D, inl);
|
|
EXPECT_EQ(1, res);
|
|
}
|
|
|
|
TEST(Calib3d_EstimateAffine3D, umeyama_3_pt)
|
|
{
|
|
std::vector<cv::Vec3d> points = {{{0.80549149, 0.8225781, 0.79949521},
|
|
{0.28906756, 0.57158557, 0.9864789},
|
|
{0.58266182, 0.65474983, 0.25078834}}};
|
|
cv::Mat R = (cv::Mat_<double>(3,3) << 0.9689135, -0.0232753, 0.2463025,
|
|
0.0236362, 0.9997195, 0.0014915,
|
|
-0.2462682, 0.0043765, 0.9691918);
|
|
cv::Vec3d t(1., 2., 3.);
|
|
cv::Affine3d transform(R, t);
|
|
std::vector<cv::Vec3d> transformed_points(points.size());
|
|
std::transform(points.begin(), points.end(), transformed_points.begin(), [transform](const cv::Vec3d v){return transform * v;});
|
|
double scale;
|
|
cv::Mat trafo_est = estimateAffine3D(points, transformed_points, &scale);
|
|
Mat R_est(trafo_est(Rect(0, 0, 3, 3)));
|
|
EXPECT_LE(cvtest::norm(R_est, R, NORM_INF), 1e-6);
|
|
Vec3d t_est = trafo_est.col(3);
|
|
EXPECT_LE(cvtest::norm(t_est, t, NORM_INF), 1e-6);
|
|
EXPECT_NEAR(scale, 1.0, 1e-6);
|
|
}
|
|
|
|
}} // namespace
|