mirror of
https://github.com/opencv/opencv.git
synced 2024-11-28 21:20:18 +08:00
46bee83005
* move array size to enum * move array size to member variable * loosen the eps of SOLVEPNP_P3P * loosen the eps in Calib3d_SolveP3P.accuracy
532 lines
18 KiB
C++
532 lines
18 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"
|
|
|
|
#ifdef HAVE_TBB
|
|
#include "tbb/task_scheduler_init.h"
|
|
#endif
|
|
|
|
using namespace cv;
|
|
using namespace std;
|
|
|
|
class CV_solvePnPRansac_Test : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_solvePnPRansac_Test()
|
|
{
|
|
eps[SOLVEPNP_ITERATIVE] = 1.0e-2;
|
|
eps[SOLVEPNP_EPNP] = 1.0e-2;
|
|
eps[SOLVEPNP_P3P] = 1.0e-2;
|
|
eps[SOLVEPNP_AP3P] = 1.0e-2;
|
|
eps[SOLVEPNP_DLS] = 1.0e-2;
|
|
eps[SOLVEPNP_UPNP] = 1.0e-2;
|
|
totalTestsCount = 10;
|
|
pointsCount = 500;
|
|
}
|
|
~CV_solvePnPRansac_Test() {}
|
|
protected:
|
|
void generate3DPointCloud(vector<Point3f>& points,
|
|
Point3f pmin = Point3f(-1, -1, 5),
|
|
Point3f pmax = Point3f(1, 1, 10))
|
|
{
|
|
RNG rng = ::theRNG(); // fix the seed to use "fixed" input 3D points
|
|
|
|
for (size_t i = 0; i < points.size(); i++)
|
|
{
|
|
float _x = rng.uniform(pmin.x, pmax.x);
|
|
float _y = rng.uniform(pmin.y, pmax.y);
|
|
float _z = rng.uniform(pmin.z, pmax.z);
|
|
points[i] = Point3f(_x, _y, _z);
|
|
}
|
|
}
|
|
|
|
void generateCameraMatrix(Mat& cameraMatrix, RNG& rng)
|
|
{
|
|
const double fcMinVal = 1e-3;
|
|
const double fcMaxVal = 100;
|
|
cameraMatrix.create(3, 3, CV_64FC1);
|
|
cameraMatrix.setTo(Scalar(0));
|
|
cameraMatrix.at<double>(0,0) = rng.uniform(fcMinVal, fcMaxVal);
|
|
cameraMatrix.at<double>(1,1) = rng.uniform(fcMinVal, fcMaxVal);
|
|
cameraMatrix.at<double>(0,2) = rng.uniform(fcMinVal, fcMaxVal);
|
|
cameraMatrix.at<double>(1,2) = rng.uniform(fcMinVal, fcMaxVal);
|
|
cameraMatrix.at<double>(2,2) = 1;
|
|
}
|
|
|
|
void generateDistCoeffs(Mat& distCoeffs, RNG& rng)
|
|
{
|
|
distCoeffs = Mat::zeros(4, 1, CV_64FC1);
|
|
for (int i = 0; i < 3; i++)
|
|
distCoeffs.at<double>(i,0) = rng.uniform(0.0, 1.0e-6);
|
|
}
|
|
|
|
void generatePose(Mat& rvec, Mat& tvec, RNG& rng)
|
|
{
|
|
const double minVal = 1.0e-3;
|
|
const double maxVal = 1.0;
|
|
rvec.create(3, 1, CV_64FC1);
|
|
tvec.create(3, 1, CV_64FC1);
|
|
for (int i = 0; i < 3; i++)
|
|
{
|
|
rvec.at<double>(i,0) = rng.uniform(minVal, maxVal);
|
|
tvec.at<double>(i,0) = rng.uniform(minVal, maxVal/10);
|
|
}
|
|
}
|
|
|
|
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
|
|
{
|
|
Mat rvec, tvec;
|
|
vector<int> inliers;
|
|
Mat trueRvec, trueTvec;
|
|
Mat intrinsics, distCoeffs;
|
|
generateCameraMatrix(intrinsics, rng);
|
|
if (method == 4) intrinsics.at<double>(1,1) = intrinsics.at<double>(0,0);
|
|
if (mode == 0)
|
|
distCoeffs = Mat::zeros(4, 1, CV_64FC1);
|
|
else
|
|
generateDistCoeffs(distCoeffs, rng);
|
|
generatePose(trueRvec, trueTvec, rng);
|
|
|
|
vector<Point2f> projectedPoints;
|
|
projectedPoints.resize(points.size());
|
|
projectPoints(Mat(points), trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints);
|
|
for (size_t i = 0; i < projectedPoints.size(); i++)
|
|
{
|
|
if (i % 20 == 0)
|
|
{
|
|
projectedPoints[i] = projectedPoints[rng.uniform(0,(int)points.size()-1)];
|
|
}
|
|
}
|
|
|
|
solvePnPRansac(points, projectedPoints, intrinsics, distCoeffs, rvec, tvec, false, pointsCount, 0.5f, 0.99, inliers, method);
|
|
|
|
bool isTestSuccess = inliers.size() >= points.size()*0.95;
|
|
|
|
double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec);
|
|
isTestSuccess = isTestSuccess && rvecDiff < epsilon[method] && tvecDiff < epsilon[method];
|
|
double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
|
|
//cout << error << " " << inliers.size() << " " << eps[method] << endl;
|
|
if (error > maxError)
|
|
maxError = error;
|
|
|
|
return isTestSuccess;
|
|
}
|
|
|
|
virtual void run(int)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
|
|
vector<Point3f> points, points_dls;
|
|
points.resize(pointsCount);
|
|
generate3DPointCloud(points);
|
|
|
|
RNG rng = ts->get_rng();
|
|
|
|
|
|
for (int mode = 0; mode < 2; mode++)
|
|
{
|
|
for (int method = 0; method < SOLVEPNP_MAX_COUNT; method++)
|
|
{
|
|
double maxError = 0;
|
|
int successfulTestsCount = 0;
|
|
for (int testIndex = 0; testIndex < totalTestsCount; testIndex++)
|
|
{
|
|
if (runTest(rng, mode, method, points, eps, maxError))
|
|
{
|
|
successfulTestsCount++;
|
|
}
|
|
}
|
|
if (successfulTestsCount < 0.7*totalTestsCount)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n",
|
|
method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
}
|
|
cout << "mode: " << mode << ", method: " << method << " -> "
|
|
<< ((double)successfulTestsCount / totalTestsCount) * 100 << "%"
|
|
<< " (err < " << maxError << ")" << endl;
|
|
}
|
|
}
|
|
}
|
|
double eps[SOLVEPNP_MAX_COUNT];
|
|
int totalTestsCount;
|
|
int pointsCount;
|
|
};
|
|
|
|
class CV_solvePnP_Test : public CV_solvePnPRansac_Test
|
|
{
|
|
public:
|
|
CV_solvePnP_Test()
|
|
{
|
|
eps[SOLVEPNP_ITERATIVE] = 1.0e-6;
|
|
eps[SOLVEPNP_EPNP] = 1.0e-6;
|
|
eps[SOLVEPNP_P3P] = 2.0e-4;
|
|
eps[SOLVEPNP_AP3P] = 1.0e-4;
|
|
eps[SOLVEPNP_DLS] = 1.0e-4;
|
|
eps[SOLVEPNP_UPNP] = 1.0e-4;
|
|
totalTestsCount = 1000;
|
|
}
|
|
|
|
~CV_solvePnP_Test() {}
|
|
protected:
|
|
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
|
|
{
|
|
Mat rvec, tvec;
|
|
Mat trueRvec, trueTvec;
|
|
Mat intrinsics, distCoeffs;
|
|
generateCameraMatrix(intrinsics, rng);
|
|
if (method == SOLVEPNP_DLS)
|
|
{
|
|
intrinsics.at<double>(1,1) = intrinsics.at<double>(0,0);
|
|
}
|
|
if (mode == 0)
|
|
{
|
|
distCoeffs = Mat::zeros(4, 1, CV_64FC1);
|
|
}
|
|
else
|
|
{
|
|
generateDistCoeffs(distCoeffs, rng);
|
|
}
|
|
generatePose(trueRvec, trueTvec, rng);
|
|
|
|
std::vector<Point3f> opoints;
|
|
switch(method)
|
|
{
|
|
case SOLVEPNP_P3P:
|
|
case SOLVEPNP_AP3P:
|
|
opoints = std::vector<Point3f>(points.begin(), points.begin()+4);
|
|
break;
|
|
case SOLVEPNP_UPNP:
|
|
opoints = std::vector<Point3f>(points.begin(), points.begin()+50);
|
|
break;
|
|
default:
|
|
opoints = points;
|
|
break;
|
|
}
|
|
|
|
vector<Point2f> projectedPoints;
|
|
projectedPoints.resize(opoints.size());
|
|
projectPoints(Mat(opoints), trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints);
|
|
|
|
bool isEstimateSuccess = solvePnP(opoints, projectedPoints, intrinsics, distCoeffs, rvec, tvec, false, method);
|
|
if (isEstimateSuccess == false)
|
|
{
|
|
return isEstimateSuccess;
|
|
}
|
|
|
|
double rvecDiff = norm(rvec-trueRvec), tvecDiff = norm(tvec-trueTvec);
|
|
bool isTestSuccess = rvecDiff < epsilon[method] && tvecDiff < epsilon[method];
|
|
|
|
double error = rvecDiff > tvecDiff ? rvecDiff : tvecDiff;
|
|
if (error > maxError)
|
|
{
|
|
maxError = error;
|
|
}
|
|
|
|
return isTestSuccess;
|
|
}
|
|
};
|
|
|
|
class CV_solveP3P_Test : public CV_solvePnPRansac_Test
|
|
{
|
|
public:
|
|
CV_solveP3P_Test()
|
|
{
|
|
eps[SOLVEPNP_P3P] = 2.0e-4;
|
|
eps[SOLVEPNP_AP3P] = 1.0e-4;
|
|
totalTestsCount = 1000;
|
|
}
|
|
|
|
~CV_solveP3P_Test() {}
|
|
protected:
|
|
virtual bool runTest(RNG& rng, int mode, int method, const vector<Point3f>& points, const double* epsilon, double& maxError)
|
|
{
|
|
std::vector<Mat> rvecs, tvecs;
|
|
Mat trueRvec, trueTvec;
|
|
Mat intrinsics, distCoeffs;
|
|
generateCameraMatrix(intrinsics, rng);
|
|
if (mode == 0)
|
|
distCoeffs = Mat::zeros(4, 1, CV_64FC1);
|
|
else
|
|
generateDistCoeffs(distCoeffs, rng);
|
|
generatePose(trueRvec, trueTvec, rng);
|
|
|
|
std::vector<Point3f> opoints;
|
|
opoints = std::vector<Point3f>(points.begin(), points.begin()+3);
|
|
|
|
vector<Point2f> projectedPoints;
|
|
projectedPoints.resize(opoints.size());
|
|
projectPoints(Mat(opoints), trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints);
|
|
|
|
int num_of_solutions = solveP3P(opoints, projectedPoints, intrinsics, distCoeffs, rvecs, tvecs, method);
|
|
if (num_of_solutions != (int) rvecs.size() || num_of_solutions != (int) tvecs.size() || num_of_solutions == 0)
|
|
return false;
|
|
|
|
double min_rvecDiff = DBL_MAX, min_tvecDiff = DBL_MAX;
|
|
for (unsigned int i = 0; i < rvecs.size(); ++i) {
|
|
double rvecDiff = norm(rvecs[i]-trueRvec);
|
|
min_rvecDiff = std::min(rvecDiff, min_rvecDiff);
|
|
}
|
|
for (unsigned int i = 0; i < tvecs.size(); ++i) {
|
|
double tvecDiff = norm(tvecs[i]-trueTvec);
|
|
min_tvecDiff = std::min(tvecDiff, min_tvecDiff);
|
|
}
|
|
bool isTestSuccess = min_rvecDiff < epsilon[method] && min_tvecDiff < epsilon[method];
|
|
|
|
double error = std::max(min_rvecDiff, min_tvecDiff);
|
|
if (error > maxError)
|
|
maxError = error;
|
|
|
|
return isTestSuccess;
|
|
}
|
|
|
|
virtual void run(int)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
|
|
vector<Point3f> points, points_dls;
|
|
points.resize(pointsCount);
|
|
generate3DPointCloud(points);
|
|
|
|
const int methodsCount = 2;
|
|
int methods[] = {SOLVEPNP_P3P, SOLVEPNP_AP3P};
|
|
RNG rng = ts->get_rng();
|
|
|
|
for (int mode = 0; mode < 2; mode++)
|
|
{
|
|
for (int method = 0; method < methodsCount; method++)
|
|
{
|
|
double maxError = 0;
|
|
int successfulTestsCount = 0;
|
|
for (int testIndex = 0; testIndex < totalTestsCount; testIndex++)
|
|
{
|
|
if (runTest(rng, mode, methods[method], points, eps, maxError))
|
|
successfulTestsCount++;
|
|
}
|
|
if (successfulTestsCount < 0.7*totalTestsCount)
|
|
{
|
|
ts->printf( cvtest::TS::LOG, "Invalid accuracy for method %d, failed %d tests from %d, maximum error equals %f, distortion mode equals %d\n",
|
|
method, totalTestsCount - successfulTestsCount, totalTestsCount, maxError, mode);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
}
|
|
cout << "mode: " << mode << ", method: " << method << " -> "
|
|
<< ((double)successfulTestsCount / totalTestsCount) * 100 << "%"
|
|
<< " (err < " << maxError << ")" << endl;
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
|
|
TEST(Calib3d_SolveP3P, accuracy) { CV_solveP3P_Test test; test.safe_run();}
|
|
TEST(Calib3d_SolvePnPRansac, accuracy) { CV_solvePnPRansac_Test test; test.safe_run(); }
|
|
TEST(Calib3d_SolvePnP, accuracy) { CV_solvePnP_Test test; test.safe_run(); }
|
|
|
|
TEST(Calib3d_SolvePnPRansac, concurrency)
|
|
{
|
|
int count = 7*13;
|
|
|
|
Mat object(1, count, CV_32FC3);
|
|
randu(object, -100, 100);
|
|
|
|
Mat camera_mat(3, 3, CV_32FC1);
|
|
randu(camera_mat, 0.5, 1);
|
|
camera_mat.at<float>(0, 1) = 0.f;
|
|
camera_mat.at<float>(1, 0) = 0.f;
|
|
camera_mat.at<float>(2, 0) = 0.f;
|
|
camera_mat.at<float>(2, 1) = 0.f;
|
|
|
|
Mat dist_coef(1, 8, CV_32F, cv::Scalar::all(0));
|
|
|
|
vector<cv::Point2f> image_vec;
|
|
Mat rvec_gold(1, 3, CV_32FC1);
|
|
randu(rvec_gold, 0, 1);
|
|
Mat tvec_gold(1, 3, CV_32FC1);
|
|
randu(tvec_gold, 0, 1);
|
|
projectPoints(object, rvec_gold, tvec_gold, camera_mat, dist_coef, image_vec);
|
|
|
|
Mat image(1, count, CV_32FC2, &image_vec[0]);
|
|
|
|
Mat rvec1, rvec2;
|
|
Mat tvec1, tvec2;
|
|
|
|
{
|
|
// limit concurrency to get deterministic result
|
|
theRNG().state = 20121010;
|
|
setNumThreads(1);
|
|
solvePnPRansac(object, image, camera_mat, dist_coef, rvec1, tvec1);
|
|
}
|
|
|
|
{
|
|
Mat rvec;
|
|
Mat tvec;
|
|
// parallel executions
|
|
for(int i = 0; i < 10; ++i)
|
|
{
|
|
cv::theRNG().state = 20121010;
|
|
solvePnPRansac(object, image, camera_mat, dist_coef, rvec, tvec);
|
|
}
|
|
}
|
|
|
|
{
|
|
// single thread again
|
|
theRNG().state = 20121010;
|
|
setNumThreads(1);
|
|
solvePnPRansac(object, image, camera_mat, dist_coef, rvec2, tvec2);
|
|
}
|
|
|
|
double rnorm = cvtest::norm(rvec1, rvec2, NORM_INF);
|
|
double tnorm = cvtest::norm(tvec1, tvec2, NORM_INF);
|
|
|
|
EXPECT_LT(rnorm, 1e-6);
|
|
EXPECT_LT(tnorm, 1e-6);
|
|
}
|
|
|
|
TEST(Calib3d_SolvePnPRansac, input_type)
|
|
{
|
|
const int numPoints = 10;
|
|
Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0.,
|
|
5.4817724002728005e+002, 2.3062194051986233e+002, 0., 0., 1.);
|
|
|
|
std::vector<cv::Point3f> points3d;
|
|
std::vector<cv::Point2f> points2d;
|
|
for (int i = 0; i < numPoints; i+=2)
|
|
{
|
|
points3d.push_back(cv::Point3i(5+i, 3, 2));
|
|
points3d.push_back(cv::Point3i(5+i, 3+i, 2+i));
|
|
points2d.push_back(cv::Point2i(0, i));
|
|
points2d.push_back(cv::Point2i(-i, i));
|
|
}
|
|
Mat R1, t1, R2, t2, R3, t3, R4, t4;
|
|
|
|
EXPECT_TRUE(solvePnPRansac(points3d, points2d, intrinsics, cv::Mat(), R1, t1));
|
|
|
|
Mat points3dMat(points3d);
|
|
Mat points2dMat(points2d);
|
|
EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R2, t2));
|
|
|
|
points3dMat = points3dMat.reshape(3, 1);
|
|
points2dMat = points2dMat.reshape(2, 1);
|
|
EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R3, t3));
|
|
|
|
points3dMat = points3dMat.reshape(1, numPoints);
|
|
points2dMat = points2dMat.reshape(1, numPoints);
|
|
EXPECT_TRUE(solvePnPRansac(points3dMat, points2dMat, intrinsics, cv::Mat(), R4, t4));
|
|
|
|
EXPECT_LE(norm(R1, R2, NORM_INF), 1e-6);
|
|
EXPECT_LE(norm(t1, t2, NORM_INF), 1e-6);
|
|
EXPECT_LE(norm(R1, R3, NORM_INF), 1e-6);
|
|
EXPECT_LE(norm(t1, t3, NORM_INF), 1e-6);
|
|
EXPECT_LE(norm(R1, R4, NORM_INF), 1e-6);
|
|
EXPECT_LE(norm(t1, t4, NORM_INF), 1e-6);
|
|
}
|
|
|
|
TEST(Calib3d_SolvePnP, double_support)
|
|
{
|
|
Matx33d intrinsics(5.4794130238156129e+002, 0., 2.9835545700043139e+002, 0.,
|
|
5.4817724002728005e+002, 2.3062194051986233e+002, 0., 0., 1.);
|
|
std::vector<cv::Point3d> points3d;
|
|
std::vector<cv::Point2d> points2d;
|
|
std::vector<cv::Point3f> points3dF;
|
|
std::vector<cv::Point2f> points2dF;
|
|
for (int i = 0; i < 10 ; i+=2)
|
|
{
|
|
points3d.push_back(cv::Point3d(5+i, 3, 2));
|
|
points3dF.push_back(cv::Point3d(5+i, 3, 2));
|
|
points3d.push_back(cv::Point3d(5+i, 3+i, 2+i));
|
|
points3dF.push_back(cv::Point3d(5+i, 3+i, 2+i));
|
|
points2d.push_back(cv::Point2d(0, i));
|
|
points2dF.push_back(cv::Point2d(0, i));
|
|
points2d.push_back(cv::Point2d(-i, i));
|
|
points2dF.push_back(cv::Point2d(-i, i));
|
|
}
|
|
Mat R,t, RF, tF;
|
|
vector<int> inliers;
|
|
|
|
solvePnPRansac(points3dF, points2dF, intrinsics, cv::Mat(), RF, tF, true, 100, 8.f, 0.999, inliers, cv::SOLVEPNP_P3P);
|
|
solvePnPRansac(points3d, points2d, intrinsics, cv::Mat(), R, t, true, 100, 8.f, 0.999, inliers, cv::SOLVEPNP_P3P);
|
|
|
|
EXPECT_LE(norm(R, Mat_<double>(RF), NORM_INF), 1e-3);
|
|
EXPECT_LE(norm(t, Mat_<double>(tF), NORM_INF), 1e-3);
|
|
}
|
|
|
|
TEST(Calib3d_SolvePnP, translation)
|
|
{
|
|
Mat cameraIntrinsic = Mat::eye(3,3, CV_32FC1);
|
|
vector<float> crvec;
|
|
crvec.push_back(0.f);
|
|
crvec.push_back(0.f);
|
|
crvec.push_back(0.f);
|
|
vector<float> ctvec;
|
|
ctvec.push_back(100.f);
|
|
ctvec.push_back(100.f);
|
|
ctvec.push_back(0.f);
|
|
vector<Point3f> p3d;
|
|
p3d.push_back(Point3f(0,0,0));
|
|
p3d.push_back(Point3f(0,0,10));
|
|
p3d.push_back(Point3f(0,10,10));
|
|
p3d.push_back(Point3f(10,10,10));
|
|
p3d.push_back(Point3f(2,5,5));
|
|
|
|
vector<Point2f> p2d;
|
|
projectPoints(p3d, crvec, ctvec, cameraIntrinsic, noArray(), p2d);
|
|
Mat rvec;
|
|
Mat tvec;
|
|
rvec =(Mat_<float>(3,1) << 0, 0, 0);
|
|
tvec = (Mat_<float>(3,1) << 100, 100, 0);
|
|
|
|
solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, true);
|
|
EXPECT_TRUE(checkRange(rvec));
|
|
EXPECT_TRUE(checkRange(tvec));
|
|
|
|
rvec =(Mat_<double>(3,1) << 0, 0, 0);
|
|
tvec = (Mat_<double>(3,1) << 100, 100, 0);
|
|
solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, true);
|
|
EXPECT_TRUE(checkRange(rvec));
|
|
EXPECT_TRUE(checkRange(tvec));
|
|
|
|
solvePnP(p3d, p2d, cameraIntrinsic, noArray(), rvec, tvec, false);
|
|
EXPECT_TRUE(checkRange(rvec));
|
|
EXPECT_TRUE(checkRange(tvec));
|
|
}
|