Open Source Computer Vision Library https://opencv.org/
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 
 

425 lines
15 KiB

/*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;
}
~CV_solvePnPRansac_Test() {}
protected:
void generate3DPointCloud(vector<Point3f>& points, Point3f pmin = Point3f(-1,
-1, 5), Point3f pmax = Point3f(1, 1, 10))
{
const Point3f delta = pmax - pmin;
for (size_t i = 0; i < points.size(); i++)
{
Point3f p(float(rand()) / RAND_MAX, float(rand()) / RAND_MAX,
float(rand()) / RAND_MAX);
p.x *= delta.x;
p.y *= delta.y;
p.z *= delta.z;
p = p + pmin;
points[i] = p;
}
}
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, 500, 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;
}
void run(int)
{
ts->set_failed_test_info(cvtest::TS::OK);
vector<Point3f> points, points_dls;
const int pointsCount = 500;
points.resize(pointsCount);
generate3DPointCloud(points);
const int methodsCount = 6;
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, method, points, eps, maxError))
successfulTestsCount++;
}
//cout << maxError << " " << successfulTestsCount << endl;
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[6];
int totalTestsCount;
};
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] = 1.0e-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 == 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);
std::vector<Point3f> opoints;
if (method == 2 || method == 5)
{
opoints = std::vector<Point3f>(points.begin(), points.begin()+4);
}
else if(method == 3)
{
opoints = std::vector<Point3f>(points.begin(), points.begin()+50);
}
else
opoints = points;
vector<Point2f> projectedPoints;
projectedPoints.resize(opoints.size());
projectPoints(Mat(opoints), trueRvec, trueTvec, intrinsics, distCoeffs, projectedPoints);
solvePnP(opoints, projectedPoints, intrinsics, distCoeffs, rvec, tvec,
false, method);
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;
}
};
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));
}