mirror of
https://github.com/opencv/opencv.git
synced 2024-11-30 14:29:49 +08:00
319 lines
11 KiB
C++
319 lines
11 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_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, 0.5, 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 = 5;
|
|
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);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
double eps[5];
|
|
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_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)
|
|
{
|
|
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(); }
|
|
|
|
|
|
#ifdef HAVE_TBB
|
|
|
|
TEST(DISABLED_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
|
|
cv::theRNG().state = 20121010;
|
|
tbb::task_scheduler_init one_thread(1);
|
|
solvePnPRansac(object, image, camera_mat, dist_coef, rvec1, tvec1);
|
|
}
|
|
|
|
if(1)
|
|
{
|
|
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
|
|
cv::theRNG().state = 20121010;
|
|
tbb::task_scheduler_init one_thread(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);
|
|
|
|
}
|
|
#endif
|