Open Source Computer Vision Library https://opencv.org/
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#include "test_precomp.hpp"
#include <string>
using namespace cv;
using namespace std;
class CV_UndistortTest : public cvtest::BaseTest
{
public:
CV_UndistortTest();
~CV_UndistortTest();
protected:
void run(int);
private:
void generate3DPointCloud(vector<Point3f>& points, Point3f pmin = Point3f(-1,
-1, 5), Point3f pmax = Point3f(1, 1, 10));
void generateCameraMatrix(Mat& cameraMatrix);
void generateDistCoeffs(Mat& distCoeffs, int count);
double thresh;
RNG rng;
};
CV_UndistortTest::CV_UndistortTest()
{
thresh = 1.0e-2;
}
CV_UndistortTest::~CV_UndistortTest() {}
void CV_UndistortTest::generate3DPointCloud(vector<Point3f>& points, Point3f pmin, Point3f pmax)
{
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 CV_UndistortTest::generateCameraMatrix(Mat& cameraMatrix)
{
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 CV_UndistortTest::generateDistCoeffs(Mat& distCoeffs, int count)
{
distCoeffs = Mat::zeros(count, 1, CV_64FC1);
for (int i = 0; i < count; i++)
distCoeffs.at<double>(i,0) = rng.uniform(0.0, 1.0e-3);
}
void CV_UndistortTest::run(int /* start_from */)
{
Mat intrinsics, distCoeffs;
generateCameraMatrix(intrinsics);
vector<Point3f> points(500);
generate3DPointCloud(points);
vector<Point2f> projectedPoints;
projectedPoints.resize(points.size());
int modelMembersCount[] = {4,5,8};
for (int idx = 0; idx < 3; idx++)
{
generateDistCoeffs(distCoeffs, modelMembersCount[idx]);
projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1), Mat::zeros(3,1,CV_64FC1), intrinsics, distCoeffs, projectedPoints);
vector<Point2f> realUndistortedPoints;
projectPoints(Mat(points), Mat::zeros(3,1,CV_64FC1), Mat::zeros(3,1,CV_64FC1), intrinsics, Mat::zeros(4,1,CV_64FC1), realUndistortedPoints);
Mat undistortedPoints;
undistortPoints(Mat(projectedPoints), undistortedPoints, intrinsics, distCoeffs);
Mat p;
perspectiveTransform(undistortedPoints, p, intrinsics);
undistortedPoints = p;
double diff = cvtest::norm(Mat(realUndistortedPoints), undistortedPoints, NORM_L2);
if (diff > thresh)
{
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
ts->set_failed_test_info(cvtest::TS::OK);
}
}
TEST(Calib3d_Undistort, accuracy) { CV_UndistortTest test; test.safe_run(); }