Remove all using directives for STL namespace and members
Made all STL usages explicit to be able automatically find all usages of particular class or function.
This commit is contained in:
Andrey Kamaev
@@ -3,31 +3,30 @@
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#include "_modelest.h"
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#include <complex>
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using namespace cv;
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using namespace std;
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using namespace cv;
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class CvEMEstimator : public CvModelEstimator2
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{
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public:
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CvEMEstimator();
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virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
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virtual int run5Point( const CvMat* _q1, const CvMat* _q2, CvMat* _ematrix );
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protected:
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bool reliable( const CvMat* m1, const CvMat* m2, const CvMat* model );
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virtual void calibrated_fivepoint_helper( double *eet, double* at );
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CvEMEstimator();
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virtual int runKernel( const CvMat* m1, const CvMat* m2, CvMat* model );
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virtual int run5Point( const CvMat* _q1, const CvMat* _q2, CvMat* _ematrix );
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protected:
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bool reliable( const CvMat* m1, const CvMat* m2, const CvMat* model );
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virtual void calibrated_fivepoint_helper( double *eet, double* at );
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virtual void computeReprojError( const CvMat* m1, const CvMat* m2,
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const CvMat* model, CvMat* error );
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};
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};
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// Input should be a vector of n 2D points or a Nx2 matrix
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Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, double focal, Point2d pp,
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int method, double prob, double threshold, OutputArray _mask)
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Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, double focal, Point2d pp,
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int method, double prob, double threshold, OutputArray _mask)
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{
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Mat points1, points2;
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_points1.getMat().copyTo(points1);
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_points2.getMat().copyTo(points2);
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Mat points1, points2;
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_points1.getMat().copyTo(points1);
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_points2.getMat().copyTo(points2);
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int npoints = points1.checkVector(2);
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CV_Assert( npoints >= 5 && points2.checkVector(2) == npoints &&
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@@ -35,231 +34,231 @@ Mat cv::findEssentialMat( InputArray _points1, InputArray _points2, double focal
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if (points1.channels() > 1)
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{
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points1 = points1.reshape(1, npoints);
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points2 = points2.reshape(1, npoints);
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points1 = points1.reshape(1, npoints);
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points2 = points2.reshape(1, npoints);
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}
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points1.convertTo(points1, CV_64F);
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points2.convertTo(points2, CV_64F);
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points1.col(0) = (points1.col(0) - pp.x) / focal;
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points2.col(0) = (points2.col(0) - pp.x) / focal;
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points1.col(1) = (points1.col(1) - pp.y) / focal;
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points2.col(1) = (points2.col(1) - pp.y) / focal;
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points1.convertTo(points1, CV_64F);
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points2.convertTo(points2, CV_64F);
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points1.col(0) = (points1.col(0) - pp.x) / focal;
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points2.col(0) = (points2.col(0) - pp.x) / focal;
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points1.col(1) = (points1.col(1) - pp.y) / focal;
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points2.col(1) = (points2.col(1) - pp.y) / focal;
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// Reshape data to fit opencv ransac function
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points1 = points1.reshape(2, 1);
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points2 = points2.reshape(2, 1);
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points1 = points1.reshape(2, 1);
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points2 = points2.reshape(2, 1);
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Mat E(3, 3, CV_64F);
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CvEMEstimator estimator;
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Mat E(3, 3, CV_64F);
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CvEMEstimator estimator;
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CvMat p1 = points1;
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CvMat p2 = points2;
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CvMat _E = E;
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CvMat* tempMask = cvCreateMat(1, npoints, CV_8U);
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assert(npoints >= 5);
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threshold /= focal;
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int count = 1;
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CvMat p1 = points1;
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CvMat p2 = points2;
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CvMat _E = E;
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CvMat* tempMask = cvCreateMat(1, npoints, CV_8U);
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assert(npoints >= 5);
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threshold /= focal;
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int count = 1;
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if (npoints == 5)
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{
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E.create(3 * 10, 3, CV_64F);
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_E = E;
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count = estimator.runKernel(&p1, &p2, &_E);
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E = E.rowRange(0, 3 * count) * 1.0;
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Mat(tempMask).setTo(true);
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E.create(3 * 10, 3, CV_64F);
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_E = E;
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count = estimator.runKernel(&p1, &p2, &_E);
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E = E.rowRange(0, 3 * count) * 1.0;
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Mat(tempMask).setTo(true);
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}
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else if (method == CV_RANSAC)
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{
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estimator.runRANSAC(&p1, &p2, &_E, tempMask, threshold, prob);
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estimator.runRANSAC(&p1, &p2, &_E, tempMask, threshold, prob);
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}
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else
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{
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estimator.runLMeDS(&p1, &p2, &_E, tempMask, prob);
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estimator.runLMeDS(&p1, &p2, &_E, tempMask, prob);
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}
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if (_mask.needed())
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{
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_mask.create(1, npoints, CV_8U, -1, true);
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Mat mask = _mask.getMat();
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Mat(tempMask).copyTo(mask);
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_mask.create(1, npoints, CV_8U, -1, true);
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Mat mask = _mask.getMat();
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Mat(tempMask).copyTo(mask);
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}
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return E;
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return E;
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}
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int cv::recoverPose( InputArray E, InputArray _points1, InputArray _points2, OutputArray _R, OutputArray _t,
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double focal, Point2d pp,
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InputOutputArray _mask)
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int cv::recoverPose( InputArray E, InputArray _points1, InputArray _points2, OutputArray _R, OutputArray _t,
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double focal, Point2d pp,
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InputOutputArray _mask)
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{
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Mat points1, points2;
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_points1.getMat().copyTo(points1);
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_points2.getMat().copyTo(points2);
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Mat points1, points2;
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_points1.getMat().copyTo(points1);
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_points2.getMat().copyTo(points2);
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int npoints = points1.checkVector(2);
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CV_Assert( npoints >= 0 && points2.checkVector(2) == npoints &&
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points1.type() == points2.type());
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if (points1.channels() > 1)
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{
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points1 = points1.reshape(1, npoints);
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points2 = points2.reshape(1, npoints);
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points1 = points1.reshape(1, npoints);
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points2 = points2.reshape(1, npoints);
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}
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points1.convertTo(points1, CV_64F);
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points2.convertTo(points2, CV_64F);
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points1.convertTo(points1, CV_64F);
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points2.convertTo(points2, CV_64F);
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points1.col(0) = (points1.col(0) - pp.x) / focal;
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points2.col(0) = (points2.col(0) - pp.x) / focal;
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points1.col(1) = (points1.col(1) - pp.y) / focal;
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points2.col(1) = (points2.col(1) - pp.y) / focal;
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points1.col(0) = (points1.col(0) - pp.x) / focal;
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points2.col(0) = (points2.col(0) - pp.x) / focal;
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points1.col(1) = (points1.col(1) - pp.y) / focal;
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points2.col(1) = (points2.col(1) - pp.y) / focal;
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points1 = points1.t();
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points2 = points2.t();
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Mat R1, R2, t;
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decomposeEssentialMat(E, R1, R2, t);
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Mat P0 = Mat::eye(3, 4, R1.type());
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Mat P1(3, 4, R1.type()), P2(3, 4, R1.type()), P3(3, 4, R1.type()), P4(3, 4, R1.type());
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P1(Range::all(), Range(0, 3)) = R1 * 1.0; P1.col(3) = t * 1.0;
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P2(Range::all(), Range(0, 3)) = R2 * 1.0; P2.col(3) = t * 1.0;
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P3(Range::all(), Range(0, 3)) = R1 * 1.0; P3.col(3) = -t * 1.0;
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P4(Range::all(), Range(0, 3)) = R2 * 1.0; P4.col(3) = -t * 1.0;
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points1 = points1.t();
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points2 = points2.t();
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// Do the cheirality check.
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Mat R1, R2, t;
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decomposeEssentialMat(E, R1, R2, t);
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Mat P0 = Mat::eye(3, 4, R1.type());
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Mat P1(3, 4, R1.type()), P2(3, 4, R1.type()), P3(3, 4, R1.type()), P4(3, 4, R1.type());
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P1(Range::all(), Range(0, 3)) = R1 * 1.0; P1.col(3) = t * 1.0;
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P2(Range::all(), Range(0, 3)) = R2 * 1.0; P2.col(3) = t * 1.0;
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P3(Range::all(), Range(0, 3)) = R1 * 1.0; P3.col(3) = -t * 1.0;
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P4(Range::all(), Range(0, 3)) = R2 * 1.0; P4.col(3) = -t * 1.0;
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// Do the cheirality check.
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// Notice here a threshold dist is used to filter
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// out far away points (i.e. infinite points) since
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// there depth may vary between postive and negtive.
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double dist = 50.0;
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Mat Q;
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triangulatePoints(P0, P1, points1, points2, Q);
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Mat mask1 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask1 = (Q.row(2) < dist) & mask1;
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Q = P1 * Q;
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mask1 = (Q.row(2) > 0) & mask1;
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mask1 = (Q.row(2) < dist) & mask1;
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// out far away points (i.e. infinite points) since
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// there depth may vary between postive and negtive.
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double dist = 50.0;
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Mat Q;
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triangulatePoints(P0, P1, points1, points2, Q);
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Mat mask1 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask1 = (Q.row(2) < dist) & mask1;
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Q = P1 * Q;
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mask1 = (Q.row(2) > 0) & mask1;
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mask1 = (Q.row(2) < dist) & mask1;
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triangulatePoints(P0, P2, points1, points2, Q);
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Mat mask2 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask2 = (Q.row(2) < dist) & mask2;
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Q = P2 * Q;
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mask2 = (Q.row(2) > 0) & mask2;
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mask2 = (Q.row(2) < dist) & mask2;
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triangulatePoints(P0, P2, points1, points2, Q);
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Mat mask2 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask2 = (Q.row(2) < dist) & mask2;
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Q = P2 * Q;
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mask2 = (Q.row(2) > 0) & mask2;
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mask2 = (Q.row(2) < dist) & mask2;
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triangulatePoints(P0, P3, points1, points2, Q);
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Mat mask3 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask3 = (Q.row(2) < dist) & mask3;
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Q = P3 * Q;
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mask3 = (Q.row(2) > 0) & mask3;
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mask3 = (Q.row(2) < dist) & mask3;
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triangulatePoints(P0, P3, points1, points2, Q);
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Mat mask3 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask3 = (Q.row(2) < dist) & mask3;
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Q = P3 * Q;
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mask3 = (Q.row(2) > 0) & mask3;
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mask3 = (Q.row(2) < dist) & mask3;
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triangulatePoints(P0, P4, points1, points2, Q);
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Mat mask4 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask4 = (Q.row(2) < dist) & mask4;
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Q = P4 * Q;
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mask4 = (Q.row(2) > 0) & mask4;
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mask4 = (Q.row(2) < dist) & mask4;
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triangulatePoints(P0, P4, points1, points2, Q);
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Mat mask4 = Q.row(2).mul(Q.row(3)) > 0;
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Q.row(0) /= Q.row(3);
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Q.row(1) /= Q.row(3);
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Q.row(2) /= Q.row(3);
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Q.row(3) /= Q.row(3);
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mask4 = (Q.row(2) < dist) & mask4;
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Q = P4 * Q;
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mask4 = (Q.row(2) > 0) & mask4;
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mask4 = (Q.row(2) < dist) & mask4;
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// If _mask is given, then use it to filter outliers.
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// If _mask is given, then use it to filter outliers.
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if (_mask.needed())
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{
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_mask.create(1, npoints, CV_8U, -1, true);
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Mat mask = _mask.getMat();
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bitwise_and(mask, mask1, mask1);
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bitwise_and(mask, mask2, mask2);
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bitwise_and(mask, mask3, mask3);
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bitwise_and(mask, mask4, mask4);
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_mask.create(1, npoints, CV_8U, -1, true);
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Mat mask = _mask.getMat();
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bitwise_and(mask, mask1, mask1);
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bitwise_and(mask, mask2, mask2);
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bitwise_and(mask, mask3, mask3);
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bitwise_and(mask, mask4, mask4);
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}
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CV_Assert(_R.needed() && _t.needed());
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_R.create(3, 3, R1.type());
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_t.create(3, 1, t.type());
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CV_Assert(_R.needed() && _t.needed());
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_R.create(3, 3, R1.type());
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_t.create(3, 1, t.type());
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int good1 = countNonZero(mask1);
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int good2 = countNonZero(mask2);
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int good3 = countNonZero(mask3);
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int good4 = countNonZero(mask4);
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int good1 = countNonZero(mask1);
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int good2 = countNonZero(mask2);
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int good3 = countNonZero(mask3);
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int good4 = countNonZero(mask4);
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if (good1 >= good2 && good1 >= good3 && good1 >= good4)
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{
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R1.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask1.copyTo(_mask.getMat());
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return good1;
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R1.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask1.copyTo(_mask.getMat());
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return good1;
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}
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else if (good2 >= good1 && good2 >= good3 && good2 >= good4)
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{
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R2.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask2.copyTo(_mask.getMat());
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return good2;
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R2.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask2.copyTo(_mask.getMat());
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return good2;
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}
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else if (good3 >= good1 && good3 >= good2 && good3 >= good4)
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{
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t = -t;
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R1.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask3.copyTo(_mask.getMat());
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return good3;
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t = -t;
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R1.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask3.copyTo(_mask.getMat());
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return good3;
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}
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else
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else
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{
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t = -t;
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R2.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask4.copyTo(_mask.getMat());
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return good4;
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t = -t;
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R2.copyTo(_R.getMat());
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t.copyTo(_t.getMat());
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if (_mask.needed()) mask4.copyTo(_mask.getMat());
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return good4;
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}
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}
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void cv::decomposeEssentialMat( InputArray _E, OutputArray _R1, OutputArray _R2, OutputArray _t )
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void cv::decomposeEssentialMat( InputArray _E, OutputArray _R1, OutputArray _R2, OutputArray _t )
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{
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Mat E;
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_E.getMat().copyTo(E);
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E = E.reshape(1, 3);
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Mat E;
|
||||
_E.getMat().copyTo(E);
|
||||
E = E.reshape(1, 3);
|
||||
|
||||
assert(E.cols == 3 && E.rows == 3);
|
||||
|
||||
Mat D, U, Vt;
|
||||
SVD::compute(E, D, U, Vt);
|
||||
|
||||
if (determinant(U) < 0) U = -U;
|
||||
if (determinant(Vt) < 0) Vt = -Vt;
|
||||
|
||||
Mat W = (Mat_<double>(3, 3) << 0, 1, 0, -1, 0, 0, 0, 0, 1);
|
||||
W.convertTo(W, E.type());
|
||||
|
||||
Mat R1, R2, t;
|
||||
R1 = U * W * Vt;
|
||||
R2 = U * W.t() * Vt;
|
||||
t = U.col(2) * 1.0;
|
||||
assert(E.cols == 3 && E.rows == 3);
|
||||
|
||||
_R1.create(3, 3, E.type());
|
||||
_R2.create(3, 3, E.type());
|
||||
_t.create(3, 1, E.type());
|
||||
R1.copyTo(_R1.getMat());
|
||||
R2.copyTo(_R2.getMat());
|
||||
t.copyTo(_t.getMat());
|
||||
Mat D, U, Vt;
|
||||
SVD::compute(E, D, U, Vt);
|
||||
|
||||
if (determinant(U) < 0) U = -U;
|
||||
if (determinant(Vt) < 0) Vt = -Vt;
|
||||
|
||||
Mat W = (Mat_<double>(3, 3) << 0, 1, 0, -1, 0, 0, 0, 0, 1);
|
||||
W.convertTo(W, E.type());
|
||||
|
||||
Mat R1, R2, t;
|
||||
R1 = U * W * Vt;
|
||||
R2 = U * W.t() * Vt;
|
||||
t = U.col(2) * 1.0;
|
||||
|
||||
_R1.create(3, 3, E.type());
|
||||
_R2.create(3, 3, E.type());
|
||||
_t.create(3, 1, E.type());
|
||||
R1.copyTo(_R1.getMat());
|
||||
R2.copyTo(_R2.getMat());
|
||||
t.copyTo(_t.getMat());
|
||||
|
||||
}
|
||||
|
||||
@@ -271,150 +270,150 @@ CvEMEstimator::CvEMEstimator()
|
||||
|
||||
int CvEMEstimator::runKernel( const CvMat* m1, const CvMat* m2, CvMat* model )
|
||||
{
|
||||
return run5Point(m1, m2, model);
|
||||
return run5Point(m1, m2, model);
|
||||
}
|
||||
|
||||
// Notice to keep compatibility with opencv ransac, q1 and q2 have
|
||||
// to be of 1 row x n col x 2 channel.
|
||||
// to be of 1 row x n col x 2 channel.
|
||||
int CvEMEstimator::run5Point( const CvMat* q1, const CvMat* q2, CvMat* ematrix )
|
||||
{
|
||||
Mat Q1 = Mat(q1).reshape(1, q1->cols);
|
||||
Mat Q2 = Mat(q2).reshape(1, q2->cols);
|
||||
Mat Q1 = Mat(q1).reshape(1, q1->cols);
|
||||
Mat Q2 = Mat(q2).reshape(1, q2->cols);
|
||||
|
||||
int n = Q1.rows;
|
||||
Mat Q(n, 9, CV_64F);
|
||||
Q.col(0) = Q1.col(0).mul( Q2.col(0) );
|
||||
Q.col(1) = Q1.col(1).mul( Q2.col(0) );
|
||||
Q.col(2) = Q2.col(0) * 1.0;
|
||||
Q.col(3) = Q1.col(0).mul( Q2.col(1) );
|
||||
Q.col(4) = Q1.col(1).mul( Q2.col(1) );
|
||||
Q.col(5) = Q2.col(1) * 1.0;
|
||||
Q.col(6) = Q1.col(0) * 1.0;
|
||||
Q.col(7) = Q1.col(1) * 1.0;
|
||||
Q.col(8) = 1.0;
|
||||
int n = Q1.rows;
|
||||
Mat Q(n, 9, CV_64F);
|
||||
Q.col(0) = Q1.col(0).mul( Q2.col(0) );
|
||||
Q.col(1) = Q1.col(1).mul( Q2.col(0) );
|
||||
Q.col(2) = Q2.col(0) * 1.0;
|
||||
Q.col(3) = Q1.col(0).mul( Q2.col(1) );
|
||||
Q.col(4) = Q1.col(1).mul( Q2.col(1) );
|
||||
Q.col(5) = Q2.col(1) * 1.0;
|
||||
Q.col(6) = Q1.col(0) * 1.0;
|
||||
Q.col(7) = Q1.col(1) * 1.0;
|
||||
Q.col(8) = 1.0;
|
||||
|
||||
Mat U, W, Vt;
|
||||
SVD::compute(Q, W, U, Vt, SVD::MODIFY_A | SVD::FULL_UV);
|
||||
|
||||
Mat EE = Mat(Vt.t()).colRange(5, 9) * 1.0;
|
||||
Mat AA(20, 10, CV_64F);
|
||||
EE = EE.t();
|
||||
calibrated_fivepoint_helper((double*)EE.data, (double*)AA.data);
|
||||
AA = AA.t();
|
||||
EE = EE.t();
|
||||
Mat U, W, Vt;
|
||||
SVD::compute(Q, W, U, Vt, SVD::MODIFY_A | SVD::FULL_UV);
|
||||
|
||||
Mat A(10, 20, CV_64F);
|
||||
int perm[20] = {0, 3, 1, 2, 4, 10, 6, 12, 5, 11, 7, 13, 16, 8, 14, 17, 9, 15, 18, 19};
|
||||
Mat EE = Mat(Vt.t()).colRange(5, 9) * 1.0;
|
||||
Mat AA(20, 10, CV_64F);
|
||||
EE = EE.t();
|
||||
calibrated_fivepoint_helper((double*)EE.data, (double*)AA.data);
|
||||
AA = AA.t();
|
||||
EE = EE.t();
|
||||
|
||||
Mat A(10, 20, CV_64F);
|
||||
int perm[20] = {0, 3, 1, 2, 4, 10, 6, 12, 5, 11, 7, 13, 16, 8, 14, 17, 9, 15, 18, 19};
|
||||
for (int i = 0; i < 20; i++)
|
||||
A.col(i) = AA.col(perm[i]) * 1.0;
|
||||
A.col(i) = AA.col(perm[i]) * 1.0;
|
||||
|
||||
|
||||
A = A.colRange(0, 10).inv() * A.colRange(10, 20);
|
||||
|
||||
double b[3 * 13];
|
||||
Mat B(3, 13, CV_64F, b);
|
||||
A = A.colRange(0, 10).inv() * A.colRange(10, 20);
|
||||
|
||||
double b[3 * 13];
|
||||
Mat B(3, 13, CV_64F, b);
|
||||
for (int i = 0; i < 3; i++)
|
||||
{
|
||||
Mat arow1 = A.row(i * 2 + 4) * 1.0;
|
||||
Mat arow2 = A.row(i * 2 + 5) * 1.0;
|
||||
Mat row1(1, 13, CV_64F, Scalar(0.0));
|
||||
Mat row2(1, 13, CV_64F, Scalar(0.0));
|
||||
Mat arow1 = A.row(i * 2 + 4) * 1.0;
|
||||
Mat arow2 = A.row(i * 2 + 5) * 1.0;
|
||||
Mat row1(1, 13, CV_64F, Scalar(0.0));
|
||||
Mat row2(1, 13, CV_64F, Scalar(0.0));
|
||||
|
||||
row1.colRange(1, 4) = arow1.colRange(0, 3) * 1.0;
|
||||
row1.colRange(5, 8) = arow1.colRange(3, 6) * 1.0;
|
||||
row1.colRange(9, 13) = arow1.colRange(6, 10) * 1.0;
|
||||
row1.colRange(1, 4) = arow1.colRange(0, 3) * 1.0;
|
||||
row1.colRange(5, 8) = arow1.colRange(3, 6) * 1.0;
|
||||
row1.colRange(9, 13) = arow1.colRange(6, 10) * 1.0;
|
||||
|
||||
row2.colRange(0, 3) = arow2.colRange(0, 3) * 1.0;
|
||||
row2.colRange(4, 7) = arow2.colRange(3, 6) * 1.0;
|
||||
row2.colRange(8, 12) = arow2.colRange(6, 10) * 1.0;
|
||||
|
||||
B.row(i) = row1 - row2;
|
||||
row2.colRange(0, 3) = arow2.colRange(0, 3) * 1.0;
|
||||
row2.colRange(4, 7) = arow2.colRange(3, 6) * 1.0;
|
||||
row2.colRange(8, 12) = arow2.colRange(6, 10) * 1.0;
|
||||
|
||||
B.row(i) = row1 - row2;
|
||||
}
|
||||
|
||||
double c[11];
|
||||
Mat coeffs(1, 11, CV_64F, c);
|
||||
c[10] = (b[0]*b[17]*b[34]+b[26]*b[4]*b[21]-b[26]*b[17]*b[8]-b[13]*b[4]*b[34]-b[0]*b[21]*b[30]+b[13]*b[30]*b[8]);
|
||||
c[9] = (b[26]*b[4]*b[22]+b[14]*b[30]*b[8]+b[13]*b[31]*b[8]+b[1]*b[17]*b[34]-b[13]*b[5]*b[34]+b[26]*b[5]*b[21]-b[0]*b[21]*b[31]-b[26]*b[17]*b[9]-b[1]*b[21]*b[30]+b[27]*b[4]*b[21]+b[0]*b[17]*b[35]-b[0]*b[22]*b[30]+b[13]*b[30]*b[9]+b[0]*b[18]*b[34]-b[27]*b[17]*b[8]-b[14]*b[4]*b[34]-b[13]*b[4]*b[35]-b[26]*b[18]*b[8]);
|
||||
c[8] = (b[14]*b[30]*b[9]+b[14]*b[31]*b[8]+b[13]*b[31]*b[9]-b[13]*b[4]*b[36]-b[13]*b[5]*b[35]+b[15]*b[30]*b[8]-b[13]*b[6]*b[34]+b[13]*b[30]*b[10]+b[13]*b[32]*b[8]-b[14]*b[4]*b[35]-b[14]*b[5]*b[34]+b[26]*b[4]*b[23]+b[26]*b[5]*b[22]+b[26]*b[6]*b[21]-b[26]*b[17]*b[10]-b[15]*b[4]*b[34]-b[26]*b[18]*b[9]-b[26]*b[19]*b[8]+b[27]*b[4]*b[22]+b[27]*b[5]*b[21]-b[27]*b[17]*b[9]-b[27]*b[18]*b[8]-b[1]*b[21]*b[31]-b[0]*b[23]*b[30]-b[0]*b[21]*b[32]+b[28]*b[4]*b[21]-b[28]*b[17]*b[8]+b[2]*b[17]*b[34]+b[0]*b[18]*b[35]-b[0]*b[22]*b[31]+b[0]*b[17]*b[36]+b[0]*b[19]*b[34]-b[1]*b[22]*b[30]+b[1]*b[18]*b[34]+b[1]*b[17]*b[35]-b[2]*b[21]*b[30]);
|
||||
c[7] = (b[14]*b[30]*b[10]+b[14]*b[32]*b[8]-b[3]*b[21]*b[30]+b[3]*b[17]*b[34]+b[13]*b[32]*b[9]+b[13]*b[33]*b[8]-b[13]*b[4]*b[37]-b[13]*b[5]*b[36]+b[15]*b[30]*b[9]+b[15]*b[31]*b[8]-b[16]*b[4]*b[34]-b[13]*b[6]*b[35]-b[13]*b[7]*b[34]+b[13]*b[30]*b[11]+b[13]*b[31]*b[10]+b[14]*b[31]*b[9]-b[14]*b[4]*b[36]-b[14]*b[5]*b[35]-b[14]*b[6]*b[34]+b[16]*b[30]*b[8]-b[26]*b[20]*b[8]+b[26]*b[4]*b[24]+b[26]*b[5]*b[23]+b[26]*b[6]*b[22]+b[26]*b[7]*b[21]-b[26]*b[17]*b[11]-b[15]*b[4]*b[35]-b[15]*b[5]*b[34]-b[26]*b[18]*b[10]-b[26]*b[19]*b[9]+b[27]*b[4]*b[23]+b[27]*b[5]*b[22]+b[27]*b[6]*b[21]-b[27]*b[17]*b[10]-b[27]*b[18]*b[9]-b[27]*b[19]*b[8]+b[0]*b[17]*b[37]-b[0]*b[23]*b[31]-b[0]*b[24]*b[30]-b[0]*b[21]*b[33]-b[29]*b[17]*b[8]+b[28]*b[4]*b[22]+b[28]*b[5]*b[21]-b[28]*b[17]*b[9]-b[28]*b[18]*b[8]+b[29]*b[4]*b[21]+b[1]*b[19]*b[34]-b[2]*b[21]*b[31]+b[0]*b[20]*b[34]+b[0]*b[19]*b[35]+b[0]*b[18]*b[36]-b[0]*b[22]*b[32]-b[1]*b[23]*b[30]-b[1]*b[21]*b[32]+b[1]*b[18]*b[35]-b[1]*b[22]*b[31]-b[2]*b[22]*b[30]+b[2]*b[17]*b[35]+b[1]*b[17]*b[36]+b[2]*b[18]*b[34]);
|
||||
c[6] = (-b[14]*b[6]*b[35]-b[14]*b[7]*b[34]-b[3]*b[22]*b[30]-b[3]*b[21]*b[31]+b[3]*b[17]*b[35]+b[3]*b[18]*b[34]+b[13]*b[32]*b[10]+b[13]*b[33]*b[9]-b[13]*b[4]*b[38]-b[13]*b[5]*b[37]-b[15]*b[6]*b[34]+b[15]*b[30]*b[10]+b[15]*b[32]*b[8]-b[16]*b[4]*b[35]-b[13]*b[6]*b[36]-b[13]*b[7]*b[35]+b[13]*b[31]*b[11]+b[13]*b[30]*b[12]+b[14]*b[32]*b[9]+b[14]*b[33]*b[8]-b[14]*b[4]*b[37]-b[14]*b[5]*b[36]+b[16]*b[30]*b[9]+b[16]*b[31]*b[8]-b[26]*b[20]*b[9]+b[26]*b[4]*b[25]+b[26]*b[5]*b[24]+b[26]*b[6]*b[23]+b[26]*b[7]*b[22]-b[26]*b[17]*b[12]+b[14]*b[30]*b[11]+b[14]*b[31]*b[10]+b[15]*b[31]*b[9]-b[15]*b[4]*b[36]-b[15]*b[5]*b[35]-b[26]*b[18]*b[11]-b[26]*b[19]*b[10]-b[27]*b[20]*b[8]+b[27]*b[4]*b[24]+b[27]*b[5]*b[23]+b[27]*b[6]*b[22]+b[27]*b[7]*b[21]-b[27]*b[17]*b[11]-b[27]*b[18]*b[10]-b[27]*b[19]*b[9]-b[16]*b[5]*b[34]-b[29]*b[17]*b[9]-b[29]*b[18]*b[8]+b[28]*b[4]*b[23]+b[28]*b[5]*b[22]+b[28]*b[6]*b[21]-b[28]*b[17]*b[10]-b[28]*b[18]*b[9]-b[28]*b[19]*b[8]+b[29]*b[4]*b[22]+b[29]*b[5]*b[21]-b[2]*b[23]*b[30]+b[2]*b[18]*b[35]-b[1]*b[22]*b[32]-b[2]*b[21]*b[32]+b[2]*b[19]*b[34]+b[0]*b[19]*b[36]-b[0]*b[22]*b[33]+b[0]*b[20]*b[35]-b[0]*b[23]*b[32]-b[0]*b[25]*b[30]+b[0]*b[17]*b[38]+b[0]*b[18]*b[37]-b[0]*b[24]*b[31]+b[1]*b[17]*b[37]-b[1]*b[23]*b[31]-b[1]*b[24]*b[30]-b[1]*b[21]*b[33]+b[1]*b[20]*b[34]+b[1]*b[19]*b[35]+b[1]*b[18]*b[36]+b[2]*b[17]*b[36]-b[2]*b[22]*b[31]);
|
||||
c[5] = (-b[14]*b[6]*b[36]-b[14]*b[7]*b[35]+b[14]*b[31]*b[11]-b[3]*b[23]*b[30]-b[3]*b[21]*b[32]+b[3]*b[18]*b[35]-b[3]*b[22]*b[31]+b[3]*b[17]*b[36]+b[3]*b[19]*b[34]+b[13]*b[32]*b[11]+b[13]*b[33]*b[10]-b[13]*b[5]*b[38]-b[15]*b[6]*b[35]-b[15]*b[7]*b[34]+b[15]*b[30]*b[11]+b[15]*b[31]*b[10]+b[16]*b[31]*b[9]-b[13]*b[6]*b[37]-b[13]*b[7]*b[36]+b[13]*b[31]*b[12]+b[14]*b[32]*b[10]+b[14]*b[33]*b[9]-b[14]*b[4]*b[38]-b[14]*b[5]*b[37]-b[16]*b[6]*b[34]+b[16]*b[30]*b[10]+b[16]*b[32]*b[8]-b[26]*b[20]*b[10]+b[26]*b[5]*b[25]+b[26]*b[6]*b[24]+b[26]*b[7]*b[23]+b[14]*b[30]*b[12]+b[15]*b[32]*b[9]+b[15]*b[33]*b[8]-b[15]*b[4]*b[37]-b[15]*b[5]*b[36]+b[29]*b[5]*b[22]+b[29]*b[6]*b[21]-b[26]*b[18]*b[12]-b[26]*b[19]*b[11]-b[27]*b[20]*b[9]+b[27]*b[4]*b[25]+b[27]*b[5]*b[24]+b[27]*b[6]*b[23]+b[27]*b[7]*b[22]-b[27]*b[17]*b[12]-b[27]*b[18]*b[11]-b[27]*b[19]*b[10]-b[28]*b[20]*b[8]-b[16]*b[4]*b[36]-b[16]*b[5]*b[35]-b[29]*b[17]*b[10]-b[29]*b[18]*b[9]-b[29]*b[19]*b[8]+b[28]*b[4]*b[24]+b[28]*b[5]*b[23]+b[28]*b[6]*b[22]+b[28]*b[7]*b[21]-b[28]*b[17]*b[11]-b[28]*b[18]*b[10]-b[28]*b[19]*b[9]+b[29]*b[4]*b[23]-b[2]*b[22]*b[32]-b[2]*b[21]*b[33]-b[1]*b[24]*b[31]+b[0]*b[18]*b[38]-b[0]*b[24]*b[32]+b[0]*b[19]*b[37]+b[0]*b[20]*b[36]-b[0]*b[25]*b[31]-b[0]*b[23]*b[33]+b[1]*b[19]*b[36]-b[1]*b[22]*b[33]+b[1]*b[20]*b[35]+b[2]*b[19]*b[35]-b[2]*b[24]*b[30]-b[2]*b[23]*b[31]+b[2]*b[20]*b[34]+b[2]*b[17]*b[37]-b[1]*b[25]*b[30]+b[1]*b[18]*b[37]+b[1]*b[17]*b[38]-b[1]*b[23]*b[32]+b[2]*b[18]*b[36]);
|
||||
c[4] = (-b[14]*b[6]*b[37]-b[14]*b[7]*b[36]+b[14]*b[31]*b[12]+b[3]*b[17]*b[37]-b[3]*b[23]*b[31]-b[3]*b[24]*b[30]-b[3]*b[21]*b[33]+b[3]*b[20]*b[34]+b[3]*b[19]*b[35]+b[3]*b[18]*b[36]-b[3]*b[22]*b[32]+b[13]*b[32]*b[12]+b[13]*b[33]*b[11]-b[15]*b[6]*b[36]-b[15]*b[7]*b[35]+b[15]*b[31]*b[11]+b[15]*b[30]*b[12]+b[16]*b[32]*b[9]+b[16]*b[33]*b[8]-b[13]*b[6]*b[38]-b[13]*b[7]*b[37]+b[14]*b[32]*b[11]+b[14]*b[33]*b[10]-b[14]*b[5]*b[38]-b[16]*b[6]*b[35]-b[16]*b[7]*b[34]+b[16]*b[30]*b[11]+b[16]*b[31]*b[10]-b[26]*b[19]*b[12]-b[26]*b[20]*b[11]+b[26]*b[6]*b[25]+b[26]*b[7]*b[24]+b[15]*b[32]*b[10]+b[15]*b[33]*b[9]-b[15]*b[4]*b[38]-b[15]*b[5]*b[37]+b[29]*b[5]*b[23]+b[29]*b[6]*b[22]+b[29]*b[7]*b[21]-b[27]*b[20]*b[10]+b[27]*b[5]*b[25]+b[27]*b[6]*b[24]+b[27]*b[7]*b[23]-b[27]*b[18]*b[12]-b[27]*b[19]*b[11]-b[28]*b[20]*b[9]-b[16]*b[4]*b[37]-b[16]*b[5]*b[36]+b[0]*b[19]*b[38]-b[0]*b[24]*b[33]+b[0]*b[20]*b[37]-b[29]*b[17]*b[11]-b[29]*b[18]*b[10]-b[29]*b[19]*b[9]+b[28]*b[4]*b[25]+b[28]*b[5]*b[24]+b[28]*b[6]*b[23]+b[28]*b[7]*b[22]-b[28]*b[17]*b[12]-b[28]*b[18]*b[11]-b[28]*b[19]*b[10]-b[29]*b[20]*b[8]+b[29]*b[4]*b[24]+b[2]*b[18]*b[37]-b[0]*b[25]*b[32]+b[1]*b[18]*b[38]-b[1]*b[24]*b[32]+b[1]*b[19]*b[37]+b[1]*b[20]*b[36]-b[1]*b[25]*b[31]+b[2]*b[17]*b[38]+b[2]*b[19]*b[36]-b[2]*b[24]*b[31]-b[2]*b[22]*b[33]-b[2]*b[23]*b[32]+b[2]*b[20]*b[35]-b[1]*b[23]*b[33]-b[2]*b[25]*b[30]);
|
||||
c[3] = (-b[14]*b[6]*b[38]-b[14]*b[7]*b[37]+b[3]*b[19]*b[36]-b[3]*b[22]*b[33]+b[3]*b[20]*b[35]-b[3]*b[23]*b[32]-b[3]*b[25]*b[30]+b[3]*b[17]*b[38]+b[3]*b[18]*b[37]-b[3]*b[24]*b[31]-b[15]*b[6]*b[37]-b[15]*b[7]*b[36]+b[15]*b[31]*b[12]+b[16]*b[32]*b[10]+b[16]*b[33]*b[9]+b[13]*b[33]*b[12]-b[13]*b[7]*b[38]+b[14]*b[32]*b[12]+b[14]*b[33]*b[11]-b[16]*b[6]*b[36]-b[16]*b[7]*b[35]+b[16]*b[31]*b[11]+b[16]*b[30]*b[12]+b[15]*b[32]*b[11]+b[15]*b[33]*b[10]-b[15]*b[5]*b[38]+b[29]*b[5]*b[24]+b[29]*b[6]*b[23]-b[26]*b[20]*b[12]+b[26]*b[7]*b[25]-b[27]*b[19]*b[12]-b[27]*b[20]*b[11]+b[27]*b[6]*b[25]+b[27]*b[7]*b[24]-b[28]*b[20]*b[10]-b[16]*b[4]*b[38]-b[16]*b[5]*b[37]+b[29]*b[7]*b[22]-b[29]*b[17]*b[12]-b[29]*b[18]*b[11]-b[29]*b[19]*b[10]+b[28]*b[5]*b[25]+b[28]*b[6]*b[24]+b[28]*b[7]*b[23]-b[28]*b[18]*b[12]-b[28]*b[19]*b[11]-b[29]*b[20]*b[9]+b[29]*b[4]*b[25]-b[2]*b[24]*b[32]+b[0]*b[20]*b[38]-b[0]*b[25]*b[33]+b[1]*b[19]*b[38]-b[1]*b[24]*b[33]+b[1]*b[20]*b[37]-b[2]*b[25]*b[31]+b[2]*b[20]*b[36]-b[1]*b[25]*b[32]+b[2]*b[19]*b[37]+b[2]*b[18]*b[38]-b[2]*b[23]*b[33]);
|
||||
c[2] = (b[3]*b[18]*b[38]-b[3]*b[24]*b[32]+b[3]*b[19]*b[37]+b[3]*b[20]*b[36]-b[3]*b[25]*b[31]-b[3]*b[23]*b[33]-b[15]*b[6]*b[38]-b[15]*b[7]*b[37]+b[16]*b[32]*b[11]+b[16]*b[33]*b[10]-b[16]*b[5]*b[38]-b[16]*b[6]*b[37]-b[16]*b[7]*b[36]+b[16]*b[31]*b[12]+b[14]*b[33]*b[12]-b[14]*b[7]*b[38]+b[15]*b[32]*b[12]+b[15]*b[33]*b[11]+b[29]*b[5]*b[25]+b[29]*b[6]*b[24]-b[27]*b[20]*b[12]+b[27]*b[7]*b[25]-b[28]*b[19]*b[12]-b[28]*b[20]*b[11]+b[29]*b[7]*b[23]-b[29]*b[18]*b[12]-b[29]*b[19]*b[11]+b[28]*b[6]*b[25]+b[28]*b[7]*b[24]-b[29]*b[20]*b[10]+b[2]*b[19]*b[38]-b[1]*b[25]*b[33]+b[2]*b[20]*b[37]-b[2]*b[24]*b[33]-b[2]*b[25]*b[32]+b[1]*b[20]*b[38]);
|
||||
c[1] = (b[29]*b[7]*b[24]-b[29]*b[20]*b[11]+b[2]*b[20]*b[38]-b[2]*b[25]*b[33]-b[28]*b[20]*b[12]+b[28]*b[7]*b[25]-b[29]*b[19]*b[12]-b[3]*b[24]*b[33]+b[15]*b[33]*b[12]+b[3]*b[19]*b[38]-b[16]*b[6]*b[38]+b[3]*b[20]*b[37]+b[16]*b[32]*b[12]+b[29]*b[6]*b[25]-b[16]*b[7]*b[37]-b[3]*b[25]*b[32]-b[15]*b[7]*b[38]+b[16]*b[33]*b[11]);
|
||||
c[0] = -b[29]*b[20]*b[12]+b[29]*b[7]*b[25]+b[16]*b[33]*b[12]-b[16]*b[7]*b[38]+b[3]*b[20]*b[38]-b[3]*b[25]*b[33];
|
||||
|
||||
std::vector<std::complex<double> > roots;
|
||||
solvePoly(coeffs, roots);
|
||||
double c[11];
|
||||
Mat coeffs(1, 11, CV_64F, c);
|
||||
c[10] = (b[0]*b[17]*b[34]+b[26]*b[4]*b[21]-b[26]*b[17]*b[8]-b[13]*b[4]*b[34]-b[0]*b[21]*b[30]+b[13]*b[30]*b[8]);
|
||||
c[9] = (b[26]*b[4]*b[22]+b[14]*b[30]*b[8]+b[13]*b[31]*b[8]+b[1]*b[17]*b[34]-b[13]*b[5]*b[34]+b[26]*b[5]*b[21]-b[0]*b[21]*b[31]-b[26]*b[17]*b[9]-b[1]*b[21]*b[30]+b[27]*b[4]*b[21]+b[0]*b[17]*b[35]-b[0]*b[22]*b[30]+b[13]*b[30]*b[9]+b[0]*b[18]*b[34]-b[27]*b[17]*b[8]-b[14]*b[4]*b[34]-b[13]*b[4]*b[35]-b[26]*b[18]*b[8]);
|
||||
c[8] = (b[14]*b[30]*b[9]+b[14]*b[31]*b[8]+b[13]*b[31]*b[9]-b[13]*b[4]*b[36]-b[13]*b[5]*b[35]+b[15]*b[30]*b[8]-b[13]*b[6]*b[34]+b[13]*b[30]*b[10]+b[13]*b[32]*b[8]-b[14]*b[4]*b[35]-b[14]*b[5]*b[34]+b[26]*b[4]*b[23]+b[26]*b[5]*b[22]+b[26]*b[6]*b[21]-b[26]*b[17]*b[10]-b[15]*b[4]*b[34]-b[26]*b[18]*b[9]-b[26]*b[19]*b[8]+b[27]*b[4]*b[22]+b[27]*b[5]*b[21]-b[27]*b[17]*b[9]-b[27]*b[18]*b[8]-b[1]*b[21]*b[31]-b[0]*b[23]*b[30]-b[0]*b[21]*b[32]+b[28]*b[4]*b[21]-b[28]*b[17]*b[8]+b[2]*b[17]*b[34]+b[0]*b[18]*b[35]-b[0]*b[22]*b[31]+b[0]*b[17]*b[36]+b[0]*b[19]*b[34]-b[1]*b[22]*b[30]+b[1]*b[18]*b[34]+b[1]*b[17]*b[35]-b[2]*b[21]*b[30]);
|
||||
c[7] = (b[14]*b[30]*b[10]+b[14]*b[32]*b[8]-b[3]*b[21]*b[30]+b[3]*b[17]*b[34]+b[13]*b[32]*b[9]+b[13]*b[33]*b[8]-b[13]*b[4]*b[37]-b[13]*b[5]*b[36]+b[15]*b[30]*b[9]+b[15]*b[31]*b[8]-b[16]*b[4]*b[34]-b[13]*b[6]*b[35]-b[13]*b[7]*b[34]+b[13]*b[30]*b[11]+b[13]*b[31]*b[10]+b[14]*b[31]*b[9]-b[14]*b[4]*b[36]-b[14]*b[5]*b[35]-b[14]*b[6]*b[34]+b[16]*b[30]*b[8]-b[26]*b[20]*b[8]+b[26]*b[4]*b[24]+b[26]*b[5]*b[23]+b[26]*b[6]*b[22]+b[26]*b[7]*b[21]-b[26]*b[17]*b[11]-b[15]*b[4]*b[35]-b[15]*b[5]*b[34]-b[26]*b[18]*b[10]-b[26]*b[19]*b[9]+b[27]*b[4]*b[23]+b[27]*b[5]*b[22]+b[27]*b[6]*b[21]-b[27]*b[17]*b[10]-b[27]*b[18]*b[9]-b[27]*b[19]*b[8]+b[0]*b[17]*b[37]-b[0]*b[23]*b[31]-b[0]*b[24]*b[30]-b[0]*b[21]*b[33]-b[29]*b[17]*b[8]+b[28]*b[4]*b[22]+b[28]*b[5]*b[21]-b[28]*b[17]*b[9]-b[28]*b[18]*b[8]+b[29]*b[4]*b[21]+b[1]*b[19]*b[34]-b[2]*b[21]*b[31]+b[0]*b[20]*b[34]+b[0]*b[19]*b[35]+b[0]*b[18]*b[36]-b[0]*b[22]*b[32]-b[1]*b[23]*b[30]-b[1]*b[21]*b[32]+b[1]*b[18]*b[35]-b[1]*b[22]*b[31]-b[2]*b[22]*b[30]+b[2]*b[17]*b[35]+b[1]*b[17]*b[36]+b[2]*b[18]*b[34]);
|
||||
c[6] = (-b[14]*b[6]*b[35]-b[14]*b[7]*b[34]-b[3]*b[22]*b[30]-b[3]*b[21]*b[31]+b[3]*b[17]*b[35]+b[3]*b[18]*b[34]+b[13]*b[32]*b[10]+b[13]*b[33]*b[9]-b[13]*b[4]*b[38]-b[13]*b[5]*b[37]-b[15]*b[6]*b[34]+b[15]*b[30]*b[10]+b[15]*b[32]*b[8]-b[16]*b[4]*b[35]-b[13]*b[6]*b[36]-b[13]*b[7]*b[35]+b[13]*b[31]*b[11]+b[13]*b[30]*b[12]+b[14]*b[32]*b[9]+b[14]*b[33]*b[8]-b[14]*b[4]*b[37]-b[14]*b[5]*b[36]+b[16]*b[30]*b[9]+b[16]*b[31]*b[8]-b[26]*b[20]*b[9]+b[26]*b[4]*b[25]+b[26]*b[5]*b[24]+b[26]*b[6]*b[23]+b[26]*b[7]*b[22]-b[26]*b[17]*b[12]+b[14]*b[30]*b[11]+b[14]*b[31]*b[10]+b[15]*b[31]*b[9]-b[15]*b[4]*b[36]-b[15]*b[5]*b[35]-b[26]*b[18]*b[11]-b[26]*b[19]*b[10]-b[27]*b[20]*b[8]+b[27]*b[4]*b[24]+b[27]*b[5]*b[23]+b[27]*b[6]*b[22]+b[27]*b[7]*b[21]-b[27]*b[17]*b[11]-b[27]*b[18]*b[10]-b[27]*b[19]*b[9]-b[16]*b[5]*b[34]-b[29]*b[17]*b[9]-b[29]*b[18]*b[8]+b[28]*b[4]*b[23]+b[28]*b[5]*b[22]+b[28]*b[6]*b[21]-b[28]*b[17]*b[10]-b[28]*b[18]*b[9]-b[28]*b[19]*b[8]+b[29]*b[4]*b[22]+b[29]*b[5]*b[21]-b[2]*b[23]*b[30]+b[2]*b[18]*b[35]-b[1]*b[22]*b[32]-b[2]*b[21]*b[32]+b[2]*b[19]*b[34]+b[0]*b[19]*b[36]-b[0]*b[22]*b[33]+b[0]*b[20]*b[35]-b[0]*b[23]*b[32]-b[0]*b[25]*b[30]+b[0]*b[17]*b[38]+b[0]*b[18]*b[37]-b[0]*b[24]*b[31]+b[1]*b[17]*b[37]-b[1]*b[23]*b[31]-b[1]*b[24]*b[30]-b[1]*b[21]*b[33]+b[1]*b[20]*b[34]+b[1]*b[19]*b[35]+b[1]*b[18]*b[36]+b[2]*b[17]*b[36]-b[2]*b[22]*b[31]);
|
||||
c[5] = (-b[14]*b[6]*b[36]-b[14]*b[7]*b[35]+b[14]*b[31]*b[11]-b[3]*b[23]*b[30]-b[3]*b[21]*b[32]+b[3]*b[18]*b[35]-b[3]*b[22]*b[31]+b[3]*b[17]*b[36]+b[3]*b[19]*b[34]+b[13]*b[32]*b[11]+b[13]*b[33]*b[10]-b[13]*b[5]*b[38]-b[15]*b[6]*b[35]-b[15]*b[7]*b[34]+b[15]*b[30]*b[11]+b[15]*b[31]*b[10]+b[16]*b[31]*b[9]-b[13]*b[6]*b[37]-b[13]*b[7]*b[36]+b[13]*b[31]*b[12]+b[14]*b[32]*b[10]+b[14]*b[33]*b[9]-b[14]*b[4]*b[38]-b[14]*b[5]*b[37]-b[16]*b[6]*b[34]+b[16]*b[30]*b[10]+b[16]*b[32]*b[8]-b[26]*b[20]*b[10]+b[26]*b[5]*b[25]+b[26]*b[6]*b[24]+b[26]*b[7]*b[23]+b[14]*b[30]*b[12]+b[15]*b[32]*b[9]+b[15]*b[33]*b[8]-b[15]*b[4]*b[37]-b[15]*b[5]*b[36]+b[29]*b[5]*b[22]+b[29]*b[6]*b[21]-b[26]*b[18]*b[12]-b[26]*b[19]*b[11]-b[27]*b[20]*b[9]+b[27]*b[4]*b[25]+b[27]*b[5]*b[24]+b[27]*b[6]*b[23]+b[27]*b[7]*b[22]-b[27]*b[17]*b[12]-b[27]*b[18]*b[11]-b[27]*b[19]*b[10]-b[28]*b[20]*b[8]-b[16]*b[4]*b[36]-b[16]*b[5]*b[35]-b[29]*b[17]*b[10]-b[29]*b[18]*b[9]-b[29]*b[19]*b[8]+b[28]*b[4]*b[24]+b[28]*b[5]*b[23]+b[28]*b[6]*b[22]+b[28]*b[7]*b[21]-b[28]*b[17]*b[11]-b[28]*b[18]*b[10]-b[28]*b[19]*b[9]+b[29]*b[4]*b[23]-b[2]*b[22]*b[32]-b[2]*b[21]*b[33]-b[1]*b[24]*b[31]+b[0]*b[18]*b[38]-b[0]*b[24]*b[32]+b[0]*b[19]*b[37]+b[0]*b[20]*b[36]-b[0]*b[25]*b[31]-b[0]*b[23]*b[33]+b[1]*b[19]*b[36]-b[1]*b[22]*b[33]+b[1]*b[20]*b[35]+b[2]*b[19]*b[35]-b[2]*b[24]*b[30]-b[2]*b[23]*b[31]+b[2]*b[20]*b[34]+b[2]*b[17]*b[37]-b[1]*b[25]*b[30]+b[1]*b[18]*b[37]+b[1]*b[17]*b[38]-b[1]*b[23]*b[32]+b[2]*b[18]*b[36]);
|
||||
c[4] = (-b[14]*b[6]*b[37]-b[14]*b[7]*b[36]+b[14]*b[31]*b[12]+b[3]*b[17]*b[37]-b[3]*b[23]*b[31]-b[3]*b[24]*b[30]-b[3]*b[21]*b[33]+b[3]*b[20]*b[34]+b[3]*b[19]*b[35]+b[3]*b[18]*b[36]-b[3]*b[22]*b[32]+b[13]*b[32]*b[12]+b[13]*b[33]*b[11]-b[15]*b[6]*b[36]-b[15]*b[7]*b[35]+b[15]*b[31]*b[11]+b[15]*b[30]*b[12]+b[16]*b[32]*b[9]+b[16]*b[33]*b[8]-b[13]*b[6]*b[38]-b[13]*b[7]*b[37]+b[14]*b[32]*b[11]+b[14]*b[33]*b[10]-b[14]*b[5]*b[38]-b[16]*b[6]*b[35]-b[16]*b[7]*b[34]+b[16]*b[30]*b[11]+b[16]*b[31]*b[10]-b[26]*b[19]*b[12]-b[26]*b[20]*b[11]+b[26]*b[6]*b[25]+b[26]*b[7]*b[24]+b[15]*b[32]*b[10]+b[15]*b[33]*b[9]-b[15]*b[4]*b[38]-b[15]*b[5]*b[37]+b[29]*b[5]*b[23]+b[29]*b[6]*b[22]+b[29]*b[7]*b[21]-b[27]*b[20]*b[10]+b[27]*b[5]*b[25]+b[27]*b[6]*b[24]+b[27]*b[7]*b[23]-b[27]*b[18]*b[12]-b[27]*b[19]*b[11]-b[28]*b[20]*b[9]-b[16]*b[4]*b[37]-b[16]*b[5]*b[36]+b[0]*b[19]*b[38]-b[0]*b[24]*b[33]+b[0]*b[20]*b[37]-b[29]*b[17]*b[11]-b[29]*b[18]*b[10]-b[29]*b[19]*b[9]+b[28]*b[4]*b[25]+b[28]*b[5]*b[24]+b[28]*b[6]*b[23]+b[28]*b[7]*b[22]-b[28]*b[17]*b[12]-b[28]*b[18]*b[11]-b[28]*b[19]*b[10]-b[29]*b[20]*b[8]+b[29]*b[4]*b[24]+b[2]*b[18]*b[37]-b[0]*b[25]*b[32]+b[1]*b[18]*b[38]-b[1]*b[24]*b[32]+b[1]*b[19]*b[37]+b[1]*b[20]*b[36]-b[1]*b[25]*b[31]+b[2]*b[17]*b[38]+b[2]*b[19]*b[36]-b[2]*b[24]*b[31]-b[2]*b[22]*b[33]-b[2]*b[23]*b[32]+b[2]*b[20]*b[35]-b[1]*b[23]*b[33]-b[2]*b[25]*b[30]);
|
||||
c[3] = (-b[14]*b[6]*b[38]-b[14]*b[7]*b[37]+b[3]*b[19]*b[36]-b[3]*b[22]*b[33]+b[3]*b[20]*b[35]-b[3]*b[23]*b[32]-b[3]*b[25]*b[30]+b[3]*b[17]*b[38]+b[3]*b[18]*b[37]-b[3]*b[24]*b[31]-b[15]*b[6]*b[37]-b[15]*b[7]*b[36]+b[15]*b[31]*b[12]+b[16]*b[32]*b[10]+b[16]*b[33]*b[9]+b[13]*b[33]*b[12]-b[13]*b[7]*b[38]+b[14]*b[32]*b[12]+b[14]*b[33]*b[11]-b[16]*b[6]*b[36]-b[16]*b[7]*b[35]+b[16]*b[31]*b[11]+b[16]*b[30]*b[12]+b[15]*b[32]*b[11]+b[15]*b[33]*b[10]-b[15]*b[5]*b[38]+b[29]*b[5]*b[24]+b[29]*b[6]*b[23]-b[26]*b[20]*b[12]+b[26]*b[7]*b[25]-b[27]*b[19]*b[12]-b[27]*b[20]*b[11]+b[27]*b[6]*b[25]+b[27]*b[7]*b[24]-b[28]*b[20]*b[10]-b[16]*b[4]*b[38]-b[16]*b[5]*b[37]+b[29]*b[7]*b[22]-b[29]*b[17]*b[12]-b[29]*b[18]*b[11]-b[29]*b[19]*b[10]+b[28]*b[5]*b[25]+b[28]*b[6]*b[24]+b[28]*b[7]*b[23]-b[28]*b[18]*b[12]-b[28]*b[19]*b[11]-b[29]*b[20]*b[9]+b[29]*b[4]*b[25]-b[2]*b[24]*b[32]+b[0]*b[20]*b[38]-b[0]*b[25]*b[33]+b[1]*b[19]*b[38]-b[1]*b[24]*b[33]+b[1]*b[20]*b[37]-b[2]*b[25]*b[31]+b[2]*b[20]*b[36]-b[1]*b[25]*b[32]+b[2]*b[19]*b[37]+b[2]*b[18]*b[38]-b[2]*b[23]*b[33]);
|
||||
c[2] = (b[3]*b[18]*b[38]-b[3]*b[24]*b[32]+b[3]*b[19]*b[37]+b[3]*b[20]*b[36]-b[3]*b[25]*b[31]-b[3]*b[23]*b[33]-b[15]*b[6]*b[38]-b[15]*b[7]*b[37]+b[16]*b[32]*b[11]+b[16]*b[33]*b[10]-b[16]*b[5]*b[38]-b[16]*b[6]*b[37]-b[16]*b[7]*b[36]+b[16]*b[31]*b[12]+b[14]*b[33]*b[12]-b[14]*b[7]*b[38]+b[15]*b[32]*b[12]+b[15]*b[33]*b[11]+b[29]*b[5]*b[25]+b[29]*b[6]*b[24]-b[27]*b[20]*b[12]+b[27]*b[7]*b[25]-b[28]*b[19]*b[12]-b[28]*b[20]*b[11]+b[29]*b[7]*b[23]-b[29]*b[18]*b[12]-b[29]*b[19]*b[11]+b[28]*b[6]*b[25]+b[28]*b[7]*b[24]-b[29]*b[20]*b[10]+b[2]*b[19]*b[38]-b[1]*b[25]*b[33]+b[2]*b[20]*b[37]-b[2]*b[24]*b[33]-b[2]*b[25]*b[32]+b[1]*b[20]*b[38]);
|
||||
c[1] = (b[29]*b[7]*b[24]-b[29]*b[20]*b[11]+b[2]*b[20]*b[38]-b[2]*b[25]*b[33]-b[28]*b[20]*b[12]+b[28]*b[7]*b[25]-b[29]*b[19]*b[12]-b[3]*b[24]*b[33]+b[15]*b[33]*b[12]+b[3]*b[19]*b[38]-b[16]*b[6]*b[38]+b[3]*b[20]*b[37]+b[16]*b[32]*b[12]+b[29]*b[6]*b[25]-b[16]*b[7]*b[37]-b[3]*b[25]*b[32]-b[15]*b[7]*b[38]+b[16]*b[33]*b[11]);
|
||||
c[0] = -b[29]*b[20]*b[12]+b[29]*b[7]*b[25]+b[16]*b[33]*b[12]-b[16]*b[7]*b[38]+b[3]*b[20]*b[38]-b[3]*b[25]*b[33];
|
||||
|
||||
std::vector<double> xs, ys, zs;
|
||||
int count = 0;
|
||||
double * e = ematrix->data.db;
|
||||
std::vector<std::complex<double> > roots;
|
||||
solvePoly(coeffs, roots);
|
||||
|
||||
std::vector<double> xs, ys, zs;
|
||||
int count = 0;
|
||||
double * e = ematrix->data.db;
|
||||
for (size_t i = 0; i < roots.size(); i++)
|
||||
{
|
||||
if (fabs(roots[i].imag()) > 1e-10) continue;
|
||||
double z1 = roots[i].real();
|
||||
double z2 = z1 * z1;
|
||||
double z3 = z2 * z1;
|
||||
double z4 = z3 * z1;
|
||||
|
||||
double bz[3][3];
|
||||
if (fabs(roots[i].imag()) > 1e-10) continue;
|
||||
double z1 = roots[i].real();
|
||||
double z2 = z1 * z1;
|
||||
double z3 = z2 * z1;
|
||||
double z4 = z3 * z1;
|
||||
|
||||
double bz[3][3];
|
||||
for (int j = 0; j < 3; j++)
|
||||
{
|
||||
const double * br = b + j * 13;
|
||||
bz[j][0] = br[0] * z3 + br[1] * z2 + br[2] * z1 + br[3];
|
||||
bz[j][1] = br[4] * z3 + br[5] * z2 + br[6] * z1 + br[7];
|
||||
bz[j][2] = br[8] * z4 + br[9] * z3 + br[10] * z2 + br[11] * z1 + br[12];
|
||||
const double * br = b + j * 13;
|
||||
bz[j][0] = br[0] * z3 + br[1] * z2 + br[2] * z1 + br[3];
|
||||
bz[j][1] = br[4] * z3 + br[5] * z2 + br[6] * z1 + br[7];
|
||||
bz[j][2] = br[8] * z4 + br[9] * z3 + br[10] * z2 + br[11] * z1 + br[12];
|
||||
}
|
||||
|
||||
Mat Bz(3, 3, CV_64F, bz);
|
||||
cv::Mat xy1;
|
||||
SVD::solveZ(Bz, xy1);
|
||||
Mat Bz(3, 3, CV_64F, bz);
|
||||
cv::Mat xy1;
|
||||
SVD::solveZ(Bz, xy1);
|
||||
|
||||
if (fabs(xy1.at<double>(2)) < 1e-10) continue;
|
||||
xs.push_back(xy1.at<double>(0) / xy1.at<double>(2));
|
||||
ys.push_back(xy1.at<double>(1) / xy1.at<double>(2));
|
||||
zs.push_back(z1);
|
||||
if (fabs(xy1.at<double>(2)) < 1e-10) continue;
|
||||
xs.push_back(xy1.at<double>(0) / xy1.at<double>(2));
|
||||
ys.push_back(xy1.at<double>(1) / xy1.at<double>(2));
|
||||
zs.push_back(z1);
|
||||
|
||||
cv::Mat Evec = EE.col(0) * xs.back() + EE.col(1) * ys.back() + EE.col(2) * zs.back() + EE.col(3);
|
||||
Evec /= norm(Evec);
|
||||
|
||||
memcpy(e + count * 9, Evec.data, 9 * sizeof(double));
|
||||
count++;
|
||||
cv::Mat Evec = EE.col(0) * xs.back() + EE.col(1) * ys.back() + EE.col(2) * zs.back() + EE.col(3);
|
||||
Evec /= norm(Evec);
|
||||
|
||||
memcpy(e + count * 9, Evec.data, 9 * sizeof(double));
|
||||
count++;
|
||||
}
|
||||
return count;
|
||||
return count;
|
||||
|
||||
}
|
||||
|
||||
// Same as the runKernel (run5Point), m1 and m2 should be
|
||||
// 1 row x n col x 2 channels.
|
||||
// And also, error has to be of CV_32FC1.
|
||||
// 1 row x n col x 2 channels.
|
||||
// And also, error has to be of CV_32FC1.
|
||||
void CvEMEstimator::computeReprojError( const CvMat* m1, const CvMat* m2,
|
||||
const CvMat* model, CvMat* error )
|
||||
{
|
||||
Mat X1(m1), X2(m2);
|
||||
int n = X1.cols;
|
||||
X1 = X1.reshape(1, n);
|
||||
X2 = X2.reshape(1, n);
|
||||
Mat X1(m1), X2(m2);
|
||||
int n = X1.cols;
|
||||
X1 = X1.reshape(1, n);
|
||||
X2 = X2.reshape(1, n);
|
||||
|
||||
X1.convertTo(X1, CV_64F);
|
||||
X2.convertTo(X2, CV_64F);
|
||||
X1.convertTo(X1, CV_64F);
|
||||
X2.convertTo(X2, CV_64F);
|
||||
|
||||
Mat E(model);
|
||||
Mat E(model);
|
||||
for (int i = 0; i < n; i++)
|
||||
{
|
||||
Mat x1 = (Mat_<double>(3, 1) << X1.at<double>(i, 0), X1.at<double>(i, 1), 1.0);
|
||||
Mat x2 = (Mat_<double>(3, 1) << X2.at<double>(i, 0), X2.at<double>(i, 1), 1.0);
|
||||
double x2tEx1 = x2.dot(E * x1);
|
||||
Mat Ex1 = E * x1;
|
||||
Mat Etx2 = E * x2;
|
||||
double a = Ex1.at<double>(0) * Ex1.at<double>(0);
|
||||
double b = Ex1.at<double>(1) * Ex1.at<double>(1);
|
||||
double c = Etx2.at<double>(0) * Etx2.at<double>(0);
|
||||
double d = Etx2.at<double>(0) * Etx2.at<double>(0);
|
||||
Mat x1 = (Mat_<double>(3, 1) << X1.at<double>(i, 0), X1.at<double>(i, 1), 1.0);
|
||||
Mat x2 = (Mat_<double>(3, 1) << X2.at<double>(i, 0), X2.at<double>(i, 1), 1.0);
|
||||
double x2tEx1 = x2.dot(E * x1);
|
||||
Mat Ex1 = E * x1;
|
||||
Mat Etx2 = E * x2;
|
||||
double a = Ex1.at<double>(0) * Ex1.at<double>(0);
|
||||
double b = Ex1.at<double>(1) * Ex1.at<double>(1);
|
||||
double c = Etx2.at<double>(0) * Etx2.at<double>(0);
|
||||
double d = Etx2.at<double>(0) * Etx2.at<double>(0);
|
||||
|
||||
error->data.fl[i] = (float)(x2tEx1 * x2tEx1 / (a + b + c + d));
|
||||
error->data.fl[i] = (float)(x2tEx1 * x2tEx1 / (a + b + c + d));
|
||||
}
|
||||
|
||||
}
|
||||
@@ -434,7 +433,7 @@ void CvEMEstimator::calibrated_fivepoint_helper( double *EE, double* A )
|
||||
double e003,e013,e023,e033,e043,e053,e063,e073,e083;
|
||||
double e103,e113,e123,e133,e143,e153,e163,e173,e183;
|
||||
double e203,e213,e223,e233,e243,e253,e263,e273,e283;
|
||||
double e303,e313,e323,e333,e343,e353,e363,e373,e383;
|
||||
double e303,e313,e323,e333,e343,e353,e363,e373,e383;
|
||||
e00 = EE[0*9 + 0 ];
|
||||
e10 = EE[1*9 + 0 ];
|
||||
e20 = EE[2*9 + 0 ];
|
||||
@@ -471,8 +470,8 @@ void CvEMEstimator::calibrated_fivepoint_helper( double *EE, double* A )
|
||||
e18 = EE[1*9 + 8 ];
|
||||
e28 = EE[2*9 + 8 ];
|
||||
e38 = EE[3*9 + 8 ];
|
||||
|
||||
|
||||
|
||||
|
||||
e002 =e00*e00;
|
||||
e102 =e10*e10;
|
||||
e202 =e20*e20;
|
||||
@@ -509,7 +508,7 @@ void CvEMEstimator::calibrated_fivepoint_helper( double *EE, double* A )
|
||||
e182 =e18*e18;
|
||||
e282 =e28*e28;
|
||||
e382 =e38*e38;
|
||||
|
||||
|
||||
e003 =e00*e00*e00;
|
||||
e103 =e10*e10*e10;
|
||||
e203 =e20*e20*e20;
|
||||
@@ -546,8 +545,8 @@ void CvEMEstimator::calibrated_fivepoint_helper( double *EE, double* A )
|
||||
e183 =e18*e18*e18;
|
||||
e283 =e28*e28*e28;
|
||||
e383 =e38*e38*e38;
|
||||
|
||||
|
||||
|
||||
|
||||
A[0 + 10*0]=0.5*e003+0.5*e00*e012+0.5*e00*e022+0.5*e00*e032+e03*e01*e04+e03*e02*e05+0.5*e00*e062+e06*e01*e07+e06*e02*e08-0.5*e00*e042-0.5*e00*e052-0.5*e00*e072-0.5*e00*e082;
|
||||
A[0 + 10*1]=e00*e11*e01+e00*e12*e02+e03*e00*e13+e03*e11*e04+e03*e01*e14+e03*e12*e05+e03*e02*e15+e13*e01*e04+e13*e02*e05+e06*e00*e16+1.5*e10*e002+0.5*e10*e012+0.5*e10*e022+0.5*e10*e062-0.5*e10*e042-0.5*e10*e052-0.5*e10*e072+0.5*e10*e032+e06*e11*e07+e06*e01*e17+e06*e12*e08+e06*e02*e18+e16*e01*e07+e16*e02*e08-e00*e14*e04-e00*e17*e07-e00*e15*e05-e00*e18*e08-0.5*e10*e082;
|
||||
A[0 + 10*2]=e16*e02*e18+e03*e12*e15+e10*e11*e01+e10*e12*e02+e03*e10*e13+e03*e11*e14+e13*e11*e04+e13*e01*e14+e13*e12*e05+e13*e02*e15+e06*e10*e16+e06*e12*e18+e06*e11*e17+e16*e11*e07+e16*e01*e17+e16*e12*e08-e10*e14*e04-e10*e17*e07-e10*e15*e05-e10*e18*e08+1.5*e00*e102+0.5*e00*e122+0.5*e00*e112+0.5*e00*e132+0.5*e00*e162-0.5*e00*e152-0.5*e00*e172-0.5*e00*e182-0.5*e00*e142;
|
||||
|
||||
Reference in New Issue
Block a user