本文整理汇总了C#中Matrix.MultiplyThis方法的典型用法代码示例。如果您正苦于以下问题：C# Matrix.MultiplyThis方法的具体用法？C# Matrix.MultiplyThis怎么用？C# Matrix.MultiplyThis使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Matrix的用法示例。
在下文中一共展示了Matrix.MultiplyThis方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: UpdateCameraMatrix
void UpdateCameraMatrix(Matrix<double> camera, CameraIndex index)
{
var RQ = camera.SubMatrix(0, 3, 0, 3).QR();
var calib = RQ.R;
if(Math.Abs(calib[2, 2] - 1) > 1e-6)
{
double scale = calib[2, 2];
camera.MultiplyThis(scale);
// NotifyPropertyChanged("CameraLeft");
RQ = camera.SubMatrix(0, 3, 0, 3).QR();
}
calib = RQ.R;
var rot = RQ.Q;
// If fx < 0 then set fx = -fx and [r11,r12,r13] = -[r11,r12,r13]
// As first row of rotation matrix is multiplied only with fx, then changing sign of both
// fx and this row won't change matrix M = K*R, and so camera matrix (same with fy, but we need to change skew also)
if(calib[0, 0] < 0)
{
calib[0, 0] = -calib[0, 0];
rot[0, 0] = -rot[0, 0];
rot[0, 1] = -rot[0, 1];
rot[0, 2] = -rot[0, 2];
}
if(calib[1, 1] < 0)
{
calib[1, 1] = -calib[1, 1];
calib[0, 1] = -calib[0, 1];
rot[1, 0] = -rot[1, 0];
rot[1, 1] = -rot[1, 1];
rot[1, 2] = -rot[1, 2];
}
var trans = -camera.SubMatrix(0, 3, 0, 3).Inverse().Multiply(camera.Column(3));
SetCalibrationMatrix(index, calib);
SetRotationMatrix(index, rot);
SetTranslationVector(index, trans);
ComputeEssentialFundamental();
}示例2: Test_EstimateCameraMatrix_Minimised
public void Test_EstimateCameraMatrix_Minimised()
{
PrepareCameraMatrix();
var points = GenerateCalibrationPoints_Random(100);
var noisedPoints = AddNoise(points, _varianceReal, _varianceImage, 200);
PrepareCalibrator(noisedPoints);
_calib.HomoPoints();
_calib.NormalizeImagePoints();
_calib.NormalizeRealPoints();
_calib._pointsNormalised = true;
_calib.CameraMatrix = _calib.FindLinearEstimationOfCameraMatrix();
// _calib.FindNormalisedVariances();
_calib.DenormaliseCameraMatrix();
_calib._pointsNormalised = false;
_eCM = _calib.CameraMatrix;
double totalDiff = 0.0;
for(int p = 0; p < _pointsCount; ++p)
{
var cp = points[p];
Vector<double> rp = new DenseVector(4);
rp[0] = cp.RealX;
rp[1] = cp.RealY;
rp[2] = cp.RealZ;
rp[3] = 1.0;
var imagePoint = _eCM * rp;
Vector2 ip = new Vector2(imagePoint[0] / imagePoint[2], imagePoint[1] / imagePoint[2]);
totalDiff += (ip - cp.Img).Length();
Assert.IsTrue((ip - cp.Img).Length() < 1.0,
"Point after linear estimation too far : " + (ip - cp.Img).Length());
}
_calib.HomoPoints();
_calib.NormalizeImagePoints();
_calib.NormalizeRealPoints();
_calib.CameraMatrix = _calib.FindLinearEstimationOfCameraMatrix();
_calib._pointsNormalised = true;
_calib.FindNormalisedVariances();
_calib.UseCovarianceMatrix = true;
var lecm = _eCM.Clone();
// Disturb camera matrix a little
// _calib.CameraMatrix = AddNoise(_calib.CameraMatrix);
_calib._miniAlg.DoComputeJacobianNumerically = true;
_calib._miniAlg.NumericalDerivativeStep = 1e-4;
_calib.MinimizeError();
// _calib.DenormaliseCameraMatrix();
_calib.DecomposeCameraMatrix();
_eCM = _calib.CameraMatrix;
var errVec = _eCM.PointwiseDivide_NoNaN(lecm);
double err = errVec.L2Norm();
double scaleK = 1.0 / _calib.CameraInternalMatrix[2, 2];
_eCM.MultiplyThis(-scaleK);
var eK = _calib.CameraInternalMatrix.Multiply(scaleK);
var eR = -_calib.CameraRotationMatrix;
var eC = -(_eCM.SubMatrix(0, 3, 0, 3).Inverse() * _eCM.Column(3));
Matrix<double> eExt = new DenseMatrix(3, 4);
eExt.SetSubMatrix(0, 0, eR);
eExt.SetColumn(3, -eR * eC);
var eCM = eK * eExt;
// var errVec = _CM.PointwiseDivide_NoNaN(_eCM);
// double err = errVec.L2Norm();
// Assert.IsTrue(
// Math.Abs(err - Math.Sqrt(12)) < Math.Sqrt(12) / 1000.0 || // max 0.1% diffrence
// (_eCM - _CM).FrobeniusNorm() < 1e-3);
double estDiff = 0;
for(int p = 0; p < _pointsCount; ++p)
{
var cp = points[p];
Vector<double> rp = new DenseVector(4);
rp[0] = cp.RealX;
rp[1] = cp.RealY;
rp[2] = cp.RealZ;
rp[3] = 1.0;
var imagePoint = _eCM * rp;
Vector2 ip = new Vector2(imagePoint[0] / imagePoint[2], imagePoint[1] / imagePoint[2]);
estDiff += (ip - cp.Img).Length();
Assert.IsTrue((ip - cp.Img).Length() < 1.5,
"Point after error minimalisation too far : " + (ip - cp.Img).Length());
}
var minialg = _calib._miniAlg;
// Test conovergence :
// ||mX-rX|| = ||mX-eX|| + ||rX-eX|| (or squared??)
// rX - real point from 'points'
// mX - measured point, noised
// eX = estimated X from result vector for 3d points and ePeX for image point
double len2_mr = 0;
//.........这里部分代码省略.........
示例3: Test_EstimateCameraMatrix_NoisedLinear
public void Test_EstimateCameraMatrix_NoisedLinear()
{
PrepareCameraMatrix();
var points = GenerateCalibrationPoints_Random();
PrepareCalibrator(
AddNoise(points, _varianceReal, _varianceImage));
_calib.HomoPoints();
_calib.NormalizeImagePoints();
_calib.NormalizeRealPoints();
_calib.CameraMatrix = _calib.FindLinearEstimationOfCameraMatrix();
_calib.DenormaliseCameraMatrix();
_calib.DecomposeCameraMatrix();
_eCM = _calib.CameraMatrix;
double scaleK = 1.0 / _calib.CameraInternalMatrix[2, 2];
_eCM.MultiplyThis(-scaleK);
var eK = _calib.CameraInternalMatrix.Multiply(scaleK);
var eR = -_calib.CameraRotationMatrix;
var eC = -(_eCM.SubMatrix(0, 3, 0, 3).Inverse() * _eCM.Column(3));
Matrix<double> eExt = new DenseMatrix(3, 4);
eExt.SetSubMatrix(0, 0, eR);
eExt.SetColumn(3, -eR * eC);
var eCM = eK * eExt;
var errVec = _CM.PointwiseDivide_NoNaN(_eCM);
double err = errVec.L2Norm();
Assert.IsTrue(
Math.Abs(err - Math.Sqrt(12)) < Math.Sqrt(12) / 50.0 || // max 2% diffrence
(_eCM - _CM).FrobeniusNorm() < 1e-3);
for(int p = 0; p < _pointsCount; ++p)
{
var cp = points[p];
Vector<double> rp = new DenseVector(4);
rp[0] = cp.RealX;
rp[1] = cp.RealY;
rp[2] = cp.RealZ;
rp[3] = 1.0;
var imagePoint = _eCM * rp;
Vector2 ip = new Vector2(imagePoint[0] / imagePoint[2], imagePoint[1] / imagePoint[2]);
Assert.IsTrue((ip - cp.Img).Length() < cp.Img.Length() / 10.0);
}
}示例4: Test_EstimateCameraMatrix_IdealLinear
public void Test_EstimateCameraMatrix_IdealLinear()
{
PrepareCameraMatrix();
var points = GenerateCalibrationPoints_Random();
PrepareCalibrator(points);
_calib.HomoPoints();
_calib.NormalizeImagePoints();
_calib.NormalizeRealPoints();
_calib.CameraMatrix = _calib.FindLinearEstimationOfCameraMatrix();
_calib.DenormaliseCameraMatrix();
_calib.DecomposeCameraMatrix();
_eCM = _calib.CameraMatrix;
double scaleK = -1.0 / _calib.CameraInternalMatrix[2, 2];
_eCM.MultiplyThis(scaleK);
Assert.IsTrue((_eCM - _CM).FrobeniusNorm() < 1e-6);
for(int p = 0; p < _pointsCount; ++p)
{
var cp = points[p];
Vector<double> rp = new DenseVector(4);
rp[0] = cp.RealX;
rp[1] = cp.RealY;
rp[2] = cp.RealZ;
rp[3] = 1.0;
var imagePoint = _eCM * rp;
Vector2 ip = new Vector2(imagePoint[0] / imagePoint[2], imagePoint[1] / imagePoint[2]);
Assert.IsTrue((ip - cp.Img).Length() < cp.Img.Length() / 100.0);
}
}