public void TestIdealMatrices()
{
List <Image <Arthmetic, double> > ptsReal = new List <Image <Arthmetic, double> >();
List <PointF> pts1 = new List <PointF>();
List <PointF> pts2 = new List <PointF>();
Random rand = new Random(1003);
for (int i = 0; i < 10; ++i)
{
var real = new Image <Arthmetic, double>(new double[, , ] {
{ { rand.Next(100, 200) } },
{ { rand.Next(100, 200) } },
{ { rand.Next(50, 100) } },
{ { 1 } },
});
ptsReal.Add(real);
var i1 = P1.Multiply(real).ToPointF();
pts1.Add(i1);
var i2 = P2.Multiply(real).ToPointF();
pts2.Add(i2);
}
MatchingResult match = new MatchingResult()
{
LeftPoints = new VectorOfPointF(pts1.ToArray()),
RightPoints = new VectorOfPointF(pts2.ToArray()),
};
var F = ComputeMatrix.F(match.LeftPoints, match.RightPoints);
var E = ComputeMatrix.E(F, K);
var svd = new Svd(E);
FindTransformation.DecomposeToRTAndTriangulate(pts1, pts2, K, E, out var RR, out var tt, out Image <Arthmetic, double> X);
var tt1 = T12.Mul(1 / T12.Norm);
var tt2 = tt.Mul(1 / tt.Norm);
var KK = EstimateCameraFromImagePair.K(F, 700, 400);
var EE = ComputeMatrix.E(F, KK);
var svd2 = new Svd(EE);
}
public void TestMatricesFromNoisedPoints()
{
List <Image <Arthmetic, double> > ptsReal = new List <Image <Arthmetic, double> >();
List <PointF> pts1 = new List <PointF>();
List <PointF> pts2 = new List <PointF>();
List <PointF> pts3 = new List <PointF>();
List <PointF> pts1Ref = new List <PointF>();
List <PointF> pts2Ref = new List <PointF>();
List <PointF> pts3Ref = new List <PointF>();
Random rand = new Random(1003);
double stddev = 3;
int pointsCount = 100;
for (int i = 0; i < pointsCount; ++i)
{
var real = new Image <Arthmetic, double>(new double[, , ] {
{ { rand.Next(100, 200) } },
{ { rand.Next(100, 200) } },
{ { rand.Next(50, 100) } },
{ { 1 } },
});
ptsReal.Add(real);
var i1 = P1.Multiply(real).ToPointF();
pts1Ref.Add(i1);
i1 = new PointF(i1.X + Noise(stddev, rand), i1.Y + Noise(stddev, rand));
pts1.Add(i1);
var i2 = P2.Multiply(real).ToPointF();
pts2Ref.Add(i2);
i2 = new PointF(i2.X + Noise(stddev, rand), i2.Y + Noise(stddev, rand));
pts2.Add(i2);
var i3 = P3.Multiply(real).ToPointF();
pts3Ref.Add(i3);
i3 = new PointF(i3.X + Noise(stddev, rand), i3.Y + Noise(stddev, rand));
pts3.Add(i3);
}
double rangeLx = pts1.Max((x) => x.X) - pts1.Min((x) => x.X);
double rangeLy = pts1.Max((x) => x.Y) - pts1.Min((x) => x.Y);
double rangeRx = pts2.Max((x) => x.X) - pts2.Min((x) => x.X);
double rangeRy = pts2.Max((x) => x.Y) - pts2.Min((x) => x.Y);
var pts1_n = new List <PointF>(pts1);
var pts2_n = new List <PointF>(pts2);
var pts3_n = new List <PointF>(pts3);
FindTransformation.NormalizePoints2d(pts1_n, out Image <Arthmetic, double> N1);
FindTransformation.NormalizePoints2d(pts2_n, out Image <Arthmetic, double> N2);
FindTransformation.NormalizePoints2d(pts3_n, out Image <Arthmetic, double> N3);
var F = ComputeMatrix.F(new VectorOfPointF(pts1_n.ToArray()), new VectorOfPointF(pts2_n.ToArray()));
var F23 = ComputeMatrix.F(new VectorOfPointF(pts2_n.ToArray()), new VectorOfPointF(pts3_n.ToArray()));
// F is normalized - lets denormalize it
F = N2.T().Multiply(F).Multiply(N1);
F23 = N3.T().Multiply(F23).Multiply(N2);
var E = ComputeMatrix.E(F, K);
var E23 = ComputeMatrix.E(F23, K);
var svd = new Svd(E);
var svd23 = new Svd(E23);
FindTransformation.DecomposeToRTAndTriangulate(pts1, pts2, K, E, out var RR, out var TT, out Image <Arthmetic, double> estReal_);
FindTransformation.DecomposeToRTAndTriangulate(pts2, pts3, K, E23, out var RR23, out var TT23, out Image <Arthmetic, double> estReal23_);
var rr0 = RotationConverter.MatrixToEulerXYZ(R12);
var rr1 = RotationConverter.MatrixToEulerXYZ(RR);
double idealScale = T23.Norm / T12.Norm;
double idealScale2 = T12.Norm / T23.Norm;
var tt0 = T12.Mul(1 / T12.Norm);
var tt1 = TT.Mul(1 / TT.Norm).Mul(T12[0, 0] * TT[0, 0] < 0 ? -1 : 1);
var tt1_23 = TT23.Mul(1 / TT23.Norm).Mul(T23[0, 0] * TT23[0, 0] < 0 ? -1 : 1);
FindTransformation.TriangulateChieral(pts1, pts2, K, RR, tt1, out var estReal12);
FindTransformation.TriangulateChieral(pts2, pts3, K, RR23, tt1_23, out var estReal23);
RansacScaleEstimation ransacModel = new RansacScaleEstimation(estReal12, estReal23, RR, ComputeMatrix.Center(tt1, RR));
int sampleSize = (int)(0.1 * pointsCount);
int minGoodPoints = (int)(0.2 * pointsCount);
int maxIterations = 1000;
double meanRefPointSize = ScaleBy3dPointsMatch.GetMeanSize(estReal12);
double threshold = meanRefPointSize * meanRefPointSize * 0.1;
var result = RANSAC.ProcessMostInliers(ransacModel, maxIterations, sampleSize, minGoodPoints, threshold, 1.0);
double scale = (double)result.BestModel;
var estRealRef23To12 = ScaleBy3dPointsMatch.TransfromBack3dPoints(RR, tt1, estReal23, scale);
double simpleScaleMean = 0.0;
for (int i = 0; i < pointsCount; ++i)
{
var p12 = new Image <Arthmetic, double>(new double[, , ] {
{ { estReal12[1, i] } },
{ { estReal12[2, i] } },
{ { estReal12[3, i] } },
});
var p23 = new Image <Arthmetic, double>(new double[, , ] {
{ { estReal23[0, i] } },
{ { estReal23[1, i] } },
{ { estReal23[2, i] } },
});
double n1 = p12.Norm;
double n2 = p23.Norm;
double simpleScale = n2 / n1;
simpleScaleMean += simpleScale;
}
simpleScaleMean /= pointsCount;
double simpleScaleMean2 = 1 / simpleScaleMean;
// TODO: compute below only on inliers
Image <Arthmetic, double> inliersOnly12 = new Image <Arthmetic, double>(result.Inliers.Count, 4);
Image <Arthmetic, double> inliersOnly12Ref = new Image <Arthmetic, double>(result.Inliers.Count, 4);
Image <Arthmetic, double> inliersOnly23 = new Image <Arthmetic, double>(result.Inliers.Count, 4);
for (int i = 0; i < result.Inliers.Count; ++i)
{
int k = result.Inliers[i];
for (int j = 0; j < 4; ++j)
{
inliersOnly12[j, i] = estReal12[j, k];
inliersOnly12Ref[j, i] = ptsReal[k][j, 0];
inliersOnly23[j, i] = estRealRef23To12[j, k];
}
}
var ptsRealM = Utils.Matrixify(ptsReal);
Errors.TraingulationError(ptsRealM, estReal12, out double mean1x, out double median1x, out List <double> errors1x);
Errors.TraingulationError(ptsRealM, estRealRef23To12, out double mean1z, out double median1z, out List <double> errors1z);
Errors.TraingulationError(inliersOnly12Ref, inliersOnly12, out double mean_in1, out double median_in1, out List <double> errors_in1);
Errors.TraingulationError(inliersOnly12Ref, inliersOnly23, out double mean_in2, out double median_in2, out List <double> errors_in2);
Errors.TraingulationError(inliersOnly12, inliersOnly23, out double mean_in3, out double median_in3, out List <double> errors_in3);
var ptsReal23M = ForwardProject3dPoints(ptsRealM, R12, C2);
Errors.TraingulationError(ptsReal23M, estReal23, out double mean1h, out double median1h, out List <double> errors1h);
var estC2 = ComputeMatrix.Center(tt1, RR);
var ptsEst23M = ForwardProject3dPoints(estReal12, RR, estC2);
Errors.TraingulationError(ptsEst23M, estReal23, out double mean1t, out double median1t, out List <double> errors1t);
int dummy = 0;
//Errors.TraingulationError(ptsReal, estReal, out double mean1, out double median1, out List<double> errors1);
//Errors.ReprojectionError(estReal, pts2, K, RR, tt1, out double mean_r1a, out double median_r1a, out List<double> _1);
//Errors.ReprojectionError(estReal, pts2Ref, K, RR, tt1, out double mean_r1b, out double median_r1b, out List<double> _2);
//Errors.ReprojectionError(estReal, pts2Ref, K, R12, tt0, out double mean_r1c, out double median_r1c, out List<double> _3);
//Errors.ReprojectionError(Errors.Matrixify(ptsReal), pts2Ref, K, RR, tt1, out double mean_r1e, out double median_r1e, out List<double> _5);
var H1 = FindTransformation.EstimateHomography(pts1, pts2, K);
var H2 = FindTransformation.EstimateHomography(pts1Ref, pts2Ref, K);
var hrr1 = RotationConverter.MatrixToEulerXYZ(H1);
var hrr2 = RotationConverter.MatrixToEulerXYZ(H2);
//var zeroT = new Image<Arthmetic, double>(1, 3);
//var H3 = RotationConverter.EulerXYZToMatrix(hrr1);
//var hrr3 = RotationConverter.MatrixToEulerXYZ(H1);
//var svdH = new Svd(H1);
bool isRotation = FindTransformation.IsPureRotation(H1);
int dummy2 = 0;
//Errors.ReprojectionError2d(pts1Ref, pts2Ref, K, H2, out double mean_h2, out double median_h2, out var err_h2);
//Errors.ReprojectionError2d(pts1, pts2, K, H1, out double mean_h1, out double median_h1, out var err_h1);
//Errors.ReprojectionError2d(pts1, pts2, K, H3, out double mean_h3, out double median_h3, out var err_h3);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, H1, zeroT, out double scale1, out double mean_h1a, out double median_h1a, out var err_h1a);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, H3, zeroT, out double scale1x, out double mean_h1ax, out double median_h1ax, out var err_h1ax);
//// Errors.ReprojectionError2dWithT(pts1, pts2, K, R12, tt0, out double scale2, out double mean_h1b, out double median_h1b, out var err_h1b);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, RR, tt1, out double scale3, out double mean_h1c, out double median_h1c, out var err_h1c);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, R12, tt1, out double scale5, out double mean_h1c1, out double median_h1c1, out var err_h1c1);
//Errors.ReprojectionError2dWithT(pts1Ref, pts2Ref, K, R12, tt0, out double scale6, out double mean_h1c2, out double median_h1c2, out var err_h1c2);
//Errors.ReprojectionError2dWithT(pts1, pts2, K, H1, tt1, out double scale4, out double mean_h1d, out double median_h1d, out var err_h1d);
//var KK = EstimateCameraFromImagePair.K(F, 600, 500);
//var EE = ComputeMatrix.E(F, KK);
//var svd2 = new Svd(EE);
//FindTransformation.DecomposeToRTAndTriangulate(pts1, pts2, KK, EE, out var RR2, out var TT2, out Image<Arthmetic, double> estReal2);
//var tt2 = TT2.Mul(1 / TT2.Norm);
//var rr2 = RotationConverter.MatrixToEulerXYZ(RR2);
//Errors.TraingulationError(ptsReal, estReal2, out double mean2, out double median2, out List<double> errors2);
//Errors.ReprojectionError(estReal2, pts2, KK, RR2, tt2, out double mean_r2a, out double median_r2a, out List<double> _1x);
//Errors.ReprojectionError(estReal2, pts2Ref, KK, RR2, tt2, out double mean_r2b, out double median_r2b, out List<double> _2x);
//Errors.ReprojectionError(estReal2, pts2Ref, KK, R12, tt0, out double mean_r2c, out double median_r2c, out List<double> _3x);
//Errors.ReprojectionError(Errors.Matrixify(ptsReal), pts2Ref, KK, RR2, tt2, out double mean_r2e, out double median_r2e, out List<double> _5x);
}