public static double[,] Expand2(double[,] source, double factor, Size requestedSize = default(Size))
{
double[,] result = requestedSize == default(Size)
? new double[(int)(source.GetLength(0) * factor), (int)(source.GetLength(1) * factor)]
: new double[requestedSize.Width, requestedSize.Height];
Resize(source, result, 1 / factor, (x, y) => Gaussian.Gaussian2D(x, y, factor / 2d * 0.75d));
return(result);
}
public static Complex[,] EsimateH2(double[,] img, double Sigma1, double Sigma2)
{
var z = GetSquaredDerectionField(img, Sigma1);
var kernel2 = KernelHelper.MakeComplexKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma2) * x, (x, y) => Gaussian.Gaussian2D(x, y, Sigma2) * (-y), KernelHelper.GetKernelSizeForGaussianSigma(Sigma2));
var I20 = ConvolutionHelper.ComplexConvolve(z, kernel2);
return(I20);
}
public static double[,] Smoothing(double[,] img, double sigma)
{
var kernel = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, sigma),
KernelHelper.GetKernelSizeForGaussianSigma(sigma));
double[,] data = ConvolutionHelper.Convolve(img, kernel);
return(data);
}
public static double[,] Reduce2(double[,] source, double factor)
{
var smoothed = ConvolutionHelper.Convolve(source,
KernelHelper.MakeKernel(
(x, y) => Gaussian.Gaussian2D(x, y, factor / 2d * 0.75d), KernelHelper.GetKernelSizeForGaussianSigma(factor / 2d * 0.75d)));
var result = new double[(int)(source.GetLength(0) / factor), (int)(source.GetLength(1) / factor)];
Resize(smoothed, result, factor, (x, y) => Gaussian.Gaussian2D(x, y, factor / 2d * 0.75d));
return(result);
}
public static Complex[,] GetSquaredDerectionField(double[,] img, double Sigma1)
{
var kernelX = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma1) * x, KernelHelper.GetKernelSizeForGaussianSigma(Sigma1));
var resultX = ConvolutionHelper.Convolve(img, kernelX);
var kernelY = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma1) * -y, KernelHelper.GetKernelSizeForGaussianSigma(Sigma1));
var resultY = ConvolutionHelper.Convolve(img, kernelY);
var preZ = KernelHelper.MakeComplexFromDouble(resultX, resultY);
var z = preZ.Select2D(x => x * x);
return(z);
}
public static Complex[,] EstimatePS(double[,] img, double Sigma1, double Sigma2)
{
var z = GetSquaredDerectionField(img, Sigma1);
var kernel2 =
KernelHelper.MakeComplexKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma2) * x / (x == 0 && y == 0 ? 1 : Math.Sqrt(x * x + y * y)),
(x, y) => Gaussian.Gaussian2D(x, y, Sigma2) * y / (x == 0 && y == 0 ? 1 : Math.Sqrt(x * x + y * y)),
KernelHelper.GetKernelSizeForGaussianSigma(Sigma2));
var I20 = ConvolutionHelper.ComplexConvolve(z, kernel2);
return(I20);
}
public static Complex[,] EstimateLS(double[,] l1, double Sigma1, double Sigma2)
{
var kernelX = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma1) * x, KernelHelper.GetKernelSizeForGaussianSigma(Sigma1));
var resultX = ConvolutionHelper.Convolve(l1, kernelX);
var kernelY = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma1) * -y, KernelHelper.GetKernelSizeForGaussianSigma(Sigma1));
var resultY = ConvolutionHelper.Convolve(l1, kernelY);
var preZ = KernelHelper.MakeComplexFromDouble(resultX, resultY);
var z = preZ.Select2D(x => x * x);
var kernel2 = KernelHelper.MakeComplexKernel((x, y) => Gaussian.Gaussian2D(x, y, Sigma2), (x, y) => 0,
KernelHelper.GetKernelSizeForGaussianSigma(Sigma2));
var I20 = ConvolutionHelper.ComplexConvolve(z, kernel2);
var I11 = ConvolutionHelper.Convolve(z.Select2D(x => x.Magnitude), kernel2.Select2D(x => x.Real));
Complex[,] LS = KernelHelper.Zip2D(I20, I11, (x, y) => x / y);
return(LS);
}
public static double[,] GenerateOrientationField(double[,] bytes)
{
double size = 1;
double avSigma = 5;
var kernelAv = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, avSigma),
KernelHelper.GetKernelSizeForGaussianSigma(avSigma));
var kernelX = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, size) * x,
KernelHelper.GetKernelSizeForGaussianSigma(size));
var dx = ConvolutionHelper.Convolve(bytes, kernelX);
var kernelY = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, size) * -y,
KernelHelper.GetKernelSizeForGaussianSigma(size));
var dy = ConvolutionHelper.Convolve(bytes, kernelY);
var Gxx = dx.Select2D(x => x * x);
var Gxy = dx.Select2D((x, row, column) => x * dy[row, column]);
var Gyy = dy.Select2D(x => x * x);
Gxx = ConvolutionHelper.Convolve(Gxx, kernelAv);
Gxy = ConvolutionHelper.Convolve(Gxy, kernelAv);
Gyy = ConvolutionHelper.Convolve(Gyy, kernelAv);
var angles = Gxx.Select2D((gxx, row, column) => 0.5 * Math.Atan2(2.0 * Gxy[row, column], gxx - Gyy[row, column]));
angles = angles.Select2D(angle => angle <= 0 ? angle + Math.PI / 2 : angle - Math.PI / 2);
//ImageHelper.SaveFieldAbove(bytes, angles, "C:\\temp\\orField.png");
//ImageHelper.SaveArray(angles, "C:\\temp\\angles.png");
return(angles);
}
public double[,] GaussFilter()
{
double[,] filterX = new double[, ] {
{ -3, 0, 3 }, { -10, 0, 10 }, { -3, 0, 3 }
};
double[,] filterY = new double[, ] {
{ 3, 10, 3 }, { 0, 0, 0 }, { -3, -10, -3 }
};
double[,] Gx = ConvolutionHelper.Convolve(pic, filterX);
double[,] Gy = ConvolutionHelper.Convolve(pic, filterY);
double[,] X2 = new double[height, width];
double[,] Y2 = new double[height, width];
double[,] XY = new double[height, width];
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
X2[i, j] = Gx[i, j] * Gx[i, j];
Y2[i, j] = Gy[i, j] * Gy[i, j];
XY[i, j] = Gx[i, j] * Gy[i, j];
}
}
double sigma = 0.5;
int size = KernelHelper.GetKernelSizeForGaussianSigma(sigma);
double[,] w = KernelHelper.MakeKernel((x, y) => Gaussian.Gaussian2D(x, y, sigma), size); //Gaussian filter
double[,] A = ConvolutionHelper.Convolve(X2, w);
double[,] B = ConvolutionHelper.Convolve(Y2, w);
double[,] C = ConvolutionHelper.Convolve(XY, w);
double[,] R = new double[height, width];
for (int i = 0; i < height; ++i)
{
for (int j = 0; j < width; ++j)
{
double[,] M = new double[2, 2];
M[0, 0] = A[i, j];
M[0, 1] = C[i, j];
M[1, 0] = C[i, j];
M[1, 1] = B[i, j];
double Tr = M[0, 0] + M[1, 1];
double Det = M[0, 0] * M[1, 1] - M[1, 0] * M[0, 1];
R[i, j] = Det / Tr;
}
}
double[,] matrix = new double[width, height];
for (int i = 0; i < width; ++i)
{
for (int j = 0; j < height; ++j)
{
matrix[i, j] = R[height - 1 - j, i];
}
}
return(matrix);
}