private void valueChangeEvent()
{
bitmapResult = BitmapOrigin.Clone() as Bitmap;
x = bitmapResult.Width;
y = bitmapResult.Height;
Complex32[] matrix = new Complex32[x * y];
for (int i = 0; i < x; i++)
{
for (int j = 0; j < y; j++)
{
matrix[i * x + j] = bitmapResult.GetPixel(i, j).R;
}
}
var ms = matrix.Clone();
Fourier.Forward2D(matrix, x, y);
//for (int i = 0; i < x; i++)
//{
// for (int j = 0; j < y; j++)
// {
// var gray = (byte)matrix[i * x + j].r
// bitmapResult.SetPixel(i, j, Color.FromArgb(matrix[i * x + j], matrix[i * x + j], matrix[i * x + j]));
// }
//}
}
/// <summary>
/// Performs a 2D-Fourier transform on data in the passed Matrix/image.
/// </summary>
/// <param name="M"></param>
/// <returns></returns>
public static double[,] FFT2Dimensional(double[,] M)
{
// Step 1: convert matrix to complex array
//double[] sampleData = Matrix2PaddedVector(M);
// AT 20180202: updated call to support newer MathNet API
var sampleData = Matrix2ComplexVector(M);
int rowCount = M.GetLength(0);
int colCount = M.GetLength(1);
// Step 2: do 2d fft
Fourier.Forward2D(sampleData, rowCount, colCount, FourierOptions.Matlab);
// Step 3: Convert complex output array to array of real magnitude values
var magnitudeMatrix = Fft2DOutputToMatrixOfMagnitude(rowCount, colCount, sampleData);
// Step 3: do the shift for array of magnitude values.
// var outputData = magnitudeMatrix; // no shifting
var outputData = fftShift(magnitudeMatrix);
return(outputData);
}
private void NBodyFFT()
{
//Compute the values v_{m, n} at the equispaced nodes, multiply the kernel matrix with the coefficients w
int n_fft_coeffs = 2 * n_interpolation_points_1d;
for (int d = 0; d < n_terms; d += 2)
{
Array.Clear(fft, 0, fft.Length);
for (int i = 0; i < n_interpolation_points_1d; i++)
{
for (int j = 0; j < n_interpolation_points_1d; j++)
{
fft[i * n_fft_coeffs + j] = new Complex(tilde_values[(i * n_interpolation_points_1d + j) * n_terms + d], tilde_values[(i * n_interpolation_points_1d + j) * n_terms + d + 1]);
}
}
Fourier.Forward2D(fft, n_fft_coeffs, n_fft_coeffs);
// Take the Hadamard product of two complex vectors
for (int i = kernel_tilde.Length - 1; i >= 0; i--)
{
fft[i] *= kernel_tilde[i];
}
// Invert the computed values at the interpolated nodes
Fourier.Inverse2D(fft, n_fft_coeffs, n_fft_coeffs);
for (int i = 0; i < n_interpolation_points_1d; i++)
{
for (int j = 0; j < n_interpolation_points_1d; j++)
{
tilde_values[(i * n_interpolation_points_1d + j) * n_terms + d] = fft[(n_interpolation_points_1d + i) * n_fft_coeffs + n_interpolation_points_1d + j].Real;
tilde_values[(i * n_interpolation_points_1d + j) * n_terms + d + 1] = fft[(n_interpolation_points_1d + i) * n_fft_coeffs + n_interpolation_points_1d + j].Imaginary;
}
}
}
}
private void ComputeKernelTilde()
{
//Evaluate the kernel at the interpolation nodes and form the embedded generating kernel vector for a circulant matrix
int n_fft_coeffs = 2 * n_interpolation_points_1d;
for (int i = 0; i < n_interpolation_points_1d; i++)
{
for (int j = 0; j < n_interpolation_points_1d; j++)
{
fft[(n_interpolation_points_1d + i) * n_fft_coeffs + n_interpolation_points_1d + j]
= fft[(n_interpolation_points_1d - i) * n_fft_coeffs + n_interpolation_points_1d + j]
= fft[(n_interpolation_points_1d + i) * n_fft_coeffs + n_interpolation_points_1d - j]
= fft[(n_interpolation_points_1d - i) * n_fft_coeffs + n_interpolation_points_1d - j]
= SquaredCauchy(tilde[0], tilde[i], tilde[j]);
}
}
// Precompute the FFT of the kernel generating matrix
Fourier.Forward2D(fft, n_fft_coeffs, n_fft_coeffs, FourierOptions.NoScaling);
for (int i = fft.Length - 1; i >= 0; i--)
{
kernel_tilde[i] = fft[i].Real;
}
}
