private void MenuSpectra_Click(object sender, EventArgs e)
{
if (!CheckImage()) return;
Bitmap bm = new Bitmap(originImage.Image);
int iw = bm.Width;
int ih = bm.Height;
if (iw == 256 && ih == 256)
{
double[] iPix = new double[iw * ih];
double[] oMod = new double[iw * ih];
for (int y = 0; y < ih; y++)
{
for (int x = 0; x < iw; x++)
{
iPix[x+y*iw] = bm.GetPixelGray(x, y);
}
}
FFT2 fft2 = new FFT2();
fft2.setData2(iw, ih, iPix);
fftData = fft2.getFFT2();
// 生成频谱图像
int u, v;
for (int y = 0; y < ih; y++)
{
for (int x = 0; x < iw; x++)
{
double tem = fftData[x+y*iw].Real * fftData[x+y*iw].Real +
fftData[x+y*iw].Imaginary * fftData[x+y*iw].Imaginary;
tem = Math.Sqrt(tem) / 100;
if (tem > 255) tem = 255;
if (x < iw / 2) u = x + iw / 2;
else u = x - iw / 2;
if (y < ih / 2) v = y + ih / 2;
else v = y - ih / 2;
oMod[u+v*iw] = tem;
}
}
for (int y = 0; y < ih; y++)
{
for (int x = 0; x < iw; x++)
{
int r = (int)oMod[x+y*iw];
try {
bm.SetPixelGray(x, y, r);
}
catch(Exception ex)
{
MessageBox.Show(ex.Message, "查看频谱", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
}
afterImage.Image = bm;
}
else
{
MessageBox.Show("仅适用于256*256图像!", "傅里叶变换", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
/// <summary>
/// 频域滤波算法
/// </summary>
/// <param name="_image">要施加滤波算法的图像</param>
/// <param name="type">滤波类型</param>
/// <param name="d0"></param>
/// <returns></returns>
public Bitmap FrequencyFilter(Bitmap _image, FilterType type, int d0)
{
Bitmap output = new Bitmap(_image);
int iw = image.Width;
int ih = image.Height;
double[] outpix = new double[iw * ih];
Complex[] complex;
for (int y = 0; y < ih; y++)
{
for (int x = 0; x < iw; x++)
{
outpix[x + y * iw] = image.GetPixelGray(x, y);
}
}
FFT2 fft2 = new FFT2();
fft2.setData2(iw, ih, outpix);
complex = fft2.getFFT2();
Complex bc = new Complex();
for (int v = 0; v < ih; v++)
{
for (int u = 0; u < iw; u++)
{
double dd = Math.Sqrt(u * u + v * v);
//double dd = u * u + v * v;
switch (type)
{
case FilterType.IdealLowPass:
if (dd <= d0) bc = new Complex(1);
else bc = new Complex(0);
//bc = new Complex(1 / (1 + 0.4142 * dd / (d0 * d0)));
break;
case FilterType.IdealHighPass:
if (dd <= d0) bc = new Complex(0);
else bc = new Complex(1);
//bc = new Complex(1 / (1 + 0.4142 * (d0 * d0)/dd));
break;
case FilterType.GaussianLowPass:
bc = new Complex(Math.Exp(-(dd * dd) / (2 * d0 * d0)));
//bc = new Complex(Math.Exp(-0.5*Math.Log(2)*dd/(d0*d0)));
break;
case FilterType.GaussianHighPass:
bc = new Complex(Math.Exp(-(dd * dd) / (2 * d0 * d0)));
//bc = new Complex(Math.Exp(-0.5 * Math.Log(2) * dd / (d0 * d0)));
bc = new Complex(1) - bc;
break;
default:
break;
}
complex[u + v * iw] = complex[u + v * iw] * bc;
}
}
//FFT2 ifft2 = new FFT2();
fft2.setData2i(iw, ih, complex);
outpix = fft2.getPixels2i();
double max = outpix[0];
for (int y = 0; y < ih; y++)
{
for (int x = 0; x < iw; x++)
{
if (max < outpix[x + y * iw])
max = outpix[x + y * iw];
}
}
for (int y = 0; y < ih; y++)
{
for (int x = 0; x < iw; x++)
{
int r = (int)(outpix[x + y * iw] * 255 / max);
output.SetPixelGray(x, y, r);
}
}
return output;
}
public Complex[] getData1i()
{
// 求共轭
for (int i = 0; i < count; i++)
{
double im = -x[i].Imaginary;
x[i].Imaginary = im;
}
fft2 = new FFT2();
fft2.setData1(x, power);
fd1i = fft2.getData1();
for (int i = 0; i < count; i++)
{
double re = fd1i[i].Real;
double im = -fd1i[i].Imaginary;
fd1i[i].Imaginary = im / count;
fd1i[i].Real = re / count;
}
return fd1i;
}
