public void ProcessImageTest()
{
double[,] diag = Matrix.Magic(5);
Bitmap input;
new MatrixToImage().Convert(diag, out input);
// Create a new Gabor filter
GaborFilter gabor = new GaborFilter();
// Apply the filter
Bitmap output = gabor.Apply(input);
double[,] actual;
new ImageToMatrix().Convert(output, out actual);
double[,] expected =
{
{ 0.192156862745098, 0.176470588235294, 0.254901960784314, 0.396078431372549, 0.529411764705882 },
{ 0.16078431372549, 0.305882352941176, 0.494117647058824, 0.635294117647059, 0.654901960784314 },
{ 0.407843137254902, 0.623529411764706, 0.737254901960784, 0.701960784313725, 0.564705882352941 },
{ 0.752941176470588, 0.815686274509804, 0.713725490196078, 0.541176470588235, 0.403921568627451 },
{ 0.847058823529412, 0.694117647058824, 0.505882352941176, 0.380392156862745, 0.329411764705882 }
};
Assert.IsTrue(expected.IsEqual(actual, 1e-6));
}
public void GaborTest1()
{
Bitmap image = Properties.Resources.lena512;
GaborFilter gabor = new GaborFilter();
Bitmap result = gabor.Apply(image);
// ImageBox.Show(result);
Assert.IsNotNull(result);
}
private void SetFilter()
{
ImageType = ImageTypes.Rgb24bpp;
Af.GaborFilter newFilter = new Af.GaborFilter();
newFilter.Theta = angle;
newFilter.Size = size;
newFilter.Gamma = gamma;
newFilter.Lambda = lambda;
newFilter.Psi = psi;
newFilter.Sigma = sigma;
imageFilter = newFilter;
}
/// <summary>
/// <para>(Accord .NET internal call)</para>
/// In image processing, a Gabor filter, named after Dennis Gabor, is a linear
/// filter used for edge detection. Frequency and orientation representations
/// of Gabor filters are similar to those of the human visual system, and they
/// have been found to be particularly appropriate for texture representation
/// and discrimination. In the spatial domain, a 2D Gabor filter is a Gaussian
/// kernel function modulated by a sinusoidal plane wave. The Gabor filters are
/// self-similar: all filters can be generated from one mother wavelet by dilation
/// and rotation.
/// </summary>
/// <param name="img">Image.</param>
/// <param name="size">The size of the filter</param>
/// <param name="sigma">The Gaussian variance for the filter.</param>
/// <param name="theta">The orientation for the filter, in radians.</param>
/// <param name="lambda">The wavelength for the filter.</param>
/// <param name="gamma">The aspect ratio for the filter.</param>
/// <param name="psi">The phase offset for the filter.</param>
/// <returns>Filtered image.</returns>
public static Gray<byte>[,] GaborFilter(this Gray<byte>[,] img, int size = 3, double sigma = 2, double theta = 0.6, double lambda = 4.0, double gamma = 0.3, double psi = 1.0)
{
GaborFilter gf = new GaborFilter
{
Size = size,
Sigma = sigma,
Theta = theta,
Lambda = lambda,
Gamma = gamma,
Psi = psi
};
return GaborFilter(img, gf);
}
/// <summary>
/// <para>(Accord .NET internal call)</para>
/// In image processing, a Gabor filter, named after Dennis Gabor, is a linear
/// filter used for edge detection. Frequency and orientation representations
/// of Gabor filters are similar to those of the human visual system, and they
/// have been found to be particularly appropriate for texture representation
/// and discrimination. In the spatial domain, a 2D Gabor filter is a Gaussian
/// kernel function modulated by a sinusoidal plane wave. The Gabor filters are
/// self-similar: all filters can be generated from one mother wavelet by dilation
/// and rotation.
/// </summary>
/// <param name="img">Image.</param>
/// <param name="gaborFilter">Gabor filter instance.
/// <para>To avoid calculating Gabor every time use this function overload that receives instance.</para>
/// </param>
/// <returns>Filtered image.</returns>
internal static Gray<byte>[,] GaborFilter(this Gray<byte>[,] img, GaborFilter gaborFilter)
{
return img.ApplyFilter(gaborFilter);
}
