/**
* Creates a Gaussian kernel with the specified properties.
*
* @param DOF 1 for 1D kernel and 2 for 2D kernel.
* @param isFloat True for F32 kernel and false for I32.
* @param numBits Number of bits in each data element. 32 or 64
* @param sigma The distributions stdev. If <= 0 then the sigma will be computed from the radius.
* @param radius Number of pixels in the kernel's radius. If <= 0 then the sigma will be computed
* from the sigma. @return The computed Gaussian kernel.
*/
public static <T extends KernelBase> T gaussian(
int DOF, boolean isFloat, int numBits, double sigma, int radius) {
if (radius <= 0) radius = FactoryKernelGaussian.radiusForSigma(sigma, 0);
else if (sigma <= 0) sigma = FactoryKernelGaussian.sigmaForRadius(radius, 0);
if (DOF == 2) {
if (numBits == 32) {
Kernel2D_F32 k = gaussian2D_F32(sigma, radius, isFloat);
if (isFloat) return (T) k;
return (T) KernelMath.convert(k, MIN_FRAC);
} else if (numBits == 64) {
Kernel2D_F64 k = gaussian2D_F64(sigma, radius, isFloat);
if (isFloat) return (T) k;
else throw new IllegalArgumentException("64bit int kernels supported");
} else {
throw new IllegalArgumentException("Bits must be 32 or 64");
}
} else if (DOF == 1) {
if (numBits == 32) {
Kernel1D_F32 k = gaussian1D_F32(sigma, radius, isFloat);
if (isFloat) return (T) k;
return (T) KernelMath.convert(k, MIN_FRAC);
} else {
throw new IllegalArgumentException("Bits must be 32 ");
}
} else {
throw new IllegalArgumentException("DOF not supported");
}
}
protected static Kernel2D_F64 gaussian2D_F64(double sigma, int radius, boolean normalize) {
Kernel1D_F64 kernel1D = gaussian1D_F64(sigma, radius, false);
Kernel2D_F64 ret = KernelMath.convolve(kernel1D, kernel1D);
if (normalize) {
KernelMath.normalizeSumToOne(ret);
}
return ret;
}
protected static Kernel1D_F64 gaussian1D_F64(double sigma, int radius, boolean normalize) {
Kernel1D_F64 ret = new Kernel1D_F64(radius * 2 + 1);
double[] gaussian = ret.data;
int index = 0;
for (int i = radius; i >= -radius; i--) {
gaussian[index++] = UtilGaussian.computePDF(0, sigma, i);
}
if (normalize) {
KernelMath.normalizeSumToOne(ret);
}
return ret;
}
/**
* Creates a 1D Gaussian kernel with the specified properties.
*
* @param order The order of the gaussian derivative.
* @param isFloat True for F32 kernel and false for I32.
* @param sigma The distributions stdev. If <= 0 then the sigma will be computed from the radius.
* @param radius Number of pixels in the kernel's radius. If <= 0 then the sigma will be computed
* from the sigma.
* @return The computed Gaussian kernel.
*/
public static <T extends Kernel1D> T derivative(
int order, boolean isFloat, double sigma, int radius) {
// zero order is a regular gaussian
if (order == 0) {
return gaussian(1, isFloat, 32, sigma, radius);
}
if (radius <= 0) radius = FactoryKernelGaussian.radiusForSigma(sigma, order);
else if (sigma <= 0) {
sigma = FactoryKernelGaussian.sigmaForRadius(radius, order);
}
Kernel1D_F32 k = derivative1D_F32(order, sigma, radius, true);
if (isFloat) return (T) k;
return (T) KernelMath.convert(k, MIN_FRAC);
}