/**
* Computes the derivative of a Gaussian kernel.
*
* @param sigma Distributions standard deviation.
* @param radius Kernel's radius.
* @param normalize
* @return The derivative of the gaussian
*/
protected static Kernel1D_F32 derivative1D_F32(
int order, double sigma, int radius, boolean normalize) {
Kernel1D_F32 ret = new Kernel1D_F32(radius * 2 + 1);
float[] gaussian = ret.data;
int index = 0;
switch (order) {
case 1:
for (int i = radius; i >= -radius; i--) {
gaussian[index++] = (float) UtilGaussian.derivative1(0, sigma, i);
}
break;
case 2:
for (int i = radius; i >= -radius; i--) {
gaussian[index++] = (float) UtilGaussian.derivative2(0, sigma, i);
}
break;
case 3:
for (int i = radius; i >= -radius; i--) {
gaussian[index++] = (float) UtilGaussian.derivative3(0, sigma, i);
}
break;
case 4:
for (int i = radius; i >= -radius; i--) {
gaussian[index++] = (float) UtilGaussian.derivative4(0, sigma, i);
}
break;
default:
throw new IllegalArgumentException("Only derivatives of order 1 to 4 are supported");
}
// multiply by the same factor as the gaussian would be normalized by
// otherwise it will effective change the intensity of the input image
if (normalize) {
double sum = 0;
for (int i = radius; i >= -radius; i--) {
sum += UtilGaussian.computePDF(0, sigma, i);
}
for (int i = 0; i < gaussian.length; i++) {
gaussian[i] /= sum;
}
}
return ret;
}
/**
* Create a gaussian kernel based on its width. Supports kernels of even or odd widths .
*
* @param sigma Sigma of the Gaussian distribution. If <= 0 then the width will be used.
* @param width How wide the kernel is. Can be even or odd.
* @return Gaussian convolution kernel.
*/
public static Kernel2D_F64 gaussianWidth(double sigma, int width) {
if (sigma <= 0) sigma = sigmaForRadius(width / 2, 0);
else if (width <= 0)
throw new IllegalArgumentException(
"Must specify the width since it doesn't know if it should be even or odd");
if (width % 2 == 0) {
int r = width / 2 - 1;
Kernel2D_F64 ret = new Kernel2D_F64(width);
double sum = 0;
for (int y = 0; y < width; y++) {
double dy = y <= r ? Math.abs(y - r) + 0.5 : Math.abs(y - r - 1) + 0.5;
for (int x = 0; x < width; x++) {
double dx = x <= r ? Math.abs(x - r) + 0.5 : Math.abs(x - r - 1) + 0.5;
double d = Math.sqrt(dx * dx + dy * dy);
double val = UtilGaussian.computePDF(0, sigma, d);
ret.set(x, y, val);
sum += val;
}
}
for (int i = 0; i < ret.data.length; i++) {
ret.data[i] /= sum;
}
return ret;
} else {
return gaussian2D_F64(sigma, width / 2, true);
}
}
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;
}