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/*
* The MIT License
*
* Copyright (c) 2016-2020 JOML
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
package org.joml.sampling;
import java.nio.FloatBuffer;
import org.joml.Math;
/**
* Generates various convolution kernels.
*
* @author Kai Burjack
*/
public class Convolution {
/**
* Generate a Gaussian convolution kernel with the given number of rows and columns, and store
* the factors in row-major order in dest.
*
* @param rows
* the number of rows (must be an odd number)
* @param cols
* the number of columns (must be an odd number)
* @param sigma
* the standard deviation of the filter kernel values
* @param dest
* will hold the kernel factors in row-major order
*/
public static void gaussianKernel(int rows, int cols, float sigma, FloatBuffer dest) {
if ((rows & 1) == 0) {
throw new IllegalArgumentException("rows must be an odd number");
}
if ((cols & 1) == 0) {
throw new IllegalArgumentException("cols must be an odd number");
}
if (dest == null) {
throw new IllegalArgumentException("dest must not be null");
}
if (dest.remaining() < rows * cols) {
throw new IllegalArgumentException("dest must have at least " + (rows * cols) + " remaining values");
}
float sum = 0.0f;
int pos = dest.position();
for (int i = 0, y = -(rows - 1) / 2; y <= (rows - 1) / 2; y++) {
for (int x = -(cols - 1) / 2; x <= (cols - 1) / 2; x++, i++) {
float k = (float) Math.exp(-(y * y + x * x) / (2.0 * sigma * sigma));
dest.put(pos + i, k);
sum += k;
}
}
for (int i = 0; i < rows * cols; i++) {
dest.put(pos + i, dest.get(pos + i) / sum);
}
}
/**
* Generate a Gaussian convolution kernel with the given number of rows and columns, and store
* the factors in row-major order in dest.
*
* @param rows
* the number of rows (must be an odd number)
* @param cols
* the number of columns (must be an odd number)
* @param sigma
* the standard deviation of the filter kernel values
* @param dest
* will hold the kernel factors in row-major order
*/
public static void gaussianKernel(int rows, int cols, float sigma, float[] dest) {
if ((rows & 1) == 0) {
throw new IllegalArgumentException("rows must be an odd number");
}
if ((cols & 1) == 0) {
throw new IllegalArgumentException("cols must be an odd number");
}
if (dest == null) {
throw new IllegalArgumentException("dest must not be null");
}
if (dest.length < rows * cols) {
throw new IllegalArgumentException("dest must have a size of at least " + (rows * cols));
}
float sum = 0.0f;
for (int i = 0, y = -(rows - 1) / 2; y <= (rows - 1) / 2; y++) {
for (int x = -(cols - 1) / 2; x <= (cols - 1) / 2; x++, i++) {
float k = (float) Math.exp(-(y * y + x * x) / (2.0 * sigma * sigma));
dest[i] = k;
sum += k;
}
}
for (int i = 0; i < rows * cols; i++) {
dest[i] = dest[i] / sum;
}
}
}
