/*
 * The MIT License
 *
 * Copyright (c) 2016-2023 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;

//#ifdef __HAS_NIO__
import java.nio.FloatBuffer;
//#endif

import org.joml.Math;

/**
 * Generates various convolution kernels.
 *
 * @author Kai Burjack
 */
public class Convolution {

//#ifdef __HAS_NIO__
    /**
     * Generate a Gaussian convolution kernel with the given number of rows and columns, and store
     * the factors in row-major order in <code>dest</code>.
     *
     * @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);
        }
    }
//#endif

    /**
     * Generate a Gaussian convolution kernel with the given number of rows and columns, and store
     * the factors in row-major order in <code>dest</code>.
     *
     * @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;
        }
    }

}
