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/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   * 	Redistributions of source code must retain the above copyright notice,
 * 	this list of conditions and the following disclaimer.
 *
 *   *	Redistributions in binary form must reproduce the above copyright notice,
 * 	this list of conditions and the following disclaimer in the documentation
 * 	and/or other materials provided with the distribution.
 *
 *   *	Neither the name of the University of Southampton nor the names of its
 * 	contributors may be used to endorse or promote products derived from this
 * 	software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.image.processing.convolution;

import static java.lang.Math.exp;

import org.openimaj.image.FImage;
import org.openimaj.math.util.FloatArrayStatsUtils;

/**
 * Simple 2D Gaussian convolution. In most cases the {@link FGaussianConvolve}
 * filter will do the same thing, but much much faster!
 *
 * @author Jonathon Hare ([email protected])
 *
 */
public class Gaussian2D extends FConvolution {

	/**
	 * Construct with given kernel size and variance.
	 *
	 * @param width
	 *            kernel width
	 * @param height
	 *            kernel height
	 * @param sigma
	 *            variance
	 */
	public Gaussian2D(int width, int height, float sigma) {
		super(createKernelImage(width, height, sigma));
	}

	/**
	 * Construct with given kernel size and variance.
	 *
	 * @param size
	 *            kernel width/height
	 * @param sigma
	 *            standard deviation
	 */
	public Gaussian2D(int size, float sigma) {
		super(createKernelImage(size, size, sigma));
	}

	/**
	 * Create a kernel image with given kernel size and standard deviation.
	 *
	 * @param size
	 *            image height/width.
	 * @param sigma
	 *            standard deviation.
	 * @return new kernel image.
	 */
	public static FImage createKernelImage(int size, float sigma) {
		return createKernelImage(size, size, sigma);
	}

	/**
	 * Create a kernel image with given kernel size and standard deviation.
	 *
	 * @param width
	 *            image width.
	 * @param height
	 *            image height.
	 * @param sigma
	 *            standard deviation.
	 * @return new kernel image.
	 */
	public static FImage createKernelImage(int width, int height, float sigma) {
		final FImage f = new FImage(width, height);
		final int hw = (width - 1) / 2;
		final int hh = (height - 1) / 2;
		final float sigmasq = sigma * sigma;

		for (int y = -hh, j = 0; y <= hh; y++, j++) {
			for (int x = -hw, i = 0; x <= hw; x++, i++) {
				final int radsqrd = x * x + y * y;
				f.pixels[j][i] = (float) exp(-radsqrd / (2 * sigmasq));
			}
		}
		final float sum = FloatArrayStatsUtils.sum(f.pixels);
		return f.divideInplace(sum);
	}
}