001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.image.processing.convolution;
031
032import static java.lang.Math.PI;
033import static java.lang.Math.exp;
034
035import org.openimaj.image.FImage;
036import org.openimaj.math.util.FloatArrayStatsUtils;
037
038/**
039 * 2D Laplacian of Gaussian filter
040 * 
041 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
042 */
043public class LaplacianOfGaussian2D extends FConvolution {
044        /**
045         * Construct with given kernel size and variance.
046         * @param width kernel width
047         * @param height kernel height
048         * @param sigma variance
049         */
050        public LaplacianOfGaussian2D(int width, int height, float sigma) {
051                super(createKernelImage(width, height, sigma));
052        }
053        
054        /**
055         * Construct with given kernel size and variance.
056         * @param size kernel width/height
057         * @param sigma variance
058         */
059        public LaplacianOfGaussian2D(int size, float sigma) {
060                super(createKernelImage(size, size, sigma));
061        }
062
063        /**
064         * Create a kernel image with given kernel size and variance.
065         * @param size image height/width.
066         * @param sigma variance.
067         * @return new kernel image.
068         */
069        public static FImage createKernelImage(int size, float sigma) {
070                return createKernelImage(size, size, sigma);
071        }
072        
073        /**
074         * Create a kernel image with given kernel size and variance.
075         * @param width image width.
076         * @param height image height.
077         * @param sigma variance.
078         * @return new kernel image.
079         */
080        public static FImage createKernelImage(int width, int height, float sigma) {
081                FImage f = new FImage(width, height);
082                int hw = (width-1)/2;
083                int hh = (height-1)/2;
084                float sigmasq = sigma * sigma;
085                float sigma4 = sigmasq*sigmasq;
086                
087                for (int y=-hh, j=0; y<hh; y++, j++) {
088                        for (int x=-hw, i=0; x<hw; x++, i++) {
089                                int radsqrd = x*x + y*y;
090                                f.pixels[j][i] = (float) (-1 / (PI*sigma4)*(1-radsqrd/(2*sigmasq))*exp(-radsqrd/(2*sigmasq)));     
091                        }
092                }
093                return f.subtractInplace(FloatArrayStatsUtils.mean(f.pixels));
094        }
095}