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.exp;
033
034import org.openimaj.image.FImage;
035import org.openimaj.math.util.FloatArrayStatsUtils;
036
037/**
038 * Simple 2D Gaussian convolution. In most cases the {@link FGaussianConvolve}
039 * filter will do the same thing, but much much faster!
040 * 
041 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
042 *
043 */
044public class Gaussian2D extends FConvolution {
045
046        /**
047         * Construct with given kernel size and variance.
048         * @param width kernel width
049         * @param height kernel height
050         * @param sigma variance
051         */
052        public Gaussian2D(int width, int height, float sigma) {
053                super(createKernelImage(width, height, sigma));
054        }
055        
056        /**
057         * Construct with given kernel size and variance.
058         * @param size kernel width/height
059         * @param sigma variance
060         */
061        public Gaussian2D(int size, float sigma) {
062                super(createKernelImage(size, size, sigma));
063        }
064
065        /**
066         * Create a kernel image with given kernel size and variance.
067         * @param size image height/width.
068         * @param sigma variance.
069         * @return new kernel image.
070         */
071        public static FImage createKernelImage(int size, float sigma) {
072                return createKernelImage(size, size, sigma);
073        }
074        
075        /**
076         * Create a kernel image with given kernel size and variance.
077         * @param width image width.
078         * @param height image height.
079         * @param sigma variance.
080         * @return new kernel image.
081         */
082        public static FImage createKernelImage(int width, int height, float sigma) {
083                FImage f = new FImage(width, height);
084                int hw = (width-1)/2;
085                int hh = (height-1)/2;
086                float sigmasq = sigma * sigma;
087                
088                for (int y=-hh, j=0; y<hh; y++, j++) {
089                        for (int x=-hw, i=0; x<hw; x++, i++) {
090                                int radsqrd = x*x + y*y;
091                                f.pixels[j][i] = (float) exp( -radsqrd/ ( 2 * sigmasq ) );                              
092                        }
093                }
094                float sum = FloatArrayStatsUtils.sum(f.pixels);
095                return f.divideInplace(sum);
096        }
097}