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.filterbank;
031
032import org.openimaj.citation.annotation.Reference;
033import org.openimaj.citation.annotation.ReferenceType;
034import org.openimaj.image.FImage;
035import org.openimaj.image.processing.convolution.SumBoxFilter;
036
037/**
038 * Implementation of Laws texture energy measures, based on the description in
039 * <a href="https://courses.cs.washington.edu/courses/cse576/book/ch7.pdf">
040 * Shapiro and Stockman Section 7.3.4</a>.
041 * <p>
042 * Nine texture energy images are created by convolving with the 16 base 5x5
043 * Laws filters, and then computing the energy for each pixel by summing the
044 * absolute pixel values in a macro window about that pixel.
045 * 
046 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
047 * 
048 */
049@Reference(
050                type = ReferenceType.Inproceedings,
051                author = { "Laws, K. I." },
052                title = "{Rapid Texture Identification}",
053                year = "1980",
054                booktitle = "Proc. SPIE Conf. Image Processing for Missile Guidance",
055                pages = { "376", "", "380" },
056                customData = {
057                                "citeulike-article-id", "2335645",
058                                "keywords", "bibtex-import",
059                                "posted-at", "2008-02-05 15:32:50",
060                                "priority", "2"
061                })
062public class LawsTexture extends LawsTextureBase {
063
064        private int macroWidth = 15;
065        private int macroHeight = 15;
066
067        /**
068         * Construct using 15*15 macro windows for the energy computation
069         */
070        public LawsTexture() {
071        }
072
073        /**
074         * Construct using the given sized macro windows for the energy computation
075         * 
076         * @param macro
077         *            the the window width and height
078         */
079        public LawsTexture(int macro) {
080                this.macroWidth = macro;
081                this.macroHeight = macro;
082        }
083
084        /**
085         * Construct using the given sized macro windows for the energy computation
086         * 
087         * @param macroWidth
088         *            the window width
089         * @param macroHeight
090         *            the window height
091         */
092        public LawsTexture(int macroWidth, int macroHeight) {
093                this.macroWidth = macroWidth;
094                this.macroHeight = macroHeight;
095        }
096
097        @Override
098        public void analyseImage(FImage in) {
099                super.analyseImage(in);
100
101                final FImage[] tmpResp = responses;
102                responses = new FImage[9];
103
104                responses[0] = absAverage(tmpResp[L5E5], tmpResp[E5L5]);
105                responses[1] = absAverage(tmpResp[L5R5], tmpResp[R5L5]);
106                responses[2] = absAverage(tmpResp[E5S5], tmpResp[S5E5]);
107                responses[3] = tmpResp[S5S5].abs();
108                responses[4] = tmpResp[R5R5].abs();
109                responses[5] = absAverage(tmpResp[L5S5], tmpResp[S5L5]);
110                responses[6] = tmpResp[E5E5].abs();
111                responses[7] = absAverage(tmpResp[E5R5], tmpResp[R5E5]);
112                responses[8] = absAverage(tmpResp[S5R5], tmpResp[R5S5]);
113
114                for (int i = 0; i < 9; i++) {
115                        responses[i] = responses[i].processInplace(new SumBoxFilter(macroWidth, macroHeight));
116                }
117        }
118
119        private FImage absAverage(FImage i1, FImage i2) {
120                final FImage img = new FImage(i1.width, i1.height);
121
122                for (int y = 0; y < img.height; y++)
123                        for (int x = 0; x < img.width; x++)
124                                img.pixels[y][x] = Math.abs(i1.pixels[y][x] + i2.pixels[y][x]) / 2;
125
126                return img;
127        }
128}