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.feature.global;
031
032import org.openimaj.citation.annotation.Reference;
033import org.openimaj.citation.annotation.ReferenceType;
034import org.openimaj.citation.annotation.References;
035import org.openimaj.feature.DoubleFV;
036import org.openimaj.feature.FeatureVectorProvider;
037import org.openimaj.image.FImage;
038import org.openimaj.image.analyser.ImageAnalyser;
039import org.openimaj.image.processing.algorithm.FourierTransform;
040
041/**
042 * Implementation of the blur estimation feature described by Ke, Tang and Jing,
043 * and Yeh et al.
044 * <p>
045 * Basically, this technique estimates the proportion of blurred pixels by
046 * thresholding the power-spectrum (magnitude) of the FFT of the image. Results
047 * are in the range 0-1. A higher number implies a sharper image.
048 * 
049 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
050 * 
051 */
052@References(
053                references = {
054                                @Reference(
055                                                type = ReferenceType.Inproceedings,
056                                                author = { "Ke, Yan", "Tang, Xiaoou", "Jing, Feng" },
057                                                title = "The Design of High-Level Features for Photo Quality Assessment",
058                                                year = "2006",
059                                                booktitle = "Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1",
060                                                pages = { "419", "", "426" },
061                                                url = "http://dx.doi.org/10.1109/CVPR.2006.303",
062                                                publisher = "IEEE Computer Society",
063                                                series = "CVPR '06",
064                                                customData = {
065                                                                "isbn", "0-7695-2597-0",
066                                                                "numpages", "8",
067                                                                "doi", "10.1109/CVPR.2006.303",
068                                                                "acmid", "1153495",
069                                                                "address", "Washington, DC, USA"
070                                }
071                                ),
072                                @Reference(
073                                                type = ReferenceType.Inproceedings,
074                                                author = { "Che-Hua Yeh", "Yuan-Chen Ho", "Brian A. Barsky", "Ming Ouhyoung" },
075                                                title = "Personalized Photograph Ranking and Selection System",
076                                                year = "2010",
077                                                booktitle = "Proceedings of ACM Multimedia",
078                                                pages = { "211", "220" },
079                                                month = "October",
080                                                customData = { "location", "Florence, Italy" }
081                                )
082})
083public class SharpPixelProportion implements ImageAnalyser<FImage>, FeatureVectorProvider<DoubleFV> {
084        double bpp = 0;
085        private float threshold = 2f;
086
087        /**
088         * Construct with a default threshold on Fourier magnitude of 2.0.
089         */
090        public SharpPixelProportion() {
091        }
092
093        /**
094         * Construct with the given threshold on Fourier magnitude.
095         * 
096         * @param threshold
097         *            the threshold
098         */
099        public SharpPixelProportion(float threshold) {
100                this.threshold = threshold;
101        }
102
103        @Override
104        public DoubleFV getFeatureVector() {
105                return new DoubleFV(new double[] { bpp });
106        }
107
108        /*
109         * (non-Javadoc)
110         * 
111         * @see
112         * org.openimaj.image.analyser.ImageAnalyser#analyseImage(org.openimaj.image
113         * .Image)
114         */
115        @Override
116        public void analyseImage(FImage image) {
117                final FourierTransform ft = new FourierTransform(image, false);
118                final FImage mag = ft.getMagnitude();
119
120                int count = 0;
121                for (int y = 0; y < mag.height; y++) {
122                        for (int x = 0; x < mag.width; x++) {
123                                if (Math.abs(mag.pixels[y][x]) > threshold)
124                                        count++;
125                        }
126                }
127                bpp = (double) count / (double) (mag.height * mag.width);
128        }
129
130        /**
131         * @return the proportion of blurred pixels (those with a Fourier magnitude
132         *         above the threshold)
133         */
134        public double getBlurredPixelProportion() {
135                return bpp;
136        }
137}