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.local.detector.dog.collector;
031
032
033import org.openimaj.feature.OrientedFeatureVector;
034import org.openimaj.image.FImage;
035import org.openimaj.image.analysis.pyramid.gaussian.GaussianOctave;
036import org.openimaj.image.feature.local.detector.dog.extractor.GradientFeatureExtractor;
037import org.openimaj.image.feature.local.detector.dog.pyramid.DoGOctaveExtremaFinder;
038import org.openimaj.image.feature.local.detector.pyramid.OctaveInterestPointFinder;
039import org.openimaj.image.feature.local.extraction.ScaleSpaceImageExtractorProperties;
040import org.openimaj.image.feature.local.keypoints.MinMaxKeypoint;
041
042/**
043 * Concrete implementation of an {@link AbstractOctaveLocalFeatureCollector}
044 * that collects {@link MinMaxKeypoint}s with the feature vector provided by the 
045 * given feature extractor. {@link MinMaxKeypoint}s contain the x, y and scale 
046 * coordinates of the interest point along with its dominant orientation and
047 * a boolean which determines whether the interest point was detected at a local
048 * minima or maxima. 
049 * 
050 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
051 */
052public class OctaveMinMaxKeypointCollector extends AbstractOctaveLocalFeatureCollector<
053        GaussianOctave<FImage>, GradientFeatureExtractor, MinMaxKeypoint, FImage> 
054{
055        protected ScaleSpaceImageExtractorProperties<FImage> extractionProperties = new ScaleSpaceImageExtractorProperties<FImage>();
056
057        /**
058         * Construct with the given feature extractor.
059         * @param featureExtractor the feature extractor.
060         */
061        public OctaveMinMaxKeypointCollector(GradientFeatureExtractor featureExtractor) {
062                super(featureExtractor);
063        }
064                
065        @Override
066        public void foundInterestPoint(OctaveInterestPointFinder<GaussianOctave<FImage>, FImage> finder, float x, float y, float octaveScale) {
067                int currentScaleIndex = finder.getCurrentScaleIndex();
068                extractionProperties.image = finder.getOctave().images[currentScaleIndex];
069                extractionProperties.scale = octaveScale;
070                extractionProperties.x = x;
071                extractionProperties.y = y;
072                
073                float octSize = finder.getOctave().octaveSize;
074                
075                boolean isMaxima = ((DoGOctaveExtremaFinder)finder).getDoGOctave().images[currentScaleIndex].pixels[Math.round(y)][Math.round(x)] > 0.0;
076                
077                addFeature(octSize * x, octSize * y, octSize * octaveScale, isMaxima);
078        }
079        
080        protected void addFeature(float imx, float imy, float imscale, boolean isMaxima) {
081                OrientedFeatureVector[] fvs = featureExtractor.extractFeature(extractionProperties);
082                
083                for (OrientedFeatureVector fv : fvs) {
084                        features.add(new MinMaxKeypoint(imx, imy, fv.orientation, imscale, fv.values, isMaxima));
085                }
086        }
087}