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.demos.sandbox.siftlike;
031
032import org.openimaj.feature.OrientedFeatureVector;
033import org.openimaj.image.DisplayUtilities;
034import org.openimaj.image.FImage;
035import org.openimaj.image.MBFImage;
036import org.openimaj.image.colour.RGBColour;
037import org.openimaj.image.feature.local.descriptor.gradient.GradientFeatureProvider;
038import org.openimaj.image.feature.local.descriptor.gradient.GradientFeatureProviderFactory;
039import org.openimaj.image.feature.local.descriptor.gradient.SIFTFeatureProvider;
040import org.openimaj.image.feature.local.detector.dog.extractor.DominantOrientationExtractor;
041import org.openimaj.image.feature.local.extraction.GradientScaleSpaceImageExtractorProperties;
042import org.openimaj.image.processing.convolution.FImageGradients;
043import org.openimaj.math.geometry.point.Point2dImpl;
044import org.openimaj.math.geometry.shape.Ellipse;
045
046import Jama.Matrix;
047
048public class EllipseGradientFeatureExtractor {
049        GradientFeatureProviderFactory factory;
050        int patchSize = 100;
051
052        public EllipseGradientFeatureExtractor() {
053                this.factory = new SIFTFeatureProvider();
054        }
055
056        public EllipseGradientFeatureExtractor(GradientFeatureProviderFactory factory) {
057                this.factory = factory;
058        }
059
060        public OrientedFeatureVector[] extract(FImage image, Ellipse ellipse) {
061                final Matrix tf = ellipse.transformMatrix();
062                final FImage patch = new FImage(patchSize, patchSize);
063                final float halfSize = patchSize / 2;
064
065                // Sample the ellipse content into a rectified image
066                for (int y = 0; y < patchSize; y++) {
067                        for (int x = 0; x < patchSize; x++) {
068                                final Point2dImpl pt = new Point2dImpl((x - halfSize) / halfSize, (y - halfSize) / halfSize);
069                                final Point2dImpl tpt = pt.transform(tf);
070                                patch.pixels[y][x] = image.getPixelInterpNative(tpt.x, tpt.y, 0);
071                        }
072                }
073
074                // now find grad mags and oris
075                final FImageGradients gmo = FImageGradients.getGradientMagnitudesAndOrientations(patch);
076
077                final GradientScaleSpaceImageExtractorProperties<FImage> props = new GradientScaleSpaceImageExtractorProperties<FImage>();
078                props.image = patch;
079                props.magnitude = gmo.magnitudes;
080                props.orientation = gmo.orientations;
081                props.x = patch.width / 2;
082                props.y = patch.height / 2;
083                props.scale = patch.height / 2 / 3; // ???
084
085                final DominantOrientationExtractor doe = new DominantOrientationExtractor();
086                final float[] oris = doe.extractFeatureRaw(props);
087
088                final MBFImage p2 = patch.toRGB();
089                for (final float o : oris) {
090                        p2.drawLine(p2.getWidth() / 2, p2.getHeight() / 2, o, 20, RGBColour.RED);
091                }
092                DisplayUtilities.display(p2);
093
094                final OrientedFeatureVector[] vectors = new OrientedFeatureVector[oris.length];
095                for (int i = 0; i < oris.length; i++) {
096                        final float ori = oris[i];
097                        final GradientFeatureProvider provider = factory.newProvider();
098
099                        // and construct the feature and sampling every pixel in the patch
100                        // note: the descriptor is actually computed over a sub-patch; there
101                        // is
102                        // a border that is used for oversampling and avoiding edge effects.
103                        final float overSample = provider.getOversamplingAmount();
104                        for (int y = 0; y < patchSize; y++) {
105                                final float yy = (y * (2 * overSample + 1) / patchSize) - overSample;
106
107                                for (int x = 0; x < patchSize; x++) {
108                                        final float xx = (x * (2 * overSample + 1) / patchSize) - overSample;
109
110                                        final float gradmag = gmo.magnitudes.pixels[y][x];
111                                        final float gradori = gmo.orientations.pixels[y][x];
112                                        provider.addSample(xx, yy, gradmag, gradori - ori);
113                                }
114                        }
115
116                        vectors[i] = provider.getFeatureVector();
117                }
118
119                return vectors;
120        }
121}