org.openimaj.image.feature.global.RuleOfThirds Maven / Gradle / Ivy
Show all versions of image-feature-extraction Show documentation
/**
* Copyright (c) 2011, The University of Southampton and the individual contributors.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
*
* * Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* * Neither the name of the University of Southampton nor the names of its
* contributors may be used to endorse or promote products derived from this
* software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
* ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
package org.openimaj.image.feature.global;
import gnu.trove.map.hash.TObjectFloatHashMap;
import gnu.trove.procedure.TObjectFloatProcedure;
import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.feature.DoubleFV;
import org.openimaj.feature.FeatureVectorProvider;
import org.openimaj.image.MBFImage;
import org.openimaj.image.analyser.ImageAnalyser;
import org.openimaj.image.pixel.ConnectedComponent;
import org.openimaj.image.pixel.PixelSet;
import org.openimaj.image.saliency.AchantaSaliency;
import org.openimaj.image.saliency.YehSaliency;
import org.openimaj.image.segmentation.FelzenszwalbHuttenlocherSegmenter;
import org.openimaj.math.geometry.point.Point2dImpl;
/**
* Implementation of the rule-of-thirds algorithm described by Yeh et al.
*
* I've assumed that the distances to the power-points should be normalized with
* respect to the image size - this isn't explicit in the paper, but given that
* the sigma of the gaussian is fixed, it seems likely...
*
* @author Jonathon Hare ([email protected])
*/
@Reference(
type = ReferenceType.Inproceedings,
author = { "Che-Hua Yeh", "Yuan-Chen Ho", "Brian A. Barsky", "Ming Ouhyoung" },
title = "Personalized Photograph Ranking and Selection System",
year = "2010",
booktitle = "Proceedings of ACM Multimedia",
pages = { "211", "220" },
month = "October",
customData = { "location", "Florence, Italy" })
public class RuleOfThirds implements ImageAnalyser, FeatureVectorProvider {
private static final double SIGMA = 0.17;
private static final Point2dImpl[] powerPoints = getPowerPoints();
YehSaliency saliencyGenerator;
private double asSum;
private double aseSum;
/**
* Construct a new {@link RuleOfThirds} with the default settings for the
* {@link YehSaliency} algorithm.
*/
public RuleOfThirds() {
saliencyGenerator = new YehSaliency();
}
/**
* Construct a new {@link RuleOfThirds} with the given values for the
* {@link YehSaliency} algorithm.
*
* @param saliencySigma
* smoothing for the {@link AchantaSaliency} class
* @param segmenterSigma
* smoothing for {@link FelzenszwalbHuttenlocherSegmenter}.
* @param k
* k value for {@link FelzenszwalbHuttenlocherSegmenter}.
* @param minSize
* minimum region size for
* {@link FelzenszwalbHuttenlocherSegmenter}.
*/
public RuleOfThirds(float saliencySigma, float segmenterSigma, float k, int minSize) {
saliencyGenerator = new YehSaliency(saliencySigma, segmenterSigma, k, minSize);
}
@Override
public DoubleFV getFeatureVector() {
if (asSum == 0)
new DoubleFV(new double[] { 0 });
return new DoubleFV(new double[] { aseSum / asSum });
}
/*
* (non-Javadoc)
*
* @see
* org.openimaj.image.processor.ImageProcessor#processImage(org.openimaj
* .image.Image)
*/
@Override
public void analyseImage(MBFImage image) {
final int width = image.getWidth();
final int height = image.getHeight();
image.analyseWith(saliencyGenerator);
final TObjectFloatHashMap componentMap = saliencyGenerator.getSaliencyComponents();
asSum = 0;
aseSum = 0;
componentMap.forEachEntry(new TObjectFloatProcedure() {
@Override
public boolean execute(ConnectedComponent c, float s) {
final double as = c.calculateArea() * s;
final double D = closestDistance(c, width, height);
asSum += as;
aseSum += as * Math.exp(-(D * D) / (2 * SIGMA));
return true;
}
});
}
private double closestDistance(PixelSet cc, int width, int height) {
final double centroid[] = cc.calculateCentroid();
double minDistance = Double.MAX_VALUE;
for (final Point2dImpl pt : powerPoints) {
final double dx = (centroid[0] / width) - pt.x;
final double dy = (centroid[1] / width) - pt.y;
final double d = dx * dx + dy * dy;
if (d < minDistance)
minDistance = d;
}
return Math.sqrt(minDistance);
}
private static Point2dImpl[] getPowerPoints() {
return new Point2dImpl[] {
new Point2dImpl(1 / 3f, 1 / 3f),
new Point2dImpl(2 / 3f, 1 / 3f),
new Point2dImpl(1 / 3f, 2 / 3f),
new Point2dImpl(2 / 3f, 2 / 3f) };
}
}