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Methods for the extraction of low-level image features, including global image features and pixel/patch classification models.
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/**
 * 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 java.util.List;

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.analysis.colour.CIEDE2000;
import org.openimaj.image.colour.ColourSpace;
import org.openimaj.image.pixel.ConnectedComponent;
import org.openimaj.image.pixel.Pixel;
import org.openimaj.image.pixel.PixelSet;
import org.openimaj.image.segmentation.FelzenszwalbHuttenlocherSegmenter;

/**
 * Implementation of a color contrast feature.
 * 
 * The feature is calculated by performing a weighted average of the average
 * colour difference of all the segments in the image.
 * 
 * @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 ColourContrast implements ImageAnalyser, FeatureVectorProvider {
	FelzenszwalbHuttenlocherSegmenter segmenter;
	double contrast;

	/**
	 * Construct the {@link ColourContrast} feature extractor using the default
	 * settings for the {@link FelzenszwalbHuttenlocherSegmenter}.
	 */
	public ColourContrast() {
		segmenter = new FelzenszwalbHuttenlocherSegmenter();
	}

	/**
	 * Construct the {@link ColourContrast} feature extractor with the given
	 * parameters for the underlying {@link FelzenszwalbHuttenlocherSegmenter}.
	 * 
	 * @param sigma
	 *            amount of blurring
	 * @param k
	 *            threshold
	 * @param minSize
	 *            minimum allowed component size
	 */
	public ColourContrast(float sigma, float k, int minSize) {
		segmenter = new FelzenszwalbHuttenlocherSegmenter(sigma, k, minSize);
	}

	@Override
	public DoubleFV getFeatureVector() {
		return new DoubleFV(new double[] { contrast });
	}

	/*
	 * (non-Javadoc)
	 * 
	 * @see
	 * org.openimaj.image.processor.ImageProcessor#processImage(org.openimaj
	 * .image.Image)
	 */
	@Override
	public void analyseImage(MBFImage image) {
		final List ccs = segmenter.segment(image);
		final MBFImage labImage = ColourSpace.convert(image, ColourSpace.CIE_Lab);
		final float[][] avgs = new float[ccs.size()][3];
		final int w = image.getWidth();
		final int h = image.getHeight();

		// calculate patch average colours
		for (int i = 0; i < avgs.length; i++) {
			for (final Pixel p : ccs.get(i).pixels) {
				final Float[] v = labImage.getPixel(p);

				avgs[i][0] += v[0];
				avgs[i][0] += v[1];
				avgs[i][0] += v[2];
			}
			final int sz = ccs.get(i).pixels.size();
			avgs[i][0] /= sz;
			avgs[i][1] /= sz;
			avgs[i][2] /= sz;
		}

		for (int i = 0; i < avgs.length; i++) {
			for (int j = i + 1; j < avgs.length; j++) {
				final PixelSet ci = ccs.get(i);
				final PixelSet cj = ccs.get(i);
				final float C = CIEDE2000.calculateDeltaE(avgs[i], avgs[j]);

				contrast += (1 - distance(ci, cj, w, h)) * (C / (ci.calculateArea() * cj.calculateArea()));
			}
		}
	}

	float distance(PixelSet c1, PixelSet c2, int w, int h) {
		final double[] cen1 = c1.calculateCentroid();
		final double[] cen2 = c2.calculateCentroid();

		final double dx = (cen1[0] - cen2[0]) / w;
		final double dy = (cen1[1] - cen2[1]) / h;

		return (float) (Math.sqrt(dx * dx + dy * dy) / Math.sqrt(2));
	}
}