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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
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 * (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.segmentation;

import org.openimaj.feature.FloatFVComparator;
import org.openimaj.image.MBFImage;
import org.openimaj.image.colour.ColourSpace;

/**
 * Simple image segmentation from grouping colours with k-means, and also
 * incorporating a spatial aspect based on pixel location.
 * 
 * @author Jonathon Hare ([email protected])
 * 
 */
public class KMSpatialColourSegmenter extends KMColourSegmenter {

	/**
	 * Construct using the given colour space and number of segments. Euclidean
	 * distance is used, and the elements of each colour band are unscaled. Up
	 * to 100 K-Means iterations will be performed.
	 * 
	 * @param colourSpace
	 *            the colour space
	 * @param K
	 *            the number of segments
	 */
	public KMSpatialColourSegmenter(ColourSpace colourSpace, int K) {
		super(colourSpace, K);
	}

	/**
	 * Construct using the given colour space, number of segments, and distance
	 * measure. The elements of each colour band are unscaled. Up to 100 K-Means
	 * iterations will be performed.
	 * 
	 * @param colourSpace
	 *            the colour space
	 * @param K
	 *            the number of segments
	 * @param distance
	 *            the distance measure
	 */
	public KMSpatialColourSegmenter(ColourSpace colourSpace, int K, FloatFVComparator distance) {
		super(colourSpace, K, distance);
	}

	/**
	 * Construct using the given colour space, number of segments, and distance
	 * measure. The elements of each colour band are by the corresponding
	 * elements in the given scaling vector; the scaling vector should be two
	 * elements longer than the number of colour bands of the target colour
	 * space such that the last two elements correspond to the scalings for the
	 * normalised x and y positions of the pixels. Up to 100 K-Means iterations
	 * will be performed.
	 * 
	 * @param colourSpace
	 *            the colour space
	 * @param scaling
	 *            the scaling vector
	 * @param K
	 *            the number of segments
	 * @param distance
	 *            the distance measure
	 */
	public KMSpatialColourSegmenter(ColourSpace colourSpace, float[] scaling, int K, FloatFVComparator distance) {
		super(colourSpace, scaling, K, distance);
	}

	/**
	 * Construct using the given colour space, number of segments, and distance
	 * measure. The elements of each colour band are by the corresponding
	 * elements in the given scaling vector; the scaling vector should be two
	 * elements longer than the number of colour bands of the target colour
	 * space such that the last two elements correspond to the scalings for the
	 * normalised x and y positions of the pixels. The k-means algorithm will
	 * iterate at most maxIters times.
	 * 
	 * @param colourSpace
	 *            the colour space
	 * @param scaling
	 *            the scaling vector
	 * @param K
	 *            the number of segments
	 * @param distance
	 *            the distance measure
	 * @param maxIters
	 *            the maximum number of iterations to perform
	 */
	public KMSpatialColourSegmenter(ColourSpace colourSpace, float[] scaling, int K, FloatFVComparator distance,
			int maxIters)
	{
		super(colourSpace, scaling, K, distance, maxIters);
	}

	@Override
	protected float[][] imageToVector(MBFImage image) {
		final int height = image.getHeight();
		final int width = image.getWidth();
		final int bands = image.numBands();

		final float[][] f = new float[height * width][bands + 2];
		for (int b = 0; b < bands; b++) {
			final float[][] band = image.getBand(b).pixels;
			final float w = scaling == null ? 1 : scaling[b];

			for (int y = 0; y < height; y++)
				for (int x = 0; x < width; x++)
					f[x + y * width][b] = band[y][x] * w;
		}
		for (int y = 0; y < height; y++) {
			for (int x = 0; x < width; x++) {
				f[x + y * width][bands] = ((float) x / (float) width) * (scaling == null ? 1 : scaling[bands]);
				f[x + y * width][bands + 1] = ((float) y / (float) height) * (scaling == null ? 1 : scaling[bands + 1]);
			}
		}

		return f;
	}
}