BlockHistogramModel

/**
 * 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:
 *
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 * 	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;
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package org.openimaj.image.pixel.statistics;

import org.openimaj.feature.FeatureVectorProvider;
import org.openimaj.image.MBFImage;
import org.openimaj.math.statistics.distribution.MultidimensionalHistogram;


/**
 * An array of multidimensional histograms calculated from image pixels
 * (assumes image is in 0-1 range)
 *
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 *
 */
public class BlockHistogramModel extends AbstractPixelStatisticsModel implements FeatureVectorProvider<MultidimensionalHistogram> {
	private static final long serialVersionUID = 1L;

	/**
	 * The histogram data
	 */
	public MultidimensionalHistogram [][] histograms;

	int blocks_x;
	int blocks_y;
	int [] dims;

	/**
	 * Construct with the given parameters
	 * @param blocks_x the number of blocks in the x-direction
	 * @param blocks_y the number of blocks in the y-direction
	 * @param nbins the number of bins in each dimension for the histograms
	 */
	public BlockHistogramModel(int blocks_x, int blocks_y, int... nbins) {
		super(nbins.length);
		this.dims = nbins;
		this.blocks_x = blocks_x;
		this.blocks_y = blocks_y;
		this.histograms = new MultidimensionalHistogram[blocks_y][blocks_x];

		for (int y=0; y<blocks_y; y++)
			for (int x=0; x<blocks_x; x++)
				histograms[y][x] = new MultidimensionalHistogram(dims);
	}

	/**
	 * @return a flattened version of the histogram array formed by
	 * concatenating the histograms of all the blocks in scan order.
	 */
	public MultidimensionalHistogram toSingleHistogram() {
		int [] newdims = new int[dims.length + 2];

		for (int i=0; i<dims.length; i++)
			newdims[i] = dims[i];
		newdims[dims.length] = blocks_x;
		newdims[dims.length+1] = blocks_y;

		MultidimensionalHistogram h = new MultidimensionalHistogram(newdims);

		for (int y=0; y<blocks_y; y++) {
			for (int x=0; x<blocks_x; x++) {
				int blkid = x + y*blocks_x;
				for (int i=0; i<histograms[y][x].values.length; i++) {
					h.values[i + blkid*histograms[y][x].values.length] = histograms[y][x].values[i];
				}
			}
		}

		return h;
	}

	protected void reset(MultidimensionalHistogram histogram) {
		for (int i=0; i<histogram.values.length; i++)
			histogram.values[i] = 0;
	}

	@Override
	public void estimateModel(MBFImage... images) {
		//reset histograms
		for (int y=0; y<blocks_y; y++)
			for (int x=0; x<blocks_x; x++)
				reset(histograms[y][x]);

		//accumulate
		for (MBFImage img : images) {
			for (int y=0; y<blocks_y; y++) {
				for (int x=0; x<blocks_x; x++) {
					accum(img, x, y);
				}
			}
		}

		//normalise
		for (int y=0; y<blocks_y; y++)
			for (int x=0; x<blocks_x; x++)
				histograms[y][x].normalise();
	}

	protected void accum(MBFImage im, int bx, int by) {
		assert (im.numBands() == ndims);

		MultidimensionalHistogram histogram = histograms[by][bx];
		int height = im.getHeight();
		int width = im.getWidth();

		int cols_per_block = width / blocks_x;
		int startx = bx*cols_per_block;
		int stopx = (1+bx)*cols_per_block;

		int rows_per_block = height / blocks_y;
		int starty = by*rows_per_block;
		int stopy = (1+by)*rows_per_block;

		if (stopx >= width) stopx = width;
		if (stopy >= height) stopy = height;

		for (int y=starty; y<stopy; y++) {
			for (int x=startx; x<stopx; x++) {
				int [] bins = new int[ndims];

				for (int i=0; i<ndims; i++) {
					bins[i] = (int)(im.getBand(i).pixels[y][x] * (histogram.nbins[i]));
					if (bins[i] >= histogram.nbins[i]) bins[i] = histogram.nbins[i] - 1;
				}

				int bin = 0;
				for (int i=0; i<ndims; i++) {
					int f = 1;
					for (int j=0; j<i; j++)
						f *= histogram.nbins[j];

					bin += f * bins[i];
				}

				histogram.values[bin]++;
			}
		}
	}

	@Override
	public String toString() {
		String s = "LocalHistogram[\n";

		for (int y=0; y<blocks_y; y++)
			for (int x=0; x<blocks_x; x++)
				s += "\t(" + x + ", " + y + ") = " + histograms[y][x].toString() + "\n";

		s += "]\n";
		return s;
	}

	@Override
	public BlockHistogramModel clone() {
		BlockHistogramModel model = new BlockHistogramModel(blocks_x, blocks_x, dims);
		model.histograms = new MultidimensionalHistogram[blocks_y][blocks_x];

		for (int y=0; y<blocks_y; y++)
			for (int x=0; x<blocks_x; x++)
				model.histograms[y][x] = histograms[y][x].clone();

		return model;
	}

	@Override
	public MultidimensionalHistogram getFeatureVector() {
		return toSingleHistogram();
	}
}