/**
 * 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
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
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();
        }
}