/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
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 * 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.
 *
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 *      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
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package org.openimaj.image.objectdetection.haar.training;

import java.util.List;

import org.openimaj.image.analysis.algorithm.SummedSqTiltAreaTable;
import org.openimaj.image.objectdetection.haar.HaarFeature;
import org.openimaj.util.array.ArrayUtils;
import org.openimaj.util.function.Operation;
import org.openimaj.util.parallel.Parallel;

public class CachedTrainingData implements HaarTrainingData {
        float[][] responses;
        boolean[] classes;
        int[][] sortedIndices;
        List<HaarFeature> features;
        int width, height;

        float computeWindowVarianceNorm(SummedSqTiltAreaTable sat) {
                final int w = width - 2;
                final int h = height - 2;

                final int x = 1; // shift by 1 scaled px to centre box
                final int y = 1;

                final float sum = sat.sum.pixels[y + h][x + w] + sat.sum.pixels[y][x] -
                                sat.sum.pixels[y + h][x] - sat.sum.pixels[y][x + w];
                final float sqSum = sat.sqSum.pixels[y + w][x + w] + sat.sqSum.pixels[y][x] -
                                sat.sqSum.pixels[y + w][x] - sat.sqSum.pixels[y][x + w];

                final float cachedInvArea = 1.0f / (w * h);
                final float mean = sum * cachedInvArea;
                float wvNorm = sqSum * cachedInvArea - mean * mean;
                wvNorm = (float) ((wvNorm >= 0) ? Math.sqrt(wvNorm) : 1);

                return wvNorm;
        }

        public CachedTrainingData(final List<SummedSqTiltAreaTable> positive, final List<SummedSqTiltAreaTable> negative,
                        final List<HaarFeature> features)
        {
                this.width = positive.get(0).sum.width - 1;
                this.height = positive.get(0).sum.height - 1;

                this.features = features;
                final int nfeatures = features.size();

                classes = new boolean[positive.size() + negative.size()];
                responses = new float[nfeatures][classes.length];
                sortedIndices = new int[nfeatures][];
                // for (int f = 0; f < nfeatures; f++) {

                Parallel.forIndex(0, nfeatures, 1, new Operation<Integer>() {

                        @Override
                        public void perform(Integer f) {
                                final HaarFeature feature = features.get(f);
                                int count = 0;

                                for (final SummedSqTiltAreaTable t : positive) {
                                        final float wvNorm = computeWindowVarianceNorm(t);
                                        responses[f][count] = feature.computeResponse(t, 0, 0) / wvNorm;
                                        classes[count] = true;
                                        ++count;
                                }

                                for (final SummedSqTiltAreaTable t : negative) {
                                        final float wvNorm = computeWindowVarianceNorm(t);
                                        responses[f][count] = feature.computeResponse(t, 0, 0) / wvNorm;
                                        classes[count] = false;
                                        ++count;
                                }

                                sortedIndices[f] = ArrayUtils.indexSort(responses[f]);
                        }
                });
        }

        @Override
        public float[] getResponses(int dimension) {
                return responses[dimension];
        }

        @Override
        public boolean[] getClasses() {
                return classes;
        }

        @Override
        public int numInstances() {
                return classes.length;
        }

        @Override
        public int numFeatures() {
                return responses.length;
        }

        @Override
        public float[] getInstanceFeature(int idx) {
                final float[] feature = new float[responses.length];

                for (int i = 0; i < feature.length; i++) {
                        feature[i] = responses[i][idx];
                }

                return feature;
        }

        @Override
        public int[] getSortedIndices(int d) {
                return sortedIndices[d];
        }

        @Override
        public HaarFeature getFeature(int dimension) {
                return features.get(dimension);
        }
}