/**
 * 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;
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package org.openimaj.image.feature.dense.gradient.dsift;

import java.util.ArrayList;
import java.util.List;

import org.openimaj.feature.local.list.LocalFeatureList;
import org.openimaj.feature.local.list.MemoryLocalFeatureList;
import org.openimaj.image.FImage;
import org.openimaj.image.Image;
import org.openimaj.image.processing.convolution.FGaussianConvolve;
import org.openimaj.image.processor.SinglebandImageProcessor;
import org.openimaj.math.geometry.shape.Rectangle;
import org.openimaj.util.array.ArrayUtils;
import org.openimaj.util.pair.IntObjectPair;

/**
 * A scale-space pyramid of dense SIFT for {@link FImage}s. Dense sift features
 * are extracted for the given bin sizes (scales). The image is optionally
 * smoothed with a Gaussian before each scale.
 * <p>
 * The {@link PyramidDenseSIFT} is not thread safe, but is reusable like the
 * {@link DenseSIFT} analyser.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * @param <IMAGE>
 *            Type of image to be processed.
 * 
 */
public class PyramidDenseSIFT<IMAGE extends Image<?, IMAGE> & SinglebandImageProcessor.Processable<Float, FImage, IMAGE>>
                extends
                AbstractDenseSIFT<IMAGE>
{
        /**
         * Scales at which the dense SIFT features are extracted. Each value is used
         * as bin size for the {@link DenseSIFT}.
         */
        int[] sizes;

        /**
         * The image is smoothed by a Gaussian kernel of standard deviation size /
         * magnificationFactor.
         */
        float magnificationFactor;

        private List<AbstractDenseSIFT<IMAGE>> levels;

        /**
         * Construct the pyramid dense sift extractor. The magnification factor is
         * used to determine how to smooth the image before extracting the features
         * at each level: the smoothing sigma at each level is the bin size at that
         * level divided by the magnification factor. If the magnification factor is
         * 0, then no smoothing will be applied at any level.
         * 
         * @param dsift
         *            the underlying dense sift extractor (typically a
         *            {@link DenseSIFT} (or {@link ApproximateDenseSIFT}) or
         *            {@link ColourDenseSIFT} depending on the image type).
         * @param magFactor
         *            the magnification factor
         * @param sizes
         *            the scales (bin sizes for dense sift)
         */
        public PyramidDenseSIFT(AbstractDenseSIFT<IMAGE> dsift, float magFactor, int... sizes) {
                this.sizes = sizes;
                this.magnificationFactor = magFactor;

                levels = new ArrayList<AbstractDenseSIFT<IMAGE>>(sizes.length);
                for (int i = 0; i < sizes.length; i++) {
                        levels.add(dsift.clone());
                }
        }

        @Override
        public void analyseImage(IMAGE image, Rectangle originalBounds) {
                final Rectangle bounds = originalBounds;

                for (int i = 0; i < sizes.length; i++) {
                        final int size = sizes[i];
                        final int offset = (int) Math.floor(3f / 2f * (ArrayUtils.maxValue(sizes) - size));

                        final IMAGE smoothed;
                        if (magnificationFactor == 0) {
                                smoothed = image;
                        } else {
                                final float sigma = size / magnificationFactor;
                                smoothed = image.process(new FGaussianConvolve(sigma));
                        }

                        // extract DSIFT
                        bounds.x = originalBounds.x + offset;
                        bounds.y = originalBounds.y + offset;

                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);
                        dsift.setBinWidth(size);
                        dsift.setBinHeight(size);
                        dsift.analyseImage(smoothed, bounds);
                }
        }

        @Override
        public LocalFeatureList<FloatDSIFTKeypoint> getFloatKeypoints() {
                final LocalFeatureList<FloatDSIFTKeypoint> kpts = new MemoryLocalFeatureList<FloatDSIFTKeypoint>(getNumOriBins()
                                * getNumBinsX() * getNumBinsY());

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);
                        kpts.addAll(dsift.getFloatKeypoints());
                }

                return kpts;
        }

        @Override
        public LocalFeatureList<ByteDSIFTKeypoint> getByteKeypoints() {
                final LocalFeatureList<ByteDSIFTKeypoint> kpts = new MemoryLocalFeatureList<ByteDSIFTKeypoint>(getNumOriBins()
                                * getNumBinsX() * getNumBinsY());

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);
                        kpts.addAll(dsift.getByteKeypoints());
                }

                return kpts;
        }

        @Override
        public LocalFeatureList<FloatDSIFTKeypoint> getFloatKeypoints(float energyThreshold) {
                final LocalFeatureList<FloatDSIFTKeypoint> kpts = new MemoryLocalFeatureList<FloatDSIFTKeypoint>(getNumOriBins()
                                * getNumBinsX() * getNumBinsY());

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);
                        kpts.addAll(dsift.getFloatKeypoints(energyThreshold));
                }

                return kpts;
        }

        @Override
        public LocalFeatureList<ByteDSIFTKeypoint> getByteKeypoints(float energyThreshold) {
                final LocalFeatureList<ByteDSIFTKeypoint> kpts = new MemoryLocalFeatureList<ByteDSIFTKeypoint>(getNumOriBins()
                                * getNumBinsX() * getNumBinsY());

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);
                        kpts.addAll(dsift.getByteKeypoints(energyThreshold));
                }

                return kpts;
        }

        /**
         * Get the SIFT descriptors from the previous call to
         * {@link #analyseImage(Image)} or {@link #analyseImage(Image, Rectangle)}
         * in the form of a list of local features with float vectors.
         * 
         * @return a list of {@link FloatDSIFTKeypoint}s.
         */
        public List<IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>> getFloatKeypointsGrouped() {
                final List<IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>> prs = new ArrayList<IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>>(
                                sizes.length);

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);

                        prs.add(new IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>(sizes[i], dsift.getFloatKeypoints()));
                }

                return prs;
        }

        /**
         * Get the SIFT descriptors from the previous call to
         * {@link #analyseImage(Image)} or {@link #analyseImage(Image, Rectangle)}
         * in the form of a list of local features with byte vectors.
         * 
         * @return a list of {@link ByteDSIFTKeypoint}s.
         */
        public List<IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>> getByteKeypointsGrouped() {
                final List<IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>> prs = new ArrayList<IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>>(
                                sizes.length);

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);

                        prs.add(new IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>(sizes[i], dsift.getByteKeypoints()));
                }

                return prs;
        }

        /**
         * Get the SIFT descriptors from the previous call to
         * {@link #analyseImage(Image)} or {@link #analyseImage(Image, Rectangle)}
         * in the form of a list of local features with float vectors. Only the
         * features with an energy above the given threshold will be returned.
         * 
         * @param energyThreshold
         *            the threshold on the feature energy
         * 
         * @return a list of {@link FloatDSIFTKeypoint}s.
         */
        public List<IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>> getFloatKeypointsGrouped(float energyThreshold) {
                final List<IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>> prs = new ArrayList<IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>>(
                                sizes.length);

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);

                        prs.add(new IntObjectPair<LocalFeatureList<FloatDSIFTKeypoint>>(sizes[i], dsift
                                        .getFloatKeypoints(energyThreshold)));
                }

                return prs;
        }

        /**
         * Get the SIFT descriptors from the previous call to
         * {@link #analyseImage(Image)} or {@link #analyseImage(Image, Rectangle)}
         * in the form of a list of local features with byte vectors. Only the
         * features with an energy above the given threshold will be returned.
         * 
         * @param energyThreshold
         *            the threshold on the feature energy
         * 
         * @return a list of {@link ByteDSIFTKeypoint}s.
         */
        public List<IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>> getByteKeypointsGrouped(float energyThreshold) {
                final List<IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>> prs = new ArrayList<IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>>(
                                sizes.length);

                for (int i = 0; i < sizes.length; i++) {
                        final AbstractDenseSIFT<IMAGE> dsift = levels.get(i);

                        prs.add(new IntObjectPair<LocalFeatureList<ByteDSIFTKeypoint>>(sizes[i],
                                        dsift.getByteKeypoints(energyThreshold)));
                }

                return prs;
        }

        /**
         * Get the computed raw dense SIFT descriptors from the previous call to
         * {@link #analyseImage(Image)} or {@link #analyseImage(Image, Rectangle)} .
         * The descriptors are grouped by the sizes at which they were extracted.
         * 
         * @return the descriptors.
         */
        public float[][][] getLevelDescriptors() {
                final float[][][] descr = new float[sizes.length][][];

                for (int i = 0; i < sizes.length; i++) {
                        descr[i] = levels.get(i).getDescriptors();
                }

                return descr;
        }

        /**
         * Get the bin sizes
         * 
         * @return the bin sizes
         */
        public int[] getSizes() {
                return sizes;
        }

        /**
         * Not supported.
         * 
         * @throws UnsupportedOperationException
         */
        @Override
        public void setBinWidth(int size) {
                throw new UnsupportedOperationException();
        }

        /**
         * Not supported.
         * 
         * @throws UnsupportedOperationException
         */
        @Override
        public void setBinHeight(int size) {
                throw new UnsupportedOperationException();
        }

        /**
         * This returns the bin size of the zeroth level only. {@inheritDoc}
         */
        @Override
        public int getBinWidth() {
                return sizes[0];
        }

        /**
         * This returns the bin size of the zeroth level only. {@inheritDoc}
         */
        @Override
        public int getBinHeight() {
                return sizes[0];
        }

        @Override
        public int getNumBinsX() {
                return levels.get(0).getNumBinsX();
        }

        @Override
        public int getNumBinsY() {
                return levels.get(0).getNumBinsY();
        }

        @Override
        public int getNumOriBins() {
                return levels.get(0).getNumOriBins();
        }

        @Override
        public float[][] getDescriptors() {
                int len = 0;
                for (int i = 0; i < sizes.length; i++) {
                        len += levels.get(i).getDescriptors().length;
                }

                final float[][] descr = new float[len][];

                int offset = 0;
                for (int i = 0; i < sizes.length; i++) {
                        final float[][] ldescr = levels.get(i).getDescriptors();
                        for (int j = 0; j < ldescr.length; j++) {
                                descr[offset++] = ldescr[j];
                        }
                }

                return descr;
        }
}