/**
 * 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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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.image.processing.convolution;

import org.openimaj.image.FImage;
import org.openimaj.image.processor.SinglebandImageProcessor;
import org.openimaj.math.matrix.MatrixUtils;

import Jama.SingularValueDecomposition;

/**
 * Base class for implementation of classes that perform convolution operations
 * on @link{FImage}s as a @link{SinglebandImageProcessor}, with the kernel
 * itself formed from and @link{FImage}.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 */
public class FConvolution implements SinglebandImageProcessor<Float, FImage> {
        /** The kernel */
        public FImage kernel;

        private ConvolveMode mode;

        interface ConvolveMode {
                public void convolve(FImage f);

                class OneD implements ConvolveMode {
                        private float[] kernel;
                        private boolean rowMode;

                        OneD(FImage image) {
                                if (image.height == 1) {
                                        this.rowMode = true;
                                        this.kernel = image.pixels[0];

                                }
                                else {
                                        this.rowMode = false;
                                        this.kernel = new float[image.height];
                                        for (int i = 0; i < image.height; i++)
                                                this.kernel[i] = image.pixels[i][0];
                                }
                        }

                        @Override
                        public void convolve(FImage f) {
                                if (this.rowMode)
                                        FImageConvolveSeparable.convolveHorizontal(f, kernel);
                                else
                                        FImageConvolveSeparable.convolveVertical(f, kernel);
                        }

                }

                class Separable implements ConvolveMode {
                        private float[] row;
                        private float[] col;

                        Separable(SingularValueDecomposition svd) {

                                final int nrows = svd.getU().getRowDimension();

                                this.row = new float[nrows];
                                this.col = new float[nrows];

                                final float factor = (float) Math.sqrt(svd.getS().get(0, 0));
                                for (int i = 0; i < nrows; i++) {
                                        this.row[i] = (float) svd.getU().get(i, 0) * factor;
                                        this.col[i] = (float) svd.getV().get(i, 0) * factor;
                                }
                        }

                        @Override
                        public void convolve(FImage f) {
                                FImageConvolveSeparable.convolveHorizontal(f, row);
                                FImageConvolveSeparable.convolveVertical(f, col);
                        }
                }

                class BruteForce implements ConvolveMode {
                        protected FImage kernel;

                        BruteForce(FImage kernel) {
                                this.kernel = kernel;
                        }

                        @Override
                        public void convolve(FImage image) {
                                final int kh = kernel.height;
                                final int kw = kernel.width;
                                final int hh = kh / 2;
                                final int hw = kw / 2;
                                final FImage clone = image.newInstance(image.width, image.height);
                                for (int y = hh; y < image.height - (kh - hh); y++) {
                                        for (int x = hw; x < image.width - (kw - hw); x++) {
                                                float sum = 0;
                                                for (int j = 0, jj = kh - 1; j < kh; j++, jj--) {
                                                        for (int i = 0, ii = kw - 1; i < kw; i++, ii--) {
                                                                final int rx = x + i - hw;
                                                                final int ry = y + j - hh;

                                                                sum += image.pixels[ry][rx] * kernel.pixels[jj][ii];
                                                        }
                                                }
                                                clone.pixels[y][x] = sum;
                                        }
                                }
                                image.internalAssign(clone);
                        }
                }
        }

        /**
         * Construct the convolution operator with the given kernel
         * 
         * @param kernel
         *            the kernel
         */
        public FConvolution(FImage kernel) {
                this.kernel = kernel;
                setup(false);
        }

        /**
         * Construct the convolution operator with the given kernel
         * 
         * @param kernel
         *            the kernel
         */
        public FConvolution(float[][] kernel) {
                this.kernel = new FImage(kernel);
                setup(false);
        }

        /**
         * Set brute-force convolution; disables kernel separation and other
         * optimisations.
         * 
         * @param brute
         */
        public void setBruteForce(boolean brute) {
                setup(brute);
        }

        private void setup(boolean brute) {
                if (brute) {
                        this.mode = new ConvolveMode.BruteForce(this.kernel);
                        return;
                }
                if (this.kernel.width == 1 || this.kernel.height == 1) {
                        this.mode = new ConvolveMode.OneD(kernel);
                }
                else {
                        MatrixUtils.matrixFromFloat(this.kernel.pixels);
                        final SingularValueDecomposition svd = new SingularValueDecomposition(
                                        MatrixUtils.matrixFromFloat(this.kernel.pixels));
                        if (svd.rank() == 1)
                                this.mode = new ConvolveMode.Separable(svd);
                        else
                                this.mode = new ConvolveMode.BruteForce(this.kernel);
                }
        }

        /*
         * (non-Javadoc)
         * 
         * @see
         * org.openimaj.image.processor.ImageProcessor#processImage(org.openimaj
         * .image.Image)
         */
        @Override
        public void processImage(FImage image) {
                mode.convolve(image);
        }

        /**
         * Return the kernel response at the x,y in the given image.
         * 
         * This method will throw an array index out of bounds if x,y requests
         * pixels outside the image bounds
         * 
         * @param x
         * @param y
         * @param image
         * @return the kernel response at the given coordinates
         */
        public float responseAt(int x, int y, FImage image) {
                float sum = 0;
                final int kh = kernel.height;
                final int kw = kernel.width;
                final int hh = kh / 2;
                final int hw = kw / 2;

                for (int j = 0, jj = kh - 1; j < kh; j++, jj--) {
                        for (int i = 0, ii = kw - 1; i < kw; i++, ii--) {
                                final int rx = x + i - hw;
                                final int ry = y + j - hh;

                                sum += image.pixels[ry][rx] * kernel.pixels[jj][ii];
                        }
                }
                return sum;
        }
}