/**
 * 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
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package org.openimaj.image.model.asm;

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

import org.openimaj.citation.annotation.Reference;
import org.openimaj.citation.annotation.ReferenceType;
import org.openimaj.citation.annotation.References;
import org.openimaj.image.Image;
import org.openimaj.image.model.landmark.LandmarkModel;
import org.openimaj.image.model.landmark.LandmarkModelFactory;
import org.openimaj.math.geometry.point.Point2d;
import org.openimaj.math.geometry.shape.PointDistributionModel;
import org.openimaj.math.geometry.shape.PointDistributionModel.Constraint;
import org.openimaj.math.geometry.shape.PointList;
import org.openimaj.math.matrix.algorithm.pca.PrincipalComponentAnalysis.ComponentSelector;
import org.openimaj.util.pair.IndependentPair;
import org.openimaj.util.pair.ObjectFloatPair;

import Jama.Matrix;

/**
 * Implementation of a basic Active Shape Model. The implementation allows different
 * types of landmark appearance models and can work with both colour and greylevel
 * images.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * 
 * @param <I> Concrete type of {@link Image}
 */
@References(references = { 
                @Reference(
                                author = { "Cootes, T. F.", "Taylor, C. J." }, 
                                title = "Statistical Models of Appearance for Computer Vision", 
                                type = ReferenceType.Unpublished,
                                month = "October",
                                year = "2001",
                                url = "http://isbe.man.ac.uk/~bim/Models/app_model.ps.gz"
                ), 
                @Reference(
                                type = ReferenceType.Inproceedings,
                                author = { "T. F. Cootes", "C. J. Taylor" },
                                title = "Active Shape Models",
                                year = "1992",
                                booktitle = "in Proceedings of the British Machine Vision Conference"
                )
})
public class ActiveShapeModel<I extends Image<?, I>> {
        private PointDistributionModel pdm;
        private LandmarkModel<I>[] landmarkModels;
        private int maxIter = 50;
        private double inlierPercentage = 0.9;
        
        /**
         * Construct an {@link ActiveShapeModel} from a pre-trained {@link PointDistributionModel}
         * and set of {@link LandmarkModel}s.
         * @param pdm the {@link PointDistributionModel}.
         * @param landmarkModels the {@link LandmarkModel}s.
         */
        public ActiveShapeModel(PointDistributionModel pdm, LandmarkModel<I>[] landmarkModels) {
                this.pdm = pdm;
                this.landmarkModels = landmarkModels;
        }
        
        /**
         * Train a new {@link ActiveShapeModel} using the given data
         * and parameters.
         * 
         * @param <I> The concrete image type.
         * @param selector the selector for choosing the number of principal components / modes of the model.
         * @param data the data to train the model from
         * @param constraint the constraint to apply to restrict the model to plausible shapes.
         * @param factory the {@link LandmarkModelFactory} for learning local appearance models
         * @return a newly trained {@link ActiveShapeModel}.
         */
        public static <I extends Image<?, I>> ActiveShapeModel<I> trainModel(ComponentSelector selector, List<IndependentPair<PointList, I>> data, Constraint constraint, LandmarkModelFactory<I> factory) {
                int nPoints = data.get(0).firstObject().size();
                
                @SuppressWarnings("unchecked")
                LandmarkModel<I>[] ppms = new LandmarkModel[nPoints];
                
                for (int i=0; i<data.size(); i++) {
                        for (int j=0; j<nPoints; j++) {
                                if (ppms[j] == null) {
                                        ppms[j] = factory.createLandmarkModel();
                                }
                        
                                PointList pl = data.get(i).firstObject();
                                
                                ppms[j].updateModel(data.get(i).secondObject(), pl.get(j), pl);
                        }
                }
                
                List<PointList> pls = new ArrayList<PointList>();
                for (IndependentPair<PointList, I> i : data)
                        pls.add(i.firstObject());
                
                PointDistributionModel pdm = new PointDistributionModel(constraint, pls);
                pdm.setNumComponents(selector);
                
                return new ActiveShapeModel<I>(pdm, ppms);
        }
        
        /**
         * Class to hold the response of a single iteration
         * of model fitting.
         * 
         * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
         *
         */
        public static class IterationResult {
                /**
                 * The percentage of points that moved less than 50% of there allowed distance
                 */
                public double fit;
                /**
                 * The updated shape in image coordinates
                 */
                public PointList shape;
                /**
                 * The model pose from model coordinates to image coordinates
                 */
                public Matrix pose;
                /**
                 * The model weight parameters
                 */
                public double [] parameters;

                protected IterationResult(Matrix pose, PointList shape, double fit, double [] parameters) {
                        this.pose = pose;
                        this.shape = shape;
                        this.fit = fit;
                        this.parameters = parameters;
                }
        }
        
        /**
         * Perform a single iteration of model fitting.
         * 
         * @param image the image to fit to
         * @param currentShape the starting shape in image coordinates
         * @return the updated shape and parameters
         */
        public IterationResult performIteration(I image, PointList currentShape) {
                PointList newShape = new PointList();
                
                int inliers = 0;
                int outliers = 0;
                //compute updated points and a score based on how far they moved
                for (int i=0; i<landmarkModels.length; i++) {
                        ObjectFloatPair<Point2d> newBest = landmarkModels[i].updatePosition(image, currentShape.get(i), currentShape);
                        newShape.points.add( newBest.first );
                        
                        float percentageFromStart = newBest.second;
                        if (percentageFromStart < 0.5)
                                inliers++;
                        else
                                outliers++;
                }
                double score = ((double)inliers) / ((double)(inliers + outliers));
                
                //find the parameters and pose that "best" model the updated points
                IndependentPair<Matrix, double[]> newModelParams = pdm.fitModel(newShape);
                
                Matrix pose = newModelParams.firstObject();
                double[] parameters = newModelParams.secondObject();
                
                //apply model parameters to get final shape for the iteration
                newShape = pdm.generateNewShape(parameters).transform(pose);
                
                return new IterationResult(pose, newShape, score, parameters);
        }
        
        /**
         * Iteratively apply {@link #performIteration(Image, PointList)} until
         * the maximum number of iterations is exceeded, or the number of
         * points that moved less than 0.5 of their maximum distance in an
         * iteration is less than the target inlier percentage.
         * 
         * @see #setInlierPercentage(double)
         * @see #setMaxIterations(int)
         * 
         * @param image the image to fit the shape to 
         * @param initialShape the initial shape in image coordinates
         * @return the fitted shape and parameters
         */
        public IterationResult fit(I image, PointList initialShape) {
                IterationResult ir = performIteration(image, initialShape);
                int count = 0;
                
                while (ir.fit < inlierPercentage  && count < maxIter) {
                        ir = performIteration(image, ir.shape);
                        count++;
                }
                
                return ir;
        }
        
        /**
         * @return the maxIter
         */
        public int getMaxIterations() {
                return maxIter;
        }

        /**
         * Set the maximum allowed number of iterations in fitting the model
         * @param maxIter the maxIter to set
         */
        public void setMaxIterations(int maxIter) {
                this.maxIter = maxIter;
        }

        /**
         * @return the inlierPercentage
         */
        public double getInlierPercentage() {
                return inlierPercentage;
        }

        /**
         * Set the target percentage of the number of points that
         * move less than 0.5 of their total possible distance within
         * an iteration to stop fitting.
         *  
         * @param inlierPercentage the inlierPercentage to set
         */
        public void setInlierPercentage(double inlierPercentage) {
                this.inlierPercentage = inlierPercentage;
        }

        /**
         * @return the learnt {@link PointDistributionModel}
         */
        public PointDistributionModel getPDM() {
                return pdm;
        }

        /**
         * @return the local landmark appearance models; one for each point in the shape.
         */
        public LandmarkModel<I>[] getLandmarkModels() {
                return landmarkModels;
        }
}