/**
    * 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
   * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
   */
  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.point.PointList;
  import org.openimaj.math.geometry.shape.PointDistributionModel;
  import org.openimaj.math.geometry.shape.PointDistributionModel.Constraint;
  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)
 	{
 		final int nPoints = data.get(0).firstObject().size();
 
 		@SuppressWarnings("unchecked")
 		final 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();
 				}
 
 				final PointList pl = data.get(i).firstObject();
 
 				ppms[j].updateModel(data.get(i).secondObject(), pl.get(j), pl);
 			}
 		}
 
 		final List<PointList> pls = new ArrayList<PointList>();
 		for (final IndependentPair<PointList, I> i : data)
 			pls.add(i.firstObject());
 
 		final 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 their 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++) {
 			final ObjectFloatPair<Point2d> newBest = landmarkModels[i].updatePosition(image, currentShape.get(i),
 					currentShape);
 			newShape.points.add(newBest.first);
 
 			final float percentageFromStart = newBest.second;
 			if (percentageFromStart < 0.5)
 				inliers++;
 			else
 				outliers++;
 		}
 		final double score = ((double) inliers) / ((double) (inliers + outliers));
 
 		// find the parameters and pose that "best" model the updated points
 		final IndependentPair<Matrix, double[]> newModelParams = pdm.fitModel(newShape);
 
 		final Matrix pose = newModelParams.firstObject();
 		final 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;
 	}
 }