/*
    * Copyright (C) 2015 Alberto Irurueta Carro (alberto@irurueta.com)
    *
    * Licensed under the Apache License, Version 2.0 (the "License");
    * you may not use this file except in compliance with the License.
    * You may obtain a copy of the License at
    *
    *         http://www.apache.org/licenses/LICENSE-2.0
    *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  package com.irurueta.geometry.estimators;
  
  import com.irurueta.geometry.AffineTransformation2D;
  import com.irurueta.geometry.CoincidentPointsException;
  import com.irurueta.geometry.CoordinatesType;
  import com.irurueta.geometry.Point2D;
  import com.irurueta.numerical.robust.LMedSRobustEstimator;
  import com.irurueta.numerical.robust.LMedSRobustEstimatorListener;
  import com.irurueta.numerical.robust.RobustEstimator;
  import com.irurueta.numerical.robust.RobustEstimatorException;
  import com.irurueta.numerical.robust.RobustEstimatorMethod;
  
  import java.util.List;
  
  /**
   * Finds the best affine 2D transformation for provided collections of matched
   * 2D points using LMedS algorithm.
   */
  public class LMedSPointCorrespondenceAffineTransformation2DRobustEstimator
          extends PointCorrespondenceAffineTransformation2DRobustEstimator {
  
      /**
       * Default value to be used for stop threshold. Stop threshold can be used
       * to keep the algorithm iterating in case that best estimated threshold
       * using median of residuals is not small enough. Once a solution is found
       * that generates a threshold below this value, the algorithm will stop.
       * The stop threshold can be used to prevent the LMedS algorithm iterating
       * too many times in cases where samples have a very similar accuracy.
       * For instance, in cases where proportion of outliers is very small (close
       * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
       * iterate for a long time trying to find the best solution when indeed
       * there is no need to do that if a reasonable threshold has already been
       * reached.
       * Because of this behaviour the stop threshold can be set to a value much
       * lower than the one typically used in RANSAC, and yet the algorithm could
       * still produce even smaller thresholds in estimated results.
       */
      public static final double DEFAULT_STOP_THRESHOLD = 1.0;
  
      /**
       * Minimum allowed stop threshold value.
       */
      public static final double MIN_STOP_THRESHOLD = 0.0;
  
      /**
       * Threshold to be used to keep the algorithm iterating in case that best
       * estimated threshold using median of residuals is not small enough. Once
       * a solution is found that generates a threshold below this value, the
       * algorithm will stop.
       * The stop threshold can be used to prevent the LMedS algorithm iterating
       * too many times in cases where samples have a very similar accuracy.
       * For instance, in cases where proportion of outliers is very small (close
       * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
       * iterate for a long time trying to find the best solution when indeed
       * there is no need to do that if a reasonable threshold has already been
       * reached.
       * Because of this behaviour the stop threshold can be set to a value much
       * lower than the one typically used in RANSAC, and yet the algorithm could
       * still produce even smaller thresholds in estimated results.
       */
      private double stopThreshold;
  
      /**
       * Constructor.
       */
      public LMedSPointCorrespondenceAffineTransformation2DRobustEstimator() {
          super();
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
  
      /**
       * Constructor with lists of points to be used to estimate an affine 2D
       * transformation.
       * Points in the list located at the same position are considered to be
       * matched. Hence, both lists must have the same size, and their size must
       * be greater or equal than MINIMUM_SIZE.
       *
       * @param inputPoints  list of input points to be used to estimate an
       *                     affine 2D transformation.
       * @param outputPoints list of output points to be used to estimate an
       *                     affine 2D transformation.
       * @throws IllegalArgumentException if provided lists of points don't have
       *                                  the same size or their size is smaller than MINIMUM_SIZE.
       */
     public LMedSPointCorrespondenceAffineTransformation2DRobustEstimator(
             final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
         super(inputPoints, outputPoints);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      */
     public LMedSPointCorrespondenceAffineTransformation2DRobustEstimator(
             final AffineTransformation2DRobustEstimatorListener listener) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor with listener and lists of points to be used to estimate an
      * affine 2D transformation.
      * Points in the list located at the same position are considered to be
      * matched. Hence, both lists must have the same size, and their size must
      * be greater or equal than MINIMUM_SIZE.
      *
      * @param listener     listener to be notified of events such as when estimation
      *                     starts, ends or its progress significantly changes.
      * @param inputPoints  list of input points to be used to estimate an
      *                     affine 2D transformation.
      * @param outputPoints list of output points to be used to estimate an
      *                     affine 2D transformation.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public LMedSPointCorrespondenceAffineTransformation2DRobustEstimator(
             final AffineTransformation2DRobustEstimatorListener listener,
             final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
         super(listener, inputPoints, outputPoints);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Returns threshold to be used to keep the algorithm iterating in case that
      * best estimated threshold using median of residuals is not small enough.
      * Once a solution is found that generates a threshold below this value, the
      * algorithm will stop.
      * The stop threshold can be used to prevent the LMedS algorithm iterating
      * too many times in cases where samples have a very similar accuracy.
      * For instance, in cases where proportion of outliers is very small (close
      * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
      * iterate for a long time trying to find the best solution when indeed
      * there is no need to do that if a reasonable threshold has already been
      * reached.
      * Because of this behaviour the stop threshold can be set to a value much
      * lower than the one typically used in RANSAC, and yet the algorithm could
      * still produce even smaller thresholds in estimated results.
      *
      * @return stop threshold to stop the algorithm prematurely when a certain
      * accuracy has been reached.
      */
     public double getStopThreshold() {
         return stopThreshold;
     }
 
     /**
      * Sets threshold to be used to keep the algorithm iterating in case that
      * best estimated threshold using median of residuals is not small enough.
      * Once a solution is found that generates a threshold below this value, the
      * algorithm will stop.
      * The stop threshold can be used to prevent the LMedS algorithm iterating
      * too many times in cases where samples have a very similar accuracy.
      * For instance, in cases where proportion of outliers is very small (close
      * to 0%), and samples are very accurate (i.e. 1e-6), the algorithm would
      * iterate for a long time trying to find the best solution when indeed
      * there is no need to do that if a reasonable threshold has already been
      * reached.
      * Because of this behaviour the stop threshold can be set to a value much
      * lower than the one typically used in RANSAC, and yet the algorithm could
      * still produce even smaller thresholds in estimated results.
      *
      * @param stopThreshold stop threshold to stop the algorithm prematurely
      *                      when a certain accuracy has been reached.
      * @throws IllegalArgumentException if provided value is zero or negative.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      */
     public void setStopThreshold(final double stopThreshold) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (stopThreshold <= MIN_STOP_THRESHOLD) {
             throw new IllegalArgumentException();
         }
 
         this.stopThreshold = stopThreshold;
     }
 
     /**
      * Estimates an affine 2D transformation using a robust estimator and
      * the best set of matched 2D point correspondences found using the robust
      * estimator.
      *
      * @return an affine 2D transformation.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      * @throws NotReadyException        if provided input data is not enough to start
      *                                  the estimation.
      * @throws RobustEstimatorException if estimation fails for any reason
      *                                  (i.e. numerical instability, no solution available, etc).
      */
     @SuppressWarnings("DuplicatedCode")
     @Override
     public AffineTransformation2D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         final var innerEstimator = new LMedSRobustEstimator<>(
                 new LMedSRobustEstimatorListener<AffineTransformation2D>() {
 
                     // point to be reused when computing residuals
                     private final Point2D testPoint = Point2D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
 
                     @Override
                     public int getTotalSamples() {
                         return inputPoints.size();
                     }
 
                     @Override
                     public int getSubsetSize() {
                         return AffineTransformation2DRobustEstimator.MINIMUM_SIZE;
                     }
 
                     @Override
                     public void estimatePreliminarSolutions(
                             final int[] samplesIndices, final List<AffineTransformation2D> solutions) {
                         final var inputPoint1 = inputPoints.get(samplesIndices[0]);
                         final var inputPoint2 = inputPoints.get(samplesIndices[1]);
                         final var inputPoint3 = inputPoints.get(samplesIndices[2]);
 
                         final var outputPoint1 = outputPoints.get(samplesIndices[0]);
                         final var outputPoint2 = outputPoints.get(samplesIndices[1]);
                         final var outputPoint3 = outputPoints.get(samplesIndices[2]);
 
                         try {
                             final var transformation = new AffineTransformation2D(inputPoint1, inputPoint2, inputPoint3,
                                     outputPoint1, outputPoint2, outputPoint3);
                             solutions.add(transformation);
                         } catch (final CoincidentPointsException e) {
                             // if points are coincident, no solution is added
                         }
                     }
 
                     @Override
                     public double computeResidual(final AffineTransformation2D currentEstimation, final int i) {
                         final var inputPoint = inputPoints.get(i);
                         final var outputPoint = outputPoints.get(i);
 
                         // transform input point and store result in mTestPoint
                         currentEstimation.transform(inputPoint, testPoint);
 
                         return outputPoint.distanceTo(testPoint);
                     }
 
                     @Override
                     public boolean isReady() {
                         return LMedSPointCorrespondenceAffineTransformation2DRobustEstimator.this.isReady();
                     }
 
                     @Override
                     public void onEstimateStart(final RobustEstimator<AffineTransformation2D> estimator) {
                         if (mListener != null) {
                             mListener.onEstimateStart(
                                     LMedSPointCorrespondenceAffineTransformation2DRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateEnd(final RobustEstimator<AffineTransformation2D> estimator) {
                         if (mListener != null) {
                             mListener.onEstimateEnd(
                                     LMedSPointCorrespondenceAffineTransformation2DRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateNextIteration(
                             final RobustEstimator<AffineTransformation2D> estimator, final int iteration) {
                         if (mListener != null) {
                             mListener.onEstimateNextIteration(
                                     LMedSPointCorrespondenceAffineTransformation2DRobustEstimator.this,
                                     iteration);
                         }
                     }
 
                     @Override
                     public void onEstimateProgressChange(
                             final RobustEstimator<AffineTransformation2D> estimator, final float progress) {
                         if (mListener != null) {
                             mListener.onEstimateProgressChange(
                                     LMedSPointCorrespondenceAffineTransformation2DRobustEstimator.this,
                                     progress);
                         }
                     }
                 });
 
         try {
             locked = true;
             inliersData = null;
             innerEstimator.setConfidence(confidence);
             innerEstimator.setMaxIterations(maxIterations);
             innerEstimator.setProgressDelta(progressDelta);
             innerEstimator.setStopThreshold(stopThreshold);
             final var transformation = innerEstimator.estimate();
             inliersData = innerEstimator.getInliersData();
             return attemptRefine(transformation);
         } catch (final com.irurueta.numerical.LockedException e) {
             throw new LockedException(e);
         } catch (final com.irurueta.numerical.NotReadyException e) {
             throw new NotReadyException(e);
         } finally {
             locked = false;
         }
     }
 
     /**
      * Returns method being used for robust estimation.
      *
      * @return method being used for robust estimation.
      */
     @Override
     public RobustEstimatorMethod getMethod() {
         return RobustEstimatorMethod.LMEDS;
     }
 
     /**
      * Gets standard deviation used for Levenberg-Marquardt fitting during
      * refinement.
      * Returned value gives an indication of how much variance each residual
      * has.
      * Typically, this value is related to the threshold used on each robust
      * estimation, since residuals of found inliers are within the range of
      * such threshold.
      *
      * @return standard deviation used for refinement.
      */
     @Override
     protected double getRefinementStandardDeviation() {
         final var inliersData = (LMedSRobustEstimator.LMedSInliersData) getInliersData();
 
         // avoid setting a threshold too strict
         final var threshold = inliersData.getEstimatedThreshold();
         return Math.max(threshold, stopThreshold);
     }
 }