/*
    * 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.algebra.AlgebraException;
  import com.irurueta.geometry.AffineTransformation2D;
  import com.irurueta.geometry.CoincidentLinesException;
  import com.irurueta.geometry.Line2D;
  import com.irurueta.numerical.robust.PROMedSRobustEstimator;
  import com.irurueta.numerical.robust.PROMedSRobustEstimatorListener;
  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 lines using PROMedS algorithm.
   */
  @SuppressWarnings("DuplicatedCode")
  public class PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator
          extends LineCorrespondenceAffineTransformation2DRobustEstimator {
  
      /**
       * 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 = 1e-6;
  
      /**
       * 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;
  
      /**
       * Quality scores corresponding to each pair of matched lines.
       * The larger the score value the better the quality of the matching.
       */
      private double[] qualityScores;
  
      /**
       * Constructor.
       */
      public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator() {
          super();
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
  
      /**
       * Constructor with lists of lines to be used to estimate an affine 2D
       * transformation.
       * Lines 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 inputLines  list of input lines to be used to estimate an affine
      *                    2D transformation.
      * @param outputLines list of output lines to be used to estimate an affine
      *                    2D transformation.
      * @throws IllegalArgumentException if provided lists of lines don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(
             final List<Line2D> inputLines, final List<Line2D> outputLines) {
         super(inputLines, outputLines);
         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 PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(
             final AffineTransformation2DRobustEstimatorListener listener) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor with listener and lists of lines to be used to estimate an
      * affine 2D transformation.
      * Lines 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 inputLines  list of input lines to be used to estimate an affine
      *                    2D transformation.
      * @param outputLines list of output lines to be used to estimate an affine
      *                    2D transformation.
      * @throws IllegalArgumentException if provided lists of lines don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(
             final AffineTransformation2DRobustEstimatorListener listener,
             final List<Line2D> inputLines, final List<Line2D> outputLines) {
         super(listener, inputLines, outputLines);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor.
      *
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      points.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(final double[] qualityScores) {
         super();
         stopThreshold = DEFAULT_STOP_THRESHOLD;
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Constructor with lists of lines to be used to estimate an affine 2D
      * transformation.
      * Lines 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 inputLines    list of input lines to be used to estimate an affine
      *                      2D transformation.
      * @param outputLines   list of output lines to be used to estimate an affine
      *                      2D transformation.
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      lines.
      * @throws IllegalArgumentException if provided lists of lines and array
      *                                  of quality scores don't have the same size or their size is smaller than
      *                                  MINIMUM_SIZE.
      */
     public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(
             final List<Line2D> inputLines, final List<Line2D> outputLines, final double[] qualityScores) {
         super(inputLines, outputLines);
 
         if (qualityScores.length != inputLines.size())
             throw new IllegalArgumentException();
 
         stopThreshold = DEFAULT_STOP_THRESHOLD;
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Constructor.
      *
      * @param listener      listener to be notified of events such as when estimation
      *                      starts, ends or its progress significantly changes.
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      lines.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(
             final AffineTransformation2DRobustEstimatorListener listener, final double[] qualityScores) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Constructor with listener and lists of lines to be used to estimate an
      * affine 2D transformation.
      * Lines 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 inputLines    list of input lines to be used to estimate an affine
      *                      2D transformation.
      * @param outputLines   list of output lines to be used to estimate an affine
      *                      2D transformation.
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      lines.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator(
             final AffineTransformation2DRobustEstimatorListener listener,
             final List<Line2D> inputLines, final List<Line2D> outputLines, final double[] qualityScores) {
         super(listener, inputLines, outputLines);
 
         if (qualityScores.length != inputLines.size()) {
             throw new IllegalArgumentException();
         }
 
         stopThreshold = DEFAULT_STOP_THRESHOLD;
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * 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;
     }
 
     /**
      * Returns quality scores corresponding to each pair of matched lines.
      * The larger the score value the better the quality of the matching.
      *
      * @return quality scores corresponding to each pair of matched lines.
      */
     @Override
     public double[] getQualityScores() {
         return qualityScores;
     }
 
     /**
      * Sets quality scores corresponding to each pair of matched lines.
      * The larger the score value the better the quality of the matching.
      *
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      lines.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     @Override
     public void setQualityScores(final double[] qualityScores) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Indicates if estimator is ready to start the affine 2D transformation
      * estimation.
      * This is true when input data (i.e. lists of matched lines and quality
      * scores) are provided and a minimum of MINIMUM_SIZE lines are available.
      *
      * @return true if estimator is ready, false otherwise.
      */
     @Override
     public boolean isReady() {
         return super.isReady() && qualityScores != null && qualityScores.length == inputLines.size();
     }
 
     /**
      * Estimates an affine 2D transformation using a robust estimator and
      * the best set of matched 2D lines 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).
      */
     @Override
     public AffineTransformation2D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         final var innerEstimator = new PROMedSRobustEstimator<>(
                 new PROMedSRobustEstimatorListener<AffineTransformation2D>() {
 
                     // line to be reused when computing residuals
                     private final Line2D mTestLine = new Line2D();
 
                     @Override
                     public double getThreshold() {
                         return stopThreshold;
                     }
 
                     @Override
                     public int getTotalSamples() {
                         return inputLines.size();
                     }
 
                     @Override
                     public int getSubsetSize() {
                         return AffineTransformation2DRobustEstimator.MINIMUM_SIZE;
                     }
 
                     @Override
                     public void estimatePreliminarSolutions(
                             final int[] samplesIndices, final List<AffineTransformation2D> solutions) {
                         final var inputLine1 = inputLines.get(samplesIndices[0]);
                         final var inputLine2 = inputLines.get(samplesIndices[1]);
                         final var inputLine3 = inputLines.get(samplesIndices[2]);
 
                         final var outputLine1 = outputLines.get(samplesIndices[0]);
                         final var outputLine2 = outputLines.get(samplesIndices[1]);
                         final var outputLine3 = outputLines.get(samplesIndices[2]);
 
                         try {
                             final var transformation = new AffineTransformation2D(inputLine1, inputLine2, inputLine3,
                                     outputLine1, outputLine2, outputLine3);
                             solutions.add(transformation);
                         } catch (final CoincidentLinesException e) {
                             // if lines are coincident, no solution is added
                         }
                     }
 
                     @Override
                     public double computeResidual(final AffineTransformation2D currentEstimation, final int i) {
                         final var inputLine = inputLines.get(i);
                         final var outputLine = outputLines.get(i);
 
                         // transform input line and store result in mTestLine
                         try {
                             currentEstimation.transform(inputLine, mTestLine);
 
                             return getResidual(outputLine, mTestLine);
                         } catch (final AlgebraException e) {
                             // this happens when internal matrix of affine transformation
                             // cannot be reverse (i.e. transformation is not well-defined,
                             // numerical instabilities, etc.)
                             return Double.MAX_VALUE;
                         }
                     }
 
                     @Override
                     public boolean isReady() {
                         return PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator.this.isReady();
                     }
 
                     @Override
                     public void onEstimateStart(final RobustEstimator<AffineTransformation2D> estimator) {
                         if (mListener != null) {
                             mListener.onEstimateStart(
                                     PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateEnd(final RobustEstimator<AffineTransformation2D> estimator) {
                         if (mListener != null) {
                             mListener.onEstimateEnd(
                                     PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateNextIteration(
                             final RobustEstimator<AffineTransformation2D> estimator, final int iteration) {
                         if (mListener != null) {
                             mListener.onEstimateNextIteration(
                                     PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator.this,
                                     iteration);
                         }
                     }
 
                     @Override
                     public void onEstimateProgressChange(
                             final RobustEstimator<AffineTransformation2D> estimator, final float progress) {
                         if (mListener != null) {
                             mListener.onEstimateProgressChange(
                                     PROMedSLineCorrespondenceAffineTransformation2DRobustEstimator.this,
                                     progress);
                         }
                     }
 
                     @Override
                     public double[] getQualityScores() {
                         return qualityScores;
                     }
                 });
 
         try {
             locked = true;
             inliersData = null;
             innerEstimator.setConfidence(confidence);
             innerEstimator.setMaxIterations(maxIterations);
             innerEstimator.setProgressDelta(progressDelta);
             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.PROMEDS;
     }
 
     /**
      * 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 = (PROMedSRobustEstimator.PROMedSInliersData) getInliersData();
 
         // avoid setting a threshold too strict
         final var threshold = inliersData.getEstimatedThreshold();
         return Math.max(threshold, stopThreshold);
     }
 
     /**
      * Sets quality scores corresponding to each pair of matched lines.
      * This method is used internally and does not check whether instance is
      * locked or not.
      *
      * @param qualityScores quality scores to be set.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE.
      */
     private void internalSetQualityScores(final double[] qualityScores) {
         if (qualityScores.length < MINIMUM_SIZE) {
             throw new IllegalArgumentException();
         }
 
         this.qualityScores = qualityScores;
     }
 }