/*
    * Copyright (C) 2017 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.CoordinatesType;
  import com.irurueta.geometry.MetricTransformation2D;
  import com.irurueta.geometry.Point2D;
  import com.irurueta.numerical.robust.PROSACRobustEstimator;
  import com.irurueta.numerical.robust.PROSACRobustEstimatorListener;
  import com.irurueta.numerical.robust.RobustEstimator;
  import com.irurueta.numerical.robust.RobustEstimatorException;
  import com.irurueta.numerical.robust.RobustEstimatorMethod;
  
  import java.util.ArrayList;
  import java.util.List;
  
  /**
   * Finds the best metric 2D transformation for provided collections of
   * matched 2D points using PROSAC algorithm.
   */
  @SuppressWarnings("DuplicatedCode")
  public class PROSACMetricTransformation2DRobustEstimator extends MetricTransformation2DRobustEstimator {
  
      /**
       * Constant defining default threshold to determine whether points are
       * inliers or not.
       * By default, 1.0 is considered a good value for cases where measures are
       * done on pixels, since typically the minimum resolution is 1 pixel.
       */
      public static final double DEFAULT_THRESHOLD = 1.0;
  
      /**
       * Minimum value that can be set as threshold.
       * Threshold must be strictly greater than 0.0.
       */
      public static final double MIN_THRESHOLD = 0.0;
  
      /**
       * Indicates that by default inliers will only be computed but not kept.
       */
      public static final boolean DEFAULT_COMPUTE_AND_KEEP_INLIERS = false;
  
      /**
       * Indicates that by default residuals will only be computed but not kept.
       */
      public static final boolean DEFAULT_COMPUTE_AND_KEEP_RESIDUALS = false;
  
      /**
       * Threshold to determine whether points are inliers or not when testing
       * possible estimation solutions.
       * The threshold refers to the amount of error (i.e. distance) a possible
       * solution has on a matched pair of points.
       */
      private double threshold;
  
      /**
       * Quality scores corresponding to each pair of matched points.
       * The larger the score value the better the quality of the matching.
       */
      private double[] qualityScores;
  
      /**
       * Indicates whether inliers must be computed and kept.
       */
      private boolean computeAndKeepInliers;
  
      /**
       * Indicates whether residuals must be computed and kept.
       */
      private boolean computeAndKeepResiduals;
  
      /**
       * Constructor.
       */
      public PROSACMetricTransformation2DRobustEstimator() {
          super();
          threshold = DEFAULT_THRESHOLD;
          computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
          computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
      }
  
      /**
       * Constructor with lists of points to be used to estimate a metric 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 a
      *                     metric 2D transformation.
      * @param outputPoints list of output points to be used to estimate a
      *                     metric 2D transformation.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
         super(inputPoints, outputPoints);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      */
     public PROSACMetricTransformation2DRobustEstimator(final MetricTransformation2DRobustEstimatorListener listener) {
         super(listener);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with listener and lists of points to be used to estimate a
      * metric 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
      *                     stars, ends or its progress significantly changes.
      * @param inputPoints  list of input points to be used to estimate a
      *                     metric 2D transformation.
      * @param outputPoints list of output points to be used to estimate a
      *                     metric 2D transformation.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener,
             final List<Point2D> inputPoints, final List<Point2D> outputPoints) {
         super(listener, inputPoints, outputPoints);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * 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 PROSACMetricTransformation2DRobustEstimator(final double[] qualityScores) {
         super();
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with lists of points to be used to estimate a metric 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 a
      *                      metric 2D transformation.
      * @param outputPoints  list of output points to be used to estimate a
      *                      metric 2D transformation.
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      points.
      * @throws IllegalArgumentException if provided lists of points and array
      *                                  of quality scores don't have the same size or their size is smaller than
      *                                  MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores) {
         super(inputPoints, outputPoints);
 
         if (qualityScores.length != inputPoints.size()) {
             throw new IllegalArgumentException();
         }
 
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * 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
      *                      points.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener, final double[] qualityScores) {
         super(listener);
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with listener and lists of points to be used to estimate a
      * metric 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
      *                      stars, ends or its progress significantly changes.
      * @param inputPoints   list of input points to be used to estimate a
      *                      metric 2D transformation.
      * @param outputPoints  list of output points to be used to estimate a
      *                      metric 2D transformation.
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      points.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener,
             final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores) {
         super(listener, inputPoints, outputPoints);
 
         if (qualityScores.length != inputPoints.size()) {
             throw new IllegalArgumentException();
         }
 
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor.
      *
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      */
     public PROSACMetricTransformation2DRobustEstimator(final boolean weakMinimumSizeAllowed) {
         super(weakMinimumSizeAllowed);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with lists of points to be used to estimate a metric 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 a
      *                               metric 2D transformation.
      * @param outputPoints           list of output points to be used to estimate a
      *                               metric 2D transformation.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
         super(inputPoints, outputPoints, weakMinimumSizeAllowed);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor.
      *
      * @param listener               listener to be notified of events such as when estimation
      *                               starts, ends or its progress significantly changes.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener, final boolean weakMinimumSizeAllowed) {
         super(listener, weakMinimumSizeAllowed);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with listener and lists of points to be used to estimate a
      * metric 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
      *                               stars, ends or its progress significantly changes.
      * @param inputPoints            list of input points to be used to estimate a
      *                               metric 2D transformation.
      * @param outputPoints           list of output points to be used to estimate a
      *                               metric 2D transformation.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener,
             final List<Point2D> inputPoints, final List<Point2D> outputPoints, final boolean weakMinimumSizeAllowed) {
         super(listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
         threshold = DEFAULT_THRESHOLD;
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor.
      *
      * @param qualityScores          quality scores corresponding to each pair of matched
      *                               points.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final double[] qualityScores, final boolean weakMinimumSizeAllowed) {
         super(weakMinimumSizeAllowed);
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with lists of points to be used to estimate a metric 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 a
      *                               metric 2D transformation.
      * @param outputPoints           list of output points to be used to estimate a
      *                               metric 2D transformation.
      * @param qualityScores          quality scores corresponding to each pair of matched
      *                               points.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      * @throws IllegalArgumentException if provided lists of points and array
      *                                  of quality scores don't have the same size or their size is smaller than
      *                                  MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final List<Point2D> inputPoints, final List<Point2D> outputPoints, final double[] qualityScores,
             final boolean weakMinimumSizeAllowed) {
         super(inputPoints, outputPoints, weakMinimumSizeAllowed);
 
         if (qualityScores.length != inputPoints.size()) {
             throw new IllegalArgumentException();
         }
 
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * 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
      *                               points.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener, final double[] qualityScores,
             final boolean weakMinimumSizeAllowed) {
         super(listener, weakMinimumSizeAllowed);
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Constructor with listener and lists of points to be used to estimate a
      * metric 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
      *                               stars, ends or its progress significantly changes.
      * @param inputPoints            list of input points to be used to estimate a
      *                               metric 2D transformation.
      * @param outputPoints           list of output points to be used to estimate a
      *                               metric 2D transformation.
      * @param qualityScores          quality scores corresponding to each pair of matched
      *                               points.
      * @param weakMinimumSizeAllowed true allows 2 points, false requires 3.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than MINIMUM_SIZE.
      */
     public PROSACMetricTransformation2DRobustEstimator(
             final MetricTransformation2DRobustEstimatorListener listener,
             final List<Point2D> inputPoints, final List<Point2D> outputPoints,
             final double[] qualityScores, final boolean weakMinimumSizeAllowed) {
         super(listener, inputPoints, outputPoints, weakMinimumSizeAllowed);
 
         if (qualityScores.length != inputPoints.size()) {
             throw new IllegalArgumentException();
         }
 
         threshold = DEFAULT_THRESHOLD;
         internalSetQualityScores(qualityScores);
         computeAndKeepInliers = DEFAULT_COMPUTE_AND_KEEP_INLIERS;
         computeAndKeepResiduals = DEFAULT_COMPUTE_AND_KEEP_RESIDUALS;
     }
 
     /**
      * Returns threshold to determine whether points are inliers or not when
      * testing possible estimation solutions.
      * The threshold refers to the amount of error (i.e. Euclidean distance) a
      * possible solution has on a matched pair of points.
      *
      * @return threshold to determine whether points are inliers or not when
      * testing possible estimation solutions.
      */
     public double getThreshold() {
         return threshold;
     }
 
     /**
      * Sets threshold to determine whether points are inliers or not when
      * testing possible estimation solutions.
      * The threshold refers to the amount of error (i.e. Euclidean distance) a
      * possible solution has on a matched pair of points.
      *
      * @param threshold threshold to determine whether points are inliers or not
      *                  when testing possible estimation solutions.
      * @throws IllegalArgumentException if provided values is equal or less than
      *                                  zero.
      * @throws LockedException          if robust estimator is locked because an
      *                                  estimation is already in progress.
      */
     public void setThreshold(final double threshold) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (threshold <= MIN_THRESHOLD) {
             throw new IllegalArgumentException();
         }
         this.threshold = threshold;
     }
 
     /**
      * Returns quality scores corresponding to each pair of matched points.
      * The larger the score value the better the quality of the matching.
      *
      * @return quality scores corresponding to each pair of matched points.
      */
     @Override
     public double[] getQualityScores() {
         return qualityScores;
     }
 
     /**
      * Sets quality scores corresponding to each pair of matched points.
      * The larger the score value the better the quality of the matching.
      *
      * @param qualityScores quality scores corresponding to each pair of matched
      *                      points.
      * @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 metric 2D transformation
      * estimation.
      * This is true when input data (i.e. lists of matched points and quality
      * scores) are provided and a minimum of MINIMUM_SIZE points are available.
      *
      * @return true if estimator is ready, false otherwise.
      */
     @Override
     public boolean isReady() {
         return super.isReady() && qualityScores != null && qualityScores.length == inputPoints.size();
     }
 
     /**
      * Indicates whether inliers must be computed and kept.
      *
      * @return true if inliers must be computed and kept, false if inliers
      * only need to be computed but not kept.
      */
     public boolean isComputeAndKeepInliersEnabled() {
         return computeAndKeepInliers;
     }
 
     /**
      * Specifies whether inliers must be computed and kept.
      *
      * @param computeAndKeepInliers true if inliers must be computed and kept,
      *                              false if inliers only need to be computed but not kept.
      * @throws LockedException if estimator is locked.
      */
     public void setComputeAndKeepInliersEnabled(final boolean computeAndKeepInliers) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.computeAndKeepInliers = computeAndKeepInliers;
     }
 
     /**
      * Indicates whether residuals must be computed and kept.
      *
      * @return true if residuals must be computed and kept, false if residuals
      * only need to be computed but not kept.
      */
     public boolean isComputeAndKeepResidualsEnabled() {
         return computeAndKeepResiduals;
     }
 
     /**
      * Specifies whether residuals must be computed and kept.
      *
      * @param computeAndKeepResiduals true if residuals must be computed and
      *                                kept, false if residuals only need to be computed but not kept.
      * @throws LockedException if estimator is locked.
      */
     public void setComputeAndKeepResidualsEnabled(final boolean computeAndKeepResiduals) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.computeAndKeepResiduals = computeAndKeepResiduals;
     }
 
     /**
      * Estimates a metric 2D transformation using a robust estimator and
      * the best set of matched 2D point correspondences found using the robust
      * estimator.
      *
      * @return a metric 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 MetricTransformation2D estimate() throws LockedException, NotReadyException, RobustEstimatorException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         final var innerEstimator = new PROSACRobustEstimator<>(
                 new PROSACRobustEstimatorListener<MetricTransformation2D>() {
 
                     // point to be reused when computing residuals
                     private final Point2D testPoint = Point2D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
 
                     private final MetricTransformation2DEstimator nonRobustEstimator =
                             new MetricTransformation2DEstimator(isWeakMinimumSizeAllowed());
 
                     private final List<Point2D> subsetInputPoints = new ArrayList<>();
                     private final List<Point2D> subsetOutputPoints = new ArrayList<>();
 
                     @Override
                     public double getThreshold() {
                         return threshold;
                     }
 
                     @Override
                     public int getTotalSamples() {
                         return inputPoints.size();
                     }
 
                     @Override
                     public int getSubsetSize() {
                         return nonRobustEstimator.getMinimumPoints();
                     }
 
                     @Override
                     public void estimatePreliminarSolutions(
                             final int[] samplesIndices, final List<MetricTransformation2D> solutions) {
                         subsetInputPoints.clear();
                         subsetOutputPoints.clear();
                         for (final var samplesIndex : samplesIndices) {
                             subsetInputPoints.add(inputPoints.get(samplesIndex));
                             subsetOutputPoints.add(outputPoints.get(samplesIndex));
                         }
 
                         try {
                             nonRobustEstimator.setPoints(subsetInputPoints, subsetOutputPoints);
                             solutions.add(nonRobustEstimator.estimate());
                         } catch (final Exception e) {
                             // if points are coincident, no solution is added
                         }
                     }
 
                     @Override
                     public double computeResidual(final MetricTransformation2D 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 PROSACMetricTransformation2DRobustEstimator.this.isReady();
                     }
 
                     @Override
                     public void onEstimateStart(final RobustEstimator<MetricTransformation2D> estimator) {
                         if (listener != null) {
                             listener.onEstimateStart(PROSACMetricTransformation2DRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateEnd(final RobustEstimator<MetricTransformation2D> estimator) {
                         if (listener != null) {
                             listener.onEstimateEnd(PROSACMetricTransformation2DRobustEstimator.this);
                         }
                     }
 
                     @Override
                     public void onEstimateNextIteration(
                             final RobustEstimator<MetricTransformation2D> estimator, final int iteration) {
                         if (listener != null) {
                             listener.onEstimateNextIteration(
                                     PROSACMetricTransformation2DRobustEstimator.this, iteration);
                         }
                     }
 
                     @Override
                     public void onEstimateProgressChange(
                             final RobustEstimator<MetricTransformation2D> estimator, final float progress) {
                         if (listener != null) {
                             listener.onEstimateProgressChange(
                                     PROSACMetricTransformation2DRobustEstimator.this, progress);
                         }
                     }
 
                     @Override
                     public double[] getQualityScores() {
                         return qualityScores;
                     }
                 });
 
         try {
             locked = true;
             inliersData = null;
             innerEstimator.setComputeAndKeepInliersEnabled(computeAndKeepInliers || refineResult);
             innerEstimator.setComputeAndKeepResidualsEnabled(computeAndKeepResiduals || refineResult);
             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.PROSAC;
     }
 
     /**
      * 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() {
         return threshold;
     }
 
     /**
      * Sets quality scores corresponding to each pair of matched points.
      * 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 < getMinimumPoints()) {
             throw new IllegalArgumentException();
         }
 
         this.qualityScores = qualityScores;
     }
 }