/*
    * 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.CoordinatesType;
  import com.irurueta.geometry.PinholeCamera;
  import com.irurueta.geometry.Point2D;
  import com.irurueta.geometry.Point3D;
  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.ArrayList;
  import java.util.List;
  
  /**
   * Finds the best pinhole camera for provided collections of matched 2D/3D
   * points using PROMedS algorithm.
   */
  @SuppressWarnings("DuplicatedCode")
  public class PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator extends
          DLTPointCorrespondencePinholeCameraRobustEstimator {
  
      /**
       * 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;
  
      /**
       * Quality scores corresponding to each pair of matched points.
       * The larger the score value the better the quality of the matching.
       */
      private double[] qualityScores;
  
      /**
       * Constructor.
       */
      public PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator() {
          super();
          stopThreshold = DEFAULT_STOP_THRESHOLD;
      }
  
      /**
       * Constructor with lists of points to be used to estimate a pinhole camera.
       * 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 MIN_NUMBER_OF_POINT_CORRESPONDENCES.
       *
      * @param points3D list of 3D points used to estimate a pinhole camera.
      * @param points2D list of corresponding projected 2D points used to
      *                 estimate a pinhole camera.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than required minimum size
      *                                  (6 correspondences).
      */
     public PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(
             final List<Point3D> points3D, final List<Point2D> points2D) {
         super(points3D, points2D);
         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 PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(
             final PinholeCameraRobustEstimatorListener listener) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
     }
 
     /**
      * Constructor with listener and lists of points to be used ot estimate a
      * pinhole camera.
      * 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 MIN_NUMBER_OF_POINT_CORRESPONDENCES.
      *
      * @param listener listener to be notified of events such as when estimation
      *                 starts, ends or its progress significantly changes.
      * @param points3D list of 3D points used to estimate a pinhole camera.
      * @param points2D list of corresponding projected 2D points used to
      *                 estimate a pinhole camera.
      * @throws IllegalArgumentException if provided lists of points don't have
      *                                  the same size or their size is smaller than required minimum size
      *                                  (6 correspondences).
      */
     public PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(
             final PinholeCameraRobustEstimatorListener listener,
             final List<Point3D> points3D, final List<Point2D> points2D) {
         super(listener, points3D, points2D);
         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 PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(final double[] qualityScores) {
         super();
         stopThreshold = DEFAULT_STOP_THRESHOLD;
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Constructor with lists of points to be used to estimate a pinhole camera.
      * 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 MIN_NUMBER_OF_POINT_CORRESPONDENCES.
      *
      * @param points3D      list of 3D points used to estimate a pinhole camera.
      * @param points2D      list of corresponding projected 2D points used to
      *                      estimate a pinhole camera.
      * @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
      *                                  6 correspondences.
      */
     public PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(
             final List<Point3D> points3D, final List<Point2D> points2D, final double[] qualityScores) {
         super(points3D, points2D);
 
         if (qualityScores.length != points3D.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
      *                      points.
      * @throws IllegalArgumentException if provided quality scores length is
      *                                  smaller than MINIMUM_SIZE (i.e. 3 samples).
      */
     public PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(
             final PinholeCameraRobustEstimatorListener listener, final double[] qualityScores) {
         super(listener);
         stopThreshold = DEFAULT_STOP_THRESHOLD;
         internalSetQualityScores(qualityScores);
     }
 
     /**
      * Constructor with listener and lists of points to be used ot estimate a
      * pinhole camera.
      * 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 MIN_NUMBER_OF_POINT_CORRESPONDENCES.
      *
      * @param listener      listener to be notified of events such as when estimation
      *                      starts, ends or its progress significantly changes.
      * @param points3D      list of 3D points used to estimate a pinhole camera.
      * @param points2D      list of corresponding projected 2D points used to
      *                      estimate a pinhole camera.
      * @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
      *                                  MIN_NUMBER_OF_POINT_CORRESPONDENCES.
      */
     public PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator(
             final PinholeCameraRobustEstimatorListener listener,
             final List<Point3D> points3D, final List<Point2D> points2D, final double[] qualityScores) {
         super(listener, points3D, points2D);
 
         if (qualityScores.length != points3D.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.
      * As in LMedS, the stop threshold can be used to prevent the PROMedS
      * 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.
      * As in LMedS, the stop threshold can be used to prevent the PROMedS
      * 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 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 affine 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 == points3D.size();
     }
 
     /**
      * 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).
      */
     @Override
     public PinholeCamera estimate() throws LockedException, NotReadyException, RobustEstimatorException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         // pinhole camera estimator using DLT (Direct Linear Transform) algorithm
         final var nonRobustEstimator = new DLTPointCorrespondencePinholeCameraEstimator();
 
         nonRobustEstimator.setLMSESolutionAllowed(false);
         nonRobustEstimator.setPointCorrespondencesNormalized(normalizeSubsetPointCorrespondences);
 
         // suggestions
         nonRobustEstimator.setSuggestSkewnessValueEnabled(isSuggestSkewnessValueEnabled());
         nonRobustEstimator.setSuggestedSkewnessValue(getSuggestedSkewnessValue());
         nonRobustEstimator.setSuggestHorizontalFocalLengthEnabled(isSuggestHorizontalFocalLengthEnabled());
         nonRobustEstimator.setSuggestedHorizontalFocalLengthValue(getSuggestedHorizontalFocalLengthValue());
         nonRobustEstimator.setSuggestVerticalFocalLengthEnabled(isSuggestVerticalFocalLengthEnabled());
         nonRobustEstimator.setSuggestedVerticalFocalLengthValue(getSuggestedVerticalFocalLengthValue());
         nonRobustEstimator.setSuggestAspectRatioEnabled(isSuggestAspectRatioEnabled());
         nonRobustEstimator.setSuggestedAspectRatioValue(getSuggestedAspectRatioValue());
         nonRobustEstimator.setSuggestPrincipalPointEnabled(isSuggestPrincipalPointEnabled());
         nonRobustEstimator.setSuggestedPrincipalPointValue(getSuggestedPrincipalPointValue());
         nonRobustEstimator.setSuggestRotationEnabled(isSuggestRotationEnabled());
         nonRobustEstimator.setSuggestedRotationValue(getSuggestedRotationValue());
         nonRobustEstimator.setSuggestCenterEnabled(isSuggestCenterEnabled());
         nonRobustEstimator.setSuggestedCenterValue(getSuggestedCenterValue());
 
         final var innerEstimator = new PROMedSRobustEstimator<>(new PROMedSRobustEstimatorListener<PinholeCamera>() {
 
             // point to be reused when computing residuals
             private final Point2D testPoint = Point2D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
 
             // 3D points for a subset of samples
             private final List<Point3D> subset3D = new ArrayList<>();
 
             // 2D points for a subset of samples
             private final List<Point2D> subset2D = new ArrayList<>();
 
             @Override
             public double getThreshold() {
                 return stopThreshold;
             }
 
             @Override
             public int getTotalSamples() {
                 return points3D.size();
             }
 
             @Override
             public int getSubsetSize() {
                 return PointCorrespondencePinholeCameraRobustEstimator.MIN_NUMBER_OF_POINT_CORRESPONDENCES;
             }
 
             @Override
             public void estimatePreliminarSolutions(final int[] samplesIndices, final List<PinholeCamera> solutions) {
                 subset3D.clear();
                 subset3D.add(points3D.get(samplesIndices[0]));
                 subset3D.add(points3D.get(samplesIndices[1]));
                 subset3D.add(points3D.get(samplesIndices[2]));
                 subset3D.add(points3D.get(samplesIndices[3]));
                 subset3D.add(points3D.get(samplesIndices[4]));
                 subset3D.add(points3D.get(samplesIndices[5]));
 
                 subset2D.clear();
                 subset2D.add(points2D.get(samplesIndices[0]));
                 subset2D.add(points2D.get(samplesIndices[1]));
                 subset2D.add(points2D.get(samplesIndices[2]));
                 subset2D.add(points2D.get(samplesIndices[3]));
                 subset2D.add(points2D.get(samplesIndices[4]));
                 subset2D.add(points2D.get(samplesIndices[5]));
 
                 try {
                     nonRobustEstimator.setLists(subset3D, subset2D);
 
                     final var cam = nonRobustEstimator.estimate();
                     solutions.add(cam);
                 } catch (final Exception e) {
                     // if points configuration is degenerate, no solution is added
                 }
             }
 
             @Override
             public double computeResidual(final PinholeCamera currentEstimation, final int i) {
                 // pick i-th points
                 final var point3D = points3D.get(i);
                 final var point2D = points2D.get(i);
 
                 // project point3D into test point
                 currentEstimation.project(point3D, testPoint);
 
                 // compare test point and 2D point
                 return testPoint.distanceTo(point2D);
             }
 
             @Override
             public boolean isReady() {
                 return PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator.this.isReady();
             }
 
             @Override
             public void onEstimateStart(final RobustEstimator<PinholeCamera> estimator) {
                 if (listener != null) {
                     listener.onEstimateStart(PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator.this);
                 }
             }
 
             @Override
             public void onEstimateEnd(final RobustEstimator<PinholeCamera> estimator) {
                 if (listener != null) {
                     listener.onEstimateEnd(PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator.this);
                 }
             }
 
             @Override
             public void onEstimateNextIteration(
                     final RobustEstimator<PinholeCamera> estimator, final int iteration) {
                 if (listener != null) {
                     listener.onEstimateNextIteration(
                             PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator.this, iteration);
                 }
             }
 
             @Override
             public void onEstimateProgressChange(final RobustEstimator<PinholeCamera> estimator, final float progress) {
                 if (listener != null) {
                     listener.onEstimateProgressChange(
                             PROMedSDLTPointCorrespondencePinholeCameraRobustEstimator.this, progress);
                 }
             }
 
             @Override
             public double[] getQualityScores() {
                 return qualityScores;
             }
         });
 
         try {
             locked = true;
             inliersData = null;
             innerEstimator.setConfidence(confidence);
             innerEstimator.setMaxIterations(maxIterations);
             innerEstimator.setProgressDelta(progressDelta);
             final var result = innerEstimator.estimate();
             inliersData = innerEstimator.getInliersData();
             return attemptRefine(result, nonRobustEstimator.getMaxSuggestionWeight());
         } 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 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(double[] qualityScores) {
         if (qualityScores.length < MIN_NUMBER_OF_POINT_CORRESPONDENCES) {
             throw new IllegalArgumentException();
         }
 
         this.qualityScores = qualityScores;
     }
 }