/*
    * 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.refiners;
  
  import com.irurueta.algebra.Matrix;
  import com.irurueta.geometry.CoordinatesType;
  import com.irurueta.geometry.PinholeCamera;
  import com.irurueta.geometry.Point2D;
  import com.irurueta.geometry.Point3D;
  import com.irurueta.geometry.estimators.LockedException;
  import com.irurueta.geometry.estimators.NotReadyException;
  import com.irurueta.numerical.EvaluationException;
  import com.irurueta.numerical.GradientEstimator;
  import com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFitter;
  import com.irurueta.numerical.fitting.LevenbergMarquardtMultiDimensionFunctionEvaluator;
  import com.irurueta.numerical.robust.InliersData;
  
  import java.util.BitSet;
  import java.util.List;
  
  /**
   * A pinhole camera refiner using point correspondences and the
   * Levenberg-Marquardt algorithm to try to decrease overall error in LMSE terms
   * among inlier samples by taking the pinhole camera matrix as a whole without
   * decomposition.
   * Typically, this refiner is used by a robust estimator, however it can also be
   * useful in some other situations.
   */
  @SuppressWarnings("DuplicatedCode")
  public class NonDecomposedPointCorrespondencePinholeCameraRefiner extends PointCorrespondencePinholeCameraRefiner {
  
      /**
       * Default value for the weight applied to errors related to suggested
       * camera parameters during computation of projection residuals.
       */
      public static final double DEFAULT_SUGGESTION_ERROR_WEIGHT = 2.0;
  
      /**
       * Dimensions for refinement.
       */
      private static final int REFINE_DIMS = 12;
  
      /**
       * Suggestion error weight. This weight is applied to errors related to
       * suggested camera parameters during computation of projection residuals.
       */
      private double suggestionErrorWeight = DEFAULT_SUGGESTION_ERROR_WEIGHT;
  
      /**
       * Constructor.
       */
      public NonDecomposedPointCorrespondencePinholeCameraRefiner() {
      }
  
      /**
       * Constructor.
       *
       * @param initialEstimation           initial estimation to be set.
       * @param keepCovariance              true if covariance of estimation must be kept after
       *                                    refinement, false otherwise.
       * @param inliers                     set indicating which of the provided matches are inliers.
       * @param residuals                   residuals for matched samples.
       * @param numInliers                  number of inliers on initial estimation.
       * @param samples1                    1st set of paired samples.
       * @param samples2                    2nd set of paired samples.
       * @param refinementStandardDeviation standard deviation used for
       *                                    Levenberg-Marquardt fitting.
       */
      public NonDecomposedPointCorrespondencePinholeCameraRefiner(
              final PinholeCamera initialEstimation, final boolean keepCovariance, final BitSet inliers,
              final double[] residuals, final int numInliers, final List<Point3D> samples1, final List<Point2D> samples2,
              final double refinementStandardDeviation) {
          super(initialEstimation, keepCovariance, inliers, residuals, numInliers, samples1, samples2,
                  refinementStandardDeviation);
      }
  
      /**
       * Constructor.
       *
       * @param initialEstimation           initial estimation to be set.
       * @param keepCovariance              true if covariance of estimation must be kept after
       *                                    refinement, false otherwise.
       * @param inliersData                 inlier data, typically obtained from a robust
       *                                    estimator.
       * @param samples1                    1st set of paired samples.
       * @param samples2                    2nd set of paired samples.
      * @param refinementStandardDeviation standard deviation used for
      *                                    Levenberg-Marquardt fitting.
      */
     public NonDecomposedPointCorrespondencePinholeCameraRefiner(
             final PinholeCamera initialEstimation, final boolean keepCovariance, final InliersData inliersData,
             final List<Point3D> samples1, final List<Point2D> samples2, final double refinementStandardDeviation) {
         super(initialEstimation, keepCovariance, inliersData, samples1, samples2, refinementStandardDeviation);
     }
 
     /**
      * Gets suggestion error weight. This weight is applied to errors related to
      * suggested camera parameters during computation of projection residuals.
      *
      * @return suggestion error weight.
      */
     public double getSuggestionErrorWeight() {
         return suggestionErrorWeight;
     }
 
     /**
      * Sets suggestion error weight. This weight is applied to errors related to
      * suggested camera parameters during computation of projection residuals.
      *
      * @param suggestionErrorWeight suggestion error weight.
      * @throws LockedException if estimator is locked.
      */
     public void setSuggestionErrorWeight(final double suggestionErrorWeight) throws LockedException {
         if (isLocked()) {
             throw new LockedException();
         }
         this.suggestionErrorWeight = suggestionErrorWeight;
     }
 
     /**
      * Refines provided initial estimation.
      * This method always sets a value into provided result instance regardless
      * of the fact that error has actually improved in LMSE terms or not.
      *
      * @param result instance where refined estimation will be stored.
      * @return true if result improves (decreases) in LMSE terms respect to
      * initial estimation, false if no improvement has been achieved.
      * @throws NotReadyException if not enough input data has been provided.
      * @throws LockedException   if estimator is locked because refinement is
      *                           already in progress.
      * @throws RefinerException  if refinement fails for some reason (e.g. unable
      *                           to converge to a result).
      */
     @Override
     public boolean refine(final PinholeCamera result) throws NotReadyException, LockedException, RefinerException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         locked = true;
 
         if (listener != null) {
             listener.onRefineStart(this, initialEstimation);
         }
 
         try {
             initialEstimation.normalize();
 
             // output values to be fitted/optimized will contain residuals
             final var y = new double[numInliers];
             // input values will contain 3D point and 2D point to compute
             // residuals
             final var nDims = Point2D.POINT2D_HOMOGENEOUS_COORDINATES_LENGTH
                     + Point3D.POINT3D_HOMOGENEOUS_COORDINATES_LENGTH;
             final var x = new Matrix(numInliers, nDims);
             final var nSamples = inliers.length();
             var pos = 0;
             final var initParams = new double[REFINE_DIMS];
             cameraToParameters(initialEstimation, initParams);
 
             final var initResidual = residualPowell(initialEstimation, initParams, suggestionErrorWeight);
 
             final var suggestionResidual = hasSuggestions() ? suggestionResidual(initParams, suggestionErrorWeight)
                     : 0.0;
             for (var i = 0; i < nSamples; i++) {
                 if (inliers.get(i)) {
                     // sample is inlier
                     final var point2D = samples2.get(i);
                     final var point3D = samples1.get(i);
                     point2D.normalize();
                     point3D.normalize();
                     x.setElementAt(pos, 0, point2D.getHomX());
                     x.setElementAt(pos, 1, point2D.getHomY());
                     x.setElementAt(pos, 2, point2D.getHomW());
                     x.setElementAt(pos, 3, point3D.getHomX());
                     x.setElementAt(pos, 4, point3D.getHomY());
                     x.setElementAt(pos, 5, point3D.getHomZ());
                     x.setElementAt(pos, 6, point3D.getHomW());
 
                     y[pos] = Math.pow(residuals[i], 2.0) + suggestionResidual;
                     pos++;
                 }
             }
 
             final var evaluator = new LevenbergMarquardtMultiDimensionFunctionEvaluator() {
 
                 private final Point2D point2D = Point2D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
 
                 private final Point3D point3D = Point3D.create(CoordinatesType.HOMOGENEOUS_COORDINATES);
 
                 private final PinholeCamera pinholeCamera = new PinholeCamera();
 
                 private final GradientEstimator gradientEstimator = new GradientEstimator(params -> {
                     parametersToCamera(params, pinholeCamera);
                     return residualLevenbergMarquardt(pinholeCamera, point3D, point2D, params, suggestionErrorWeight);
                 });
 
                 @Override
                 public int getNumberOfDimensions() {
                     return nDims;
                 }
 
                 @Override
                 public double[] createInitialParametersArray() {
                     return initParams;
                 }
 
                 @Override
                 public double evaluate(
                         final int i, final double[] point, final double[] params, final double[] derivatives)
                         throws EvaluationException {
                     point2D.setHomogeneousCoordinates(point[0], point[1], point[2]);
                     point3D.setHomogeneousCoordinates(point[3], point[4], point[5], point[6]);
 
                     point2D.normalize();
                     point3D.normalize();
 
                     parametersToCamera(params, pinholeCamera);
                     final var y = residualLevenbergMarquardt(pinholeCamera, point3D, point2D, params,
                             suggestionErrorWeight);
                     gradientEstimator.gradient(params, derivatives);
 
                     return y;
                 }
             };
 
             final var fitter = new LevenbergMarquardtMultiDimensionFitter(evaluator, x, y, refinementStandardDeviation);
 
             fitter.fit();
 
             final var finalParams = fitter.getA();
 
             parametersToCamera(finalParams, result);
 
             if (keepCovariance) {
                 covariance = fitter.getCovar();
             }
 
             final var finalResidual = residualPowell(result, finalParams, suggestionErrorWeight);
             final var errorDecreased = finalResidual < initResidual;
 
             if (listener != null) {
                 listener.onRefineEnd(this, initialEstimation, result,
                         errorDecreased);
             }
 
             return errorDecreased;
 
         } catch (final Exception e) {
             throw new RefinerException(e);
         } finally {
             locked = false;
         }
     }
 }