/*
    * 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.AlgebraException;
  import com.irurueta.algebra.Matrix;
  import com.irurueta.geometry.Line2D;
  import com.irurueta.geometry.ProjectiveTransformation2D;
  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 2D projective transformation refiner using line correspondences.
   * This class takes into account an initial estimation, inlier line matches
   * and their residuals to find a solution that minimizes error of inliers in
   * LMSE terms.
   * Typically, a refiner is used by a robust estimator, however it can also be
   * useful in some other situations.
   */
  @SuppressWarnings("DuplicatedCode")
  public class LineCorrespondenceProjectiveTransformation2DRefiner extends
          ProjectiveTransformation2DRefiner<Line2D, Line2D> {
  
      /**
       * Line to be reused when computing residuals.
       */
      private final Line2D residualTestLine = new Line2D();
  
      /**
       * Constructor.
       */
      public LineCorrespondenceProjectiveTransformation2DRefiner() {
      }
  
      /**
       * 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 LineCorrespondenceProjectiveTransformation2DRefiner(
              final ProjectiveTransformation2D initialEstimation,
              final boolean keepCovariance, final BitSet inliers, final double[] residuals,
              final int numInliers, final List<Line2D> samples1, final List<Line2D> 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 LineCorrespondenceProjectiveTransformation2DRefiner(
              final ProjectiveTransformation2D initialEstimation,
              final boolean keepCovariance, final InliersData inliersData,
              final List<Line2D> samples1, final List<Line2D> samples2,
              final double refinementStandardDeviation) {
          super(initialEstimation, keepCovariance, inliersData, samples1, samples2, refinementStandardDeviation);
      }
  
     /**
      * 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 (error 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 ProjectiveTransformation2D result) throws NotReadyException, LockedException,
             RefinerException {
         if (isLocked()) {
             throw new LockedException();
         }
         if (!isReady()) {
             throw new NotReadyException();
         }
 
         locked = true;
 
         if (listener != null) {
             listener.onRefineStart(this, initialEstimation);
         }
 
         initialEstimation.normalize();
 
         final var initialTotalResidual = totalResidual(initialEstimation);
 
         try {
             final var initParams = new double[
                     ProjectiveTransformation2D.HOM_COORDS * ProjectiveTransformation2D.HOM_COORDS];
             // copy values
             System.arraycopy(initialEstimation.getT().getBuffer(), 0, initParams, 0, initParams.length);
 
             // output values to be fitted/optimized will contain residuals
             final var y = new double[numInliers];
             // input values will contain 2 sets of 2D lines to compute residuals
             final var nDims = 2 * Line2D.LINE_NUMBER_PARAMS;
             final var x = new Matrix(numInliers, nDims);
             final var nSamples = inliers.length();
             var pos = 0;
             for (var i = 0; i < nSamples; i++) {
                 if (inliers.get(i)) {
                     // sample is inlier
                     final var inputLine = samples1.get(i);
                     final var outputLine = samples2.get(i);
                     inputLine.normalize();
                     outputLine.normalize();
                     x.setElementAt(pos, 0, inputLine.getA());
                     x.setElementAt(pos, 1, inputLine.getB());
                     x.setElementAt(pos, 2, inputLine.getC());
                     x.setElementAt(pos, 3, outputLine.getA());
                     x.setElementAt(pos, 4, outputLine.getB());
                     x.setElementAt(pos, 5, outputLine.getC());
 
                     y[pos] = residuals[i];
                     pos++;
                 }
             }
 
             final var evaluator = new LevenbergMarquardtMultiDimensionFunctionEvaluator() {
 
                 private final Line2D inputLine = new Line2D();
 
                 private final Line2D outputLine = new Line2D();
 
                 private final ProjectiveTransformation2D transformation = new ProjectiveTransformation2D();
 
                 private final GradientEstimator gradientEstimator = new GradientEstimator(params -> {
                     // copy values
                     System.arraycopy(params, 0, transformation.getT().getBuffer(), 0, params.length);
                     return residual(transformation, inputLine, outputLine);
                 });
 
                 @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 {
                     inputLine.setParameters(point[0], point[1], point[2]);
                     outputLine.setParameters(point[3], point[4], point[5]);
 
                     // copy values
                     System.arraycopy(params, 0, transformation.getT().getBuffer(), 0, params.length);
 
                     final var y = residual(transformation, inputLine, outputLine);
                     gradientEstimator.gradient(params, derivatives);
 
                     return y;
 
                 }
             };
 
             final var fitter = new LevenbergMarquardtMultiDimensionFitter(evaluator, x, y,
                     getRefinementStandardDeviation());
 
             fitter.fit();
 
             // obtain estimated params
             final var params = fitter.getA();
 
             // update transformation
 
             // copy values
             System.arraycopy(params, 0, result.getT().getBuffer(), 0, params.length);
 
             if (keepCovariance) {
                 // keep covariance
                 covariance = fitter.getCovar();
             }
 
             final var finalTotalResidual = totalResidual(result);
             final var errorDecreased = finalTotalResidual < initialTotalResidual;
 
             if (listener != null) {
                 listener.onRefineEnd(this, initialEstimation, result, errorDecreased);
             }
 
             return errorDecreased;
 
         } catch (final Exception e) {
             throw new RefinerException(e);
         } finally {
             locked = false;
         }
     }
 
     /**
      * Computes the residual between the affine transformation and a pair of
      * matched lines.
      *
      * @param transformation a transformation.
      * @param inputLine      input 2D line.
      * @param outputLine     output 2D line.
      * @return residual.
      */
     private double residual(final ProjectiveTransformation2D transformation, final Line2D inputLine,
                             final Line2D outputLine) {
         try {
             inputLine.normalize();
             outputLine.normalize();
 
             transformation.transform(inputLine, residualTestLine);
             return 1.0 - Math.abs(outputLine.dotProduct(residualTestLine));
         } catch (final AlgebraException e) {
             return 1.0;
         }
     }
 
     /**
      * Computes total residual among all provided inlier samples.
      *
      * @param transformation a transformation.
      * @return total residual.
      */
     private double totalResidual(final ProjectiveTransformation2D transformation) {
         var result = 0.0;
 
         final var nSamples = inliers.length();
         for (var i = 0; i < nSamples; i++) {
             if (inliers.get(i)) {
                 // sample is inlier
                 final var inputLine = samples1.get(i);
                 final var outputLine = samples2.get(i);
                 inputLine.normalize();
                 outputLine.normalize();
                 result += residual(transformation, inputLine, outputLine);
             }
         }
 
         return result;
     }
 }