/*=========================================================================

  Program:   Visualization Toolkit
  Module:    vtkRansacPlaneModel.cxx
  Author: Pierre Guilbert

  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
  All rights reserved.
  See Copyright.txt or http://www.kitware.com/Copyright.htm for details.

  This software is distributed WITHOUT ANY WARRANTY; without even
  the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
  PURPOSE.  See the above copyright notice for more information.

=========================================================================*/

#include "vtkRansacPlaneModel.h"

#include "vtkConversions.h"

#include <iostream>

#include <vtkPointData.h>
#include <vtkInformationVector.h>
#include <vtkNew.h>
#include <vtkObjectFactory.h>
#include <vtkPoints.h>
#include <vtkUnsignedIntArray.h>
#include <vtkDoubleArray.h>

#include <Eigen/Dense>

//----------------------------------------------------------------------------
struct RansacSampleInfo
{
  RansacSampleInfo(unsigned int nInliers, unsigned int index1,
                   unsigned int index2, unsigned int index3)
  {
    this->NInliers = nInliers;
    this->Index1 = index1;
    this->Index2 = index2;
    this->Index3 = index3;
  }

  unsigned int NInliers;
  unsigned int Index1;
  unsigned int Index2;
  unsigned int Index3;
};

//----------------------------------------------------------------------------
void RefineRansac(std::vector<Eigen::Vector3d >& Points, vtkPolyData* output,
                  RansacSampleInfo sampleInfo, double threshold, double PlaneParam[4])
{
  // Create inliers / outliers array information
  vtkNew<vtkUnsignedIntArray> inliersArray;
  inliersArray->SetName("ransac_plane_inliers");

  // compute plane PlaneParameters
  Eigen::Vector3d pointPlane = Points[sampleInfo.Index1];
  Eigen::Vector3d normalPlane = (Points[sampleInfo.Index3] - pointPlane).cross(Points[sampleInfo.Index2] - pointPlane);
  normalPlane.normalize();

  // compute inliers
  std::vector<Eigen::Vector3d > inliersPoints;
  for (unsigned int k = 0; k < Points.size(); ++k)
  {
    if (std::abs((Points[k] - pointPlane).dot(normalPlane)) < threshold)
    {
      inliersPoints.push_back(Points[k]);
      inliersArray->InsertNextValue(1);
    }
    else
    {
      inliersArray->InsertNextValue(0);
    }
  }

  // Now, compute the best plane using all inliers
  Eigen::MatrixXd centeredSamples(3, inliersPoints.size());
  Eigen::Vector3d center = Eigen::Vector3d::Zero();
  for (unsigned int k = 0; k < inliersPoints.size(); ++k)
  {
    centeredSamples.col(k) = inliersPoints[k];
    center += inliersPoints[k];
  }
  center /= static_cast<double>(inliersPoints.size());
  for (unsigned int k = 0; k < inliersPoints.size(); ++k)
  {
    centeredSamples.col(k) -= center;
  }
  Eigen::Matrix3d varianceCovariance = centeredSamples * centeredSamples.transpose();
  varianceCovariance /= static_cast<double>(inliersPoints.size());

  // since the variance covariance matrix is a real
  // symmetric matrix it can be diagonalized in a orthonormal
  // basis. We will use the AutoAdjoint eigen solver
  Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d > eigenSolver(varianceCovariance);

  // PlaneParameters
  normalPlane = eigenSolver.eigenvectors().col(0);
  pointPlane = center;
  PlaneParam[0] = normalPlane(0);
  PlaneParam[1] = normalPlane(1);
  PlaneParam[2] = normalPlane(2);
  PlaneParam[3] = -normalPlane.dot(pointPlane);

  output->GetPointData()->AddArray(inliersArray.Get());
}


//----------------------------------------------------------------------------
unsigned int ComputeNumberOfInlier(std::vector<Eigen::Vector3d >& Points, Eigen::Vector3d planePoint,
                                   Eigen::Vector3d planeNormal, double threshold)
{
  unsigned int nInliers = 0;
  for (unsigned int k = 0; k < Points.size(); ++k)
  {
    if (std::abs((Points[k] - planePoint).dot(planeNormal)) < threshold)
    {
      nInliers++;
    }
  }

  return nInliers;
}

// Implementation of the New function
vtkStandardNewMacro(vtkRansacPlaneModel)

//-----------------------------------------------------------------------------
int vtkRansacPlaneModel::RequestData(vtkInformation *vtkNotUsed(request),
  vtkInformationVector **inputVector, vtkInformationVector *outputVector)
{
  // Save the previous plane estimate
  double prevPlaneEst[4];
  std::copy(this->PlaneParam, this->PlaneParam + 4, prevPlaneEst);

  // Get the input
  vtkPolyData * input = vtkPolyData::GetData(inputVector[0]->GetInformationObject(0));

  // Get the output
  vtkPolyData *output = vtkPolyData::GetData(outputVector->GetInformationObject(0));
  output->ShallowCopy(input);

  // Convert the point cloud in Eigen data structure point cloud
  std::vector<Eigen::Vector3d> Points = vtkPointsToEigenVector(input->GetPoints());

  // Create a random order of points index
  std::vector<double> randomIndex(input->GetNumberOfPoints(), 0);
  for (unsigned int k = 0; k < randomIndex.size(); ++k)
  {
    randomIndex[k] = k;
  }
  std::random_shuffle(randomIndex.begin(), randomIndex.end());

  // information variable about ransac iterations
  bool shouldStopRansac = false;
  unsigned int iterationMade = 0;
  unsigned int iterationPointer = 0;

  // keep information of iterations
  std::vector<RansacSampleInfo> samplesInfo;
  unsigned int maxInliers = 0;
  unsigned int indexMaxInliers = 0;

  // Affine plane PlaneParameters
  Eigen::Vector3d planeNormal, planePoint;

  // indicate if ransac has "converged"
  bool hasConverged = false;

  // Ransac loop
  while (!shouldStopRansac)
  {
    // if we went throught the all random index we need
    // to random shuffle again
    if ((iterationPointer + 2) >= randomIndex.size())
    {
      std::random_shuffle(randomIndex.begin(), randomIndex.end());
      iterationPointer = 0;
    }

    // Compute current sample plane PlaneParameters
    planePoint = Points[randomIndex[iterationPointer]];
    planeNormal = (Points[randomIndex[iterationPointer + 2]] - planePoint).cross(Points[randomIndex[iterationPointer + 1]] - planePoint);
    planeNormal.normalize();

    // Compute the number of inliers / outliers
    unsigned int nInliers = ComputeNumberOfInlier(Points, planePoint, planeNormal, this->Threshold);

    // keep info
    RansacSampleInfo info(nInliers, randomIndex[iterationPointer], randomIndex[iterationPointer + 1], randomIndex[iterationPointer + 2]);
    samplesInfo.push_back(info);

    if (nInliers > maxInliers)
    {
      maxInliers = nInliers;
      indexMaxInliers = iterationMade;
    }

    // Check that the number of inliers is enought to
    // break the ransac algorithm loop
    if (nInliers > input->GetNumberOfPoints() * this->RatioInliersRequired)
    {
      shouldStopRansac = true;
      hasConverged = true;
    }

    // check if the maximum iteration has been reached
    if (iterationMade > this->MaxRansacIteration)
    {
      shouldStopRansac = true;
    }

    // Updates iterations index
    iterationMade++;
    iterationPointer += 3;
  }

  // Now refine using all inliers
  RefineRansac(Points, output, samplesInfo[indexMaxInliers], this->Threshold, this->PlaneParam);

  // output info
  std::cout << "ransac algorithm has converged: " << hasConverged << std::endl;
  std::cout << "number of iteration made: " << iterationMade << std::endl;
  std::cout << "number of inliers: " << maxInliers << ", " << samplesInfo[indexMaxInliers].NInliers << std::endl;
  std::cout << "plane PlaneParams: [" << this->PlaneParam[0] << "," << this->PlaneParam[1] << "," << this->PlaneParam[2] << "," << this->PlaneParam[3] << "]" << std::endl;

  // flip normal if needed
  if (this->PlaneParam[2] < 0)
  {
    this->PlaneParam[0] *= -1.0;
    this->PlaneParam[1] *= -1.0;
    this->PlaneParam[2] *= -1.0;
    this->PlaneParam[3] *= -1.0;
  }

  double d = this->PlaneParam[3];
  Eigen::Vector3d n(this->PlaneParam[0], this->PlaneParam[1], this->PlaneParam[2]);
  n.normalize();

  // previous normal and bias estimation
  Eigen::Vector3d nPrev(prevPlaneEst[0], prevPlaneEst[1], prevPlaneEst[2]);
  double dPrev = prevPlaneEst[3];

  double newEstimationWeight = 1.0 - this->PreviousEstimationWeight;  // how much do we trust the new plane estimate

  if (this->TemporalAveraging && std::abs(nPrev.norm() - 1) < 1e-3)   // if the previous estimate is valid
  {
    // if the angle between the new normal estimate and the previous is larger than the threshold,
    // the current normal is discarded
    if (std::asin(n.cross(nPrev).norm()) > vtkMath::RadiansFromDegrees(MaxTemporalAngleChange))
    {
      n = nPrev;
      d = dPrev;
    }
    else
    {
      // update the plane estimate with the current one
      n = newEstimationWeight * n + this->PreviousEstimationWeight * nPrev;
      d = newEstimationWeight * d + this->PreviousEstimationWeight * dPrev;
    }
  }
  n.normalize();

  this->PlaneParam[0] = n[0];
  this->PlaneParam[1] = n[1];
  this->PlaneParam[2] = n[2];
  this->PlaneParam[3] = d;

  // transform output if enabled
  if (this->AlignOutput)
  {
    Eigen::Vector3d v = n.cross(Eigen::Vector3d::UnitZ());
    double angle = std::asin(v.norm());
    Eigen::AngleAxisd rot(angle, v.normalized());
    Eigen::Vector3d shift(0.0, 0.0, d);

    // transform points
    for (auto& pt : Points)
    {
      pt =  rot * pt + shift;
    }
    // copy points to output
    output->SetPoints(eigenVectorToVTKPoints(Points));
  }

  // Add model params to field data
  vtkSmartPointer<vtkDoubleArray> paramdata = vtkSmartPointer<vtkDoubleArray>::New();
  paramdata->SetName("PlaneParam");
  paramdata->SetNumberOfComponents(4);
  paramdata->InsertNextTuple(this->PlaneParam);
  output->GetFieldData()->AddArray(paramdata);

  return 1;
}
