// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
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// Author: sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/local_parameterization.h"

#include "ceres/householder_vector.h"
#include "ceres/internal/eigen.h"
#include "ceres/rotation.h"
#include "glog/logging.h"

namespace ceres {

using std::vector;

LocalParameterization::~LocalParameterization() {
}

bool LocalParameterization::MultiplyByJacobian(const double* x,
                                               const int num_rows,
                                               const double* global_matrix,
                                               double* local_matrix) const {
  Matrix jacobian(GlobalSize(), LocalSize());
  if (!ComputeJacobian(x, jacobian.data())) {
    return false;
  }

  MatrixRef(local_matrix, num_rows, LocalSize()) =
      ConstMatrixRef(global_matrix, num_rows, GlobalSize()) * jacobian;
  return true;
}

IdentityParameterization::IdentityParameterization(const int size)
    : size_(size) {
  CHECK_GT(size, 0);
}

bool IdentityParameterization::Plus(const double* x,
                                    const double* delta,
                                    double* x_plus_delta) const {
  VectorRef(x_plus_delta, size_) =
      ConstVectorRef(x, size_) + ConstVectorRef(delta, size_);
  return true;
}

bool IdentityParameterization::ComputeJacobian(const double* x,
                                               double* jacobian) const {
  MatrixRef(jacobian, size_, size_) = Matrix::Identity(size_, size_);
  return true;
}

bool IdentityParameterization::MultiplyByJacobian(const double* x,
                                                  const int num_cols,
                                                  const double* global_matrix,
                                                  double* local_matrix) const {
  std::copy(global_matrix,
            global_matrix + num_cols * GlobalSize(),
            local_matrix);
  return true;
}

SubsetParameterization::SubsetParameterization(
    int size,
    const vector<int>& constant_parameters)
    : local_size_(size - constant_parameters.size()),
      constancy_mask_(size, 0) {
  CHECK_GT(constant_parameters.size(), 0)
      << "The set of constant parameters should contain at least "
      << "one element. If you do not wish to hold any parameters "
      << "constant, then do not use a SubsetParameterization";

  vector<int> constant = constant_parameters;
  sort(constant.begin(), constant.end());
  CHECK(unique(constant.begin(), constant.end()) == constant.end())
      << "The set of constant parameters cannot contain duplicates";
  CHECK_LT(constant_parameters.size(), size)
      << "Number of parameters held constant should be less "
      << "than the size of the parameter block. If you wish "
      << "to hold the entire parameter block constant, then a "
      << "efficient way is to directly mark it as constant "
      << "instead of using a LocalParameterization to do so.";
  CHECK_GE(*min_element(constant.begin(), constant.end()), 0);
  CHECK_LT(*max_element(constant.begin(), constant.end()), size);

  for (int i = 0; i < constant_parameters.size(); ++i) {
    constancy_mask_[constant_parameters[i]] = 1;
  }
}

bool SubsetParameterization::Plus(const double* x,
                                  const double* delta,
                                  double* x_plus_delta) const {
  for (int i = 0, j = 0; i < constancy_mask_.size(); ++i) {
    if (constancy_mask_[i]) {
      x_plus_delta[i] = x[i];
    } else {
      x_plus_delta[i] = x[i] + delta[j++];
    }
  }
  return true;
}

bool SubsetParameterization::ComputeJacobian(const double* x,
                                             double* jacobian) const {
  MatrixRef m(jacobian, constancy_mask_.size(), local_size_);
  m.setZero();
  for (int i = 0, j = 0; i < constancy_mask_.size(); ++i) {
    if (!constancy_mask_[i]) {
      m(i, j++) = 1.0;
    }
  }
  return true;
}

bool SubsetParameterization::MultiplyByJacobian(const double* x,
                                               const int num_rows,
                                               const double* global_matrix,
                                               double* local_matrix) const {
  for (int row = 0; row < num_rows; ++row) {
    for (int col = 0, j = 0; col < constancy_mask_.size(); ++col) {
      if (!constancy_mask_[col]) {
        local_matrix[row * LocalSize() + j++] =
            global_matrix[row * GlobalSize() + col];
      }
    }
  }
  return true;
}

bool QuaternionParameterization::Plus(const double* x,
                                      const double* delta,
                                      double* x_plus_delta) const {
  const double norm_delta =
      sqrt(delta[0] * delta[0] + delta[1] * delta[1] + delta[2] * delta[2]);
  if (norm_delta > 0.0) {
    const double sin_delta_by_delta = (sin(norm_delta) / norm_delta);
    double q_delta[4];
    q_delta[0] = cos(norm_delta);
    q_delta[1] = sin_delta_by_delta * delta[0];
    q_delta[2] = sin_delta_by_delta * delta[1];
    q_delta[3] = sin_delta_by_delta * delta[2];
    QuaternionProduct(q_delta, x, x_plus_delta);
  } else {
    for (int i = 0; i < 4; ++i) {
      x_plus_delta[i] = x[i];
    }
  }
  return true;
}

bool QuaternionParameterization::ComputeJacobian(const double* x,
                                                 double* jacobian) const {
  jacobian[0] = -x[1]; jacobian[1]  = -x[2]; jacobian[2]  = -x[3];  // NOLINT
  jacobian[3] =  x[0]; jacobian[4]  =  x[3]; jacobian[5]  = -x[2];  // NOLINT
  jacobian[6] = -x[3]; jacobian[7]  =  x[0]; jacobian[8]  =  x[1];  // NOLINT
  jacobian[9] =  x[2]; jacobian[10] = -x[1]; jacobian[11] =  x[0];  // NOLINT
  return true;
}

HomogeneousVectorParameterization::HomogeneousVectorParameterization(int size)
    : size_(size) {
  CHECK_GT(size_, 1) << "The size of the homogeneous vector needs to be "
                     << "greater than 1.";
}

bool HomogeneousVectorParameterization::Plus(const double* x_ptr,
                                             const double* delta_ptr,
                                             double* x_plus_delta_ptr) const {
  ConstVectorRef x(x_ptr, size_);
  ConstVectorRef delta(delta_ptr, size_ - 1);
  VectorRef x_plus_delta(x_plus_delta_ptr, size_);

  const double norm_delta = delta.norm();

  if (norm_delta == 0.0) {
    x_plus_delta = x;
    return true;
  }

  // Map the delta from the minimum representation to the over parameterized
  // homogeneous vector. See section A6.9.2 on page 624 of Hartley & Zisserman
  // (2nd Edition) for a detailed description.  Note there is a typo on Page
  // 625, line 4 so check the book errata.
  const double norm_delta_div_2 = 0.5 * norm_delta;
  const double sin_delta_by_delta = sin(norm_delta_div_2) /
      norm_delta_div_2;

  Vector y(size_);
  y.head(size_ - 1) = 0.5 * sin_delta_by_delta * delta;
  y(size_ - 1) = cos(norm_delta_div_2);

  Vector v(size_);
  double beta;
  internal::ComputeHouseholderVector<double>(x, &v, &beta);

  // Apply the delta update to remain on the unit sphere. See section A6.9.3
  // on page 625 of Hartley & Zisserman (2nd Edition) for a detailed
  // description.
  x_plus_delta = x.norm() * (y -  v * (beta * (v.transpose() * y)));

  return true;
}

bool HomogeneousVectorParameterization::ComputeJacobian(
    const double* x_ptr, double* jacobian_ptr) const {
  ConstVectorRef x(x_ptr, size_);
  MatrixRef jacobian(jacobian_ptr, size_, size_ - 1);

  Vector v(size_);
  double beta;
  internal::ComputeHouseholderVector<double>(x, &v, &beta);

  // The Jacobian is equal to J = 0.5 * H.leftCols(size_ - 1) where H is the
  // Householder matrix (H = I - beta * v * v').
  for (int i = 0; i < size_ - 1; ++i) {
    jacobian.col(i) = -0.5 * beta * v(i) * v;
    jacobian.col(i)(i) += 0.5;
  }
  jacobian *= x.norm();

  return true;
}

}  // namespace ceres