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#ifndef PCL_SURFACE_IMPL_BILATERAL_UPSAMPLING_H_
#define PCL_SURFACE_IMPL_BILATERAL_UPSAMPLING_H_

#include <pcl/surface/bilateral_upsampling.h>
#include <algorithm>
#include <pcl/console/print.h>

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::BilateralUpsampling<PointInT, PointOutT>::process (pcl::PointCloud<PointOutT> &output)
{
  // Copy the header
  output.header = input_->header;

  if (!initCompute ())
  {
    output.width = output.height = 0;
    output.points.clear ();
    return;
  }

  if (input_->isOrganized () == false)
  {
    PCL_ERROR ("Input cloud is not organized.\n");
    return;
  }

  // Invert projection matrix
  unprojection_matrix_ = projection_matrix_.inverse ();

  for (int i = 0; i < 3; ++i)
  {
    for (int j = 0; j < 3; ++j)
      printf ("%f ", unprojection_matrix_(i, j));

    printf ("\n");
  }


  // Perform the actual surface reconstruction
  performProcessing (output);

  deinitCompute ();
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> void
pcl::BilateralUpsampling<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
    output.resize (input_->size ());
    float nan = std::numeric_limits<float>::quiet_NaN ();

    Eigen::MatrixXf val_exp_depth_matrix;
    Eigen::VectorXf val_exp_rgb_vector;
    computeDistances (val_exp_depth_matrix, val_exp_rgb_vector);

    for (int x = 0; x < static_cast<int> (input_->width); ++x)
      for (int y = 0; y < static_cast<int> (input_->height); ++y)
      {
        int start_window_x = std::max (x - window_size_, 0),
            start_window_y = std::max (y - window_size_, 0),
            end_window_x = std::min (x + window_size_, static_cast<int> (input_->width)),
            end_window_y = std::min (y + window_size_, static_cast<int> (input_->height));

        float sum = 0.0f,
            norm_sum = 0.0f;

        for (int x_w = start_window_x; x_w < end_window_x; ++ x_w)
          for (int y_w = start_window_y; y_w < end_window_y; ++ y_w)
          {
            float val_exp_depth = val_exp_depth_matrix (static_cast<Eigen::MatrixXf::Index> (x - x_w + window_size_),
                                                        static_cast<Eigen::MatrixXf::Index> (y - y_w + window_size_));

            Eigen::VectorXf::Index d_color = static_cast<Eigen::VectorXf::Index> (
                std::abs (input_->points[y_w * input_->width + x_w].r - input_->points[y * input_->width + x].r) +
                std::abs (input_->points[y_w * input_->width + x_w].g - input_->points[y * input_->width + x].g) +
                std::abs (input_->points[y_w * input_->width + x_w].b - input_->points[y * input_->width + x].b));

            float val_exp_rgb = val_exp_rgb_vector (d_color);

            if (std::isfinite (input_->points[y_w*input_->width + x_w].z))
            {
              sum += val_exp_depth * val_exp_rgb * input_->points[y_w*input_->width + x_w].z;
              norm_sum += val_exp_depth * val_exp_rgb;
            }
          }

        output.points[y*input_->width + x].r = input_->points[y*input_->width + x].r;
        output.points[y*input_->width + x].g = input_->points[y*input_->width + x].g;
        output.points[y*input_->width + x].b = input_->points[y*input_->width + x].b;

        if (norm_sum != 0.0f)
        {
          float depth = sum / norm_sum;
          Eigen::Vector3f pc (static_cast<float> (x) * depth, static_cast<float> (y) * depth, depth);
          Eigen::Vector3f pw (unprojection_matrix_ * pc);
          output.points[y*input_->width + x].x = pw[0];
          output.points[y*input_->width + x].y = pw[1];
          output.points[y*input_->width + x].z = pw[2];
        }
        else
        {
          output.points[y*input_->width + x].x = nan;
          output.points[y*input_->width + x].y = nan;
          output.points[y*input_->width + x].z = nan;
        }
      }

    output.header = input_->header;
    output.width = input_->width;
    output.height = input_->height;
}


template <typename PointInT, typename PointOutT> void
pcl::BilateralUpsampling<PointInT, PointOutT>::computeDistances (Eigen::MatrixXf &val_exp_depth, Eigen::VectorXf &val_exp_rgb)
{
  val_exp_depth.resize (2*window_size_+1,2*window_size_+1);
  val_exp_rgb.resize (3*255+1);

  int j = 0;
  for (int dx = -window_size_; dx < window_size_+1; ++dx)
  {
    int i = 0;
    for (int dy = -window_size_; dy < window_size_+1; ++dy)
    {
      float val_exp = std::exp (- (dx*dx + dy*dy) / (2.0f * static_cast<float> (sigma_depth_ * sigma_depth_)));
      val_exp_depth(i,j) = val_exp;
      i++;
    }
    j++;
  }

  for (int d_color = 0; d_color < 3*255+1; d_color++)
  {
    float val_exp = std::exp (- d_color * d_color / (2.0f * sigma_color_ * sigma_color_));
    val_exp_rgb(d_color) = val_exp;
  }
}


#define PCL_INSTANTIATE_BilateralUpsampling(T,OutT) template class PCL_EXPORTS pcl::BilateralUpsampling<T,OutT>;


#endif /* PCL_SURFACE_IMPL_BILATERAL_UPSAMPLING_H_ */
