/*
 * Software License Agreement (BSD License)
 *
 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2010-2011, Willow Garage, Inc.
 *  Copyright (c) 2012-, Open Perception, Inc.
 *
 *  All rights reserved.
 *
 *  Redistribution and use in source and binary forms, with or without
 *  modification, are permitted provided that the following conditions
 *  are met:
 *
 *   * Redistributions of source code must retain the above copyright
 *     notice, this list of conditions and the following disclaimer.
 *   * Redistributions in binary form must reproduce the above
 *     copyright notice, this list of conditions and the following
 *     disclaimer in the documentation and/or other materials provided
 *     with the distribution.
 *   * Neither the name of the copyright holder(s) nor the names of its
 *     contributors may be used to endorse or promote products derived
 *     from this software without specific prior written permission.
 *
 *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
 *  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
 *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
 *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
 *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
 *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
 *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
 *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
 *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
 *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 *  POSSIBILITY OF SUCH DAMAGE.
 *
 * $Id$
 *
 */

#ifndef PCL_FEATURES_IMPL_FPFH_OMP_H_
#define PCL_FEATURES_IMPL_FPFH_OMP_H_

#include <pcl/features/fpfh_omp.h>

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::FPFHEstimationOMP<PointInT, PointNT, PointOutT>::setNumberOfThreads (unsigned int nr_threads)
{
  if (nr_threads == 0)
#ifdef _OPENMP
    threads_ = omp_get_num_procs();
#else
    threads_ = 1;
#endif
  else
    threads_ = nr_threads;
}

//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::FPFHEstimationOMP<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
  std::vector<int> spfh_indices_vec;
  std::vector<int> spfh_hist_lookup (surface_->points.size ());

  // Build a list of (unique) indices for which we will need to compute SPFH signatures
  // (We need an SPFH signature for every point that is a neighbor of any point in input_[indices_])
  if (surface_ != input_ ||
      indices_->size () != surface_->points.size ())
  { 
    std::vector<int> nn_indices (k_); // \note These resizes are irrelevant for a radiusSearch ().
    std::vector<float> nn_dists (k_); 

    std::set<int> spfh_indices_set;
    for (size_t idx = 0; idx < indices_->size (); ++idx)
    {
      int p_idx = (*indices_)[idx];
      if (!isFinite ((*input_)[p_idx]) ||
          this->searchForNeighbors (p_idx, search_parameter_, nn_indices, nn_dists) == 0)
        continue;
      
      spfh_indices_set.insert (nn_indices.begin (), nn_indices.end ());
    }
    spfh_indices_vec.resize (spfh_indices_set.size ());
    std::copy (spfh_indices_set.begin (), spfh_indices_set.end (), spfh_indices_vec.begin ());
  }
  else
  {
    // Special case: When a feature must be computed at every point, there is no need for a neighborhood search
    spfh_indices_vec.resize (indices_->size ());
    for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx)
      spfh_indices_vec[idx] = idx;
  }

  // Initialize the arrays that will store the SPFH signatures
  size_t data_size = spfh_indices_vec.size ();
  hist_f1_.setZero (data_size, nr_bins_f1_);
  hist_f2_.setZero (data_size, nr_bins_f2_);
  hist_f3_.setZero (data_size, nr_bins_f3_);

  std::vector<int> nn_indices (k_); // \note These resizes are irrelevant for a radiusSearch ().
  std::vector<float> nn_dists (k_); 

  // Compute SPFH signatures for every point that needs them

#ifdef _OPENMP
#pragma omp parallel for shared (spfh_hist_lookup) private (nn_indices, nn_dists) num_threads(threads_)
#endif
  for (int i = 0; i < static_cast<int> (spfh_indices_vec.size ()); ++i)
  {
    // Get the next point index
    int p_idx = spfh_indices_vec[i];

    // Find the neighborhood around p_idx
    if (!isFinite ((*input_)[p_idx]) ||
        this->searchForNeighbors (*surface_, p_idx, search_parameter_, nn_indices, nn_dists) == 0)
      continue;

    // Estimate the SPFH signature around p_idx
    this->computePointSPFHSignature (*surface_, *normals_, p_idx, i, nn_indices, hist_f1_, hist_f2_, hist_f3_);

    // Populate a lookup table for converting a point index to its corresponding row in the spfh_hist_* matrices
    spfh_hist_lookup[p_idx] = i;
  }

  // Initialize the array that will store the FPFH signature
  int nr_bins = nr_bins_f1_ + nr_bins_f2_ + nr_bins_f3_;

  nn_indices.clear();
  nn_dists.clear();

  // Iterate over the entire index vector
#ifdef _OPENMP
#pragma omp parallel for shared (output) private (nn_indices, nn_dists) num_threads(threads_)
#endif
  for (int idx = 0; idx < static_cast<int> (indices_->size ()); ++idx)
  {
    // Find the indices of point idx's neighbors...
    if (!isFinite ((*input_)[(*indices_)[idx]]) ||
        this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
    {
      for (int d = 0; d < nr_bins; ++d)
        output.points[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
  
      output.is_dense = false;
      continue;
    }


    // ... and remap the nn_indices values so that they represent row indices in the spfh_hist_* matrices 
    // instead of indices into surface_->points
    for (size_t i = 0; i < nn_indices.size (); ++i)
      nn_indices[i] = spfh_hist_lookup[nn_indices[i]];

    // Compute the FPFH signature (i.e. compute a weighted combination of local SPFH signatures) ...
    Eigen::VectorXf fpfh_histogram = Eigen::VectorXf::Zero (nr_bins);
    weightPointSPFHSignature (hist_f1_, hist_f2_, hist_f3_, nn_indices, nn_dists, fpfh_histogram);

    // ...and copy it into the output cloud
    for (int d = 0; d < nr_bins; ++d)
      output.points[idx].histogram[d] = fpfh_histogram[d];
  }

}

#define PCL_INSTANTIATE_FPFHEstimationOMP(T,NT,OutT) template class PCL_EXPORTS pcl::FPFHEstimationOMP<T,NT,OutT>;

#endif    // PCL_FEATURES_IMPL_FPFH_OMP_H_ 

