shot_omp.hpp

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#ifndef PCL_FEATURES_IMPL_SHOT_OMP_H_
#define PCL_FEATURES_IMPL_SHOT_OMP_H_

#include <pcl/features/shot_omp.h>
#include <pcl/common/time.h>
#include <pcl/features/shot_lrf_omp.h>

template<typename PointInT, typename PointNT, typename PointOutT, typename PointRFT> bool
pcl::SHOTEstimationOMP<PointInT, PointNT, PointOutT, PointRFT>::initCompute ()
{
  if (!FeatureFromNormals<PointInT, PointNT, PointOutT>::initCompute ())
  {
    PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
    return (false);
  }

  // SHOT cannot work with k-search
  if (this->getKSearch () != 0)
  {
    PCL_ERROR(
      "[pcl::%s::initCompute] Error! Search method set to k-neighborhood. Call setKSearch(0) and setRadiusSearch( radius ) to use this class.\n",
      getClassName().c_str ());
    return (false);
  }

  // Default LRF estimation alg: SHOTLocalReferenceFrameEstimationOMP
  typename boost::shared_ptr<SHOTLocalReferenceFrameEstimationOMP<PointInT, PointRFT> > lrf_estimator(new SHOTLocalReferenceFrameEstimationOMP<PointInT, PointRFT>());
  lrf_estimator->setRadiusSearch (search_radius_);
  lrf_estimator->setInputCloud (input_);
  lrf_estimator->setIndices (indices_);
  lrf_estimator->setNumberOfThreads(threads_);

  if (!fake_surface_)
    lrf_estimator->setSearchSurface(surface_);

  if (!FeatureWithLocalReferenceFrames<PointInT, PointRFT>::initLocalReferenceFrames (indices_->size (), lrf_estimator))
  {
    PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
    return (false);
  }

  return (true);
}

template<typename PointInT, typename PointNT, typename PointOutT, typename PointRFT> bool
pcl::SHOTColorEstimationOMP<PointInT, PointNT, PointOutT, PointRFT>::initCompute ()
{
  if (!FeatureFromNormals<PointInT, PointNT, PointOutT>::initCompute ())
  {
    PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
    return (false);
  }

  // SHOT cannot work with k-search
  if (this->getKSearch () != 0)
  {
    PCL_ERROR(
      "[pcl::%s::initCompute] Error! Search method set to k-neighborhood. Call setKSearch(0) and setRadiusSearch( radius ) to use this class.\n",
      getClassName().c_str ());
    return (false);
  }

  // Default LRF estimation alg: SHOTLocalReferenceFrameEstimationOMP
  typename boost::shared_ptr<SHOTLocalReferenceFrameEstimationOMP<PointInT, PointRFT> > lrf_estimator(new SHOTLocalReferenceFrameEstimationOMP<PointInT, PointRFT>());
  lrf_estimator->setRadiusSearch (search_radius_);
  lrf_estimator->setInputCloud (input_);
  lrf_estimator->setIndices (indices_);
  lrf_estimator->setNumberOfThreads(threads_);

  if (!fake_surface_)
    lrf_estimator->setSearchSurface(surface_);

  if (!FeatureWithLocalReferenceFrames<PointInT, PointRFT>::initLocalReferenceFrames (indices_->size (), lrf_estimator))
  {
    PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
    return (false);
  }

  return (true);
}

template<typename PointInT, typename PointNT, typename PointRFT> bool
pcl::SHOTEstimationOMP<PointInT, PointNT, pcl::SHOT, PointRFT>::initCompute ()
{
  if (!FeatureFromNormals<PointInT, PointNT, pcl::SHOT>::initCompute ())
  {
    PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
    return (false);
  }

  // SHOT cannot work with k-search
  if (this->getKSearch () != 0)
  {
    PCL_ERROR(
      "[pcl::%s::initCompute] Error! Search method set to k-neighborhood. Call setKSearch(0) and setRadiusSearch( radius ) to use this class.\n",
      getClassName().c_str ());
    return (false);
  }

  // Default LRF estimation alg: SHOTLocalReferenceFrameEstimationOMP
  typename boost::shared_ptr<SHOTLocalReferenceFrameEstimationOMP<PointInT, PointRFT> > lrf_estimator(new SHOTLocalReferenceFrameEstimationOMP<PointInT, PointRFT>());
  lrf_estimator->setRadiusSearch (search_radius_);
  lrf_estimator->setInputCloud (input_);
  lrf_estimator->setIndices (indices_);
  lrf_estimator->setNumberOfThreads(threads_);

  if (!fake_surface_)
    lrf_estimator->setSearchSurface(surface_);

  if (!FeatureWithLocalReferenceFrames<PointInT, PointRFT>::initLocalReferenceFrames (indices_->size (), lrf_estimator))
  {
    PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
    return (false);
  }

  return (true);
}

template<typename PointInT, typename PointNT, typename PointOutT, typename PointRFT> void
pcl::SHOTEstimationOMP<PointInT, PointNT, PointOutT, PointRFT>::computeFeature (PointCloudOut &output)
{

  if (threads_ < 0)
    threads_ = 1;

  descLength_ = nr_grid_sector_ * (nr_shape_bins_ + 1);

  sqradius_ = search_radius_ * search_radius_;
  radius3_4_ = (search_radius_ * 3) / 4;
  radius1_4_ = search_radius_ / 4;
  radius1_2_ = search_radius_ / 2;

  assert(descLength_ == 352);

  int data_size = static_cast<int> (indices_->size ());
  Eigen::VectorXf *shot = new Eigen::VectorXf[threads_];

  for (int i = 0; i < threads_; i++)
    shot[i].setZero (descLength_);

  output.is_dense = true;
  // Iterating over the entire index vector
  #pragma omp parallel for num_threads(threads_)
  for (int idx = 0; idx < data_size; ++idx)
  {
#ifdef _OPENMP
    int tid = omp_get_thread_num ();
#else
    int tid = 0;
#endif

    bool lrf_is_nan = false;
    const PointRFT& current_frame = (*frames_)[idx];
    if (!pcl_isfinite (current_frame.rf[0]) ||
        !pcl_isfinite (current_frame.rf[4]) ||
        !pcl_isfinite (current_frame.rf[11]))
    {
      PCL_WARN ("[pcl::%s::computeFeature] The local reference frame is not valid! Aborting description of point with index %d\n",
        getClassName ().c_str (), (*indices_)[idx]);
      lrf_is_nan = true;
    }

    // Allocate enough space to hold the results
    // \note This resize is irrelevant for a radiusSearch ().
    std::vector<int> nn_indices (k_);
    std::vector<float> nn_dists (k_);

    if (!isFinite ((*input_)[(*indices_)[idx]]) || lrf_is_nan || this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices,
                                                                                           nn_dists) == 0)
    {
      // Copy into the resultant cloud
      for (int d = 0; d < shot[tid].size (); ++d)
        output.points[idx].descriptor[d] = std::numeric_limits<float>::quiet_NaN ();
      for (int d = 0; d < 9; ++d)
        output.points[idx].rf[d] = std::numeric_limits<float>::quiet_NaN ();

      output.is_dense = false;
      continue;
    }

    // Estimate the SHOT at each patch
    this->computePointSHOT (idx, nn_indices, nn_dists, shot[tid]);

    // Copy into the resultant cloud
    for (int d = 0; d < shot[tid].size (); ++d)
      output.points[idx].descriptor[d] = shot[tid][d];
    for (int d = 0; d < 9; ++d)
      output.points[idx].rf[d] = frames_->points[idx].rf[(4 * (d / 3) + (d % 3))];
  }
  delete[] shot;
}

template<typename PointInT, typename PointNT, typename PointRFT> void
pcl::SHOTEstimationOMP<PointInT, PointNT, pcl::SHOT, PointRFT>::computeFeature (PointCloudOut &output)
{

  if (threads_ < 0)
    threads_ = 1;

  descLength_ = nr_grid_sector_ * (nr_shape_bins_ + 1);

  sqradius_ = search_radius_ * search_radius_;
  radius3_4_ = (search_radius_ * 3) / 4;
  radius1_4_ = search_radius_ / 4;
  radius1_2_ = search_radius_ / 2;

  for (size_t idx = 0; idx < indices_->size (); ++idx)
    output.points[idx].descriptor.resize (descLength_);

  int data_size = static_cast<int> (indices_->size ());
  Eigen::VectorXf *shot = new Eigen::VectorXf[threads_];

  for (int i = 0; i < threads_; i++)
    shot[i].setZero (descLength_);

  output.is_dense = true;
  // Iterating over the entire index vector
  #pragma omp parallel for num_threads(threads_)
  for (int idx = 0; idx < data_size; ++idx)
  {
#ifdef _OPENMP
    int tid = omp_get_thread_num ();
#else
    int tid = 0;
#endif

    bool lrf_is_nan = false;
    const PointRFT& current_frame = (*frames_)[idx];
    if (!pcl_isfinite (current_frame.rf[0]) ||
        !pcl_isfinite (current_frame.rf[4]) ||
        !pcl_isfinite (current_frame.rf[11]))
    {
      PCL_WARN ("[pcl::%s::computeFeature] The local reference frame is not valid! Aborting description of point with index %d\n",
        getClassName ().c_str (), (*indices_)[idx]);
      lrf_is_nan = true;
    }

    // Allocate enough space to hold the results
    // \note This resize is irrelevant for a radiusSearch ().
    std::vector<int> nn_indices (k_);
    std::vector<float> nn_dists (k_);

    if (!isFinite ((*input_)[(*indices_)[idx]]) || lrf_is_nan || this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices,
                                                                                           nn_dists) == 0)
    {
      // Copy into the resultant cloud
      for (int d = 0; d < shot[tid].size (); ++d)
        output.points[idx].descriptor[d] = std::numeric_limits<float>::quiet_NaN ();
      for (int d = 0; d < 9; ++d)
        output.points[idx].rf[d] = std::numeric_limits<float>::quiet_NaN ();

      output.is_dense = false;
      continue;
    }

    // Estimate the SHOT at each patch
    this->computePointSHOT (idx, nn_indices, nn_dists, shot[tid]);

    // Copy into the resultant cloud
    for (int d = 0; d < shot[tid].size (); ++d)
      output.points[idx].descriptor[d] = shot[tid][d];
    for (int d = 0; d < 9; ++d)
      output.points[idx].rf[d] = frames_->points[idx].rf[(4 * (d / 3) + (d % 3))];
  }
  delete[] shot;
}

template <typename PointInT, typename PointNT, typename PointOutT, typename PointRFT> void
pcl::SHOTColorEstimationOMP<PointInT, PointNT, PointOutT, PointRFT>::computeFeature (PointCloudOut &output)
{
  if (threads_ < 0)
    threads_ = 1;

  descLength_ = (b_describe_shape_) ? nr_grid_sector_ * (nr_shape_bins_ + 1) : 0;
  descLength_ += (b_describe_color_) ? nr_grid_sector_ * (nr_color_bins_ + 1) : 0;

  assert( (!b_describe_color_ && b_describe_shape_ && descLength_ == 352) ||
          (b_describe_color_ && !b_describe_shape_ && descLength_ == 992) ||
          (b_describe_color_ && b_describe_shape_ && descLength_ == 1344)
        );

  sqradius_ = search_radius_ * search_radius_;
  radius3_4_ = (search_radius_ * 3) / 4;
  radius1_4_ = search_radius_ / 4;
  radius1_2_ = search_radius_ / 2;

  int data_size = static_cast<int> (indices_->size ());
  Eigen::VectorXf *shot = new Eigen::VectorXf[threads_];

  for (int i = 0; i < threads_; i++)
    shot[i].setZero (descLength_);

  output.is_dense = true;
  // Iterating over the entire index vector
#pragma omp parallel for num_threads(threads_)
  for (int idx = 0; idx < data_size; ++idx)
  {
#ifdef _OPENMP
    int tid = omp_get_thread_num ();
#else
    int tid = 0;
#endif
    // Allocate enough space to hold the results
    // \note This resize is irrelevant for a radiusSearch ().
    std::vector<int> nn_indices (k_);
    std::vector<float> nn_dists (k_);

    bool lrf_is_nan = false;
    const PointRFT& current_frame = (*frames_)[idx];
    if (!pcl_isfinite (current_frame.rf[0]) ||
        !pcl_isfinite (current_frame.rf[4]) ||
        !pcl_isfinite (current_frame.rf[11]))
    {
      PCL_WARN ("[pcl::%s::computeFeature] The local reference frame is not valid! Aborting description of point with index %d\n",
        getClassName ().c_str (), (*indices_)[idx]);
      lrf_is_nan = true;
    }

    if (!isFinite ((*input_)[(*indices_)[idx]]) ||
        lrf_is_nan ||
        this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
    {
      // Copy into the resultant cloud
      for (int d = 0; d < shot[tid].size (); ++d)
        output.points[idx].descriptor[d] = std::numeric_limits<float>::quiet_NaN ();
      for (int d = 0; d < 9; ++d)
        output.points[idx].rf[d] = std::numeric_limits<float>::quiet_NaN ();

      output.is_dense = false;
      continue;
    }

    // Estimate the SHOT at each patch
    this->computePointSHOT (idx, nn_indices, nn_dists, shot[tid]);

    // Copy into the resultant cloud
    for (int d = 0; d < shot[tid].size (); ++d)
      output.points[idx].descriptor[d] = shot[tid][d];
    for (int d = 0; d < 9; ++d)
      output.points[idx].rf[d] = frames_->points[idx].rf[ (4*(d/3) + (d%3)) ];
  }

  delete[] shot;
}

template<typename PointNT, typename PointRFT> void
pcl::SHOTEstimationOMP<pcl::PointXYZRGBA, PointNT, pcl::SHOT, PointRFT>::computeFeature (PointCloudOut &output)
{
  if (threads_ < 0)
    threads_ = 1;

  descLength_ = (b_describe_shape_) ? nr_grid_sector_ * (nr_shape_bins_ + 1) : 0;
  descLength_ += (b_describe_color_) ? nr_grid_sector_ * (nr_color_bins_ + 1) : 0;

  sqradius_ = search_radius_ * search_radius_;
  radius3_4_ = (search_radius_ * 3) / 4;
  radius1_4_ = search_radius_ / 4;
  radius1_2_ = search_radius_ / 2;

 //if (output.points[0].descriptor.size () != static_cast<size_t> (descLength_))
  for (size_t idx = 0; idx < indices_->size (); ++idx)
    output.points[idx].descriptor.resize (descLength_);

  int data_size = static_cast<int> (indices_->size ());
  Eigen::VectorXf *shot = new Eigen::VectorXf[threads_];

  for (int i = 0; i < threads_; i++)
    shot[i].setZero (descLength_);

  output.is_dense = true;
  // Iterating over the entire index vector
#pragma omp parallel for num_threads(threads_)
  for (int idx = 0; idx < data_size; ++idx)
  {
#ifdef _OPENMP
    int tid = omp_get_thread_num ();
#else
    int tid = 0;
#endif
    // Allocate enough space to hold the results
    // \note This resize is irrelevant for a radiusSearch ().
    std::vector<int> nn_indices (k_);
    std::vector<float> nn_dists (k_);

    bool lrf_is_nan = false;
    const PointRFT& current_frame = (*frames_)[idx];
    if (!pcl_isfinite (current_frame.rf[0]) ||
        !pcl_isfinite (current_frame.rf[4]) ||
        !pcl_isfinite (current_frame.rf[11]))
    {
      PCL_WARN ("[pcl::%s::computeFeature] The local reference frame is not valid! Aborting description of point with index %d\n",
        getClassName ().c_str (), (*indices_)[idx]);
      lrf_is_nan = true;
    }

    if (!isFinite ((*input_)[(*indices_)[idx]]) ||
        lrf_is_nan ||
        this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
    {
      // Copy into the resultant cloud
      for (int d = 0; d < shot[tid].size (); ++d)
        output.points[idx].descriptor[d] = std::numeric_limits<float>::quiet_NaN ();
      for (int d = 0; d < 9; ++d)
        output.points[idx].rf[d] = std::numeric_limits<float>::quiet_NaN ();

      output.is_dense = false;
      continue;
    }

    // Estimate the SHOT at each patch
    this->computePointSHOT (idx, nn_indices, nn_dists, shot[tid]);

    // Copy into the resultant cloud
    for (int d = 0; d < shot[tid].size (); ++d)
      output.points[idx].descriptor[d] = shot[tid][d];
    for (int d = 0; d < 9; ++d)
      output.points[idx].rf[d] = frames_->points[idx].rf[ (4*(d/3) + (d%3)) ];
  }

  delete[] shot;
}

#define PCL_INSTANTIATE_SHOTEstimationOMP(T,NT,OutT,RFT) template class PCL_EXPORTS pcl::SHOTEstimationOMP<T,NT,OutT,RFT>;
#define PCL_INSTANTIATE_SHOTColorEstimationOMP(T,NT,OutT,RFT) template class PCL_EXPORTS pcl::SHOTColorEstimationOMP<T,NT,OutT,RFT>;

#endif    // PCL_FEATURES_IMPL_SHOT_OMP_H_