template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
// Compute the number of coefficients
nr_coeff_ = (order_ + 1) * (order_ + 2) / 2;
// Allocate enough space to hold the results of nearest neighbor searches
// \note resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
// For all points
for (size_t cp = 0; cp < indices_->size (); ++cp)
{
// Get the initial estimates of point positions and their neighborhoods
if (!searchForNeighbors ((*indices_)[cp], nn_indices, nn_sqr_dists))
continue;
// Check the number of nearest neighbors for normal estimation (and later
// for polynomial fit as well)
if (nn_indices.size () < 3)
continue;
PointCloudOut projected_points;
NormalCloud projected_points_normals;
// Get a plane approximating the local surface's tangent and project point onto it
int index = (*indices_)[cp];
computeMLSPointNormal (index, nn_indices, nn_sqr_dists, projected_points, projected_points_normals, *corresponding_input_indices_, mls_results_[index]);
// Copy all information from the input cloud to the output points (not doing any interpolation)
for (size_t pp = 0; pp < projected_points.size (); ++pp)
copyMissingFields (input_->points[(*indices_)[cp]], projected_points[pp]);
// Append projected points to output
output.insert (output.end (), projected_points.begin (), projected_points.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals.begin (), projected_points_normals.end ());
}
// Perform the distinct-cloud or voxel-grid upsampling
performUpsampling (output);
}
void ResamplerTest::testUpsamplingTriangle()
{
generateTriangularSignal();
std::cout << "Test Upsampling Triangle" << std::endl;
Resampler resampler(44100);
performUpsampling(resampler);
AudioBuffer tmpInputBuffer(TMP_LOWSMPLR_BUFFER_LENGTH, AudioFormat::MONO());
AudioBuffer tmpOutputBuffer(TMP_HIGHSMPLR_BUFFER_LENGTH, AudioFormat(16000, 1));
tmpInputBuffer.copy(inputBuffer);
std::cout << "Input Buffer" << std::endl;
print_buffer(*tmpInputBuffer.getChannel(0));
tmpOutputBuffer.copy(outputBuffer);
std::cout << "Output Buffer" << std::endl;
print_buffer(*tmpOutputBuffer.getChannel(0));
}
void ResamplerTest::testUpsamplingRamp()
{
// generate input samples and store them in inputBuffer
generateRamp();
std::cout << "Test Upsampling Ramp" << std::endl;
Resampler resampler(44100);
performUpsampling(resampler);
AudioBuffer tmpInputBuffer(TMP_LOWSMPLR_BUFFER_LENGTH, AudioFormat::MONO());
AudioBuffer tmpOutputBuffer(TMP_HIGHSMPLR_BUFFER_LENGTH, AudioFormat(16000, 1));
tmpInputBuffer.copy(inputBuffer);
std::cout << "Input Buffer" << std::endl;
print_buffer(*tmpInputBuffer.getChannel(0));
tmpOutputBuffer.copy(outputBuffer);
std::cout << "Output Buffer" << std::endl;
print_buffer(*tmpOutputBuffer.getChannel(0));
}
template <typename PointInT, typename PointOutT> void
pcl::MovingLeastSquares<PointInT, PointOutT>::performProcessing (PointCloudOut &output)
{
// Compute the number of coefficients
nr_coeff_ = (order_ + 1) * (order_ + 2) / 2;
#ifdef _OPENMP
// (Maximum) number of threads
const unsigned int threads = threads_ == 0 ? 1 : threads_;
// Create temporaries for each thread in order to avoid synchronization
typename PointCloudOut::CloudVectorType projected_points (threads);
typename NormalCloud::CloudVectorType projected_points_normals (threads);
std::vector<PointIndices> corresponding_input_indices (threads);
#endif
// For all points
#ifdef _OPENMP
#pragma omp parallel for schedule (dynamic,1000) num_threads (threads)
#endif
for (int cp = 0; cp < static_cast<int> (indices_->size ()); ++cp)
{
// Allocate enough space to hold the results of nearest neighbor searches
// \note resize is irrelevant for a radiusSearch ().
std::vector<int> nn_indices;
std::vector<float> nn_sqr_dists;
// Get the initial estimates of point positions and their neighborhoods
if (searchForNeighbors ((*indices_)[cp], nn_indices, nn_sqr_dists))
{
// Check the number of nearest neighbors for normal estimation (and later for polynomial fit as well)
if (nn_indices.size () >= 3)
{
// This thread's ID (range 0 to threads-1)
#ifdef _OPENMP
const int tn = omp_get_thread_num ();
// Size of projected points before computeMLSPointNormal () adds points
size_t pp_size = projected_points[tn].size ();
#else
PointCloudOut projected_points;
NormalCloud projected_points_normals;
#endif
// Get a plane approximating the local surface's tangent and project point onto it
const int index = (*indices_)[cp];
size_t mls_result_index = 0;
if (cache_mls_results_)
mls_result_index = index; // otherwise we give it a dummy location.
#ifdef _OPENMP
computeMLSPointNormal (index, nn_indices, projected_points[tn], projected_points_normals[tn], corresponding_input_indices[tn], mls_results_[mls_result_index]);
// Copy all information from the input cloud to the output points (not doing any interpolation)
for (size_t pp = pp_size; pp < projected_points[tn].size (); ++pp)
copyMissingFields (input_->points[(*indices_)[cp]], projected_points[tn][pp]);
#else
computeMLSPointNormal (index, nn_indices, projected_points, projected_points_normals, *corresponding_input_indices_, mls_results_[mls_result_index]);
// Append projected points to output
output.insert (output.end (), projected_points.begin (), projected_points.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals.begin (), projected_points_normals.end ());
#endif
}
}
}
#ifdef _OPENMP
// Combine all threads' results into the output vectors
for (unsigned int tn = 0; tn < threads; ++tn)
{
output.insert (output.end (), projected_points[tn].begin (), projected_points[tn].end ());
corresponding_input_indices_->indices.insert (corresponding_input_indices_->indices.end (),
corresponding_input_indices[tn].indices.begin (), corresponding_input_indices[tn].indices.end ());
if (compute_normals_)
normals_->insert (normals_->end (), projected_points_normals[tn].begin (), projected_points_normals[tn].end ());
}
#endif
// Perform the distinct-cloud or voxel-grid upsampling
performUpsampling (output);
}
