crh.hpp
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* $Id: cvfh.hpp 5311 2012-03-26 22:02:04Z aaldoma $
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#ifndef PCL_FEATURES_IMPL_CRH_H_
#define PCL_FEATURES_IMPL_CRH_H_
#include <pcl/features/crh.h>
#include <pcl/common/fft/kiss_fftr.h>
#include <pcl/common/common.h>
#include <pcl/common/transforms.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT>
void
pcl::CRHEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Check if input was set
if (!normals_)
{
PCL_ERROR ("[pcl::%s::computeFeature] No input dataset containing normals was given!\n", getClassName ().c_str ());
output.width = output.height = 0;
output.points.clear ();
return;
}
if (normals_->points.size () != surface_->points.size ())
{
PCL_ERROR ("[pcl::%s::computeFeature] The number of points in the input dataset differs from the number of points in the dataset containing the normals!\n", getClassName ().c_str ());
output.width = output.height = 0;
output.points.clear ();
return;
}
Eigen::Vector3f plane_normal;
plane_normal[0] = -centroid_[0];
plane_normal[1] = -centroid_[1];
plane_normal[2] = -centroid_[2];
Eigen::Vector3f z_vector = Eigen::Vector3f::UnitZ ();
plane_normal.normalize ();
Eigen::Vector3f axis = plane_normal.cross (z_vector);
double rotation = -asin (axis.norm ());
axis.normalize ();
int nbins = nbins_;
int bin_angle = 360 / nbins;
Eigen::Affine3f transformPC (Eigen::AngleAxisf (static_cast<float> (rotation), axis));
pcl::PointCloud<pcl::PointNormal> grid;
grid.points.resize (indices_->size ());
for (size_t i = 0; i < indices_->size (); i++)
{
grid.points[i].getVector4fMap () = surface_->points[(*indices_)[i]].getVector4fMap ();
grid.points[i].getNormalVector4fMap () = normals_->points[(*indices_)[i]].getNormalVector4fMap ();
}
pcl::transformPointCloudWithNormals (grid, grid, transformPC);
//fill spatial data vector and the zero-initialize or "value-initialize" an array on c++,
// the initialization is made with () after the [nbins]
kiss_fft_scalar * spatial_data = new kiss_fft_scalar[nbins]();
float sum_w = 0, w = 0;
int bin = 0;
for (size_t i = 0; i < grid.points.size (); ++i)
{
bin = static_cast<int> ((((atan2 (grid.points[i].normal_y, grid.points[i].normal_x) + M_PI) * 180 / M_PI) / bin_angle)) % nbins;
w = std::sqrt (grid.points[i].normal_y * grid.points[i].normal_y + grid.points[i].normal_x * grid.points[i].normal_x);
sum_w += w;
spatial_data[bin] += w;
}
for (int i = 0; i < nbins; ++i)
spatial_data[i] /= sum_w;
kiss_fft_cpx * freq_data = new kiss_fft_cpx[nbins / 2 + 1];
kiss_fftr_cfg mycfg = kiss_fftr_alloc (nbins, 0, NULL, NULL);
kiss_fftr (mycfg, spatial_data, freq_data);
output.points.resize (1);
output.width = output.height = 1;
output.points[0].histogram[0] = freq_data[0].r / freq_data[0].r; //dc
int k = 1;
for (int i = 1; i < (nbins / 2); i++, k += 2)
{
output.points[0].histogram[k] = freq_data[i].r / freq_data[0].r;
output.points[0].histogram[k + 1] = freq_data[i].i / freq_data[0].r;
}
output.points[0].histogram[nbins - 1] = freq_data[nbins / 2].r / freq_data[0].r; //nyquist
delete[] spatial_data;
delete[] freq_data;
}
#define PCL_INSTANTIATE_CRHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::CRHEstimation<T,NT,OutT>;
#endif // PCL_FEATURES_IMPL_CRH_H_
kiss_fft_cpx::i
kiss_fft_scalar i
Definition: kiss_fft.h:59
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::transformPointCloudWithNormals
void transformPointCloudWithNormals(const pcl::PointCloud< PointT > &cloud_in, pcl::PointCloud< PointT > &cloud_out, const Eigen::Transform< Scalar, 3, Eigen::Affine > &transform, bool copy_all_fields=true)
Transform a point cloud and rotate its normals using an Eigen transform.
Definition: transforms.hpp:149
common.h
Define standard C methods and C++ classes that are common to all methods.
pcl::PointCloud< pcl::PointNormal >
kiss_fft_cpx
Definition: kiss_fft.h:57
kiss_fft_cpx::r
kiss_fft_scalar r
Definition: kiss_fft.h:58
pcl::CRHEstimation
CRHEstimation estimates the Camera Roll Histogram (CRH) descriptor for a given point cloud dataset co...
Definition: crh.h:61