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ppfrgb.hpp
/*
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#ifndef PCL_FEATURES_IMPL_PPFRGB_H_
#define PCL_FEATURES_IMPL_PPFRGB_H_
#include <pcl/features/ppfrgb.h>
#include <pcl/features/pfhrgb.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT>
pcl::PPFRGBEstimation<PointInT, PointNT, PointOutT>::PPFRGBEstimation ()
: FeatureFromNormals <PointInT, PointNT, PointOutT> ()
{
feature_name_ = "PPFRGBEstimation";
// Slight hack in order to pass the check for the presence of a search method in Feature::initCompute ()
Feature<PointInT, PointOutT>::tree_.reset (new pcl::search::KdTree <PointInT> ());
Feature<PointInT, PointOutT>::search_radius_ = 1.0f;
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PPFRGBEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
// Initialize output container - overwrite the sizes done by Feature::initCompute ()
output.resize (indices_->size () * input_->size ());
output.height = 1;
output.width = output.size ();
// Compute point pair features for every pair of points in the cloud
for (std::size_t index_i = 0; index_i < indices_->size (); ++index_i)
{
std::size_t i = (*indices_)[index_i];
for (std::size_t j = 0 ; j < input_->size (); ++j)
{
PointOutT p;
if (i != j)
{
if (pcl::computeRGBPairFeatures
((*input_)[i].getVector4fMap (), (*normals_)[i].getNormalVector4fMap (), (*input_)[i].getRGBVector4i (),
(*input_)[j].getVector4fMap (), (*normals_)[j].getNormalVector4fMap (), (*input_)[j].getRGBVector4i (),
p.f1, p.f2, p.f3, p.f4, p.r_ratio, p.g_ratio, p.b_ratio))
{
// Calculate alpha_m angle
Eigen::Vector3f model_reference_point = (*input_)[i].getVector3fMap (),
model_reference_normal = (*normals_)[i].getNormalVector3fMap (),
model_point = (*input_)[j].getVector3fMap ();
Eigen::AngleAxisf rotation_mg (std::acos (model_reference_normal.dot (Eigen::Vector3f::UnitX ())),
model_reference_normal.cross (Eigen::Vector3f::UnitX ()).normalized ());
Eigen::Affine3f transform_mg = Eigen::Translation3f ( rotation_mg * ((-1) * model_reference_point)) * rotation_mg;
Eigen::Vector3f model_point_transformed = transform_mg * model_point;
float angle = std::atan2 ( -model_point_transformed(2), model_point_transformed(1));
if (std::sin (angle) * model_point_transformed(2) < 0.0f)
angle *= (-1);
p.alpha_m = -angle;
}
else
{
PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), i, j);
p.f1 = p.f2 = p.f3 = p.f4 = p.alpha_m = p.r_ratio = p.g_ratio = p.b_ratio = 0.f;
}
}
// Do not calculate the feature for identity pairs (i, i) as they are not used
// in the following computations
else
p.f1 = p.f2 = p.f3 = p.f4 = p.alpha_m = p.r_ratio = p.g_ratio = p.b_ratio = 0.f;
output[index_i*input_->size () + j] = p;
}
}
}
//////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT>
pcl::PPFRGBRegionEstimation<PointInT, PointNT, PointOutT>::PPFRGBRegionEstimation ()
: FeatureFromNormals <PointInT, PointNT, PointOutT> ()
{
feature_name_ = "PPFRGBEstimation";
}
//////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointNT, typename PointOutT> void
pcl::PPFRGBRegionEstimation<PointInT, PointNT, PointOutT>::computeFeature (PointCloudOut &output)
{
PCL_INFO ("before computing output size: %u\n", output.size ());
output.resize (indices_->size ());
for (std::size_t index_i = 0; index_i < indices_->size (); ++index_i)
{
auto i = (*indices_)[index_i];
pcl::Indices nn_indices;
std::vector<float> nn_distances;
tree_->radiusSearch (i, static_cast<float> (search_radius_), nn_indices, nn_distances);
PointOutT average_feature_nn;
average_feature_nn.alpha_m = 0;
average_feature_nn.f1 = average_feature_nn.f2 = average_feature_nn.f3 = average_feature_nn.f4 =
average_feature_nn.r_ratio = average_feature_nn.g_ratio = average_feature_nn.b_ratio = 0.0f;
for (const auto &j : nn_indices)
{
if (i != j)
{
float f1, f2, f3, f4, r_ratio, g_ratio, b_ratio;
if (pcl::computeRGBPairFeatures
((*input_)[i].getVector4fMap (), (*normals_)[i].getNormalVector4fMap (), (*input_)[i].getRGBVector4i (),
(*input_)[j].getVector4fMap (), (*normals_)[j].getNormalVector4fMap (), (*input_)[j].getRGBVector4i (),
f1, f2, f3, f4, r_ratio, g_ratio, b_ratio))
{
average_feature_nn.f1 += f1;
average_feature_nn.f2 += f2;
average_feature_nn.f3 += f3;
average_feature_nn.f4 += f4;
average_feature_nn.r_ratio += r_ratio;
average_feature_nn.g_ratio += g_ratio;
average_feature_nn.b_ratio += b_ratio;
}
else
{
PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), i, j);
}
}
}
float normalization_factor = static_cast<float> (nn_indices.size ());
average_feature_nn.f1 /= normalization_factor;
average_feature_nn.f2 /= normalization_factor;
average_feature_nn.f3 /= normalization_factor;
average_feature_nn.f4 /= normalization_factor;
average_feature_nn.r_ratio /= normalization_factor;
average_feature_nn.g_ratio /= normalization_factor;
average_feature_nn.b_ratio /= normalization_factor;
output[index_i] = average_feature_nn;
}
PCL_INFO ("Output size: %zu\n", static_cast<std::size_t>(output.size ()));
}
#define PCL_INSTANTIATE_PPFRGBEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PPFRGBEstimation<T,NT,OutT>;
#define PCL_INSTANTIATE_PPFRGBRegionEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PPFRGBRegionEstimation<T,NT,OutT>;
#endif // PCL_FEATURES_IMPL_PPFRGB_H_
pcl::FeatureFromNormals
Definition feature.h:309
pcl::Feature
Feature represents the base feature class.
Definition feature.h:107
pcl::Feature::feature_name_
std::string feature_name_
The feature name.
Definition feature.h:220
pcl::PPFRGBEstimation
Definition ppfrgb.h:46
pcl::PPFRGBEstimation::PPFRGBEstimation
PPFRGBEstimation()
Empty Constructor.
Definition ppfrgb.hpp:46
pcl::PPFRGBRegionEstimation
Definition ppfrgb.h:72
pcl::PPFRGBRegionEstimation::PPFRGBRegionEstimation
PPFRGBRegionEstimation()
Definition ppfrgb.hpp:114
pcl::computeRGBPairFeatures
PCL_EXPORTS bool computeRGBPairFeatures(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4i &colors1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, const Eigen::Vector4i &colors2, float &f1, float &f2, float &f3, float &f4, float &f5, float &f6, float &f7)
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133