keypoint.hpp
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#ifndef PCL_KEYPOINT_IMPL_H_
#define PCL_KEYPOINT_IMPL_H_
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> bool
pcl::Keypoint<PointInT, PointOutT>::initCompute ()
{
if (!PCLBase<PointInT>::initCompute ())
return (false);
// Initialize the spatial locator
if (!tree_)
{
if (input_->isOrganized ())
tree_.reset (new pcl::search::OrganizedNeighbor<PointInT> ());
else
tree_.reset (new pcl::search::KdTree<PointInT> (false));
}
// If no search surface has been defined, use the input dataset as the search surface itself
if (!surface_)
surface_ = input_;
// Send the surface dataset to the spatial locator
tree_->setInputCloud (surface_);
// Do a fast check to see if the search parameters are well defined
if (search_radius_ != 0.0)
{
if (k_ != 0)
{
PCL_ERROR ("[pcl::%s::initCompute] Both radius (%f) and K (%d) defined! Set one of them to zero first and then re-run compute ().\n", getClassName ().c_str (), search_radius_, k_);
return (false);
}
// Use the radiusSearch () function
search_parameter_ = search_radius_;
if (surface_ == input_) // if the two surfaces are the same
{
// Declare the search locator definition
search_method_ = [this] (int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_distances)
{
return tree_->radiusSearch (index, radius, k_indices, k_distances, 0);
};
}
else
{
// Declare the search locator definition
search_method_surface_ = [this] (const PointCloudIn &cloud, int index, double radius, std::vector<int> &k_indices, std::vector<float> &k_distances)
{
return tree_->radiusSearch (cloud, index, radius, k_indices, k_distances, 0);
};
}
}
else
{
if (k_ != 0) // Use the nearestKSearch () function
{
search_parameter_ = k_;
if (surface_ == input_) // if the two surfaces are the same
{
// Declare the search locator definition
search_method_ = [this] (int index, int k, std::vector<int> &k_indices, std::vector<float> &k_distances)
{
return tree_->nearestKSearch (index, k, k_indices, k_distances);
};
}
else
{
// Declare the search locator definition
search_method_surface_ = [this] (const PointCloudIn &cloud, int index, int k, std::vector<int> &k_indices, std::vector<float> &k_distances)
{
return tree_->nearestKSearch (cloud, index, k, k_indices, k_distances);
};
}
}
else
{
PCL_ERROR ("[pcl::%s::initCompute] Neither radius nor K defined! Set one of them to a positive number first and then re-run compute ().\n", getClassName ().c_str ());
return (false);
}
}
keypoints_indices_.reset (new pcl::PointIndices);
keypoints_indices_->indices.reserve (input_->size ());
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
template <typename PointInT, typename PointOutT> inline void
pcl::Keypoint<PointInT, PointOutT>::compute (PointCloudOut &output)
{
if (!initCompute ())
{
PCL_ERROR ("[pcl::%s::compute] initCompute failed!\n", getClassName ().c_str ());
return;
}
// Perform the actual computation
detectKeypoints (output);
deinitCompute ();
// Reset the surface
if (input_ == surface_)
surface_.reset ();
}
#endif //#ifndef PCL_KEYPOINT_IMPL_H_
pcl::Keypoint::initCompute
virtual bool initCompute()
Definition: keypoint.hpp:43
pcl::PCLBase
PCL base class.
Definition: pcl_base.h:69
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: projection_matrix.h:52
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:61
pcl::PointIndices
Definition: PointIndices.h:12
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.
Definition: organized.h:62
pcl::Keypoint::compute
void compute(PointCloudOut &output)
Base method for key point detection for all points given in <setInputCloud (), setIndices ()> using t...
Definition: keypoint.hpp:129