Point Cloud Library (PCL) 1.12.0
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bilateral.hpp
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39
40#ifndef PCL_FILTERS_BILATERAL_IMPL_H_
41#define PCL_FILTERS_BILATERAL_IMPL_H_
42
43#include <pcl/filters/bilateral.h>
44#include <pcl/search/organized.h> // for OrganizedNeighbor
45#include <pcl/search/kdtree.h> // for KdTree
46
47//////////////////////////////////////////////////////////////////////////////////////////////
48template <typename PointT> double
50 const Indices &indices,
51 const std::vector<float> &distances)
52{
53 double BF = 0, W = 0;
54
55 // For each neighbor
56 for (std::size_t n_id = 0; n_id < indices.size (); ++n_id)
57 {
58 int id = indices[n_id];
59 // Compute the difference in intensity
60 double intensity_dist = std::abs ((*input_)[pid].intensity - (*input_)[id].intensity);
61
62 // Compute the Gaussian intensity weights both in Euclidean and in intensity space
63 double dist = std::sqrt (distances[n_id]);
64 double weight = kernel (dist, sigma_s_) * kernel (intensity_dist, sigma_r_);
65
66 // Calculate the bilateral filter response
67 BF += weight * (*input_)[id].intensity;
68 W += weight;
69 }
70 return (BF / W);
71}
72
73//////////////////////////////////////////////////////////////////////////////////////////////
74template <typename PointT> void
76{
77 // Check if sigma_s has been given by the user
78 if (sigma_s_ == 0)
79 {
80 PCL_ERROR ("[pcl::BilateralFilter::applyFilter] Need a sigma_s value given before continuing.\n");
81 return;
82 }
83 // In case a search method has not been given, initialize it using some defaults
84 if (!tree_)
85 {
86 // For organized datasets, use an OrganizedNeighbor
87 if (input_->isOrganized ())
88 tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
89 // For unorganized data, use a FLANN kdtree
90 else
91 tree_.reset (new pcl::search::KdTree<PointT> (false));
92 }
93 tree_->setInputCloud (input_);
94
96 std::vector<float> k_distances;
97
98 // Copy the input data into the output
99 output = *input_;
100
101 // For all the indices given (equal to the entire cloud if none given)
102 for (const auto& idx : (*indices_))
103 {
104 // Perform a radius search to find the nearest neighbors
105 tree_->radiusSearch (idx, sigma_s_ * 2, k_indices, k_distances);
106
107 // Overwrite the intensity value with the computed average
108 output[idx].intensity = static_cast<float> (computePointWeight (idx, k_indices, k_distances));
109 }
110}
111
112#define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter<T>;
113
114#endif // PCL_FILTERS_BILATERAL_IMPL_H_
115
void applyFilter(PointCloud &output) override
Filter the input data and store the results into output.
Definition bilateral.hpp:75
double computePointWeight(const int pid, const Indices &indices, const std::vector< float > &distances)
Compute the intensity average for a single point.
Definition bilateral.hpp:49
Iterator class for point clouds with or without given indices.
ConstCloudIterator(const PointCloud< PointT > &cloud)
std::size_t size() const
Size of the range the iterator is going through.
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.
Definition organized.h:61
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133