Point Cloud Library (PCL) 1.15.0
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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#include <pcl/common/point_tests.h> // for isXYZFinite
47
48//////////////////////////////////////////////////////////////////////////////////////////////
49template <typename PointT> double
51 const Indices &indices,
52 const std::vector<float> &distances)
53{
54 double BF = 0, W = 0;
55
56 // For each neighbor
57 for (std::size_t n_id = 0; n_id < indices.size (); ++n_id)
58 {
59 int id = indices[n_id];
60 // Compute the difference in intensity
61 double intensity_dist = std::abs ((*input_)[pid].intensity - (*input_)[id].intensity);
62
63 // Compute the Gaussian intensity weights both in Euclidean and in intensity space
64 double dist = std::sqrt (distances[n_id]);
65 double weight = kernel (dist, sigma_s_) * kernel (intensity_dist, sigma_r_);
66
67 // Calculate the bilateral filter response
68 BF += weight * (*input_)[id].intensity;
69 W += weight;
70 }
71 return (BF / W);
72}
73
74//////////////////////////////////////////////////////////////////////////////////////////////
75template <typename PointT> void
77{
78 // Check if sigma_s has been given by the user
79 if (sigma_s_ == 0)
80 {
81 PCL_ERROR ("[pcl::BilateralFilter::applyFilter] Need a sigma_s value given before continuing.\n");
82 return;
83 }
84 // In case a search method has not been given, initialize it using some defaults
85 if (!tree_)
86 {
87 // For organized datasets, use an OrganizedNeighbor
88 if (input_->isOrganized ())
89 tree_.reset (new pcl::search::OrganizedNeighbor<PointT> ());
90 // For unorganized data, use a FLANN kdtree
91 else
92 tree_.reset (new pcl::search::KdTree<PointT> (false));
93 }
94 tree_->setInputCloud (input_);
95
96 Indices k_indices;
97 std::vector<float> k_distances;
98
99 // Copy the input data into the output
100 output = *input_;
101
102 // For all the indices given (equal to the entire cloud if none given)
103 for (const auto& idx : (*indices_))
104 {
105 if (input_->is_dense || pcl::isXYZFinite((*input_)[idx]))
106 {
107 // Perform a radius search to find the nearest neighbors
108 tree_->radiusSearch (idx, sigma_s_ * 2, k_indices, k_distances);
109
110 // Overwrite the intensity value with the computed average
111 output[idx].intensity = static_cast<float> (computePointWeight (idx, k_indices, k_distances));
112 }
113 }
114}
115
116#define PCL_INSTANTIATE_BilateralFilter(T) template class PCL_EXPORTS pcl::BilateralFilter<T>;
117
118#endif // PCL_FILTERS_BILATERAL_IMPL_H_
119
void applyFilter(PointCloud &output) override
Filter the input data and store the results into output.
Definition bilateral.hpp:76
double computePointWeight(const int pid, const Indices &indices, const std::vector< float > &distances)
Compute the intensity average for a single point.
Definition bilateral.hpp:50
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 neighbor search in organized projectable point clo...
Definition organized.h:66
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
constexpr bool isXYZFinite(const PointT &) noexcept