Point Cloud Library (PCL) 1.13.0
moment_invariants.hpp
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40
41#ifndef PCL_FEATURES_IMPL_MOMENT_INVARIANTS_H_
42#define PCL_FEATURES_IMPL_MOMENT_INVARIANTS_H_
43
44#include <pcl/features/moment_invariants.h>
45#include <pcl/common/centroid.h>
46
47//////////////////////////////////////////////////////////////////////////////////////////////
48template <typename PointInT, typename PointOutT> void
50 const pcl::PointCloud<PointInT> &cloud, const pcl::Indices &indices,
51 float &j1, float &j2, float &j3)
52{
53 // Estimate the XYZ centroid
54 compute3DCentroid (cloud, indices, xyz_centroid_);
55
56 // Initialize the centralized moments
57 float mu200 = 0, mu020 = 0, mu002 = 0, mu110 = 0, mu101 = 0, mu011 = 0;
58
59 // Iterate over the nearest neighbors set
60 for (const auto &index : indices)
61 {
62 // Demean the points
63 temp_pt_[0] = cloud[index].x - xyz_centroid_[0];
64 temp_pt_[1] = cloud[index].y - xyz_centroid_[1];
65 temp_pt_[2] = cloud[index].z - xyz_centroid_[2];
66
67 mu200 += temp_pt_[0] * temp_pt_[0];
68 mu020 += temp_pt_[1] * temp_pt_[1];
69 mu002 += temp_pt_[2] * temp_pt_[2];
70 mu110 += temp_pt_[0] * temp_pt_[1];
71 mu101 += temp_pt_[0] * temp_pt_[2];
72 mu011 += temp_pt_[1] * temp_pt_[2];
73 }
74
75 // Save the moment invariants
76 j1 = mu200 + mu020 + mu002;
77 j2 = mu200*mu020 + mu200*mu002 + mu020*mu002 - mu110*mu110 - mu101*mu101 - mu011*mu011;
78 j3 = mu200*mu020*mu002 + 2*mu110*mu101*mu011 - mu002*mu110*mu110 - mu020*mu101*mu101 - mu200*mu011*mu011;
79}
80
81//////////////////////////////////////////////////////////////////////////////////////////////
82template <typename PointInT, typename PointOutT> void
84 const pcl::PointCloud<PointInT> &cloud, float &j1, float &j2, float &j3)
85{
86 // Estimate the XYZ centroid
87 compute3DCentroid (cloud, xyz_centroid_);
88
89 // Initialize the centralized moments
90 float mu200 = 0, mu020 = 0, mu002 = 0, mu110 = 0, mu101 = 0, mu011 = 0;
91
92 // Iterate over the nearest neighbors set
93 for (const auto& point: cloud.points)
94 {
95 // Demean the points
96 temp_pt_[0] = point.x - xyz_centroid_[0];
97 temp_pt_[1] = point.y - xyz_centroid_[1];
98 temp_pt_[2] = point.z - xyz_centroid_[2];
99
100 mu200 += temp_pt_[0] * temp_pt_[0];
101 mu020 += temp_pt_[1] * temp_pt_[1];
102 mu002 += temp_pt_[2] * temp_pt_[2];
103 mu110 += temp_pt_[0] * temp_pt_[1];
104 mu101 += temp_pt_[0] * temp_pt_[2];
105 mu011 += temp_pt_[1] * temp_pt_[2];
106 }
107
108 // Save the moment invariants
109 j1 = mu200 + mu020 + mu002;
110 j2 = mu200*mu020 + mu200*mu002 + mu020*mu002 - mu110*mu110 - mu101*mu101 - mu011*mu011;
111 j3 = mu200*mu020*mu002 + 2*mu110*mu101*mu011 - mu002*mu110*mu110 - mu020*mu101*mu101 - mu200*mu011*mu011;
112}
113
114//////////////////////////////////////////////////////////////////////////////////////////////
115template <typename PointInT, typename PointOutT> void
117{
118 // Allocate enough space to hold the results
119 // \note This resize is irrelevant for a radiusSearch ().
120 pcl::Indices nn_indices (k_);
121 std::vector<float> nn_dists (k_);
122
123 output.is_dense = true;
124 // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
125 if (input_->is_dense)
126 {
127 // Iterating over the entire index vector
128 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
129 {
130 if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
131 {
132 output[idx].j1 = output[idx].j2 = output[idx].j3 = std::numeric_limits<float>::quiet_NaN ();
133 output.is_dense = false;
134 continue;
135 }
136
137 computePointMomentInvariants (*surface_, nn_indices,
138 output[idx].j1, output[idx].j2, output[idx].j3);
139 }
140 }
141 else
142 {
143 // Iterating over the entire index vector
144 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
145 {
146 if (!isFinite ((*input_)[(*indices_)[idx]]) ||
147 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
148 {
149 output[idx].j1 = output[idx].j2 = output[idx].j3 = std::numeric_limits<float>::quiet_NaN ();
150 output.is_dense = false;
151 continue;
152 }
153
154 computePointMomentInvariants (*surface_, nn_indices,
155 output[idx].j1, output[idx].j2, output[idx].j3);
156 }
157 }
158}
159
160#define PCL_INSTANTIATE_MomentInvariantsEstimation(T,NT) template class PCL_EXPORTS pcl::MomentInvariantsEstimation<T,NT>;
161
162#endif // PCL_FEATURES_IMPL_MOMENT_INVARIANTS_H_
163
Define methods for centroid estimation and covariance matrix calculus.
void computeFeature(PointCloudOut &output) override
Estimate moment invariants for all points given in <setInputCloud (), setIndices ()> using the surfac...
void computePointMomentInvariants(const pcl::PointCloud< PointInT > &cloud, const pcl::Indices &indices, float &j1, float &j2, float &j3)
Compute the 3 moment invariants (j1, j2, j3) for a given set of points, using their indices.
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:395
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition: centroid.hpp:56
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition: point_tests.h:55
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133