Point Cloud Library (PCL) 1.13.0
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pfh.hpp
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38
39#pragma once
40
41#include <pcl/features/pfh.h>
42#include <pcl/features/pfh_tools.h> // for computePairFeatures
43
44#include <pcl/common/point_tests.h> // for pcl::isFinite
45
46
47//////////////////////////////////////////////////////////////////////////////////////////////
48template <typename PointInT, typename PointNT, typename PointOutT> bool
50 const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
51 int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
52{
53 pcl::computePairFeatures (cloud[p_idx].getVector4fMap (), normals[p_idx].getNormalVector4fMap (),
54 cloud[q_idx].getVector4fMap (), normals[q_idx].getNormalVector4fMap (),
55 f1, f2, f3, f4);
56 return (true);
57}
58
59//////////////////////////////////////////////////////////////////////////////////////////////
60template <typename PointInT, typename PointNT, typename PointOutT> void
62 const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
63 const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
64{
65 int h_index, h_p;
66
67 // Clear the resultant point histogram
68 pfh_histogram.setZero ();
69
70 // Factorization constant
71 float hist_incr = 100.0f / static_cast<float> (indices.size () * (indices.size () - 1) / 2);
72
73 std::pair<int, int> key;
74 bool key_found = false;
75
76 // Iterate over all the points in the neighborhood
77 for (std::size_t i_idx = 0; i_idx < indices.size (); ++i_idx)
78 {
79 for (std::size_t j_idx = 0; j_idx < i_idx; ++j_idx)
80 {
81 // If the 3D points are invalid, don't bother estimating, just continue
82 if (!isFinite (cloud[indices[i_idx]]) || !isFinite (cloud[indices[j_idx]]))
83 continue;
84
85 if (use_cache_)
86 {
87 // In order to create the key, always use the smaller index as the first key pair member
88 int p1, p2;
89 // if (indices[i_idx] >= indices[j_idx])
90 // {
91 p1 = indices[i_idx];
92 p2 = indices[j_idx];
93 // }
94 // else
95 // {
96 // p1 = indices[j_idx];
97 // p2 = indices[i_idx];
98 // }
99 key = std::pair<int, int> (p1, p2);
100
101 // Check to see if we already estimated this pair in the global hashmap
102 auto fm_it = feature_map_.find (key);
103 if (fm_it != feature_map_.end ())
104 {
105 pfh_tuple_ = fm_it->second;
106 key_found = true;
107 }
108 else
109 {
110 // Compute the pair NNi to NNj
111 if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
112 pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
113 continue;
114
115 key_found = false;
116 }
117 }
118 else
119 if (!computePairFeatures (cloud, normals, indices[i_idx], indices[j_idx],
120 pfh_tuple_[0], pfh_tuple_[1], pfh_tuple_[2], pfh_tuple_[3]))
121 continue;
122
123 // Normalize the f1, f2, f3 features and push them in the histogram
124 f_index_[0] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[0] + M_PI) * d_pi_)));
125 if (f_index_[0] < 0) f_index_[0] = 0;
126 if (f_index_[0] >= nr_split) f_index_[0] = nr_split - 1;
127
128 f_index_[1] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[1] + 1.0) * 0.5)));
129 if (f_index_[1] < 0) f_index_[1] = 0;
130 if (f_index_[1] >= nr_split) f_index_[1] = nr_split - 1;
131
132 f_index_[2] = static_cast<int> (std::floor (nr_split * ((pfh_tuple_[2] + 1.0) * 0.5)));
133 if (f_index_[2] < 0) f_index_[2] = 0;
134 if (f_index_[2] >= nr_split) f_index_[2] = nr_split - 1;
135
136 // Copy into the histogram
137 h_index = 0;
138 h_p = 1;
139 for (const int &d : f_index_)
140 {
141 h_index += h_p * d;
142 h_p *= nr_split;
143 }
144 pfh_histogram[h_index] += hist_incr;
145
146 if (use_cache_ && !key_found)
147 {
148 // Save the value in the hashmap
149 feature_map_[key] = pfh_tuple_;
150
151 // Use a maximum cache so that we don't go overboard on RAM usage
152 key_list_.push (key);
153 // Check to see if we need to remove an element due to exceeding max_size
154 if (key_list_.size () > max_cache_size_)
155 {
156 // Remove the oldest element.
157 feature_map_.erase (key_list_.front ());
158 key_list_.pop ();
159 }
160 }
161 }
162 }
163}
164
165//////////////////////////////////////////////////////////////////////////////////////////////
166template <typename PointInT, typename PointNT, typename PointOutT> void
168{
169 // Clear the feature map
170 feature_map_.clear ();
171 std::queue<std::pair<int, int> > empty;
172 std::swap (key_list_, empty);
173
174 pfh_histogram_.setZero (nr_subdiv_ * nr_subdiv_ * nr_subdiv_);
175
176 // Allocate enough space to hold the results
177 // \note This resize is irrelevant for a radiusSearch ().
178 pcl::Indices nn_indices (k_);
179 std::vector<float> nn_dists (k_);
180
181 output.is_dense = true;
182 // Save a few cycles by not checking every point for NaN/Inf values if the cloud is set to dense
183 if (input_->is_dense)
184 {
185 // Iterating over the entire index vector
186 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
187 {
188 if (this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
189 {
190 for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
191 output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
192
193 output.is_dense = false;
194 continue;
195 }
196
197 // Estimate the PFH signature at each patch
198 computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);
199
200 // Copy into the resultant cloud
201 for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
202 output[idx].histogram[d] = pfh_histogram_[d];
203 }
204 }
205 else
206 {
207 // Iterating over the entire index vector
208 for (std::size_t idx = 0; idx < indices_->size (); ++idx)
209 {
210 if (!isFinite ((*input_)[(*indices_)[idx]]) ||
211 this->searchForNeighbors ((*indices_)[idx], search_parameter_, nn_indices, nn_dists) == 0)
212 {
213 for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
214 output[idx].histogram[d] = std::numeric_limits<float>::quiet_NaN ();
215
216 output.is_dense = false;
217 continue;
218 }
219
220 // Estimate the PFH signature at each patch
221 computePointPFHSignature (*surface_, *normals_, nn_indices, nr_subdiv_, pfh_histogram_);
222
223 // Copy into the resultant cloud
224 for (Eigen::Index d = 0; d < pfh_histogram_.size (); ++d)
225 output[idx].histogram[d] = pfh_histogram_[d];
226 }
227 }
228}
229
230#define PCL_INSTANTIATE_PFHEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::PFHEstimation<T,NT,OutT>;
231
void computePointPFHSignature(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, const pcl::Indices &indices, int nr_split, Eigen::VectorXf &pfh_histogram)
Estimate the PFH (Point Feature Histograms) individual signatures of the three angular (f1,...
Definition pfh.hpp:61
bool computePairFeatures(const pcl::PointCloud< PointInT > &cloud, const pcl::PointCloud< PointNT > &normals, int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
Definition pfh.hpp:49
void computeFeature(PointCloudOut &output) override
Estimate the Point Feature Histograms (PFH) descriptors at a set of points given by <setInputCloud ()...
Definition pfh.hpp:167
typename Feature< PointInT, PointOutT >::PointCloudOut PointCloudOut
Definition pfh.h:95
PointCloud represents the base class in PCL for storing collections of 3D points.
PCL_EXPORTS bool computePairFeatures(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, float &f1, float &f2, float &f3, float &f4)
Compute the 4-tuple representation containing the three angles and one distance between two points re...
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
#define M_PI
Definition pcl_macros.h:201