Point Cloud Library (PCL) 1.12.0
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euclidean_cluster_comparator.h
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39
40#pragma once
41
42#include <set> // for std::set
43#include <pcl/memory.h>
44#include <pcl/pcl_macros.h>
45#include <pcl/point_types.h>
46#include <pcl/segmentation/comparator.h>
47
48
49namespace pcl
50{
51 /** \brief EuclideanClusterComparator is a comparator used for finding clusters based on euclidian distance.
52 *
53 * \author Alex Trevor
54 */
55 template<typename PointT, typename PointLT = pcl::Label>
57 {
58 protected:
59
61
62 public:
63 using typename Comparator<PointT>::PointCloud;
65
69
70 using Ptr = shared_ptr<EuclideanClusterComparator<PointT, PointLT> >;
71 using ConstPtr = shared_ptr<const EuclideanClusterComparator<PointT, PointLT> >;
72
73 using ExcludeLabelSet = std::set<std::uint32_t>;
74 using ExcludeLabelSetPtr = shared_ptr<ExcludeLabelSet>;
75 using ExcludeLabelSetConstPtr = shared_ptr<const ExcludeLabelSet>;
76
77 /** \brief Default constructor for EuclideanClusterComparator. */
79
80 void
81 setInputCloud (const PointCloudConstPtr& cloud) override
82 {
83 input_ = cloud;
84 Eigen::Matrix3f rot = input_->sensor_orientation_.toRotationMatrix ();
85 z_axis_ = rot.col (2);
86 }
87
88 /** \brief Set the tolerance in meters for difference in perpendicular distance (d component of plane equation) to the plane between neighboring points, to be considered part of the same plane.
89 * \param[in] distance_threshold the tolerance in meters
90 * \param depth_dependent
91 */
92 inline void
93 setDistanceThreshold (float distance_threshold, bool depth_dependent)
94 {
95 distance_threshold_ = distance_threshold;
96 depth_dependent_ = depth_dependent;
97 }
98
99 /** \brief Get the distance threshold in meters (d component of plane equation) between neighboring points, to be considered part of the same plane. */
100 inline float
102 {
103 return distance_threshold_;
104 }
105
106 /** \brief Get if depth dependent */
107 inline bool
109 {
110 return depth_dependent_;
111 }
112
113 /** \brief Set label cloud
114 * \param[in] labels The label cloud
115 */
116 void
117 setLabels (const PointCloudLPtr& labels)
118 {
119 labels_ = labels;
120 }
121
122 /** \brief Get labels */
123 const PointCloudLPtr&
124 getLabels() const
125 {
126 return labels_;
127 }
128
129 /** \brief Get exlude labels */
132 {
133 return exclude_labels_;
134 }
135
136 /** \brief Set labels in the label cloud to exclude.
137 * \param exclude_labels a vector of bools corresponding to whether or not a given label should be considered
138 */
139 void
141 {
142 exclude_labels_ = exclude_labels;
143 }
144
145 /** \brief Compare points at two indices by their euclidean distance
146 * \param idx1 The first index for the comparison
147 * \param idx2 The second index for the comparison
148 */
149 bool
150 compare (int idx1, int idx2) const override
151 {
153 {
154 assert (labels_->size () == input_->size ());
155 const std::uint32_t &label1 = (*labels_)[idx1].label;
156 const std::uint32_t &label2 = (*labels_)[idx2].label;
157
158 const std::set<std::uint32_t>::const_iterator it1 = exclude_labels_->find (label1);
159 if (it1 == exclude_labels_->end ())
160 return false;
161
162 const std::set<std::uint32_t>::const_iterator it2 = exclude_labels_->find (label2);
163 if (it2 == exclude_labels_->end ())
164 return false;
165 }
166
167 float dist_threshold = distance_threshold_;
169 {
170 Eigen::Vector3f vec = (*input_)[idx1].getVector3fMap ();
171 float z = vec.dot (z_axis_);
172 dist_threshold *= z * z;
173 }
174
175 const float dist = ((*input_)[idx1].getVector3fMap ()
176 - (*input_)[idx2].getVector3fMap ()).norm ();
177 return (dist < dist_threshold);
178 }
179
180 protected:
181
182
183 /** \brief Set of labels with similar size as the input point cloud,
184 * aggregating points into groups based on a similar label identifier.
185 *
186 * It needs to be set in conjunction with the \ref exclude_labels_
187 * member in order to provided a masking functionality.
188 */
190
191 /** \brief Specifies which labels should be excluded com being clustered.
192 *
193 * If a label is not specified, it's assumed by default that it's
194 * intended be excluded
195 */
197
198 float distance_threshold_ = 0.005f;
199
200 bool depth_dependent_ = false;
201
202 Eigen::Vector3f z_axis_;
203 };
204}
Comparator is the base class for comparators that compare two points given some function.
Definition comparator.h:55
PointCloudConstPtr input_
Definition comparator.h:100
typename PointCloud::ConstPtr PointCloudConstPtr
Definition comparator.h:59
EuclideanClusterComparator is a comparator used for finding clusters based on euclidian distance.
ExcludeLabelSetConstPtr exclude_labels_
Specifies which labels should be excluded com being clustered.
float getDistanceThreshold() const
Get the distance threshold in meters (d component of plane equation) between neighboring points,...
bool compare(int idx1, int idx2) const override
Compare points at two indices by their euclidean distance.
bool getDepthDependent() const
Get if depth dependent.
const ExcludeLabelSetConstPtr & getExcludeLabels() const
Get exlude labels.
void setExcludeLabels(const ExcludeLabelSetConstPtr &exclude_labels)
Set labels in the label cloud to exclude.
const PointCloudLPtr & getLabels() const
Get labels.
void setInputCloud(const PointCloudConstPtr &cloud) override
Set the input cloud for the comparator.
shared_ptr< const EuclideanClusterComparator< PointT, PointLT > > ConstPtr
PointCloudLPtr labels_
Set of labels with similar size as the input point cloud, aggregating points into groups based on a s...
typename PointCloudL::ConstPtr PointCloudLConstPtr
shared_ptr< ExcludeLabelSet > ExcludeLabelSetPtr
shared_ptr< EuclideanClusterComparator< PointT, PointLT > > Ptr
shared_ptr< const ExcludeLabelSet > ExcludeLabelSetConstPtr
void setDistanceThreshold(float distance_threshold, bool depth_dependent)
Set the tolerance in meters for difference in perpendicular distance (d component of plane equation) ...
EuclideanClusterComparator()=default
Default constructor for EuclideanClusterComparator.
void setLabels(const PointCloudLPtr &labels)
Set label cloud.
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
Defines all the PCL implemented PointT point type structures.
Defines functions, macros and traits for allocating and using memory.
Defines all the PCL and non-PCL macros used.
A point structure representing Euclidean xyz coordinates, and the RGB color.