Point Cloud Library (PCL)  1.11.1
segment_differences.h
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37 
38 #pragma once
39 
40 #include <pcl/pcl_base.h>
41 #include <pcl/pcl_macros.h>
42 #include <pcl/search/pcl_search.h>
43 
44 namespace pcl
45 {
46  ////////////////////////////////////////////////////////////////////////////////////////////
47  /** \brief Obtain the difference between two aligned point clouds as another point cloud, given a distance threshold.
48  * \param src the input point cloud source
49  * \param threshold the distance threshold (tolerance) for point correspondences. (e.g., check if f a point p1 from
50  * src has a correspondence > threshold than a point p2 from tgt)
51  * \param tree the spatial locator (e.g., kd-tree) used for nearest neighbors searching built over the target cloud
52  * \param output the resultant output point cloud difference
53  * \ingroup segmentation
54  */
55  template <typename PointT>
57  const pcl::PointCloud<PointT> &src,
58  double threshold,
59  const typename pcl::search::Search<PointT>::Ptr &tree,
60  pcl::PointCloud<PointT> &output);
61 
62  template <typename PointT>
63  PCL_DEPRECATED(1, 12, "tgt parameter is not used; it is deprecated and will be removed in future releases")
65  const pcl::PointCloud<PointT> &src,
66  const pcl::PointCloud<PointT> & /* tgt */,
67  double threshold,
68  const typename pcl::search::Search<PointT>::Ptr &tree,
69  pcl::PointCloud<PointT> &output)
70  {
71  getPointCloudDifference<PointT> (src, threshold, tree, output);
72  }
73 
74  ////////////////////////////////////////////////////////////////////////////////////////////
75  ////////////////////////////////////////////////////////////////////////////////////////////
76  ////////////////////////////////////////////////////////////////////////////////////////////
77  /** \brief @b SegmentDifferences obtains the difference between two spatially
78  * aligned point clouds and returns the difference between them for a maximum
79  * given distance threshold.
80  * \author Radu Bogdan Rusu
81  * \ingroup segmentation
82  */
83  template <typename PointT>
84  class SegmentDifferences: public PCLBase<PointT>
85  {
87 
88  public:
90  using PointCloudPtr = typename PointCloud::Ptr;
92 
94  using KdTreePtr = typename KdTree::Ptr;
95 
98 
99  /** \brief Empty constructor. */
101  tree_ (), target_ (), distance_threshold_ (0)
102  {};
103 
104  /** \brief Provide a pointer to the target dataset against which we
105  * compare the input cloud given in setInputCloud
106  *
107  * \param cloud the target PointCloud dataset
108  */
109  inline void
110  setTargetCloud (const PointCloudConstPtr &cloud) { target_ = cloud; }
111 
112  /** \brief Get a pointer to the input target point cloud dataset. */
113  inline PointCloudConstPtr const
114  getTargetCloud () { return (target_); }
115 
116  /** \brief Provide a pointer to the search object.
117  * \param tree a pointer to the spatial search object.
118  */
119  inline void
120  setSearchMethod (const KdTreePtr &tree) { tree_ = tree; }
121 
122  /** \brief Get a pointer to the search method used. */
123  inline KdTreePtr
124  getSearchMethod () { return (tree_); }
125 
126  /** \brief Set the maximum distance tolerance (squared) between corresponding
127  * points in the two input datasets.
128  *
129  * \param sqr_threshold the squared distance tolerance as a measure in L2 Euclidean space
130  */
131  inline void
132  setDistanceThreshold (double sqr_threshold) { distance_threshold_ = sqr_threshold; }
133 
134  /** \brief Get the squared distance tolerance between corresponding points as a
135  * measure in the L2 Euclidean space.
136  */
137  inline double
139 
140  /** \brief Segment differences between two input point clouds.
141  * \param output the resultant difference between the two point clouds as a PointCloud
142  */
143  void
144  segment (PointCloud &output);
145 
146  protected:
147  // Members derived from the base class
148  using BasePCLBase::input_;
149  using BasePCLBase::indices_;
152 
153  /** \brief A pointer to the spatial search object. */
155 
156  /** \brief The input target point cloud dataset. */
158 
159  /** \brief The distance tolerance (squared) as a measure in the L2
160  * Euclidean space between corresponding points.
161  */
163 
164  /** \brief Class getName method. */
165  virtual std::string
166  getClassName () const { return ("SegmentDifferences"); }
167  };
168 }
169 
170 #ifdef PCL_NO_PRECOMPILE
171 #include <pcl/segmentation/impl/segment_differences.hpp>
172 #endif
PCL base class.
Definition: pcl_base.h:73
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:150
typename PointCloud::Ptr PointCloudPtr
Definition: pcl_base.h:76
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: pcl_base.h:77
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: pcl_base.h:153
bool initCompute()
This method should get called before starting the actual computation.
Definition: pcl_base.hpp:138
PointIndices::ConstPtr PointIndicesConstPtr
Definition: pcl_base.h:80
bool deinitCompute()
This method should get called after finishing the actual computation.
Definition: pcl_base.hpp:171
PointIndices::Ptr PointIndicesPtr
Definition: pcl_base.h:79
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:181
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:429
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:430
SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the ...
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
void setTargetCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the target dataset against which we compare the input cloud given in setInputClo...
double getDistanceThreshold()
Get the squared distance tolerance between corresponding points as a measure in the L2 Euclidean spac...
void segment(PointCloud &output)
Segment differences between two input point clouds.
SegmentDifferences()
Empty constructor.
KdTreePtr tree_
A pointer to the spatial search object.
PointCloudConstPtr target_
The input target point cloud dataset.
PointCloudConstPtr const getTargetCloud()
Get a pointer to the input target point cloud dataset.
KdTreePtr getSearchMethod()
Get a pointer to the search method used.
typename KdTree::Ptr KdTreePtr
void setDistanceThreshold(double sqr_threshold)
Set the maximum distance tolerance (squared) between corresponding points in the two input datasets.
double distance_threshold_
The distance tolerance (squared) as a measure in the L2 Euclidean space between corresponding points.
virtual std::string getClassName() const
Class getName method.
Generic search class.
Definition: search.h:75
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition: search.h:81
void getPointCloudDifference(const pcl::PointCloud< PointT > &src, double threshold, const typename pcl::search::Search< PointT >::Ptr &tree, pcl::PointCloud< PointT > &output)
Obtain the difference between two aligned point clouds as another point cloud, given a distance thres...
Defines all the PCL and non-PCL macros used.
#define PCL_DEPRECATED(Major, Minor, Message)
macro for compatibility across compilers and help remove old deprecated items for the Major....
Definition: pcl_macros.h:147
shared_ptr< ::pcl::PointIndices > Ptr
Definition: PointIndices.h:15
shared_ptr< const ::pcl::PointIndices > ConstPtr
Definition: PointIndices.h:16
A point structure representing Euclidean xyz coordinates, and the RGB color.