Point Cloud Library (PCL)  1.10.1-dev
cppf.hpp
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40 
41 #ifndef PCL_FEATURES_IMPL_CPPF_H_
42 #define PCL_FEATURES_IMPL_CPPF_H_
43 
44 #include <pcl/features/cppf.h>
45 #include <pcl/features/pfh.h>
46 
47 //////////////////////////////////////////////////////////////////////////////////////////////
48 template <typename PointInT, typename PointNT, typename PointOutT>
50  : FeatureFromNormals <PointInT, PointNT, PointOutT> ()
51 {
52  feature_name_ = "CPPFEstimation";
53  // Slight hack in order to pass the check for the presence of a search method in Feature::initCompute ()
56 }
57 
58 
59 //////////////////////////////////////////////////////////////////////////////////////////////
60 template <typename PointInT, typename PointNT, typename PointOutT> void
62 {
63  // Initialize output container
64  output.points.clear ();
65  output.points.reserve (indices_->size () * input_->points.size ());
66  output.is_dense = true;
67  // Compute point pair features for every pair of points in the cloud
68  for (const auto& i: *indices_)
69  {
70  for (std::size_t j = 0 ; j < input_->points.size (); ++j)
71  {
72  PointOutT p;
73  // No need to calculate feature for identity pair (i, j) as they aren't used in future calculations
74  // @TODO: resolve issue with comparison in a better manner
75  if (static_cast<std::size_t>(i) != j)
76  {
77  if (
78  pcl::computeCPPFPairFeature (input_->points[i].getVector4fMap (),
79  normals_->points[i].getNormalVector4fMap (),
80  input_->points[i].getRGBVector4i (),
81  input_->points[j].getVector4fMap (),
82  normals_->points[j].getNormalVector4fMap (),
83  input_->points[j].getRGBVector4i (),
84  p.f1, p.f2, p.f3, p.f4, p.f5, p.f6, p.f7, p.f8, p.f9, p.f10))
85  {
86  // Calculate alpha_m angle
87  Eigen::Vector3f model_reference_point = input_->points[i].getVector3fMap (),
88  model_reference_normal = normals_->points[i].getNormalVector3fMap (),
89  model_point = input_->points[j].getVector3fMap ();
90  Eigen::AngleAxisf rotation_mg (std::acos (model_reference_normal.dot (Eigen::Vector3f::UnitX ())),
91  model_reference_normal.cross (Eigen::Vector3f::UnitX ()).normalized ());
92  Eigen::Affine3f transform_mg = Eigen::Translation3f ( rotation_mg * ((-1) * model_reference_point)) * rotation_mg;
93 
94  Eigen::Vector3f model_point_transformed = transform_mg * model_point;
95  float angle = std::atan2 ( -model_point_transformed(2), model_point_transformed(1));
96  if (std::sin (angle) * model_point_transformed(2) < 0.0f)
97  angle *= (-1);
98  p.alpha_m = -angle;
99  }
100  else
101  {
102  PCL_ERROR ("[pcl::%s::computeFeature] Computing pair feature vector between points %lu and %lu went wrong.\n", getClassName ().c_str (), i, j);
103  p.f1 = p.f2 = p.f3 = p.f4 = p.f5 = p.f6 = p.f7 = p.f8 = p.f9 = p.f10 = p.alpha_m = std::numeric_limits<float>::quiet_NaN ();
104  output.is_dense = false;
105  }
106  }
107  else
108  {
109  p.f1 = p.f2 = p.f3 = p.f4 = p.f5 = p.f6 = p.f7 = p.f8 = p.f9 = p.f10 = p.alpha_m = std::numeric_limits<float>::quiet_NaN ();
110  output.is_dense = false;
111  }
112 
113  output.points.push_back (p);
114  }
115  }
116  // overwrite the sizes done by Feature::initCompute ()
117  output.height = 1;
118  output.width = static_cast<std::uint32_t> (output.points.size ());
119 }
120 
121 #define PCL_INSTANTIATE_CPPFEstimation(T,NT,OutT) template class PCL_EXPORTS pcl::CPPFEstimation<T,NT,OutT>;
122 
123 
124 #endif // PCL_FEATURES_IMPL_CPPF_H_
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition: kdtree.h:61
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:396
PCL_EXPORTS bool computeCPPFPairFeature(const Eigen::Vector4f &p1, const Eigen::Vector4f &n1, const Eigen::Vector4i &c1, const Eigen::Vector4f &p2, const Eigen::Vector4f &n2, const Eigen::Vector4i &c2, float &f1, float &f2, float &f3, float &f4, float &f5, float &f6, float &f7, float &f8, float &f9, float &f10)
Class that calculates the "surflet" features for each pair in the given pointcloud.
Definition: cppf.h:87
std::string feature_name_
The feature name.
Definition: feature.h:223
CPPFEstimation()
Empty Constructor.
Definition: cppf.hpp:49
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: pcl_base.h:151
const std::string & getClassName() const
Get a string representation of the name of this class.
Definition: feature.h:247
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:399
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:401
PointCloudNConstPtr normals_
A pointer to the input dataset that contains the point normals of the XYZ dataset.
Definition: feature.h:355
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:404
PointCloudConstPtr input_
The input point cloud dataset.
Definition: pcl_base.h:148
Feature represents the base feature class.
Definition: feature.h:106