Point Cloud Library (PCL)  1.10.0-dev
sac_model_sphere.h
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40 
41 #pragma once
42 
43 #include <pcl/sample_consensus/sac_model.h>
44 #include <pcl/sample_consensus/model_types.h>
45 
46 namespace pcl
47 {
48  /** \brief SampleConsensusModelSphere defines a model for 3D sphere segmentation.
49  * The model coefficients are defined as:
50  * - \b center.x : the X coordinate of the sphere's center
51  * - \b center.y : the Y coordinate of the sphere's center
52  * - \b center.z : the Z coordinate of the sphere's center
53  * - \b radius : the sphere's radius
54  *
55  * \author Radu B. Rusu
56  * \ingroup sample_consensus
57  */
58  template <typename PointT>
60  {
61  public:
68 
72 
75 
76  /** \brief Constructor for base SampleConsensusModelSphere.
77  * \param[in] cloud the input point cloud dataset
78  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
79  */
81  bool random = false)
82  : SampleConsensusModel<PointT> (cloud, random)
83  {
84  model_name_ = "SampleConsensusModelSphere";
85  sample_size_ = 4;
86  model_size_ = 4;
87  }
88 
89  /** \brief Constructor for base SampleConsensusModelSphere.
90  * \param[in] cloud the input point cloud dataset
91  * \param[in] indices a vector of point indices to be used from \a cloud
92  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
93  */
95  const std::vector<int> &indices,
96  bool random = false)
97  : SampleConsensusModel<PointT> (cloud, indices, random)
98  {
99  model_name_ = "SampleConsensusModelSphere";
100  sample_size_ = 4;
101  model_size_ = 4;
102  }
103 
104  /** \brief Empty destructor */
106 
107  /** \brief Copy constructor.
108  * \param[in] source the model to copy into this
109  */
112  {
113  *this = source;
114  model_name_ = "SampleConsensusModelSphere";
115  }
116 
117  /** \brief Copy constructor.
118  * \param[in] source the model to copy into this
119  */
122  {
124  return (*this);
125  }
126 
127  /** \brief Check whether the given index samples can form a valid sphere model, compute the model
128  * coefficients from these samples and store them internally in model_coefficients.
129  * The sphere coefficients are: x, y, z, R.
130  * \param[in] samples the point indices found as possible good candidates for creating a valid model
131  * \param[out] model_coefficients the resultant model coefficients
132  */
133  bool
134  computeModelCoefficients (const std::vector<int> &samples,
135  Eigen::VectorXf &model_coefficients) const override;
136 
137  /** \brief Compute all distances from the cloud data to a given sphere model.
138  * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
139  * \param[out] distances the resultant estimated distances
140  */
141  void
142  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
143  std::vector<double> &distances) const override;
144 
145  /** \brief Select all the points which respect the given model coefficients as inliers.
146  * \param[in] model_coefficients the coefficients of a sphere model that we need to compute distances to
147  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
148  * \param[out] inliers the resultant model inliers
149  */
150  void
151  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
152  const double threshold,
153  std::vector<int> &inliers) override;
154 
155  /** \brief Count all the points which respect the given model coefficients as inliers.
156  *
157  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
158  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
159  * \return the resultant number of inliers
160  */
161  std::size_t
162  countWithinDistance (const Eigen::VectorXf &model_coefficients,
163  const double threshold) const override;
164 
165  /** \brief Recompute the sphere coefficients using the given inlier set and return them to the user.
166  * @note: these are the coefficients of the sphere model after refinement (e.g. after SVD)
167  * \param[in] inliers the data inliers found as supporting the model
168  * \param[in] model_coefficients the initial guess for the optimization
169  * \param[out] optimized_coefficients the resultant recomputed coefficients after non-linear optimization
170  */
171  void
172  optimizeModelCoefficients (const std::vector<int> &inliers,
173  const Eigen::VectorXf &model_coefficients,
174  Eigen::VectorXf &optimized_coefficients) const override;
175 
176  /** \brief Create a new point cloud with inliers projected onto the sphere model.
177  * \param[in] inliers the data inliers that we want to project on the sphere model
178  * \param[in] model_coefficients the coefficients of a sphere model
179  * \param[out] projected_points the resultant projected points
180  * \param[in] copy_data_fields set to true if we need to copy the other data fields
181  * \todo implement this.
182  */
183  void
184  projectPoints (const std::vector<int> &inliers,
185  const Eigen::VectorXf &model_coefficients,
186  PointCloud &projected_points,
187  bool copy_data_fields = true) const override;
188 
189  /** \brief Verify whether a subset of indices verifies the given sphere model coefficients.
190  * \param[in] indices the data indices that need to be tested against the sphere model
191  * \param[in] model_coefficients the sphere model coefficients
192  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
193  */
194  bool
195  doSamplesVerifyModel (const std::set<int> &indices,
196  const Eigen::VectorXf &model_coefficients,
197  const double threshold) const override;
198 
199  /** \brief Return a unique id for this model (SACMODEL_SPHERE). */
200  inline pcl::SacModel getModelType () const override { return (SACMODEL_SPHERE); }
201 
202  protected:
205 
206  /** \brief Check whether a model is valid given the user constraints.
207  * \param[in] model_coefficients the set of model coefficients
208  */
209  bool
210  isModelValid (const Eigen::VectorXf &model_coefficients) const override
211  {
212  if (!SampleConsensusModel<PointT>::isModelValid (model_coefficients))
213  return (false);
214 
215  if (radius_min_ != -std::numeric_limits<double>::max() && model_coefficients[3] < radius_min_)
216  return (false);
217  if (radius_max_ != std::numeric_limits<double>::max() && model_coefficients[3] > radius_max_)
218  return (false);
219 
220  return (true);
221  }
222 
223  /** \brief Check if a sample of indices results in a good sample of points
224  * indices.
225  * \param[in] samples the resultant index samples
226  */
227  bool
228  isSampleGood(const std::vector<int> &samples) const override;
229 
230  private:
231  struct OptimizationFunctor : pcl::Functor<float>
232  {
233  /** Functor constructor
234  * \param[in] indices the indices of data points to evaluate
235  * \param[in] estimator pointer to the estimator object
236  */
237  OptimizationFunctor (const pcl::SampleConsensusModelSphere<PointT> *model, const std::vector<int>& indices) :
238  pcl::Functor<float> (indices.size ()), model_ (model), indices_ (indices) {}
239 
240  /** Cost function to be minimized
241  * \param[in] x the variables array
242  * \param[out] fvec the resultant functions evaluations
243  * \return 0
244  */
245  int
246  operator() (const Eigen::VectorXf &x, Eigen::VectorXf &fvec) const
247  {
248  Eigen::Vector4f cen_t;
249  cen_t[3] = 0;
250  for (int i = 0; i < values (); ++i)
251  {
252  // Compute the difference between the center of the sphere and the datapoint X_i
253  cen_t[0] = model_->input_->points[indices_[i]].x - x[0];
254  cen_t[1] = model_->input_->points[indices_[i]].y - x[1];
255  cen_t[2] = model_->input_->points[indices_[i]].z - x[2];
256 
257  // g = sqrt ((x-a)^2 + (y-b)^2 + (z-c)^2) - R
258  fvec[i] = std::sqrt (cen_t.dot (cen_t)) - x[3];
259  }
260  return (0);
261  }
262 
264  const std::vector<int> &indices_;
265  };
266  };
267 }
268 
269 #ifdef PCL_NO_PRECOMPILE
270 #include <pcl/sample_consensus/impl/sac_model_sphere.hpp>
271 #endif
double radius_min_
The minimum and maximum radius limits for the model.
Definition: sac_model.h:537
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:45
unsigned int model_size_
The number of coefficients in the model.
Definition: sac_model.h:564
Base functor all the models that need non linear optimization must define their own one and implement...
Definition: sac_model.h:642
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the sphere coefficients using the given inlier set and return them to the user...
bool isSampleGood(const std::vector< int > &samples) const override
Check if a sample of indices results in a good sample of points indices.
typename PointCloud::Ptr PointCloudPtr
Definition: sac_model.h:73
void projectPoints(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the sphere model.
SampleConsensusModel represents the base model class.
Definition: sac_model.h:68
SampleConsensusModelSphere(const PointCloudConstPtr &cloud, const std::vector< int > &indices, bool random=false)
Constructor for base SampleConsensusModelSphere.
std::string model_name_
The model name.
Definition: sac_model.h:523
pcl::SacModel getModelType() const override
Return a unique id for this model (SACMODEL_SPHERE).
bool doSamplesVerifyModel(const std::set< int > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given sphere model coefficients.
bool isModelValid(const Eigen::VectorXf &model_coefficients) const override
Check whether a model is valid given the user constraints.
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: sac_model.h:72
SampleConsensusModelSphere & operator=(const SampleConsensusModelSphere &source)
Copy constructor.
SampleConsensusModelSphere(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelSphere.
SacModel
Definition: model_types.h:45
shared_ptr< const SampleConsensusModel< pcl::PointXYZRGB > > ConstPtr
Definition: sac_model.h:77
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition: sac_model.h:529
~SampleConsensusModelSphere()
Empty destructor.
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, std::vector< int > &inliers) override
Select all the points which respect the given model coefficients as inliers.
A point structure representing Euclidean xyz coordinates, and the RGB color.
shared_ptr< SampleConsensusModel< pcl::PointXYZRGB > > Ptr
Definition: sac_model.h:76
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid sphere model, compute the model coefficients f...
boost::shared_ptr< T > shared_ptr
Alias for boost::shared_ptr.
Definition: pcl_macros.h:90
SampleConsensusModelSphere defines a model for 3D sphere segmentation.
unsigned int sample_size_
The size of a sample from which the model is computed.
Definition: sac_model.h:561
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all distances from the cloud data to a given sphere model.
SampleConsensusModelSphere(const SampleConsensusModelSphere &source)
Copy constructor.