Point Cloud Library (PCL)  1.11.1
sac_model_sphere.hpp
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40 
41 #ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
42 #define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
43 
44 #include <pcl/sample_consensus/eigen.h>
45 #include <pcl/sample_consensus/sac_model_sphere.h>
46 
47 //////////////////////////////////////////////////////////////////////////
48 template <typename PointT> bool
50 {
51  if (samples.size () != sample_size_)
52  {
53  PCL_ERROR ("[pcl::SampleConsensusModelSphere::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
54  return (false);
55  }
56  return (true);
57 }
58 
59 //////////////////////////////////////////////////////////////////////////
60 template <typename PointT> bool
62  const Indices &samples, Eigen::VectorXf &model_coefficients) const
63 {
64  // Need 4 samples
65  if (samples.size () != sample_size_)
66  {
67  PCL_ERROR ("[pcl::SampleConsensusModelSphere::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
68  return (false);
69  }
70 
71  Eigen::Matrix4f temp;
72  for (int i = 0; i < 4; i++)
73  {
74  temp (i, 0) = (*input_)[samples[i]].x;
75  temp (i, 1) = (*input_)[samples[i]].y;
76  temp (i, 2) = (*input_)[samples[i]].z;
77  temp (i, 3) = 1;
78  }
79  float m11 = temp.determinant ();
80  if (m11 == 0)
81  {
82  return (false); // the points don't define a sphere!
83  }
84 
85  for (int i = 0; i < 4; ++i)
86  {
87  temp (i, 0) = ((*input_)[samples[i]].x) * ((*input_)[samples[i]].x) +
88  ((*input_)[samples[i]].y) * ((*input_)[samples[i]].y) +
89  ((*input_)[samples[i]].z) * ((*input_)[samples[i]].z);
90  }
91  float m12 = temp.determinant ();
92 
93  for (int i = 0; i < 4; ++i)
94  {
95  temp (i, 1) = temp (i, 0);
96  temp (i, 0) = (*input_)[samples[i]].x;
97  }
98  float m13 = temp.determinant ();
99 
100  for (int i = 0; i < 4; ++i)
101  {
102  temp (i, 2) = temp (i, 1);
103  temp (i, 1) = (*input_)[samples[i]].y;
104  }
105  float m14 = temp.determinant ();
106 
107  for (int i = 0; i < 4; ++i)
108  {
109  temp (i, 0) = temp (i, 2);
110  temp (i, 1) = (*input_)[samples[i]].x;
111  temp (i, 2) = (*input_)[samples[i]].y;
112  temp (i, 3) = (*input_)[samples[i]].z;
113  }
114  float m15 = temp.determinant ();
115 
116  // Center (x , y, z)
117  model_coefficients.resize (model_size_);
118  model_coefficients[0] = 0.5f * m12 / m11;
119  model_coefficients[1] = 0.5f * m13 / m11;
120  model_coefficients[2] = 0.5f * m14 / m11;
121  // Radius
122  model_coefficients[3] = std::sqrt (model_coefficients[0] * model_coefficients[0] +
123  model_coefficients[1] * model_coefficients[1] +
124  model_coefficients[2] * model_coefficients[2] - m15 / m11);
125 
126  return (true);
127 }
128 
129 //////////////////////////////////////////////////////////////////////////
130 template <typename PointT> void
132  const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
133 {
134  // Check if the model is valid given the user constraints
135  if (!isModelValid (model_coefficients))
136  {
137  distances.clear ();
138  return;
139  }
140  distances.resize (indices_->size ());
141 
142  // Iterate through the 3d points and calculate the distances from them to the sphere
143  for (std::size_t i = 0; i < indices_->size (); ++i)
144  {
145  // Calculate the distance from the point to the sphere as the difference between
146  //dist(point,sphere_origin) and sphere_radius
147  distances[i] = std::abs (std::sqrt (
148  ( (*input_)[(*indices_)[i]].x - model_coefficients[0] ) *
149  ( (*input_)[(*indices_)[i]].x - model_coefficients[0] ) +
150 
151  ( (*input_)[(*indices_)[i]].y - model_coefficients[1] ) *
152  ( (*input_)[(*indices_)[i]].y - model_coefficients[1] ) +
153 
154  ( (*input_)[(*indices_)[i]].z - model_coefficients[2] ) *
155  ( (*input_)[(*indices_)[i]].z - model_coefficients[2] )
156  ) - model_coefficients[3]);
157  }
158 }
159 
160 //////////////////////////////////////////////////////////////////////////
161 template <typename PointT> void
163  const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
164 {
165  // Check if the model is valid given the user constraints
166  if (!isModelValid (model_coefficients))
167  {
168  inliers.clear ();
169  return;
170  }
171 
172  inliers.clear ();
173  error_sqr_dists_.clear ();
174  inliers.reserve (indices_->size ());
175  error_sqr_dists_.reserve (indices_->size ());
176 
177  // Iterate through the 3d points and calculate the distances from them to the sphere
178  for (std::size_t i = 0; i < indices_->size (); ++i)
179  {
180  double distance = std::abs (std::sqrt (
181  ( (*input_)[(*indices_)[i]].x - model_coefficients[0] ) *
182  ( (*input_)[(*indices_)[i]].x - model_coefficients[0] ) +
183 
184  ( (*input_)[(*indices_)[i]].y - model_coefficients[1] ) *
185  ( (*input_)[(*indices_)[i]].y - model_coefficients[1] ) +
186 
187  ( (*input_)[(*indices_)[i]].z - model_coefficients[2] ) *
188  ( (*input_)[(*indices_)[i]].z - model_coefficients[2] )
189  ) - model_coefficients[3]);
190  // Calculate the distance from the point to the sphere as the difference between
191  // dist(point,sphere_origin) and sphere_radius
192  if (distance < threshold)
193  {
194  // Returns the indices of the points whose distances are smaller than the threshold
195  inliers.push_back ((*indices_)[i]);
196  error_sqr_dists_.push_back (static_cast<double> (distance));
197  }
198  }
199 }
200 
201 //////////////////////////////////////////////////////////////////////////
202 template <typename PointT> std::size_t
204  const Eigen::VectorXf &model_coefficients, const double threshold) const
205 {
206  // Check if the model is valid given the user constraints
207  if (!isModelValid (model_coefficients))
208  return (0);
209 
210  std::size_t nr_p = 0;
211 
212  // Iterate through the 3d points and calculate the distances from them to the sphere
213  for (std::size_t i = 0; i < indices_->size (); ++i)
214  {
215  // Calculate the distance from the point to the sphere as the difference between
216  // dist(point,sphere_origin) and sphere_radius
217  if (std::abs (std::sqrt (
218  ( (*input_)[(*indices_)[i]].x - model_coefficients[0] ) *
219  ( (*input_)[(*indices_)[i]].x - model_coefficients[0] ) +
220 
221  ( (*input_)[(*indices_)[i]].y - model_coefficients[1] ) *
222  ( (*input_)[(*indices_)[i]].y - model_coefficients[1] ) +
223 
224  ( (*input_)[(*indices_)[i]].z - model_coefficients[2] ) *
225  ( (*input_)[(*indices_)[i]].z - model_coefficients[2] )
226  ) - model_coefficients[3]) < threshold)
227  nr_p++;
228  }
229  return (nr_p);
230 }
231 
232 //////////////////////////////////////////////////////////////////////////
233 template <typename PointT> void
235  const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
236 {
237  optimized_coefficients = model_coefficients;
238 
239  // Needs a set of valid model coefficients
240  if (!isModelValid (model_coefficients))
241  {
242  PCL_ERROR ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] Given model is invalid!\n");
243  return;
244  }
245 
246  // Need more than the minimum sample size to make a difference
247  if (inliers.size () <= sample_size_)
248  {
249  PCL_ERROR ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
250  return;
251  }
252 
253  OptimizationFunctor functor (this, inliers);
254  Eigen::NumericalDiff<OptimizationFunctor> num_diff (functor);
255  Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, float> lm (num_diff);
256  int info = lm.minimize (optimized_coefficients);
257 
258  // Compute the L2 norm of the residuals
259  PCL_DEBUG ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] LM solver finished with exit code %i, having a residual norm of %g. \nInitial solution: %g %g %g %g \nFinal solution: %g %g %g %g\n",
260  info, lm.fvec.norm (), model_coefficients[0], model_coefficients[1], model_coefficients[2], model_coefficients[3], optimized_coefficients[0], optimized_coefficients[1], optimized_coefficients[2], optimized_coefficients[3]);
261 }
262 
263 //////////////////////////////////////////////////////////////////////////
264 template <typename PointT> void
266  const Indices &, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool) const
267 {
268  // Needs a valid model coefficients
269  if (!isModelValid (model_coefficients))
270  {
271  PCL_ERROR ("[pcl::SampleConsensusModelSphere::projectPoints] Given model is invalid!\n");
272  return;
273  }
274 
275  // Allocate enough space and copy the basics
276  projected_points.points.resize (input_->size ());
277  projected_points.header = input_->header;
278  projected_points.width = input_->width;
279  projected_points.height = input_->height;
280  projected_points.is_dense = input_->is_dense;
281 
282  PCL_WARN ("[pcl::SampleConsensusModelSphere::projectPoints] Not implemented yet.\n");
283  projected_points.points = input_->points;
284 }
285 
286 //////////////////////////////////////////////////////////////////////////
287 template <typename PointT> bool
289  const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
290 {
291  // Needs a valid model coefficients
292  if (!isModelValid (model_coefficients))
293  {
294  PCL_ERROR ("[pcl::SampleConsensusModelSphere::doSamplesVerifyModel] Given model is invalid!\n");
295  return (false);
296  }
297 
298  for (const auto &index : indices)
299  {
300  // Calculate the distance from the point to the sphere as the difference between
301  //dist(point,sphere_origin) and sphere_radius
302  if (std::abs (sqrt (
303  ( (*input_)[index].x - model_coefficients[0] ) *
304  ( (*input_)[index].x - model_coefficients[0] ) +
305  ( (*input_)[index].y - model_coefficients[1] ) *
306  ( (*input_)[index].y - model_coefficients[1] ) +
307  ( (*input_)[index].z - model_coefficients[2] ) *
308  ( (*input_)[index].z - model_coefficients[2] )
309  ) - model_coefficients[3]) > threshold)
310  {
311  return (false);
312  }
313  }
314 
315  return (true);
316 }
317 
318 #define PCL_INSTANTIATE_SampleConsensusModelSphere(T) template class PCL_EXPORTS pcl::SampleConsensusModelSphere<T>;
319 
320 #endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
321 
pcl::SampleConsensusModelSphere::countWithinDistance
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.
Definition: sac_model_sphere.hpp:203
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:416
pcl::geometry::distance
float distance(const PointT &p1, const PointT &p2)
Definition: geometry.h:60
pcl::SampleConsensusModelSphere::isSampleGood
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_sphere.hpp:49
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:411
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:181
pcl::SampleConsensusModelSphere::getDistancesToModel
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.
Definition: sac_model_sphere.hpp:131
pcl::SampleConsensusModelSphere::optimizeModelCoefficients
void optimizeModelCoefficients(const Indices &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.
Definition: sac_model_sphere.hpp:234
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:414
pcl::SampleConsensusModelSphere::computeModelCoefficients
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid sphere model, compute the model coefficients f...
Definition: sac_model_sphere.hpp:61
pcl::PointCloud::is_dense
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:419
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:408
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:141
pcl::SampleConsensusModelSphere::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given sphere model coefficients.
Definition: sac_model_sphere.hpp:288
pcl::SampleConsensusModelSphere::projectPoints
void projectPoints(const Indices &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.
Definition: sac_model_sphere.hpp:265
pcl::SampleConsensusModelSphere::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_sphere.hpp:162