sac_model_sphere.hpp
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
#include <pcl/sample_consensus/eigen.h>
#include <pcl/sample_consensus/sac_model_sphere.h>
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelSphere<PointT>::isSampleGood (const std::vector<int> &) const
{
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelSphere<PointT>::computeModelCoefficients (
const std::vector<int> &samples, Eigen::VectorXf &model_coefficients)
{
// Need 4 samples
if (samples.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelSphere::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
return (false);
}
Eigen::Matrix4f temp;
for (int i = 0; i < 4; i++)
{
temp (i, 0) = input_->points[samples[i]].x;
temp (i, 1) = input_->points[samples[i]].y;
temp (i, 2) = input_->points[samples[i]].z;
temp (i, 3) = 1;
}
float m11 = temp.determinant ();
if (m11 == 0)
return (false); // the points don't define a sphere!
for (int i = 0; i < 4; ++i)
temp (i, 0) = (input_->points[samples[i]].x) * (input_->points[samples[i]].x) +
(input_->points[samples[i]].y) * (input_->points[samples[i]].y) +
(input_->points[samples[i]].z) * (input_->points[samples[i]].z);
float m12 = temp.determinant ();
for (int i = 0; i < 4; ++i)
{
temp (i, 1) = temp (i, 0);
temp (i, 0) = input_->points[samples[i]].x;
}
float m13 = temp.determinant ();
for (int i = 0; i < 4; ++i)
{
temp (i, 2) = temp (i, 1);
temp (i, 1) = input_->points[samples[i]].y;
}
float m14 = temp.determinant ();
for (int i = 0; i < 4; ++i)
{
temp (i, 0) = temp (i, 2);
temp (i, 1) = input_->points[samples[i]].x;
temp (i, 2) = input_->points[samples[i]].y;
temp (i, 3) = input_->points[samples[i]].z;
}
float m15 = temp.determinant ();
// Center (x , y, z)
model_coefficients.resize (4);
model_coefficients[0] = 0.5f * m12 / m11;
model_coefficients[1] = 0.5f * m13 / m11;
model_coefficients[2] = 0.5f * m14 / m11;
// Radius
model_coefficients[3] = sqrtf (
model_coefficients[0] * model_coefficients[0] +
model_coefficients[1] * model_coefficients[1] +
model_coefficients[2] * model_coefficients[2] - m15 / m11);
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelSphere<PointT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances)
{
// Check if the model is valid given the user constraints
if (!isModelValid (model_coefficients))
{
distances.clear ();
return;
}
distances.resize (indices_->size ());
// Iterate through the 3d points and calculate the distances from them to the sphere
for (size_t i = 0; i < indices_->size (); ++i)
// Calculate the distance from the point to the sphere as the difference between
//dist(point,sphere_origin) and sphere_radius
distances[i] = fabs (sqrtf (
( input_->points[(*indices_)[i]].x - model_coefficients[0] ) *
( input_->points[(*indices_)[i]].x - model_coefficients[0] ) +
( input_->points[(*indices_)[i]].y - model_coefficients[1] ) *
( input_->points[(*indices_)[i]].y - model_coefficients[1] ) +
( input_->points[(*indices_)[i]].z - model_coefficients[2] ) *
( input_->points[(*indices_)[i]].z - model_coefficients[2] )
) - model_coefficients[3]);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelSphere<PointT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, std::vector<int> &inliers)
{
// Check if the model is valid given the user constraints
if (!isModelValid (model_coefficients))
{
inliers.clear ();
return;
}
int nr_p = 0;
inliers.resize (indices_->size ());
error_sqr_dists_.resize (indices_->size ());
// Iterate through the 3d points and calculate the distances from them to the sphere
for (size_t i = 0; i < indices_->size (); ++i)
{
double distance = fabs (sqrtf (
( input_->points[(*indices_)[i]].x - model_coefficients[0] ) *
( input_->points[(*indices_)[i]].x - model_coefficients[0] ) +
( input_->points[(*indices_)[i]].y - model_coefficients[1] ) *
( input_->points[(*indices_)[i]].y - model_coefficients[1] ) +
( input_->points[(*indices_)[i]].z - model_coefficients[2] ) *
( input_->points[(*indices_)[i]].z - model_coefficients[2] )
) - model_coefficients[3]);
// Calculate the distance from the point to the sphere as the difference between
// dist(point,sphere_origin) and sphere_radius
if (distance < threshold)
{
// Returns the indices of the points whose distances are smaller than the threshold
inliers[nr_p] = (*indices_)[i];
error_sqr_dists_[nr_p] = static_cast<double> (distance);
++nr_p;
}
}
inliers.resize (nr_p);
error_sqr_dists_.resize (nr_p);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> int
pcl::SampleConsensusModelSphere<PointT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold)
{
// Check if the model is valid given the user constraints
if (!isModelValid (model_coefficients))
return (0);
int nr_p = 0;
// Iterate through the 3d points and calculate the distances from them to the sphere
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the sphere as the difference between
// dist(point,sphere_origin) and sphere_radius
if (fabs (sqrtf (
( input_->points[(*indices_)[i]].x - model_coefficients[0] ) *
( input_->points[(*indices_)[i]].x - model_coefficients[0] ) +
( input_->points[(*indices_)[i]].y - model_coefficients[1] ) *
( input_->points[(*indices_)[i]].y - model_coefficients[1] ) +
( input_->points[(*indices_)[i]].z - model_coefficients[2] ) *
( input_->points[(*indices_)[i]].z - model_coefficients[2] )
) - model_coefficients[3]) < threshold)
nr_p++;
}
return (nr_p);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelSphere<PointT>::optimizeModelCoefficients (
const std::vector<int> &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
{
optimized_coefficients = model_coefficients;
// Needs a set of valid model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return;
}
// Need at least 4 samples
if (inliers.size () <= 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelSphere::optimizeModelCoefficients] Not enough inliers found to support a model (%lu)! Returning the same coefficients.\n", inliers.size ());
return;
}
tmp_inliers_ = &inliers;
OptimizationFunctor functor (static_cast<int> (inliers.size ()), this);
Eigen::NumericalDiff<OptimizationFunctor> num_diff (functor);
Eigen::LevenbergMarquardt<Eigen::NumericalDiff<OptimizationFunctor>, float> lm (num_diff);
int info = lm.minimize (optimized_coefficients);
// Compute the L2 norm of the residuals
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",
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]);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelSphere<PointT>::projectPoints (
const std::vector<int> &, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool)
{
// Needs a valid model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelSphere::projectPoints] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return;
}
// Allocate enough space and copy the basics
projected_points.points.resize (input_->points.size ());
projected_points.header = input_->header;
projected_points.width = input_->width;
projected_points.height = input_->height;
projected_points.is_dense = input_->is_dense;
PCL_WARN ("[pcl::SampleConsensusModelSphere::projectPoints] Not implemented yet.\n");
projected_points.points = input_->points;
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelSphere<PointT>::doSamplesVerifyModel (
const std::set<int> &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
{
// Needs a valid model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelSphere::doSamplesVerifyModel] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return (false);
}
for (std::set<int>::const_iterator it = indices.begin (); it != indices.end (); ++it)
// Calculate the distance from the point to the sphere as the difference between
//dist(point,sphere_origin) and sphere_radius
if (fabs (sqrt (
( input_->points[*it].x - model_coefficients[0] ) *
( input_->points[*it].x - model_coefficients[0] ) +
( input_->points[*it].y - model_coefficients[1] ) *
( input_->points[*it].y - model_coefficients[1] ) +
( input_->points[*it].z - model_coefficients[2] ) *
( input_->points[*it].z - model_coefficients[2] )
) - model_coefficients[3]) > threshold)
return (false);
return (true);
}
#define PCL_INSTANTIATE_SampleConsensusModelSphere(T) template class PCL_EXPORTS pcl::SampleConsensusModelSphere<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_SPHERE_H_
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::SampleConsensusModelSphere::optimizeModelCoefficients
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
Recompute the sphere coefficients using the given inlier set and return them to the user...
Definition: sac_model_sphere.hpp:226
pcl::SampleConsensusModelSphere::projectPoints
void projectPoints(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true)
Create a new point cloud with inliers projected onto the sphere model.
Definition: sac_model_sphere.hpp:259
pcl::PointCloud::height
uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:415
pcl::PointCloud::width
uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:413
pcl::SampleConsensusModelSphere::isSampleGood
bool isSampleGood(const std::vector< int > &samples) const
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_sphere.hpp:49
pcl::SampleConsensusModelSphere::countWithinDistance
virtual int countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold)
Count all the points which respect the given model coefficients as inliers.
Definition: sac_model_sphere.hpp:195
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points. ...
Definition: projection_matrix.h:53
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:407
pcl::SampleConsensusModelSphere::computeModelCoefficients
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients)
Check whether the given index samples can form a valid sphere model, compute the model coefficients f...
Definition: sac_model_sphere.hpp:56
pcl::SampleConsensusModelSphere::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< int > &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
Verify whether a subset of indices verifies the given sphere model coefficients.
Definition: sac_model_sphere.hpp:282
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values).
Definition: point_cloud.h:418
pcl::SampleConsensusModelSphere::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances)
Compute all distances from the cloud data to a given sphere model.
Definition: sac_model_sphere.hpp:123
pcl::SampleConsensusModelSphere::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, std::vector< int > &inliers)
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_sphere.hpp:152