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ransac.hpp
/*
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* Point Cloud Library (PCL) - www.pointclouds.org
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_RANSAC_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_RANSAC_H_
#include <pcl/sample_consensus/ransac.h>
#ifdef _OPENMP
#include <omp.h>
#endif
#if defined _OPENMP && _OPENMP >= 201107 // We need OpenMP 3.1 for the atomic constructs
#define OPENMP_AVAILABLE_RANSAC true
#else
#define OPENMP_AVAILABLE_RANSAC false
#endif
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::RandomSampleConsensus<PointT>::computeModel (int)
{
// Warn and exit if no threshold was set
if (threshold_ == std::numeric_limits<double>::max())
{
PCL_ERROR ("[pcl::RandomSampleConsensus::computeModel] No threshold set!\n");
return (false);
}
iterations_ = 0;
std::size_t n_best_inliers_count = 0;
double k = std::numeric_limits<double>::max();
Indices selection;
Eigen::VectorXf model_coefficients (sac_model_->getModelSize ());
const double log_probability = std::log (1.0 - probability_);
const double one_over_indices = 1.0 / static_cast<double> (sac_model_->getIndices ()->size ());
unsigned skipped_count = 0;
// suppress infinite loops by just allowing 10 x maximum allowed iterations for invalid model parameters!
const unsigned max_skip = max_iterations_ * 10;
int threads = threads_;
if (threads >= 0)
{
#if OPENMP_AVAILABLE_RANSAC
if (threads == 0)
{
threads = omp_get_num_procs();
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Automatic number of threads requested, choosing %i threads.\n", threads);
}
#else
// Parallelization desired, but not available
PCL_WARN ("[pcl::RandomSampleConsensus::computeModel] Parallelization is requested, but OpenMP 3.1 is not available! Continuing without parallelization.\n");
threads = -1;
#endif
}
#if OPENMP_AVAILABLE_RANSAC
#pragma omp parallel if(threads > 0) num_threads(threads) shared(k, skipped_count, n_best_inliers_count) firstprivate(selection, model_coefficients) // would be nice to have a default(none)-clause here, but then some compilers complain about the shared const variables
#endif
{
#if OPENMP_AVAILABLE_RANSAC
if (omp_in_parallel())
#pragma omp master
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Computing in parallel with up to %i threads.\n", omp_get_num_threads());
else
#endif
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Computing not parallel.\n");
// Iterate
while (true) // infinite loop with four possible breaks
{
// Get X samples which satisfy the model criteria
#if OPENMP_AVAILABLE_RANSAC
#pragma omp critical(samples)
#endif
{
sac_model_->getSamples (iterations_, selection); // The random number generator used when choosing the samples should not be called in parallel
}
if (selection.empty ())
{
PCL_ERROR ("[pcl::RandomSampleConsensus::computeModel] No samples could be selected!\n");
break;
}
// Search for inliers in the point cloud for the current plane model M
if (!sac_model_->computeModelCoefficients (selection, model_coefficients)) // This function has to be thread-safe
{
//++iterations_;
unsigned skipped_count_tmp;
#if OPENMP_AVAILABLE_RANSAC
#pragma omp atomic capture
#endif
skipped_count_tmp = ++skipped_count;
if (skipped_count_tmp < max_skip)
continue;
else
break;
}
// Select the inliers that are within threshold_ from the model
//sac_model_->selectWithinDistance (model_coefficients, threshold_, inliers);
//if (inliers.empty () && k > 1.0)
// continue;
std::size_t n_inliers_count = sac_model_->countWithinDistance (model_coefficients, threshold_); // This functions has to be thread-safe. Most work is done here
std::size_t n_best_inliers_count_tmp;
#if OPENMP_AVAILABLE_RANSAC
#pragma omp atomic read
#endif
n_best_inliers_count_tmp = n_best_inliers_count;
if (n_inliers_count > n_best_inliers_count_tmp) // This condition is false most of the time, and the critical region is not entered, hopefully leading to more efficient concurrency
{
#if OPENMP_AVAILABLE_RANSAC
#pragma omp critical(update) // n_best_inliers_count, model_, model_coefficients_, k are shared and read/write must be protected
#endif
{
// Better match ?
if (n_inliers_count > n_best_inliers_count)
{
n_best_inliers_count = n_inliers_count; // This write and the previous read of n_best_inliers_count must be consecutive and must not be interrupted!
n_best_inliers_count_tmp = n_best_inliers_count;
// Save the current model/inlier/coefficients selection as being the best so far
model_ = selection;
model_coefficients_ = model_coefficients;
// Compute the k parameter (k=std::log(z)/std::log(1-w^n))
const double w = static_cast<double> (n_best_inliers_count) * one_over_indices;
double p_outliers = 1.0 - std::pow (w, static_cast<double> (selection.size ())); // Probability that selection is contaminated by at least one outlier
p_outliers = (std::max) (std::numeric_limits<double>::epsilon (), p_outliers); // Avoid division by -Inf
p_outliers = (std::min) (1.0 - std::numeric_limits<double>::epsilon (), p_outliers); // Avoid division by 0.
k = log_probability / std::log (p_outliers);
}
} // omp critical
}
int iterations_tmp;
double k_tmp;
#if OPENMP_AVAILABLE_RANSAC
#pragma omp atomic capture
#endif
iterations_tmp = ++iterations_;
#if OPENMP_AVAILABLE_RANSAC
#pragma omp atomic read
#endif
k_tmp = k;
#if OPENMP_AVAILABLE_RANSAC
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Trial %d out of %f: %u inliers (best is: %u so far) (thread %d).\n", iterations_tmp, k_tmp, n_inliers_count, n_best_inliers_count_tmp, omp_get_thread_num());
#else
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Trial %d out of %f: %u inliers (best is: %u so far).\n", iterations_tmp, k_tmp, n_inliers_count, n_best_inliers_count_tmp);
#endif
if (iterations_tmp > k_tmp)
break;
if (iterations_tmp > max_iterations_)
{
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] RANSAC reached the maximum number of trials.\n");
break;
}
} // while
} // omp parallel
PCL_DEBUG ("[pcl::RandomSampleConsensus::computeModel] Model: %lu size, %u inliers.\n", model_.size (), n_best_inliers_count);
if (model_.empty ())
{
PCL_ERROR ("[pcl::RandomSampleConsensus::computeModel] RANSAC found no model.\n");
inliers_.clear ();
return (false);
}
// Get the set of inliers that correspond to the best model found so far
sac_model_->selectWithinDistance (model_coefficients_, threshold_, inliers_);
return (true);
}
#define PCL_INSTANTIATE_RandomSampleConsensus(T) template class PCL_EXPORTS pcl::RandomSampleConsensus<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_RANSAC_H_
pcl::RandomSampleConsensus::computeModel
bool computeModel(int debug_verbosity_level=0) override
Compute the actual model and find the inliers.
Definition ransac.hpp:57
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133