本文整理匯總了C++中pcl::Correspondences::resize方法的典型用法代碼示例。如果您正苦於以下問題：C++ Correspondences::resize方法的具體用法？C++ Correspondences::resize怎麽用？C++ Correspondences::resize使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類pcl::Correspondences的用法示例。
在下文中一共展示了Correspondences::resize方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的C++代碼示例。
示例1: deinitCompute
template <typename PointSource, typename PointTarget, typename NormalT> void
pcl::registration::CorrespondenceEstimationBackProjection<PointSource, PointTarget, NormalT>::determineCorrespondences (
pcl::Correspondences &correspondences, double max_distance)
{
if (!initCompute ())
return;
typedef typename pcl::traits::fieldList<PointTarget>::type FieldListTarget;
correspondences.resize (indices_->size ());
std::vector<int> nn_indices (k_);
std::vector<float> nn_dists (k_);
float min_dist = std::numeric_limits<float>::max ();
int min_index = 0;
pcl::Correspondence corr;
unsigned int nr_valid_correspondences = 0;
// Check if the template types are the same. If true, avoid a copy.
// Both point types MUST be registered using the POINT_CLOUD_REGISTER_POINT_STRUCT macro!
if (isSamePointType<PointSource, PointTarget> ())
{
PointTarget pt;
// Iterate over the input set of source indices
for (std::vector<int>::const_iterator idx_i = indices_->begin (); idx_i != indices_->end (); ++idx_i)
{
tree_->nearestKSearch (input_->points[*idx_i], k_, nn_indices, nn_dists);
// Among the K nearest neighbours find the one with minimum perpendicular distance to the normal
min_dist = std::numeric_limits<float>::max ();
// Find the best correspondence
for (size_t j = 0; j < nn_indices.size (); j++)
{
float cos_angle = source_normals_->points[*idx_i].normal_x * target_normals_->points[nn_indices[j]].normal_x +
source_normals_->points[*idx_i].normal_y * target_normals_->points[nn_indices[j]].normal_y +
source_normals_->points[*idx_i].normal_z * target_normals_->points[nn_indices[j]].normal_z ;
float dist = nn_dists[min_index] * (2.0f - cos_angle * cos_angle);
if (dist < min_dist)
{
min_dist = dist;
min_index = j;
}
}
if (min_dist > max_distance)
continue;
corr.index_query = *idx_i;
corr.index_match = nn_indices[min_index];
corr.distance = nn_dists[min_index];//min_dist;
correspondences[nr_valid_correspondences++] = corr;
}
}
else
{
PointTarget pt;
// Iterate over the input set of source indices
for (std::vector<int>::const_iterator idx_i = indices_->begin (); idx_i != indices_->end (); ++idx_i)
{
tree_->nearestKSearch (input_->points[*idx_i], k_, nn_indices, nn_dists);
// Among the K nearest neighbours find the one with minimum perpendicular distance to the normal
min_dist = std::numeric_limits<float>::max ();
// Find the best correspondence
for (size_t j = 0; j < nn_indices.size (); j++)
{
PointSource pt_src;
// Copy the source data to a target PointTarget format so we can search in the tree
pcl::for_each_type <FieldListTarget> (pcl::NdConcatenateFunctor <PointSource, PointTarget> (
input_->points[*idx_i],
pt_src));
float cos_angle = source_normals_->points[*idx_i].normal_x * target_normals_->points[nn_indices[j]].normal_x +
source_normals_->points[*idx_i].normal_y * target_normals_->points[nn_indices[j]].normal_y +
source_normals_->points[*idx_i].normal_z * target_normals_->points[nn_indices[j]].normal_z ;
float dist = nn_dists[min_index] * (2.0f - cos_angle * cos_angle);
if (dist < min_dist)
{
min_dist = dist;
min_index = j;
}
}
if (min_dist > max_distance)
continue;
corr.index_query = *idx_i;
corr.index_match = nn_indices[min_index];
corr.distance = nn_dists[min_index];//min_dist;
correspondences[nr_valid_correspondences++] = corr;
}
}
correspondences.resize (nr_valid_correspondences);
deinitCompute ();
}