bsfm::FlannDescriptorKDTree Class Reference

#include <flann_descriptor_kdtree.h>

Public Member Functions
	FlannDescriptorKDTree ()
	~FlannDescriptorKDTree ()
void	AddDescriptor (Descriptor &descriptor)
void	AddDescriptors (std::vector< Descriptor > &descriptors)
bool	NearestNeighbor (Descriptor &query, int &nn_index, double &nn_distance)

Private Attributes
std::shared_ptr< flann::Index< flann::L2< double > > >	index_

Detailed Description

Definition at line 60 of file flann_descriptor_kdtree.h.

Constructor & Destructor Documentation

bsfm::FlannDescriptorKDTree::FlannDescriptorKDTree

(

)

Definition at line 42 of file flann_descriptor_kdtree.cpp.

{}

bsfm::FlannDescriptorKDTree::~FlannDescriptorKDTree

(

)

Definition at line 44 of file flann_descriptor_kdtree.cpp.

                                              {
  // Free memory from descriptors in the kd tree.
  if (index_ != nullptr) {
    for (size_t ii = 0; ii < index_->size(); ++ii) {
      double* descriptor = index_->getPoint(ii);
      delete[] descriptor;
    }
  }
}

Member Function Documentation

void bsfm::FlannDescriptorKDTree::AddDescriptor

(

Descriptor &

descriptor

)

Definition at line 55 of file flann_descriptor_kdtree.cpp.

                                                                {

  // Copy the input descriptor into FLANN's Matrix type.
  const size_t cols = descriptor.size();
  flann::Matrix<double> flann_descriptor(new double[cols], 1, cols);
  for (size_t ii = 0; ii < cols; ++ii) {
    flann_descriptor[0][ii] = descriptor(ii);
  }

  // If this is the first point in the index, create the index and exit.
  if (index_ == nullptr) {
    // Single kd-tree. No approximation.
    const int kNumRandomizedKDTrees = 1;
    index_.reset(new flann::Index<flann::L2<double> >(
        flann_descriptor, flann::KDTreeIndexParams(kNumRandomizedKDTrees)));
    index_->buildIndex();
    return;
  }

  // If the index is already created, add the data point to the index. Rebuild
  // every time the index doubles in size to occasionally rebalance the kd tree.
  const int kRebuildThreshold = 2;
  index_->addPoints(flann_descriptor, kRebuildThreshold);
}

void bsfm::FlannDescriptorKDTree::AddDescriptors

(

std::vector< Descriptor > &

descriptors

)

Definition at line 81 of file flann_descriptor_kdtree.cpp.

                                        {
  for (auto& descriptor : descriptors) {
    AddDescriptor(descriptor);
  }
}

bool bsfm::FlannDescriptorKDTree::NearestNeighbor	(	Descriptor &	query,
		int &	nn_index,
		double &	nn_distance
	)

Definition at line 89 of file flann_descriptor_kdtree.cpp.

                                                                 {
  if (index_ == nullptr) {
    VLOG(1) << "Index has not been built. Descriptors must be added before "
               "querying the kd tree";
    return false;
  }

  // Convert the input descriptor to the FLANN format. We can use Eigen's memory
  // here, since we will have our answer before leaving function scope.
  flann::Matrix<double> flann_query(query.data(), 1, index_->veclen());

  // Search the kd tree for the nearest neighbor to the query.
  std::vector< std::vector<int> > query_match_indices;
  std::vector< std::vector<double> > query_distances;

  const int kOneNearestNeighbor = 1;
  int num_neighbors_found = index_->knnSearch(
      flann_query, query_match_indices, query_distances, kOneNearestNeighbor,
      flann::SearchParams(flann::FLANN_CHECKS_UNLIMITED) /* no approx */);

  // If we found a nearest neighbor, assign output.
  if (num_neighbors_found > 0) {
    nn_index = query_match_indices[0][0];
    nn_distance = query_distances[0][0];
  }

  return num_neighbors_found > 0;
}

Member Data Documentation

std::shared_ptr< flann::Index<flann::L2<double> > > bsfm::FlannDescriptorKDTree::index_

private

Definition at line 78 of file flann_descriptor_kdtree.h.

---

The documentation for this class was generated from the following files:

• /Users/eanelson/Git/berkeley_sfm/src/cpp/sfm/flann_descriptor_kdtree.h
• /Users/eanelson/Git/berkeley_sfm/src/cpp/sfm/flann_descriptor_kdtree.cpp