TEST (PCL, KdTreeFLANN_radiusSearch)
{
KdTreeFLANN<MyPoint> kdtree;
kdtree.setInputCloud (cloud.makeShared ());
MyPoint test_point(0.0f, 0.0f, 0.0f);
double max_dist = 0.15;
set<int> brute_force_result;
for (unsigned int i=0; i<cloud.points.size(); ++i)
if (euclideanDistance(cloud.points[i], test_point) < max_dist)
brute_force_result.insert(i);
vector<int> k_indices;
vector<float> k_distances;
kdtree.radiusSearch (test_point, max_dist, k_indices, k_distances, 100);
//cout << k_indices.size()<<"=="<<brute_force_result.size()<<"?\n";
for (size_t i = 0; i < k_indices.size (); ++i)
{
set<int>::iterator brute_force_result_it = brute_force_result.find (k_indices[i]);
bool ok = brute_force_result_it != brute_force_result.end ();
//if (!ok) cerr << k_indices[i] << " is not correct...\n";
//else cerr << k_indices[i] << " is correct...\n";
EXPECT_EQ (ok, true);
if (ok)
brute_force_result.erase (brute_force_result_it);
}
//for (set<int>::const_iterator it=brute_force_result.begin(); it!=brute_force_result.end(); ++it)
//cerr << "FLANN missed "<<*it<<"\n";
bool error = brute_force_result.size () > 0;
//if (error) cerr << "Missed too many neighbors!\n";
EXPECT_EQ (error, false);
{
KdTreeFLANN<MyPoint> kdtree;
kdtree.setInputCloud (cloud_big.makeShared ());
// preallocate indices and dists arrays
k_indices.resize(cloud_big.points.size ());
k_distances.resize(cloud_big.points.size ());
ScopeTime scopeTime ("FLANN radiusSearch");
{
for (size_t i = 0; i < cloud_big.points.size (); ++i)
kdtree.radiusSearch (cloud_big.points[i], 0.1, k_indices, k_distances);
}
}
{
KdTreeFLANN<MyPoint> kdtree (false);
kdtree.setInputCloud (cloud_big.makeShared ());
// preallocate indices and dists arrays
k_indices.resize(cloud_big.points.size ());
k_distances.resize(cloud_big.points.size ());
ScopeTime scopeTime ("FLANN radiusSearch (unsorted results)");
{
for (size_t i = 0; i < cloud_big.points.size (); ++i)
kdtree.radiusSearch (cloud_big.points[i], 0.1, k_indices, k_distances);
}
}
}
TEST (PCL, KdTreeFLANN_radiusSearchEigen)
{
KdTreeFLANN<Eigen::MatrixXf> kdtree;
kdtree.setInputCloud (cloud_eigen.makeShared ());
kdtree.setInputCloud (cloud_eigen.makeShared ());
Eigen::VectorXf test_point = Eigen::Vector3f (0.0f, 0.0f, 0.0f);
double max_dist = 0.15;
set<int> brute_force_result;
for (int i = 0; i < cloud_eigen.points.rows (); ++i)
if ((cloud_eigen.points.row (i) - test_point.transpose ()).norm () < max_dist)
brute_force_result.insert (i);
vector<int> k_indices;
vector<float> k_distances;
kdtree.radiusSearch (test_point, max_dist, k_indices, k_distances);
for (size_t i = 0; i < k_indices.size (); ++i)
{
set<int>::iterator brute_force_result_it = brute_force_result.find (k_indices[i]);
bool ok = brute_force_result_it != brute_force_result.end ();
EXPECT_EQ (ok, true);
if (ok)
brute_force_result.erase (brute_force_result_it);
}
bool error = brute_force_result.size () > 0;
EXPECT_EQ (error, false);
{
KdTreeFLANN<Eigen::MatrixXf> kdtree;
kdtree.setInputCloud (cloud_eigen.makeShared ());
// preallocate indices and dists arrays
k_indices.resize (cloud_eigen.points.rows ());
k_distances.resize (cloud_eigen.points.rows ());
ScopeTime scopeTime ("FLANN radiusSearch");
{
for (int i = 0; i < cloud_eigen.points.rows (); ++i)
kdtree.radiusSearch (cloud_eigen.points.row (i), 0.1, k_indices, k_distances);
}
}
{
KdTreeFLANN<Eigen::MatrixXf> kdtree (false);
kdtree.setInputCloud (cloud_eigen.makeShared ());
// preallocate indices and dists arrays
k_indices.resize (cloud_eigen.points.rows ());
k_distances.resize (cloud_eigen.points.rows ());
ScopeTime scopeTime ("FLANN radiusSearch (unsorted results)");
{
for (int i = 0; i < cloud_eigen.points.rows (); ++i)
kdtree.radiusSearch (cloud_eigen.points.row (i), 0.1, k_indices, k_distances);
}
}
}
TEST (PCL, KdTreeFLANN_32_vs_64_bit)
{
KdTreeFLANN<PointXYZ> tree;
tree.setInputCloud (cloud_in);
std::vector<std::vector<int> > nn_indices_vector;
for (size_t i = 0; i < cloud_in->size (); ++i)
if (isFinite ((*cloud_in)[i]))
{
std::vector<int> nn_indices;
std::vector<float> nn_dists;
tree.radiusSearch ((*cloud_in)[i], 0.02, nn_indices, nn_dists);
nn_indices_vector.push_back (nn_indices);
}
for (size_t vec_i = 0; vec_i < nn_indices_vector.size (); ++vec_i)
{
char str[512];
sprintf (str, "point_%d", int (vec_i));
boost::optional<boost::property_tree::ptree&> tree = xml_property_tree.get_child_optional (str);
if (!tree)
FAIL ();
int vec_size = tree.get ().get<int> ("size");
EXPECT_EQ (vec_size, nn_indices_vector[vec_i].size ());
for (size_t n_i = 0; n_i < nn_indices_vector[vec_i].size (); ++n_i)
{
sprintf (str, "nn_%d", int (n_i));
int neighbor_index = tree.get ().get<int> (str);
EXPECT_EQ (neighbor_index, nn_indices_vector[vec_i][n_i]);
}
}
}
