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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "open3d/geometry/KDTreeFlann.h"
#include "pybind/docstring.h"
#include "pybind/geometry/geometry.h"
#include "pybind/geometry/geometry_trampoline.h"
namespace open3d {
namespace geometry {
void pybind_kdtreeflann_declarations(py::module &m) {
py::class_<KDTreeSearchParam> kdtreesearchparam(
m, "KDTreeSearchParam", "Base class for KDTree search parameters.");
// open3d.geometry.KDTreeSearchParam.Type
py::enum_<KDTreeSearchParam::SearchType> kdtree_search_param_type(
kdtreesearchparam, "Type", py::arithmetic());
kdtree_search_param_type
.value("KNNSearch", KDTreeSearchParam::SearchType::Knn)
.value("RadiusSearch", KDTreeSearchParam::SearchType::Radius)
.value("HybridSearch", KDTreeSearchParam::SearchType::Hybrid)
.export_values();
kdtree_search_param_type.attr("__doc__") = docstring::static_property(
py::cpp_function([](py::handle arg) -> std::string {
return "Enum class for Geometry types.";
}),
py::none(), py::none(), "");
py::class_<KDTreeSearchParamKNN> kdtreesearchparam_knn(
m, "KDTreeSearchParamKNN", kdtreesearchparam,
"KDTree search parameters for pure KNN search.");
py::class_<KDTreeSearchParamRadius> kdtreesearchparam_radius(
m, "KDTreeSearchParamRadius", kdtreesearchparam,
"KDTree search parameters for pure radius search.");
py::class_<KDTreeSearchParamHybrid> kdtreesearchparam_hybrid(
m, "KDTreeSearchParamHybrid", kdtreesearchparam,
"KDTree search parameters for hybrid KNN and radius search.");
py::class_<KDTreeFlann, std::shared_ptr<KDTreeFlann>> kdtreeflann(
m, "KDTreeFlann", "KDTree with FLANN for nearest neighbor search.");
}
void pybind_kdtreeflann_definitions(py::module &m) {
// open3d.geometry.KDTreeSearchParam
auto kdtreesearchparam = static_cast<py::class_<KDTreeSearchParam>>(
m.attr("KDTreeSearchParam"));
kdtreesearchparam.def("get_search_type", &KDTreeSearchParam::GetSearchType,
"Get the search type (KNN, Radius, Hybrid) for the "
"search parameter.");
docstring::ClassMethodDocInject(m, "KDTreeSearchParam", "get_search_type");
// open3d.geometry.KDTreeSearchParamKNN
auto kdtreesearchparam_knn = static_cast<py::class_<KDTreeSearchParamKNN>>(
m.attr("KDTreeSearchParamKNN"));
kdtreesearchparam_knn.def(py::init<int>(), "knn"_a = 30)
.def("__repr__",
[](const KDTreeSearchParamKNN ¶m) {
return fmt::format(
"KDTreeSearchParamKNN("
"knn={})",
param.knn_);
})
.def_readwrite("knn", &KDTreeSearchParamKNN::knn_,
"Number of the neighbors that will be searched.");
// open3d.geometry.KDTreeSearchParamRadius
auto kdtreesearchparam_radius =
static_cast<py::class_<KDTreeSearchParamRadius>>(
m.attr("KDTreeSearchParamRadius"));
kdtreesearchparam_radius.def(py::init<double>(), "radius"_a)
.def("__repr__",
[](const KDTreeSearchParamRadius ¶m) {
return fmt::format(
"KDTreeSearchParamRadius("
"radius={})",
param.radius_);
})
.def_readwrite("radius", &KDTreeSearchParamRadius::radius_,
"Search radius.");
// open3d.geometry.KDTreeSearchParamHybrid
auto kdtreesearchparam_hybrid =
static_cast<py::class_<KDTreeSearchParamHybrid>>(
m.attr("KDTreeSearchParamHybrid"));
kdtreesearchparam_hybrid
.def(py::init<double, int>(), "radius"_a, "max_nn"_a)
.def("__repr__",
[](const KDTreeSearchParamHybrid ¶m) {
return fmt::format(
"KDTreeSearchParamHybrid("
"radius={}, "
"max_nn={})",
param.radius_, param.max_nn_);
})
.def_readwrite("radius", &KDTreeSearchParamHybrid::radius_,
"Search radius.")
.def_readwrite(
"max_nn", &KDTreeSearchParamHybrid::max_nn_,
"At maximum, ``max_nn`` neighbors will be searched.");
// open3d.geometry.KDTreeFlann
auto kdtreeflann =
static_cast<py::class_<KDTreeFlann>>(m.attr("KDTreeFlann"));
static const std::unordered_map<std::string, std::string>
map_kd_tree_flann_method_docs = {
{"query", "The input query point."},
{"radius", "Search radius."},
{"max_nn",
"At maximum, ``max_nn`` neighbors will be searched."},
{"knn", "``knn`` neighbors will be searched."},
{"feature", "Feature data."},
{"data", "Matrix data."}};
kdtreeflann.def(py::init<>())
.def(py::init<const Eigen::MatrixXd &>(), "data"_a)
.def("set_matrix_data", &KDTreeFlann::SetMatrixData,
"Sets the data for the KDTree from a matrix.", "data"_a)
.def(py::init<const Geometry &>(), "geometry"_a)
.def("set_geometry", &KDTreeFlann::SetGeometry,
"Sets the data for the KDTree from geometry.", "geometry"_a)
.def(py::init<const pipelines::registration::Feature &>(),
"feature"_a)
.def("set_feature", &KDTreeFlann::SetFeature,
"Sets the data for the KDTree from the feature data.",
"feature"_a)
// Although these C++ style functions are fast by orders of
// magnitudes when similar queries are performed for a large number
// of times and memory management is involved, we prefer not to
// expose them in Python binding. Considering writing C++ functions
// if performance is an issue.
//.def("search_vector_3d_in_place",
//&KDTreeFlann::Search<Eigen::Vector3d>,
// "query"_a, "search_param"_a, "indices"_a, "distance2"_a)
//.def("search_knn_vector_3d_in_place",
// &KDTreeFlann::SearchKNN<Eigen::Vector3d>,
// "query"_a, "knn"_a, "indices"_a, "distance2"_a)
//.def("search_radius_vector_3d_in_place",
// &KDTreeFlann::SearchRadius<Eigen::Vector3d>, "query"_a,
// "radius"_a, "indices"_a, "distance2"_a)
//.def("search_hybrid_vector_3d_in_place",
// &KDTreeFlann::SearchHybrid<Eigen::Vector3d>, "query"_a,
// "radius"_a, "max_nn"_a, "indices"_a, "distance2"_a)
.def(
"search_vector_3d",
[](const KDTreeFlann &tree, const Eigen::Vector3d &query,
const KDTreeSearchParam ¶m) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.Search(query, param, indices, distance2);
if (k < 0)
throw std::runtime_error(
"search_vector_3d() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "search_param"_a)
.def(
"search_knn_vector_3d",
[](const KDTreeFlann &tree, const Eigen::Vector3d &query,
int knn) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.SearchKNN(query, knn, indices, distance2);
if (k < 0)
throw std::runtime_error(
"search_knn_vector_3d() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "knn"_a)
.def(
"search_radius_vector_3d",
[](const KDTreeFlann &tree, const Eigen::Vector3d &query,
double radius) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.SearchRadius(query, radius, indices,
distance2);
if (k < 0)
throw std::runtime_error(
"search_radius_vector_3d() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "radius"_a)
.def(
"search_hybrid_vector_3d",
[](const KDTreeFlann &tree, const Eigen::Vector3d &query,
double radius, int max_nn) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.SearchHybrid(query, radius, max_nn,
indices, distance2);
if (k < 0)
throw std::runtime_error(
"search_hybrid_vector_3d() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "radius"_a, "max_nn"_a)
.def(
"search_vector_xd",
[](const KDTreeFlann &tree, const Eigen::VectorXd &query,
const KDTreeSearchParam ¶m) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.Search(query, param, indices, distance2);
if (k < 0)
throw std::runtime_error(
"search_vector_xd() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "search_param"_a)
.def(
"search_knn_vector_xd",
[](const KDTreeFlann &tree, const Eigen::VectorXd &query,
int knn) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.SearchKNN(query, knn, indices, distance2);
if (k < 0)
throw std::runtime_error(
"search_knn_vector_xd() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "knn"_a)
.def(
"search_radius_vector_xd",
[](const KDTreeFlann &tree, const Eigen::VectorXd &query,
double radius) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.SearchRadius(query, radius, indices,
distance2);
if (k < 0)
throw std::runtime_error(
"search_radius_vector_xd() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "radius"_a)
.def(
"search_hybrid_vector_xd",
[](const KDTreeFlann &tree, const Eigen::VectorXd &query,
double radius, int max_nn) {
std::vector<int> indices;
std::vector<double> distance2;
int k = tree.SearchHybrid(query, radius, max_nn,
indices, distance2);
if (k < 0)
throw std::runtime_error(
"search_hybrid_vector_xd() error!");
return std::make_tuple(k, indices, distance2);
},
"query"_a, "radius"_a, "max_nn"_a);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_hybrid_vector_3d",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_hybrid_vector_xd",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_knn_vector_3d",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_knn_vector_xd",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_radius_vector_3d",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_radius_vector_xd",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_vector_3d",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "search_vector_xd",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "set_feature",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "set_geometry",
map_kd_tree_flann_method_docs);
docstring::ClassMethodDocInject(m, "KDTreeFlann", "set_matrix_data",
map_kd_tree_flann_method_docs);
}
} // namespace geometry
} // namespace open3d
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