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// ----------------------------------------------------------------------------
// - Open3D: www.open3d.org -
// ----------------------------------------------------------------------------
// Copyright (c) 2018-2024 www.open3d.org
// SPDX-License-Identifier: MIT
// ----------------------------------------------------------------------------
#include "open3d/geometry/PointCloud.h"
#include <vector>
#include "open3d/camera/PinholeCameraIntrinsic.h"
#include "open3d/geometry/Image.h"
#include "open3d/geometry/RGBDImage.h"
#include "pybind/docstring.h"
#include "pybind/geometry/geometry.h"
#include "pybind/geometry/geometry_trampoline.h"
namespace open3d {
namespace geometry {
void pybind_pointcloud_declarations(py::module &m) {
py::class_<PointCloud, PyGeometry3D<PointCloud>,
std::shared_ptr<PointCloud>, Geometry3D>
pointcloud(m, "PointCloud",
"PointCloud class. A point cloud consists of point "
"coordinates, and optionally point colors and point "
"normals.");
}
void pybind_pointcloud_definitions(py::module &m) {
auto pointcloud =
static_cast<py::class_<PointCloud, PyGeometry3D<PointCloud>,
std::shared_ptr<PointCloud>, Geometry3D>>(
m.attr("PointCloud"));
py::detail::bind_default_constructor<PointCloud>(pointcloud);
py::detail::bind_copy_functions<PointCloud>(pointcloud);
pointcloud
.def(py::init<const std::vector<Eigen::Vector3d> &>(),
"Create a PointCloud from points", "points"_a)
.def("__repr__",
[](const PointCloud &pcd) {
return std::string("PointCloud with ") +
std::to_string(pcd.points_.size()) + " points.";
})
.def(py::self + py::self)
.def(py::self += py::self)
.def("has_points", &PointCloud::HasPoints,
"Returns ``True`` if the point cloud contains points.")
.def("has_normals", &PointCloud::HasNormals,
"Returns ``True`` if the point cloud contains point normals.")
.def("has_colors", &PointCloud::HasColors,
"Returns ``True`` if the point cloud contains point colors.")
.def("has_covariances", &PointCloud::HasCovariances,
"Returns ``True`` if the point cloud contains covariances.")
.def("normalize_normals", &PointCloud::NormalizeNormals,
"Normalize point normals to length 1.")
.def("paint_uniform_color", &PointCloud::PaintUniformColor,
"color"_a,
"Assigns each point in the PointCloud the same color.")
.def("select_by_index", &PointCloud::SelectByIndex,
"Function to select points from input pointcloud into output "
"pointcloud.",
"indices"_a, "invert"_a = false)
.def("voxel_down_sample", &PointCloud::VoxelDownSample,
"Function to downsample input pointcloud into output "
"pointcloud with "
"a voxel. Normals and colors are averaged if they exist.",
"voxel_size"_a)
.def("voxel_down_sample_and_trace",
&PointCloud::VoxelDownSampleAndTrace,
"Function to downsample using PointCloud::VoxelDownSample. "
"Also records point cloud index before downsampling",
"voxel_size"_a, "min_bound"_a, "max_bound"_a,
"approximate_class"_a = false)
.def("uniform_down_sample", &PointCloud::UniformDownSample,
"Function to downsample input pointcloud into output "
"pointcloud uniformly. The sample is performed in the order "
"of the points with the 0-th point always chosen, not at "
"random.",
"every_k_points"_a)
.def("random_down_sample", &PointCloud::RandomDownSample,
"Function to downsample input pointcloud into output "
"pointcloud randomly. The sample is generated by randomly "
"sampling the indexes from the point cloud.",
"sampling_ratio"_a)
.def("farthest_point_down_sample",
&PointCloud::FarthestPointDownSample,
"Downsamples input pointcloud into output pointcloud with a "
"set of points has farthest distance. The sample is performed "
"by selecting the farthest point from previous selected "
"points iteratively.",
"num_samples"_a,
"Index to start downsampling from. Valid index is a "
"non-negative number less than number of points in the "
"input pointcloud.",
"start_index"_a = 0)
.def("crop",
(std::shared_ptr<PointCloud>(PointCloud::*)(
const AxisAlignedBoundingBox &, bool) const) &
PointCloud::Crop,
"Function to crop input pointcloud into output pointcloud",
"bounding_box"_a, "invert"_a = false)
.def("crop",
(std::shared_ptr<PointCloud>(PointCloud::*)(
const OrientedBoundingBox &, bool) const) &
PointCloud::Crop,
"Function to crop input pointcloud into output pointcloud",
"bounding_box"_a, "invert"_a = false)
.def("remove_non_finite_points", &PointCloud::RemoveNonFinitePoints,
"Removes all points from the point cloud that have a nan "
"entry, or infinite entries. It also removes the "
"corresponding attributes associated with the non-finite "
"point such as normals, covariances and color entries. It "
"doesn't re-computes these attributes after removing "
"non-finite points. ",
"remove_nan"_a = true, "remove_infinite"_a = true)
.def("remove_duplicated_points",
&PointCloud::RemoveDuplicatedPoints,
"Removes duplicated points, i.e., points that "
"have identical coordinates. It also removes the "
"corresponding attributes associated with the non-finite "
"point such as normals, covariances and color entries. It "
"doesn't re-computes these attributes after removing "
"duplicated points.")
.def("remove_radius_outlier", &PointCloud::RemoveRadiusOutliers,
"Removes points that have neighbors less than nb_points in a "
"sphere of a given radius",
"nb_points"_a, "radius"_a, "print_progress"_a = false)
.def("remove_statistical_outlier",
&PointCloud::RemoveStatisticalOutliers,
"Removes points that are further away from their neighbors in "
"average.",
"nb_neighbors"_a, "std_ratio"_a, "print_progress"_a = false)
.def("estimate_normals", &PointCloud::EstimateNormals,
"Function to compute the normals of a point cloud. Normals "
"are oriented with respect to the input point cloud if "
"normals exist",
"search_param"_a = KDTreeSearchParamKNN(),
"fast_normal_computation"_a = true)
.def("orient_normals_to_align_with_direction",
&PointCloud::OrientNormalsToAlignWithDirection,
"Function to orient the normals of a point cloud",
"orientation_reference"_a = Eigen::Vector3d(0.0, 0.0, 1.0))
.def("orient_normals_towards_camera_location",
&PointCloud::OrientNormalsTowardsCameraLocation,
"Function to orient the normals of a point cloud",
"camera_location"_a = Eigen::Vector3d(0.0, 0.0, 0.0))
.def("orient_normals_consistent_tangent_plane",
&PointCloud::OrientNormalsConsistentTangentPlane,
"Function to orient the normals with respect to consistent "
"tangent planes",
"k"_a, "lambda_penalty"_a = 0.0, "cos_alpha_tol"_a = 1.0)
.def("compute_point_cloud_distance",
&PointCloud::ComputePointCloudDistance,
"For each point in the source point cloud, compute the "
"distance to the target point cloud.",
"target"_a)
.def_static(
"estimate_point_covariances",
&PointCloud::EstimatePerPointCovariances,
"Static function to compute the covariance matrix for "
"each "
"point in the given point cloud, doesn't change the input",
"input"_a, "search_param"_a = KDTreeSearchParamKNN())
.def("estimate_covariances", &PointCloud::EstimateCovariances,
"Function to compute the covariance matrix for each point "
"in the point cloud",
"search_param"_a = KDTreeSearchParamKNN())
.def("compute_mean_and_covariance",
&PointCloud::ComputeMeanAndCovariance,
"Function to compute the mean and covariance matrix of a "
"point cloud.")
.def("compute_mahalanobis_distance",
&PointCloud::ComputeMahalanobisDistance,
"Function to compute the Mahalanobis distance for points in a "
"point cloud. See: "
"https://en.wikipedia.org/wiki/Mahalanobis_distance.")
.def("compute_nearest_neighbor_distance",
&PointCloud::ComputeNearestNeighborDistance,
"Function to compute the distance from a point to its nearest "
"neighbor in the point cloud")
.def("compute_convex_hull", &PointCloud::ComputeConvexHull,
"joggle_inputs"_a = false, R"doc(
Computes the convex hull of the point cloud.
Args:
joggle_inputs (bool): If True allows the algorithm to add random noise to
the points to work around degenerate inputs. This adds the 'QJ'
option to the qhull command.
Returns:
tuple(open3d.geometry.TriangleMesh, list): The triangle mesh of the convex
hull and the list of point indices that are part of the convex hull.
)doc")
.def("hidden_point_removal", &PointCloud::HiddenPointRemoval,
"Removes hidden points from a point cloud and returns a mesh "
"of the remaining points. Based on Katz et al. 'Direct "
"Visibility of Point Sets', 2007. Additional information "
"about the choice of radius for noisy point clouds can be "
"found in Mehra et. al. 'Visibility of Noisy Point Cloud "
"Data', 2010.",
"camera_location"_a, "radius"_a)
.def("cluster_dbscan", &PointCloud::ClusterDBSCAN,
"Cluster PointCloud using the DBSCAN algorithm Ester et al., "
"'A Density-Based Algorithm for Discovering Clusters in Large "
"Spatial Databases with Noise', 1996. Returns a list of point "
"labels, -1 indicates noise according to the algorithm.",
"eps"_a, "min_points"_a, "print_progress"_a = false)
.def("segment_plane", &PointCloud::SegmentPlane,
"Segments a plane in the point cloud using the RANSAC "
"algorithm.",
"distance_threshold"_a, "ransac_n"_a, "num_iterations"_a,
"probability"_a = 0.99999999)
.def("detect_planar_patches", &PointCloud::DetectPlanarPatches,
R"doc(
Detects planar patches in the point cloud using a robust statistics-based approach.
Returns:
A list of detected planar patches, represented as
OrientedBoundingBox objects, with the third column (z) of R indicating
the planar patch normal vector. The extent in the z direction is
non-zero so that the OrientedBoundingBox contains the points that
contribute to the plane detection.
)doc",
"normal_variance_threshold_deg"_a = 60,
"coplanarity_deg"_a = 75, "outlier_ratio"_a = 0.75,
"min_plane_edge_length"_a = 0.0, "min_num_points"_a = 0,
"search_param"_a = KDTreeSearchParamKNN())
.def_static(
"create_from_depth_image",
&PointCloud::CreateFromDepthImage,
R"(Factory function to create a pointcloud from a depth image and a
camera. Given depth value d at (u, v) image coordinate, the corresponding 3d point is:
- z = d / depth_scale
- x = (u - cx) * z / fx
- y = (v - cy) * z / fy)",
"depth"_a, "intrinsic"_a,
"extrinsic"_a = Eigen::Matrix4d::Identity(),
"depth_scale"_a = 1000.0, "depth_trunc"_a = 1000.0,
"stride"_a = 1, "project_valid_depth_only"_a = true)
.def_static(
"create_from_rgbd_image", &PointCloud::CreateFromRGBDImage,
"Factory function to create a pointcloud from an RGB-D "
"image and a camera. Given depth value d at (u, "
"v) image coordinate, the corresponding 3d point is:\n\n"
R"( - z = d / depth_scale
- x = (u - cx) * z / fx
- y = (v - cy) * z / fy)",
"image"_a, "intrinsic"_a,
"extrinsic"_a = Eigen::Matrix4d::Identity(),
"project_valid_depth_only"_a = true)
.def_readwrite("points", &PointCloud::points_,
"``float64`` array of shape ``(num_points, 3)``, "
"use ``numpy.asarray()`` to access data: Points "
"coordinates.")
.def_readwrite("normals", &PointCloud::normals_,
"``float64`` array of shape ``(num_points, 3)``, "
"use ``numpy.asarray()`` to access data: Points "
"normals.")
.def_readwrite(
"colors", &PointCloud::colors_,
"``float64`` array of shape ``(num_points, 3)``, "
"range ``[0, 1]`` , use ``numpy.asarray()`` to access "
"data: RGB colors of points.")
.def_readwrite("covariances", &PointCloud::covariances_,
"``float64`` array of shape ``(num_points, 3, 3)``, "
"use ``numpy.asarray()`` to access data: Points "
"covariances.");
docstring::ClassMethodDocInject(m, "PointCloud", "has_colors");
docstring::ClassMethodDocInject(m, "PointCloud", "has_normals");
docstring::ClassMethodDocInject(m, "PointCloud", "has_points");
docstring::ClassMethodDocInject(m, "PointCloud", "normalize_normals");
docstring::ClassMethodDocInject(
m, "PointCloud", "paint_uniform_color",
{{"color", "RGB color for the PointCloud."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "select_by_index",
{{"indices", "Indices of points to be selected."},
{"invert",
"Set to ``True`` to invert the selection of indices."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "voxel_down_sample",
{{"voxel_size", "Voxel size to downsample into."},
{"invert", "set to ``True`` to invert the selection of indices"}});
docstring::ClassMethodDocInject(
m, "PointCloud", "voxel_down_sample_and_trace",
{{"voxel_size", "Voxel size to downsample into."},
{"min_bound", "Minimum coordinate of voxel boundaries"},
{"max_bound", "Maximum coordinate of voxel boundaries"}});
docstring::ClassMethodDocInject(
m, "PointCloud", "uniform_down_sample",
{{"every_k_points",
"Sample rate, the selected point indices are [0, k, 2k, ...]"}});
docstring::ClassMethodDocInject(
m, "PointCloud", "random_down_sample",
{{"sampling_ratio",
"Sampling ratio, the ratio of number of selected points to total "
"number of points[0-1]"}});
docstring::ClassMethodDocInject(
m, "PointCloud", "crop",
{{"bounding_box", "AxisAlignedBoundingBox to crop points"},
{"invert", "optional boolean to invert cropping"}});
docstring::ClassMethodDocInject(
m, "PointCloud", "remove_non_finite_points",
{{"remove_nan", "Remove NaN values from the PointCloud"},
{"remove_infinite",
"Remove infinite values from the PointCloud"}});
docstring::ClassMethodDocInject(m, "PointCloud",
"remove_duplicated_points");
docstring::ClassMethodDocInject(
m, "PointCloud", "remove_radius_outlier",
{{"nb_points", "Number of points within the radius."},
{"radius", "Radius of the sphere."},
{"print_progress", "Set to True to print progress bar."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "remove_statistical_outlier",
{{"nb_neighbors", "Number of neighbors around the target point."},
{"std_ratio", "Standard deviation ratio."},
{"print_progress", "Set to True to print progress bar."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "estimate_normals",
{{"search_param",
"The KDTree search parameters for neighborhood search."},
{"fast_normal_computation",
"If true, the normal estimation uses a non-iterative method to "
"extract the eigenvector from the covariance matrix. This is "
"faster, but is not as numerical stable."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "orient_normals_to_align_with_direction",
{{"orientation_reference",
"Normals are oriented with respect to orientation_reference."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "orient_normals_towards_camera_location",
{{"camera_location",
"Normals are oriented with towards the camera_location."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "orient_normals_consistent_tangent_plane",
{{"k",
"Number of k nearest neighbors used in constructing the "
"Riemannian graph used to propagate normal orientation."}});
docstring::ClassMethodDocInject(m, "PointCloud",
"compute_point_cloud_distance",
{{"target", "The target point cloud."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "estimate_point_covariances",
{{"input", "The input point cloud."},
{"search_param",
"The KDTree search parameters for neighborhood search."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "estimate_covariances",
{{"search_param",
"The KDTree search parameters for neighborhood search."}});
docstring::ClassMethodDocInject(m, "PointCloud",
"compute_mean_and_covariance");
docstring::ClassMethodDocInject(m, "PointCloud",
"compute_mahalanobis_distance");
docstring::ClassMethodDocInject(m, "PointCloud",
"compute_nearest_neighbor_distance");
docstring::ClassMethodDocInject(
m, "PointCloud", "hidden_point_removal",
{{"input", "The input point cloud."},
{"camera_location",
"All points not visible from that location will be removed"},
{"radius", "The radius of the sperical projection"}});
docstring::ClassMethodDocInject(
m, "PointCloud", "cluster_dbscan",
{{"eps",
"Density parameter that is used to find neighbouring points."},
{"min_points", "Minimum number of points to form a cluster."},
{"print_progress",
"If true the progress is visualized in the console."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "segment_plane",
{{"distance_threshold",
"Max distance a point can be from the plane model, and still be "
"considered an inlier."},
{"ransac_n",
"Number of initial points to be considered inliers in each "
"iteration."},
{"num_iterations", "Number of iterations."},
{"probability",
"Expected probability of finding the optimal plane."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "detect_planar_patches",
{
{"normal_similarity",
"Angle threshold based on robust statistics for planarity "
"test. Larger values allow more noisy planes to be "
"detected."},
{"coplanarity",
"Angle threshold based on robust statistics for planarity "
"test. Smaller values allow more noisy planes to be "
"detected."},
{"outlier_ratio",
"Maximum allowable ratio of outliers "
"associated to a plane."},
{"min_plane_edge_length",
"Minimum edge length of plane's long "
"edge before being rejected."},
{"min_num_points",
"Minimum number of points allowable for "
"fitting planes."},
{"search_param",
"The KDTree search parameters for "
"neighborhood search."},
});
docstring::ClassMethodDocInject(
m, "PointCloud", "create_from_depth_image",
{{"depth",
"The input depth image can be either a float image, or a "
"uint16_t image."},
{"intrinsic", "Intrinsic parameters of the camera."},
{"extrnsic", "Extrinsic parameters of the camera."},
{"depth_scale", "The depth is scaled by 1 / depth_scale."},
{"depth_trunc", "Truncated at depth_trunc distance."},
{"stride",
"Sampling factor to support coarse point cloud extraction."}});
docstring::ClassMethodDocInject(
m, "PointCloud", "create_from_rgbd_image",
{{"image", "The input image."},
{"intrinsic", "Intrinsic parameters of the camera."},
{"extrnsic", "Extrinsic parameters of the camera."}});
}
} // namespace geometry
} // namespace open3d
|
