Introduction¶
This is a small python binding to the pointcloud library. Currently, the following parts of the API are wrapped (all methods operate on PointXYZRGB) point types
- I/O and integration; saving and loading PCD files
- segmentation
- SAC
- smoothing
- filtering
The code tries to follow the Point Cloud API, and also provides helper function for interacting with NumPy.
Point clouds can be viewed as NumPy arrays, so modifying them is possible using all the familiar NumPy functionality:
import numpy as np
import pcl
p = pcl.PointCloud(10) # "empty" point cloud
a = np.asarray(p) # NumPy view on the cloud
a[:] = 0 # fill with zeros
print(p[3]) # prints (0.0, 0.0, 0.0)
a[:, 0] = 1 # set x coordinates to 1
print(p[3]) # prints (1.0, 0.0, 0.0)
More samples can be found in the examples directory, and in the unit tests.
This library is developed for use in our Project Patty, see this repository for more interesting examples. Also, the reading and writing of LAS files is implemented there.
This work was supported by Strawlab and the Netherlands eScience Center
A note about types¶
Point Cloud is a heavily templated API, and consequently mapping this into Python using Cython is challenging.
It is written in Cython, and implements enough hard bits of the API (from Cythons perspective, i.e the template/smart_ptr bits) to provide a foundation for someone wishing to carry on.
API Documentation¶
For API documentation, look at our gh-pages branch For deficiencies in this documentation, please consult the PCL API docs, and the PCL tutorials.
Pointcloud class¶
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class
pcl.BasePointCloud¶ -
extract(self, pyindices, bool negative=False)¶ Given a list of indices of points in the pointcloud, return a new pointcloud containing only those points.
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from_array(self, ndarray arr)¶ Fill this object from a 2D numpy array (float32)
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from_file(self, char *f)¶ Fill this pointcloud from a file (a local path). Only pcd files supported currently.
Deprecated; use pcl.load instead.
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from_list(self, _list)¶ Fill this pointcloud from a list of 3-tuples
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get_point(self, npy_intp row, npy_intp col)¶ Return a point (6-tuple) at the given row/column
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height¶ property containing the height of the point cloud
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is_dense¶ property containing whether the cloud is dense or not
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make_kdtree_flann(self)¶ Return a pcl.kdTreeFLANN object with this object set as the input-cloud
Deprecated: use the pcl.KdTreeFLANN constructor on this cloud.
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make_moving_least_squares(self)¶ Return a pcl.MovingLeastSquares object with this object as input cloud.
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make_octree(self, double resolution)¶ Return a pcl.octree object with this object set as the input-cloud
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make_passthrough_filter(self)¶ Return a pcl.PassThroughFilter object with this object set as the input-cloud
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make_segmenter(self)¶ Return a pcl.Segmentation object with this object set as the input-cloud
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make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1.0)¶ Return a pcl.SegmentationNormal object with this object set as the input-cloud
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make_statistical_outlier_filter(self)¶ Return a pcl.StatisticalOutlierRemovalFilter object with this object set as the input-cloud
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make_voxel_grid_filter(self)¶ Return a pcl.VoxelGridFilter object with this object set as the input-cloud
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resize(self, npy_intp x)¶
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sensor_orientation¶
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sensor_origin¶
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size¶ property containing the number of points in the point cloud
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to_file(self, char const *fname, bool ascii=True)¶ Save pointcloud to a file in PCD format.
Deprecated: use pcl.save instead.
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width¶ property containing the width of the point cloud
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class
pcl.BasePyPointCloud¶ -
to_list()¶ Return this object as a list of tuples.
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transform(t)¶ Apply rigid transformation t, in-place.
- t : ndarray, shape (4, 4)
- Rigid transformation.
- self : point cloud
- Returns self, for convenience.
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class
pcl.KdTreeFLANN¶ Finds k nearest neighbours from points in another pointcloud to points in a reference pointcloud.
Must be constructed from the reference point cloud, which is copied, so changed to pc are not reflected in KdTreeFLANN(pc).
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nearest_k_search_for_cloud(self, BasePointCloud pc, int k=1)¶ Find the k nearest neighbours and squared distances for all points in the pointcloud. Results are in ndarrays, size (pc.size, k) Returns: (k_indices, k_sqr_distances)
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nearest_k_search_for_point(self, BasePointCloud pc, int index, int k=1)¶ Find the k nearest neighbours and squared distances for the point at pc[index]. Results are in ndarrays, size (k) Returns: (k_indices, k_sqr_distances)
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class
pcl.MovingLeastSquares¶ Smoothing class which is an implementation of the MLS (Moving Least Squares) algorithm for data smoothing and improved normal estimation.
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process(self)¶ Apply the smoothing according to the previously set values and return a new pointcloud
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set_polynomial_fit(self, bool fit)¶ Sets whether the surface and normal are approximated using a polynomial, or only via tangent estimation.
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set_polynomial_order(self, bool order)¶ Set the order of the polynomial to be fit.
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set_search_radius(self, double radius)¶ Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting.
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class
pcl.OctreePointCloud(double resolution)¶ Octree pointcloud
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add_points_from_input_cloud(self)¶ Add points from input point cloud to octree.
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define_bounding_box(self)¶ Investigate dimensions of pointcloud data set and define corresponding bounding box for octree.
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delete_tree(self)¶ Delete the octree structure and its leaf nodes.
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delete_voxel_at_point(self, point)¶ Delete leaf node / voxel at given point.
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get_occupied_voxel_centers(self)¶ Get list of centers of all occupied voxels.
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is_voxel_occupied_at_point(self, point)¶ Check if voxel at given point coordinates exist.
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set_input_cloud(self, BasePointCloud pc)¶ Provide a pointer to the input data set.
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class
pcl.OctreePointCloudSearch¶ Octree pointcloud search
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radius_search(self, point, double radius, unsigned int max_nn=0)¶ Search for all neighbors of query point that are within a given radius.
Returns: (k_indices, k_sqr_distances)
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class
pcl.PassThroughFilter¶ Passes points in a cloud based on constraints for one particular field of the point type
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filter(self)¶ Apply the filter according to the previously set parameters and return a new pointcloud
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set_filter_field_name(self, field_name)¶
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set_filter_limits(self, float filter_min, float filter_max)¶
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class
pcl.PointCloud¶ 3-d point cloud (no color information).
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get_point(row, col)¶ Return point (3-tuple) at the given row/column.
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to_array()¶ Return this object as a 2D numpy array (float32).
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class
pcl.PointCloudXYZRGB¶ 3-d point cloud with color information.
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get_point(row, col)¶ Return point (3-tuple) at the given row/column.
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to_array()¶ Return this object as a 2D numpy array (float32).
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class
pcl.Segmentation¶ Segmentation class for Sample Consensus methods and models
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segment(self)¶
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set_distance_threshold(self, float d)¶
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set_method_type(self, int m)¶
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set_model_type(self, SacModel m)¶
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set_optimize_coefficients(self, bool b)¶
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class
pcl.SegmentationNormal¶ Segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation.
Due to Cython limitations this should derive from pcl.Segmentation, but is currently unable to do so.
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segment(self)¶
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set_axis(self, double ax, double ay, double az)¶
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set_distance_threshold(self, float d)¶
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set_eps_angle(self, double ea)¶
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set_max_iterations(self, int i)¶
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set_method_type(self, int m)¶
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set_model_type(self, SacModel m)¶
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set_normal_distance_weight(self, float f)¶
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set_optimize_coefficients(self, bool b)¶
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set_radius_limits(self, float f1, float f2)¶
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class
pcl.Sequence¶ All the operations on a read-only sequence.
Concrete subclasses must override __new__ or __init__, __getitem__, and __len__.
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count(value) → integer -- return number of occurrences of value¶
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index(value) → integer -- return first index of value.¶ Raises ValueError if the value is not present.
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class
pcl.StatisticalOutlierRemovalFilter¶ Filter class uses point neighborhood statistics to filter outlier data.
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filter(self)¶ Apply the filter according to the previously set parameters and return a new pointcloud
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mean_k¶
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negative¶
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set_mean_k(self, int k)¶ Set the number of points (k) to use for mean distance estimation.
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set_negative(self, bool negative)¶ Set whether the indices should be returned, or all points except the indices.
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set_std_dev_mul_thresh(self, double std_mul)¶ Set the standard deviation multiplier threshold.
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stddev_mul_thresh¶
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class
pcl.VoxelGridFilter¶ Assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.
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filter(self)¶ Apply the filter according to the previously set parameters and return a new pointcloud
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set_leaf_size(self, float x, float y, float z)¶ Set the voxel grid leaf size.
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pcl.float_to_rgb(p_rgb)¶
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pcl.load(path, format=None, loadRGB=True)¶ Load pointcloud from path.
Currently supports PCD and PLY files.
Format should be “pcd”, “ply”, or None to infer from the pathname.
An optional loadRGB parameter is included to provide the option of loading or ignoring colour information.
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pcl.save(cloud, path, format=None, binary=False)¶ Save pointcloud to file.
Format should be “pcd”, “ply”, or None to infer from the pathname.
Registration functions¶
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pcl.registration.gicp(BasePointCloud source, BasePointCloud target, max_iter=None)¶ Align source to target using generalized iterative closest point (GICP).
- source : point cloud
- Source point cloud.
- target : point cloud
- Target point cloud.
- max_iter : integer, optional
- Maximum number of iterations. If not given, uses the default number hardwired into PCL.
- converged : bool
- Whether the ICP algorithm converged in at most max_iter steps.
- transf : np.ndarray, shape = [4, 4]
- Transformation matrix.
- estimate : point cloud
- Transformed version of source. Will be of the same type as source.
- fitness : float
- Sum of squares error in the estimated transformation.
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pcl.registration.ia_ransac(BasePointCloud source, BasePointCloud target, max_iter=None, radius=0.05, minSampleDistance=0.05, maxCorrespondenceDistance=0.2)¶ An implementation of the initial alignment algorithm described in section IV of “Fast Point Feature Histograms (FPFH) for 3D Registration,” Rusu et al.
- source : PointCloud
- Source point cloud.
- target : PointCloud
- Target point cloud.
- max_iter : integer, optional
- Maximum number of iterations. If not given, uses the default number hardwired into PCL.
- radius : double
- Radius in which neighbours are searched for in the NormalEstimation algorithm.
- minSampleDistance : double
- Set the minimum distances between samples.
- maxCorrespondenceDistance : double
- Set the maximum distance threshold between two correspondent points in source <-> target.
- converged : bool
- Whether the ICP algorithm converged in at most max_iter steps.
- estimate : PointCloud
- Transformed version of source.
- fitness : float
- Sum of squares error in the estimated transformation.
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pcl.registration.icp(BasePointCloud source, BasePointCloud target, max_iter=None, transformationEpsilon=None, euclideanFitnessEpsilon=None)¶ Align source to target using iterative closest point (ICP).
- source : point cloud
- Source point cloud.
- target : point cloud
- Target point cloud.
- max_iter : integer, optional
- Maximum number of iterations. If not given, uses the pcl default (10) hardwired into PCL.
- transformationEpsilon : double, optional
- Maximum difference between two consecutive transformations, before the algorithm is considered to have converged. If not given, uses the pcl default (0)
- euclideanFitnessEpsilon : double, optional
- Maximum Euclidean error between two consecutive steps in the ICP loop, before the algorithm is considered to have converged. If not given, uses the pcl default (max)
- converged : bool
- Whether the ICP algorithm converged in at most max_iter steps.
- transf : np.ndarray, shape = [4, 4]
- Transformation matrix.
- estimate : point cloud
- Transformed version of source. Will be of the same type as source.
- fitness : float
- Sum of squares error in the estimated transformation.
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pcl.registration.icp_nl(BasePointCloud source, BasePointCloud target, max_iter=None)¶ Align source to target using generalized non-linear ICP (ICP-NL).
- source : point cloud
- Source point cloud.
- target : point cloud
- Target point cloud.
- max_iter : integer, optional
- Maximum number of iterations. If not given, uses the default number hardwired into PCL.
- converged : bool
- Whether the ICP algorithm converged in at most max_iter steps.
- transf : np.ndarray, shape = [4, 4]
- Transformation matrix.
- estimate : point cloud
- Transformed version of source. Will be of the same type as source.
- fitness : float
- Sum of squares error in the estimated transformation.