pcl模块
├──── pcl.find_library(name)
None
├──── pcl.save(cloud, path, format=None, binary=False)
Save pointcloud to file.
Format should be "pcd", "ply", or None to infer from the pathname.
├──── pcl._infer_format(path, format)
None
├──── pcl._encode(path)
None
├──── pcl.load_XYZRGBA(path, format=None)
Load pointcloud from path.
Currently supports PCD and PLY files.
Format should be "pcd", "ply", "obj", or None to infer from the pathname.
├──── pcl.save_PointNormal(cloud, path, format=None, binary=False)
Save pointcloud to file.
Format should be "pcd", "ply", or None to infer from the pathname.
├──── pcl.load_XYZI(path, format=None)
Load pointcloud from path.
Currently supports PCD and PLY files.
Format should be "pcd", "ply", "obj", or None to infer from the pathname.
├──── pcl.load_PointWithViewpoint(path, format=None)
Load pointcloud from path.
Currently supports PCD and PLY files.
Format should be "pcd", "ply", "obj", or None to infer from the pathname.
├──── pcl.load_XYZRGB(path, format=None)
Load pointcloud from path.
Currently supports PCD and PLY files.
Format should be "pcd", "ply", "obj", or None to infer from the pathname.
├──── pcl.save_XYZRGBA(cloud, path, format=None, binary=False)
Save pointcloud to file.
Format should be "pcd", "ply", or None to infer from the pathname.
├──── pcl.load(path, format=None)
Load pointcloud from path.
Currently supports PCD and PLY files.
Format should be "pcd", "ply", "obj", or None to infer from the pathname.
├──── pcl.RadiusOutlierRemoval
└──── set_radius_search(self, double radius)
└──── set_MinNeighborsInRadius(self, int min_pts)
└──── get_radius_search(self)
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── get_MinNeighborsInRadius(self)
├──── pcl.SampleConsensusModelRegistration
├──── pcl.OctreePointCloudSearch_PointXYZRGBA
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
└──── radius_search(self, point, double radius, unsigned int max_nn=0)
Search for all neighbors of query point that are within a given radius
├──── pcl.Segmentation
└──── set_method_type(self, int m)
└──── set_distance_threshold(self, float d)
└──── set_MaxIterations(self, int count)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.KdTreeFLANN_PointXYZI
└──── nearest_k_search_for_point(self, PointCloud_PointXYZI pc, int index, int k=1)
Find the k nearest neighbours and squared distances for the point
at pc[index]
└──── nearest_k_search_for_cloud(self, PointCloud_PointXYZI pc, int k=1)
Find the k nearest neighbours and squared distances for all points
in the pointcloud
├──── pcl.StatisticalOutlierRemovalFilter_PointXYZRGB
└──── set_mean_k(self, int k)
Set the number of points (k) to use for mean distance estimation
└──── set_negative(self, bool negative)
Set whether the indices should be returned, or all points except the indices
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud_PointXYZRGB pc)
└──── set_std_dev_mul_thresh(self, double std_mul)
Set the standard deviation multiplier threshold
├──── pcl.ConcaveHull_PointXYZRGBA
└──── set_Alpha(self, double d)
└──── reconstruct(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.Segmentation_PointXYZI
└──── set_method_type(self, int m)
└──── set_distance_threshold(self, float d)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.NormalEstimation
└──── compute(self)
└──── set_SearchMethod(self, KdTree kdtree)
└──── set_RadiusSearch(self, double param)
└──── set_KSearch(self, int param)
├──── pcl.RandomSampleConsensus
└──── set_DistanceThreshold(self, double param)
└──── get_Inliers(self)
└──── computeModel(self)
├──── pcl.PointCloud_PointXYZRGB
└──── from_list(self, _list)
Fill this pointcloud from a list of 4-tuples
└──── __reduce__(self)
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── _to_ply_file(self, const char *f, bool binary=False)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── extract(self, pyindices, bool negative=False)
Given a list of indices of points in the pointcloud, return a
new pointcloud containing only those points
└──── make_voxel_grid_filter(self)
Return a pcl
└──── make_segmenter(self)
Return a pcl
└──── _from_obj_file(self, const char *s)
└──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
└──── make_passthrough_filter(self)
Return a pcl
└──── _from_ply_file(self, const char *s)
└──── make_kdtree_flann(self)
Return a pcl
└──── _from_pcd_file(self, const char *s)
└──── resize(self, npy_intp x)
└──── make_statistical_outlier_filter(self)
Return a pcl
└──── from_file(self, char *f)
Fill this pointcloud from a file (a local path)
└──── _to_pcd_file(self, const char *f, bool binary=False)
└──── to_list(self)
Return this object as a list of 4-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (3-tuple) at the given row/column
└──── make_moving_least_squares(self)
Return a pcl
└──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
├──── pcl.OctreePointCloud2Buf_PointXYZRGB
└──── set_input_cloud(self, PointCloud_PointXYZRGB pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.ConcaveHull
└──── set_Alpha(self, double d)
└──── reconstruct(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.OctreePointCloud_PointXYZI
└──── set_input_cloud(self, PointCloud_PointXYZI pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.ConditionalRemoval
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_KeepOrganized(self, flag)
├──── pcl.PointCloud_Normal
└──── from_list(self, _list)
Fill this pointcloud from a list of 4-tuples
└──── __reduce__(self)
└──── to_list(self)
Return this object as a list of 4-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (4-tuple) at the given row/column
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── resize(self, npy_intp x)
├──── pcl.KdTreeFLANN_PointXYZRGB
└──── nearest_k_search_for_point(self, PointCloud_PointXYZRGB pc, int index, int k=1)
Find the k nearest neighbours and squared distances for the point
at pc[index]
└──── nearest_k_search_for_cloud(self, PointCloud_PointXYZRGB pc, int k=1)
Find the k nearest neighbours and squared distances for all points
in the pointcloud
├──── pcl.OctreePointCloud2Buf
└──── set_input_cloud(self, PointCloud pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.Vertices
└──── from_list(self, _list)
Fill this pointcloud from a list of 3-tuples
└──── to_list(self)
Return this object as a list of 3-tuples
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── resize(self, npy_intp x)
├──── pcl.PointCloud_PointXYZI
└──── from_list(self, _list)
Fill this pointcloud from a list of 4-tuples
└──── __reduce__(self)
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── _to_ply_file(self, const char *f, bool binary=False)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── extract(self, pyindices, bool negative=False)
Given a list of indices of points in the pointcloud, return a
new pointcloud containing only those points
└──── make_voxel_grid_filter(self)
Return a pcl
└──── make_segmenter(self)
Return a pcl
└──── _from_obj_file(self, const char *s)
└──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
└──── make_passthrough_filter(self)
Return a pcl
└──── _from_ply_file(self, const char *s)
└──── make_kdtree_flann(self)
Return a pcl
└──── _from_pcd_file(self, const char *s)
└──── resize(self, npy_intp x)
└──── make_statistical_outlier_filter(self)
Return a pcl
└──── from_file(self, char *f)
Fill this pointcloud from a file (a local path)
└──── _to_pcd_file(self, const char *f, bool binary=False)
└──── to_list(self)
Return this object as a list of 4-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (4-tuple) at the given row/column
└──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
├──── pcl.SampleConsensusModelStick
├──── pcl.StatisticalOutlierRemovalFilter_PointXYZI
└──── set_mean_k(self, int k)
Set the number of points (k) to use for mean distance estimation
└──── set_negative(self, bool negative)
Set whether the indices should be returned, or all points except the indices
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud_PointXYZI pc)
└──── set_std_dev_mul_thresh(self, double std_mul)
Set the standard deviation multiplier threshold
├──── pcl.OctreePointCloud
└──── set_input_cloud(self, PointCloud pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.RegionGrowing
└──── get_SmoothModeFlag(self)
└──── get_ResidualThreshold(self)
└──── get_MaxClusterSize(self)
└──── get_ResidualTestFlag(self)
└──── get_MinClusterSize(self)
└──── set_MinClusterSize(self, int min)
└──── set_CurvatureThreshold(self, float curvature)
└──── get_CurvatureTestFlag(self)
└──── get_SmoothnessThreshold(self)
└──── get_SegmentFromPoint(self, int index)
└──── set_MaxClusterSize(self, int max)
└──── set_ResidualThreshold(self, float residual)
└──── set_SmoothModeFlag(self, bool value)
└──── set_SearchMethod(self, KdTree kdtree)
└──── set_ResidualTestFlag(self, bool value)
└──── set_InputNormals(self, PointCloud_Normal normals)
└──── get_CurvatureThreshold(self)
└──── set_SmoothnessThreshold(self, float theta)
└──── set_NumberOfNeighbours(self, int neighbour_number)
└──── set_CurvatureTestFlag(self, bool value)
└──── Extract(self)
└──── get_NumberOfNeighbours(self)
├──── pcl.OctreePointCloudChangeDetector_PointXYZRGBA
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── get_PointIndicesFromNewVoxels(self)
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
├──── pcl.EuclideanClusterExtraction
└──── set_ClusterTolerance(self, double b)
└──── set_SearchMethod(self, KdTree kdtree)
└──── set_MinClusterSize(self, int min)
└──── set_MaxClusterSize(self, int max)
└──── Extract(self)
├──── pcl.VoxelGridFilter
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.OctreePointCloud_PointXYZRGB
└──── set_input_cloud(self, PointCloud_PointXYZRGB pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.OctreePointCloud2Buf_PointXYZRGBA
└──── set_input_cloud(self, PointCloud_PointXYZRGBA pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.Segmentation_PointXYZRGB
└──── set_method_type(self, int m)
└──── set_distance_threshold(self, float d)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.Segmentation_PointXYZRGBA_Normal
└──── set_method_type(self, int m)
└──── get_eps_angle(self)
└──── get_min_max_opening_angle(self)
└──── set_distance_threshold(self, float d)
└──── set_axis(self, double ax1, double ax2, double ax3)
└──── set_radius_limits(self, float f1, float f2)
└──── get_axis(self)
└──── set_max_iterations(self, int i)
└──── set_min_max_opening_angle(self, double min_angle, double max_angle)
Set the minimum and maximum cone opening angles in radians for a cone model
└──── set_eps_angle(self, double ea)
└──── set_normal_distance_weight(self, float f)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.StatisticalOutlierRemovalFilter
└──── set_mean_k(self, int k)
Set the number of points (k) to use for mean distance estimation
└──── set_negative(self, bool negative)
Set whether the indices should be returned, or all points except the indices
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud pc)
└──── set_std_dev_mul_thresh(self, double std_mul)
Set the standard deviation multiplier threshold
├──── pcl.SampleConsensusModelLine
├──── pcl.VoxelGridFilter_PointXYZI
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.PassThroughFilter_PointXYZRGBA
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new PointCloud_PointXYZRGBA
└──── set_filter_limits(self, float filter_min, float filter_max)
└──── set_filter_field_name(self, field_name)
├──── pcl.OctreePointCloudSearch_PointXYZI
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
└──── radius_search(self, point, double radius, unsigned int max_nn=0)
Search for all neighbors of query point that are within a given radius
├──── pcl.MovingLeastSquares_PointXYZRGB
└──── set_polynomial_fit(self, bool fit)
Sets whether the surface and normal are approximated using a polynomial,
or only via tangent estimation
└──── set_polynomial_order(self, bool order)
Set the order of the polynomial to be fit
└──── set_search_radius(self, double radius)
Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting
└──── process(self)
Apply the smoothing according to the previously set values and return
a new pointcloud
├──── pcl.PointCloud_PointWithViewpoint
└──── from_file(self, char *f)
Fill this pointcloud from a file (a local path)
└──── from_list(self, _list)
Fill this pointcloud from a list of 6-tuples
└──── __reduce__(self)
└──── to_list(self)
Return this object as a list of 6-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (6-tuple) at the given row/column
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── _from_ply_file(self, const char *s)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
└──── _from_pcd_file(self, const char *s)
└──── resize(self, npy_intp x)
├──── pcl.SegmentationNormal
└──── set_method_type(self, int m)
└──── get_eps_angle(self)
└──── get_min_max_opening_angle(self)
└──── set_distance_threshold(self, float d)
└──── set_axis(self, double ax1, double ax2, double ax3)
└──── set_radius_limits(self, float f1, float f2)
└──── get_axis(self)
└──── set_max_iterations(self, int i)
└──── set_min_max_opening_angle(self, double min_angle, double max_angle)
Set the minimum and maximum cone opening angles in radians for a cone model
└──── set_eps_angle(self, double ea)
└──── set_normal_distance_weight(self, float f)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.OctreePointCloud_PointXYZRGBA
└──── set_input_cloud(self, PointCloud_PointXYZRGBA pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.VoxelGridFilter_PointXYZRGB
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.OctreePointCloudSearch
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── nearest_k_search_for_point(self, PointCloud pc, int index, int k=1)
Find the k nearest neighbours and squared distances for the point
at pc[index]
└──── VoxelSearch(self, PointCloud pc)
Search for all neighbors of query point that are within a given voxel
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── nearest_k_search_for_cloud(self, PointCloud pc, int k=1)
Find the k nearest neighbours and squared distances for all points
in the pointcloud
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
└──── radius_search(self, point, double radius, unsigned int max_nn=0)
Search for all neighbors of query point that are within a given radius
├──── pcl.OctreePointCloudChangeDetector_PointXYZI
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── get_PointIndicesFromNewVoxels(self)
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
├──── pcl.KdTreeFLANN_PointXYZRGBA
└──── nearest_k_search_for_point(self, PointCloud_PointXYZRGBA pc, int index, int k=1)
Find the k nearest neighbours and squared distances for the point
at pc[index]
└──── nearest_k_search_for_cloud(self, PointCloud_PointXYZRGBA pc, int k=1)
Find the k nearest neighbours and squared distances for all points
in the pointcloud
├──── pcl.PointCloud_PointNormal
└──── from_list(self, _list)
Fill this pointcloud from a list of 4-tuples
└──── __reduce__(self)
└──── to_list(self)
Return this object as a list of 3-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (3-tuple) at the given row/column
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── resize(self, npy_intp x)
├──── pcl.StatisticalOutlierRemovalFilter_PointXYZRGBA
└──── set_mean_k(self, int k)
Set the number of points (k) to use for mean distance estimation
└──── set_negative(self, bool negative)
Set whether the indices should be returned, or all points except the indices
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud_PointXYZRGBA pc)
└──── set_std_dev_mul_thresh(self, double std_mul)
Set the standard deviation multiplier threshold
├──── pcl.ConditionAnd
└──── add_Comparison2(self, field_name, CompareOp2 compOp, double thresh)
├──── pcl.ConcaveHull_PointXYZRGB
└──── set_Alpha(self, double d)
└──── reconstruct(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.GeneralizedIterativeClosestPoint
└──── gicp(self, PointCloud source, PointCloud target, max_iter=None)
Align source to target using generalized iterative closest point (GICP)
├──── pcl.MovingLeastSquares
└──── set_polynomial_fit(self, bool fit)
Sets whether the surface and normal are approximated using a polynomial,
or only via tangent estimation
└──── set_polynomial_order(self, bool order)
Set the order of the polynomial to be fit
└──── set_search_radius(self, double radius)
Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting
└──── set_Search_Method(self, KdTree kdtree)
└──── set_Compute_Normals(self, bool flag)
└──── process(self)
Apply the smoothing according to the previously set values and return
a new PointCloud
├──── pcl.KdTree
├──── pcl.VFHEstimation
└──── set_SearchMethod(self, KdTree kdtree)
└──── set_KSearch(self, int param)
├──── pcl.IterativeClosestPointNonLinear
└──── icp_nl(self, PointCloud source, PointCloud target, max_iter=None)
Align source to target using generalized non-linear ICP (ICP-NL)
├──── pcl.ProjectInliers
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── get_copy_all_data(self)
└──── get_model_type(self)
└──── set_model_type(self, SacModel m)
└──── set_copy_all_data(self, bool m)
├──── pcl.ApproximateVoxelGrid_PointXYZRGBA
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud_PointXYZRGBA pc)
├──── pcl.ApproximateVoxelGrid_PointXYZRGB
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud_PointXYZRGB pc)
├──── pcl.OctreePointCloud2Buf_PointXYZI
└──── set_input_cloud(self, PointCloud_PointXYZI pc)
Provide a pointer to the input data set
└──── delete_tree(self)
Delete the octree structure and its leaf nodes
├──── pcl.ApproximateVoxelGrid_PointXYZI
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud_PointXYZI pc)
├──── pcl.OctreePointCloudChangeDetector_PointXYZRGB
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── get_PointIndicesFromNewVoxels(self)
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
├──── pcl.SampleConsensusModelPlane
├──── pcl.PointCloud_PointXYZRGBA
└──── from_list(self, _list)
Fill this pointcloud from a list of 4-tuples
└──── __reduce__(self)
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── _to_ply_file(self, const char *f, bool binary=False)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── extract(self, pyindices, bool negative=False)
Given a list of indices of points in the pointcloud, return a
new pointcloud containing only those points
└──── make_voxel_grid_filter(self)
Return a pcl
└──── make_segmenter(self)
Return a pcl
└──── _from_obj_file(self, const char *s)
└──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
└──── make_passthrough_filter(self)
Return a pcl
└──── _from_ply_file(self, const char *s)
└──── make_kdtree_flann(self)
Return a pcl
└──── _from_pcd_file(self, const char *s)
└──── resize(self, npy_intp x)
└──── make_statistical_outlier_filter(self)
Return a pcl
└──── from_file(self, char *f)
Fill this pointcloud from a file (a local path)
└──── _to_pcd_file(self, const char *f, bool binary=False)
└──── to_list(self)
Return this object as a list of 3-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (3-tuple) at the given row/column
└──── make_moving_least_squares(self)
Return a pcl
└──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
├──── pcl.SampleConsensusModelSphere
├──── pcl.KdTreeFLANN
└──── radius_search_for_cloud(self, PointCloud pc, double radius, unsigned int max_nn=0)
Find the radius and squared distances for all points
in the pointcloud
└──── nearest_k_search_for_point(self, PointCloud pc, int index, int k=1)
Find the k nearest neighbours and squared distances for the point
at pc[index]
└──── nearest_k_search_for_cloud(self, PointCloud pc, int k=1)
Find the k nearest neighbours and squared distances for all points
in the pointcloud
├──── pcl.SampleConsensusModelCylinder
├──── pcl.MomentOfInertiaEstimation
└──── get_AABB(self)
└──── get_EigenVectors(self)
└──── compute(self)
└──── get_EigenValues(self)
└──── get_Eccentricity(self)
└──── get_OBB(self)
└──── set_InputCloud(self, PointCloud pc)
└──── get_MassCenter(self)
└──── get_MomentOfInertia(self)
├──── pcl.NormalDistributionsTransform
└──── set_OulierRatio(self, double outlier_ratio)
└──── get_TransformationProbability(self)
└──── set_StepSize(self, double step_size)
└──── get_OulierRatio(self)
└──── set_InputTarget(self)
└──── set_Resolution(self, float resolution)
└──── get_FinalNumIteration(self)
└──── get_Resolution(self)
└──── get_StepSize(self)
├──── pcl.SampleConsensusModel
├──── pcl.CropBox
└──── set_Rotation(self, rx, ry, rz)
└──── set_Min(self, minx, miny, minz, mins)
└──── get_Negative(self)
└──── set_MinMax(self, minx, miny, minz, mins, maxx, maxy, maxz, maxs)
└──── set_Negative(self, bool flag)
└──── set_Max(self, maxx, maxy, maxz, maxs)
└──── get_RemovedIndices(self)
└──── filter(self)
└──── set_Translation(self, tx, ty, tz)
└──── set_InputCloud(self, PointCloud pc)
├──── pcl.MovingLeastSquares_PointXYZRGBA
└──── set_polynomial_fit(self, bool fit)
Sets whether the surface and normal are approximated using a polynomial,
or only via tangent estimation
└──── set_polynomial_order(self, bool order)
Set the order of the polynomial to be fit
└──── set_search_radius(self, double radius)
Set the sphere radius that is to be used for determining the k-nearest neighbors used for fitting
└──── process(self)
Apply the smoothing according to the previously set values and return
a new pointcloud
├──── pcl.IntegralImageNormalEstimation
└──── set_NormalEstimation_Method_SIMPLE_3D_GRADIENT(self)
└──── set_NormalEstimation_Method_COVARIANCE_MATRIX(self)
└──── set_NormalSmoothingSize(self, double param)
└──── set_NormalEstimation_Method_AVERAGE_3D_GRADIENT(self)
└──── set_MaxDepthChange_Factor(self, double param)
└──── set_NormalEstimation_Method_AVERAGE_DEPTH_CHANGE(self)
└──── compute(self)
├──── pcl.VoxelGridFilter_PointXYZRGBA
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.Segmentation_PointXYZRGBA
└──── set_method_type(self, int m)
└──── set_distance_threshold(self, float d)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.PassThroughFilter
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_filter_limits(self, float filter_min, float filter_max)
└──── set_filter_field_name(self, field_name)
├──── pcl.OctreePointCloudChangeDetector
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── get_PointIndicesFromNewVoxels(self)
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
└──── switchBuffers(self)
├──── pcl.HarrisKeypoint3D
└──── compute(self)
└──── set_NonMaxSupression(self, bool param)
└──── set_RadiusSearch(self, double param)
└──── set_Radius(self, float param)
├──── pcl.ConcaveHull_PointXYZI
└──── set_Alpha(self, double d)
└──── reconstruct(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
├──── pcl.CropHull
└──── Filtering(self, PointCloud outputCloud)
└──── filter(self)
└──── set_InputCloud(self, PointCloud pc)
└──── SetParameter(self, PointCloud points, Vertices vt)
├──── pcl.Segmentation_PointXYZRGB_Normal
└──── set_method_type(self, int m)
└──── get_eps_angle(self)
└──── get_min_max_opening_angle(self)
└──── set_distance_threshold(self, float d)
└──── set_axis(self, double ax1, double ax2, double ax3)
└──── set_radius_limits(self, float f1, float f2)
└──── get_axis(self)
└──── set_max_iterations(self, int i)
└──── set_min_max_opening_angle(self, double min_angle, double max_angle)
Set the minimum and maximum cone opening angles in radians for a cone model
└──── set_eps_angle(self, double ea)
└──── set_normal_distance_weight(self, float f)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.PassThroughFilter_PointXYZI
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new PointCloud_PointXYZI
└──── set_filter_limits(self, float filter_min, float filter_max)
└──── set_filter_field_name(self, field_name)
├──── pcl.ApproximateVoxelGrid
└──── set_leaf_size(self, float x, float y, float z)
Set the voxel grid leaf size
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new pointcloud
└──── set_InputCloud(self, PointCloud pc)
├──── pcl.OctreePointCloudSearch_PointXYZRGB
└──── define_bounding_box(self)
Investigate dimensions of pointcloud data set and define corresponding bounding box for octree
└──── add_points_from_input_cloud(self)
Add points from input point cloud to octree
└──── is_voxel_occupied_at_point(self, point)
Check if voxel at given point coordinates exist
└──── delete_voxel_at_point(self, point)
Delete leaf node / voxel at given point
└──── get_occupied_voxel_centers(self)
Get list of centers of all occupied voxels
└──── radius_search(self, point, double radius, unsigned int max_nn=0)
Search for all neighbors of query point that are within a given radius
├──── pcl.PointCloud
└──── make_octreeChangeDetector(self, double resolution)
Return a pcl
└──── from_list(self, _list)
Fill this pointcloud from a list of 3-tuples
└──── make_kdtree(self)
Return a pcl
└──── __reduce__(self)
└──── make_ConcaveHull(self)
Return a pcl
└──── from_array(self, ndarray arr)
Fill this object from a 2D numpy array (float32)
└──── _to_ply_file(self, const char *f, bool binary=False)
└──── make_RangeImage(self)
└──── to_array(self)
Return this object as a 2D numpy array (float32)
└──── make_ConditionalRemoval(self, ConditionAnd range_conf)
Return a pcl
└──── extract(self, pyindices, bool negative=False)
Given a list of indices of points in the pointcloud, return a
new pointcloud containing only those points
└──── make_GeneralizedIterativeClosestPoint(self)
└──── make_octreeSearch(self, double resolution)
Return a pcl
└──── make_octree(self, double resolution)
Return a pcl
└──── make_ProjectInliers(self)
Return a pclfil
└──── make_NormalEstimation(self)
└──── make_RegionGrowing(self, int ksearch=-1, double searchRadius=-1
└──── make_IterativeClosestPointNonLinear(self)
└──── make_voxel_grid_filter(self)
Return a pcl
└──── make_segmenter(self)
Return a pcl
└──── _from_obj_file(self, const char *s)
└──── to_file(self, const char *fname, bool ascii=True)
Save pointcloud to a file in PCD format
└──── make_ApproximateVoxelGrid(self)
Return a pcl
└──── make_VFHEstimation(self)
└──── make_EuclideanClusterExtraction(self)
└──── make_ConditionAnd(self)
Return a pcl
└──── make_passthrough_filter(self)
Return a pcl
└──── _from_ply_file(self, const char *s)
└──── make_kdtree_flann(self)
Return a pcl
└──── _from_pcd_file(self, const char *s)
└──── make_RadiusOutlierRemoval(self)
Return a pclfil
└──── resize(self, npy_intp x)
└──── make_statistical_outlier_filter(self)
Return a pcl
└──── from_file(self, char *f)
Fill this pointcloud from a file (a local path)
└──── _to_pcd_file(self, const char *f, bool binary=False)
└──── make_cropbox(self)
Return a pcl
└──── make_MomentOfInertiaEstimation(self)
└──── make_IterativeClosestPoint(self)
└──── to_list(self)
Return this object as a list of 3-tuples
└──── get_point(self, npy_intp row, npy_intp col)
Return a point (3-tuple) at the given row/column
└──── make_HarrisKeypoint3D(self)
Return a pcl
└──── make_moving_least_squares(self)
Return a pcl
└──── make_IntegralImageNormalEstimation(self)
Return a pcl
└──── make_segmenter_normals(self, int ksearch=-1, double searchRadius=-1
└──── make_crophull(self)
Return a pcl
├──── pcl.RangeImages
└──── CreateFromPointCloud(self, PointCloud cloud, float angular_resolution, float max_angle_width, float max_angle_height, CoordinateFrame2 coordinate_frame, float noise_level, float min_range, int border_size)
└──── SetUnseenToMaxRange(self)
└──── SetAngularResolution(self, float angular_resolution_x, float angular_resolution_y)
└──── IntegrateFarRanges(self, PointCloud_PointWithViewpoint viewpoint)
├──── pcl.IterativeClosestPoint
└──── icp(self, PointCloud source, PointCloud target, max_iter=None)
Align source to target using iterative closest point (ICP)
└──── set_InputTarget(self, PointCloud cloud)
├──── pcl.Segmentation_PointXYZI_Normal
└──── set_method_type(self, int m)
└──── get_eps_angle(self)
└──── get_min_max_opening_angle(self)
└──── set_distance_threshold(self, float d)
└──── set_axis(self, double ax1, double ax2, double ax3)
└──── set_radius_limits(self, float f1, float f2)
└──── get_axis(self)
└──── set_max_iterations(self, int i)
└──── set_min_max_opening_angle(self, double min_angle, double max_angle)
Set the minimum and maximum cone opening angles in radians for a cone model
└──── set_eps_angle(self, double ea)
└──── set_normal_distance_weight(self, float f)
└──── set_model_type(self, SacModel m)
└──── segment(self)
└──── set_optimize_coefficients(self, bool b)
├──── pcl.PassThroughFilter_PointXYZRGB
└──── filter(self)
Apply the filter according to the previously set parameters and return
a new PointCloud_PointXYZRGB
└──── set_filter_limits(self, float filter_min, float filter_max)
└──── set_filter_field_name(self, field_name)
visualization模块
这里visual 为pcl.pcl_visualization
├──── visual.PointCloudColorHandleringRandom
├──── visual.PointCloudColorHandleringGenericField
├──── visual.PointCloudGeometryHandlerCustom
├──── visual.CloudViewing
└──── ShowColorACloud(self, PointCloud_PointXYZRGBA pc, cloudname=b'cloud')
└──── ShowMonochromeCloud(self, PointCloud pc, cloudname=b'cloud')
└──── WasStopped(self, int millis_to_wait=1)
└──── ShowColorCloud(self, PointCloud_PointXYZRGB pc, cloudname=b'cloud')
└──── ShowGrayCloud(self, PointCloud_PointXYZI pc, cloudname=b'cloud')
├──── visual.PointCloudGeometryHandleringCustom
├──── visual.PointCloudColorHandleringCustom
├──── visual.PointCloudColorHandleringHSVField
├──── visual.PCLHistogramViewing
└──── SpinOnce(self, int time=1, bool force_redraw=False)
└──── Spin(self)
└──── AddFeatureHistogram(self, PointCloud cloud, int hsize, cloudname, int win_width=640, int win_height=200)
├──── visual.PointCloudGeometryHandleringXYZ
├──── visual.PCLVisualizering
└──── AddPlane(self)
└──── ResetStoppedFlag(self)
└──── AddCube(self, double min_x, double max_x, double min_y, double max_y, double min_z, double max_z, double r, double g, double b, name)
└──── SetFullScreen(self, bool mode)
└──── RemoveAllShapes(self, int viewport)
└──── AddPointCloud_ColorHandler(self, PointCloud cloud, PointCloudColorHandleringCustom color_handler, id=b'cloud', viewport=0)
└──── SetPointCloudRenderingProperties(self, int propType, int propValue, propName=b'cloud')
└──── AddLine(self)
└──── RemoveAllPointClouds(self, int viewport)
└──── SetBackgroundColor(self, int r, int g, int b)
└──── AddSphere(self)
└──── AddCylinder(self)
└──── RemoveShape(self, string id, int viewport)
└──── SpinOnce(self, int millis_to_wait=1, bool force_redraw=False)
└──── Spin(self)
└──── AddCircle(self)
└──── InitCameraParameters(self)
└──── AddCoordinateSystem(self, double scale=1
└──── WasStopped(self)
└──── RemovePointCloud(self, string id, int viewport)
└──── AddCone(self)
└──── UpdateText(self, string text, int xpos, int ypos, id)
└──── SetWindowBorders(self, bool mode)
└──── AddText(self, string text, int xpos, int ypos, id, int viewport)
└──── RemovePolygonMesh(self, string id, int viewport)
└──── removeCoordinateSystem(self, int viewport)
└──── AddPointCloudNormals(self, PointCloud cloud, PointCloud_Normal normal, int level=100, double scale=0
└──── Close(self)
└──── RemoveText3D(self, string id, int viewport)
└──── AddPointCloud(self, PointCloud cloud, id=b'cloud', int viewport=0)
├──── visual.PointCloudColorHandleringRGBField
├──── visual.PointCloudGeometryHandleringSurfaceNormal
一些常量
('SACMODEL_LINE', 1)
('SACMODEL_PARALLEL_PLANE', 15)
('SACMODEL_CYLINDER', 5)
('SAC_PROSAC', 6)
('SAC_LMEDS', 1)
('SACMODEL_PARALLEL_LINES', 10)
('SAC_MSAC', 2)
('SACMODEL_REGISTRATION', 13)
('SAC_RRANSAC', 3)
('SACMODEL_NORMAL_PARALLEL_PLANE', 16)
('SACMODEL_NORMAL_SPHERE', 12)
('SACMODEL_CIRCLE2D', 2)
('SACMODEL_TORUS', 7)
('SACMODEL_STICK', 17)
('SACMODEL_CIRCLE3D', 3)
('SAC_RMSAC', 4)
('SACMODEL_NORMAL_PLANE', 11)
('SACMODEL_PERPENDICULAR_PLANE', 9)
('SAC_RANSAC', 0)
('SACMODEL_CONE', 6)
('SAC_MLESAC', 5)
('SACMODEL_SPHERE', 4)
('SACMODEL_PARALLEL_LINE', 8)
('SACMODEL_PLANE', 0)
('PCLVISUALIZER_OPACITY', 1)
('PCLVISUALIZER_REPRESENTATION', 5)
('PCLVISUALIZER_FONT_SIZE', 3)
('PCLVISUALIZER_POINT_SIZE', 0)
('PCLVISUALIZER_REPRESENTATION_WIREFRAME', 1)
('PCLVISUALIZER_IMMEDIATE_RENDERING', 6)
('PCLVISUALIZER_REPRESENTATION_POINTS', 0)
('PCLVISUALIZER_REPRESENTATION_SURFACE', 2)
('PCLVISUALIZER_LINE_WIDTH', 2)
('PCLVISUALIZER_COLOR', 4)
