python-pcl简易文档（不包含自建函数与pcl

python-pcl简易文档

pcl模块
一些常量

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 returna 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 pointat pc[index]└──── nearest_k_search_for_cloud(self, PointCloud_PointXYZI pc, int k=1)Find the k nearest neighbours and squared distances for all pointsin 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 returna 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 returna 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 returna 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 returna 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 pointat pc[index]└──── nearest_k_search_for_cloud(self, PointCloud_PointXYZRGB pc, int k=1)Find the k nearest neighbours and squared distances for all pointsin 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 returna 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 returna 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 returna 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 returna new pointcloud├──── pcl.PassThroughFilter_PointXYZRGBA└──── filter(self)Apply the filter according to the previously set parameters and returna 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 returna 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 returna 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 pointat 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 pointsin 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 pointat pc[index]└──── nearest_k_search_for_cloud(self, PointCloud_PointXYZRGBA pc, int k=1)Find the k nearest neighbours and squared distances for all pointsin 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 returna 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 returna 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 returna 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 returna 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 returna 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 returna 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 returna 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 pointsin 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 pointat pc[index]└──── nearest_k_search_for_cloud(self, PointCloud pc, int k=1)Find the k nearest neighbours and squared distances for all pointsin 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 returna 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 returna 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 returna 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 returna 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 returna 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 returna 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 returna new PointCloud_PointXYZRGB└──── set_filter_limits(self, float filter_min, float filter_max)└──── set_filter_field_name(self, field_name)

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)

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python-pcl简易文档（不包含自建函数与pcl_grabber包）

python-pcl简易文档

pcl模块

一些常量

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