转：https://blog.csdn.net/wyqkx/article/details/84188736
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)