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# -*- coding: utf-8 -*-
from libc.stddef cimport size_t
from libcpp.vector cimport vector
from libcpp.string cimport string
from libcpp cimport bool
# main
cimport pcl_defs as cpp
cimport pcl_features as pclftr
# boost
from boost_shared_ptr cimport shared_ptr
###############################################################################
# Types
###############################################################################
### base class ###
# keypoint.h
# template <typename PointInT, typename PointOutT>
# class Keypoint : public PCLBase<PointInT>
cdef extern from "pcl/keypoints/keypoint.h" namespace "pcl":
cdef cppclass Keypoint[In, Out](cpp.PCLBase[In]):
Keypoint ()
# public:
# brief Provide a pointer to the input dataset that we need to estimate features at every point for.
# param cloud the const boost shared pointer to a PointCloud message
# void setSearchSurface (const PointCloudInConstPtr &cloud)
# void setSearchSurface (const PointCloud[In] &cloud)
# brief Get a pointer to the surface point cloud dataset.
# PointCloudInConstPtr getSearchSurface ()
# PointCloud[In] getSearchSurface ()
# brief Provide a pointer to the search object.
# param tree a pointer to the spatial search object.
# void setSearchMethod (const KdTreePtr &tree)
# void setSearchMethod (-.KdTree &tree)
# brief Get a pointer to the search method used.
# KdTreePtr getSearchMethod ()
# -.KdTree getSearchMethod ()
# brief Get the internal search parameter.
double getSearchParameter ()
# brief Set the number of k nearest neighbors to use for the feature estimation.
# param k the number of k-nearest neighbors
void setKSearch (int k)
# brief get the number of k nearest neighbors used for the feature estimation. */
int getKSearch ()
# brief Set the sphere radius that is to be used for determining the nearest neighbors used for the key point detection
# param radius the sphere radius used as the maximum distance to consider a point a neighbor
void setRadiusSearch (double radius)
# brief Get the sphere radius used for determining the neighbors. */
double getRadiusSearch ()
# brief Base method for key point detection for all points given in <setInputCloud (), setIndices ()> using
# the surface in setSearchSurface () and the spatial locator in setSearchMethod ()
# param output the resultant point cloud model dataset containing the estimated features
# inline void compute (PointCloudOut &output);
void compute (cpp.PointCloud[Out] &output)
# brief Search for k-nearest neighbors using the spatial locator from \a setSearchmethod, and the given surface
# from \a setSearchSurface.
# param index the index of the query point
# param parameter the search parameter (either k or radius)
# param indices the resultant vector of indices representing the k-nearest neighbors
# param distances the resultant vector of distances representing the distances from the query point to the
# k-nearest neighbors
# inline int searchForNeighbors (int index, double parameter, vector[int] &indices, vector[float] &distances)
int searchForNeighbors (int index, double parameter, vector[int] &indices, vector[float] &distances)
###
# harris_keypoint3D.h (1.6.0)
# harris_3d.h (1.7.2)
# template <typename PointInT, typename PointOutT, typename NormalT = pcl::Normal>
# class HarrisKeypoint3D : public Keypoint<PointInT, PointOutT>
cdef extern from "pcl/keypoints/harris_keypoint3D.h" namespace "pcl":
cdef cppclass HarrisKeypoint3D[In, Out, NormalT](Keypoint[In, Out]):
HarrisKeypoint3D ()
# HarrisKeypoint3D (ResponseMethod method = HARRIS, float radius = 0.01f, float threshold = 0.0f)
# typedef typename Keypoint<PointInT, PointOutT>::PointCloudIn PointCloudIn;
# typedef typename Keypoint<PointInT, PointOutT>::PointCloudOut PointCloudOut;
# typedef typename Keypoint<PointInT, PointOutT>::KdTree KdTree;
# typedef typename PointCloudIn::ConstPtr PointCloudInConstPtr;
# typedef typename pcl::PointCloud<NormalT> PointCloudN;
# typedef typename PointCloudN::Ptr PointCloudNPtr;
# typedef typename PointCloudN::ConstPtr PointCloudNConstPtr;
# typedef enum {HARRIS = 1, NOBLE, LOWE, TOMASI, CURVATURE} ResponseMethod;
# brief Set the method of the response to be calculated.
# param[in] type
# void setMethod (ResponseMethod type)
# void setMethod (ResponseMethod2 type)
void setMethod (int type)
# * \brief Set the radius for normal estimation and non maxima supression.
# * \param[in] radius
# void setRadius (float radius)
void setRadius (float radius)
# * \brief Set the threshold value for detecting corners. This is only evaluated if non maxima suppression is turned on.
# * \brief note non maxima suppression needs to be activated in order to use this feature.
# * \param[in] threshold
void setThreshold (float threshold)
# * \brief Whether non maxima suppression should be applied or the response for each point should be returned
# * \note this value needs to be turned on in order to apply thresholding and refinement
# * \param[in] nonmax default is false
# void setNonMaxSupression (bool = false)
void setNonMaxSupression (bool param)
# * \brief Whether the detected key points should be refined or not. If turned of, the key points are a subset of the original point cloud. Otherwise the key points may be arbitrary.
# * \brief note non maxima supression needs to be on in order to use this feature.
# * \param[in] do_refine
void setRefine (bool do_refine)
# * \brief Set normals if precalculated normals are available.
# * \param normals
# void setNormals (const PointCloudNPtr &normals)
# void setNormals (const cpp.PointCloud[NormalT] &normals)
# * \brief Provide a pointer to a dataset to add additional information
# * to estimate the features for every point in the input dataset. This
# * is optional, if this is not set, it will only use the data in the
# * input cloud to estimate the features. This is useful when you only
# * need to compute the features for a downsampled cloud.
# * \param[in] cloud a pointer to a PointCloud message
# virtual void setSearchSurface (const PointCloudInConstPtr &cloud)
# void setSearchSurface (const PointCloudInConstPtr &cloud)
# * \brief Initialize the scheduler and set the number of threads to use.
# * \param nr_threads the number of hardware threads to use (-1 sets the value back to automatic)
# inline void setNumberOfThreads (int nr_threads)
void setNumberOfThreads (int nr_threads)
ctypedef HarrisKeypoint3D[cpp.PointXYZ, cpp.PointXYZI, cpp.Normal] HarrisKeypoint3D_t
ctypedef HarrisKeypoint3D[cpp.PointXYZI, cpp.PointXYZI, cpp.Normal] HarrisKeypoint3D_PointXYZI_t
ctypedef HarrisKeypoint3D[cpp.PointXYZRGB, cpp.PointXYZI, cpp.Normal] HarrisKeypoint3D_PointXYZRGB_t
ctypedef HarrisKeypoint3D[cpp.PointXYZRGBA, cpp.PointXYZI, cpp.Normal] HarrisKeypoint3D_PointXYZRGBA_t
ctypedef shared_ptr[HarrisKeypoint3D[cpp.PointXYZ, cpp.PointXYZI, cpp.Normal]] HarrisKeypoint3DPtr_t
ctypedef shared_ptr[HarrisKeypoint3D[cpp.PointXYZI, cpp.PointXYZI, cpp.Normal]] HarrisKeypoint3D_PointXYZI_Ptr_t
ctypedef shared_ptr[HarrisKeypoint3D[cpp.PointXYZRGB, cpp.PointXYZI, cpp.Normal]] HarrisKeypoint3D_PointXYZRGB_Ptr_t
ctypedef shared_ptr[HarrisKeypoint3D[cpp.PointXYZRGBA, cpp.PointXYZI, cpp.Normal]] HarrisKeypoint3D_PointXYZRGBA_Ptr_t
###
# narf_keypoint.h
# class PCL_EXPORTS NarfKeypoint : public Keypoint<PointWithRange, int>
cdef extern from "pcl/keypoints/narf_keypoint.h" namespace "pcl":
cdef cppclass NarfKeypoint(Keypoint[cpp.PointWithRange, int]):
NarfKeypoint ()
NarfKeypoint (pclftr.RangeImageBorderExtractor range_image_border_extractor, float support_size)
# NarfKeypoint (RangeImageBorderExtractor* range_image_border_extractor=NULL, float support_size=-1.0f);
# public:
# // =====TYPEDEFS=====
# typedef Keypoint<PointWithRange, int> BaseClass;
# typedef Keypoint<PointWithRange, int>::PointCloudOut PointCloudOut;
# // =====PUBLIC STRUCTS=====
# //! Parameters used in this class
# cdef struct Parameters
# {
# Parameters() : support_size(-1.0f), max_no_of_interest_points(-1), min_distance_between_interest_points(0.25f),
# optimal_distance_to_high_surface_change(0.25), min_interest_value(0.45f),
# min_surface_change_score(0.2f), optimal_range_image_patch_size(10),
# distance_for_additional_points(0.0f), add_points_on_straight_edges(false),
# do_non_maximum_suppression(true), no_of_polynomial_approximations_per_point(0),
# max_no_of_threads(1), use_recursive_scale_reduction(false),
# calculate_sparse_interest_image(true) {}
#
# float support_size; //!< This defines the area 'covered' by an interest point (in meters)
# int max_no_of_interest_points; //!< The maximum number of interest points that will be returned
# float min_distance_between_interest_points; /**< Minimum distance between maximas
# * (this is a factor for support_size, i.e. the distance is
# * min_distance_between_interest_points*support_size) */
# float optimal_distance_to_high_surface_change; /**< The distance we want keep between keypoints and areas
# * of high surface change
# * (this is a factor for support_size, i.e., the distance is
# * optimal_distance_to_high_surface_change*support_size) */
# float min_interest_value; //!< The minimum value to consider a point as an interest point
# float min_surface_change_score; //!< The minimum value of the surface change score to consider a point
# int optimal_range_image_patch_size; /**< The size (in pixels) of the image patches from which the interest value
# * should be computed. This influences, which range image is selected from
# * the scale space to compute the interest value of a pixel at a certain
# * distance. */
# // TODO:
# float distance_for_additional_points; /**< All points in this distance to a found maximum, that
# * are above min_interest_value are also added as interest points
# * (this is a factor for support_size, i.e. the distance is
# * distance_for_additional_points*support_size) */
# bool add_points_on_straight_edges; /**< If this is set to true, there will also be interest points on
# * straight edges, e.g., just indicating an area of high surface change */
# bool do_non_maximum_suppression; /**< If this is set to false there will be much more points
# * (can be used to spread points over the whole scene
# * (combined with a low min_interest_value)) */
# bool no_of_polynomial_approximations_per_point; /**< If this is >0, the exact position of the interest point is
# determined using bivariate polynomial approximations of the
# interest values of the area. */
# int max_no_of_threads; //!< The maximum number of threads this code is allowed to use with OPNEMP
# bool use_recursive_scale_reduction; /**< Try to decrease runtime by extracting interest points at lower reolution
# * in areas that contain enough points, i.e., have lower range. */
# bool calculate_sparse_interest_image; /**< Use some heuristics to decide which areas of the interest image
# can be left out to improve the runtime. */
# };
#
# =====PUBLIC METHODS=====
# Erase all data calculated for the current range image
void clearData ()
# //! Set the RangeImageBorderExtractor member (required)
# void setRangeImageBorderExtractor (RangeImageBorderExtractor* range_image_border_extractor);
void setRangeImageBorderExtractor (pclftr.RangeImageBorderExtractor range_image_border_extractor)
# //! Get the RangeImageBorderExtractor member
# RangeImageBorderExtractor* getRangeImageBorderExtractor ()
pclftr.RangeImageBorderExtractor getRangeImageBorderExtractor ()
# //! Set the RangeImage member of the RangeImageBorderExtractor
# void setRangeImage (const RangeImage* range_image)
# void setRangeImage (const RangeImage_Ptr range_image)
# /** Extract interest value per image point */
# float* getInterestImage () { calculateInterestImage(); return interest_image_;}
# float[] getInterestImage ()
# //! Extract maxima from an interest image
# const ::pcl::PointCloud<InterestPoint>& getInterestPoints () { calculateInterestPoints(); return *interest_points_;}
# //! Set all points in the image that are interest points to true, the rest to false
# const std::vector<bool>& getIsInterestPointImage ()
# //! Getter for the parameter struct
# Parameters& getParameters ()
# //! Getter for the range image of range_image_border_extractor_
# const RangeImage& getRangeImage ();
# //! Overwrite the compute function of the base class
# void compute (PointCloudOut& output);
# ingroup keypoints
# operator
# inline std::ostream& operator << (std::ostream& os, const NarfKeypoint::Parameters& p)
ctypedef NarfKeypoint NarfKeypoint_t
ctypedef shared_ptr[NarfKeypoint] NarfKeypointPtr_t
###
# sift_keypoint.h
# template <typename PointInT, typename PointOutT>
# class SIFTKeypoint : public Keypoint<PointInT, PointOutT>
cdef extern from "pcl/keypoints/sift_keypoint.h" namespace "pcl":
cdef cppclass SIFTKeypoint[In, Out](Keypoint[In, Out]):
SIFTKeypoint ()
# public:
# /** \brief Specify the range of scales over which to search for keypoints
# * \param min_scale the standard deviation of the smallest scale in the scale space
# * \param nr_octaves the number of octaves (i.e. doublings of scale) to compute
# * \param nr_scales_per_octave the number of scales to compute within each octave
void setScales (float min_scale, int nr_octaves, int nr_scales_per_octave)
# /** \brief Provide a threshold to limit detection of keypoints without sufficient contrast
# * \param min_contrast the minimum contrast required for detection
void setMinimumContrast (float min_contrast)
# pcl::SIFTKeypoint<pcl::PointNormal, pcl::PointWithScale> sift;
ctypedef SIFTKeypoint[cpp.PointNormal, cpp.PointWithScale] SIFTKeypoint_t
ctypedef shared_ptr[SIFTKeypoint[cpp.PointNormal, cpp.PointWithScale]] SIFTKeypointPtr_t
###
# smoothed_surfaces_keypoint.h
# template <typename PointT, typename PointNT>
# class SmoothedSurfacesKeypoint : public Keypoint <PointT, PointT>
cdef extern from "pcl/keypoints/smoothed_surfaces_keypoint.h" namespace "pcl":
cdef cppclass SmoothedSurfacesKeypoint[In, Out](Keypoint[In, Out]):
SmoothedSurfacesKeypoint ()
# public:
# void addSmoothedPointCloud (const PointCloudTConstPtr &cloud, const PointCloudNTConstPtr &normals, KdTreePtr &kdtree, float &scale);
void resetClouds ()
# inline void setNeighborhoodConstant (float neighborhood_constant)
# inline float getNeighborhoodConstant ()
# inline void setInputNormals (const PointCloudNTConstPtr &normals)
# inline void setInputScale (float input_scale)
# void detectKeypoints (PointCloudT &output);
###
# uniform_sampling.h
# template <typename PointInT>
# class UniformSampling: public Keypoint<PointInT, int>
cdef extern from "pcl/keypoints/uniform_sampling.h" namespace "pcl":
cdef cppclass UniformSampling[In](Keypoint[In, int]):
UniformSampling ()
# public:
# brief Set the 3D grid leaf size.
# param radius the 3D grid leaf size
void setRadiusSearch (double radius)
ctypedef UniformSampling[cpp.PointXYZ] UniformSampling_t
ctypedef UniformSampling[cpp.PointXYZI] UniformSampling_PointXYZI_t
ctypedef UniformSampling[cpp.PointXYZRGB] UniformSampling_PointXYZRGB_t
ctypedef UniformSampling[cpp.PointXYZRGBA] UniformSampling_PointXYZRGBA_t
ctypedef shared_ptr[UniformSampling[cpp.PointXYZ]] UniformSamplingPtr_t
ctypedef shared_ptr[UniformSampling[cpp.PointXYZI]] UniformSampling_PointXYZI_Ptr_t
ctypedef shared_ptr[UniformSampling[cpp.PointXYZRGB]] UniformSampling_PointXYZRGB_Ptr_t
ctypedef shared_ptr[UniformSampling[cpp.PointXYZRGBA]] UniformSampling_PointXYZRGBA_Ptr_t
###
###############################################################################
# Enum
###############################################################################
# 1.6.0
# NG : use Template parameters Class Internal
# typedef enum {HARRIS = 1, NOBLE, LOWE, TOMASI, CURVATURE} ResponseMethod;
# 1.7.2
# NG : use Template parameters Class Internal
# RESPONSEMETHOD_HARRIS "pcl::HarrisKeypoint3D::HARRIS",
# RESPONSEMETHOD_NOBLE "pcl::HarrisKeypoint3D::NOBLE",
# RESPONSEMETHOD_LOWE "pcl::HarrisKeypoint3D::LOWE",
# RESPONSEMETHOD_TOMASI "pcl::HarrisKeypoint3D::TOMASI",
# RESPONSEMETHOD_CURVATURE "pcl::HarrisKeypoint3D::CURVATURE"
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