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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 eigen as eigen3
# boost
from boost_shared_ptr cimport shared_ptr
### base class ###
# person_classifier.h
# namespace pcl
# namespace people
# template <typename PointT> class PersonClassifier;
# template <typename PointT>
# class PersonClassifier
cdef extern from "pcl/people/person_classifier.h" namespace "pcl::people":
cdef cppclass PersonClassifier:
PersonClassifier()
# protected:
# /** \brief Height of the image patch to classify. */
# int window_height_;
# /** \brief Width of the image patch to classify. */
# int window_width_;
# /** \brief SVM offset. */
# float SVM_offset_;
# /** \brief SVM weights vector. */
# std::vector<float> SVM_weights_;
# public:
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef boost::shared_ptr<PointCloud> PointCloudPtr;
# /** \brief Load SVM parameters from a text file.
# * \param[in] svm_filename Filename containing SVM parameters.
# * \return true if SVM has been correctly set, false otherwise.
bool loadSVMFromFile (string svm_filename)
# * \brief Set trained SVM for person confidence estimation.
# * \param[in] window_height Detection window height.
# * \param[in] window_width Detection window width.
# * \param[in] SVM_weights SVM weights vector.
# * \param[in] SVM_offset SVM offset.
void setSVM (int window_height, int window_width, vector[float] SVM_weights, float SVM_offset)
# * \brief Get trained SVM for person confidence estimation.
# * \param[out] window_height Detection window height.
# * \param[out] window_width Detection window width.
# * \param[out] SVM_weights SVM weights vector.
# * \param[out] SVM_offset SVM offset.
# void getSVM (int& window_height, int& window_width, vector[float]& SVM_weights, float& SVM_offset);
# * \brief Resize an image represented by a pointcloud containing RGB information.
# * \param[in] input_image A pointer to a pointcloud containing RGB information.
# * \param[out] output_image A pointer to the output pointcloud.
# * \param[in] width Output width.
# * \param[in] height Output height.
# void resize (PointCloudPtr& input_image, PointCloudPtr& output_image, int width, int height)
# * \brief Copies an image and makes a black border around it, where the source image is not present.
# * \param[in] input_image A pointer to a pointcloud containing RGB information.
# * \param[out] output_image A pointer to the output pointcloud.
# * \param[in] xmin x coordinate of the top-left point of the bbox to copy from the input image.
# * \param[in] ymin y coordinate of the top-left point of the bbox to copy from the input image.
# * \param[in] width Output width.
# * \param[in] height Output height.
# void copyMakeBorder (PointCloudPtr& input_image, PointCloudPtr& output_image, int xmin, int ymin, int width, int height)
# * \brief Classify the given portion of image.
# * \param[in] height The height of the image patch to classify, in pixels.
# * \param[in] xc The x-coordinate of the center of the image patch to classify, in pixels.
# * \param[in] yc The y-coordinate of the center of the image patch to classify, in pixels.
# * \param[in] image The whole image (pointer to a point cloud containing RGB information) containing the object to classify.
# * \return The classification score given by the SVM.
# double evaluate (float height, float xc, float yc, PointCloudPtr& image)
# * \brief Compute person confidence for a given PersonCluster.
# * \param[in] image The input image (pointer to a point cloud containing RGB information).
# * \param[in] bottom Theoretical bottom point of the cluster projected to the image.
# * \param[in] top Theoretical top point of the cluster projected to the image.
# * \param[in] centroid Theoretical centroid point of the cluster projected to the image.
# * \param[in] vertical If true, the sensor is considered to be vertically placed (portrait mode).
# * \return The person confidence.
# double evaluate (PointCloudPtr& image, Eigen::Vector3f& bottom, Eigen::Vector3f& top, Eigen::Vector3f& centroid, bool vertical)
###
# person_cluster.h
# namespace pcl
# namespace people
# /** \brief @b PersonCluster represents a class for representing information about a cluster containing a person.
# * \author Filippo Basso, Matteo Munaro
# * \ingroup people
# */
# template <typename PointT> class PersonCluster;
# template <typename PointT> bool operator<(const PersonCluster<PointT>& c1, const PersonCluster<PointT>& c2);
#
# template <typename PointT>
# class PersonCluster
cdef extern from "pcl/people/person_cluster.h" namespace "pcl::people":
cdef cppclass PersonCluster:
PersonClassifier()
# PersonCluster (
# const PointCloudPtr& input_cloud,
# const pcl::PointIndices& indices,
# const Eigen::VectorXf& ground_coeffs,
# float sqrt_ground_coeffs,
# bool head_centroid,
# bool vertical = false);
# protected:
# bool head_centroid_;
# /** \brief Minimum x coordinate of the cluster points. */
# float min_x_;
# /** \brief Minimum y coordinate of the cluster points. */
# float min_y_;
# /** \brief Minimum z coordinate of the cluster points. */
# float min_z_;
# /** \brief Maximum x coordinate of the cluster points. */
# float max_x_;
# /** \brief Maximum y coordinate of the cluster points. */
# float max_y_;
# /** \brief Maximum z coordinate of the cluster points. */
# float max_z_;
# /** \brief Sum of x coordinates of the cluster points. */
# float sum_x_;
# /** \brief Sum of y coordinates of the cluster points. */
# float sum_y_;
# /** \brief Sum of z coordinates of the cluster points. */
# float sum_z_;
# /** \brief Number of cluster points. */
# int n_;
# /** \brief x coordinate of the cluster centroid. */
# float c_x_;
# /** \brief y coordinate of the cluster centroid. */
# float c_y_;
# /** \brief z coordinate of the cluster centroid. */
# float c_z_;
# /** \brief Cluster height from the ground plane. */
# float height_;
# /** \brief Cluster distance from the sensor. */
# float distance_;
# /** \brief Cluster centroid horizontal angle with respect to z axis. */
# float angle_;
# /** \brief Maximum angle of the cluster points. */
# float angle_max_;
# /** \brief Minimum angle of the cluster points. */
# float angle_min_;
# /** \brief Cluster top point. */
# Eigen::Vector3f top_;
# /** \brief Cluster bottom point. */
# Eigen::Vector3f bottom_;
# /** \brief Cluster centroid. */
# Eigen::Vector3f center_;
# /** \brief Theoretical cluster top. */
# Eigen::Vector3f ttop_;
# /** \brief Theoretical cluster bottom (lying on the ground plane). */
# Eigen::Vector3f tbottom_;
# /** \brief Theoretical cluster center (between ttop_ and tbottom_). */
# Eigen::Vector3f tcenter_;
# /** \brief Vector containing the minimum coordinates of the cluster. */
# Eigen::Vector3f min_;
# /** \brief Vector containing the maximum coordinates of the cluster. */
# Eigen::Vector3f max_;
# /** \brief Point cloud indices of the cluster points. */
# pcl::PointIndices points_indices_;
# /** \brief If true, the sensor is considered to be vertically placed (portrait mode). */
# bool vertical_;
# /** \brief PersonCluster HOG confidence. */
# float person_confidence_;
# public:
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef boost::shared_ptr<PointCloud> PointCloudPtr;
# typedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# * \brief Returns the height of the cluster.
# * \return the height of the cluster.
float getHeight ();
# * \brief Update the height of the cluster.
# * \param[in] ground_coeffs The coefficients of the ground plane.
# * \return the height of the cluster.
# float updateHeight (const Eigen::VectorXf& ground_coeffs);
# * \brief Update the height of the cluster.
# * \param[in] ground_coeffs The coefficients of the ground plane.
# * \param[in] sqrt_ground_coeffs The norm of the vector [a, b, c] where a, b and c are the first
# * three coefficients of the ground plane (ax + by + cz + d = 0).
# * \return the height of the cluster.
# float updateHeight (const Eigen::VectorXf& ground_coeffs, float sqrt_ground_coeffs);
# * \brief Returns the distance of the cluster from the sensor.
# * \return the distance of the cluster (its centroid) from the sensor without considering the
# * y dimension.
float getDistance ()
# * \brief Returns the angle formed by the cluster's centroid with respect to the sensor (in radians).
# * \return the angle formed by the cluster's centroid with respect to the sensor (in radians).
float getAngle ()
# * \brief Returns the minimum angle formed by the cluster with respect to the sensor (in radians).
# * \return the minimum angle formed by the cluster with respect to the sensor (in radians).
float getAngleMin ()
# * \brief Returns the maximum angle formed by the cluster with respect to the sensor (in radians).
# * \return the maximum angle formed by the cluster with respect to the sensor (in radians).
float getAngleMax ()
# * \brief Returns the indices of the point cloud points corresponding to the cluster.
# * \return the indices of the point cloud points corresponding to the cluster.
# pcl::PointIndices& getIndices ();
# * \brief Returns the theoretical top point.
# * \return the theoretical top point.
# Eigen::Vector3f& getTTop ();
# * \brief Returns the theoretical bottom point.
# * \return the theoretical bottom point.
# Eigen::Vector3f& getTBottom ();
# * \brief Returns the theoretical centroid (at half height).
# * \return the theoretical centroid (at half height).
# Eigen::Vector3f& getTCenter ();
# * \brief Returns the top point.
# * \return the top point.
# Eigen::Vector3f& getTop ();
# * \brief Returns the bottom point.
# * \return the bottom point.
# Eigen::Vector3f& getBottom ();
# * \brief Returns the centroid.
# * \return the centroid.
# Eigen::Vector3f& getCenter ();
# //Eigen::Vector3f& getTMax();
# * \brief Returns the point formed by min x, min y and min z.
# * \return the point formed by min x, min y and min z.
# Eigen::Vector3f& getMin ();
# * \brief Returns the point formed by max x, max y and max z.
# * \return the point formed by max x, max y and max z.
# Eigen::Vector3f& getMax ();
# /**
# * \brief Returns the HOG confidence.
# * \return the HOG confidence.
# */
# float
# getPersonConfidence ();
# /**
# * \brief Returns the number of points of the cluster.
# * \return the number of points of the cluster.
# */
# int getNumberPoints ();
# /**
# * \brief Sets the cluster height.
# * \param[in] height
# */
# void setHeight (float height);
# /**
# * \brief Sets the HOG confidence.
# * \param[in] confidence
# */
# void setPersonConfidence (float confidence);
# /**
# * \brief Draws the theoretical 3D bounding box of the cluster in the PCL visualizer.
# * \param[in] viewer PCL visualizer.
# * \param[in] person_number progressive number representing the person.
# */
# void drawTBoundingBox (pcl::visualization::PCLVisualizer& viewer, int person_number);
# /**
# * \brief For sorting purpose: sort by distance.
# */
# friend bool operator< <>(const PersonCluster<PointT>& c1, const PersonCluster<PointT>& c2);
# protected:
# /**
# * \brief PersonCluster initialization.
# */
# void init(
# const PointCloudPtr& input_cloud,
# const pcl::PointIndices& indices,
# const Eigen::VectorXf& ground_coeffs,
# float sqrt_ground_coeffs,
# bool head_centroid,
# bool vertical);
###
# ground_based_people_detection_app.h
# namespace pcl
# namespace people
# template <typename PointT>
# class GroundBasedPeopleDetectionApp
# public:
cdef extern from "pcl/people/ground_based_people_detection_app.h" namespace "pcl::people":
cdef cppclass GroundBasedPeopleDetectionApp[PointT]:
GroundBasedPeopleDetectionApp()
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef boost::shared_ptr<PointCloud> PointCloudPtr;
# typedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# * \brief Set the pointer to the input cloud.
# * \param[in] cloud A pointer to the input cloud.
# void setInputCloud (PointCloudPtr& cloud);
void setInputCloud (sharedPtr[cpp.PointCloud[PointT]] cloud)
# * \brief Set the ground coefficients.
# * \param[in] ground_coeffs Vector containing the four plane coefficients.
# void setGround (Eigen::VectorXf& ground_coeffs);
# * \brief Set the transformation matrix, which is used in order to transform the given point cloud, the ground plane and the intrinsics matrix to the internal coordinate frame.
# * \param[in] cloud A pointer to the input cloud.
void setTransformation (eigen3.Matrix3f& transformation);
# * \brief Set sampling factor.
# * \param[in] sampling_factor Value of the downsampling factor (in each dimension) which is applied to the raw point cloud (default = 1.).
void setSamplingFactor (int sampling_factor);
# * \brief Set voxel size.
# * \param[in] voxel_size Value of the voxel dimension (default = 0.06m.).
void setVoxelSize (float voxel_size);
# * \brief Set intrinsic parameters of the RGB camera.
# * \param[in] intrinsics_matrix RGB camera intrinsic parameters matrix.
void setIntrinsics (eigen3.Matrix3f intrinsics_matrix);
# * \brief Set SVM-based person classifier.
# * \param[in] person_classifier Needed for people detection on RGB data.
# void setClassifier (pcl::people::PersonClassifier<pcl::RGB> person_classifier);
# * \brief Set the field of view of the point cloud in z direction.
# * \param[in] min The beginning of the field of view in z-direction, should be usually set to zero.
# * \param[in] max The end of the field of view in z-direction.
void setFOV (float min, float max)
# * \brief Set sensor orientation (vertical = true means portrait mode, vertical = false means landscape mode).
# * \param[in] vertical Set landscape/portait camera orientation (default = false).
void setSensorPortraitOrientation (bool vertical)
# * \brief Set head_centroid_ to true (person centroid is in the head) or false (person centroid is the whole body centroid).
# * \param[in] head_centroid Set the location of the person centroid (head or body center) (default = true).
void setHeadCentroid (bool head_centroid)
# * \brief Set minimum and maximum allowed height and width for a person cluster.
# * \param[in] min_height Minimum allowed height for a person cluster (default = 1.3).
# * \param[in] max_height Maximum allowed height for a person cluster (default = 2.3).
# * \param[in] min_width Minimum width for a person cluster (default = 0.1).
# * \param[in] max_width Maximum width for a person cluster (default = 8.0).
void setPersonClusterLimits (float min_height, float max_height, float min_width, float max_width)
# * \brief Set minimum distance between persons' heads.
# * \param[in] heads_minimum_distance Minimum allowed distance between persons' heads (default = 0.3).
void setMinimumDistanceBetweenHeads (float heads_minimum_distance)
# * \brief Get the minimum and maximum allowed height and width for a person cluster.
# * \param[out] min_height Minimum allowed height for a person cluster.
# * \param[out] max_height Maximum allowed height for a person cluster.
# * \param[out] min_width Minimum width for a person cluster.
# * \param[out] max_width Maximum width for a person cluster.
void getPersonClusterLimits (float& min_height, float& max_height, float& min_width, float& max_width);
# * \brief Get minimum and maximum allowed number of points for a person cluster.
# * \param[out] min_points Minimum allowed number of points for a person cluster.
# * \param[out] max_points Maximum allowed number of points for a person cluster.
void getDimensionLimits (int& min_points, int& max_points)
# * \brief Get minimum distance between persons' heads.
float getMinimumDistanceBetweenHeads ()
# * \brief Get floor coefficients.
# Eigen::VectorXf getGround ();
# * \brief Get the filtered point cloud.
# PointCloudPtr getFilteredCloud ();
# * \brief Get pointcloud after voxel grid filtering and ground removal.
# PointCloudPtr getNoGroundCloud ();
# * \brief Extract RGB information from a point cloud and output the corresponding RGB point cloud.
# * \param[in] input_cloud A pointer to a point cloud containing also RGB information.
# * \param[out] output_cloud A pointer to a RGB point cloud.
# void extractRGBFromPointCloud (PointCloudPtr input_cloud, pcl::PointCloud<pcl::RGB>::Ptr& output_cloud);
# * \brief Swap rows/cols dimensions of a RGB point cloud (90 degrees counterclockwise rotation).
# * \param[in,out] cloud A pointer to a RGB point cloud.
# void swapDimensions (pcl::PointCloud<pcl::RGB>::Ptr& cloud);
# * \brief Estimates min_points_ and max_points_ based on the minimal and maximal cluster size and the voxel size.
void updateMinMaxPoints ()
# * \brief Applies the transformation to the input point cloud.
void applyTransformationPointCloud ()
# * \brief Applies the transformation to the ground plane.
void applyTransformationGround ()
# * \brief Applies the transformation to the intrinsics matrix.
void applyTransformationIntrinsics ()
# * \brief Reduces the input cloud to one point per voxel and limits the field of view.
void filter ()
# * \brief Perform people detection on the input data and return people clusters information.
# * \param[out] clusters Vector of PersonCluster.
# * \return true if the compute operation is successful, false otherwise.
# bool compute (std::vector<pcl::people::PersonCluster<PointT> >& clusters);
# protected:
# /** \brief sampling factor used to downsample the point cloud */
# int sampling_factor_;
# /** \brief voxel size */
# float voxel_size_;
# /** \brief ground plane coefficients */
# Eigen::VectorXf ground_coeffs_;
# /** \brief flag stating whether the ground coefficients have been set or not */
# bool ground_coeffs_set_;
# /** \brief the transformed ground coefficients */
# Eigen::VectorXf ground_coeffs_transformed_;
# /** \brief ground plane normalization factor */
# float sqrt_ground_coeffs_;
# /** \brief rotation matrix which transforms input point cloud to internal people tracker coordinate frame */
# Eigen::Matrix3f transformation_;
# /** \brief flag stating whether the transformation matrix has been set or not */
# bool transformation_set_;
# /** \brief pointer to the input cloud */
# PointCloudPtr cloud_;
# /** \brief pointer to the filtered cloud */
# PointCloudPtr cloud_filtered_;
# /** \brief pointer to the cloud after voxel grid filtering and ground removal */
# PointCloudPtr no_ground_cloud_;
# /** \brief pointer to a RGB cloud corresponding to cloud_ */
# pcl::PointCloud<pcl::RGB>::Ptr rgb_image_;
# /** \brief person clusters maximum height from the ground plane */
# float max_height_;
# /** \brief person clusters minimum height from the ground plane */
# float min_height_;
# /** \brief person clusters maximum width, used to estimate how many points maximally represent a person cluster */
# float max_width_;
# /** \brief person clusters minimum width, used to estimate how many points minimally represent a person cluster */
# float min_width_;
# /** \brief the beginning of the field of view in z-direction, should be usually set to zero */
# float min_fov_;
# /** \brief the end of the field of view in z-direction */
# float max_fov_;
# /** \brief if true, the sensor is considered to be vertically placed (portrait mode) */
# bool vertical_;
# /** \brief if true, the person centroid is computed as the centroid of the cluster points belonging to the head;
# * if false, the person centroid is computed as the centroid of the whole cluster points (default = true) */
# bool head_centroid_; // if true, the person centroid is computed as the centroid of the cluster points belonging to the head (default = true)
# // if false, the person centroid is computed as the centroid of the whole cluster points
# /** \brief maximum number of points for a person cluster */
# int max_points_;
# /** \brief minimum number of points for a person cluster */
# int min_points_;
# /** \brief minimum distance between persons' heads */
# float heads_minimum_distance_;
# /** \brief intrinsic parameters matrix of the RGB camera */
# Eigen::Matrix3f intrinsics_matrix_;
# /** \brief flag stating whether the intrinsics matrix has been set or not */
# bool intrinsics_matrix_set_;
# /** \brief the transformed intrinsics matrix */
# Eigen::Matrix3f intrinsics_matrix_transformed_;
# /** \brief SVM-based person classifier */
# pcl::people::PersonClassifier<pcl::RGB> person_classifier_;
# /** \brief flag stating if the classifier has been set or not */
# bool person_classifier_set_flag_;
###
# head_based_subcluster.h
# namespace pcl
# namespace people
# /** \brief @b HeadBasedSubclustering represents a class for searching for people inside a HeightMap2D based on a 3D head detection algorithm
# * \author Matteo Munaro
# * \ingroup people
# */
# template <typename PointT> class HeadBasedSubclustering;
#
# template <typename PointT>
# class HeadBasedSubclustering
cdef extern from "pcl/people/head_based_subcluster.h" namespace "pcl::people":
cdef cppclass HeadBasedSubclustering[PointT]:
HeadBasedSubclustering()
# public:
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef boost::shared_ptr<PointCloud> PointCloudPtr;
# typedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# * \brief Compute subclusters and return them into a vector of PersonCluster.
# * \param[in] clusters Vector of PersonCluster.
# void subcluster (std::vector<pcl::people::PersonCluster<PointT> >& clusters);
# /**
# * \brief Merge clusters close in floor coordinates.
# *
# * \param[in] input_clusters Input vector of PersonCluster.
# * \param[in] output_clusters Output vector of PersonCluster (after merging).
# */
# void
# mergeClustersCloseInFloorCoordinates (std::vector<pcl::people::PersonCluster<PointT> >& input_clusters,
# std::vector<pcl::people::PersonCluster<PointT> >& output_clusters);
# /**
# * \brief Create subclusters centered on the heads position from the current cluster.
# *
# * \param[in] cluster A PersonCluster.
# * \param[in] maxima_number_after_filtering Number of local maxima to use as centers of the new cluster.
# * \param[in] maxima_cloud_indices_filtered Cloud indices of local maxima to use as centers of the new cluster.
# * \param[out] subclusters Output vector of PersonCluster objects derived from the input cluster.
# */
# void
# createSubClusters (pcl::people::PersonCluster<PointT>& cluster, int maxima_number_after_filtering, std::vector<int>& maxima_cloud_indices_filtered,
# std::vector<pcl::people::PersonCluster<PointT> >& subclusters);
# /**
# * \brief Set input cloud.
# *
# * \param[in] cloud A pointer to the input point cloud.
# */
# void
# setInputCloud (PointCloudPtr& cloud);
# /**
# * \brief Set the ground coefficients.
# *
# * \param[in] ground_coeffs The ground plane coefficients.
# */
# void
# setGround (Eigen::VectorXf& ground_coeffs);
# /**
# * \brief Set sensor orientation to landscape mode (false) or portrait mode (true).
# *
# * \param[in] vertical Landscape (false) or portrait (true) mode (default = false).
# */
# void
# setSensorPortraitOrientation (bool vertical);
# /**
# * \brief Set head_centroid_ to true (person centroid is in the head) or false (person centroid is the whole body centroid).
# *
# * \param[in] head_centroid Set the location of the person centroid (head or body center) (default = true).
# */
# void
# setHeadCentroid (bool head_centroid);
# /**
# * \brief Set initial cluster indices.
# *
# * \param[in] cluster_indices Point cloud indices corresponding to the initial clusters (before subclustering).
# */
# void
# setInitialClusters (std::vector<pcl::PointIndices>& cluster_indices);
# /**
# * \brief Set minimum and maximum allowed height for a person cluster.
# *
# * \param[in] min_height Minimum allowed height for a person cluster (default = 1.3).
# * \param[in] max_height Maximum allowed height for a person cluster (default = 2.3).
# */
# void
# setHeightLimits (float min_height, float max_height);
# /**
# * \brief Set minimum and maximum allowed number of points for a person cluster.
# *
# * \param[in] min_points Minimum allowed number of points for a person cluster.
# * \param[in] max_points Maximum allowed number of points for a person cluster.
# */
# void
# setDimensionLimits (int min_points, int max_points);
# /**
# * \brief Set minimum distance between persons' heads.
# *
# * \param[in] heads_minimum_distance Minimum allowed distance between persons' heads (default = 0.3).
# */
# void
# setMinimumDistanceBetweenHeads (float heads_minimum_distance);
# /**
# * \brief Get minimum and maximum allowed height for a person cluster.
# *
# * \param[out] min_height Minimum allowed height for a person cluster.
# * \param[out] max_height Maximum allowed height for a person cluster.
# */
# void
# getHeightLimits (float& min_height, float& max_height);
# /**
# * \brief Get minimum and maximum allowed number of points for a person cluster.
# *
# * \param[out] min_points Minimum allowed number of points for a person cluster.
# * \param[out] max_points Maximum allowed number of points for a person cluster.
# */
# void
# getDimensionLimits (int& min_points, int& max_points);
# /**
# * \brief Get minimum distance between persons' heads.
# */
# float
# getMinimumDistanceBetweenHeads ();
# protected:
# /** \brief ground plane coefficients */
# Eigen::VectorXf ground_coeffs_;
# /** \brief ground plane normalization factor */
# float sqrt_ground_coeffs_;
# /** \brief initial clusters indices */
# std::vector<pcl::PointIndices> cluster_indices_;
# /** \brief pointer to the input cloud */
# PointCloudPtr cloud_;
# /** \brief person clusters maximum height from the ground plane */
# float max_height_;
# /** \brief person clusters minimum height from the ground plane */
# float min_height_;
# /** \brief if true, the sensor is considered to be vertically placed (portrait mode) */
# bool vertical_;
# /** \brief if true, the person centroid is computed as the centroid of the cluster points belonging to the head
# if false, the person centroid is computed as the centroid of the whole cluster points (default = true) */
# bool head_centroid_;
# /** \brief maximum number of points for a person cluster */
# int max_points_;
# /** \brief minimum number of points for a person cluster */
# int min_points_;
# /** \brief minimum distance between persons' heads */
# float heads_minimum_distance_;
###
# height_map_2d.h
# namespace pcl
# namespace people
# /** \brief @b HeightMap2D represents a class for creating a 2D height map from a point cloud and searching for its local maxima
# * \author Matteo Munaro
# * \ingroup people
# */
# template <typename PointT> class HeightMap2D;
# template <typename PointT>
cdef extern from "pcl/people/height_map_2d.h" namespace "pcl::people":
cdef cppclass HeightMap2D:
HeightMap2D()
# public:
# typedef pcl::PointCloud<PointT> PointCloud;
# typedef boost::shared_ptr<PointCloud> PointCloudPtr;
# typedef boost::shared_ptr<const PointCloud> PointCloudConstPtr;
# /**
# * \brief Compute the height map with the projection of cluster points onto the ground plane.
# * \param[in] cluster The PersonCluster used to compute the height map.
# */
# void
# compute (pcl::people::PersonCluster<PointT>& cluster);
# /**
# * \brief Compute local maxima of the height map.
# */
# void
# searchLocalMaxima ();
# /**
# * \brief Filter maxima of the height map by imposing a minimum distance between them.
# */
# void
# filterMaxima ();
# /**
# * \brief Set initial cluster indices.
# *
# * \param[in] cloud A pointer to the input cloud.
# */
# void
# setInputCloud (PointCloudPtr& cloud);
# /**
# * \brief Set the ground coefficients.
# *
# * \param[in] ground_coeffs The ground plane coefficients.
# */
# void
# setGround (Eigen::VectorXf& ground_coeffs);
# /**
# * \brief Set bin size for the height map.
# *
# * \param[in] bin_size Bin size for the height map (default = 0.06).
# */
# void
# setBinSize (float bin_size);
# /**
# * \brief Set minimum distance between maxima.
# *
# * \param[in] minimum_distance_between_maxima Minimum allowed distance between maxima (default = 0.3).
# */
# void
# setMinimumDistanceBetweenMaxima (float minimum_distance_between_maxima);
# /**
# * \brief Set sensor orientation to landscape mode (false) or portrait mode (true).
# *
# * \param[in] vertical Landscape (false) or portrait (true) mode (default = false).
# */
# void
# setSensorPortraitOrientation (bool vertical);
# /**
# * \brief Get the height map as a vector of int.
# */
# std::vector<int>& getHeightMap ();
# /**
# * \brief Get bin size for the height map.
# */
# float getBinSize ();
# /**
# * \brief Get minimum distance between maxima of the height map.
# */
# float getMinimumDistanceBetweenMaxima ();
# /**
# * \brief Return the maxima number after the filterMaxima method.
# */
# int& getMaximaNumberAfterFiltering ();
# /**
# * \brief Return the point cloud indices corresponding to the maxima computed after the filterMaxima method.
# */
# std::vector<int>& getMaximaCloudIndicesFiltered ();
# protected:
# /** \brief ground plane coefficients */
# Eigen::VectorXf ground_coeffs_;
# /** \brief ground plane normalization factor */
# float sqrt_ground_coeffs_;
# /** \brief pointer to the input cloud */
# PointCloudPtr cloud_;
# /** \brief if true, the sensor is considered to be vertically placed (portrait mode) */
# bool vertical_;
# /** \brief vector with maximum height values for every bin (height map) */
# std::vector<int> buckets_;
# /** \brief indices of the pointcloud points with maximum height for every bin */
# std::vector<int> buckets_cloud_indices_;
# /** \brief bin dimension */
# float bin_size_;
# /** \brief number of local maxima in the height map */
# int maxima_number_;
# /** \brief contains the position of the maxima in the buckets vector */
# std::vector<int> maxima_indices_;
# /** \brief contains the point cloud position of the maxima (indices of the point cloud) */
# std::vector<int> maxima_cloud_indices_;
# /** \brief number of local maxima after filtering */
# int maxima_number_after_filtering_;
# /** \brief contains the position of the maxima in the buckets array after filtering */
# std::vector<int> maxima_indices_filtered_;
# /** \brief contains the point cloud position of the maxima after filtering */
# std::vector<int> maxima_cloud_indices_filtered_;
# /** \brief minimum allowed distance between maxima */
# float min_dist_between_maxima_;
###
# hog.h
# namespace pcl
# namespace people
# /** \brief @b HOG represents a class for computing the HOG descriptor described in
# * Dalal, N. and Triggs, B., "Histograms of oriented gradients for human detection", CVPR 2005.
# * \author Matteo Munaro, Stefano Ghidoni, Stefano Michieletto
# * \ingroup people
# */
# class PCL_EXPORTS HOG
cdef extern from "pcl/people/hog.h" namespace "pcl::people":
cdef cppclass HOG:
HOG()
# public:
# /**
# * \brief Compute gradient magnitude and orientation at each location (uses sse).
# *
# * \param[in] I Image as array of float.
# * \param[in] h Image height.
# * \param[in] w Image width.
# * \param[in] d Image number of channels.
# * \param[out] M Gradient magnitude for each image point.
# * \param[out] O Gradient orientation for each image point.
# */
# void gradMag ( float *I, int h, int w, int d, float *M, float *O ) const;
# /**
# * \brief Compute n_orients gradient histograms per bin_size x bin_size block of pixels.
# *
# * \param[in] M Gradient magnitude for each image point.
# * \param[in] O Gradient orientation for each image point.
# * \param[in] h Image height.
# * \param[in] w Image width.
# * \param[in] bin_size Spatial bin size.
# * \param[in] n_orients Number of orientation bins.
# * \param[in] soft_bin If true, each pixel can contribute to multiple spatial bins (using bilinear interpolation).
# * \param[out] H Gradient histograms.
# */
# void gradHist ( float *M, float *O, int h, int w, int bin_size, int n_orients, bool soft_bin, float *H) const;
# /**
# * \brief Normalize histogram of gradients.
# *
# * \param[in] H Gradient histograms.
# * \param[in] h Image height.
# * \param[in] w Image width.
# * \param[in] bin_size Spatial bin size.
# * \param[in] n_orients Number of orientation bins.
# * \param[in] clip Value at which to clip histogram bins.
# * \param[out] G Normalized gradient histograms.
# */
# void normalization ( float *H, int h, int w, int bin_size, int n_orients, float clip, float *G ) const;
# /**
# * \brief Compute HOG descriptor.
# *
# * \param[in] I Image as array of float between 0 and 1.
# * \param[in] h Image height.
# * \param[in] w Image width.
# * \param[in] n_channels Image number of channels.
# * \param[in] bin_size Spatial bin size.
# * \param[in] n_orients Number of orientation bins.
# * \param[in] soft_bin If true, each pixel can contribute to multiple spatial bins (using bilinear interpolation).
# * \param[out] descriptor HOG descriptor.
# */
# void compute (float *I, int h, int w, int n_channels, int bin_size, int n_orients, bool soft_bin, float *descriptor);
# /**
# * \brief Compute HOG descriptor with default parameters.
# * \param[in] I Image as array of float between 0 and 1.
# * \param[out] descriptor HOG descriptor.
# */
# void compute (float *I, float *descriptor) const;
# protected:
# /** \brief image height (default = 128) */
# int h_;
# /** \brief image width (default = 64) */
# int w_;
# /** \brief image number of channels (default = 3) */
# int n_channels_;
# /** \brief spatial bin size (default = 8) */
# int bin_size_;
# /** \brief number of orientation bins (default = 9) */
# int n_orients_;
# /** \brief if true, each pixel can contribute to multiple spatial bins (using bilinear interpolation) (default = true) */
# bool soft_bin_;
# /** \brief value at which to clip histogram bins (default = 0.2) */
# float clip_;
###
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