/home/laradmin/lar/perception/planarobstacles/mtt/src/mtt_clustering.cpp File Reference

Clustering related functions. More...

#include <mtt/mtt_clustering.h>

Include dependency graph for mtt_clustering.cpp:

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Functions
void	calc_cluster_props (vector< t_clustersPtr > &clusters, t_data &data)
void	calc_cluster_props (t_cluster **clusters, int size, t_data *data, t_config *)
	Calculates cluster properties.
void	calc_object_props (vector< t_objectPtr > &objects)
	Computes object properties, such as centroid and size.
void	clean_objets (vector< t_objectPtr > &objects)
	Frees memory associated with objects.
bool	clustering (t_data &data, vector< t_clustersPtr > &clustersPtr, t_config *config, t_flag *flags)
t_cluster **	clustering (t_data *data, int *count, t_config *config, t_flag *flags)
	Performs clustering operation.
double	ClusteringThreshold (double r1, double t1, double r2, double t2, t_config *config)
bool	clusters2objects (vector< t_objectPtr > &objectsPtr, vector< t_clustersPtr > &clusters, t_data &data, t_config &config)
	Converts clusters of points into objects using recursive line fitting.
double	dietmayer_threshold (double r, t_config *config)
	Computes dietmayer clustering threshold.
void	FlagCollisionWithOcclusion (t_cluster **clusters, int object_size, t_data *data, t_config *)
void	free_lines (vector< t_objectPtr > &objects)
	Removes lines from memory.
void	recursive_IEPF (t_objectPtr &object, t_data &data, int start, int end, t_config &config)
	Recursive iterative end point fit. Single step.
void	recursive_line_fitting (t_objectPtr &object, t_cluster &cluster, t_data &data, t_config &config)
	Recursive line fitting, complete process.
void	remove_border_points (t_cluster **clusters, int size, int npoints)
	Removes cluster border points.
void	remove_small_clusters (t_cluster **clusters, int *size, int threshold)
	Removes clusters based on their size.

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Detailed Description

Clustering related functions.

Definition in file mtt_clustering.cpp.

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Function Documentation

void calc_cluster_props	(	vector< t_clustersPtr > &	clusters,
		t_data &	data
	)

Definition at line 331 of file mtt_clustering.cpp.

void calc_cluster_props	(	t_cluster **	clusters,
		int	size,
		t_data *	data,
		t_config *	config
	)

Calculates cluster properties.

Parameters:

	clusters	clusters to be drawn
	size	number of clusters
	data	general data storage vector
	config	general config structure

Returns:

void

Definition at line 307 of file mtt_clustering.cpp.

void calc_object_props

(

vector< t_objectPtr > &

objects

)

Computes object properties, such as centroid and size.

Parameters:

objects

list of objects

Returns:

void

Definition at line 378 of file mtt_clustering.cpp.

void clean_objets

(

vector< t_objectPtr > &

objects

)

Frees memory associated with objects.

Parameters:

objects

Input objects

Returns:

void

Definition at line 418 of file mtt_clustering.cpp.

bool clustering	(	t_data &	data,
		vector< t_clustersPtr > &	clustersPtr,
		t_config *	config,
		t_flag *	flags
	)

Definition at line 196 of file mtt_clustering.cpp.

t_cluster** clustering	(	t_data *	data,
		int *	count,
		t_config *	config,
		t_flag *	flags
	)

Performs clustering operation.

Parameters:

	data	general data storage vector
	count	number of clusters
	config	general config structure
	flags	general flags structure

Returns:

clusters

Definition at line 113 of file mtt_clustering.cpp.

double ClusteringThreshold	(	double	r1,
		double	t1,
		double	r2,
		double	t2,
		t_config *	config
	)

Definition at line 68 of file mtt_clustering.cpp.

bool clusters2objects	(	vector< t_objectPtr > &	objectsPtr,
		vector< t_clustersPtr > &	clusters,
		t_data &	data,
		t_config &	config
	)

Converts clusters of points into objects using recursive line fitting.

Parameters:

	objectsPtr	Output objects
	clusters	Input clusters
	data	Data points
	config	general config structure

Returns:

always true

Definition at line 355 of file mtt_clustering.cpp.

double dietmayer_threshold	(	double	r,
		t_config *	config
	)

Computes dietmayer clustering threshold.

Parameters:

	r
	config	general config structure

Returns:

dietmayer threshold

Definition at line 97 of file mtt_clustering.cpp.

void FlagCollisionWithOcclusion	(	t_cluster **	clusters,
		int	object_size,
		t_data *	data,
		t_config *
	)

Definition at line 34 of file mtt_clustering.cpp.

void free_lines

(

vector< t_objectPtr > &

objects

)

Removes lines from memory.

Parameters:

objects

list of objects

Returns:

void

Definition at line 476 of file mtt_clustering.cpp.

void recursive_IEPF	(	t_objectPtr &	object,
		t_data &	data,
		int	start,
		int	end,
		t_config &	config
	)

Recursive iterative end point fit. Single step.

Parameters:

	object	single object
	data	data points
	start	start point
	end	end point
	config	general config structure

Returns:

void

This functions malloc a line to work with, each time it mallocs a line it increments the number of lines object data

Definition at line 423 of file mtt_clustering.cpp.

void recursive_line_fitting	(	t_objectPtr &	object,
		t_cluster &	cluster,
		t_data &	data,
		t_config &	config
	)

Recursive line fitting, complete process.

Parameters:

	object	single object
	cluster	single cluster
	data	data points
	config	general config structure

Returns:

void

Definition at line 482 of file mtt_clustering.cpp.

void remove_border_points	(	t_cluster **	clusters,
		int	size,
		int	npoints
	)

Removes cluster border points.

Parameters:

	clusters	clusters to be drawn
	size	number of clusters
	npoints	number of points to be removed

Returns:

void

Definition at line 291 of file mtt_clustering.cpp.

void remove_small_clusters	(	t_cluster **	clusters,
		int *	size,
		int	threshold
	)

Removes clusters based on their size.

Parameters:

	clusters	clusters to be drawn
	size	number of clusters
	threshold	number of points a cluster must have not to be deleted

Returns:

void

Definition at line 270 of file mtt_clustering.cpp.