WaldBoost object detector from [112] . More...

#include "xobjdetect.hpp"

Inheritance diagram for cv::xobjdetect::WaldBoost:

Public Member Functions
virtual float	predict (const Ptr< FeatureEvaluator > &feature_evaluator) const =0
	Predict objects class given object that can compute object features. More...

virtual std::vector< int >	train (Mat &data, const Mat &labels, bool use_fast_log=false)=0
	Train WaldBoost cascade for given data. More...

Public Member Functions inherited from cv::Algorithm
	Algorithm ()

virtual	~Algorithm ()

virtual void	clear ()
	Clears the algorithm state. More...

virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read. More...

virtual String	getDefaultName () const

virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage. More...

virtual void	save (const String &filename) const

virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage. More...

Additional Inherited Members
Static Public Member Functions inherited from cv::Algorithm
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file. More...

template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String. More...

template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node. More...

Detailed Description

WaldBoost object detector from [112] .

Member Function Documentation

virtual float cv::xobjdetect::WaldBoost::predict ( const Ptr< FeatureEvaluator > & feature_evaluator ) const

pure virtual

Predict objects class given object that can compute object features.

Returns unnormed confidence value — measure of confidence that object is from class +1.

Parameters

feature_evaluator object that can compute features by demand

virtual std::vector<int> cv::xobjdetect::WaldBoost::train	(	Mat &	data,
		const Mat &	labels,
		bool	use_fast_log = `false`
	)

pure virtual

Train WaldBoost cascade for given data.

Returns feature indices chosen for cascade. Feature enumeration starts from 0.

Parameters

data	matrix of feature values, size M x N, one feature per row
labels	matrix of samples class labels, size 1 x N. Labels can be from {-1, +1}
use_fast_log

The documentation for this class was generated from the following file:

/builds/master-contrib_docs-mac/opencv_contrib/modules/xobjdetect/include/opencv2/xobjdetect.hpp