Structured forests for fast edge detection

Classes
class	cv::ximgproc::RFFeatureGetter
class	cv::ximgproc::StructuredEdgeDetection
	Class implementing edge detection algorithm from [50] : More...

Functions
Ptr< RFFeatureGetter >	cv::ximgproc::createRFFeatureGetter ()
Ptr< StructuredEdgeDetection >	cv::ximgproc::createStructuredEdgeDetection (const String &model, Ptr< const RFFeatureGetter > howToGetFeatures=Ptr< RFFeatureGetter >())

Detailed Description

This module contains implementations of modern structured edge detection algorithms, i.e. algorithms which somehow takes into account pixel affinities in natural images.

Function Documentation

createRFFeatureGetter()

Ptr<RFFeatureGetter> cv::ximgproc::createRFFeatureGetter

(

)

Python:
	retval	=	cv.ximgproc.createRFFeatureGetter(		)

#include <opencv2/ximgproc/structured_edge_detection.hpp>

createStructuredEdgeDetection()

Ptr<StructuredEdgeDetection> cv::ximgproc::createStructuredEdgeDetection	(	const String &	model,
		Ptr< const RFFeatureGetter >	howToGetFeatures = Ptr< RFFeatureGetter >()
	)

Python:
	retval	=	cv.ximgproc.createStructuredEdgeDetection(	model[, howToGetFeatures]	)

#include <opencv2/ximgproc/structured_edge_detection.hpp>

The only constructor

Parameters

model	: name of the file where the model is stored
howToGetFeatures	: optional object inheriting from RFFeatureGetter. You need it only if you would like to train your own forest, pass NULL otherwise