Structured forests for fast edge detection

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.

Classes
class	cv::ximgproc::RFFeatureGetter
class	cv::ximgproc::StructuredEdgeDetection
	Class implementing edge detection algorithm from [70] : More...

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

Function Documentation

createRFFeatureGetter()

Ptr< RFFeatureGetter > cv::ximgproc::createRFFeatureGetter

(

)

Python:
	cv.ximgproc.createRFFeatureGetter(		) ->	retval

#include <opencv2/ximgproc/structured_edge_detection.hpp>

createStructuredEdgeDetection()

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

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

#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