cv::mcc::CCheckerDetector Class Reference

A class to find the positions of the ColorCharts in the image. More...

#include <opencv2/mcc/checker_detector.hpp>

Collaboration diagram for cv::mcc::CCheckerDetector:

Public Member Functions
virtual Ptr< mcc::CChecker >	getBestColorChecker ()=0
	Get the best color checker. By the best it means the one detected with the highest confidence.
virtual std::vector< Ptr< CChecker > >	getListColorChecker ()=0
	Get the list of all detected colorcheckers.
virtual bool	process (InputArray image, const TYPECHART chartType, const int nc=1, bool useNet=false, const Ptr< DetectorParameters > &params=DetectorParameters::create())=0
	Find the ColorCharts in the given image.
virtual bool	process (InputArray image, const TYPECHART chartType, const std::vector< Rect > &regionsOfInterest, const int nc=1, bool useNet=false, const Ptr< DetectorParameters > &params=DetectorParameters::create())=0
	Find the ColorCharts in the given image.
virtual bool	setNet (dnn::Net net)=0
	Set the net which will be used to find the approximate bounding boxes for the color charts.
Public Member Functions inherited from cv::Algorithm
	Algorithm ()
virtual	~Algorithm ()
virtual void	clear ()
	Clears the algorithm state.
virtual bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
virtual String	getDefaultName () const
virtual void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage.
virtual void	save (const String &filename) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

Static Public Member Functions
static Ptr< CCheckerDetector >	create ()
	Returns the implementation of the CCheckerDetector.
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.
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String.
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node.

Additional Inherited Members
Protected Member Functions inherited from cv::Algorithm
void	writeFormat (FileStorage &fs) const

Detailed Description

A class to find the positions of the ColorCharts in the image.

Member Function Documentation

create()

static Ptr< CCheckerDetector > cv::mcc::CCheckerDetector::create ( )

static

Python:
	cv.mcc.CCheckerDetector.create(		) ->	retval
	cv.mcc.CCheckerDetector_create(		) ->	retval

Returns the implementation of the CCheckerDetector.

getBestColorChecker()

virtual Ptr< mcc::CChecker > cv::mcc::CCheckerDetector::getBestColorChecker ( )

pure virtual

Python:
	cv.mcc.CCheckerDetector.getBestColorChecker(		) ->	retval

Get the best color checker. By the best it means the one detected with the highest confidence.

Returns

checker A single colorchecker, if atleast one colorchecker was detected, 'nullptr' otherwise.

getListColorChecker()

virtual std::vector< Ptr< CChecker > > cv::mcc::CCheckerDetector::getListColorChecker ( )

pure virtual

Python:
	cv.mcc.CCheckerDetector.getListColorChecker(		) ->	retval

Get the list of all detected colorcheckers.

Returns

checkers vector of colorcheckers

process() [1/2]

virtual bool cv::mcc::CCheckerDetector::process ( InputArray image, const TYPECHART chartType, const int nc = 1, bool useNet = false, const Ptr< DetectorParameters > & params = DetectorParameters::create() )

pure virtual

Python:
	cv.mcc.CCheckerDetector.process(	image, chartType[, nc[, useNet[, params]]]	) ->	retval
	cv.mcc.CCheckerDetector.processWithROI(	image, chartType, regionsOfInterest[, nc[, useNet[, params]]]	) ->	retval

Find the ColorCharts in the given image.

Differs from the above one only in the arguments.

This version searches for the chart in the full image.

The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker()

Parameters

image	image in color space BGR
chartType	type of the chart to detect
nc	number of charts in the image, if you don't know the exact then keeping this number high helps.
useNet	if it is true the network provided using the setNet() is used for preliminary search for regions where chart could be present, inside the regionsOfInterest provied.
params	parameters of the detection system. More information about them can be found in the struct DetectorParameters.

Returns

true if atleast one chart is detected otherwise false

process() [2/2]

virtual bool cv::mcc::CCheckerDetector::process ( InputArray image, const TYPECHART chartType, const std::vector< Rect > & regionsOfInterest, const int nc = 1, bool useNet = false, const Ptr< DetectorParameters > & params = DetectorParameters::create() )

pure virtual

Python:
	cv.mcc.CCheckerDetector.process(	image, chartType[, nc[, useNet[, params]]]	) ->	retval
	cv.mcc.CCheckerDetector.processWithROI(	image, chartType, regionsOfInterest[, nc[, useNet[, params]]]	) ->	retval

Find the ColorCharts in the given image.

The found charts are not returned but instead stored in the detector, these can be accessed later on using getBestColorChecker() and getListColorChecker()

Parameters

image	image in color space BGR
chartType	type of the chart to detect
regionsOfInterest	regions of image to look for the chart, if it is empty, charts are looked for in the entire image
nc	number of charts in the image, if you don't know the exact then keeping this number high helps.
useNet	if it is true the network provided using the setNet() is used for preliminary search for regions where chart could be present, inside the regionsOfInterest provied.
params	parameters of the detection system. More information about them can be found in the struct DetectorParameters.

Returns

true if atleast one chart is detected otherwise false

setNet()

virtual bool cv::mcc::CCheckerDetector::setNet ( dnn::Net net )

pure virtual

Python:
	cv.mcc.CCheckerDetector.setNet(	net	) ->	retval

Set the net which will be used to find the approximate bounding boxes for the color charts.

It is not necessary to use this, but this usually results in better detection rate.

Parameters

net	the neural network, if the network in empty, then the function will return false.

Returns

true if it was able to set the detector's network, false otherwise.

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

• opencv2/mcc/checker_detector.hpp