cv::xphoto::LearningBasedWB Class Reference

More sophisticated learning-based automatic white balance algorithm. More...

#include <opencv2/xphoto/white_balance.hpp>

Collaboration diagram for cv::xphoto::LearningBasedWB:

Public Member Functions
virtual void	extractSimpleFeatures (InputArray src, OutputArray dst)=0
	Implements the feature extraction part of the algorithm.
virtual int	getHistBinNum () const =0
	Defines the size of one dimension of a three-dimensional RGB histogram that is used internally by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth (e.g. 256 bins for a 12 bit image).
virtual int	getRangeMaxVal () const =0
	Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images)
virtual float	getSaturationThreshold () const =0
	Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds \(\texttt{saturation_threshold}\times\texttt{range_max_val}\) are ignored.
virtual void	setHistBinNum (int val)=0
	Defines the size of one dimension of a three-dimensional RGB histogram that is used internally by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth (e.g. 256 bins for a 12 bit image).
virtual void	setRangeMaxVal (int val)=0
	Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images)
virtual void	setSaturationThreshold (float val)=0
	Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds \(\texttt{saturation_threshold}\times\texttt{range_max_val}\) are ignored.
Public Member Functions inherited from cv::xphoto::WhiteBalancer
virtual void	balanceWhite (InputArray src, OutputArray dst)=0
	Applies white balancing to the input image.
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
void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage.
void	write (FileStorage &fs, const String &name) const

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

Detailed Description

More sophisticated learning-based automatic white balance algorithm.

As GrayworldWB, this algorithm works by applying different gains to the input image channels, but their computation is a bit more involved compared to the simple gray-world assumption. More details about the algorithm can be found in [55] .

To mask out saturated pixels this function uses only pixels that satisfy the following condition:

\[ \frac{\textrm{max}(R,G,B)}{\texttt{range_max_val}} < \texttt{saturation_thresh} \]

Currently supports images of type CV_8UC3 and CV_16UC3.

Member Function Documentation

extractSimpleFeatures()

virtual void cv::xphoto::LearningBasedWB::extractSimpleFeatures ( InputArray  src, OutputArray  dst  )

pure virtual

Python:
	cv.xphoto.LearningBasedWB.extractSimpleFeatures(	src[, dst]	) ->	dst

Implements the feature extraction part of the algorithm.

In accordance with [55] , computes the following features for the input image:

1. Chromaticity of an average (R,G,B) tuple
2. Chromaticity of the brightest (R,G,B) tuple (while ignoring saturated pixels)
3. Chromaticity of the dominant (R,G,B) tuple (the one that has the highest value in the RGB histogram)
4. Mode of the chromaticity palette, that is constructed by taking 300 most common colors according to the RGB histogram and projecting them on the chromaticity plane. Mode is the most high-density point of the palette, which is computed by a straightforward fixed-bandwidth kernel density estimator with a Epanechnikov kernel function.

Parameters

src	Input three-channel image (BGR color space is assumed).
dst	An array of four (r,g) chromaticity tuples corresponding to the features listed above.

getHistBinNum()

virtual int cv::xphoto::LearningBasedWB::getHistBinNum ( ) const

pure virtual

Python:
	cv.xphoto.LearningBasedWB.getHistBinNum(		) ->	retval

Defines the size of one dimension of a three-dimensional RGB histogram that is used internally by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth (e.g. 256 bins for a 12 bit image).

See also

setHistBinNum

getRangeMaxVal()

virtual int cv::xphoto::LearningBasedWB::getRangeMaxVal ( ) const

pure virtual

Python:
	cv.xphoto.LearningBasedWB.getRangeMaxVal(		) ->	retval

Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images)

See also

setRangeMaxVal

getSaturationThreshold()

virtual float cv::xphoto::LearningBasedWB::getSaturationThreshold ( ) const

pure virtual

Python:
	cv.xphoto.LearningBasedWB.getSaturationThreshold(		) ->	retval

Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds \(\texttt{saturation_threshold}\times\texttt{range_max_val}\) are ignored.

See also

setSaturationThreshold

setHistBinNum()

virtual void cv::xphoto::LearningBasedWB::setHistBinNum ( int  val )

pure virtual

Python:
	cv.xphoto.LearningBasedWB.setHistBinNum(	val	) ->	None

Defines the size of one dimension of a three-dimensional RGB histogram that is used internally by the algorithm. It often makes sense to increase the number of bins for images with higher bit depth (e.g. 256 bins for a 12 bit image).

See also

getHistBinNum

setRangeMaxVal()

virtual void cv::xphoto::LearningBasedWB::setRangeMaxVal ( int  val )

pure virtual

Python:
	cv.xphoto.LearningBasedWB.setRangeMaxVal(	val	) ->	None

Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images)

See also

getRangeMaxVal

setSaturationThreshold()

virtual void cv::xphoto::LearningBasedWB::setSaturationThreshold ( float  val )

pure virtual

Python:
	cv.xphoto.LearningBasedWB.setSaturationThreshold(	val	) ->	None

Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds \(\texttt{saturation_threshold}\times\texttt{range_max_val}\) are ignored.

See also

getSaturationThreshold

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

• opencv2/xphoto/white_balance.hpp