cv::xphoto::LearningBasedWB クラス

More sophisticated learning-based automatic white balance algorithm. [詳解]

#include <white_balance.hpp>

cv::xphoto::WhiteBalancerを継承しています。

公開メンバ関数
virtual CV_WRAP void	extractSimpleFeatures (InputArray src, OutputArray dst)=0
	Implements the feature extraction part of the algorithm. [詳解]
virtual CV_WRAP int	getRangeMaxVal () const =0
	Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images) [詳解]
virtual CV_WRAP void	setRangeMaxVal (int val)=0
	Maximum possible value of the input image (e.g. 255 for 8 bit images, 4095 for 12 bit images) [詳解]
virtual CV_WRAP float	getSaturationThreshold () const =0
	Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds are ignored. [詳解]
virtual CV_WRAP void	setSaturationThreshold (float val)=0
	Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds are ignored. [詳解]
virtual CV_WRAP 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). [詳解]
virtual CV_WRAP 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). [詳解]
基底クラス cv::xphoto::WhiteBalancer に属する継承公開メンバ関数
virtual CV_WRAP void	balanceWhite (InputArray src, OutputArray dst)=0
	Applies white balancing to the input image [詳解]
基底クラス cv::Algorithm に属する継承公開メンバ関数
virtual CV_WRAP void	clear ()
	Clears the algorithm state [詳解]
virtual void	write (FileStorage &fs) const
	Stores algorithm parameters in a file storage [詳解]
CV_WRAP void	write (const Ptr< FileStorage > &fs, const String &name=String()) const
	simplified API for language bindings これはオーバーロードされたメンバ関数です。利便性のために用意されています。元の関数との違いは引き数のみです。
virtual CV_WRAP void	read (const FileNode &fn)
	Reads algorithm parameters from a file storage [詳解]
virtual CV_WRAP bool	empty () const
	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read [詳解]
virtual CV_WRAP void	save (const String &filename) const
virtual CV_WRAP String	getDefaultName () const

その他の継承メンバ
基底クラス cv::Algorithm に属する継承静的公開メンバ関数
template<typename _Tp >
static Ptr< _Tp >	read (const FileNode &fn)
	Reads algorithm from the file node [詳解]
template<typename _Tp >
static Ptr< _Tp >	load (const String &filename, const String &objname=String())
	Loads algorithm from the file [詳解]
template<typename _Tp >
static Ptr< _Tp >	loadFromString (const String &strModel, const String &objname=String())
	Loads algorithm from a String [詳解]
基底クラス cv::Algorithm に属する継承限定公開メンバ関数
void	writeFormat (FileStorage &fs) const

詳解

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 [Cheng2015] .

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

Currently supports images of type CV_8UC3 and CV_16UC3.

関数詳解

extractSimpleFeatures()

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

pure virtual

Implements the feature extraction part of the algorithm.

In accordance with [Cheng2015] , 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.

引数

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

pure virtual

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).

参照

setHistBinNum

getRangeMaxVal()

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

pure virtual

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

参照

setRangeMaxVal

getSaturationThreshold()

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

pure virtual

Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds are ignored.

参照

setSaturationThreshold

setHistBinNum()

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

pure virtual

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).

参照

getHistBinNum

setRangeMaxVal()

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

pure virtual

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

参照

getRangeMaxVal

setSaturationThreshold()

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

pure virtual

Threshold that is used to determine saturated pixels, i.e. pixels where at least one of the channels exceeds are ignored.

参照

getSaturationThreshold

---

このクラス詳解は次のファイルから抽出されました:

• white_balance.hpp