Class cv::xphoto::LearningBasedWB#

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

#include <opencv2/xphoto/white_balance.hpp>

Collaboration diagram for cv::xphoto::LearningBasedWB:

Public Member Functions#

Public Member Functions inherited from cv::xphoto::WhiteBalancer

Return	Name	Description
`void`	`balanceWhite(` `InputArray src,` `OutputArray dst )`	Applies white balancing to the input image.

Public Member Functions inherited from cv::Algorithm

Return	Name	Description
	`Algorithm()`
	`~Algorithm()`
`void`	`clear()`	Clears the algorithm state.
`bool`	`empty()`	Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read.
`String`	`getDefaultName()`
`void`	`read(const FileNode & fn)`	Reads algorithm parameters from a file storage.
`void`	`save(const String & filename)`
`void`	`write(` `const Ptr< FileStorage > & fs,` `const String & name = String() )`
`void`	`write(FileStorage & fs)`	Stores algorithm parameters in a file storage.
`void`	`write(` `FileStorage & fs,` `const String & name )`

Static Public Member Functions#

Static Public Member Functions inherited from cv::Algorithm

Return	Name	Description
`static Ptr< _Tp >`	`load(` `const String & filename,` `const String & objname = String() )`	Loads algorithm from the file.
`static Ptr< _Tp >`	`loadFromString(` `const String & strModel,` `const String & objname = String() )`	Loads algorithm from a String.
`static Ptr< _Tp >`	`read(const FileNode & fn)`	Reads algorithm from the file node.

Additional Inherited Members#

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 Cheng2015 .

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()#

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

Python:

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

Implements the feature extraction part of the algorithm.

In accordance with [62] , computes the following features for the input image:

Chromaticity of an average (R,G,B) tuple
Chromaticity of the brightest (R,G,B) tuple (while ignoring saturated pixels)
Chromaticity of the dominant (R,G,B) tuple (the one that has the highest value in the RGB histogram)
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()#

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

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()#

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

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()#

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

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()#

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

Python:

cv.xphoto.LearningBasedWB.setHistBinNum(val)

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()#

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

Python:

cv.xphoto.LearningBasedWB.setRangeMaxVal(val)

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

See also

getRangeMaxVal

setSaturationThreshold()#

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

Python:

cv.xphoto.LearningBasedWB.setSaturationThreshold(val)

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

Source file#

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

opencv2/xphoto/white_balance.hpp