Implementation of a bayesian auto white balance algorithm. More...

Inheritance diagram for libcamera::ipa::AwbBayes:

Collaboration diagram for libcamera::ipa::AwbBayes:

Public Member Functions
int	init (const YamlObject &tuningData) override
	Initialize the algorithm with the given tuning data. More...

AwbResult	calculateAwb (const AwbStats &stats, unsigned int lux) override
	Calculate AWB data from the given statistics. More...

RGB< double >	gainsFromColourTemperature (double temperatureK) override
	Compute white balance gains from a colour temperature. More...

void	handleControls (const ControlList &controls) override

Public Member Functions inherited from libcamera::ipa::AwbAlgorithm
const ControlInfoMap::Map &	controls () const
	Get the controls info map for this algorithm. More...

virtual void	handleControls ([[maybe_unused]] const ControlList &controls)
	Handle the controls supplied in a request. More...

Additional Inherited Members
Protected Member Functions inherited from libcamera::ipa::AwbAlgorithm
int	parseModeConfigs (const YamlObject &tuningData, const ControlValue &def={})
	Parse the mode configurations from the tuning data. More...

Protected Attributes inherited from libcamera::ipa::AwbAlgorithm
ControlInfoMap::Map	controls_
	Controls info map for the controls provided by the algorithm.

std::map< controls::AwbModeEnum, AwbAlgorithm::ModeConfig >	modes_
	Map of all configured modes. More...

Detailed Description

Implementation of a bayesian auto white balance algorithm.

In a bayesian AWB algorithm the auto white balance estimation is improved by taking the likelihood of a given lightsource based on the estimated lux level into account. E.g. If it is very bright we can assume that we are outside and that colour temperatures around 6500 are preferred.

The second part of this algorithm is the search for the most likely colour temperature. It is implemented in AwbBayes::coarseSearch() and in AwbBayes::fineSearch(). The search works very well without prior likelihoods and therefore the algorithm itself provides very good results even without prior likelihoods.

Member Function Documentation

◆ calculateAwb()

AwbResult libcamera::ipa::AwbBayes::calculateAwb	(	const AwbStats &	stats,
		unsigned int	lux
	)

overridevirtual

Calculate AWB data from the given statistics.

Parameters

[in]	stats	The statistics to use for the calculation
[in]	lux	The lux value of the scene

Calculate an AwbResult object from the given statistics and lux value. A lux value of 0 means it is unknown or invalid and the algorithm shall ignore it.

Returns: The AWB result

Implements libcamera::ipa::AwbAlgorithm.

◆ gainsFromColourTemperature()

RGB< double > libcamera::ipa::AwbBayes::gainsFromColourTemperature ( double colourTemperature )

overridevirtual

Compute white balance gains from a colour temperature.

Parameters

[in] colourTemperature The colour temperature in Kelvin

Compute the white balance gains from a colourTemperature. This function does not take any statistics into account. It is used to compute the colour gains when the user manually specifies a colour temperature.