[PATCH v2 15/17] libipa: awb_bayes: Add logging of value limits

[Dan Scally]

Hi Stefan

On 23/01/2025 11:41, Stefan Klug wrote:
> When tuning the AWB algorithm it is more helpful to get a feeling for the
> value ranges than to get verbose output of every single step. Add a
> small utility class to track the limits and log them.
>
> Signed-off-by: Stefan Klug <stefan.klug at ideasonboard.com>

Neat idea, I think it probably should be its own thing rather than embedded in awb_bayes actually, 
but that can be done anytime:


Reviewed-by: Daniel Scally <dan.scally at ideasonboard.com>

>
> ---
>
> Changes in v2:
> - Added this commit
> ---
>   src/ipa/libipa/awb_bayes.cpp | 57 ++++++++++++++++++++++++++++++++++--
>   1 file changed, 55 insertions(+), 2 deletions(-)
>
> diff --git a/src/ipa/libipa/awb_bayes.cpp b/src/ipa/libipa/awb_bayes.cpp
> index 8ab6ed661a3e..aaa8c7a663ad 100644
> --- a/src/ipa/libipa/awb_bayes.cpp
> +++ b/src/ipa/libipa/awb_bayes.cpp
> @@ -50,6 +50,44 @@ namespace libcamera {
>   
>   LOG_DECLARE_CATEGORY(Awb)
>   
> +namespace {
> +
> +template<typename T>
> +class LimitsRecorder
> +{
> +public:
> +	LimitsRecorder()
> +		: min_(std::numeric_limits<T>::max()),
> +		  max_(std::numeric_limits<T>::min())
> +	{
> +	}
> +
> +	void record(const T& value)
> +	{
> +		min_ = std::min(min_, value);
> +		max_ = std::max(max_, value);
> +	}
> +
> +	const T& min() const { return min_; }
> +	const T& max() const { return max_; }
> +private:
> +	T min_;
> +	T max_;
> +};
> +
> +
> +
> +#ifndef __DOXYGEN__
> +template<typename T>
> +std::ostream &operator<<(std::ostream &out, const LimitsRecorder<T> &v)
> +{
> +	out << "[ " << v.min() << ", " << v.max() << " ]";
> +	return out;
> +}
> +#endif
> +
> +} /* namespace */
> +
>   namespace ipa {
>   
>   /**
> @@ -277,6 +315,8 @@ double AwbBayes::coarseSearch(const ipa::Pwl &prior, const AwbStats &stats) cons
>   	double t = currentMode_->ctLo;
>   	int spanR = -1;
>   	int spanB = -1;
> +	LimitsRecorder<double> errorLimits;
> +	LimitsRecorder<double> priorLogLikelihoodLimits;
>   
>   	/* Step down the CT curve evaluating log likelihood. */
>   	while (true) {
> @@ -287,6 +327,9 @@ double AwbBayes::coarseSearch(const ipa::Pwl &prior, const AwbStats &stats) cons
>   		double priorLogLikelihood = prior.eval(prior.domain().clamp(t));
>   		double finalLogLikelihood = delta2Sum - priorLogLikelihood;
>   
> +		errorLimits.record(delta2Sum);
> +		priorLogLikelihoodLimits.record(priorLogLikelihood);
> +
>   		LOG(Awb, Debug) << "Coarse search t: " << t
>   				<< " gains: " << gains
>   				<< " error: " << delta2Sum
> @@ -308,7 +351,9 @@ double AwbBayes::coarseSearch(const ipa::Pwl &prior, const AwbStats &stats) cons
>   	}
>   
>   	t = points[bestPoint].x();
> -	LOG(Awb, Debug) << "Coarse search found CT " << t;
> +	LOG(Awb, Debug) << "Coarse search found CT " << t
> +			<< " error limits:" << errorLimits
> +			<< " prior log likelihood limits:" << priorLogLikelihoodLimits;
>   
>   	/*
>   	 * We have the best point of the search, but refine it with a quadratic
> @@ -352,6 +397,9 @@ void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior
>   	Pwl::Point bestRB;
>   	double transverseRange = transverseNeg_ + transversePos_;
>   	const int maxNumDeltas = 12;
> +	LimitsRecorder<double> errorLimits;
> +	LimitsRecorder<double> priorLogLikelihoodLimits;
> +
>   
>   	/* a transverse step approximately every 0.01 r/b units */
>   	int numDeltas = floor(transverseRange * 100 + 0.5) + 1;
> @@ -366,6 +414,7 @@ void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior
>   		double tTest = t + i * step;
>   		double priorLogLikelihood =
>   			prior.eval(prior.domain().clamp(tTest));
> +		priorLogLikelihoodLimits.record(priorLogLikelihood);
>   		Pwl::Point rbStart{ { ctR_.eval(tTest, &spanR),
>   				      ctB_.eval(tTest, &spanB) } };
>   		Pwl::Point samples[maxNumDeltas];
> @@ -384,6 +433,7 @@ void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior
>   			Pwl::Point rbTest = rbStart + transverse * p.x();
>   			RGB<double> gains({ 1 / rbTest[0], 1.0, 1 / rbTest[1] });
>   			double delta2Sum = stats.computeColourError(gains);
> +			errorLimits.record(delta2Sum);
>   			p.y() = delta2Sum - priorLogLikelihood;
>   
>   			if (p.y() < samples[bestPoint].y())
> @@ -401,6 +451,7 @@ void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior
>   		Pwl::Point rbTest = rbStart + transverse * bestOffset;
>   		RGB<double> gains({ 1 / rbTest[0], 1.0, 1 / rbTest[1] });
>   		double delta2Sum = stats.computeColourError(gains);
> +		errorLimits.record(delta2Sum);
>   		double finalLogLikelihood = delta2Sum - priorLogLikelihood;
>   		LOG(Awb, Debug)
>   			<< "Fine search t: " << tTest
> @@ -421,7 +472,9 @@ void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior
>   	r = bestRB[0];
>   	b = bestRB[1];
>   	LOG(Awb, Debug)
> -		<< "Fine search found t " << t << " r " << r << " b " << b;
> +		<< "Fine search found t " << t << " r " << r << " b " << b
> +		<< " error limits: " << errorLimits
> +		<< " prior log likelihood limits: " << priorLogLikelihoodLimits;
>   }
>   
>   /**