diff options
| -rw-r--r-- | src/ipa/libipa/awb.cpp | 5 | ||||
| -rw-r--r-- | src/ipa/libipa/awb_bayes.cpp | 8 | ||||
| -rw-r--r-- | utils/tuning/config-example.yaml | 12 |
3 files changed, 15 insertions, 10 deletions
diff --git a/src/ipa/libipa/awb.cpp b/src/ipa/libipa/awb.cpp index 62b69dd9..6157bd43 100644 --- a/src/ipa/libipa/awb.cpp +++ b/src/ipa/libipa/awb.cpp @@ -57,8 +57,9 @@ namespace ipa { * applied. To keep the actual implementations computationally inexpensive, * the squared colour error shall be returned. * - * If the awb statistics provide multiple zones, the sum over all zones needs to - * calculated. + * If the awb statistics provide multiple zones, the average of the individual + * squared errors shall be returned. Averaging/normalizing is necessary so that + * the numeric dimensions are the same on all hardware platforms. * * \return The computed error value */ diff --git a/src/ipa/libipa/awb_bayes.cpp b/src/ipa/libipa/awb_bayes.cpp index c3e0b69b..e75bfcd6 100644 --- a/src/ipa/libipa/awb_bayes.cpp +++ b/src/ipa/libipa/awb_bayes.cpp @@ -234,6 +234,10 @@ int AwbBayes::readPriors(const YamlObject &tuningData) auto &pwl = priors[lux]; for (const auto &[ct, prob] : ctToProbability) { + if (prob < 1e-6) { + LOG(Awb, Error) << "Prior probability must be larger than 1e-6"; + return -EINVAL; + } pwl.append(ct, prob); } } @@ -323,7 +327,7 @@ double AwbBayes::coarseSearch(const ipa::Pwl &prior, const AwbStats &stats) cons double b = ctB_.eval(t, &spanB); RGB<double> gains({ 1 / r, 1.0, 1 / b }); double delta2Sum = stats.computeColourError(gains); - double priorLogLikelihood = prior.eval(prior.domain().clamp(t)); + double priorLogLikelihood = log(prior.eval(prior.domain().clamp(t))); double finalLogLikelihood = delta2Sum - priorLogLikelihood; errorLimits.record(delta2Sum); @@ -406,7 +410,7 @@ void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior for (int i = -nsteps; i <= nsteps; i++) { double tTest = t + i * step; double priorLogLikelihood = - prior.eval(prior.domain().clamp(tTest)); + log(prior.eval(prior.domain().clamp(tTest))); priorLogLikelihoodLimits.record(priorLogLikelihood); Pwl::Point rbStart{ { ctR_.eval(tTest, &spanR), ctB_.eval(tTest, &spanB) } }; diff --git a/utils/tuning/config-example.yaml b/utils/tuning/config-example.yaml index 1bbb2757..5593eaef 100644 --- a/utils/tuning/config-example.yaml +++ b/utils/tuning/config-example.yaml @@ -7,21 +7,21 @@ general: awb: # Algorithm can either be 'grey' or 'bayes' algorithm: bayes - # Priors is only used for the bayes algorithm. They are defined in - # logarithmic space. A good staring point is: + # Priors is only used for the bayes algorithm. They are defined in linear + # space. A good staring point is: # - lux: 0 # ct: [ 2000, 3000, 13000 ] - # probability: [ 1.0, 0.0, 0.0 ] + # probability: [ 1.005, 1.0, 1.0 ] # - lux: 800 # ct: [ 2000, 6000, 13000 ] - # probability: [ 0.0, 2.0, 2.0 ] + # probability: [ 1.0, 1.01, 1.01 ] # - lux: 1500 # ct: [ 2000, 4000, 6000, 6500, 7000, 13000 ] - # probability: [ 0.0, 1.0, 6.0, 7.0, 1.0, 1.0 ] + # probability: [ 1.0, 1.005, 1.032, 1.037, 1.01, 1.01 ] priors: - lux: 0 ct: [ 2000, 13000 ] - probability: [ 0.0, 0.0 ] + probability: [ 1.0, 1.0 ] AwbMode: AwbAuto: lo: 2500 |