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This will make it easier to add new AWB algorithms in the future. Signed-off-by: Peter Bailey Reviewed-by: David Plowman Reviewed-by: Naushir Patuck --- src/ipa/rpi/controller/meson.build | 1 + src/ipa/rpi/controller/rpi/awb.cpp | 409 +++------------------ src/ipa/rpi/controller/rpi/awb.h | 99 ++--- src/ipa/rpi/controller/rpi/awb_bayes.cpp | 444 +++++++++++++++++++++++ 4 files changed, 533 insertions(+), 420 deletions(-) create mode 100644 src/ipa/rpi/controller/rpi/awb_bayes.cpp diff --git a/src/ipa/rpi/controller/meson.build b/src/ipa/rpi/controller/meson.build index c13c4853..c8637906 100644 --- a/src/ipa/rpi/controller/meson.build +++ b/src/ipa/rpi/controller/meson.build @@ -10,6 +10,7 @@ rpi_ipa_controller_sources = files([ 'rpi/agc_channel.cpp', 'rpi/alsc.cpp', 'rpi/awb.cpp', + 'rpi/awb_bayes.cpp', 'rpi/black_level.cpp', 'rpi/cac.cpp', 'rpi/ccm.cpp', diff --git a/src/ipa/rpi/controller/rpi/awb.cpp b/src/ipa/rpi/controller/rpi/awb.cpp index 365b595f..de5fa59b 100644 --- a/src/ipa/rpi/controller/rpi/awb.cpp +++ b/src/ipa/rpi/controller/rpi/awb.cpp @@ -1,20 +1,14 @@ /* SPDX-License-Identifier: BSD-2-Clause */ /* - * Copyright (C) 2019, Raspberry Pi Ltd + * Copyright (C) 2025, Raspberry Pi Ltd * * AWB control algorithm */ - -#include -#include -#include - -#include +#include "awb.h" #include "../lux_status.h" #include "alsc_status.h" -#include "awb.h" using namespace RPiController; using namespace libcamera; @@ -23,39 +17,6 @@ LOG_DEFINE_CATEGORY(RPiAwb) constexpr double kDefaultCT = 4500.0; -#define NAME "rpi.awb" - -/* - * todo - the locking in this algorithm needs some tidying up as has been done - * elsewhere (ALSC and AGC). - */ - -int AwbMode::read(const libcamera::YamlObject ¶ms) -{ - auto value = params["lo"].get(); - if (!value) - return -EINVAL; - ctLo = *value; - - value = params["hi"].get(); - if (!value) - return -EINVAL; - ctHi = *value; - - return 0; -} - -int AwbPrior::read(const libcamera::YamlObject ¶ms) -{ - auto value = params["lux"].get(); - if (!value) - return -EINVAL; - lux = *value; - - prior = params["prior"].get(ipa::Pwl{}); - return prior.empty() ? -EINVAL : 0; -} - static int readCtCurve(ipa::Pwl &ctR, ipa::Pwl &ctB, const libcamera::YamlObject ¶ms) { if (params.size() % 3) { @@ -92,11 +53,25 @@ static int readCtCurve(ipa::Pwl &ctR, ipa::Pwl &ctB, const libcamera::YamlObject return 0; } +int AwbMode::read(const libcamera::YamlObject ¶ms) +{ + auto value = params["lo"].get(); + if (!value) + return -EINVAL; + ctLo = *value; + + value = params["hi"].get(); + if (!value) + return -EINVAL; + ctHi = *value; + + return 0; +} + int AwbConfig::read(const libcamera::YamlObject ¶ms) { int ret; - bayes = params["bayes"].get(1); framePeriod = params["frame_period"].get(10); startupFrames = params["startup_frames"].get(10); convergenceFrames = params["convergence_frames"].get(3); @@ -111,23 +86,6 @@ int AwbConfig::read(const libcamera::YamlObject ¶ms) ctBInverse = ctB.inverse().first; } - if (params.contains("priors")) { - for (const auto &p : params["priors"].asList()) { - AwbPrior prior; - ret = prior.read(p); - if (ret) - return ret; - if (!priors.empty() && prior.lux <= priors.back().lux) { - LOG(RPiAwb, Error) << "AwbConfig: Prior must be ordered in increasing lux value"; - return -EINVAL; - } - priors.push_back(prior); - } - if (priors.empty()) { - LOG(RPiAwb, Error) << "AwbConfig: no AWB priors configured"; - return -EINVAL; - } - } if (params.contains("modes")) { for (const auto &[key, value] : params["modes"].asDict()) { ret = modes[key].read(value); @@ -142,13 +100,10 @@ int AwbConfig::read(const libcamera::YamlObject ¶ms) } } - minPixels = params["min_pixels"].get(16.0); - minG = params["min_G"].get(32); - minRegions = params["min_regions"].get(10); deltaLimit = params["delta_limit"].get(0.2); - coarseStep = params["coarse_step"].get(0.2); transversePos = params["transverse_pos"].get(0.01); transverseNeg = params["transverse_neg"].get(0.01); + if (transversePos <= 0 || transverseNeg <= 0) { LOG(RPiAwb, Error) << "AwbConfig: transverse_pos/neg must be > 0"; return -EINVAL; @@ -157,29 +112,21 @@ int AwbConfig::read(const libcamera::YamlObject ¶ms) sensitivityR = params["sensitivity_r"].get(1.0); sensitivityB = params["sensitivity_b"].get(1.0); - if (bayes) { - if (ctR.empty() || ctB.empty() || priors.empty() || - defaultMode == nullptr) { - LOG(RPiAwb, Warning) - << "Bayesian AWB mis-configured - switch to Grey method"; - bayes = false; - } - } - whitepointR = params["whitepoint_r"].get(0.0); - whitepointB = params["whitepoint_b"].get(0.0); - if (bayes == false) + if (hasCtCurve() && defaultMode != nullptr) { + greyWorld = false; + } else { + greyWorld = true; sensitivityR = sensitivityB = 1.0; /* nor do sensitivities make any sense */ - /* - * The biasProportion parameter adds a small proportion of the counted - * pixles to a region biased to the biasCT colour temperature. - * - * A typical value for biasProportion would be between 0.05 to 0.1. - */ - biasProportion = params["bias_proportion"].get(0.0); - biasCT = params["bias_ct"].get(kDefaultCT); + } + return 0; } +bool AwbConfig::hasCtCurve() const +{ + return !ctR.empty() && !ctB.empty(); +} + Awb::Awb(Controller *controller) : AwbAlgorithm(controller) { @@ -199,16 +146,6 @@ Awb::~Awb() asyncThread_.join(); } -char const *Awb::name() const -{ - return NAME; -} - -int Awb::read(const libcamera::YamlObject ¶ms) -{ - return config_.read(params); -} - void Awb::initialise() { frameCount_ = framePhase_ = 0; @@ -217,7 +154,7 @@ void Awb::initialise() * just in case the first few frames don't have anything meaningful in * them. */ - if (!config_.ctR.empty() && !config_.ctB.empty()) { + if (!config_.greyWorld) { syncResults_.temperatureK = config_.ctR.domain().clamp(4000); syncResults_.gainR = 1.0 / config_.ctR.eval(syncResults_.temperatureK); syncResults_.gainG = 1.0; @@ -282,7 +219,7 @@ void Awb::setManualGains(double manualR, double manualB) syncResults_.gainR = prevSyncResults_.gainR = manualR_; syncResults_.gainG = prevSyncResults_.gainG = 1.0; syncResults_.gainB = prevSyncResults_.gainB = manualB_; - if (config_.bayes) { + if (!config_.greyWorld) { /* Also estimate the best corresponding colour temperature from the curves. */ double ctR = config_.ctRInverse.eval(config_.ctRInverse.domain().clamp(1 / manualR_)); double ctB = config_.ctBInverse.eval(config_.ctBInverse.domain().clamp(1 / manualB_)); @@ -294,7 +231,7 @@ void Awb::setManualGains(double manualR, double manualB) void Awb::setColourTemperature(double temperatureK) { - if (!config_.bayes) { + if (config_.greyWorld) { LOG(RPiAwb, Warning) << "AWB uncalibrated - cannot set colour temperature"; return; } @@ -433,10 +370,10 @@ void Awb::asyncFunc() } } -static void generateStats(std::vector &zones, - StatisticsPtr &stats, double minPixels, - double minG, Metadata &globalMetadata, - double biasProportion, double biasCtR, double biasCtB) +void Awb::generateStats(std::vector &zones, + StatisticsPtr &stats, double minPixels, + double minG, Metadata &globalMetadata, + double biasProportion, double biasCtR, double biasCtB) { std::scoped_lock l(globalMetadata); @@ -450,9 +387,9 @@ static void generateStats(std::vector &zones, zone.R = region.val.rSum / region.counted; zone.B = region.val.bSum / region.counted; /* - * Add some bias samples to allow the search to tend to a - * bias CT in failure cases. - */ + * Add some bias samples to allow the search to tend to a + * bias CT in failure cases. + */ const unsigned int proportion = biasProportion * region.counted; zone.R += proportion * biasCtR; zone.B += proportion * biasCtB; @@ -469,29 +406,7 @@ static void generateStats(std::vector &zones, } } -void Awb::prepareStats() -{ - zones_.clear(); - /* - * LSC has already been applied to the stats in this pipeline, so stop - * any LSC compensation. We also ignore config_.fast in this version. - */ - const double biasCtR = config_.bayes ? config_.ctR.eval(config_.biasCT) : 0; - const double biasCtB = config_.bayes ? config_.ctB.eval(config_.biasCT) : 0; - generateStats(zones_, statistics_, config_.minPixels, - config_.minG, getGlobalMetadata(), - config_.biasProportion, biasCtR, biasCtB); - /* - * apply sensitivities, so values appear to come from our "canonical" - * sensor. - */ - for (auto &zone : zones_) { - zone.R *= config_.sensitivityR; - zone.B *= config_.sensitivityB; - } -} - -double Awb::computeDelta2Sum(double gainR, double gainB) +double Awb::computeDelta2Sum(double gainR, double gainB, double whitepointR, double whitepointB) { /* * Compute the sum of the squared colour error (non-greyness) as it @@ -499,8 +414,8 @@ double Awb::computeDelta2Sum(double gainR, double gainB) */ double delta2Sum = 0; for (auto &z : zones_) { - double deltaR = gainR * z.R - 1 - config_.whitepointR; - double deltaB = gainB * z.B - 1 - config_.whitepointB; + double deltaR = gainR * z.R - 1 - whitepointR; + double deltaB = gainB * z.B - 1 - whitepointB; double delta2 = deltaR * deltaR + deltaB * deltaB; /* LOG(RPiAwb, Debug) << "deltaR " << deltaR << " deltaB " << deltaB << " delta2 " << delta2; */ delta2 = std::min(delta2, config_.deltaLimit); @@ -509,39 +424,14 @@ double Awb::computeDelta2Sum(double gainR, double gainB) return delta2Sum; } -ipa::Pwl Awb::interpolatePrior() +double Awb::interpolateQuadatric(libcamera::ipa::Pwl::Point const &a, + libcamera::ipa::Pwl::Point const &b, + libcamera::ipa::Pwl::Point const &c) { /* - * Interpolate the prior log likelihood function for our current lux - * value. - */ - if (lux_ <= config_.priors.front().lux) - return config_.priors.front().prior; - else if (lux_ >= config_.priors.back().lux) - return config_.priors.back().prior; - else { - int idx = 0; - /* find which two we lie between */ - while (config_.priors[idx + 1].lux < lux_) - idx++; - double lux0 = config_.priors[idx].lux, - lux1 = config_.priors[idx + 1].lux; - return ipa::Pwl::combine(config_.priors[idx].prior, - config_.priors[idx + 1].prior, - [&](double /*x*/, double y0, double y1) { - return y0 + (y1 - y0) * - (lux_ - lux0) / (lux1 - lux0); - }); - } -} - -static double interpolateQuadatric(ipa::Pwl::Point const &a, ipa::Pwl::Point const &b, - ipa::Pwl::Point const &c) -{ - /* - * Given 3 points on a curve, find the extremum of the function in that - * interval by fitting a quadratic. - */ + * Given 3 points on a curve, find the extremum of the function in that + * interval by fitting a quadratic. + */ const double eps = 1e-3; ipa::Pwl::Point ca = c - a, ba = b - a; double denominator = 2 * (ba.y() * ca.x() - ca.y() * ba.x()); @@ -554,180 +444,6 @@ static double interpolateQuadatric(ipa::Pwl::Point const &a, ipa::Pwl::Point con return a.y() < c.y() - eps ? a.x() : (c.y() < a.y() - eps ? c.x() : b.x()); } -double Awb::coarseSearch(ipa::Pwl const &prior) -{ - points_.clear(); /* assume doesn't deallocate memory */ - size_t bestPoint = 0; - double t = mode_->ctLo; - int spanR = 0, spanB = 0; - /* Step down the CT curve evaluating log likelihood. */ - while (true) { - double r = config_.ctR.eval(t, &spanR); - double b = config_.ctB.eval(t, &spanB); - double gainR = 1 / r, gainB = 1 / b; - double delta2Sum = computeDelta2Sum(gainR, gainB); - double priorLogLikelihood = prior.eval(prior.domain().clamp(t)); - double finalLogLikelihood = delta2Sum - priorLogLikelihood; - LOG(RPiAwb, Debug) - << "t: " << t << " gain R " << gainR << " gain B " - << gainB << " delta2_sum " << delta2Sum - << " prior " << priorLogLikelihood << " final " - << finalLogLikelihood; - points_.push_back(ipa::Pwl::Point({ t, finalLogLikelihood })); - if (points_.back().y() < points_[bestPoint].y()) - bestPoint = points_.size() - 1; - if (t == mode_->ctHi) - break; - /* for even steps along the r/b curve scale them by the current t */ - t = std::min(t + t / 10 * config_.coarseStep, mode_->ctHi); - } - t = points_[bestPoint].x(); - LOG(RPiAwb, Debug) << "Coarse search found CT " << t; - /* - * We have the best point of the search, but refine it with a quadratic - * interpolation around its neighbours. - */ - if (points_.size() > 2) { - unsigned long bp = std::min(bestPoint, points_.size() - 2); - bestPoint = std::max(1UL, bp); - t = interpolateQuadatric(points_[bestPoint - 1], - points_[bestPoint], - points_[bestPoint + 1]); - LOG(RPiAwb, Debug) - << "After quadratic refinement, coarse search has CT " - << t; - } - return t; -} - -void Awb::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior) -{ - int spanR = -1, spanB = -1; - config_.ctR.eval(t, &spanR); - config_.ctB.eval(t, &spanB); - double step = t / 10 * config_.coarseStep * 0.1; - int nsteps = 5; - double rDiff = config_.ctR.eval(t + nsteps * step, &spanR) - - config_.ctR.eval(t - nsteps * step, &spanR); - double bDiff = config_.ctB.eval(t + nsteps * step, &spanB) - - config_.ctB.eval(t - nsteps * step, &spanB); - ipa::Pwl::Point transverse({ bDiff, -rDiff }); - if (transverse.length2() < 1e-6) - return; - /* - * unit vector orthogonal to the b vs. r function (pointing outwards - * with r and b increasing) - */ - transverse = transverse / transverse.length(); - double bestLogLikelihood = 0, bestT = 0, bestR = 0, bestB = 0; - double transverseRange = config_.transverseNeg + config_.transversePos; - const int maxNumDeltas = 12; - /* a transverse step approximately every 0.01 r/b units */ - int numDeltas = floor(transverseRange * 100 + 0.5) + 1; - numDeltas = numDeltas < 3 ? 3 : (numDeltas > maxNumDeltas ? maxNumDeltas : numDeltas); - /* - * Step down CT curve. March a bit further if the transverse range is - * large. - */ - nsteps += numDeltas; - for (int i = -nsteps; i <= nsteps; i++) { - double tTest = t + i * step; - double priorLogLikelihood = - prior.eval(prior.domain().clamp(tTest)); - double rCurve = config_.ctR.eval(tTest, &spanR); - double bCurve = config_.ctB.eval(tTest, &spanB); - /* x will be distance off the curve, y the log likelihood there */ - ipa::Pwl::Point points[maxNumDeltas]; - int bestPoint = 0; - /* Take some measurements transversely *off* the CT curve. */ - for (int j = 0; j < numDeltas; j++) { - points[j][0] = -config_.transverseNeg + - (transverseRange * j) / (numDeltas - 1); - ipa::Pwl::Point rbTest = ipa::Pwl::Point({ rCurve, bCurve }) + - transverse * points[j].x(); - double rTest = rbTest.x(), bTest = rbTest.y(); - double gainR = 1 / rTest, gainB = 1 / bTest; - double delta2Sum = computeDelta2Sum(gainR, gainB); - points[j][1] = delta2Sum - priorLogLikelihood; - LOG(RPiAwb, Debug) - << "At t " << tTest << " r " << rTest << " b " - << bTest << ": " << points[j].y(); - if (points[j].y() < points[bestPoint].y()) - bestPoint = j; - } - /* - * We have NUM_DELTAS points transversely across the CT curve, - * now let's do a quadratic interpolation for the best result. - */ - bestPoint = std::max(1, std::min(bestPoint, numDeltas - 2)); - ipa::Pwl::Point rbTest = ipa::Pwl::Point({ rCurve, bCurve }) + - transverse * interpolateQuadatric(points[bestPoint - 1], - points[bestPoint], - points[bestPoint + 1]); - double rTest = rbTest.x(), bTest = rbTest.y(); - double gainR = 1 / rTest, gainB = 1 / bTest; - double delta2Sum = computeDelta2Sum(gainR, gainB); - double finalLogLikelihood = delta2Sum - priorLogLikelihood; - LOG(RPiAwb, Debug) - << "Finally " - << tTest << " r " << rTest << " b " << bTest << ": " - << finalLogLikelihood - << (finalLogLikelihood < bestLogLikelihood ? " BEST" : ""); - if (bestT == 0 || finalLogLikelihood < bestLogLikelihood) - bestLogLikelihood = finalLogLikelihood, - bestT = tTest, bestR = rTest, bestB = bTest; - } - t = bestT, r = bestR, b = bestB; - LOG(RPiAwb, Debug) - << "Fine search found t " << t << " r " << r << " b " << b; -} - -void Awb::awbBayes() -{ - /* - * May as well divide out G to save computeDelta2Sum from doing it over - * and over. - */ - for (auto &z : zones_) - z.R = z.R / (z.G + 1), z.B = z.B / (z.G + 1); - /* - * Get the current prior, and scale according to how many zones are - * valid... not entirely sure about this. - */ - ipa::Pwl prior = interpolatePrior(); - prior *= zones_.size() / (double)(statistics_->awbRegions.numRegions()); - prior.map([](double x, double y) { - LOG(RPiAwb, Debug) << "(" << x << "," << y << ")"; - }); - double t = coarseSearch(prior); - double r = config_.ctR.eval(t); - double b = config_.ctB.eval(t); - LOG(RPiAwb, Debug) - << "After coarse search: r " << r << " b " << b << " (gains r " - << 1 / r << " b " << 1 / b << ")"; - /* - * Not entirely sure how to handle the fine search yet. Mostly the - * estimated CT is already good enough, but the fine search allows us to - * wander transverely off the CT curve. Under some illuminants, where - * there may be more or less green light, this may prove beneficial, - * though I probably need more real datasets before deciding exactly how - * this should be controlled and tuned. - */ - fineSearch(t, r, b, prior); - LOG(RPiAwb, Debug) - << "After fine search: r " << r << " b " << b << " (gains r " - << 1 / r << " b " << 1 / b << ")"; - /* - * Write results out for the main thread to pick up. Remember to adjust - * the gains from the ones that the "canonical sensor" would require to - * the ones needed by *this* sensor. - */ - asyncResults_.temperatureK = t; - asyncResults_.gainR = 1.0 / r * config_.sensitivityR; - asyncResults_.gainG = 1.0; - asyncResults_.gainB = 1.0 / b * config_.sensitivityB; -} - void Awb::awbGrey() { LOG(RPiAwb, Debug) << "Grey world AWB"; @@ -765,32 +481,3 @@ void Awb::awbGrey() asyncResults_.gainG = 1.0; asyncResults_.gainB = gainB; } - -void Awb::doAwb() -{ - prepareStats(); - LOG(RPiAwb, Debug) << "Valid zones: " << zones_.size(); - if (zones_.size() > config_.minRegions) { - if (config_.bayes) - awbBayes(); - else - awbGrey(); - LOG(RPiAwb, Debug) - << "CT found is " - << asyncResults_.temperatureK - << " with gains r " << asyncResults_.gainR - << " and b " << asyncResults_.gainB; - } - /* - * we're done with these; we may as well relinquish our hold on the - * pointer. - */ - statistics_.reset(); -} - -/* Register algorithm with the system. */ -static Algorithm *create(Controller *controller) -{ - return (Algorithm *)new Awb(controller); -} -static RegisterAlgorithm reg(NAME, &create); diff --git a/src/ipa/rpi/controller/rpi/awb.h b/src/ipa/rpi/controller/rpi/awb.h index 2fb91254..8b2d8d1d 100644 --- a/src/ipa/rpi/controller/rpi/awb.h +++ b/src/ipa/rpi/controller/rpi/awb.h @@ -1,42 +1,33 @@ /* SPDX-License-Identifier: BSD-2-Clause */ /* - * Copyright (C) 2019, Raspberry Pi Ltd + * Copyright (C) 2025, Raspberry Pi Ltd * * AWB control algorithm */ #pragma once -#include #include +#include #include -#include - #include "../awb_algorithm.h" #include "../awb_status.h" -#include "../statistics.h" - #include "libipa/pwl.h" namespace RPiController { -/* Control algorithm to perform AWB calculations. */ - struct AwbMode { int read(const libcamera::YamlObject ¶ms); double ctLo; /* low CT value for search */ double ctHi; /* high CT value for search */ }; -struct AwbPrior { - int read(const libcamera::YamlObject ¶ms); - double lux; /* lux level */ - libcamera::ipa::Pwl prior; /* maps CT to prior log likelihood for this lux level */ -}; - struct AwbConfig { - AwbConfig() : defaultMode(nullptr) {} + AwbConfig() + : defaultMode(nullptr) {} int read(const libcamera::YamlObject ¶ms); + bool hasCtCurve() const; + /* Only repeat the AWB calculation every "this many" frames */ uint16_t framePeriod; /* number of initial frames for which speed taken as 1.0 (maximum) */ @@ -47,27 +38,13 @@ struct AwbConfig { libcamera::ipa::Pwl ctB; /* function maps CT to b (= B/G) */ libcamera::ipa::Pwl ctRInverse; /* inverse of ctR */ libcamera::ipa::Pwl ctBInverse; /* inverse of ctB */ - /* table of illuminant priors at different lux levels */ - std::vector priors; + /* AWB "modes" (determines the search range) */ std::map modes; AwbMode *defaultMode; /* mode used if no mode selected */ - /* - * minimum proportion of pixels counted within AWB region for it to be - * "useful" - */ - double minPixels; - /* minimum G value of those pixels, to be regarded a "useful" */ - uint16_t minG; - /* - * number of AWB regions that must be "useful" in order to do the AWB - * calculation - */ - uint32_t minRegions; + /* clamp on colour error term (so as not to penalise non-grey excessively) */ double deltaLimit; - /* step size control in coarse search */ - double coarseStep; /* how far to wander off CT curve towards "more purple" */ double transversePos; /* how far to wander off CT curve towards "more green" */ @@ -82,14 +59,8 @@ struct AwbConfig { * sensor's B/G) */ double sensitivityB; - /* The whitepoint (which we normally "aim" for) can be moved. */ - double whitepointR; - double whitepointB; - bool bayes; /* use Bayesian algorithm */ - /* proportion of counted samples to add for the search bias */ - double biasProportion; - /* CT target for the search bias */ - double biasCT; + + bool greyWorld; /* don't use the ct curve when in grey world mode */ }; class Awb : public AwbAlgorithm @@ -97,9 +68,7 @@ class Awb : public AwbAlgorithm public: Awb(Controller *controller = NULL); ~Awb(); - char const *name() const override; - void initialise() override; - int read(const libcamera::YamlObject ¶ms) override; + virtual void initialise() override; unsigned int getConvergenceFrames() const override; void initialValues(double &gainR, double &gainB) override; void setMode(std::string const &name) override; @@ -110,6 +79,11 @@ public: void switchMode(CameraMode const &cameraMode, Metadata *metadata) override; void prepare(Metadata *imageMetadata) override; void process(StatisticsPtr &stats, Metadata *imageMetadata) override; + + static double interpolateQuadatric(libcamera::ipa::Pwl::Point const &a, + libcamera::ipa::Pwl::Point const &b, + libcamera::ipa::Pwl::Point const &c); + struct RGB { RGB(double r = 0, double g = 0, double b = 0) : R(r), G(g), B(b) @@ -123,10 +97,30 @@ public: } }; -private: - bool isAutoEnabled() const; +protected: /* configuration is read-only, and available to both threads */ AwbConfig config_; + /* + * The following are for the asynchronous thread to use, though the main + * thread can set/reset them if the async thread is known to be idle: + */ + std::vector zones_; + StatisticsPtr statistics_; + double lux_; + AwbMode *mode_; + AwbStatus asyncResults_; + + virtual void doAwb() = 0; + virtual void prepareStats() = 0; + double computeDelta2Sum(double gainR, double gainB, double whitepointR, double whitepointB); + void awbGrey(); + static void generateStats(std::vector &zones, + StatisticsPtr &stats, double minPixels, + double minG, Metadata &globalMetadata, + double biasProportion, double biasCtR, double biasCtB); + +private: + bool isAutoEnabled() const; std::thread asyncThread_; void asyncFunc(); /* asynchronous thread function */ std::mutex mutex_; @@ -152,6 +146,7 @@ private: AwbStatus syncResults_; AwbStatus prevSyncResults_; std::string modeName_; + /* * The following are for the asynchronous thread to use, though the main * thread can set/reset them if the async thread is known to be idle: @@ -159,20 +154,6 @@ private: void restartAsync(StatisticsPtr &stats, double lux); /* copy out the results from the async thread so that it can be restarted */ void fetchAsyncResults(); - StatisticsPtr statistics_; - AwbMode *mode_; - double lux_; - AwbStatus asyncResults_; - void doAwb(); - void awbBayes(); - void awbGrey(); - void prepareStats(); - double computeDelta2Sum(double gainR, double gainB); - libcamera::ipa::Pwl interpolatePrior(); - double coarseSearch(libcamera::ipa::Pwl const &prior); - void fineSearch(double &t, double &r, double &b, libcamera::ipa::Pwl const &prior); - std::vector zones_; - std::vector points_; /* manual r setting */ double manualR_; /* manual b setting */ @@ -196,4 +177,4 @@ static inline Awb::RGB operator*(Awb::RGB const &rgb, double d) return d * rgb; } -} /* namespace RPiController */ +} // namespace RPiController diff --git a/src/ipa/rpi/controller/rpi/awb_bayes.cpp b/src/ipa/rpi/controller/rpi/awb_bayes.cpp new file mode 100644 index 00000000..09233cec --- /dev/null +++ b/src/ipa/rpi/controller/rpi/awb_bayes.cpp @@ -0,0 +1,444 @@ +/* SPDX-License-Identifier: BSD-2-Clause */ +/* + * Copyright (C) 2019, Raspberry Pi Ltd + * + * AWB control algorithm + */ + +#include +#include +#include +#include +#include +#include + +#include + +#include + +#include "../awb_algorithm.h" +#include "../awb_status.h" +#include "../lux_status.h" +#include "../statistics.h" +#include "libipa/pwl.h" + +#include "alsc_status.h" +#include "awb.h" + +using namespace libcamera; + +LOG_DECLARE_CATEGORY(RPiAwb) + +constexpr double kDefaultCT = 4500.0; + +#define NAME "rpi.awb" + +/* + * todo - the locking in this algorithm needs some tidying up as has been done + * elsewhere (ALSC and AGC). + */ + +namespace RPiController { + +struct AwbPrior { + int read(const libcamera::YamlObject ¶ms); + double lux; /* lux level */ + libcamera::ipa::Pwl prior; /* maps CT to prior log likelihood for this lux level */ +}; + +struct AwbBayesConfig { + AwbBayesConfig() {} + int read(const libcamera::YamlObject ¶ms, AwbConfig &config); + /* table of illuminant priors at different lux levels */ + std::vector priors; + /* + * minimum proportion of pixels counted within AWB region for it to be + * "useful" + */ + double minPixels; + /* minimum G value of those pixels, to be regarded a "useful" */ + uint16_t minG; + /* + * number of AWB regions that must be "useful" in order to do the AWB + * calculation + */ + uint32_t minRegions; + /* step size control in coarse search */ + double coarseStep; + /* The whitepoint (which we normally "aim" for) can be moved. */ + double whitepointR; + double whitepointB; + bool bayes; /* use Bayesian algorithm */ + /* proportion of counted samples to add for the search bias */ + double biasProportion; + /* CT target for the search bias */ + double biasCT; +}; + +class AwbBayes : public Awb +{ +public: + AwbBayes(Controller *controller = NULL); + ~AwbBayes(); + char const *name() const override; + int read(const libcamera::YamlObject ¶ms) override; + +protected: + void prepareStats() override; + void doAwb() override; + +private: + AwbBayesConfig bayesConfig_; + void awbBayes(); + libcamera::ipa::Pwl interpolatePrior(); + double coarseSearch(libcamera::ipa::Pwl const &prior); + void fineSearch(double &t, double &r, double &b, libcamera::ipa::Pwl const &prior); + std::vector points_; +}; + +int AwbPrior::read(const libcamera::YamlObject ¶ms) +{ + auto value = params["lux"].get(); + if (!value) + return -EINVAL; + lux = *value; + + prior = params["prior"].get(ipa::Pwl{}); + return prior.empty() ? -EINVAL : 0; +} + +int AwbBayesConfig::read(const libcamera::YamlObject ¶ms, AwbConfig &config) +{ + int ret; + + bayes = params["bayes"].get(1); + + if (params.contains("priors")) { + for (const auto &p : params["priors"].asList()) { + AwbPrior prior; + ret = prior.read(p); + if (ret) + return ret; + if (!priors.empty() && prior.lux <= priors.back().lux) { + LOG(RPiAwb, Error) << "AwbConfig: Prior must be ordered in increasing lux value"; + return -EINVAL; + } + priors.push_back(prior); + } + if (priors.empty()) { + LOG(RPiAwb, Error) << "AwbConfig: no AWB priors configured"; + return -EINVAL; + } + } + + minPixels = params["min_pixels"].get(16.0); + minG = params["min_G"].get(32); + minRegions = params["min_regions"].get(10); + coarseStep = params["coarse_step"].get(0.2); + + if (bayes) { + if (!config.hasCtCurve() || priors.empty() || + config.defaultMode == nullptr) { + LOG(RPiAwb, Warning) + << "Bayesian AWB mis-configured - switch to Grey method"; + bayes = false; + } + } + whitepointR = params["whitepoint_r"].get(0.0); + whitepointB = params["whitepoint_b"].get(0.0); + if (bayes == false) { + config.sensitivityR = config.sensitivityB = 1.0; /* nor do sensitivities make any sense */ + config.greyWorld = true; /* prevent the ct curve being used in manual mode */ + } + /* + * The biasProportion parameter adds a small proportion of the counted + * pixles to a region biased to the biasCT colour temperature. + * + * A typical value for biasProportion would be between 0.05 to 0.1. + */ + biasProportion = params["bias_proportion"].get(0.0); + biasCT = params["bias_ct"].get(kDefaultCT); + return 0; +} + +AwbBayes::AwbBayes(Controller *controller) + : Awb(controller) +{ +} + +AwbBayes::~AwbBayes() +{ +} + +char const *AwbBayes::name() const +{ + return NAME; +} + +int AwbBayes::read(const libcamera::YamlObject ¶ms) +{ + int ret; + + ret = config_.read(params); + if (ret) + return ret; + + ret = bayesConfig_.read(params, config_); + if (ret) + return ret; + + return 0; +} + +void AwbBayes::prepareStats() +{ + zones_.clear(); + /* + * LSC has already been applied to the stats in this pipeline, so stop + * any LSC compensation. We also ignore config_.fast in this version. + */ + const double biasCtR = bayesConfig_.bayes ? config_.ctR.eval(bayesConfig_.biasCT) : 0; + const double biasCtB = bayesConfig_.bayes ? config_.ctB.eval(bayesConfig_.biasCT) : 0; + generateStats(zones_, statistics_, bayesConfig_.minPixels, + bayesConfig_.minG, getGlobalMetadata(), + bayesConfig_.biasProportion, biasCtR, biasCtB); + /* + * apply sensitivities, so values appear to come from our "canonical" + * sensor. + */ + for (auto &zone : zones_) { + zone.R *= config_.sensitivityR; + zone.B *= config_.sensitivityB; + } +} + +ipa::Pwl AwbBayes::interpolatePrior() +{ + /* + * Interpolate the prior log likelihood function for our current lux + * value. + */ + if (lux_ <= bayesConfig_.priors.front().lux) + return bayesConfig_.priors.front().prior; + else if (lux_ >= bayesConfig_.priors.back().lux) + return bayesConfig_.priors.back().prior; + else { + int idx = 0; + /* find which two we lie between */ + while (bayesConfig_.priors[idx + 1].lux < lux_) + idx++; + double lux0 = bayesConfig_.priors[idx].lux, + lux1 = bayesConfig_.priors[idx + 1].lux; + return ipa::Pwl::combine(bayesConfig_.priors[idx].prior, + bayesConfig_.priors[idx + 1].prior, + [&](double /*x*/, double y0, double y1) { + return y0 + (y1 - y0) * + (lux_ - lux0) / (lux1 - lux0); + }); + } +} + +double AwbBayes::coarseSearch(ipa::Pwl const &prior) +{ + points_.clear(); /* assume doesn't deallocate memory */ + size_t bestPoint = 0; + double t = mode_->ctLo; + int spanR = 0, spanB = 0; + /* Step down the CT curve evaluating log likelihood. */ + while (true) { + double r = config_.ctR.eval(t, &spanR); + double b = config_.ctB.eval(t, &spanB); + double gainR = 1 / r, gainB = 1 / b; + double delta2Sum = computeDelta2Sum(gainR, gainB, bayesConfig_.whitepointR, bayesConfig_.whitepointB); + double priorLogLikelihood = prior.eval(prior.domain().clamp(t)); + double finalLogLikelihood = delta2Sum - priorLogLikelihood; + LOG(RPiAwb, Debug) + << "t: " << t << " gain R " << gainR << " gain B " + << gainB << " delta2_sum " << delta2Sum + << " prior " << priorLogLikelihood << " final " + << finalLogLikelihood; + points_.push_back(ipa::Pwl::Point({ t, finalLogLikelihood })); + if (points_.back().y() < points_[bestPoint].y()) + bestPoint = points_.size() - 1; + if (t == mode_->ctHi) + break; + /* for even steps along the r/b curve scale them by the current t */ + t = std::min(t + t / 10 * bayesConfig_.coarseStep, mode_->ctHi); + } + t = points_[bestPoint].x(); + LOG(RPiAwb, Debug) << "Coarse search found CT " << t; + /* + * We have the best point of the search, but refine it with a quadratic + * interpolation around its neighbours. + */ + if (points_.size() > 2) { + unsigned long bp = std::min(bestPoint, points_.size() - 2); + bestPoint = std::max(1UL, bp); + t = interpolateQuadatric(points_[bestPoint - 1], + points_[bestPoint], + points_[bestPoint + 1]); + LOG(RPiAwb, Debug) + << "After quadratic refinement, coarse search has CT " + << t; + } + return t; +} + +void AwbBayes::fineSearch(double &t, double &r, double &b, ipa::Pwl const &prior) +{ + int spanR = -1, spanB = -1; + config_.ctR.eval(t, &spanR); + config_.ctB.eval(t, &spanB); + double step = t / 10 * bayesConfig_.coarseStep * 0.1; + int nsteps = 5; + double rDiff = config_.ctR.eval(t + nsteps * step, &spanR) - + config_.ctR.eval(t - nsteps * step, &spanR); + double bDiff = config_.ctB.eval(t + nsteps * step, &spanB) - + config_.ctB.eval(t - nsteps * step, &spanB); + ipa::Pwl::Point transverse({ bDiff, -rDiff }); + if (transverse.length2() < 1e-6) + return; + /* + * unit vector orthogonal to the b vs. r function (pointing outwards + * with r and b increasing) + */ + transverse = transverse / transverse.length(); + double bestLogLikelihood = 0, bestT = 0, bestR = 0, bestB = 0; + double transverseRange = config_.transverseNeg + config_.transversePos; + const int maxNumDeltas = 12; + /* a transverse step approximately every 0.01 r/b units */ + int numDeltas = floor(transverseRange * 100 + 0.5) + 1; + numDeltas = numDeltas < 3 ? 3 : (numDeltas > maxNumDeltas ? maxNumDeltas : numDeltas); + /* + * Step down CT curve. March a bit further if the transverse range is + * large. + */ + nsteps += numDeltas; + for (int i = -nsteps; i <= nsteps; i++) { + double tTest = t + i * step; + double priorLogLikelihood = + prior.eval(prior.domain().clamp(tTest)); + double rCurve = config_.ctR.eval(tTest, &spanR); + double bCurve = config_.ctB.eval(tTest, &spanB); + /* x will be distance off the curve, y the log likelihood there */ + ipa::Pwl::Point points[maxNumDeltas]; + int bestPoint = 0; + /* Take some measurements transversely *off* the CT curve. */ + for (int j = 0; j < numDeltas; j++) { + points[j][0] = -config_.transverseNeg + + (transverseRange * j) / (numDeltas - 1); + ipa::Pwl::Point rbTest = ipa::Pwl::Point({ rCurve, bCurve }) + + transverse * points[j].x(); + double rTest = rbTest.x(), bTest = rbTest.y(); + double gainR = 1 / rTest, gainB = 1 / bTest; + double delta2Sum = computeDelta2Sum(gainR, gainB, bayesConfig_.whitepointR, bayesConfig_.whitepointB); + points[j][1] = delta2Sum - priorLogLikelihood; + LOG(RPiAwb, Debug) + << "At t " << tTest << " r " << rTest << " b " + << bTest << ": " << points[j].y(); + if (points[j].y() < points[bestPoint].y()) + bestPoint = j; + } + /* + * We have NUM_DELTAS points transversely across the CT curve, + * now let's do a quadratic interpolation for the best result. + */ + bestPoint = std::max(1, std::min(bestPoint, numDeltas - 2)); + ipa::Pwl::Point rbTest = ipa::Pwl::Point({ rCurve, bCurve }) + + transverse * interpolateQuadatric(points[bestPoint - 1], + points[bestPoint], + points[bestPoint + 1]); + double rTest = rbTest.x(), bTest = rbTest.y(); + double gainR = 1 / rTest, gainB = 1 / bTest; + double delta2Sum = computeDelta2Sum(gainR, gainB, bayesConfig_.whitepointR, bayesConfig_.whitepointB); + double finalLogLikelihood = delta2Sum - priorLogLikelihood; + LOG(RPiAwb, Debug) + << "Finally " + << tTest << " r " << rTest << " b " << bTest << ": " + << finalLogLikelihood + << (finalLogLikelihood < bestLogLikelihood ? " BEST" : ""); + if (bestT == 0 || finalLogLikelihood < bestLogLikelihood) + bestLogLikelihood = finalLogLikelihood, + bestT = tTest, bestR = rTest, bestB = bTest; + } + t = bestT, r = bestR, b = bestB; + LOG(RPiAwb, Debug) + << "Fine search found t " << t << " r " << r << " b " << b; +} + +void AwbBayes::awbBayes() +{ + /* + * May as well divide out G to save computeDelta2Sum from doing it over + * and over. + */ + for (auto &z : zones_) + z.R = z.R / (z.G + 1), z.B = z.B / (z.G + 1); + /* + * Get the current prior, and scale according to how many zones are + * valid... not entirely sure about this. + */ + ipa::Pwl prior = interpolatePrior(); + prior *= zones_.size() / (double)(statistics_->awbRegions.numRegions()); + prior.map([](double x, double y) { + LOG(RPiAwb, Debug) << "(" << x << "," << y << ")"; + }); + double t = coarseSearch(prior); + double r = config_.ctR.eval(t); + double b = config_.ctB.eval(t); + LOG(RPiAwb, Debug) + << "After coarse search: r " << r << " b " << b << " (gains r " + << 1 / r << " b " << 1 / b << ")"; + /* + * Not entirely sure how to handle the fine search yet. Mostly the + * estimated CT is already good enough, but the fine search allows us to + * wander transverely off the CT curve. Under some illuminants, where + * there may be more or less green light, this may prove beneficial, + * though I probably need more real datasets before deciding exactly how + * this should be controlled and tuned. + */ + fineSearch(t, r, b, prior); + LOG(RPiAwb, Debug) + << "After fine search: r " << r << " b " << b << " (gains r " + << 1 / r << " b " << 1 / b << ")"; + /* + * Write results out for the main thread to pick up. Remember to adjust + * the gains from the ones that the "canonical sensor" would require to + * the ones needed by *this* sensor. + */ + asyncResults_.temperatureK = t; + asyncResults_.gainR = 1.0 / r * config_.sensitivityR; + asyncResults_.gainG = 1.0; + asyncResults_.gainB = 1.0 / b * config_.sensitivityB; +} + +void AwbBayes::doAwb() +{ + prepareStats(); + LOG(RPiAwb, Debug) << "Valid zones: " << zones_.size(); + if (zones_.size() > bayesConfig_.minRegions) { + if (bayesConfig_.bayes) + awbBayes(); + else + awbGrey(); + LOG(RPiAwb, Debug) + << "CT found is " + << asyncResults_.temperatureK + << " with gains r " << asyncResults_.gainR + << " and b " << asyncResults_.gainB; + } + /* + * we're done with these; we may as well relinquish our hold on the + * pointer. + */ + statistics_.reset(); +} + +/* Register algorithm with the system. */ +static Algorithm *create(Controller *controller) +{ + return (Algorithm *)new AwbBayes(controller); +} +static RegisterAlgorithm reg(NAME, &create); 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Tue, 27 Jan 2026 04:06:08 -0800 (PST) Received: from davidp-pi5.pitowers.org ([2a00:1098:3142:1f:88ea:c658:5b20:5e46]) by smtp.gmail.com with ESMTPSA id ffacd0b85a97d-435b1c30293sm38221267f8f.19.2026.01.27.04.06.07 (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Tue, 27 Jan 2026 04:06:07 -0800 (PST) From: David Plowman To: libcamera-devel@lists.libcamera.org Cc: Peter Bailey , David Plowman , Naushir Patuck Subject: [PATCH v5 2/4] ipa: rpi: controller: awb: Add Neural Network AWB Date: Tue, 27 Jan 2026 11:59:57 +0000 Message-ID: <20260127120604.6560-3-david.plowman@raspberrypi.com> X-Mailer: git-send-email 2.47.3 In-Reply-To: <20260127120604.6560-1-david.plowman@raspberrypi.com> References: <20260127120604.6560-1-david.plowman@raspberrypi.com> MIME-Version: 1.0 X-BeenThere: libcamera-devel@lists.libcamera.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: libcamera-devel-bounces@lists.libcamera.org Sender: "libcamera-devel" From: Peter Bailey Add an AWB algorithm which uses neural networks. Signed-off-by: Peter Bailey Reviewed-by: David Plowman Reviewed-by: Naushir Patuck --- meson_options.txt | 5 + src/ipa/rpi/controller/meson.build | 9 + src/ipa/rpi/controller/rpi/awb_nn.cpp | 456 ++++++++++++++++++++++++++ 3 files changed, 470 insertions(+) create mode 100644 src/ipa/rpi/controller/rpi/awb_nn.cpp diff --git a/meson_options.txt b/meson_options.txt index c052e85a..07847294 100644 --- a/meson_options.txt +++ b/meson_options.txt @@ -76,6 +76,11 @@ option('qcam', value : 'auto', description : 'Compile the qcam test application') +option('rpi-awb-nn', + type : 'feature', + value : 'auto', + description : 'Enable the Raspberry Pi Neural Network AWB algorithm') + option('test', type : 'boolean', value : false, diff --git a/src/ipa/rpi/controller/meson.build b/src/ipa/rpi/controller/meson.build index c8637906..03ee7c20 100644 --- a/src/ipa/rpi/controller/meson.build +++ b/src/ipa/rpi/controller/meson.build @@ -32,6 +32,15 @@ rpi_ipa_controller_deps = [ libcamera_private, ] +tflite_dep = dependency('tensorflow-lite', required : get_option('rpi-awb-nn')) + +if tflite_dep.found() + rpi_ipa_controller_sources += files([ + 'rpi/awb_nn.cpp', + ]) + rpi_ipa_controller_deps += tflite_dep +endif + rpi_ipa_controller_lib = static_library('rpi_ipa_controller', rpi_ipa_controller_sources, include_directories : libipa_includes, dependencies : rpi_ipa_controller_deps) diff --git a/src/ipa/rpi/controller/rpi/awb_nn.cpp b/src/ipa/rpi/controller/rpi/awb_nn.cpp new file mode 100644 index 00000000..395add85 --- /dev/null +++ b/src/ipa/rpi/controller/rpi/awb_nn.cpp @@ -0,0 +1,456 @@ +/* SPDX-License-Identifier: BSD-2-Clause */ +/* + * Copyright (C) 2025, Raspberry Pi Ltd + * + * AWB control algorithm using neural network + * + * The AWB Neural Network algorithm can be run entirely with the code here + * and the suppllied TFLite models. Those interested in the full model + * definitions, or who may want to re-train the models should visit + * + * https://github.com/raspberrypi/awb_nn + * + * where you will find full source code for the models, the full datasets + * used for training our supplied models, and full instructions for capturing + * your own images and re-training the models for your own use cases. + */ + +#include +#include +#include + +#include +#include + +#include +#include +#include + +#include "../awb_algorithm.h" +#include "../awb_status.h" +#include "../lux_status.h" +#include "libipa/pwl.h" + +#include "alsc_status.h" +#include "awb.h" + +using namespace libcamera; + +LOG_DECLARE_CATEGORY(RPiAwb) + +constexpr double kDefaultCT = 4500.0; + +/* + * The neural networks are trained to work on images rendered at a canonical + * colour temperature. That value is 5000K, which must be reproduced here. + */ +constexpr double kNetworkCanonicalCT = 5000.0; + +#define NAME "rpi.nn.awb" + +namespace RPiController { + +struct AwbNNConfig { + AwbNNConfig() {} + int read(const libcamera::YamlObject ¶ms, AwbConfig &config); + + /* An empty model will check default locations for model.tflite */ + std::string model; + float minTemp; + float maxTemp; + + bool enableNn; + + /* CCM matrix for canonical network CT */ + double ccm[9]; +}; + +class AwbNN : public Awb +{ +public: + AwbNN(Controller *controller = NULL); + ~AwbNN(); + char const *name() const override; + void initialise() override; + int read(const libcamera::YamlObject ¶ms) override; + +protected: + void doAwb() override; + void prepareStats() override; + +private: + bool isAutoEnabled() const; + AwbNNConfig nnConfig_; + void transverseSearch(double t, double &r, double &b); + RGB processZone(RGB zone, float red_gain, float blue_gain); + void awbNN(); + void loadModel(); + + libcamera::Size zoneSize_; + std::unique_ptr model_; + std::unique_ptr interpreter_; +}; + +int AwbNNConfig::read(const libcamera::YamlObject ¶ms, AwbConfig &config) +{ + model = params["model"].get(""); + minTemp = params["min_temp"].get(2800.0); + maxTemp = params["max_temp"].get(7600.0); + + for (int i = 0; i < 9; i++) + ccm[i] = params["ccm"][i].get(0.0); + + enableNn = params["enable_nn"].get(1); + + if (enableNn) { + if (!config.hasCtCurve()) { + LOG(RPiAwb, Error) << "CT curve not specified"; + enableNn = false; + } + + if (!model.empty() && model.find(".tflite") == std::string::npos) { + LOG(RPiAwb, Error) << "Model must be a .tflite file"; + enableNn = false; + } + + bool validCcm = true; + for (int i = 0; i < 9; i++) + if (ccm[i] == 0.0) + validCcm = false; + + if (!validCcm) { + LOG(RPiAwb, Error) << "CCM not specified or invalid"; + enableNn = false; + } + + if (!enableNn) { + LOG(RPiAwb, Warning) << "Neural Network AWB mis-configured - switch to Grey method"; + } + } + + if (!enableNn) { + config.sensitivityR = config.sensitivityB = 1.0; + config.greyWorld = true; + } + + return 0; +} + +AwbNN::AwbNN(Controller *controller) + : Awb(controller) +{ + zoneSize_ = getHardwareConfig().awbRegions; +} + +AwbNN::~AwbNN() +{ +} + +char const *AwbNN::name() const +{ + return NAME; +} + +int AwbNN::read(const libcamera::YamlObject ¶ms) +{ + int ret; + + ret = config_.read(params); + if (ret) + return ret; + + ret = nnConfig_.read(params, config_); + if (ret) + return ret; + + return 0; +} + +static bool checkTensorShape(TfLiteTensor *tensor, const int *expectedDims, const int expectedDimsSize) +{ + if (tensor->dims->size != expectedDimsSize) + return false; + + for (int i = 0; i < tensor->dims->size; i++) { + if (tensor->dims->data[i] != expectedDims[i]) { + return false; + } + } + return true; +} + +static std::string buildDimString(const int *dims, const int dimsSize) +{ + std::string s = "["; + for (int i = 0; i < dimsSize; i++) { + s += std::to_string(dims[i]); + if (i < dimsSize - 1) + s += ","; + else + s += "]"; + } + return s; +} + +void AwbNN::loadModel() +{ + std::string modelPath; + if (getTarget() == "bcm2835") { + modelPath = "/ipa/rpi/vc4/awb_model.tflite"; + } else { + modelPath = "/ipa/rpi/pisp/awb_model.tflite"; + } + + if (nnConfig_.model.empty()) { + std::string root = utils::libcameraSourcePath(); + if (!root.empty()) { + modelPath = root + modelPath; + } else { + modelPath = LIBCAMERA_DATA_DIR + modelPath; + } + + if (!File::exists(modelPath)) { + LOG(RPiAwb, Error) << "No model file found in standard locations"; + nnConfig_.enableNn = false; + return; + } + } else { + modelPath = nnConfig_.model; + } + + LOG(RPiAwb, Debug) << "Attempting to load model from: " << modelPath; + + model_ = tflite::FlatBufferModel::BuildFromFile(modelPath.c_str()); + + if (!model_) { + LOG(RPiAwb, Error) << "Failed to load model from " << modelPath; + nnConfig_.enableNn = false; + return; + } + + tflite::MutableOpResolver resolver; + tflite::ops::builtin::BuiltinOpResolver builtin_resolver; + resolver.AddAll(builtin_resolver); + tflite::InterpreterBuilder(*model_, resolver)(&interpreter_); + if (!interpreter_) { + LOG(RPiAwb, Error) << "Failed to build interpreter for model " << nnConfig_.model; + nnConfig_.enableNn = false; + return; + } + + interpreter_->AllocateTensors(); + TfLiteTensor *inputTensor = interpreter_->input_tensor(0); + TfLiteTensor *inputLuxTensor = interpreter_->input_tensor(1); + TfLiteTensor *outputTensor = interpreter_->output_tensor(0); + if (!inputTensor || !inputLuxTensor || !outputTensor) { + LOG(RPiAwb, Error) << "Model missing input or output tensor"; + nnConfig_.enableNn = false; + return; + } + + const int expectedInputDims[] = { 1, (int)zoneSize_.height, (int)zoneSize_.width, 3 }; + const int expectedInputLuxDims[] = { 1 }; + const int expectedOutputDims[] = { 1 }; + + if (!checkTensorShape(inputTensor, expectedInputDims, 4)) { + LOG(RPiAwb, Error) << "Model input tensor dimension mismatch. Expected: " << buildDimString(expectedInputDims, 4) + << ", Got: " << buildDimString(inputTensor->dims->data, inputTensor->dims->size); + nnConfig_.enableNn = false; + return; + } + + if (!checkTensorShape(inputLuxTensor, expectedInputLuxDims, 1)) { + LOG(RPiAwb, Error) << "Model input lux tensor dimension mismatch. Expected: " << buildDimString(expectedInputLuxDims, 1) + << ", Got: " << buildDimString(inputLuxTensor->dims->data, inputLuxTensor->dims->size); + nnConfig_.enableNn = false; + return; + } + + if (!checkTensorShape(outputTensor, expectedOutputDims, 1)) { + LOG(RPiAwb, Error) << "Model output tensor dimension mismatch. Expected: " << buildDimString(expectedOutputDims, 1) + << ", Got: " << buildDimString(outputTensor->dims->data, outputTensor->dims->size); + nnConfig_.enableNn = false; + return; + } + + if (inputTensor->type != kTfLiteFloat32 || inputLuxTensor->type != kTfLiteFloat32 || outputTensor->type != kTfLiteFloat32) { + LOG(RPiAwb, Error) << "Model input and output tensors must be float32"; + nnConfig_.enableNn = false; + return; + } + + LOG(RPiAwb, Info) << "Model loaded successfully from " << modelPath; + LOG(RPiAwb, Debug) << "Model validation successful - Input Image: " + << buildDimString(expectedInputDims, 4) + << ", Input Lux: " << buildDimString(expectedInputLuxDims, 1) + << ", Output: " << buildDimString(expectedOutputDims, 1) << " floats"; +} + +void AwbNN::initialise() +{ + Awb::initialise(); + + if (nnConfig_.enableNn) { + loadModel(); + if (!nnConfig_.enableNn) { + LOG(RPiAwb, Warning) << "Neural Network AWB failed to load - switch to Grey method"; + config_.greyWorld = true; + config_.sensitivityR = config_.sensitivityB = 1.0; + } + } +} + +void AwbNN::prepareStats() +{ + zones_.clear(); + /* + * LSC has already been applied to the stats in this pipeline, so stop + * any LSC compensation. We also ignore config_.fast in this version. + */ + generateStats(zones_, statistics_, 0.0, 0.0, getGlobalMetadata(), 0.0, 0.0, 0.0); + /* + * apply sensitivities, so values appear to come from our "canonical" + * sensor. + */ + for (auto &zone : zones_) { + zone.R *= config_.sensitivityR; + zone.B *= config_.sensitivityB; + } +} + +void AwbNN::transverseSearch(double t, double &r, double &b) +{ + int spanR = -1, spanB = -1; + config_.ctR.eval(t, &spanR); + config_.ctB.eval(t, &spanB); + + const int diff = 10; + double rDiff = config_.ctR.eval(t + diff, &spanR) - + config_.ctR.eval(t - diff, &spanR); + double bDiff = config_.ctB.eval(t + diff, &spanB) - + config_.ctB.eval(t - diff, &spanB); + + ipa::Pwl::Point transverse({ bDiff, -rDiff }); + if (transverse.length2() < 1e-6) + return; + + transverse = transverse / transverse.length(); + double transverseRange = config_.transverseNeg + config_.transversePos; + const int maxNumDeltas = 12; + int numDeltas = floor(transverseRange * 100 + 0.5) + 1; + numDeltas = numDeltas < 3 ? 3 : (numDeltas > maxNumDeltas ? maxNumDeltas : numDeltas); + + ipa::Pwl::Point points[maxNumDeltas]; + int bestPoint = 0; + + for (int i = 0; i < numDeltas; i++) { + points[i][0] = -config_.transverseNeg + + (transverseRange * i) / (numDeltas - 1); + ipa::Pwl::Point rbTest = ipa::Pwl::Point({ r, b }) + + transverse * points[i].x(); + double rTest = rbTest.x(), bTest = rbTest.y(); + double gainR = 1 / rTest, gainB = 1 / bTest; + double delta2Sum = computeDelta2Sum(gainR, gainB, 0.0, 0.0); + points[i][1] = delta2Sum; + if (points[i].y() < points[bestPoint].y()) + bestPoint = i; + } + + bestPoint = std::clamp(bestPoint, 1, numDeltas - 2); + ipa::Pwl::Point rbBest = ipa::Pwl::Point({ r, b }) + + transverse * interpolateQuadatric(points[bestPoint - 1], + points[bestPoint], + points[bestPoint + 1]); + double rBest = rbBest.x(), bBest = rbBest.y(); + + r = rBest, b = bBest; +} + +AwbNN::RGB AwbNN::processZone(AwbNN::RGB zone, float redGain, float blueGain) +{ + /* + * Renders the pixel at canonical network colour temperature + */ + RGB zoneGains = zone; + + zoneGains.R *= redGain; + zoneGains.G *= 1.0; + zoneGains.B *= blueGain; + + RGB zoneCcm; + + zoneCcm.R = nnConfig_.ccm[0] * zoneGains.R + nnConfig_.ccm[1] * zoneGains.G + nnConfig_.ccm[2] * zoneGains.B; + zoneCcm.G = nnConfig_.ccm[3] * zoneGains.R + nnConfig_.ccm[4] * zoneGains.G + nnConfig_.ccm[5] * zoneGains.B; + zoneCcm.B = nnConfig_.ccm[6] * zoneGains.R + nnConfig_.ccm[7] * zoneGains.G + nnConfig_.ccm[8] * zoneGains.B; + + return zoneCcm; +} + +void AwbNN::awbNN() +{ + float *inputData = interpreter_->typed_input_tensor(0); + float *inputLux = interpreter_->typed_input_tensor(1); + + float redGain = 1.0 / config_.ctR.eval(kNetworkCanonicalCT); + float blueGain = 1.0 / config_.ctB.eval(kNetworkCanonicalCT); + + for (uint i = 0; i < zoneSize_.height; i++) { + for (uint j = 0; j < zoneSize_.width; j++) { + uint zoneIdx = i * zoneSize_.width + j; + + RGB processedZone = processZone(zones_[zoneIdx] * (1.0 / 65535), redGain, blueGain); + uint baseIdx = zoneIdx * 3; + + inputData[baseIdx + 0] = static_cast(processedZone.R); + inputData[baseIdx + 1] = static_cast(processedZone.G); + inputData[baseIdx + 2] = static_cast(processedZone.B); + } + } + + inputLux[0] = static_cast(lux_); + + TfLiteStatus status = interpreter_->Invoke(); + if (status != kTfLiteOk) { + LOG(RPiAwb, Error) << "Model inference failed with status: " << status; + return; + } + + float *outputData = interpreter_->typed_output_tensor(0); + + double t = outputData[0]; + + LOG(RPiAwb, Debug) << "Model output temperature: " << t; + + t = std::clamp(t, mode_->ctLo, mode_->ctHi); + + double r = config_.ctR.eval(t); + double b = config_.ctB.eval(t); + + transverseSearch(t, r, b); + + LOG(RPiAwb, Debug) << "After transverse search: Temperature: " << t << " Red gain: " << 1.0 / r << " Blue gain: " << 1.0 / b; + + asyncResults_.temperatureK = t; + asyncResults_.gainR = 1.0 / r * config_.sensitivityR; + asyncResults_.gainG = 1.0; + asyncResults_.gainB = 1.0 / b * config_.sensitivityB; +} + +void AwbNN::doAwb() +{ + prepareStats(); + if (zones_.size() == (zoneSize_.width * zoneSize_.height) && nnConfig_.enableNn) + awbNN(); + else + awbGrey(); + statistics_.reset(); +} + +/* Register algorithm with the system. */ +static Algorithm *create(Controller *controller) +{ + return (Algorithm *)new AwbNN(controller); +} +static RegisterAlgorithm reg(NAME, &create); + +} /* namespace RPiController */ From patchwork Tue Jan 27 11:59:58 2026 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: David Plowman X-Patchwork-Id: 25973 Return-Path: X-Original-To: parsemail@patchwork.libcamera.org Delivered-To: parsemail@patchwork.libcamera.org Received: from lancelot.ideasonboard.com (lancelot.ideasonboard.com [92.243.16.209]) by patchwork.libcamera.org (Postfix) with ESMTPS id 326B9C32E7 for ; Tue, 27 Jan 2026 12:06:15 +0000 (UTC) Received: from lancelot.ideasonboard.com (localhost [IPv6:::1]) by lancelot.ideasonboard.com (Postfix) with ESMTP id 9D83F61FD9; Tue, 27 Jan 2026 13:06:13 +0100 (CET) Authentication-Results: lancelot.ideasonboard.com; dkim=pass (2048-bit key; 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Tue, 27 Jan 2026 04:06:09 -0800 (PST) Received: from davidp-pi5.pitowers.org ([2a00:1098:3142:1f:88ea:c658:5b20:5e46]) by smtp.gmail.com with ESMTPSA id ffacd0b85a97d-435b1c30293sm38221267f8f.19.2026.01.27.04.06.08 (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Tue, 27 Jan 2026 04:06:08 -0800 (PST) From: David Plowman To: libcamera-devel@lists.libcamera.org Cc: Peter Bailey , David Plowman , Naushir Patuck Subject: [PATCH v5 3/4] ipa: rpi: controller: Ignore algorithms that are not enabled Date: Tue, 27 Jan 2026 11:59:58 +0000 Message-ID: <20260127120604.6560-4-david.plowman@raspberrypi.com> X-Mailer: git-send-email 2.47.3 In-Reply-To: <20260127120604.6560-1-david.plowman@raspberrypi.com> References: <20260127120604.6560-1-david.plowman@raspberrypi.com> MIME-Version: 1.0 X-BeenThere: libcamera-devel@lists.libcamera.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: libcamera-devel-bounces@lists.libcamera.org Sender: "libcamera-devel" From: Peter Bailey Algorithms may now contain an "enabled" field which can be set to false to disable it and prevent it from being loaded. If not present, algorithms are treated as enabled by default for backwards compatability. We additionally prevent duplicate versions of the same algorithm type (such as AWB) from loading, and flag an error. This will prevent undefined behaviour if (for example) two distinct AWB algorithms are trying to run simultaneously. Signed-off-by: Peter Bailey Reviewed-by: David Plowman Reviewed-by: Naushir Patuck --- src/ipa/rpi/controller/controller.cpp | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/src/ipa/rpi/controller/controller.cpp b/src/ipa/rpi/controller/controller.cpp index df45dcd3..88de6f36 100644 --- a/src/ipa/rpi/controller/controller.cpp +++ b/src/ipa/rpi/controller/controller.cpp @@ -145,6 +145,14 @@ int Controller::read(char const *filename) int Controller::createAlgorithm(const std::string &name, const YamlObject ¶ms) { + /* Any algorithm may be disabled by setting "enabled" to false. */ + bool enabled = params["enabled"].get(true); + LOG(RPiController, Debug) + << "Algorithm " << name << ": " + << (enabled ? "enabled" : "disabled"); + if (!enabled) + return 0; + auto it = getAlgorithms().find(name); if (it == getAlgorithms().end()) { LOG(RPiController, Warning) @@ -152,6 +160,16 @@ int Controller::createAlgorithm(const std::string &name, const YamlObject ¶m return 0; } + /* Do not allow duplicate versions of algorithms (e.g. AWB) to run. */ + size_t pos = name.find_last_of('.'); + std::string const &algoType = + pos == std::string::npos ? name : name.substr(pos + 1); + if (getAlgorithm(algoType)) { + LOG(RPiController, Error) + << "Algorithm type '" << algoType << "' already exists"; + return -1; + } + Algorithm *algo = (*it->second)(this); int ret = algo->read(params); if (ret) From patchwork Tue Jan 27 11:59:59 2026 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: David Plowman X-Patchwork-Id: 25974 Return-Path: X-Original-To: parsemail@patchwork.libcamera.org Delivered-To: parsemail@patchwork.libcamera.org Received: from lancelot.ideasonboard.com (lancelot.ideasonboard.com [92.243.16.209]) by patchwork.libcamera.org (Postfix) with ESMTPS id 26D44C32EA for ; 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Tue, 27 Jan 2026 04:06:09 -0800 (PST) Received: from davidp-pi5.pitowers.org ([2a00:1098:3142:1f:88ea:c658:5b20:5e46]) by smtp.gmail.com with ESMTPSA id ffacd0b85a97d-435b1c30293sm38221267f8f.19.2026.01.27.04.06.09 (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Tue, 27 Jan 2026 04:06:09 -0800 (PST) From: David Plowman To: libcamera-devel@lists.libcamera.org Cc: Peter Bailey , David Plowman , Naushir Patuck Subject: [PATCH v5 4/4] ipa: rpi: pisp: vc4: Update tuning files for new AWB Date: Tue, 27 Jan 2026 11:59:59 +0000 Message-ID: <20260127120604.6560-5-david.plowman@raspberrypi.com> X-Mailer: git-send-email 2.47.3 In-Reply-To: <20260127120604.6560-1-david.plowman@raspberrypi.com> References: <20260127120604.6560-1-david.plowman@raspberrypi.com> MIME-Version: 1.0 X-BeenThere: libcamera-devel@lists.libcamera.org X-Mailman-Version: 2.1.29 Precedence: list List-Id: List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , Errors-To: libcamera-devel-bounces@lists.libcamera.org Sender: "libcamera-devel" From: Peter Bailey Update the tuning files to include the new AWB algorithm. It is enabled by setting "enabled" to true for the AWB algorithm that you want, and the same field to false for the one you don't want. Note that you may enable only one of the two algorithms! The AWB models themselves are not included with libcamera. They will be supplied from the Raspberry Pi software repositories. Signed-off-by: Peter Bailey Reviewed-by: David Plowman Reviewed-by: Naushir Patuck --- src/ipa/rpi/pisp/data/imx219.json | 65 ++++++++++++++++ src/ipa/rpi/pisp/data/imx296.json | 64 ++++++++++++++++ src/ipa/rpi/pisp/data/imx296_16mm.json | 64 ++++++++++++++++ src/ipa/rpi/pisp/data/imx296_6mm.json | 64 ++++++++++++++++ src/ipa/rpi/pisp/data/imx477.json | 65 ++++++++++++++++ src/ipa/rpi/pisp/data/imx477_16mm.json | 65 ++++++++++++++++ src/ipa/rpi/pisp/data/imx477_6mm.json | 65 ++++++++++++++++ src/ipa/rpi/pisp/data/imx477_scientific.json | 79 ++++++++++++++++++++ src/ipa/rpi/pisp/data/imx708.json | 64 ++++++++++++++++ src/ipa/rpi/pisp/data/imx708_wide.json | 64 ++++++++++++++++ src/ipa/rpi/pisp/data/ov5647.json | 65 ++++++++++++++++ src/ipa/rpi/vc4/data/imx219.json | 66 ++++++++++++++++ src/ipa/rpi/vc4/data/imx296.json | 66 ++++++++++++++++ src/ipa/rpi/vc4/data/imx477.json | 71 ++++++++++++++++++ src/ipa/rpi/vc4/data/imx708.json | 74 ++++++++++++++++++ src/ipa/rpi/vc4/data/imx708_wide.json | 64 ++++++++++++++++ src/ipa/rpi/vc4/data/ov5647.json | 66 ++++++++++++++++ 17 files changed, 1131 insertions(+) diff --git a/src/ipa/rpi/pisp/data/imx219.json b/src/ipa/rpi/pisp/data/imx219.json index 727f8e2d..7aea190a 100644 --- a/src/ipa/rpi/pisp/data/imx219.json +++ b/src/ipa/rpi/pisp/data/imx219.json @@ -109,6 +109,7 @@ { "rpi.awb": { + "enabled": true, "priors": [ { "lux": 0, @@ -195,6 +196,70 @@ "transverse_neg": 0.034 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 7700 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8000 + } + }, + "ct_curve": + [ + 2860.0, 0.9514, 0.4156, + 2960.0, 0.9289, 0.4372, + 3603.0, 0.8305, 0.5251, + 4650.0, 0.6756, 0.6433, + 5858.0, 0.6193, 0.6807, + 7580.0, 0.5019, 0.7495 + ], + "sensitivity_r": 1.0, + "sensitivity_b": 1.0, + "transverse_pos": 0.03392, + "transverse_neg": 0.034, + "ccm": + [ + 2.2229345364238413, -0.7596721523178808, -0.46326238410596027, + -0.6834893874172185, 2.7118816887417223, -1.02839940397351, + -0.2613746357615894, -0.668015927152318, 1.9293905629139072 + ], + "enable_nn": 1 + } + }, { "rpi.agc": { diff --git a/src/ipa/rpi/pisp/data/imx296.json b/src/ipa/rpi/pisp/data/imx296.json index d833c837..f03f875e 100644 --- a/src/ipa/rpi/pisp/data/imx296.json +++ b/src/ipa/rpi/pisp/data/imx296.json @@ -109,6 +109,7 @@ { "rpi.awb": { + "enabled": true, "priors": [ { "lux": 0, @@ -194,6 +195,69 @@ "transverse_neg": 0.02154 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 7700 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8000 + } + }, + "ct_curve": + [ + 2875.0, 0.4699, 0.3209, + 3610.0, 0.4089, 0.4265, + 4640.0, 0.3281, 0.5417, + 5912.0, 0.2992, 0.5771, + 7630.0, 0.2285, 0.6524 + ], + "sensitivity_r": 1.0, + "sensitivity_b": 1.0, + "transverse_pos": 0.01783, + "transverse_neg": 0.02154, + "ccm": + [ + 2.1073753846153847, -0.8054946153846154, -0.30188076923076923, + -0.43306999999999995, 2.162828076923077, -0.7297680769230768, + -0.126655, -0.5027626923076922, 1.6294176923076922 + ], + "enable_nn": 1 + } + }, { "rpi.agc": { diff --git a/src/ipa/rpi/pisp/data/imx296_16mm.json b/src/ipa/rpi/pisp/data/imx296_16mm.json index 62344533..91f0a379 100644 --- a/src/ipa/rpi/pisp/data/imx296_16mm.json +++ b/src/ipa/rpi/pisp/data/imx296_16mm.json @@ -109,6 +109,7 @@ { "rpi.awb": { + "enabled": true, "priors": [ { "lux": 0, @@ -194,6 +195,69 @@ "transverse_neg": 0.02154 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 7700 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8000 + } + }, + "ct_curve": + [ + 2875.0, 0.4699, 0.3209, + 3610.0, 0.4089, 0.4265, + 4640.0, 0.3281, 0.5417, + 5912.0, 0.2992, 0.5771, + 7630.0, 0.2285, 0.6524 + ], + "sensitivity_r": 1.0, + "sensitivity_b": 1.0, + "transverse_pos": 0.01783, + "transverse_neg": 0.02154, + "ccm": + [ + 2.1073753846153847, -0.8054946153846154, -0.30188076923076923, + -0.43306999999999995, 2.162828076923077, -0.7297680769230768, + -0.126655, -0.5027626923076922, 1.6294176923076922 + ], + "enable_nn": 1 + } + }, { "rpi.agc": { diff --git a/src/ipa/rpi/pisp/data/imx296_6mm.json b/src/ipa/rpi/pisp/data/imx296_6mm.json index c71a0327..30c4a2ad 100644 --- a/src/ipa/rpi/pisp/data/imx296_6mm.json +++ b/src/ipa/rpi/pisp/data/imx296_6mm.json @@ -109,6 +109,7 @@ { "rpi.awb": { + "enabled": true, "priors": [ { "lux": 0, @@ -194,6 +195,69 @@ "transverse_neg": 0.02154 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 7700 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8000 + } + }, + "ct_curve": + [ + 2875.0, 0.4699, 0.3209, + 3610.0, 0.4089, 0.4265, + 4640.0, 0.3281, 0.5417, + 5912.0, 0.2992, 0.5771, + 7630.0, 0.2285, 0.6524 + ], + "sensitivity_r": 1.0, + "sensitivity_b": 1.0, + "transverse_pos": 0.01783, + "transverse_neg": 0.02154, + "ccm": + [ + 2.1073753846153847, -0.8054946153846154, -0.30188076923076923, + 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a/src/ipa/rpi/vc4/data/imx219.json b/src/ipa/rpi/vc4/data/imx219.json index 8098889e..d40c38b1 100644 --- a/src/ipa/rpi/vc4/data/imx219.json +++ b/src/ipa/rpi/vc4/data/imx219.json @@ -41,6 +41,7 @@ { "rpi.awb": { + "enabled": true, "priors": [ { "lux": 0, @@ -128,6 +129,71 @@ "transverse_neg": 0.04881 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 8000 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8600 + } + }, + "ct_curve": + [ + 2498.0, 0.9309, 0.3599, + 2911.0, 0.8682, 0.4283, + 2919.0, 0.8358, 0.4621, + 3627.0, 0.7646, 0.5327, + 4600.0, 0.6079, 0.6721, + 5716.0, 0.5712, 0.7017, + 8575.0, 0.4331, 0.8037 + ], + "sensitivity_r": 1.05, + "sensitivity_b": 1.05, + "transverse_pos": 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a/src/ipa/rpi/vc4/data/imx708.json b/src/ipa/rpi/vc4/data/imx708.json index ec17243d..3ae24087 100644 --- a/src/ipa/rpi/vc4/data/imx708.json +++ b/src/ipa/rpi/vc4/data/imx708.json @@ -41,6 +41,7 @@ { "rpi.awb": { + "enabled": true, "priors": [ { "lux": 0, @@ -136,6 +137,79 @@ "transverse_neg": 0.03061 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 8000 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8600 + } + }, + "ct_curve": + [ + 2498.0, 0.8733, 0.2606, + 2821.0, 0.7707, 0.3245, + 2925.0, 0.7338, 0.3499, + 2926.0, 0.7193, 0.3603, + 2951.0, 0.7144, 0.3639, + 2954.0, 0.7111, 0.3663, + 3578.0, 0.6038, 0.4516, + 3717.0, 0.5861, 0.4669, + 3784.0, 0.5786, 0.4737, + 4485.0, 0.5113, 0.5368, + 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true, "priors": [ { "lux": 0, @@ -128,6 +129,71 @@ "transverse_neg": 0.04313 } }, + { + "rpi.nn.awb": + { + "enabled": false, + "modes": + { + "auto": + { + "lo": 2500, + "hi": 8000 + }, + "incandescent": + { + "lo": 2500, + "hi": 3000 + }, + "tungsten": + { + "lo": 3000, + "hi": 3500 + }, + "fluorescent": + { + "lo": 4000, + "hi": 4700 + }, + "indoor": + { + "lo": 3000, + "hi": 5000 + }, + "daylight": + { + "lo": 5500, + "hi": 6500 + }, + "cloudy": + { + "lo": 7000, + "hi": 8600 + } + }, + "ct_curve": + [ + 2500.0, 1.0289, 0.4503, + 2803.0, 0.9428, 0.5108, + 2914.0, 0.9406, 0.5127, + 3605.0, 0.8261, 0.6249, + 4540.0, 0.7331, 0.7533, + 5699.0, 0.6715, 0.8627, + 8625.0, 0.6081, 1.0012 + ], + "sensitivity_r": 1.05, + "sensitivity_b": 1.05, + "transverse_pos": 0.0321, + "transverse_neg": 0.04313, + "ccm": + [ + 2.041588151260504, -0.5494553781512606, -0.49214025210084034, + -0.5116488235294118, 1.9901442857142857, -0.47849546218487393, + -0.10519773109243696, -0.641700168067227, 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