From 177df04d2b7f357ebe41f1a9809ab68b6f948082 Mon Sep 17 00:00:00 2001 From: Naushir Patuck Date: Wed, 27 Jul 2022 09:55:17 +0100 Subject: ipa: raspberrypi: Code refactoring to match style guidelines Refactor all the source files in src/ipa/raspberrypi/ to match the recommended formatting guidelines for the libcamera project. The vast majority of changes in this commit comprise of switching from snake_case to CamelCase, and starting class member functions with a lower case character. Signed-off-by: Naushir Patuck Reviewed-by: Laurent Pinchart Signed-off-by: Laurent Pinchart --- src/ipa/raspberrypi/controller/rpi/awb.cpp | 566 ++++++++++++++--------------- 1 file changed, 276 insertions(+), 290 deletions(-) (limited to 'src/ipa/raspberrypi/controller/rpi/awb.cpp') diff --git a/src/ipa/raspberrypi/controller/rpi/awb.cpp b/src/ipa/raspberrypi/controller/rpi/awb.cpp index d4c93447..07791e8b 100644 --- a/src/ipa/raspberrypi/controller/rpi/awb.cpp +++ b/src/ipa/raspberrypi/controller/rpi/awb.cpp @@ -24,33 +24,33 @@ LOG_DEFINE_CATEGORY(RPiAwb) // todo - the locking in this algorithm needs some tidying up as has been done // elsewhere (ALSC and AGC). -void AwbMode::Read(boost::property_tree::ptree const ¶ms) +void AwbMode::read(boost::property_tree::ptree const ¶ms) { - ct_lo = params.get("lo"); - ct_hi = params.get("hi"); + ctLo = params.get("lo"); + ctHi = params.get("hi"); } -void AwbPrior::Read(boost::property_tree::ptree const ¶ms) +void AwbPrior::read(boost::property_tree::ptree const ¶ms) { lux = params.get("lux"); - prior.Read(params.get_child("prior")); + prior.read(params.get_child("prior")); } -static void read_ct_curve(Pwl &ct_r, Pwl &ct_b, - boost::property_tree::ptree const ¶ms) +static void readCtCurve(Pwl &ctR, Pwl &ctB, + boost::property_tree::ptree const ¶ms) { int num = 0; for (auto it = params.begin(); it != params.end(); it++) { double ct = it->second.get_value(); - assert(it == params.begin() || ct != ct_r.Domain().end); + assert(it == params.begin() || ct != ctR.domain().end); if (++it == params.end()) throw std::runtime_error( "AwbConfig: incomplete CT curve entry"); - ct_r.Append(ct, it->second.get_value()); + ctR.append(ct, it->second.get_value()); if (++it == params.end()) throw std::runtime_error( "AwbConfig: incomplete CT curve entry"); - ct_b.Append(ct, it->second.get_value()); + ctB.append(ct, it->second.get_value()); num++; } if (num < 2) @@ -58,22 +58,21 @@ static void read_ct_curve(Pwl &ct_r, Pwl &ct_b, "AwbConfig: insufficient points in CT curve"); } -void AwbConfig::Read(boost::property_tree::ptree const ¶ms) +void AwbConfig::read(boost::property_tree::ptree const ¶ms) { bayes = params.get("bayes", 1); - frame_period = params.get("frame_period", 10); - startup_frames = params.get("startup_frames", 10); - convergence_frames = params.get("convergence_frames", 3); + framePeriod = params.get("framePeriod", 10); + startupFrames = params.get("startupFrames", 10); + convergenceFrames = params.get("convergence_frames", 3); speed = params.get("speed", 0.05); if (params.get_child_optional("ct_curve")) - read_ct_curve(ct_r, ct_b, params.get_child("ct_curve")); + readCtCurve(ctR, ctB, params.get_child("ct_curve")); if (params.get_child_optional("priors")) { for (auto &p : params.get_child("priors")) { AwbPrior prior; - prior.Read(p.second); + prior.read(p.second); if (!priors.empty() && prior.lux <= priors.back().lux) - throw std::runtime_error( - "AwbConfig: Prior must be ordered in increasing lux value"); + throw std::runtime_error("AwbConfig: Prior must be ordered in increasing lux value"); priors.push_back(prior); } if (priors.empty()) @@ -82,177 +81,170 @@ void AwbConfig::Read(boost::property_tree::ptree const ¶ms) } if (params.get_child_optional("modes")) { for (auto &p : params.get_child("modes")) { - modes[p.first].Read(p.second); - if (default_mode == nullptr) - default_mode = &modes[p.first]; + modes[p.first].read(p.second); + if (defaultMode == nullptr) + defaultMode = &modes[p.first]; } - if (default_mode == nullptr) - throw std::runtime_error( - "AwbConfig: no AWB modes configured"); + if (defaultMode == nullptr) + throw std::runtime_error("AwbConfig: no AWB modes configured"); } - min_pixels = params.get("min_pixels", 16.0); - min_G = params.get("min_G", 32); - min_regions = params.get("min_regions", 10); - delta_limit = params.get("delta_limit", 0.2); - coarse_step = params.get("coarse_step", 0.2); - transverse_pos = params.get("transverse_pos", 0.01); - transverse_neg = params.get("transverse_neg", 0.01); - if (transverse_pos <= 0 || transverse_neg <= 0) - throw std::runtime_error( - "AwbConfig: transverse_pos/neg must be > 0"); - sensitivity_r = params.get("sensitivity_r", 1.0); - sensitivity_b = params.get("sensitivity_b", 1.0); + minPixels = params.get("min_pixels", 16.0); + minG = params.get("min_G", 32); + minRegions = params.get("min_regions", 10); + deltaLimit = params.get("delta_limit", 0.2); + coarseStep = params.get("coarse_step", 0.2); + transversePos = params.get("transverse_pos", 0.01); + transverseNeg = params.get("transverse_neg", 0.01); + if (transversePos <= 0 || transverseNeg <= 0) + throw std::runtime_error("AwbConfig: transverse_pos/neg must be > 0"); + sensitivityR = params.get("sensitivity_r", 1.0); + sensitivityB = params.get("sensitivity_b", 1.0); if (bayes) { - if (ct_r.Empty() || ct_b.Empty() || priors.empty() || - default_mode == nullptr) { + if (ctR.empty() || ctB.empty() || priors.empty() || + defaultMode == nullptr) { LOG(RPiAwb, Warning) << "Bayesian AWB mis-configured - switch to Grey method"; bayes = false; } } - fast = params.get( - "fast", bayes); // default to fast for Bayesian, otherwise slow - whitepoint_r = params.get("whitepoint_r", 0.0); - whitepoint_b = params.get("whitepoint_b", 0.0); + fast = params.get("fast", bayes); // default to fast for Bayesian, otherwise slow + whitepointR = params.get("whitepoint_r", 0.0); + whitepointB = params.get("whitepoint_b", 0.0); if (bayes == false) - sensitivity_r = sensitivity_b = - 1.0; // nor do sensitivities make any sense + sensitivityR = sensitivityB = 1.0; // nor do sensitivities make any sense } Awb::Awb(Controller *controller) : AwbAlgorithm(controller) { - async_abort_ = async_start_ = async_started_ = async_finished_ = false; + asyncAbort_ = asyncStart_ = asyncStarted_ = asyncFinished_ = false; mode_ = nullptr; - manual_r_ = manual_b_ = 0.0; - first_switch_mode_ = true; - async_thread_ = std::thread(std::bind(&Awb::asyncFunc, this)); + manualR_ = manualB_ = 0.0; + firstSwitchMode_ = true; + asyncThread_ = std::thread(std::bind(&Awb::asyncFunc, this)); } Awb::~Awb() { { std::lock_guard lock(mutex_); - async_abort_ = true; + asyncAbort_ = true; } - async_signal_.notify_one(); - async_thread_.join(); + asyncSignal_.notify_one(); + asyncThread_.join(); } -char const *Awb::Name() const +char const *Awb::name() const { return NAME; } -void Awb::Read(boost::property_tree::ptree const ¶ms) +void Awb::read(boost::property_tree::ptree const ¶ms) { - config_.Read(params); + config_.read(params); } -void Awb::Initialise() +void Awb::initialise() { - frame_count_ = frame_phase_ = 0; + frameCount_ = framePhase_ = 0; // Put something sane into the status that we are filtering towards, // just in case the first few frames don't have anything meaningful in // them. - if (!config_.ct_r.Empty() && !config_.ct_b.Empty()) { - sync_results_.temperature_K = config_.ct_r.Domain().Clip(4000); - sync_results_.gain_r = - 1.0 / config_.ct_r.Eval(sync_results_.temperature_K); - sync_results_.gain_g = 1.0; - sync_results_.gain_b = - 1.0 / config_.ct_b.Eval(sync_results_.temperature_K); + if (!config_.ctR.empty() && !config_.ctB.empty()) { + syncResults_.temperatureK = config_.ctR.domain().clip(4000); + syncResults_.gainR = 1.0 / config_.ctR.eval(syncResults_.temperatureK); + syncResults_.gainG = 1.0; + syncResults_.gainB = 1.0 / config_.ctB.eval(syncResults_.temperatureK); } else { // random values just to stop the world blowing up - sync_results_.temperature_K = 4500; - sync_results_.gain_r = sync_results_.gain_g = - sync_results_.gain_b = 1.0; + syncResults_.temperatureK = 4500; + syncResults_.gainR = syncResults_.gainG = syncResults_.gainB = 1.0; } - prev_sync_results_ = sync_results_; - async_results_ = sync_results_; + prevSyncResults_ = syncResults_; + asyncResults_ = syncResults_; } -bool Awb::IsPaused() const +bool Awb::isPaused() const { return false; } -void Awb::Pause() +void Awb::pause() { // "Pause" by fixing everything to the most recent values. - manual_r_ = sync_results_.gain_r = prev_sync_results_.gain_r; - manual_b_ = sync_results_.gain_b = prev_sync_results_.gain_b; - sync_results_.gain_g = prev_sync_results_.gain_g; - sync_results_.temperature_K = prev_sync_results_.temperature_K; + manualR_ = syncResults_.gainR = prevSyncResults_.gainR; + manualB_ = syncResults_.gainB = prevSyncResults_.gainB; + syncResults_.gainG = prevSyncResults_.gainG; + syncResults_.temperatureK = prevSyncResults_.temperatureK; } -void Awb::Resume() +void Awb::resume() { - manual_r_ = 0.0; - manual_b_ = 0.0; + manualR_ = 0.0; + manualB_ = 0.0; } -unsigned int Awb::GetConvergenceFrames() const +unsigned int Awb::getConvergenceFrames() const { // If not in auto mode, there is no convergence // to happen, so no need to drop any frames - return zero. if (!isAutoEnabled()) return 0; else - return config_.convergence_frames; + return config_.convergenceFrames; } -void Awb::SetMode(std::string const &mode_name) +void Awb::setMode(std::string const &modeName) { - mode_name_ = mode_name; + modeName_ = modeName; } -void Awb::SetManualGains(double manual_r, double manual_b) +void Awb::setManualGains(double manualR, double manualB) { // If any of these are 0.0, we swich back to auto. - manual_r_ = manual_r; - manual_b_ = manual_b; - // If not in auto mode, set these values into the sync_results which + manualR_ = manualR; + manualB_ = manualB; + // If not in auto mode, set these values into the syncResults which // means that Prepare() will adopt them immediately. if (!isAutoEnabled()) { - sync_results_.gain_r = prev_sync_results_.gain_r = manual_r_; - sync_results_.gain_g = prev_sync_results_.gain_g = 1.0; - sync_results_.gain_b = prev_sync_results_.gain_b = manual_b_; + syncResults_.gainR = prevSyncResults_.gainR = manualR_; + syncResults_.gainG = prevSyncResults_.gainG = 1.0; + syncResults_.gainB = prevSyncResults_.gainB = manualB_; } } -void Awb::SwitchMode([[maybe_unused]] CameraMode const &camera_mode, +void Awb::switchMode([[maybe_unused]] CameraMode const &cameraMode, Metadata *metadata) { // On the first mode switch we'll have no meaningful colour // temperature, so try to dead reckon one if in manual mode. - if (!isAutoEnabled() && first_switch_mode_ && config_.bayes) { - Pwl ct_r_inverse = config_.ct_r.Inverse(); - Pwl ct_b_inverse = config_.ct_b.Inverse(); - double ct_r = ct_r_inverse.Eval(ct_r_inverse.Domain().Clip(1 / manual_r_)); - double ct_b = ct_b_inverse.Eval(ct_b_inverse.Domain().Clip(1 / manual_b_)); - prev_sync_results_.temperature_K = (ct_r + ct_b) / 2; - sync_results_.temperature_K = prev_sync_results_.temperature_K; + if (!isAutoEnabled() && firstSwitchMode_ && config_.bayes) { + Pwl ctRInverse = config_.ctR.inverse(); + Pwl ctBInverse = config_.ctB.inverse(); + double ctR = ctRInverse.eval(ctRInverse.domain().clip(1 / manualR_)); + double ctB = ctBInverse.eval(ctBInverse.domain().clip(1 / manualB_)); + prevSyncResults_.temperatureK = (ctR + ctB) / 2; + syncResults_.temperatureK = prevSyncResults_.temperatureK; } // Let other algorithms know the current white balance values. - metadata->Set("awb.status", prev_sync_results_); - first_switch_mode_ = false; + metadata->set("awb.status", prevSyncResults_); + firstSwitchMode_ = false; } bool Awb::isAutoEnabled() const { - return manual_r_ == 0.0 || manual_b_ == 0.0; + return manualR_ == 0.0 || manualB_ == 0.0; } void Awb::fetchAsyncResults() { LOG(RPiAwb, Debug) << "Fetch AWB results"; - async_finished_ = false; - async_started_ = false; + asyncFinished_ = false; + asyncStarted_ = false; // It's possible manual gains could be set even while the async // thread was running, so only copy the results if still in auto mode. if (isAutoEnabled()) - sync_results_ = async_results_; + syncResults_ = asyncResults_; } void Awb::restartAsync(StatisticsPtr &stats, double lux) @@ -261,75 +253,74 @@ void Awb::restartAsync(StatisticsPtr &stats, double lux) // this makes a new reference which belongs to the asynchronous thread statistics_ = stats; // store the mode as it could technically change - auto m = config_.modes.find(mode_name_); + auto m = config_.modes.find(modeName_); mode_ = m != config_.modes.end() ? &m->second - : (mode_ == nullptr ? config_.default_mode : mode_); + : (mode_ == nullptr ? config_.defaultMode : mode_); lux_ = lux; - frame_phase_ = 0; - async_started_ = true; - size_t len = mode_name_.copy(async_results_.mode, - sizeof(async_results_.mode) - 1); - async_results_.mode[len] = '\0'; + framePhase_ = 0; + asyncStarted_ = true; + size_t len = modeName_.copy(asyncResults_.mode, + sizeof(asyncResults_.mode) - 1); + asyncResults_.mode[len] = '\0'; { std::lock_guard lock(mutex_); - async_start_ = true; + asyncStart_ = true; } - async_signal_.notify_one(); + asyncSignal_.notify_one(); } -void Awb::Prepare(Metadata *image_metadata) +void Awb::prepare(Metadata *imageMetadata) { - if (frame_count_ < (int)config_.startup_frames) - frame_count_++; - double speed = frame_count_ < (int)config_.startup_frames + if (frameCount_ < (int)config_.startupFrames) + frameCount_++; + double speed = frameCount_ < (int)config_.startupFrames ? 1.0 : config_.speed; LOG(RPiAwb, Debug) - << "frame_count " << frame_count_ << " speed " << speed; + << "frame_count " << frameCount_ << " speed " << speed; { std::unique_lock lock(mutex_); - if (async_started_ && async_finished_) + if (asyncStarted_ && asyncFinished_) fetchAsyncResults(); } // Finally apply IIR filter to results and put into metadata. - memcpy(prev_sync_results_.mode, sync_results_.mode, - sizeof(prev_sync_results_.mode)); - prev_sync_results_.temperature_K = - speed * sync_results_.temperature_K + - (1.0 - speed) * prev_sync_results_.temperature_K; - prev_sync_results_.gain_r = speed * sync_results_.gain_r + - (1.0 - speed) * prev_sync_results_.gain_r; - prev_sync_results_.gain_g = speed * sync_results_.gain_g + - (1.0 - speed) * prev_sync_results_.gain_g; - prev_sync_results_.gain_b = speed * sync_results_.gain_b + - (1.0 - speed) * prev_sync_results_.gain_b; - image_metadata->Set("awb.status", prev_sync_results_); + memcpy(prevSyncResults_.mode, syncResults_.mode, + sizeof(prevSyncResults_.mode)); + prevSyncResults_.temperatureK = speed * syncResults_.temperatureK + + (1.0 - speed) * prevSyncResults_.temperatureK; + prevSyncResults_.gainR = speed * syncResults_.gainR + + (1.0 - speed) * prevSyncResults_.gainR; + prevSyncResults_.gainG = speed * syncResults_.gainG + + (1.0 - speed) * prevSyncResults_.gainG; + prevSyncResults_.gainB = speed * syncResults_.gainB + + (1.0 - speed) * prevSyncResults_.gainB; + imageMetadata->set("awb.status", prevSyncResults_); LOG(RPiAwb, Debug) - << "Using AWB gains r " << prev_sync_results_.gain_r << " g " - << prev_sync_results_.gain_g << " b " - << prev_sync_results_.gain_b; + << "Using AWB gains r " << prevSyncResults_.gainR << " g " + << prevSyncResults_.gainG << " b " + << prevSyncResults_.gainB; } -void Awb::Process(StatisticsPtr &stats, Metadata *image_metadata) +void Awb::process(StatisticsPtr &stats, Metadata *imageMetadata) { // Count frames since we last poked the async thread. - if (frame_phase_ < (int)config_.frame_period) - frame_phase_++; - LOG(RPiAwb, Debug) << "frame_phase " << frame_phase_; + if (framePhase_ < (int)config_.framePeriod) + framePhase_++; + LOG(RPiAwb, Debug) << "frame_phase " << framePhase_; // We do not restart the async thread if we're not in auto mode. if (isAutoEnabled() && - (frame_phase_ >= (int)config_.frame_period || - frame_count_ < (int)config_.startup_frames)) { + (framePhase_ >= (int)config_.framePeriod || + frameCount_ < (int)config_.startupFrames)) { // Update any settings and any image metadata that we need. - struct LuxStatus lux_status = {}; - lux_status.lux = 400; // in case no metadata - if (image_metadata->Get("lux.status", lux_status) != 0) + struct LuxStatus luxStatus = {}; + luxStatus.lux = 400; // in case no metadata + if (imageMetadata->get("lux.status", luxStatus) != 0) LOG(RPiAwb, Debug) << "No lux metadata found"; - LOG(RPiAwb, Debug) << "Awb lux value is " << lux_status.lux; + LOG(RPiAwb, Debug) << "Awb lux value is " << luxStatus.lux; - if (async_started_ == false) - restartAsync(stats, lux_status.lux); + if (asyncStarted_ == false) + restartAsync(stats, luxStatus.lux); } } @@ -338,32 +329,32 @@ void Awb::asyncFunc() while (true) { { std::unique_lock lock(mutex_); - async_signal_.wait(lock, [&] { - return async_start_ || async_abort_; + asyncSignal_.wait(lock, [&] { + return asyncStart_ || asyncAbort_; }); - async_start_ = false; - if (async_abort_) + asyncStart_ = false; + if (asyncAbort_) break; } doAwb(); { std::lock_guard lock(mutex_); - async_finished_ = true; + asyncFinished_ = true; } - sync_signal_.notify_one(); + syncSignal_.notify_one(); } } -static void generate_stats(std::vector &zones, - bcm2835_isp_stats_region *stats, double min_pixels, - double min_G) +static void generateStats(std::vector &zones, + bcm2835_isp_stats_region *stats, double minPixels, + double minG) { for (int i = 0; i < AWB_STATS_SIZE_X * AWB_STATS_SIZE_Y; i++) { Awb::RGB zone; double counted = stats[i].counted; - if (counted >= min_pixels) { + if (counted >= minPixels) { zone.G = stats[i].g_sum / counted; - if (zone.G >= min_G) { + if (zone.G >= minG) { zone.R = stats[i].r_sum / counted; zone.B = stats[i].b_sum / counted; zones.push_back(zone); @@ -377,32 +368,33 @@ 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. - generate_stats(zones_, statistics_->awb_stats, config_.min_pixels, - config_.min_G); + generateStats(zones_, statistics_->awb_stats, config_.minPixels, + config_.minG); // we're done with these; we may as well relinquish our hold on the // pointer. statistics_.reset(); // apply sensitivities, so values appear to come from our "canonical" // sensor. - for (auto &zone : zones_) - zone.R *= config_.sensitivity_r, - zone.B *= config_.sensitivity_b; + for (auto &zone : zones_) { + zone.R *= config_.sensitivityR; + zone.B *= config_.sensitivityB; + } } -double Awb::computeDelta2Sum(double gain_r, double gain_b) +double Awb::computeDelta2Sum(double gainR, double gainB) { // Compute the sum of the squared colour error (non-greyness) as it // appears in the log likelihood equation. - double delta2_sum = 0; + double delta2Sum = 0; for (auto &z : zones_) { - double delta_r = gain_r * z.R - 1 - config_.whitepoint_r; - double delta_b = gain_b * z.B - 1 - config_.whitepoint_b; - double delta2 = delta_r * delta_r + delta_b * delta_b; - //LOG(RPiAwb, Debug) << "delta_r " << delta_r << " delta_b " << delta_b << " delta2 " << delta2; - delta2 = std::min(delta2, config_.delta_limit); - delta2_sum += delta2; + double deltaR = gainR * z.R - 1 - config_.whitepointR; + double deltaB = gainB * z.B - 1 - config_.whitepointB; + double delta2 = deltaR * deltaR + deltaB * deltaB; + //LOG(RPiAwb, Debug) << "deltaR " << deltaR << " deltaB " << deltaB << " delta2 " << delta2; + delta2 = std::min(delta2, config_.deltaLimit); + delta2Sum += delta2; } - return delta2_sum; + return delta2Sum; } Pwl Awb::interpolatePrior() @@ -420,7 +412,7 @@ Pwl Awb::interpolatePrior() idx++; double lux0 = config_.priors[idx].lux, lux1 = config_.priors[idx + 1].lux; - return Pwl::Combine(config_.priors[idx].prior, + return Pwl::combine(config_.priors[idx].prior, config_.priors[idx + 1].prior, [&](double /*x*/, double y0, double y1) { return y0 + (y1 - y0) * @@ -429,62 +421,60 @@ Pwl Awb::interpolatePrior() } } -static double interpolate_quadatric(Pwl::Point const &A, Pwl::Point const &B, - Pwl::Point const &C) +static double interpolateQuadatric(Pwl::Point const &a, Pwl::Point const &b, + Pwl::Point const &c) { // Given 3 points on a curve, find the extremum of the function in that // interval by fitting a quadratic. const double eps = 1e-3; - Pwl::Point CA = C - A, BA = B - A; - double denominator = 2 * (BA.y * CA.x - CA.y * BA.x); + Pwl::Point ca = c - a, ba = b - a; + double denominator = 2 * (ba.y * ca.x - ca.y * ba.x); if (abs(denominator) > eps) { - double numerator = BA.y * CA.x * CA.x - CA.y * BA.x * BA.x; - double result = numerator / denominator + A.x; - return std::max(A.x, std::min(C.x, result)); + double numerator = ba.y * ca.x * ca.x - ca.y * ba.x * ba.x; + double result = numerator / denominator + a.x; + return std::max(a.x, std::min(c.x, result)); } // has degenerated to straight line segment - return A.y < C.y - eps ? A.x : (C.y < A.y - eps ? C.x : B.x); + return a.y < c.y - eps ? a.x : (c.y < a.y - eps ? c.x : b.x); } double Awb::coarseSearch(Pwl const &prior) { points_.clear(); // assume doesn't deallocate memory - size_t best_point = 0; - double t = mode_->ct_lo; - int span_r = 0, span_b = 0; + 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_.ct_r.Eval(t, &span_r); - double b = config_.ct_b.Eval(t, &span_b); - double gain_r = 1 / r, gain_b = 1 / b; - double delta2_sum = computeDelta2Sum(gain_r, gain_b); - double prior_log_likelihood = - prior.Eval(prior.Domain().Clip(t)); - double final_log_likelihood = delta2_sum - prior_log_likelihood; + 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().clip(t)); + double finalLogLikelihood = delta2Sum - priorLogLikelihood; LOG(RPiAwb, Debug) - << "t: " << t << " gain_r " << gain_r << " gain_b " - << gain_b << " delta2_sum " << delta2_sum - << " prior " << prior_log_likelihood << " final " - << final_log_likelihood; - points_.push_back(Pwl::Point(t, final_log_likelihood)); - if (points_.back().y < points_[best_point].y) - best_point = points_.size() - 1; - if (t == mode_->ct_hi) + << "t: " << t << " gain R " << gainR << " gain B " + << gainB << " delta2_sum " << delta2Sum + << " prior " << priorLogLikelihood << " final " + << finalLogLikelihood; + points_.push_back(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_.coarse_step, - mode_->ct_hi); + t = std::min(t + t / 10 * config_.coarseStep, mode_->ctHi); } - t = points_[best_point].x; + 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(best_point, points_.size() - 2); - best_point = std::max(1UL, bp); - t = interpolate_quadatric(points_[best_point - 1], - points_[best_point], - points_[best_point + 1]); + 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; @@ -494,80 +484,76 @@ double Awb::coarseSearch(Pwl const &prior) void Awb::fineSearch(double &t, double &r, double &b, Pwl const &prior) { - int span_r = -1, span_b = -1; - config_.ct_r.Eval(t, &span_r); - config_.ct_b.Eval(t, &span_b); - double step = t / 10 * config_.coarse_step * 0.1; + 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 r_diff = config_.ct_r.Eval(t + nsteps * step, &span_r) - - config_.ct_r.Eval(t - nsteps * step, &span_r); - double b_diff = config_.ct_b.Eval(t + nsteps * step, &span_b) - - config_.ct_b.Eval(t - nsteps * step, &span_b); - Pwl::Point transverse(b_diff, -r_diff); - if (transverse.Len2() < 1e-6) + 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); + Pwl::Point transverse(bDiff, -rDiff); + if (transverse.len2() < 1e-6) return; // unit vector orthogonal to the b vs. r function (pointing outwards // with r and b increasing) - transverse = transverse / transverse.Len(); - double best_log_likelihood = 0, best_t = 0, best_r = 0, best_b = 0; - double transverse_range = - config_.transverse_neg + config_.transverse_pos; - const int MAX_NUM_DELTAS = 12; + transverse = transverse / transverse.len(); + 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 num_deltas = floor(transverse_range * 100 + 0.5) + 1; - num_deltas = num_deltas < 3 ? 3 : - (num_deltas > MAX_NUM_DELTAS ? MAX_NUM_DELTAS : num_deltas); + 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 += num_deltas; + nsteps += numDeltas; for (int i = -nsteps; i <= nsteps; i++) { - double t_test = t + i * step; - double prior_log_likelihood = - prior.Eval(prior.Domain().Clip(t_test)); - double r_curve = config_.ct_r.Eval(t_test, &span_r); - double b_curve = config_.ct_b.Eval(t_test, &span_b); + double tTest = t + i * step; + double priorLogLikelihood = + prior.eval(prior.domain().clip(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 - Pwl::Point points[MAX_NUM_DELTAS]; - int best_point = 0; + Pwl::Point points[maxNumDeltas]; + int bestPoint = 0; // Take some measurements transversely *off* the CT curve. - for (int j = 0; j < num_deltas; j++) { - points[j].x = -config_.transverse_neg + - (transverse_range * j) / (num_deltas - 1); - Pwl::Point rb_test = Pwl::Point(r_curve, b_curve) + - transverse * points[j].x; - double r_test = rb_test.x, b_test = rb_test.y; - double gain_r = 1 / r_test, gain_b = 1 / b_test; - double delta2_sum = computeDelta2Sum(gain_r, gain_b); - points[j].y = delta2_sum - prior_log_likelihood; + for (int j = 0; j < numDeltas; j++) { + points[j].x = -config_.transverseNeg + + (transverseRange * j) / (numDeltas - 1); + Pwl::Point rbTest = 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].y = delta2Sum - priorLogLikelihood; LOG(RPiAwb, Debug) - << "At t " << t_test << " r " << r_test << " b " - << b_test << ": " << points[j].y; - if (points[j].y < points[best_point].y) - best_point = j; + << "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. - best_point = std::max(1, std::min(best_point, num_deltas - 2)); - Pwl::Point rb_test = - Pwl::Point(r_curve, b_curve) + - transverse * - interpolate_quadatric(points[best_point - 1], - points[best_point], - points[best_point + 1]); - double r_test = rb_test.x, b_test = rb_test.y; - double gain_r = 1 / r_test, gain_b = 1 / b_test; - double delta2_sum = computeDelta2Sum(gain_r, gain_b); - double final_log_likelihood = delta2_sum - prior_log_likelihood; + bestPoint = std::max(1, std::min(bestPoint, numDeltas - 2)); + Pwl::Point rbTest = 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 " - << t_test << " r " << r_test << " b " << b_test << ": " - << final_log_likelihood - << (final_log_likelihood < best_log_likelihood ? " BEST" : ""); - if (best_t == 0 || final_log_likelihood < best_log_likelihood) - best_log_likelihood = final_log_likelihood, - best_t = t_test, best_r = r_test, best_b = b_test; + << tTest << " r " << rTest << " b " << bTest << ": " + << finalLogLikelihood + << (finalLogLikelihood < bestLogLikelihood ? " BEST" : ""); + if (bestT == 0 || finalLogLikelihood < bestLogLikelihood) + bestLogLikelihood = finalLogLikelihood, + bestT = tTest, bestR = rTest, bestB = bTest; } - t = best_t, r = best_r, b = best_b; + t = bestT, r = bestR, b = bestB; LOG(RPiAwb, Debug) << "Fine search found t " << t << " r " << r << " b " << b; } @@ -582,12 +568,12 @@ void Awb::awbBayes() // valid... not entirely sure about this. Pwl prior = interpolatePrior(); prior *= zones_.size() / (double)(AWB_STATS_SIZE_X * AWB_STATS_SIZE_Y); - prior.Map([](double x, double y) { + prior.map([](double x, double y) { LOG(RPiAwb, Debug) << "(" << x << "," << y << ")"; }); double t = coarseSearch(prior); - double r = config_.ct_r.Eval(t); - double b = config_.ct_b.Eval(t); + 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 << ")"; @@ -604,10 +590,10 @@ void Awb::awbBayes() // 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. - async_results_.temperature_K = t; - async_results_.gain_r = 1.0 / r * config_.sensitivity_r; - async_results_.gain_g = 1.0; - async_results_.gain_b = 1.0 / b * config_.sensitivity_b; + asyncResults_.temperatureK = t; + asyncResults_.gainR = 1.0 / r * config_.sensitivityR; + asyncResults_.gainG = 1.0; + asyncResults_.gainB = 1.0 / b * config_.sensitivityB; } void Awb::awbGrey() @@ -617,51 +603,51 @@ void Awb::awbGrey() // that we can sort them to exclude the extreme gains. We could // consider some variations, such as normalising all the zones first, or // doing an L2 average etc. - std::vector &derivs_R(zones_); - std::vector derivs_B(derivs_R); - std::sort(derivs_R.begin(), derivs_R.end(), + std::vector &derivsR(zones_); + std::vector derivsB(derivsR); + std::sort(derivsR.begin(), derivsR.end(), [](RGB const &a, RGB const &b) { return a.G * b.R < b.G * a.R; }); - std::sort(derivs_B.begin(), derivs_B.end(), + std::sort(derivsB.begin(), derivsB.end(), [](RGB const &a, RGB const &b) { return a.G * b.B < b.G * a.B; }); // Average the middle half of the values. - int discard = derivs_R.size() / 4; - RGB sum_R(0, 0, 0), sum_B(0, 0, 0); - for (auto ri = derivs_R.begin() + discard, - bi = derivs_B.begin() + discard; - ri != derivs_R.end() - discard; ri++, bi++) - sum_R += *ri, sum_B += *bi; - double gain_r = sum_R.G / (sum_R.R + 1), - gain_b = sum_B.G / (sum_B.B + 1); - async_results_.temperature_K = 4500; // don't know what it is - async_results_.gain_r = gain_r; - async_results_.gain_g = 1.0; - async_results_.gain_b = gain_b; + int discard = derivsR.size() / 4; + RGB sumR(0, 0, 0), sumB(0, 0, 0); + for (auto ri = derivsR.begin() + discard, + bi = derivsB.begin() + discard; + ri != derivsR.end() - discard; ri++, bi++) + sumR += *ri, sumB += *bi; + double gainR = sumR.G / (sumR.R + 1), + gainB = sumB.G / (sumB.B + 1); + asyncResults_.temperatureK = 4500; // don't know what it is + asyncResults_.gainR = gainR; + asyncResults_.gainG = 1.0; + asyncResults_.gainB = gainB; } void Awb::doAwb() { prepareStats(); LOG(RPiAwb, Debug) << "Valid zones: " << zones_.size(); - if (zones_.size() > config_.min_regions) { + if (zones_.size() > config_.minRegions) { if (config_.bayes) awbBayes(); else awbGrey(); LOG(RPiAwb, Debug) << "CT found is " - << async_results_.temperature_K - << " with gains r " << async_results_.gain_r - << " and b " << async_results_.gain_b; + << asyncResults_.temperatureK + << " with gains r " << asyncResults_.gainR + << " and b " << asyncResults_.gainB; } } // Register algorithm with the system. -static Algorithm *Create(Controller *controller) +static Algorithm *create(Controller *controller) { return (Algorithm *)new Awb(controller); } -static RegisterAlgorithm reg(NAME, &Create); +static RegisterAlgorithm reg(NAME, &create); -- cgit v1.2.1