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-rw-r--r--src/ipa/raspberrypi/controller/rpi/awb.cpp566
1 files changed, 276 insertions, 290 deletions
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 &params)
+void AwbMode::read(boost::property_tree::ptree const &params)
{
- ct_lo = params.get<double>("lo");
- ct_hi = params.get<double>("hi");
+ ctLo = params.get<double>("lo");
+ ctHi = params.get<double>("hi");
}
-void AwbPrior::Read(boost::property_tree::ptree const &params)
+void AwbPrior::read(boost::property_tree::ptree const &params)
{
lux = params.get<double>("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 &params)
+static void readCtCurve(Pwl &ctR, Pwl &ctB,
+ boost::property_tree::ptree const &params)
{
int num = 0;
for (auto it = params.begin(); it != params.end(); it++) {
double ct = it->second.get_value<double>();
- 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<double>());
+ ctR.append(ct, it->second.get_value<double>());
if (++it == params.end())
throw std::runtime_error(
"AwbConfig: incomplete CT curve entry");
- ct_b.Append(ct, it->second.get_value<double>());
+ ctB.append(ct, it->second.get_value<double>());
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 &params)
+void AwbConfig::read(boost::property_tree::ptree const &params)
{
bayes = params.get<int>("bayes", 1);
- frame_period = params.get<uint16_t>("frame_period", 10);
- startup_frames = params.get<uint16_t>("startup_frames", 10);
- convergence_frames = params.get<unsigned int>("convergence_frames", 3);
+ framePeriod = params.get<uint16_t>("framePeriod", 10);
+ startupFrames = params.get<uint16_t>("startupFrames", 10);
+ convergenceFrames = params.get<unsigned int>("convergence_frames", 3);
speed = params.get<double>("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 &params)
}
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<double>("min_pixels", 16.0);
- min_G = params.get<uint16_t>("min_G", 32);
- min_regions = params.get<uint32_t>("min_regions", 10);
- delta_limit = params.get<double>("delta_limit", 0.2);
- coarse_step = params.get<double>("coarse_step", 0.2);
- transverse_pos = params.get<double>("transverse_pos", 0.01);
- transverse_neg = params.get<double>("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<double>("sensitivity_r", 1.0);
- sensitivity_b = params.get<double>("sensitivity_b", 1.0);
+ minPixels = params.get<double>("min_pixels", 16.0);
+ minG = params.get<uint16_t>("min_G", 32);
+ minRegions = params.get<uint32_t>("min_regions", 10);
+ deltaLimit = params.get<double>("delta_limit", 0.2);
+ coarseStep = params.get<double>("coarse_step", 0.2);
+ transversePos = params.get<double>("transverse_pos", 0.01);
+ transverseNeg = params.get<double>("transverse_neg", 0.01);
+ if (transversePos <= 0 || transverseNeg <= 0)
+ throw std::runtime_error("AwbConfig: transverse_pos/neg must be > 0");
+ sensitivityR = params.get<double>("sensitivity_r", 1.0);
+ sensitivityB = params.get<double>("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<int>(
- "fast", bayes); // default to fast for Bayesian, otherwise slow
- whitepoint_r = params.get<double>("whitepoint_r", 0.0);
- whitepoint_b = params.get<double>("whitepoint_b", 0.0);
+ fast = params.get<int>("fast", bayes); // default to fast for Bayesian, otherwise slow
+ whitepointR = params.get<double>("whitepoint_r", 0.0);
+ whitepointB = params.get<double>("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<std::mutex> 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 &params)
+void Awb::read(boost::property_tree::ptree const &params)
{
- 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<std::mutex> 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<std::mutex> 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<std::mutex> 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<std::mutex> lock(mutex_);
- async_finished_ = true;
+ asyncFinished_ = true;
}
- sync_signal_.notify_one();
+ syncSignal_.notify_one();
}
}
-static void generate_stats(std::vector<Awb::RGB> &zones,
- bcm2835_isp_stats_region *stats, double min_pixels,
- double min_G)
+static void generateStats(std::vector<Awb::RGB> &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<RGB> &derivs_R(zones_);
- std::vector<RGB> derivs_B(derivs_R);
- std::sort(derivs_R.begin(), derivs_R.end(),
+ std::vector<RGB> &derivsR(zones_);
+ std::vector<RGB> 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);
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-10 05:33:30 +0000