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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* awb.cpp - AWB control algorithm
*/
#include <assert.h>
#include <functional>
#include <libcamera/base/log.h>
#include "../lux_status.h"
#include "awb.h"
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiAwb)
#define NAME "rpi.awb"
static constexpr unsigned int AwbStatsSizeX = DEFAULT_AWB_REGIONS_X;
static constexpr unsigned int AwbStatsSizeY = DEFAULT_AWB_REGIONS_Y;
/*
* 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<double>();
if (!value)
return -EINVAL;
ctLo = *value;
value = params["hi"].get<double>();
if (!value)
return -EINVAL;
ctHi = *value;
return 0;
}
int AwbPrior::read(const libcamera::YamlObject ¶ms)
{
auto value = params["lux"].get<double>();
if (!value)
return -EINVAL;
lux = *value;
return prior.read(params["prior"]);
}
static int readCtCurve(Pwl &ctR, Pwl &ctB, const libcamera::YamlObject ¶ms)
{
if (params.size() % 3) {
LOG(RPiAwb, Error) << "AwbConfig: incomplete CT curve entry";
return -EINVAL;
}
if (params.size() < 6) {
LOG(RPiAwb, Error) << "AwbConfig: insufficient points in CT curve";
return -EINVAL;
}
const auto &list = params.asList();
for (auto it = list.begin(); it != list.end(); it++) {
auto value = it->get<double>();
if (!value)
return -EINVAL;
double ct = *value;
assert(it == list.begin() || ct != ctR.domain().end);
value = (++it)->get<double>();
if (!value)
return -EINVAL;
ctR.append(ct, *value);
value = (++it)->get<double>();
if (!value)
return -EINVAL;
ctB.append(ct, *value);
}
return 0;
}
int AwbConfig::read(const libcamera::YamlObject ¶ms)
{
int ret;
bayes = params["bayes"].get<int>(1);
framePeriod = params["frame_period"].get<uint16_t>(10);
startupFrames = params["startup_frames"].get<uint16_t>(10);
convergenceFrames = params["convergence_frames"].get<unsigned int>(3);
speed = params["speed"].get<double>(0.05);
if (params.contains("ct_curve")) {
ret = readCtCurve(ctR, ctB, params["ct_curve"]);
if (ret)
return ret;
}
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 ret;
}
}
if (params.contains("modes")) {
for (const auto &[key, value] : params["modes"].asDict()) {
ret = modes[key].read(value);
if (ret)
return ret;
if (defaultMode == nullptr)
defaultMode = &modes[key];
}
if (defaultMode == nullptr) {
LOG(RPiAwb, Error) << "AwbConfig: no AWB modes configured";
return -EINVAL;
}
}
minPixels = params["min_pixels"].get<double>(16.0);
minG = params["min_G"].get<uint16_t>(32);
minRegions = params["min_regions"].get<uint32_t>(10);
deltaLimit = params["delta_limit"].get<double>(0.2);
coarseStep = params["coarse_step"].get<double>(0.2);
transversePos = params["transverse_pos"].get<double>(0.01);
transverseNeg = params["transverse_neg"].get<double>(0.01);
if (transversePos <= 0 || transverseNeg <= 0) {
LOG(RPiAwb, Error) << "AwbConfig: transverse_pos/neg must be > 0";
return -EINVAL;
}
sensitivityR = params["sensitivity_r"].get<double>(1.0);
sensitivityB = params["sensitivity_b"].get<double>(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;
}
}
fast = params[fast].get<int>(bayes); /* default to fast for Bayesian, otherwise slow */
whitepointR = params["whitepoint_r"].get<double>(0.0);
whitepointB = params["whitepoint_b"].get<double>(0.0);
if (bayes == false)
sensitivityR = sensitivityB = 1.0; /* nor do sensitivities make any sense */
return 0;
}
Awb::Awb(Controller *controller)
: AwbAlgorithm(controller)
{
asyncAbort_ = asyncStart_ = asyncStarted_ = asyncFinished_ = false;
mode_ = nullptr;
manualR_ = manualB_ = 0.0;
firstSwitchMode_ = true;
asyncThread_ = std::thread(std::bind(&Awb::asyncFunc, this));
}
Awb::~Awb()
{
{
std::lock_guard<std::mutex> lock(mutex_);
asyncAbort_ = true;
}
asyncSignal_.notify_one();
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;
/*
* 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_.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 */
syncResults_.temperatureK = 4500;
syncResults_.gainR = syncResults_.gainG = syncResults_.gainB = 1.0;
}
prevSyncResults_ = syncResults_;
asyncResults_ = syncResults_;
}
bool Awb::isPaused() const
{
return false;
}
void Awb::pause()
{
/* "Pause" by fixing everything to the most recent values. */
manualR_ = syncResults_.gainR = prevSyncResults_.gainR;
manualB_ = syncResults_.gainB = prevSyncResults_.gainB;
syncResults_.gainG = prevSyncResults_.gainG;
syncResults_.temperatureK = prevSyncResults_.temperatureK;
}
void Awb::resume()
{
manualR_ = 0.0;
manualB_ = 0.0;
}
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_.convergenceFrames;
}
void Awb::setMode(std::string const &modeName)
{
modeName_ = modeName;
}
void Awb::setManualGains(double manualR, double manualB)
{
/* If any of these are 0.0, we swich back to auto. */
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()) {
syncResults_.gainR = prevSyncResults_.gainR = manualR_;
syncResults_.gainG = prevSyncResults_.gainG = 1.0;
syncResults_.gainB = prevSyncResults_.gainB = manualB_;
}
}
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() && 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", prevSyncResults_);
firstSwitchMode_ = false;
}
bool Awb::isAutoEnabled() const
{
return manualR_ == 0.0 || manualB_ == 0.0;
}
void Awb::fetchAsyncResults()
{
LOG(RPiAwb, Debug) << "Fetch AWB results";
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())
syncResults_ = asyncResults_;
}
void Awb::restartAsync(StatisticsPtr &stats, double lux)
{
LOG(RPiAwb, Debug) << "Starting AWB calculation";
/* 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(modeName_);
mode_ = m != config_.modes.end()
? &m->second
: (mode_ == nullptr ? config_.defaultMode : mode_);
lux_ = lux;
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_);
asyncStart_ = true;
}
asyncSignal_.notify_one();
}
void Awb::prepare(Metadata *imageMetadata)
{
if (frameCount_ < (int)config_.startupFrames)
frameCount_++;
double speed = frameCount_ < (int)config_.startupFrames
? 1.0
: config_.speed;
LOG(RPiAwb, Debug)
<< "frame_count " << frameCount_ << " speed " << speed;
{
std::unique_lock<std::mutex> lock(mutex_);
if (asyncStarted_ && asyncFinished_)
fetchAsyncResults();
}
/* Finally apply IIR filter to results and put into metadata. */
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 " << prevSyncResults_.gainR << " g "
<< prevSyncResults_.gainG << " b "
<< prevSyncResults_.gainB;
}
void Awb::process(StatisticsPtr &stats, Metadata *imageMetadata)
{
/* Count frames since we last poked the async thread. */
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() &&
(framePhase_ >= (int)config_.framePeriod ||
frameCount_ < (int)config_.startupFrames)) {
/* Update any settings and any image metadata that we need. */
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 " << luxStatus.lux;
if (asyncStarted_ == false)
restartAsync(stats, luxStatus.lux);
}
}
void Awb::asyncFunc()
{
while (true) {
{
std::unique_lock<std::mutex> lock(mutex_);
asyncSignal_.wait(lock, [&] {
return asyncStart_ || asyncAbort_;
});
asyncStart_ = false;
if (asyncAbort_)
break;
}
doAwb();
{
std::lock_guard<std::mutex> lock(mutex_);
asyncFinished_ = true;
}
syncSignal_.notify_one();
}
}
static void generateStats(std::vector<Awb::RGB> &zones,
bcm2835_isp_stats_region *stats, double minPixels,
double minG)
{
for (unsigned int i = 0; i < AwbStatsSizeX * AwbStatsSizeY; i++) {
Awb::RGB zone;
double counted = stats[i].counted;
if (counted >= minPixels) {
zone.G = stats[i].g_sum / counted;
if (zone.G >= minG) {
zone.R = stats[i].r_sum / counted;
zone.B = stats[i].b_sum / counted;
zones.push_back(zone);
}
}
}
}
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.
*/
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_.sensitivityR;
zone.B *= config_.sensitivityB;
}
}
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 delta2Sum = 0;
for (auto &z : zones_) {
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 delta2Sum;
}
Pwl Awb::interpolatePrior()
{
/*
* 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 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(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);
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));
}
/* has degenerated to straight line segment */
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 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().clip(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(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, 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);
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 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().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[maxNumDeltas];
int bestPoint = 0;
/* Take some measurements transversely *off* the CT curve. */
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 " << 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));
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 "
<< 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.
*/
Pwl prior = interpolatePrior();
prior *= zones_.size() / (double)(AwbStatsSizeX * AwbStatsSizeY);
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
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