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/* SPDX-License-Identifier: BSD-2-Clause */
/*
* Copyright (C) 2019, Raspberry Pi Ltd
*
* alsc.cpp - ALSC (auto lens shading correction) control algorithm
*/
#include <functional>
#include <math.h>
#include <numeric>
#include <libcamera/base/log.h>
#include <libcamera/base/span.h>
#include "../awb_status.h"
#include "alsc.h"
/* Raspberry Pi ALSC (Auto Lens Shading Correction) algorithm. */
using namespace RPiController;
using namespace libcamera;
LOG_DEFINE_CATEGORY(RPiAlsc)
#define NAME "rpi.alsc"
static const int X = AlscCellsX;
static const int Y = AlscCellsY;
static const int XY = X * Y;
static const double InsufficientData = -1.0;
Alsc::Alsc(Controller *controller)
: Algorithm(controller)
{
asyncAbort_ = asyncStart_ = asyncStarted_ = asyncFinished_ = false;
asyncThread_ = std::thread(std::bind(&Alsc::asyncFunc, this));
}
Alsc::~Alsc()
{
{
std::lock_guard<std::mutex> lock(mutex_);
asyncAbort_ = true;
}
asyncSignal_.notify_one();
asyncThread_.join();
}
char const *Alsc::name() const
{
return NAME;
}
static int generateLut(double *lut, const libcamera::YamlObject ¶ms)
{
double cstrength = params["corner_strength"].get<double>(2.0);
if (cstrength <= 1.0) {
LOG(RPiAlsc, Error) << "corner_strength must be > 1.0";
return -EINVAL;
}
double asymmetry = params["asymmetry"].get<double>(1.0);
if (asymmetry < 0) {
LOG(RPiAlsc, Error) << "asymmetry must be >= 0";
return -EINVAL;
}
double f1 = cstrength - 1, f2 = 1 + sqrt(cstrength);
double R2 = X * Y / 4 * (1 + asymmetry * asymmetry);
int num = 0;
for (int y = 0; y < Y; y++) {
for (int x = 0; x < X; x++) {
double dy = y - Y / 2 + 0.5,
dx = (x - X / 2 + 0.5) * asymmetry;
double r2 = (dx * dx + dy * dy) / R2;
lut[num++] =
(f1 * r2 + f2) * (f1 * r2 + f2) /
(f2 * f2); /* this reproduces the cos^4 rule */
}
}
return 0;
}
static int readLut(double *lut, const libcamera::YamlObject ¶ms)
{
if (params.size() != XY) {
LOG(RPiAlsc, Error) << "Invalid number of entries in LSC table";
return -EINVAL;
}
int num = 0;
for (const auto &p : params.asList()) {
auto value = p.get<double>();
if (!value)
return -EINVAL;
lut[num++] = *value;
}
return 0;
}
static int readCalibrations(std::vector<AlscCalibration> &calibrations,
const libcamera::YamlObject ¶ms,
std::string const &name)
{
if (params.contains(name)) {
double lastCt = 0;
for (const auto &p : params[name].asList()) {
auto value = p["ct"].get<double>();
if (!value)
return -EINVAL;
double ct = *value;
if (ct <= lastCt) {
LOG(RPiAlsc, Error)
<< "Entries in " << name << " must be in increasing ct order";
return -EINVAL;
}
AlscCalibration calibration;
calibration.ct = lastCt = ct;
const libcamera::YamlObject &table = p["table"];
if (table.size() != XY) {
LOG(RPiAlsc, Error)
<< "Incorrect number of values for ct "
<< ct << " in " << name;
return -EINVAL;
}
int num = 0;
for (const auto &elem : table.asList()) {
value = elem.get<double>();
if (!value)
return -EINVAL;
calibration.table[num++] = *value;
}
calibrations.push_back(calibration);
LOG(RPiAlsc, Debug)
<< "Read " << name << " calibration for ct " << ct;
}
}
return 0;
}
int Alsc::read(const libcamera::YamlObject ¶ms)
{
config_.framePeriod = params["frame_period"].get<uint16_t>(12);
config_.startupFrames = params["startup_frames"].get<uint16_t>(10);
config_.speed = params["speed"].get<double>(0.05);
double sigma = params["sigma"].get<double>(0.01);
config_.sigmaCr = params["sigma_Cr"].get<double>(sigma);
config_.sigmaCb = params["sigma_Cb"].get<double>(sigma);
config_.minCount = params["min_count"].get<double>(10.0);
config_.minG = params["min_G"].get<uint16_t>(50);
config_.omega = params["omega"].get<double>(1.3);
config_.nIter = params["n_iter"].get<uint32_t>(X + Y);
config_.luminanceStrength =
params["luminance_strength"].get<double>(1.0);
for (int i = 0; i < XY; i++)
config_.luminanceLut[i] = 1.0;
int ret = 0;
if (params.contains("corner_strength"))
ret = generateLut(config_.luminanceLut, params);
else if (params.contains("luminance_lut"))
ret = readLut(config_.luminanceLut, params["luminance_lut"]);
else
LOG(RPiAlsc, Warning)
<< "no luminance table - assume unity everywhere";
if (ret)
return ret;
ret = readCalibrations(config_.calibrationsCr, params, "calibrations_Cr");
if (ret)
return ret;
ret = readCalibrations(config_.calibrationsCb, params, "calibrations_Cb");
if (ret)
return ret;
config_.defaultCt = params["default_ct"].get<double>(4500.0);
config_.threshold = params["threshold"].get<double>(1e-3);
config_.lambdaBound = params["lambda_bound"].get<double>(0.05);
return 0;
}
static double getCt(Metadata *metadata, double defaultCt);
static void getCalTable(double ct, std::vector<AlscCalibration> const &calibrations,
double calTable[XY]);
static void resampleCalTable(double const calTableIn[XY], CameraMode const &cameraMode,
double calTableOut[XY]);
static void compensateLambdasForCal(double const calTable[XY], double const oldLambdas[XY],
double newLambdas[XY]);
static void addLuminanceToTables(double results[3][Y][X], double const lambdaR[XY], double lambdaG,
double const lambdaB[XY], double const luminanceLut[XY],
double luminanceStrength);
void Alsc::initialise()
{
frameCount2_ = frameCount_ = framePhase_ = 0;
firstTime_ = true;
ct_ = config_.defaultCt;
/* The lambdas are initialised in the SwitchMode. */
}
void Alsc::waitForAysncThread()
{
if (asyncStarted_) {
asyncStarted_ = false;
std::unique_lock<std::mutex> lock(mutex_);
syncSignal_.wait(lock, [&] {
return asyncFinished_;
});
asyncFinished_ = false;
}
}
static bool compareModes(CameraMode const &cm0, CameraMode const &cm1)
{
/*
* Return true if the modes crop from the sensor significantly differently,
* or if the user transform has changed.
*/
if (cm0.transform != cm1.transform)
return true;
int leftDiff = abs(cm0.cropX - cm1.cropX);
int topDiff = abs(cm0.cropY - cm1.cropY);
int rightDiff = fabs(cm0.cropX + cm0.scaleX * cm0.width -
cm1.cropX - cm1.scaleX * cm1.width);
int bottomDiff = fabs(cm0.cropY + cm0.scaleY * cm0.height -
cm1.cropY - cm1.scaleY * cm1.height);
/*
* These thresholds are a rather arbitrary amount chosen to trigger
* when carrying on with the previously calculated tables might be
* worse than regenerating them (but without the adaptive algorithm).
*/
int thresholdX = cm0.sensorWidth >> 4;
int thresholdY = cm0.sensorHeight >> 4;
return leftDiff > thresholdX || rightDiff > thresholdX ||
topDiff > thresholdY || bottomDiff > thresholdY;
}
void Alsc::switchMode(CameraMode const &cameraMode,
[[maybe_unused]] Metadata *metadata)
{
/*
* We're going to start over with the tables if there's any "significant"
* change.
*/
bool resetTables = firstTime_ || compareModes(cameraMode_, cameraMode);
/* Believe the colour temperature from the AWB, if there is one. */
ct_ = getCt(metadata, ct_);
/* Ensure the other thread isn't running while we do this. */
waitForAysncThread();
cameraMode_ = cameraMode;
/*
* We must resample the luminance table like we do the others, but it's
* fixed so we can simply do it up front here.
*/
resampleCalTable(config_.luminanceLut, cameraMode_, luminanceTable_);
if (resetTables) {
/*
* Upon every "table reset", arrange for something sensible to be
* generated. Construct the tables for the previous recorded colour
* temperature. In order to start over from scratch we initialise
* the lambdas, but the rest of this code then echoes the code in
* doAlsc, without the adaptive algorithm.
*/
for (int i = 0; i < XY; i++)
lambdaR_[i] = lambdaB_[i] = 1.0;
double calTableR[XY], calTableB[XY], calTableTmp[XY];
getCalTable(ct_, config_.calibrationsCr, calTableTmp);
resampleCalTable(calTableTmp, cameraMode_, calTableR);
getCalTable(ct_, config_.calibrationsCb, calTableTmp);
resampleCalTable(calTableTmp, cameraMode_, calTableB);
compensateLambdasForCal(calTableR, lambdaR_, asyncLambdaR_);
compensateLambdasForCal(calTableB, lambdaB_, asyncLambdaB_);
addLuminanceToTables(syncResults_, asyncLambdaR_, 1.0, asyncLambdaB_,
luminanceTable_, config_.luminanceStrength);
memcpy(prevSyncResults_, syncResults_, sizeof(prevSyncResults_));
framePhase_ = config_.framePeriod; /* run the algo again asap */
firstTime_ = false;
}
}
void Alsc::fetchAsyncResults()
{
LOG(RPiAlsc, Debug) << "Fetch ALSC results";
asyncFinished_ = false;
asyncStarted_ = false;
memcpy(syncResults_, asyncResults_, sizeof(syncResults_));
}
double getCt(Metadata *metadata, double defaultCt)
{
AwbStatus awbStatus;
awbStatus.temperatureK = defaultCt; /* in case nothing found */
if (metadata->get("awb.status", awbStatus) != 0)
LOG(RPiAlsc, Debug) << "no AWB results found, using "
<< awbStatus.temperatureK;
else
LOG(RPiAlsc, Debug) << "AWB results found, using "
<< awbStatus.temperatureK;
return awbStatus.temperatureK;
}
static void copyStats(bcm2835_isp_stats_region regions[XY], StatisticsPtr &stats,
AlscStatus const &status)
{
bcm2835_isp_stats_region *inputRegions = stats->awb_stats;
double *rTable = (double *)status.r;
double *gTable = (double *)status.g;
double *bTable = (double *)status.b;
for (int i = 0; i < XY; i++) {
regions[i].r_sum = inputRegions[i].r_sum / rTable[i];
regions[i].g_sum = inputRegions[i].g_sum / gTable[i];
regions[i].b_sum = inputRegions[i].b_sum / bTable[i];
regions[i].counted = inputRegions[i].counted;
/* (don't care about the uncounted value) */
}
}
void Alsc::restartAsync(StatisticsPtr &stats, Metadata *imageMetadata)
{
LOG(RPiAlsc, Debug) << "Starting ALSC calculation";
/*
* Get the current colour temperature. It's all we need from the
* metadata. Default to the last CT value (which could be the default).
*/
ct_ = getCt(imageMetadata, ct_);
/*
* We have to copy the statistics here, dividing out our best guess of
* the LSC table that the pipeline applied to them.
*/
AlscStatus alscStatus;
if (imageMetadata->get("alsc.status", alscStatus) != 0) {
LOG(RPiAlsc, Warning)
<< "No ALSC status found for applied gains!";
for (int y = 0; y < Y; y++)
for (int x = 0; x < X; x++) {
alscStatus.r[y][x] = 1.0;
alscStatus.g[y][x] = 1.0;
alscStatus.b[y][x] = 1.0;
}
}
copyStats(statistics_, stats, alscStatus);
framePhase_ = 0;
asyncStarted_ = true;
{
std::lock_guard<std::mutex> lock(mutex_);
asyncStart_ = true;
}
asyncSignal_.notify_one();
}
void Alsc::prepare(Metadata *imageMetadata)
{
/*
* Count frames since we started, and since we last poked the async
* thread.
*/
if (frameCount_ < (int)config_.startupFrames)
frameCount_++;
double speed = frameCount_ < (int)config_.startupFrames
? 1.0
: config_.speed;
LOG(RPiAlsc, Debug)
<< "frame count " << frameCount_ << " speed " << speed;
{
std::unique_lock<std::mutex> lock(mutex_);
if (asyncStarted_ && asyncFinished_)
fetchAsyncResults();
}
/* Apply IIR filter to results and program into the pipeline. */
double *ptr = (double *)syncResults_,
*pptr = (double *)prevSyncResults_;
for (unsigned int i = 0; i < sizeof(syncResults_) / sizeof(double); i++)
pptr[i] = speed * ptr[i] + (1.0 - speed) * pptr[i];
/* Put output values into status metadata. */
AlscStatus status;
memcpy(status.r, prevSyncResults_[0], sizeof(status.r));
memcpy(status.g, prevSyncResults_[1], sizeof(status.g));
memcpy(status.b, prevSyncResults_[2], sizeof(status.b));
imageMetadata->set("alsc.status", status);
}
void Alsc::process(StatisticsPtr &stats, Metadata *imageMetadata)
{
/*
* Count frames since we started, and since we last poked the async
* thread.
*/
if (framePhase_ < (int)config_.framePeriod)
framePhase_++;
if (frameCount2_ < (int)config_.startupFrames)
frameCount2_++;
LOG(RPiAlsc, Debug) << "frame_phase " << framePhase_;
if (framePhase_ >= (int)config_.framePeriod ||
frameCount2_ < (int)config_.startupFrames) {
if (asyncStarted_ == false)
restartAsync(stats, imageMetadata);
}
}
void Alsc::asyncFunc()
{
while (true) {
{
std::unique_lock<std::mutex> lock(mutex_);
asyncSignal_.wait(lock, [&] {
return asyncStart_ || asyncAbort_;
});
asyncStart_ = false;
if (asyncAbort_)
break;
}
doAlsc();
{
std::lock_guard<std::mutex> lock(mutex_);
asyncFinished_ = true;
}
syncSignal_.notify_one();
}
}
void getCalTable(double ct, std::vector<AlscCalibration> const &calibrations,
double calTable[XY])
{
if (calibrations.empty()) {
for (int i = 0; i < XY; i++)
calTable[i] = 1.0;
LOG(RPiAlsc, Debug) << "no calibrations found";
} else if (ct <= calibrations.front().ct) {
memcpy(calTable, calibrations.front().table, XY * sizeof(double));
LOG(RPiAlsc, Debug) << "using calibration for "
<< calibrations.front().ct;
} else if (ct >= calibrations.back().ct) {
memcpy(calTable, calibrations.back().table, XY * sizeof(double));
LOG(RPiAlsc, Debug) << "using calibration for "
<< calibrations.back().ct;
} else {
int idx = 0;
while (ct > calibrations[idx + 1].ct)
idx++;
double ct0 = calibrations[idx].ct, ct1 = calibrations[idx + 1].ct;
LOG(RPiAlsc, Debug)
<< "ct is " << ct << ", interpolating between "
<< ct0 << " and " << ct1;
for (int i = 0; i < XY; i++)
calTable[i] =
(calibrations[idx].table[i] * (ct1 - ct) +
calibrations[idx + 1].table[i] * (ct - ct0)) /
(ct1 - ct0);
}
}
void resampleCalTable(double const calTableIn[XY],
CameraMode const &cameraMode, double calTableOut[XY])
{
/*
* Precalculate and cache the x sampling locations and phases to save
* recomputing them on every row.
*/
int xLo[X], xHi[X];
double xf[X];
double scaleX = cameraMode.sensorWidth /
(cameraMode.width * cameraMode.scaleX);
double xOff = cameraMode.cropX / (double)cameraMode.sensorWidth;
double x = .5 / scaleX + xOff * X - .5;
double xInc = 1 / scaleX;
for (int i = 0; i < X; i++, x += xInc) {
xLo[i] = floor(x);
xf[i] = x - xLo[i];
xHi[i] = std::min(xLo[i] + 1, X - 1);
xLo[i] = std::max(xLo[i], 0);
if (!!(cameraMode.transform & libcamera::Transform::HFlip)) {
xLo[i] = X - 1 - xLo[i];
xHi[i] = X - 1 - xHi[i];
}
}
/* Now march over the output table generating the new values. */
double scaleY = cameraMode.sensorHeight /
(cameraMode.height * cameraMode.scaleY);
double yOff = cameraMode.cropY / (double)cameraMode.sensorHeight;
double y = .5 / scaleY + yOff * Y - .5;
double yInc = 1 / scaleY;
for (int j = 0; j < Y; j++, y += yInc) {
int yLo = floor(y);
double yf = y - yLo;
int yHi = std::min(yLo + 1, Y - 1);
yLo = std::max(yLo, 0);
if (!!(cameraMode.transform & libcamera::Transform::VFlip)) {
yLo = Y - 1 - yLo;
yHi = Y - 1 - yHi;
}
double const *rowAbove = calTableIn + X * yLo;
double const *rowBelow = calTableIn + X * yHi;
for (int i = 0; i < X; i++) {
double above = rowAbove[xLo[i]] * (1 - xf[i]) +
rowAbove[xHi[i]] * xf[i];
double below = rowBelow[xLo[i]] * (1 - xf[i]) +
rowBelow[xHi[i]] * xf[i];
*(calTableOut++) = above * (1 - yf) + below * yf;
}
}
}
/* Calculate chrominance statistics (R/G and B/G) for each region. */
static_assert(XY == AWB_REGIONS, "ALSC/AWB statistics region mismatch");
static void calculateCrCb(bcm2835_isp_stats_region *awbRegion, double cr[XY],
double cb[XY], uint32_t minCount, uint16_t minG)
{
for (int i = 0; i < XY; i++) {
bcm2835_isp_stats_region &zone = awbRegion[i];
if (zone.counted <= minCount ||
zone.g_sum / zone.counted <= minG) {
cr[i] = cb[i] = InsufficientData;
continue;
}
cr[i] = zone.r_sum / (double)zone.g_sum;
cb[i] = zone.b_sum / (double)zone.g_sum;
}
}
static void applyCalTable(double const calTable[XY], double C[XY])
{
for (int i = 0; i < XY; i++)
if (C[i] != InsufficientData)
C[i] *= calTable[i];
}
void compensateLambdasForCal(double const calTable[XY],
double const oldLambdas[XY],
double newLambdas[XY])
{
double minNewLambda = std::numeric_limits<double>::max();
for (int i = 0; i < XY; i++) {
newLambdas[i] = oldLambdas[i] * calTable[i];
minNewLambda = std::min(minNewLambda, newLambdas[i]);
}
for (int i = 0; i < XY; i++)
newLambdas[i] /= minNewLambda;
}
[[maybe_unused]] static void printCalTable(double const C[XY])
{
printf("table: [\n");
for (int j = 0; j < Y; j++) {
for (int i = 0; i < X; i++) {
printf("%5.3f", 1.0 / C[j * X + i]);
if (i != X - 1 || j != Y - 1)
printf(",");
}
printf("\n");
}
printf("]\n");
}
/*
* Compute weight out of 1.0 which reflects how similar we wish to make the
* colours of these two regions.
*/
static double computeWeight(double Ci, double Cj, double sigma)
{
if (Ci == InsufficientData || Cj == InsufficientData)
return 0;
double diff = (Ci - Cj) / sigma;
return exp(-diff * diff / 2);
}
/* Compute all weights. */
static void computeW(double const C[XY], double sigma, double W[XY][4])
{
for (int i = 0; i < XY; i++) {
/* Start with neighbour above and go clockwise. */
W[i][0] = i >= X ? computeWeight(C[i], C[i - X], sigma) : 0;
W[i][1] = i % X < X - 1 ? computeWeight(C[i], C[i + 1], sigma) : 0;
W[i][2] = i < XY - X ? computeWeight(C[i], C[i + X], sigma) : 0;
W[i][3] = i % X ? computeWeight(C[i], C[i - 1], sigma) : 0;
}
}
/* Compute M, the large but sparse matrix such that M * lambdas = 0. */
static void constructM(double const C[XY], double const W[XY][4],
double M[XY][4])
{
double epsilon = 0.001;
for (int i = 0; i < XY; i++) {
/*
* Note how, if C[i] == INSUFFICIENT_DATA, the weights will all
* be zero so the equation is still set up correctly.
*/
int m = !!(i >= X) + !!(i % X < X - 1) + !!(i < XY - X) +
!!(i % X); /* total number of neighbours */
/* we'll divide the diagonal out straight away */
double diagonal = (epsilon + W[i][0] + W[i][1] + W[i][2] + W[i][3]) * C[i];
M[i][0] = i >= X ? (W[i][0] * C[i - X] + epsilon / m * C[i]) / diagonal : 0;
M[i][1] = i % X < X - 1 ? (W[i][1] * C[i + 1] + epsilon / m * C[i]) / diagonal : 0;
M[i][2] = i < XY - X ? (W[i][2] * C[i + X] + epsilon / m * C[i]) / diagonal : 0;
M[i][3] = i % X ? (W[i][3] * C[i - 1] + epsilon / m * C[i]) / diagonal : 0;
}
}
/*
* In the compute_lambda_ functions, note that the matrix coefficients for the
* left/right neighbours are zero down the left/right edges, so we don't need
* need to test the i value to exclude them.
*/
static double computeLambdaBottom(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + X] +
M[i][3] * lambda[i - 1];
}
static double computeLambdaBottomStart(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + X];
}
static double computeLambdaInterior(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][0] * lambda[i - X] + M[i][1] * lambda[i + 1] +
M[i][2] * lambda[i + X] + M[i][3] * lambda[i - 1];
}
static double computeLambdaTop(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][0] * lambda[i - X] + M[i][1] * lambda[i + 1] +
M[i][3] * lambda[i - 1];
}
static double computeLambdaTopEnd(int i, double const M[XY][4],
double lambda[XY])
{
return M[i][0] * lambda[i - X] + M[i][3] * lambda[i - 1];
}
/* Gauss-Seidel iteration with over-relaxation. */
static double gaussSeidel2Sor(double const M[XY][4], double omega,
double lambda[XY], double lambdaBound)
{
const double min = 1 - lambdaBound, max = 1 + lambdaBound;
double oldLambda[XY];
int i;
for (i = 0; i < XY; i++)
oldLambda[i] = lambda[i];
lambda[0] = computeLambdaBottomStart(0, M, lambda);
lambda[0] = std::clamp(lambda[0], min, max);
for (i = 1; i < X; i++) {
lambda[i] = computeLambdaBottom(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
for (; i < XY - X; i++) {
lambda[i] = computeLambdaInterior(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
for (; i < XY - 1; i++) {
lambda[i] = computeLambdaTop(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
}
lambda[i] = computeLambdaTopEnd(i, M, lambda);
lambda[i] = std::clamp(lambda[i], min, max);
/*
* Also solve the system from bottom to top, to help spread the updates
* better.
*/
lambda[i] = computeLambdaTopEnd(i, M, lambda);
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