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path: root/utils/raspberrypi/ctt/ctt_image_load.py
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2024-06-13utils: raspberrypi: ctt: Changed CTT handling of VC4 and PiSPBen Benson
Changed how users select which platform to tune for. Now users specify a command line argument, '-t', to specify which target platform. Signed-off-by: Ben Benson <ben.benson@raspberrypi.com> Signed-off-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Tested-by: Naushir Patuck <naush@raspberrypi.com> Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Signed-off-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
2024-06-13utils: raspberrypi: ctt: Added CAC support to the CTTBen Benson
Added the ability to tune the chromatic aberration correction within the ctt. There are options for cac_only or to tune as part of a larger tuning process. CTT will now recognise any files that begin with "cac" as being chromatic aberration tuning files. Signed-off-by: Ben Benson <ben.benson@raspberrypi.com> Signed-off-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Tested-by: Naushir Patuck <naush@raspberrypi.com> Acked-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Signed-off-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
2024-05-08libcamera: Drop file name from header comment blocksLaurent Pinchart
Source files in libcamera start by a comment block header, which includes the file name and a one-line description of the file contents. While the latter is useful to get a quick overview of the file contents at a glance, the former is mostly a source of inconvenience. The name in the comments can easily get out of sync with the file name when files are renamed, and copy & paste during development have often lead to incorrect names being used to start with. Readers of the source code are expected to know which file they're looking it. Drop the file name from the header comment block. The change was generated with the following script: ---------------------------------------- dirs="include/libcamera src test utils" declare -rA patterns=( ['c']=' \* ' ['cpp']=' \* ' ['h']=' \* ' ['py']='# ' ['sh']='# ' ) for ext in ${!patterns[@]} ; do files=$(for dir in $dirs ; do find $dir -name "*.${ext}" ; done) pattern=${patterns[${ext}]} for file in $files ; do name=$(basename ${file}) sed -i "s/^\(${pattern}\)${name} - /\1/" "$file" done done ---------------------------------------- This misses several files that are out of sync with the comment block header. Those will be addressed separately and manually. Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Daniel Scally <dan.scally@ideasonboard.com>
2022-08-03utils: raspberrypi: ctt: dng_load_image: Work with DNG files from Picamera2William Vinnicombe
The DNG specification is based on the TIFF file format and recommends storing the raw image data in a SubIFD and the Exif tags in an Exif IFD. Other options are allowed, even if not recommended, such as storing both the raw image data and the Exif data in IFD0, as done by the TIFF/EP specification. libcamera-apps use pyexiv2 to produce DNG files, following the DNG recommendation, while applications based on picamera2 use PiDNG, which adopts the TIFF/EP structure. Why it does so is not currently clear (see https://github.com/schoolpost/PiDNG/issues/65 for discussions on this topic), but as files based on the DNG and TIFF/EP variants exist in the wild, both need to be supported by ctt. Add code to identify which tags are being used, and then load the metadata from the correct tags. Signed-off-by: William Vinnicombe <william.vinnicombe@raspberrypi.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
2022-07-27raspberrypi: Update Copyright statement in all Raspberry Pi source filesNaushir Patuck
s/Raspberry Pi (Trading) Limited/Raspberry Pi Ltd/ to reflect the new Raspberry Pi entity name. Signed-off-by: Naushir Patuck <naush@raspberrypi.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
2022-07-06utils: raspberrypi: ctt: load_image: Ignore JPEG files with no raw dataWilliam Vinnicombe
The load_image function would throw errors with JPEG or JPG files containing no raw data. Prevent throwing these errors by returning 0 if an error has occurred. Signed-off-by: William Vinnicombe <william.vinnicombe@raspberrypi.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E302Laurent Pinchart
E302 expected 2 blank lines, found 0 Note that issues are still flagged, due to the use of docstrings as multi-lines comments. This will be addressed separately. Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E305Laurent Pinchart
E305 expected 2 blank lines after class or function definition Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E722Laurent Pinchart
E722 do not use bare 'except' Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E711 and E712Laurent Pinchart
E711 comparison to None should be 'if cond is None:' E711 comparison to None should be 'if cond is not None:' E712 comparison to False should be 'if cond is False:' or 'if not cond:' E712 comparison to True should be 'if cond is True:' or 'if cond:' Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E261 and E262Laurent Pinchart
E261 at least two spaces before inline comment E262 inline comment should start with '# ' Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E228Laurent Pinchart
E228 missing whitespace around modulo operator Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E203Laurent Pinchart
E203 whitespace before ':' Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-13utils: raspberrypi: ctt: Fix pycodestyle E231Laurent Pinchart
E231 missing whitespace after ',' E231 missing whitespace after ':' Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com> Reviewed-by: David Plowman <david.plowman@raspberrypi.com>
2020-05-11libcamera: utils: Raspberry Pi Camera Tuning ToolNaushir Patuck
Initial implementation of the Raspberry Pi (BCM2835) Camera Tuning Tool. All code is licensed under the BSD-2-Clause terms. Copyright (c) 2019-2020 Raspberry Pi Trading Ltd. Signed-off-by: Naushir Patuck <naush@raspberrypi.com> Acked-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com> Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
generated by cgit v1.2.1 (git 2.18.0) at 2025-05-31 09:40:09 +0000
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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 <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 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(Array2D<double> &lut, const libcamera::YamlObject &params)
{
	/* These must be signed ints for the co-ordinate calculations below. */
	int X = lut.dimensions().width, Y = lut.dimensions().height;
	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(Array2D<double> &lut, const libcamera::YamlObject &params)
{
	if (params.size() != lut.size()) {
		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 &params,
			    std::string const &name, const Size &size)
{
	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() != size.width * size.height) {
				LOG(RPiAlsc, Error)
					<< "Incorrect number of values for ct "
					<< ct << " in " << name;
				return -EINVAL;
			}

			int num = 0;
			calibration.table.resize(size);
			for (const auto &elem : table.asList()) {
				value = elem.get<double>();
				if (!value)
					return -EINVAL;
				calibration.table[num++] = *value;
			}

			calibrations.push_back(std::move(calibration));
			LOG(RPiAlsc, Debug)
				<< "Read " << name << " calibration for ct " << ct;
		}
	}
	return 0;
}

int Alsc::read(const libcamera::YamlObject &params)
{
	config_.tableSize = getHardwareConfig().awbRegions;
	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>(config_.tableSize.width + config_.tableSize.height);
	config_.luminanceStrength =
		params["luminance_strength"].get<double>(1.0);

	config_.luminanceLut.resize(config_.tableSize, 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",
			       config_.tableSize);
	if (ret)
		return ret;
	ret = readCalibrations(config_.calibrationsCb, params, "calibrations_Cb",
			       config_.tableSize);
	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,
			Array2D<double> &calTable);
static void resampleCalTable(const Array2D<double> &calTableIn, CameraMode const &cameraMode,
			     Array2D<double> &calTableOut);
static void compensateLambdasForCal(const Array2D<double> &calTable,
				    const Array2D<double> &oldLambdas,
				    Array2D<double> &newLambdas);
static void addLuminanceToTables(std::array<Array2D<double>, 3> &results,
				 const Array2D<double> &lambdaR, double lambdaG,
				 const Array2D<double> &lambdaB,
				 const Array2D<double> &luminanceLut,
				 double luminanceStrength);

void Alsc::initialise()
{
	frameCount2_ = frameCount_ = framePhase_ = 0;
	firstTime_ = true;
	ct_ = config_.defaultCt;

	const size_t XY = config_.tableSize.width * config_.tableSize.height;

	for (auto &r : syncResults_)
		r.resize(config_.tableSize);
	for (auto &r : prevSyncResults_)
		r.resize(config_.tableSize);
	for (auto &r : asyncResults_)
		r.resize(config_.tableSize);

	luminanceTable_.resize(config_.tableSize);
	asyncLambdaR_.resize(config_.tableSize);
	asyncLambdaB_.resize(config_.tableSize);
	/* The lambdas are initialised in the SwitchMode. */
	lambdaR_.resize(config_.tableSize);
	lambdaB_.resize(config_.tableSize);

	/* Temporaries for the computations, but sensible to allocate this up-front! */
	for (auto &c : tmpC_)
		c.resize(config_.tableSize);
	for (auto &m : tmpM_)
		m.resize(XY);
}

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.
		 */
		std::fill(lambdaR_.begin(), lambdaR_.end(), 1.0);
		std::fill(lambdaB_.begin(), lambdaB_.end(), 1.0);
		Array2D<double> &calTableR = tmpC_[0], &calTableB = tmpC_[1], &calTableTmp = tmpC_[2];
		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);
		prevSyncResults_ = syncResults_;
		framePhase_ = config_.framePeriod; /* run the algo again asap */
		firstTime_ = false;
	}
}

void Alsc::fetchAsyncResults()
{
	LOG(RPiAlsc, Debug) << "Fetch ALSC results";
	asyncFinished_ = false;
	asyncStarted_ = false;
	syncResults_ = asyncResults_;
}

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(RgbyRegions &regions, StatisticsPtr &stats,
		      AlscStatus const &status)
{
	if (!regions.numRegions())
		regions.init(stats->awbRegions.size());

	const std::vector<double> &rTable = status.r;
	const std::vector<double> &gTable = status.g;
	const std::vector<double> &bTable = status.b;
	for (unsigned int i = 0; i < stats->awbRegions.numRegions(); i++) {
		auto r = stats->awbRegions.get(i);
		r.val.rSum = static_cast<uint64_t>(r.val.rSum / rTable[i]);
		r.val.gSum = static_cast<uint64_t>(r.val.gSum / gTable[i]);
		r.val.bSum = static_cast<uint64_t>(r.val.bSum / bTable[i]);
		regions.set(i, r);
	}
}

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!";
		alscStatus.r.resize(config_.tableSize.width * config_.tableSize.height, 1.0);
		alscStatus.g.resize(config_.tableSize.width * config_.tableSize.height, 1.0);
		alscStatus.b.resize(config_.tableSize.width * config_.tableSize.height, 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. */
	for (unsigned int j = 0; j < syncResults_.size(); j++) {
		for (unsigned int i = 0; i < syncResults_[j].size(); i++)
			prevSyncResults_[j][i] = speed * syncResults_[j][i] + (1.0 - speed) * prevSyncResults_[j][i];
	}
	/* Put output values into status metadata. */
	AlscStatus status;
	status.r = prevSyncResults_[0].data();
	status.g = prevSyncResults_[1].data();
	status.b = prevSyncResults_[2].data();
	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,
		 Array2D<double> &calTable)
{
	if (calibrations.empty()) {
		std::fill(calTable.begin(), calTable.end(), 1.0);
		LOG(RPiAlsc, Debug) << "no calibrations found";
	} else if (ct <= calibrations.front().ct) {
		calTable = calibrations.front().table;
		LOG(RPiAlsc, Debug) << "using calibration for "
				    << calibrations.front().ct;
	} else if (ct >= calibrations.back().ct) {
		calTable = calibrations.back().table;
		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 (unsigned int i = 0; i < calTable.size(); i++)
			calTable[i] =
				(calibrations[idx].table[i] * (ct1 - ct) +
				 calibrations[idx + 1].table[i] * (ct - ct0)) /
				(ct1 - ct0);
	}
}

void resampleCalTable(const Array2D<double> &calTableIn,
		      CameraMode const &cameraMode,
		      Array2D<double> &calTableOut)
{
	int X = calTableIn.dimensions().width;
	int Y = calTableIn.dimensions().height;

	/*
	 * 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.ptr() + X * yLo;
		double const *rowBelow = calTableIn.ptr() + X * yHi;
		double *out = calTableOut.ptr() + X * j;
		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];
			*(out++) = above * (1 - yf) + below * yf;
		}
	}
}

/* Calculate chrominance statistics (R/G and B/G) for each region. */
static void calculateCrCb(const RgbyRegions &awbRegion, Array2D<double> &cr,
			  Array2D<double> &cb, uint32_t minCount, uint16_t minG)
{
	for (unsigned int i = 0; i < cr.size(); i++) {
		auto s = awbRegion.get(i);

		if (s.counted <= minCount || s.val.gSum / s.counted <= minG) {
			cr[i] = cb[i] = InsufficientData;
			continue;
		}

		cr[i] = s.val.rSum / (double)s.val.gSum;
		cb[i] = s.val.bSum / (double)s.val.gSum;
	}
}

static void applyCalTable(const Array2D<double> &calTable, Array2D<double> &C)
{
	for (unsigned int i = 0; i < C.size(); i++)
		if (C[i] != InsufficientData)
			C[i] *= calTable[i];
}

void compensateLambdasForCal(const Array2D<double> &calTable,
			     const Array2D<double> &oldLambdas,
			     Array2D<double> &newLambdas)
{
	double minNewLambda = std::numeric_limits<double>::max();
	for (unsigned int i = 0; i < newLambdas.size(); i++) {
		newLambdas[i] = oldLambdas[i] * calTable[i];
		minNewLambda = std::min(minNewLambda, newLambdas[i]);
	}
	for (unsigned int i = 0; i < newLambdas.size(); i++)
		newLambdas[i] /= minNewLambda;
}

[[maybe_unused]] static void printCalTable(const Array2D<double> &C)
{
	const Size &size = C.dimensions();
	printf("table: [\n");
	for (unsigned int j = 0; j < size.height; j++) {
		for (unsigned int i = 0; i < size.width; i++) {
			printf("%5.3f", 1.0 / C[j * size.width + i]);
			if (i != size.width - 1 || j != size.height - 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(const Array2D<double> &C, double sigma,
		     SparseArray<double> &W)
{
	size_t XY = C.size();
	size_t X = C.dimensions().width;

	for (unsigned 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(const Array2D<double> &C,
		       const SparseArray<double> &W,
		       SparseArray<double> &M)
{
	size_t XY = C.size();
	size_t X = C.dimensions().width;

	double epsilon = 0.001;
	for (unsigned 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, const SparseArray<double> &M,
				  Array2D<double> &lambda)
{
	return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + lambda.dimensions().width] +
	       M[i][3] * lambda[i - 1];
}
static double computeLambdaBottomStart(int i, const SparseArray<double> &M,
				       Array2D<double> &lambda)
{
	return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + lambda.dimensions().width];
}
static double computeLambdaInterior(int i, const SparseArray<double> &M,
				    Array2D<double> &lambda)
{
	return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][1] * lambda[i + 1] +
	       M[i][2] * lambda[i + lambda.dimensions().width] + M[i][3] * lambda[i - 1];
}
static double computeLambdaTop(int i, const SparseArray<double> &M,
			       Array2D<double> &lambda)
{
	return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][1] * lambda[i + 1] +
	       M[i][3] * lambda[i - 1];
}
static double computeLambdaTopEnd(int i, const SparseArray<double> &M,
				  Array2D<double> &lambda)
{
	return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][3] * lambda[i - 1];
}

/* Gauss-Seidel iteration with over-relaxation. */
static double gaussSeidel2Sor(const SparseArray<double> &M, double omega,
			      Array2D<double> &lambda, double lambdaBound)
{
	int XY = lambda.size();
	int X = lambda.dimensions().width;
	const double min = 1 - lambdaBound, max = 1 + lambdaBound;
	Array2D<double> oldLambda = lambda;
	int 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);
	lambda[i] = std::clamp(lambda[i], min, max);
	for (i = XY - 2; i >= XY - X; i--) {
		lambda[i] = computeLambdaTop(i, M, lambda);
		lambda[i] = std::clamp(lambda[i], min, max);
	}
	for (; i >= X; i--) {
		lambda[i] = computeLambdaInterior(i, M, lambda);
		lambda[i] = std::clamp(lambda[i], min, max);
	}
	for (; i >= 1; i--) {
		lambda[i] = computeLambdaBottom(i, M, lambda);
		lambda[i] = std::clamp(lambda[i], min, max);
	}
	lambda[0] = computeLambdaBottomStart(0, M, lambda);
	lambda[0] = std::clamp(lambda[0], min, max);
	double maxDiff = 0;
	for (i = 0; i < XY; i++) {
		lambda[i] = oldLambda[i] + (lambda[i] - oldLambda[i]) * omega;
		if (fabs(lambda[i] - oldLambda[i]) > fabs(maxDiff))
			maxDiff = lambda[i] - oldLambda[i];
	}
	return maxDiff;
}

/* Normalise the values so that the smallest value is 1. */
static void normalise(Array2D<double> &results)
{
	double minval = *std::min_element(results.begin(), results.end());
	std::for_each(results.begin(), results.end(),
		      [minval](double val) { return val / minval; });
}

/* Rescale the values so that the average value is 1. */
static void reaverage(Array2D<double> &data)
{
	double sum = std::accumulate(data.begin(), data.end(), 0.0);
	double ratio = 1 / (sum / data.size());
	std::for_each(data.begin(), data.end(),
		      [ratio](double val) { return val * ratio; });
}

static void runMatrixIterations(const Array2D<double> &C,
				Array2D<double> &lambda,
				const SparseArray<double> &W,
				SparseArray<double> &M, double omega,
				unsigned int nIter, double threshold, double lambdaBound)
{
	constructM(C, W, M);
	double lastMaxDiff = std::numeric_limits<double>::max();
	for (unsigned int i = 0; i < nIter; i++) {
		double maxDiff = fabs(gaussSeidel2Sor(M, omega, lambda, lambdaBound));
		if (maxDiff < threshold) {
			LOG(RPiAlsc, Debug)
				<< "Stop after " << i + 1 << " iterations";
			break;
		}
		/*
		 * this happens very occasionally (so make a note), though
		 * doesn't seem to matter
		 */
		if (maxDiff > lastMaxDiff)
			LOG(RPiAlsc, Debug)
				<< "Iteration " << i << ": maxDiff gone up "
				<< lastMaxDiff << " to " << maxDiff;
		lastMaxDiff = maxDiff;
	}
	/* We're going to normalise the lambdas so the total average is 1. */
	reaverage(lambda);
}

static void addLuminanceRb(Array2D<double> &result, const Array2D<double> &lambda,
			   const Array2D<double> &luminanceLut,
			   double luminanceStrength)
{
	for (unsigned int i = 0; i < result.size(); i++)
		result[i] = lambda[i] * ((luminanceLut[i] - 1) * luminanceStrength + 1);
}

static void addLuminanceG(Array2D<double> &result, double lambda,
			  const Array2D<double> &luminanceLut,
			  double luminanceStrength)
{
	for (unsigned int i = 0; i < result.size(); i++)
		result[i] = lambda * ((luminanceLut[i] - 1) * luminanceStrength + 1);
}

void addLuminanceToTables(std::array<Array2D<double>, 3> &results,
			  const Array2D<double> &lambdaR,
			  double lambdaG, const Array2D<double> &lambdaB,
			  const Array2D<double> &luminanceLut,
			  double luminanceStrength)
{
	addLuminanceRb(results[0], lambdaR, luminanceLut, luminanceStrength);
	addLuminanceG(results[1], lambdaG, luminanceLut, luminanceStrength);
	addLuminanceRb(results[2], lambdaB, luminanceLut, luminanceStrength);
	for (auto &r : results)
		normalise(r);
}

void Alsc::doAlsc()
{
	Array2D<double> &cr = tmpC_[0], &cb = tmpC_[1], &calTableR = tmpC_[2],
			&calTableB = tmpC_[3], &calTableTmp = tmpC_[4];
	SparseArray<double> &wr = tmpM_[0], &wb = tmpM_[1], &M = tmpM_[2];

	/*
	 * Calculate our R/B ("Cr"/"Cb") colour statistics, and assess which are
	 * usable.
	 */
	calculateCrCb(statistics_, cr, cb, config_.minCount, config_.minG);
	/*
	 * Fetch the new calibrations (if any) for this CT. Resample them in
	 * case the camera mode is not full-frame.
	 */
	getCalTable(ct_, config_.calibrationsCr, calTableTmp);
	resampleCalTable(calTableTmp, cameraMode_, calTableR);
	getCalTable(ct_, config_.calibrationsCb, calTableTmp);
	resampleCalTable(calTableTmp, cameraMode_, calTableB);
	/*
	 * You could print out the cal tables for this image here, if you're
	 * tuning the algorithm...
	 * Apply any calibration to the statistics, so the adaptive algorithm
	 * makes only the extra adjustments.
	 */
	applyCalTable(calTableR, cr);
	applyCalTable(calTableB, cb);
	/* Compute weights between zones. */
	computeW(cr, config_.sigmaCr, wr);
	computeW(cb, config_.sigmaCb, wb);
	/* Run Gauss-Seidel iterations over the resulting matrix, for R and B. */
	runMatrixIterations(cr, lambdaR_, wr, M, config_.omega, config_.nIter,
			    config_.threshold, config_.lambdaBound);
	runMatrixIterations(cb, lambdaB_, wb, M, config_.omega, config_.nIter,
			    config_.threshold, config_.lambdaBound);
	/*
	 * Fold the calibrated gains into our final lambda values. (Note that on
	 * the next run, we re-start with the lambda values that don't have the
	 * calibration gains included.)
	 */
	compensateLambdasForCal(calTableR, lambdaR_, asyncLambdaR_);
	compensateLambdasForCal(calTableB, lambdaB_, asyncLambdaB_);
	/* Fold in the luminance table at the appropriate strength. */
	addLuminanceToTables(asyncResults_, asyncLambdaR_, 1.0,
			     asyncLambdaB_, luminanceTable_,
			     config_.luminanceStrength);
}

/* Register algorithm with the system. */
static Algorithm *create(Controller *controller)
{
	return (Algorithm *)new Alsc(controller);
}
static RegisterAlgorithm reg(NAME, &create);
generated by cgit v1.2.1 (git 2.18.0) at 2025-05-31 09:40:09 +0000