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/* SPDX-License-Identifier: LGPL-2.1-or-later */
/*
* Copyright (C) 2021, Ideas On Board
*
* AGC/AEC mean-based control algorithm
*/
#include "agc.h"
#include <algorithm>
#include <chrono>
#include <libcamera/base/log.h>
#include <libcamera/base/utils.h>
#include <libcamera/control_ids.h>
#include <libcamera/ipa/core_ipa_interface.h>
#include "libipa/colours.h"
#include "libipa/histogram.h"
/**
* \file agc.h
*/
namespace libcamera {
using namespace std::literals::chrono_literals;
namespace ipa::ipu3::algorithms {
/**
* \class Agc
* \brief A mean-based auto-exposure algorithm
*
* This algorithm calculates an exposure time and an analogue gain so that the
* average value of the green channel of the brightest 2% of pixels approaches
* 0.5. The AWB gains are not used here, and all cells in the grid have the same
* weight, like an average-metering case. In this metering mode, the camera uses
* light information from the entire scene and creates an average for the final
* exposure setting, giving no weighting to any particular portion of the
* metered area.
*
* Reference: Battiato, Messina & Castorina. (2008). Exposure
* Correction for Imaging Devices: An Overview. 10.1201/9781420054538.ch12.
*/
LOG_DEFINE_CATEGORY(IPU3Agc)
/* Minimum limit for analogue gain value */
static constexpr double kMinAnalogueGain = 1.0;
/* \todo Honour the FrameDurationLimits control instead of hardcoding a limit */
static constexpr utils::Duration kMaxExposureTime = 60ms;
/* Histogram constants */
static constexpr uint32_t knumHistogramBins = 256;
Agc::Agc()
: minExposureTime_(0s), maxExposureTime_(0s)
{
}
/**
* \brief Initialise the AGC algorithm from tuning files
* \param[in] context The shared IPA context
* \param[in] tuningData The YamlObject containing Agc tuning data
*
* This function calls the base class' tuningData parsers to discover which
* control values are supported.
*
* \return 0 on success or errors from the base class
*/
int Agc::init(IPAContext &context, const YamlObject &tuningData)
{
int ret;
ret = parseTuningData(tuningData);
if (ret)
return ret;
context.ctrlMap.merge(controls());
return 0;
}
/**
* \brief Configure the AGC given a configInfo
* \param[in] context The shared IPA context
* \param[in] configInfo The IPA configuration data
*
* \return 0
*/
int Agc::configure(IPAContext &context,
[[maybe_unused]] const IPAConfigInfo &configInfo)
{
const IPASessionConfiguration &configuration = context.configuration;
IPAActiveState &activeState = context.activeState;
stride_ = configuration.grid.stride;
bdsGrid_ = configuration.grid.bdsGrid;
minExposureTime_ = configuration.agc.minExposureTime;
maxExposureTime_ = std::min(configuration.agc.maxExposureTime,
kMaxExposureTime);
minAnalogueGain_ = std::max(configuration.agc.minAnalogueGain, kMinAnalogueGain);
maxAnalogueGain_ = configuration.agc.maxAnalogueGain;
/* Configure the default exposure and gain. */
activeState.agc.gain = minAnalogueGain_;
activeState.agc.exposure = 10ms / configuration.sensor.lineDuration;
context.activeState.agc.constraintMode = constraintModes().begin()->first;
context.activeState.agc.exposureMode = exposureModeHelpers().begin()->first;
/* \todo Run this again when FrameDurationLimits is passed in */
setLimits(minExposureTime_, maxExposureTime_, minAnalogueGain_,
maxAnalogueGain_);
resetFrameCount();
return 0;
}
Histogram Agc::parseStatistics(const ipu3_uapi_stats_3a *stats,
const ipu3_uapi_grid_config &grid)
{
uint32_t hist[knumHistogramBins] = { 0 };
rgbTriples_.clear();
for (unsigned int cellY = 0; cellY < grid.height; cellY++) {
for (unsigned int cellX = 0; cellX < grid.width; cellX++) {
uint32_t cellPosition = cellY * stride_ + cellX;
const ipu3_uapi_awb_set_item *cell =
reinterpret_cast<const ipu3_uapi_awb_set_item *>(
&stats->awb_raw_buffer.meta_data[cellPosition]);
rgbTriples_.push_back({
cell->R_avg,
(cell->Gr_avg + cell->Gb_avg) / 2,
cell->B_avg
});
/*
* Store the average green value to estimate the
* brightness. Even the overexposed pixels are
* taken into account.
*/
hist[(cell->Gr_avg + cell->Gb_avg) / 2]++;
}
}
return Histogram(Span<uint32_t>(hist));
}
/**
* \brief Estimate the relative luminance of the frame with a given gain
* \param[in] gain The gain to apply in estimating luminance
*
* The estimation is based on the AWB statistics for the current frame. Red,
* green and blue averages for all cells are first multiplied by the gain, and
* then saturated to approximate the sensor behaviour at high brightness
* values. The approximation is quite rough, as it doesn't take into account
* non-linearities when approaching saturation.
*
* The relative luminance (Y) is computed from the linear RGB components using
* the Rec. 601 formula. The values are normalized to the [0.0, 1.0] range,
* where 1.0 corresponds to a theoretical perfect reflector of 100% reference
* white.
*
* More detailed information can be found in:
* https://en.wikipedia.org/wiki/Relative_luminance
*
* \return The relative luminance of the frame
*/
double Agc::estimateLuminance(double gain) const
{
RGB<double> sum{ 0.0 };
for (unsigned int i = 0; i < rgbTriples_.size(); i++) {
sum.r() += std::min(std::get<0>(rgbTriples_[i]) * gain, 255.0);
sum.g() += std::min(std::get<1>(rgbTriples_[i]) * gain, 255.0);
sum.b() += std::min(std::get<2>(rgbTriples_[i]) * gain, 255.0);
}
RGB<double> gains{{ rGain_, gGain_, bGain_ }};
double ySum = rec601LuminanceFromRGB(sum * gains);
return ySum / (bdsGrid_.height * bdsGrid_.width) / 255;
}
/**
* \brief Process IPU3 statistics, and run AGC operations
* \param[in] context The shared IPA context
* \param[in] frame The current frame sequence number
* \param[in] frameContext The current frame context
* \param[in] stats The IPU3 statistics and ISP results
* \param[out] metadata Metadata for the frame, to be filled by the algorithm
*
* Identify the current image brightness, and use that to estimate the optimal
* new exposure and gain for the scene.
*/
void Agc::process(IPAContext &context, [[maybe_unused]] const uint32_t frame,
IPAFrameContext &frameContext,
const ipu3_uapi_stats_3a *stats,
ControlList &metadata)
{
Histogram hist = parseStatistics(stats, context.configuration.grid.bdsGrid);
rGain_ = context.activeState.awb.gains.red;
gGain_ = context.activeState.awb.gains.blue;
bGain_ = context.activeState.awb.gains.green;
/*
* The Agc algorithm needs to know the effective exposure value that was
* applied to the sensor when the statistics were collected.
*/
utils::Duration exposureTime = context.configuration.sensor.lineDuration
* frameContext.sensor.exposure;
double analogueGain = frameContext.sensor.gain;
utils::Duration effectiveExposureValue = exposureTime * analogueGain;
utils::Duration newExposureTime;
double aGain, dGain;
std::tie(newExposureTime, aGain, dGain) =
calculateNewEv(context.activeState.agc.constraintMode,
context.activeState.agc.exposureMode, hist,
effectiveExposureValue);
LOG(IPU3Agc, Debug)
<< "Divided up exposure time, analogue gain and digital gain are "
<< newExposureTime << ", " << aGain << " and " << dGain;
IPAActiveState &activeState = context.activeState;
/* Update the estimated exposure time and gain. */
activeState.agc.exposure = newExposureTime / context.configuration.sensor.lineDuration;
activeState.agc.gain = aGain;
metadata.set(controls::AnalogueGain, frameContext.sensor.gain);
metadata.set(controls::ExposureTime, exposureTime.get<std::micro>());
/* \todo Use VBlank value calculated from each frame exposure. */
uint32_t vTotal = context.configuration.sensor.size.height
+ context.configuration.sensor.defVBlank;
utils::Duration frameDuration = context.configuration.sensor.lineDuration
* vTotal;
metadata.set(controls::FrameDuration, frameDuration.get<std::micro>());
}
REGISTER_IPA_ALGORITHM(Agc, "Agc")
} /* namespace ipa::ipu3::algorithms */
} /* namespace libcamera */
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