Add support to the rkisp1 AGC to read histogram weights from the tuning file. As controls for selecting the metering mode are not yet supported, for now hardcode the matrix metering mode, which is the same as what the AGC previously hardcoded. Signed-off-by: Paul Elder <paul.elder@ideasonboard.com> Reviewed-by: Stefan Klug <stefan.klug@ideasonboard.com> Reviewed-by: Daniel Scally <dan.scally@ideasonboard.com>
412 lines
14 KiB
C++
412 lines
14 KiB
C++
/* SPDX-License-Identifier: LGPL-2.1-or-later */
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/*
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* Copyright (C) 2021-2022, Ideas On Board
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*
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* AGC/AEC mean-based control algorithm
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*/
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#include "agc.h"
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#include <algorithm>
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#include <chrono>
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#include <cmath>
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#include <libcamera/base/log.h>
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#include <libcamera/base/utils.h>
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#include <libcamera/control_ids.h>
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#include <libcamera/ipa/core_ipa_interface.h>
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#include "libcamera/internal/yaml_parser.h"
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#include "libipa/histogram.h"
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/**
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* \file agc.h
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*/
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namespace libcamera {
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using namespace std::literals::chrono_literals;
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namespace ipa::rkisp1::algorithms {
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/**
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* \class Agc
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* \brief A mean-based auto-exposure algorithm
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*/
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LOG_DEFINE_CATEGORY(RkISP1Agc)
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int Agc::parseMeteringModes(IPAContext &context, const YamlObject &tuningData)
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{
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if (!tuningData.isDictionary()) {
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LOG(RkISP1Agc, Error)
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<< "'AeMeteringMode' parameter not found in tuning file";
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return -EINVAL;
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}
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for (const auto &[key, value] : tuningData.asDict()) {
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if (controls::AeMeteringModeNameValueMap.find(key) ==
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controls::AeMeteringModeNameValueMap.end()) {
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LOG(RkISP1Agc, Warning)
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<< "Skipping unknown metering mode '" << key << "'";
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continue;
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}
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std::vector<uint8_t> weights =
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value.getList<uint8_t>().value_or(std::vector<uint8_t>{});
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if (weights.size() != context.hw->numHistogramWeights) {
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LOG(RkISP1Agc, Warning)
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<< "Failed to read metering mode'" << key << "'";
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continue;
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}
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meteringModes_[controls::AeMeteringModeNameValueMap.at(key)] = weights;
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}
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if (meteringModes_.empty()) {
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LOG(RkISP1Agc, Warning)
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<< "No metering modes read from tuning file; defaulting to matrix";
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int32_t meteringModeId = controls::AeMeteringModeNameValueMap.at("MeteringMatrix");
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std::vector<uint8_t> weights(context.hw->numHistogramWeights, 1);
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meteringModes_[meteringModeId] = weights;
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}
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return 0;
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}
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uint8_t Agc::computeHistogramPredivider(Size &size, enum rkisp1_cif_isp_histogram_mode mode)
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{
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/*
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* The maximum number of pixels that could potentially be in one bin is
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* if all the pixels of the image are in it, multiplied by 3 for the
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* three color channels. The counter for each bin is 16 bits wide, so
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* `factor` thus contains the number of times we'd wrap around. This is
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* obviously the number of pixels that we need to skip to make sure
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* that we don't wrap around, but we compute the square root of it
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* instead, as the skip that we need to program is for both the x and y
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* directions.
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*
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* Even though it looks like dividing into a counter of 65536 would
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* overflow by 1, this is apparently fine according to the hardware
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* documentation, and this successfully gets the expected documented
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* predivider size for cases where:
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* (width / predivider) * (height / predivider) * 3 == 65536.
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*
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* There's a bit of extra rounding math to make sure the rounding goes
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* the correct direction so that the square of the step is big enough
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* to encompass the `factor` number of pixels that we need to skip.
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*
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* \todo Take into account weights. That is, if the weights are low
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* enough we can potentially reduce the predivider to increase
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* precision. This needs some investigation however, as this hardware
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* behavior is undocumented and is only an educated guess.
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*/
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int count = mode == RKISP1_CIF_ISP_HISTOGRAM_MODE_RGB_COMBINED ? 3 : 1;
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double factor = size.width * size.height * count / 65536.0;
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double root = std::sqrt(factor);
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uint8_t predivider;
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if (std::pow(std::floor(root), 2) < factor)
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predivider = static_cast<uint8_t>(std::ceil(root));
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else
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predivider = static_cast<uint8_t>(std::floor(root));
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return std::clamp<uint8_t>(predivider, 3, 127);
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}
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Agc::Agc()
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{
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supportsRaw_ = true;
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}
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/**
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* \brief Initialise the AGC algorithm from tuning files
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* \param[in] context The shared IPA context
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* \param[in] tuningData The YamlObject containing Agc tuning data
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*
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* This function calls the base class' tuningData parsers to discover which
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* control values are supported.
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*
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* \return 0 on success or errors from the base class
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*/
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int Agc::init(IPAContext &context, const YamlObject &tuningData)
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{
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int ret;
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ret = parseTuningData(tuningData);
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if (ret)
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return ret;
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const YamlObject &yamlMeteringModes = tuningData["AeMeteringMode"];
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ret = parseMeteringModes(context, yamlMeteringModes);
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if (ret)
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return ret;
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context.ctrlMap.merge(controls());
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return 0;
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}
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/**
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* \brief Configure the AGC given a configInfo
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* \param[in] context The shared IPA context
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* \param[in] configInfo The IPA configuration data
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*
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* \return 0
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*/
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int Agc::configure(IPAContext &context, const IPACameraSensorInfo &configInfo)
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{
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/* Configure the default exposure and gain. */
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context.activeState.agc.automatic.gain = context.configuration.sensor.minAnalogueGain;
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context.activeState.agc.automatic.exposure =
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10ms / context.configuration.sensor.lineDuration;
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context.activeState.agc.manual.gain = context.activeState.agc.automatic.gain;
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context.activeState.agc.manual.exposure = context.activeState.agc.automatic.exposure;
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context.activeState.agc.autoEnabled = !context.configuration.raw;
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context.activeState.agc.constraintMode = constraintModes().begin()->first;
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context.activeState.agc.exposureMode = exposureModeHelpers().begin()->first;
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/*
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* Define the measurement window for AGC as a centered rectangle
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* covering 3/4 of the image width and height.
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*/
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context.configuration.agc.measureWindow.h_offs = configInfo.outputSize.width / 8;
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context.configuration.agc.measureWindow.v_offs = configInfo.outputSize.height / 8;
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context.configuration.agc.measureWindow.h_size = 3 * configInfo.outputSize.width / 4;
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context.configuration.agc.measureWindow.v_size = 3 * configInfo.outputSize.height / 4;
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/* \todo Run this again when FrameDurationLimits is passed in */
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setLimits(context.configuration.sensor.minShutterSpeed,
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context.configuration.sensor.maxShutterSpeed,
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context.configuration.sensor.minAnalogueGain,
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context.configuration.sensor.maxAnalogueGain);
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resetFrameCount();
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return 0;
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}
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/**
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* \copydoc libcamera::ipa::Algorithm::queueRequest
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*/
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void Agc::queueRequest(IPAContext &context,
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[[maybe_unused]] const uint32_t frame,
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IPAFrameContext &frameContext,
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const ControlList &controls)
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{
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auto &agc = context.activeState.agc;
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if (!context.configuration.raw) {
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const auto &agcEnable = controls.get(controls::AeEnable);
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if (agcEnable && *agcEnable != agc.autoEnabled) {
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agc.autoEnabled = *agcEnable;
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LOG(RkISP1Agc, Debug)
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<< (agc.autoEnabled ? "Enabling" : "Disabling")
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<< " AGC";
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}
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}
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const auto &exposure = controls.get(controls::ExposureTime);
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if (exposure && !agc.autoEnabled) {
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agc.manual.exposure = *exposure * 1.0us
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/ context.configuration.sensor.lineDuration;
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LOG(RkISP1Agc, Debug)
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<< "Set exposure to " << agc.manual.exposure;
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}
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const auto &gain = controls.get(controls::AnalogueGain);
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if (gain && !agc.autoEnabled) {
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agc.manual.gain = *gain;
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LOG(RkISP1Agc, Debug) << "Set gain to " << agc.manual.gain;
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}
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frameContext.agc.autoEnabled = agc.autoEnabled;
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if (!frameContext.agc.autoEnabled) {
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frameContext.agc.exposure = agc.manual.exposure;
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frameContext.agc.gain = agc.manual.gain;
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}
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}
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/**
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* \copydoc libcamera::ipa::Algorithm::prepare
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*/
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void Agc::prepare(IPAContext &context, const uint32_t frame,
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IPAFrameContext &frameContext, rkisp1_params_cfg *params)
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{
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if (frameContext.agc.autoEnabled) {
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frameContext.agc.exposure = context.activeState.agc.automatic.exposure;
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frameContext.agc.gain = context.activeState.agc.automatic.gain;
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}
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/* \todo Remove this when we can set the below with controls */
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if (frame > 0)
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return;
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/* Configure the measurement window. */
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params->meas.aec_config.meas_window = context.configuration.agc.measureWindow;
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/* Use a continuous method for measure. */
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params->meas.aec_config.autostop = RKISP1_CIF_ISP_EXP_CTRL_AUTOSTOP_0;
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/* Estimate Y as (R + G + B) x (85/256). */
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params->meas.aec_config.mode = RKISP1_CIF_ISP_EXP_MEASURING_MODE_1;
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params->module_cfg_update |= RKISP1_CIF_ISP_MODULE_AEC;
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params->module_ens |= RKISP1_CIF_ISP_MODULE_AEC;
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params->module_en_update |= RKISP1_CIF_ISP_MODULE_AEC;
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/* Configure histogram. */
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params->meas.hst_config.meas_window = context.configuration.agc.measureWindow;
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/* Produce the luminance histogram. */
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params->meas.hst_config.mode = RKISP1_CIF_ISP_HISTOGRAM_MODE_Y_HISTOGRAM;
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/* Set an average weighted histogram. */
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Span<uint8_t> weights{
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params->meas.hst_config.hist_weight,
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context.hw->numHistogramWeights
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};
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/* \todo Get this from control */
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std::vector<uint8_t> &modeWeights = meteringModes_.at(controls::MeteringMatrix);
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std::copy(modeWeights.begin(), modeWeights.end(), weights.begin());
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struct rkisp1_cif_isp_window window = params->meas.hst_config.meas_window;
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Size windowSize = { window.h_size, window.v_size };
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params->meas.hst_config.histogram_predivider =
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computeHistogramPredivider(windowSize,
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static_cast<rkisp1_cif_isp_histogram_mode>(params->meas.hst_config.mode));
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/* Update the configuration for histogram. */
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params->module_cfg_update |= RKISP1_CIF_ISP_MODULE_HST;
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/* Enable the histogram measure unit. */
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params->module_ens |= RKISP1_CIF_ISP_MODULE_HST;
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params->module_en_update |= RKISP1_CIF_ISP_MODULE_HST;
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}
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void Agc::fillMetadata(IPAContext &context, IPAFrameContext &frameContext,
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ControlList &metadata)
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{
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utils::Duration exposureTime = context.configuration.sensor.lineDuration
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* frameContext.sensor.exposure;
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metadata.set(controls::AnalogueGain, frameContext.sensor.gain);
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metadata.set(controls::ExposureTime, exposureTime.get<std::micro>());
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/* \todo Use VBlank value calculated from each frame exposure. */
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uint32_t vTotal = context.configuration.sensor.size.height
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+ context.configuration.sensor.defVBlank;
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utils::Duration frameDuration = context.configuration.sensor.lineDuration
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* vTotal;
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metadata.set(controls::FrameDuration, frameDuration.get<std::micro>());
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}
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/**
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* \brief Estimate the relative luminance of the frame with a given gain
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* \param[in] gain The gain to apply to the frame
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*
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* This function estimates the average relative luminance of the frame that
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* would be output by the sensor if an additional \a gain was applied.
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*
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* The estimation is based on the AE statistics for the current frame. Y
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* averages for all cells are first multiplied by the gain, and then saturated
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* to approximate the sensor behaviour at high brightness values. The
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* approximation is quite rough, as it doesn't take into account non-linearities
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* when approaching saturation. In this case, saturating after the conversion to
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* YUV doesn't take into account the fact that the R, G and B components
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* contribute differently to the relative luminance.
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*
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* The values are normalized to the [0.0, 1.0] range, where 1.0 corresponds to a
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* theoretical perfect reflector of 100% reference white.
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*
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* More detailed information can be found in:
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* https://en.wikipedia.org/wiki/Relative_luminance
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*
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* \return The relative luminance
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*/
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double Agc::estimateLuminance(double gain) const
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{
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double ySum = 0.0;
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/* Sum the averages, saturated to 255. */
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for (uint8_t expMean : expMeans_)
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ySum += std::min(expMean * gain, 255.0);
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/* \todo Weight with the AWB gains */
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return ySum / expMeans_.size() / 255;
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}
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/**
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* \brief Process RkISP1 statistics, and run AGC operations
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* \param[in] context The shared IPA context
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* \param[in] frame The frame context sequence number
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* \param[in] frameContext The current frame context
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* \param[in] stats The RKISP1 statistics and ISP results
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* \param[out] metadata Metadata for the frame, to be filled by the algorithm
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*
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* Identify the current image brightness, and use that to estimate the optimal
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* new exposure and gain for the scene.
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*/
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void Agc::process(IPAContext &context, [[maybe_unused]] const uint32_t frame,
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IPAFrameContext &frameContext, const rkisp1_stat_buffer *stats,
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ControlList &metadata)
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{
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if (!stats) {
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fillMetadata(context, frameContext, metadata);
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return;
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}
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/*
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* \todo Verify that the exposure and gain applied by the sensor for
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* this frame match what has been requested. This isn't a hard
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* requirement for stability of the AGC (the guarantee we need in
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* automatic mode is a perfect match between the frame and the values
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* we receive), but is important in manual mode.
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*/
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const rkisp1_cif_isp_stat *params = &stats->params;
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ASSERT(stats->meas_type & RKISP1_CIF_ISP_STAT_AUTOEXP);
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/* The lower 4 bits are fractional and meant to be discarded. */
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Histogram hist({ params->hist.hist_bins, context.hw->numHistogramBins },
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[](uint32_t x) { return x >> 4; });
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expMeans_ = { params->ae.exp_mean, context.hw->numAeCells };
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/*
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* The Agc algorithm needs to know the effective exposure value that was
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* applied to the sensor when the statistics were collected.
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*/
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utils::Duration exposureTime = context.configuration.sensor.lineDuration
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* frameContext.sensor.exposure;
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double analogueGain = frameContext.sensor.gain;
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utils::Duration effectiveExposureValue = exposureTime * analogueGain;
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utils::Duration shutterTime;
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double aGain, dGain;
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std::tie(shutterTime, aGain, dGain) =
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calculateNewEv(context.activeState.agc.constraintMode,
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context.activeState.agc.exposureMode,
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hist, effectiveExposureValue);
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LOG(RkISP1Agc, Debug)
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<< "Divided up shutter, analogue gain and digital gain are "
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<< shutterTime << ", " << aGain << " and " << dGain;
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IPAActiveState &activeState = context.activeState;
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/* Update the estimated exposure and gain. */
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activeState.agc.automatic.exposure = shutterTime / context.configuration.sensor.lineDuration;
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activeState.agc.automatic.gain = aGain;
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fillMetadata(context, frameContext, metadata);
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expMeans_ = {};
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}
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REGISTER_IPA_ALGORITHM(Agc, "Agc")
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} /* namespace ipa::rkisp1::algorithms */
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} /* namespace libcamera */
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