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 /* SPDX-License-Identifier: LGPL-2.1-or-later */ /* * Copyright (C) 2021, Ideas On Board * * ipu3_agc.cpp - AGC/AEC mean-based control algorithm */ #include "agc.h" #include #include #include #include #include #include #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 a shutter 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) /* Limits for analogue gain values */ static constexpr double kMinAnalogueGain = 1.0; static constexpr double kMaxAnalogueGain = 8.0; /* \todo Honour the FrameDurationLimits control instead of hardcoding a limit */ static constexpr utils::Duration kMaxShutterSpeed = 60ms; /* Histogram constants */ static constexpr uint32_t knumHistogramBins = 256; /* Target value to reach for the top 2% of the histogram */ static constexpr double kEvGainTarget = 0.5; /* Number of frames to wait before calculating stats on minimum exposure */ static constexpr uint32_t kNumStartupFrames = 10; /* * Relative luminance target. * * It's a number that's chosen so that, when the camera points at a grey * target, the resulting image brightness is considered right. */ static constexpr double kRelativeLuminanceTarget = 0.16; Agc::Agc() : frameCount_(0), minShutterSpeed_(0s), maxShutterSpeed_(0s), filteredExposure_(0s) { } /** * \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) { IPASessionConfiguration &configuration = context.configuration; IPAFrameContext &frameContext = context.frameContext; stride_ = configuration.grid.stride; minShutterSpeed_ = context.configuration.agc.minShutterSpeed; maxShutterSpeed_ = std::min(context.configuration.agc.maxShutterSpeed, kMaxShutterSpeed); minAnalogueGain_ = std::max(context.configuration.agc.minAnalogueGain, kMinAnalogueGain); maxAnalogueGain_ = std::min(context.configuration.agc.maxAnalogueGain, kMaxAnalogueGain); /* Configure the default exposure and gain. */ frameContext.agc.gain = std::max(minAnalogueGain_, kMinAnalogueGain); frameContext.agc.exposure = 10ms / configuration.sensor.lineDuration; frameCount_ = 0; return 0; } /** * \brief Estimate the mean value of the top 2% of the histogram * \param[in] stats The statistics computed by the ImgU * \param[in] grid The grid used to store the statistics in the IPU3 * \return The mean value of the top 2% of the histogram */ double Agc::measureBrightness(const ipu3_uapi_stats_3a *stats, const ipu3_uapi_grid_config &grid) const { /* Initialise the histogram array */ uint32_t hist[knumHistogramBins] = { 0 }; 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( &stats->awb_raw_buffer.meta_data[cellPosition] ); uint8_t gr = cell->Gr_avg; uint8_t gb = cell->Gb_avg; /* * Store the average green value to estimate the * brightness. Even the overexposed pixels are * taken into account. */ hist[(gr + gb) / 2]++; } } /* Estimate the quantile mean of the top 2% of the histogram. */ return Histogram(Span(hist)).interQuantileMean(0.98, 1.0); } /** * \brief Apply a filter on the exposure value to limit the speed of changes * \param[in] exposureValue The target exposure from the AGC algorithm * * The speed of the filter is adaptive, and will produce the target quicker * during startup, or when the target exposure is within 20% of the most recent * filter output. * * \return The filtered exposure */ utils::Duration Agc::filterExposure(utils::Duration exposureValue) { double speed = 0.2; /* Adapt instantly if we are in startup phase. */ if (frameCount_ < kNumStartupFrames) speed = 1.0; /* * If we are close to the desired result, go faster to avoid making * multiple micro-adjustments. * \todo Make this customisable? */ if (filteredExposure_ < 1.2 * exposureValue && filteredExposure_ > 0.8 * exposureValue) speed = sqrt(speed); filteredExposure_ = speed * exposureValue + filteredExposure_ * (1.0 - speed); LOG(IPU3Agc, Debug) << "After filtering, exposure " << filteredExposure_; return filteredExposure_; } /** * \brief Estimate the new exposure and gain values * \param[inout] frameContext The shared IPA frame Context * \param[in] yGain The gain calculated based on the relative luminance target * \param[in] iqMeanGain The gain calculated based on the relative luminance target */ void Agc::computeExposure(IPAContext &context, double yGain, double iqMeanGain) { const IPASessionConfiguration &configuration = context.configuration; IPAFrameContext &frameContext = context.frameContext; /* Get the effective exposure and gain applied on the sensor. */ uint32_t exposure = frameContext.sensor.exposure; double analogueGain = frameContext.sensor.gain; /* Use the highest of the two gain estimates. */ double evGain = std::max(yGain, iqMeanGain); /* Consider within 1% of the target as correctly exposed */ if (utils::abs_diff(evGain, 1.0) < 0.01) LOG(IPU3Agc, Debug) << "We are well exposed (evGain = " << evGain << ")"; /* extracted from Rpi::Agc::computeTargetExposure */ /* Calculate the shutter time in seconds */ utils::Duration currentShutter = exposure * configuration.sensor.lineDuration; /* * Update the exposure value for the next computation using the values * of exposure and gain really used by the sensor. */ utils::Duration effectiveExposureValue = currentShutter * analogueGain; LOG(IPU3Agc, Debug) << "Actual total exposure " << currentShutter * analogueGain << " Shutter speed " << currentShutter << " Gain " << analogueGain << " Needed ev gain " << evGain; /* * Calculate the current exposure value for the scene as the latest * exposure value applied multiplied by the new estimated gain. */ utils::Duration exposureValue = effectiveExposureValue * evGain; /* Clamp the exposure value to the min and max authorized */ utils::Duration maxTotalExposure = maxShutterSpeed_ * maxAnalogueGain_; exposureValue = std::min(exposureValue, maxTotalExposure); LOG(IPU3Agc, Debug) << "Target total exposure " << exposureValue << ", maximum is " << maxTotalExposure; /* * Filter the exposure. * \todo: estimate if we need to desaturate */ exposureValue = filterExposure(exposureValue); /* * Divide the exposure value as new exposure and gain values. * * Push the shutter time up to the maximum first, and only then * increase the gain. */ utils::Duration shutterTime = std::clamp(exposureValue / minAnalogueGain_, minShutterSpeed_, maxShutterSpeed_); double stepGain = std::clamp(exposureValue / shutterTime, minAnalogueGain_, maxAnalogueGain_); LOG(IPU3Agc, Debug) << "Divided up shutter and gain are " << shutterTime << " and " << stepGain; /* Update the estimated exposure and gain. */ frameContext.agc.exposure = shutterTime / configuration.sensor.lineDuration; frameContext.agc.gain = stepGain; } /** * \brief Estimate the relative luminance of the frame with a given gain * \param[in] frameContext The shared IPA frame context * \param[in] grid The grid used to store the statistics in the IPU3 * \param[in] stats The IPU3 statistics and ISP results * \param[in] gain The gain to apply to the frame * \return The relative luminance * * This function estimates the average relative luminance of the frame that * would be output by the sensor if an additional \a gain was applied. * * 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 */ double Agc::estimateLuminance(IPAFrameContext &frameContext, const ipu3_uapi_grid_config &grid, const ipu3_uapi_stats_3a *stats, double gain) { double redSum = 0, greenSum = 0, blueSum = 0; /* Sum the per-channel averages, saturated to 255. */ 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( &stats->awb_raw_buffer.meta_data[cellPosition] ); const uint8_t G_avg = (cell->Gr_avg + cell->Gb_avg) / 2; redSum += std::min(cell->R_avg * gain, 255.0); greenSum += std::min(G_avg * gain, 255.0); blueSum += std::min(cell->B_avg * gain, 255.0); } } /* * Apply the AWB gains to approximate colours correctly, use the Rec. * 601 formula to calculate the relative luminance, and normalize it. */ double ySum = redSum * frameContext.awb.gains.red * 0.299 + greenSum * frameContext.awb.gains.green * 0.587 + blueSum * frameContext.awb.gains.blue * 0.114; return ySum / (grid.height * grid.width) / 255; } /** * \brief Process IPU3 statistics, and run AGC operations * \param[in] context The shared IPA context * \param[in] stats The IPU3 statistics and ISP results * * Identify the current image brightness, and use that to estimate the optimal * new exposure and gain for the scene. */ void Agc::process(IPAContext &context, const ipu3_uapi_stats_3a *stats) { /* * Estimate the gain needed to have the proportion of pixels in a given * desired range. iqMean is the mean value of the top 2% of the * cumulative histogram, and we want it to be as close as possible to a * configured target. */ double iqMean = measureBrightness(stats, context.configuration.grid.bdsGrid); double iqMeanGain = kEvGainTarget * knumHistogramBins / iqMean; /* * Estimate the gain needed to achieve a relative luminance target. To * account for non-linearity caused by saturation, the value needs to be * estimated in an iterative process, as multiplying by a gain will not * increase the relative luminance by the same factor if some image * regions are saturated. */ double yGain = 1.0; double yTarget = kRelativeLuminanceTarget; for (unsigned int i = 0; i < 8; i++) { double yValue = estimateLuminance(context.frameContext, context.configuration.grid.bdsGrid, stats, yGain); double extraGain = std::min(10.0, yTarget / (yValue + .001)); yGain *= extraGain; LOG(IPU3Agc, Debug) << "Y value: " << yValue << ", Y target: " << yTarget << ", gives gain " << yGain; if (extraGain < 1.01) break; } computeExposure(context, yGain, iqMeanGain); frameCount_++; } } /* namespace ipa::ipu3::algorithms */ } /* namespace libcamera */