libcamera/vivid.git - libcamera pipeline handler for VIVID

Age	Commit message (Expand)	Author
2020-11-11	utils: ipc: import mojo	Paul Elder

/* SPDX-License-Identifier: LGPL-2.1-or-later */ /* * Copyright (C) 2021, Red Hat * * af.cpp - IPU3 auto focus algorithm */ #include "af.h" #include <algorithm> #include <chrono> #include <cmath> #include <fcntl.h> #include <numeric> #include <sys/ioctl.h> #include <sys/stat.h> #include <sys/types.h> #include <unistd.h> #include <linux/videodev2.h> #include <libcamera/base/log.h> #include <libcamera/ipa/core_ipa_interface.h> #include "libipa/histogram.h" /** * \file af.h */ /* * Static variables from ChromiumOS Intel Camera HAL and ia_imaging library: * - https://chromium.googlesource.com/chromiumos/platform/arc-camera/+/master/hal/intel/psl/ipu3/statsConverter/ipu3-stats.h * - https://chromium.googlesource.com/chromiumos/platform/camera/+/refs/heads/main/hal/intel/ipu3/include/ia_imaging/af_public.h */ /** The minimum horizontal grid dimension. */ static constexpr uint8_t kAfMinGridWidth = 16; /** The minimum vertical grid dimension. */ static constexpr uint8_t kAfMinGridHeight = 16; /** The maximum horizontal grid dimension. */ static constexpr uint8_t kAfMaxGridWidth = 32; /** The maximum vertical grid dimension. */ static constexpr uint8_t kAfMaxGridHeight = 24; /** The minimum value of Log2 of the width of the grid cell. */ static constexpr uint16_t kAfMinGridBlockWidth = 4; /** The minimum value of Log2 of the height of the grid cell. */ static constexpr uint16_t kAfMinGridBlockHeight = 3; /** The maximum value of Log2 of the width of the grid cell. */ static constexpr uint16_t kAfMaxGridBlockWidth = 6; /** The maximum value of Log2 of the height of the grid cell. */ static constexpr uint16_t kAfMaxGridBlockHeight = 6; /** The number of blocks in vertical axis per slice. */ static constexpr uint16_t kAfDefaultHeightPerSlice = 2; namespace libcamera { using namespace std::literals::chrono_literals; namespace ipa::ipu3::algorithms { LOG_DEFINE_CATEGORY(IPU3Af) /** * Maximum focus steps of the VCM control * \todo should be obtained from the VCM driver */ static constexpr uint32_t kMaxFocusSteps = 1023; /* Minimum focus step for searching appropriate focus */ static constexpr uint32_t kCoarseSearchStep = 30; static constexpr uint32_t kFineSearchStep = 1; /* Max ratio of variance change, 0.0 < kMaxChange < 1.0 */ static constexpr double kMaxChange = 0.5; /* The numbers of frame to be ignored, before performing focus scan. */ static constexpr uint32_t kIgnoreFrame = 10; /* Fine scan range 0 < kFineRange < 1 */ static constexpr double kFineRange = 0.05; /* Settings for IPU3 AF filter */ static struct ipu3_uapi_af_filter_config afFilterConfigDefault = { .y1_coeff_0 = { 0, 1, 3, 7 }, .y1_coeff_1 = { 11, 13, 1, 2 }, .y1_coeff_2 = { 8, 19, 34, 242 }, .y1_sign_vec = 0x7fdffbfe, .y2_coeff_0 = { 0, 1, 6, 6 }, .y2_coeff_1 = { 13, 25, 3, 0 }, .y2_coeff_2 = { 25, 3, 177, 254 }, .y2_sign_vec = 0x4e53ca72, .y_calc = { 8, 8, 8, 8 }, .nf = { 0, 9, 0, 9, 0 }, }; /** * \class Af * \brief An auto-focus algorithm based on IPU3 statistics * * This algorithm is used to determine the position of the lens to make a * focused image. The IPU3 AF processing block computes the statistics that * are composed by two types of filtered value and stores in a AF buffer. * Typically, for a clear image, it has a relatively higher contrast than a * blurred one. Therefore, if an image with the highest contrast can be * found through the scan, the position of the len indicates to a clearest * image. */ Af::Af() : focus_(0), bestFocus_(0), currentVariance_(0.0), previousVariance_(0.0), coarseCompleted_(false), fineCompleted_(false) { } /** * \copydoc libcamera::ipa::Algorithm::prepare */ void Af::prepare(IPAContext &context, ipu3_uapi_params *params) { const struct ipu3_uapi_grid_config &grid = context.configuration.af.afGrid; params->acc_param.af.grid_cfg = grid; params->acc_param.af.filter_config = afFilterConfigDefault; /* Enable AF processing block */ params->use.acc_af = 1; } /** * \brief Configure the Af given a configInfo * \param[in] context The shared IPA context * \param[in] configInfo The IPA configuration data * \return 0 on success, a negative error code otherwise */ int Af::configure(IPAContext &context, const IPAConfigInfo &configInfo) { struct ipu3_uapi_grid_config &grid = context.configuration.af.afGrid; grid.width = kAfMinGridWidth; grid.height = kAfMinGridHeight; grid.block_width_log2 = kAfMinGridBlockWidth; grid.block_height_log2 = kAfMinGridBlockHeight; /* * \todo - while this clamping code is effectively a no-op, it satisfies * the compiler that the constant definitions of the hardware limits * are used, and paves the way to support dynamic grid sizing in the * future. While the block_{width,height}_log2 remain assigned to the * minimum, this code should be optimized out by the compiler. */ grid.width = std::clamp(grid.width, kAfMinGridWidth, kAfMaxGridWidth); grid.height = std::clamp(grid.height, kAfMinGridHeight, kAfMaxGridHeight); grid.block_width_log2 = std::clamp(grid.block_width_log2, kAfMinGridBlockWidth, kAfMaxGridBlockWidth); grid.block_height_log2 = std::clamp(grid.block_height_log2, kAfMinGridBlockHeight, kAfMaxGridBlockHeight); grid.height_per_slice = kAfDefaultHeightPerSlice; /* Position the AF grid in the center of the BDS output. */ Rectangle bds(configInfo.bdsOutputSize); Size gridSize(grid.width << grid.block_width_log2, grid.height << grid.block_height_log2); /* * \todo - Support request metadata * - Set the ROI based on any input controls in the request * - Return the AF ROI as metadata in the Request */ Rectangle roi = gridSize.centeredTo(bds.center()); Point start = roi.topLeft(); /* x_start and y_start should be even */ grid.x_start = utils::alignDown(start.x, 2); grid.y_start = utils::alignDown(start.y, 2); grid.y_start |= IPU3_UAPI_GRID_Y_START_EN; /* Initial max focus step */ maxStep_ = kMaxFocusSteps; /* Initial frame ignore counter */ afIgnoreFrameReset(); /* Initial focus value */ context.activeState.af.focus = 0; /* Maximum variance of the AF statistics */ context.activeState.af.maxVariance = 0; /* The stable AF value flag. if it is true, the AF should be in a stable state. */ context.activeState.af.stable = false; return 0; } /** * \brief AF coarse scan * \param[in] context The shared IPA context * * Find a near focused image using a coarse step. The step is determined by * kCoarseSearchStep. */ void Af::afCoarseScan(IPAContext &context) { if (coarseCompleted_) return; if (afNeedIgnoreFrame()) return; if (afScan(context, kCoarseSearchStep)) { coarseCompleted_ = true; context.activeState.af.maxVariance = 0; focus_ = context.activeState.af.focus - (context.activeState.af.focus * kFineRange); context.activeState.af.focus = focus_; previousVariance_ = 0; maxStep_ = std::clamp(focus_ + static_cast<uint32_t>((focus_ * kFineRange)), 0U, kMaxFocusSteps); } } /** * \brief AF fine scan * \param[in] context The shared IPA context * * Find an optimum lens position with moving 1 step for each search. */ void Af::afFineScan(IPAContext &context) { if (!coarseCompleted_) return; if (afNeedIgnoreFrame()) return; if (afScan(context, kFineSearchStep)) { context.activeState.af.stable = true; fineCompleted_ = true; } } /** * \brief AF reset * \param[in] context The shared IPA context * * Reset all the parameters to start over the AF process. */ void Af::afReset(IPAContext &context) { if (afNeedIgnoreFrame()) return; context.activeState.af.maxVariance = 0; context.activeState.af.focus = 0; focus_ = 0; context.activeState.af.stable = false; ignoreCounter_ = kIgnoreFrame; previousVariance_ = 0.0; coarseCompleted_ = false; fineCompleted_ = false; maxStep_ = kMaxFocusSteps; } /** * \brief AF variance comparison. * \param[in] context The IPA context * \param[in] min_step The VCM movement step. * * We always pick the largest variance to replace the previous one. The image * with a larger variance also indicates it is a clearer image than previous * one. If we find a negative derivative, we return immediately. * * \return True, if it finds a AF value. */ bool Af::afScan(IPAContext &context, int min_step) { if (focus_ > maxStep_) { /* If reach the max step, move lens to the position. */ context.activeState.af.focus = bestFocus_; return true; } else { /* * Find the maximum of the variance by estimating its * derivative. If the direction changes, it means we have * passed a maximum one step before. */ if ((currentVariance_ - context.activeState.af.maxVariance) >= -(context.activeState.af.maxVariance * 0.1)) { /* * Positive and zero derivative: * The variance is still increasing. The focus could be * increased for the next comparison. Also, the max variance * and previous focus value are updated. */ bestFocus_ = focus_; focus_ += min_step; context.activeState.af.focus = focus_; context.activeState.af.maxVariance = currentVariance_; } else { /* * Negative derivative: * The variance starts to decrease which means the maximum * variance is found. Set focus step to previous good one * then return immediately. */ context.activeState.af.focus = bestFocus_; return true; } } previousVariance_ = currentVariance_; LOG(IPU3Af, Debug) << " Previous step is " << bestFocus_ << " Current step is " << focus_; return false; } /** * \brief Determine the frame to be ignored. * \return Return True if the frame should be ignored, false otherwise */ bool Af::afNeedIgnoreFrame() { if (ignoreCounter_ == 0) return false; else ignoreCounter_--; return true; } /** * \brief Reset frame ignore counter. */ void Af::afIgnoreFrameReset() { ignoreCounter_ = kIgnoreFrame; } /** * \brief Estimate variance * \param[in] y_items The AF filter data set from the IPU3 statistics buffer * \param[in] isY1 Selects between filter Y1 or Y2 to calculate the variance * * Calculate the mean of the data set provided by \a y_item, and then calculate * the variance of that data set from the mean. * * The operation can work on one of two sets of values contained within the * y_item data set supplied by the IPU3. The two data sets are the results of * both the Y1 and Y2 filters which are used to support coarse (Y1) and fine * (Y2) calculations of the contrast. * * \return The variance of the values in the data set \a y_item selected by \a isY1 */ double Af::afEstimateVariance(Span<const y_table_item_t> y_items, bool isY1) { uint32_t total = 0; double mean; double var_sum = 0; for (auto y : y_items) total += isY1 ? y.y1_avg : y.y2_avg; mean = total / y_items.size(); for (auto y : y_items) { double avg = isY1 ? y.y1_avg : y.y2_avg; var_sum += pow(avg - mean, 2); } return var_sum / y_items.size(); } /** * \brief Determine out-of-focus situation. * \param[in] context The IPA context. * * Out-of-focus means that the variance change rate for a focused and a new * variance is greater than a threshold. * * \return True if the variance threshold is crossed indicating lost focus, * false otherwise */ bool Af::afIsOutOfFocus(IPAContext &context) { const uint32_t diff_var = std::abs(currentVariance_ - context.activeState.af.maxVariance); const double var_ratio = diff_var / context.activeState.af.maxVariance; LOG(IPU3Af, Debug) << "Variance change rate: " << var_ratio << " Current VCM step: " << context.activeState.af.focus; if (var_ratio > kMaxChange) return true; else return false; } /** * \brief Determine the max contrast image and lens position. * \param[in] context The IPA context. * \param[in] frameContext The current frame context * \param[in] stats The statistics buffer of IPU3. * * Ideally, a clear image also has a relatively higher contrast. So, every * image for each focus step should be tested to find an optimal focus step. * * The Hill Climbing Algorithm[1] is used to find the maximum variance of the * AF statistics which is the AF output of IPU3. The focus step is increased * then the variance of the AF statistics are estimated. If it finds the * negative derivative we have just passed the peak, and we infer that the best * focus is found. * * [1] Hill Climbing Algorithm, https://en.wikipedia.org/wiki/Hill_climbing */ void Af::process(IPAContext &context, [[maybe_unused]] IPAFrameContext &frameContext, const ipu3_uapi_stats_3a *stats) { /* Evaluate the AF buffer length */ uint32_t afRawBufferLen = context.configuration.af.afGrid.width * context.configuration.af.afGrid.height; ASSERT(afRawBufferLen < IPU3_UAPI_AF_Y_TABLE_MAX_SIZE); Span<const y_table_item_t> y_items(reinterpret_cast<const y_table_item_t *>(&stats->af_raw_buffer.y_table), afRawBufferLen); /* * Calculate the mean and the variance of AF statistics for a given grid. * For coarse: y1 are used. * For fine: y2 results are used. */ currentVariance_ = afEstimateVariance(y_items, !coarseCompleted_); if (!context.activeState.af.stable) { afCoarseScan(context); afFineScan(context); } else { if (afIsOutOfFocus(context)) afReset(context); else afIgnoreFrameReset(); } } REGISTER_IPA_ALGORITHM(Af, "Af") } /* namespace ipa::ipu3::algorithms */ } /* namespace libcamera */