From eb605eab5b3df765082549bda2dc45215cda16f9 Mon Sep 17 00:00:00 2001 From: David Plowman Date: Mon, 25 Jan 2021 18:48:56 +0000 Subject: ipa: raspberrypi: awb: Replace Raspberry Pi debug with libcamera debug Signed-off-by: David Plowman Reviewed-by: Kieran Bingham Reviewed-by: Laurent Pinchart Signed-off-by: Laurent Pinchart --- src/ipa/raspberrypi/controller/rpi/awb.cpp | 97 ++++++++++++++++-------------- 1 file changed, 53 insertions(+), 44 deletions(-) (limited to 'src/ipa/raspberrypi/controller') diff --git a/src/ipa/raspberrypi/controller/rpi/awb.cpp b/src/ipa/raspberrypi/controller/rpi/awb.cpp index f66c2b29..62337b13 100644 --- a/src/ipa/raspberrypi/controller/rpi/awb.cpp +++ b/src/ipa/raspberrypi/controller/rpi/awb.cpp @@ -5,12 +5,16 @@ * awb.cpp - AWB control algorithm */ -#include "../logging.hpp" +#include "libcamera/internal/log.h" + #include "../lux_status.h" #include "awb.hpp" using namespace RPiController; +using namespace libcamera; + +LOG_DEFINE_CATEGORY(RPiAwb) #define NAME "rpi.awb" @@ -58,7 +62,6 @@ static void read_ct_curve(Pwl &ct_r, Pwl &ct_b, void AwbConfig::Read(boost::property_tree::ptree const ¶ms) { - RPI_LOG("AwbConfig"); bayes = params.get("bayes", 1); frame_period = params.get("frame_period", 10); startup_frames = params.get("startup_frames", 10); @@ -104,8 +107,8 @@ void AwbConfig::Read(boost::property_tree::ptree const ¶ms) if (bayes) { if (ct_r.Empty() || ct_b.Empty() || priors.empty() || default_mode == nullptr) { - RPI_WARN( - "Bayesian AWB mis-configured - switch to Grey method"); + LOG(RPiAwb, Warning) + << "Bayesian AWB mis-configured - switch to Grey method"; bayes = false; } } @@ -220,7 +223,7 @@ void Awb::SwitchMode([[maybe_unused]] CameraMode const &camera_mode, void Awb::fetchAsyncResults() { - RPI_LOG("Fetch AWB results"); + LOG(RPiAwb, Debug) << "Fetch AWB results"; async_finished_ = false; async_started_ = false; sync_results_ = async_results_; @@ -229,7 +232,7 @@ void Awb::fetchAsyncResults() void Awb::restartAsync(StatisticsPtr &stats, std::string const &mode_name, double lux) { - RPI_LOG("Starting AWB thread"); + LOG(RPiAwb, Debug) << "Starting AWB calculation"; // this makes a new reference which belongs to the asynchronous thread statistics_ = stats; // store the mode as it could technically change @@ -254,13 +257,12 @@ void Awb::Prepare(Metadata *image_metadata) double speed = frame_count_ < (int)config_.startup_frames ? 1.0 : config_.speed; - RPI_LOG("Awb: frame_count " << frame_count_ << " speed " << speed); + LOG(RPiAwb, Debug) + << "frame_count " << frame_count_ << " speed " << speed; { std::unique_lock lock(mutex_); - if (async_started_ && async_finished_) { - RPI_LOG("AWB thread finished"); + if (async_started_ && async_finished_) fetchAsyncResults(); - } } // Finally apply IIR filter to results and put into metadata. memcpy(prev_sync_results_.mode, sync_results_.mode, @@ -275,9 +277,10 @@ void Awb::Prepare(Metadata *image_metadata) prev_sync_results_.gain_b = speed * sync_results_.gain_b + (1.0 - speed) * prev_sync_results_.gain_b; image_metadata->Set("awb.status", prev_sync_results_); - RPI_LOG("Using AWB gains r " << prev_sync_results_.gain_r << " g " - << prev_sync_results_.gain_g << " b " - << prev_sync_results_.gain_b); + LOG(RPiAwb, Debug) + << "Using AWB gains r " << prev_sync_results_.gain_r << " g " + << prev_sync_results_.gain_g << " b " + << prev_sync_results_.gain_b; } void Awb::Process(StatisticsPtr &stats, Metadata *image_metadata) @@ -287,7 +290,7 @@ void Awb::Process(StatisticsPtr &stats, Metadata *image_metadata) frame_phase_++; if (frame_count2_ < (int)config_.startup_frames) frame_count2_++; - RPI_LOG("Awb: frame_phase " << frame_phase_); + LOG(RPiAwb, Debug) << "frame_phase " << frame_phase_; if (frame_phase_ >= (int)config_.frame_period || frame_count2_ < (int)config_.startup_frames) { // Update any settings and any image metadata that we need. @@ -299,14 +302,12 @@ void Awb::Process(StatisticsPtr &stats, Metadata *image_metadata) struct LuxStatus lux_status = {}; lux_status.lux = 400; // in case no metadata if (image_metadata->Get("lux.status", lux_status) != 0) - RPI_LOG("No lux metadata found"); - RPI_LOG("Awb lux value is " << lux_status.lux); + LOG(RPiAwb, Debug) << "No lux metadata found"; + LOG(RPiAwb, Debug) << "Awb lux value is " << lux_status.lux; std::unique_lock lock(mutex_); - if (async_started_ == false) { - RPI_LOG("AWB thread starting"); + if (async_started_ == false) restartAsync(stats, mode_name, lux_status.lux); - } } } @@ -375,7 +376,7 @@ double Awb::computeDelta2Sum(double gain_r, double gain_b) double delta_r = gain_r * z.R - 1 - config_.whitepoint_r; double delta_b = gain_b * z.B - 1 - config_.whitepoint_b; double delta2 = delta_r * delta_r + delta_b * delta_b; - //RPI_LOG("delta_r " << delta_r << " delta_b " << delta_b << " delta2 " << delta2); + //LOG(RPiAwb, Debug) << "delta_r " << delta_r << " delta_b " << delta_b << " delta2 " << delta2; delta2 = std::min(delta2, config_.delta_limit); delta2_sum += delta2; } @@ -438,10 +439,11 @@ double Awb::coarseSearch(Pwl const &prior) double prior_log_likelihood = prior.Eval(prior.Domain().Clip(t)); double final_log_likelihood = delta2_sum - prior_log_likelihood; - RPI_LOG("t: " << t << " gain_r " << gain_r << " gain_b " - << gain_b << " delta2_sum " << delta2_sum - << " prior " << prior_log_likelihood << " final " - << final_log_likelihood); + LOG(RPiAwb, Debug) + << "t: " << t << " gain_r " << gain_r << " gain_b " + << gain_b << " delta2_sum " << delta2_sum + << " prior " << prior_log_likelihood << " final " + << final_log_likelihood; points_.push_back(Pwl::Point(t, final_log_likelihood)); if (points_.back().y < points_[best_point].y) best_point = points_.size() - 1; @@ -452,7 +454,7 @@ double Awb::coarseSearch(Pwl const &prior) mode_->ct_hi); } t = points_[best_point].x; - RPI_LOG("Coarse search found CT " << t); + LOG(RPiAwb, Debug) << "Coarse search found CT " << t; // We have the best point of the search, but refine it with a quadratic // interpolation around its neighbours. if (points_.size() > 2) { @@ -461,8 +463,9 @@ double Awb::coarseSearch(Pwl const &prior) t = interpolate_quadatric(points_[best_point - 1], points_[best_point], points_[best_point + 1]); - RPI_LOG("After quadratic refinement, coarse search has CT " - << t); + LOG(RPiAwb, Debug) + << "After quadratic refinement, coarse search has CT " + << t; } return t; } @@ -514,8 +517,9 @@ void Awb::fineSearch(double &t, double &r, double &b, Pwl const &prior) double gain_r = 1 / r_test, gain_b = 1 / b_test; double delta2_sum = computeDelta2Sum(gain_r, gain_b); points[j].y = delta2_sum - prior_log_likelihood; - RPI_LOG("At t " << t_test << " r " << r_test << " b " - << b_test << ": " << points[j].y); + LOG(RPiAwb, Debug) + << "At t " << t_test << " r " << r_test << " b " + << b_test << ": " << points[j].y; if (points[j].y < points[best_point].y) best_point = j; } @@ -532,17 +536,18 @@ void Awb::fineSearch(double &t, double &r, double &b, Pwl const &prior) double gain_r = 1 / r_test, gain_b = 1 / b_test; double delta2_sum = computeDelta2Sum(gain_r, gain_b); double final_log_likelihood = delta2_sum - prior_log_likelihood; - RPI_LOG("Finally " + LOG(RPiAwb, Debug) + << "Finally " << t_test << " r " << r_test << " b " << b_test << ": " << final_log_likelihood - << (final_log_likelihood < best_log_likelihood ? " BEST" - : "")); + << (final_log_likelihood < best_log_likelihood ? " BEST" : ""); if (best_t == 0 || final_log_likelihood < best_log_likelihood) best_log_likelihood = final_log_likelihood, best_t = t_test, best_r = r_test, best_b = b_test; } t = best_t, r = best_r, b = best_b; - RPI_LOG("Fine search found t " << t << " r " << r << " b " << b); + LOG(RPiAwb, Debug) + << "Fine search found t " << t << " r " << r << " b " << b; } void Awb::awbBayes() @@ -556,13 +561,14 @@ void Awb::awbBayes() Pwl prior = interpolatePrior(); prior *= zones_.size() / (double)(AWB_STATS_SIZE_X * AWB_STATS_SIZE_Y); prior.Map([](double x, double y) { - RPI_LOG("(" << x << "," << y << ")"); + LOG(RPiAwb, Debug) << "(" << x << "," << y << ")"; }); double t = coarseSearch(prior); double r = config_.ct_r.Eval(t); double b = config_.ct_b.Eval(t); - RPI_LOG("After coarse search: r " << r << " b " << b << " (gains r " - << 1 / r << " b " << 1 / b << ")"); + LOG(RPiAwb, Debug) + << "After coarse search: r " << r << " b " << b << " (gains r " + << 1 / r << " b " << 1 / b << ")"; // Not entirely sure how to handle the fine search yet. Mostly the // estimated CT is already good enough, but the fine search allows us to // wander transverely off the CT curve. Under some illuminants, where @@ -570,8 +576,9 @@ void Awb::awbBayes() // though I probably need more real datasets before deciding exactly how // this should be controlled and tuned. fineSearch(t, r, b, prior); - RPI_LOG("After fine search: r " << r << " b " << b << " (gains r " - << 1 / r << " b " << 1 / b << ")"); + LOG(RPiAwb, Debug) + << "After fine search: r " << r << " b " << b << " (gains r " + << 1 / r << " b " << 1 / b << ")"; // Write results out for the main thread to pick up. Remember to adjust // the gains from the ones that the "canonical sensor" would require to // the ones needed by *this* sensor. @@ -583,7 +590,7 @@ void Awb::awbBayes() void Awb::awbGrey() { - RPI_LOG("Grey world AWB"); + LOG(RPiAwb, Debug) << "Grey world AWB"; // Make a separate list of the derivatives for each of red and blue, so // that we can sort them to exclude the extreme gains. We could // consider some variations, such as normalising all the zones first, or @@ -620,21 +627,23 @@ void Awb::doAwb() async_results_.gain_r = manual_r_; async_results_.gain_g = 1.0; async_results_.gain_b = manual_b_; - RPI_LOG("Using manual white balance: gain_r " + LOG(RPiAwb, Debug) + << "Using manual white balance: gain_r " << async_results_.gain_r << " gain_b " - << async_results_.gain_b); + << async_results_.gain_b; } else { prepareStats(); - RPI_LOG("Valid zones: " << zones_.size()); + LOG(RPiAwb, Debug) << "Valid zones: " << zones_.size(); if (zones_.size() > config_.min_regions) { if (config_.bayes) awbBayes(); else awbGrey(); - RPI_LOG("CT found is " + LOG(RPiAwb, Debug) + << "CT found is " << async_results_.temperature_K << " with gains r " << async_results_.gain_r - << " and b " << async_results_.gain_b); + << " and b " << async_results_.gain_b; } } } -- cgit v1.2.1