[libcamera-devel] [PATCH 3/5] ipa: raspberrypi: awb: Replace Raspberry Pi debug with libcamera debug
Laurent Pinchart
laurent.pinchart at ideasonboard.com
Sat Jan 23 12:15:40 CET 2021
Hi David,
Thank you for the patch.
Same comments here as for the previous patches.
On Fri, Jan 22, 2021 at 10:22:09AM +0000, David Plowman wrote:
> Signed-off-by: David Plowman <david.plowman at raspberrypi.com>
> ---
> src/ipa/raspberrypi/controller/rpi/awb.cpp | 92 +++++++++++-----------
> 1 file changed, 47 insertions(+), 45 deletions(-)
>
> diff --git a/src/ipa/raspberrypi/controller/rpi/awb.cpp b/src/ipa/raspberrypi/controller/rpi/awb.cpp
> index f66c2b29..34f3644b 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 +62,7 @@ static void read_ct_curve(Pwl &ct_r, Pwl &ct_b,
>
> void AwbConfig::Read(boost::property_tree::ptree const ¶ms)
> {
> - RPI_LOG("AwbConfig");
> + LOG(RPiAwb, Debug) << "AwbConfig";
> bayes = params.get<int>("bayes", 1);
> frame_period = params.get<uint16_t>("frame_period", 10);
> startup_frames = params.get<uint16_t>("startup_frames", 10);
> @@ -104,8 +108,7 @@ 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 thread";
> // this makes a new reference which belongs to the asynchronous thread
> statistics_ = stats;
> // store the mode as it could technically change
> @@ -254,11 +257,11 @@ 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) << "Awb: frame_count " << frame_count_ << " speed " << speed;
> {
> std::unique_lock<std::mutex> lock(mutex_);
> if (async_started_ && async_finished_) {
> - RPI_LOG("AWB thread finished");
> + LOG(RPiAwb, Debug) << "AWB thread finished";
> fetchAsyncResults();
> }
> }
> @@ -275,9 +278,9 @@ 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) << "Awb: 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.
> @@ -299,12 +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<std::mutex> lock(mutex_);
> if (async_started_ == false) {
> - RPI_LOG("AWB thread starting");
> + LOG(RPiAwb, Debug) << "AWB thread starting";
> restartAsync(stats, mode_name, lux_status.lux);
> }
> }
> @@ -375,7 +378,7 @@ 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 +441,10 @@ 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 +455,7 @@ 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 +464,8 @@ 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 +517,8 @@ 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 +535,16 @@ 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 "
> - << t_test << " r " << r_test << " b " << b_test << ": "
> - << final_log_likelihood
> - << (final_log_likelihood < best_log_likelihood ? " BEST"
> - : ""));
> + LOG(RPiAwb, Debug) << "Finally "
> + << t_test << " r " << r_test << " b " << b_test << ": "
> + << final_log_likelihood
> + << (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 +558,13 @@ 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 +572,8 @@ 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 +585,7 @@ 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 +622,21 @@ 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 "
> - << async_results_.gain_r << " gain_b "
> - << async_results_.gain_b);
> + LOG(RPiAwb, Debug) << "Using manual white balance: gain_r "
> + << async_results_.gain_r << " 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 "
> - << async_results_.temperature_K
> - << " with gains r " << async_results_.gain_r
> - << " and b " << async_results_.gain_b);
> + LOG(RPiAwb, Debug) << "CT found is "
> + << async_results_.temperature_K
> + << " with gains r " << async_results_.gain_r
> + << " and b " << async_results_.gain_b;
> }
> }
> }
--
Regards,
Laurent Pinchart
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