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-rw-r--r--src/ipa/raspberrypi/controller/rpi/awb.cpp97
1 files changed, 53 insertions, 44 deletions
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 &params)
{
- RPI_LOG("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 +107,8 @@ void AwbConfig::Read(boost::property_tree::ptree const &params)
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<std::mutex> 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<std::mutex> 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;
}
}
}
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-03 19:17:30 +0000