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ipa: raspberrypi: awb: Replace Raspberry Pi debug with libcamera debug

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Signed-off-by: David Plowman <david.plowman@raspberrypi.com>
Reviewed-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Reviewed-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
Signed-off-by: Laurent Pinchart <laurent.pinchart@ideasonboard.com>
This commit is contained in:
David Plowman 2021-01-25 18:48:56 +00:00 • committed by Laurent Pinchart
parent b2186c2b89
commit eb605eab5b
1 changed files with 53 additions and 44 deletions
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97
src/ipa/raspberrypi/controller/rpi/awb.cpp
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@ -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;
}
}
}