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-rw-r--r--src/ipa/raspberrypi/controller/rpi/alsc.cpp58
-rw-r--r--src/ipa/raspberrypi/controller/rpi/alsc.hpp1
2 files changed, 44 insertions, 15 deletions
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.cpp b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
index be3d1ae4..e575c14a 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.cpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
@@ -4,9 +4,12 @@
*
* alsc.cpp - ALSC (auto lens shading correction) control algorithm
*/
+
#include <math.h>
+#include <numeric>
#include <libcamera/base/log.h>
+#include <libcamera/base/span.h>
#include "../awb_status.h"
#include "alsc.hpp"
@@ -149,6 +152,7 @@ void Alsc::Read(boost::property_tree::ptree const &params)
read_calibrations(config_.calibrations_Cb, params, "calibrations_Cb");
config_.default_ct = params.get<double>("default_ct", 4500.0);
config_.threshold = params.get<double>("threshold", 1e-3);
+ config_.lambda_bound = params.get<double>("lambda_bound", 0.05);
}
static double get_ct(Metadata *metadata, double default_ct);
@@ -610,30 +614,47 @@ static double compute_lambda_top_end(int i, double const M[XY][4],
// Gauss-Seidel iteration with over-relaxation.
static double gauss_seidel2_SOR(double const M[XY][4], double omega,
- double lambda[XY])
+ double lambda[XY], double lambda_bound)
{
+ const double min = 1 - lambda_bound, max = 1 + lambda_bound;
double old_lambda[XY];
int i;
for (i = 0; i < XY; i++)
old_lambda[i] = lambda[i];
lambda[0] = compute_lambda_bottom_start(0, M, lambda);
- for (i = 1; i < X; i++)
+ lambda[0] = std::clamp(lambda[0], min, max);
+ for (i = 1; i < X; i++) {
lambda[i] = compute_lambda_bottom(i, M, lambda);
- for (; i < XY - X; i++)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i < XY - X; i++) {
lambda[i] = compute_lambda_interior(i, M, lambda);
- for (; i < XY - 1; i++)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i < XY - 1; i++) {
lambda[i] = compute_lambda_top(i, M, lambda);
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
lambda[i] = compute_lambda_top_end(i, M, lambda);
+ lambda[i] = std::clamp(lambda[i], min, max);
// Also solve the system from bottom to top, to help spread the updates
// better.
lambda[i] = compute_lambda_top_end(i, M, lambda);
- for (i = XY - 2; i >= XY - X; i--)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ for (i = XY - 2; i >= XY - X; i--) {
lambda[i] = compute_lambda_top(i, M, lambda);
- for (; i >= X; i--)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i >= X; i--) {
lambda[i] = compute_lambda_interior(i, M, lambda);
- for (; i >= 1; i--)
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
+ for (; i >= 1; i--) {
lambda[i] = compute_lambda_bottom(i, M, lambda);
+ lambda[i] = std::clamp(lambda[i], min, max);
+ }
lambda[0] = compute_lambda_bottom_start(0, M, lambda);
+ lambda[0] = std::clamp(lambda[0], min, max);
double max_diff = 0;
for (i = 0; i < XY; i++) {
lambda[i] = old_lambda[i] + (lambda[i] - old_lambda[i]) * omega;
@@ -653,15 +674,24 @@ static void normalise(double *ptr, size_t n)
ptr[i] /= minval;
}
+// Rescale the values so that the average value is 1.
+static void reaverage(Span<double> data)
+{
+ double sum = std::accumulate(data.begin(), data.end(), 0.0);
+ double ratio = 1 / (sum / data.size());
+ for (double &d : data)
+ d *= ratio;
+}
+
static void run_matrix_iterations(double const C[XY], double lambda[XY],
double const W[XY][4], double omega,
- int n_iter, double threshold)
+ int n_iter, double threshold, double lambda_bound)
{
double M[XY][4];
construct_M(C, W, M);
double last_max_diff = std::numeric_limits<double>::max();
for (int i = 0; i < n_iter; i++) {
- double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda));
+ double max_diff = fabs(gauss_seidel2_SOR(M, omega, lambda, lambda_bound));
if (max_diff < threshold) {
LOG(RPiAlsc, Debug)
<< "Stop after " << i + 1 << " iterations";
@@ -675,10 +705,8 @@ static void run_matrix_iterations(double const C[XY], double lambda[XY],
<< last_max_diff << " to " << max_diff;
last_max_diff = max_diff;
}
- // We're going to normalise the lambdas so the smallest is 1. Not sure
- // this is really necessary as they get renormalised later, but I
- // suppose it does stop these quantities from wandering off...
- normalise(lambda, XY);
+ // We're going to normalise the lambdas so the total average is 1.
+ reaverage({ lambda, XY });
}
static void add_luminance_rb(double result[XY], double const lambda[XY],
@@ -737,9 +765,9 @@ void Alsc::doAlsc()
compute_W(Cb, config_.sigma_Cb, Wb);
// Run Gauss-Seidel iterations over the resulting matrix, for R and B.
run_matrix_iterations(Cr, lambda_r_, Wr, config_.omega, config_.n_iter,
- config_.threshold);
+ config_.threshold, config_.lambda_bound);
run_matrix_iterations(Cb, lambda_b_, Wb, config_.omega, config_.n_iter,
- config_.threshold);
+ config_.threshold, config_.lambda_bound);
// Fold the calibrated gains into our final lambda values. (Note that on
// the next run, we re-start with the lambda values that don't have the
// calibration gains included.)
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.hpp b/src/ipa/raspberrypi/controller/rpi/alsc.hpp
index 9616b99e..d1dbe0d1 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.hpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.hpp
@@ -41,6 +41,7 @@ struct AlscConfig {
std::vector<AlscCalibration> calibrations_Cb;
double default_ct; // colour temperature if no metadata found
double threshold; // iteration termination threshold
+ double lambda_bound; // upper/lower bound for lambda from a value of 1
};
class Alsc : public Algorithm