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authorDavid Plowman <david.plowman@raspberrypi.com>2023-03-27 13:20:25 +0100
committerKieran Bingham <kieran.bingham@ideasonboard.com>2023-03-31 13:29:18 +0100
commita82d08973f09f951e1de016ed8c1137663e7a58c (patch)
treeccc070f9731152fcf787949504ca04572fa71f17 /src/ipa/raspberrypi/controller/rpi
parentc557de126e551133a345ae7bd8d1b58188769ac9 (diff)
ipa: raspberrypi: alsc: Use a better type name for sparse arrays
The algorithm uses the data type std::vector<std::array<double, 4>> to represent the large sparse matrices that are XY (X, Y being the ALSC grid size) high but with only 4 non-zero elements on each row. Replace this slightly long type name by SparseArray<double>. No functional changes. Signed-off-by: David Plowman <david.plowman@raspberrypi.com> Reviewed-by: Naushir Patuck <naush@raspberrypi.com> Reviewed-by: Jacopo Mondi <jacopo.mondi@ideasonboard.com> Signed-off-by: Kieran Bingham <kieran.bingham@ideasonboard.com>
Diffstat (limited to 'src/ipa/raspberrypi/controller/rpi')
-rw-r--r--src/ipa/raspberrypi/controller/rpi/alsc.cpp24
-rw-r--r--src/ipa/raspberrypi/controller/rpi/alsc.h10
2 files changed, 21 insertions, 13 deletions
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.cpp b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
index 524c4809..3a2e8fe0 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.cpp
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.cpp
@@ -607,7 +607,7 @@ static double computeWeight(double Ci, double Cj, double sigma)
/* Compute all weights. */
static void computeW(const Array2D<double> &C, double sigma,
- std::vector<std::array<double, 4>> &W)
+ SparseArray<double> &W)
{
size_t XY = C.size();
size_t X = C.dimensions().width;
@@ -623,8 +623,8 @@ static void computeW(const Array2D<double> &C, double sigma,
/* Compute M, the large but sparse matrix such that M * lambdas = 0. */
static void constructM(const Array2D<double> &C,
- const std::vector<std::array<double, 4>> &W,
- std::vector<std::array<double, 4>> &M)
+ const SparseArray<double> &W,
+ SparseArray<double> &M)
{
size_t XY = C.size();
size_t X = C.dimensions().width;
@@ -651,37 +651,37 @@ static void constructM(const Array2D<double> &C,
* left/right neighbours are zero down the left/right edges, so we don't need
* need to test the i value to exclude them.
*/
-static double computeLambdaBottom(int i, const std::vector<std::array<double, 4>> &M,
+static double computeLambdaBottom(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + lambda.dimensions().width] +
M[i][3] * lambda[i - 1];
}
-static double computeLambdaBottomStart(int i, const std::vector<std::array<double, 4>> &M,
+static double computeLambdaBottomStart(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][1] * lambda[i + 1] + M[i][2] * lambda[i + lambda.dimensions().width];
}
-static double computeLambdaInterior(int i, const std::vector<std::array<double, 4>> &M,
+static double computeLambdaInterior(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][1] * lambda[i + 1] +
M[i][2] * lambda[i + lambda.dimensions().width] + M[i][3] * lambda[i - 1];
}
-static double computeLambdaTop(int i, const std::vector<std::array<double, 4>> &M,
+static double computeLambdaTop(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][1] * lambda[i + 1] +
M[i][3] * lambda[i - 1];
}
-static double computeLambdaTopEnd(int i, const std::vector<std::array<double, 4>> &M,
+static double computeLambdaTopEnd(int i, const SparseArray<double> &M,
Array2D<double> &lambda)
{
return M[i][0] * lambda[i - lambda.dimensions().width] + M[i][3] * lambda[i - 1];
}
/* Gauss-Seidel iteration with over-relaxation. */
-static double gaussSeidel2Sor(const std::vector<std::array<double, 4>> &M, double omega,
+static double gaussSeidel2Sor(const SparseArray<double> &M, double omega,
Array2D<double> &lambda, double lambdaBound)
{
int XY = lambda.size();
@@ -753,8 +753,8 @@ static void reaverage(Array2D<double> &data)
static void runMatrixIterations(const Array2D<double> &C,
Array2D<double> &lambda,
- const std::vector<std::array<double, 4>> &W,
- std::vector<std::array<double, 4>> &M, double omega,
+ const SparseArray<double> &W,
+ SparseArray<double> &M, double omega,
unsigned int nIter, double threshold, double lambdaBound)
{
constructM(C, W, M);
@@ -813,7 +813,7 @@ void Alsc::doAlsc()
{
Array2D<double> &cr = tmpC_[0], &cb = tmpC_[1], &calTableR = tmpC_[2],
&calTableB = tmpC_[3], &calTableTmp = tmpC_[4];
- std::vector<std::array<double, 4>> &wr = tmpM_[0], &wb = tmpM_[1], &M = tmpM_[2];
+ SparseArray<double> &wr = tmpM_[0], &wb = tmpM_[1], &M = tmpM_[2];
/*
* Calculate our R/B ("Cr"/"Cb") colour statistics, and assess which are
diff --git a/src/ipa/raspberrypi/controller/rpi/alsc.h b/src/ipa/raspberrypi/controller/rpi/alsc.h
index 1ab61299..0b6d9478 100644
--- a/src/ipa/raspberrypi/controller/rpi/alsc.h
+++ b/src/ipa/raspberrypi/controller/rpi/alsc.h
@@ -68,6 +68,14 @@ private:
std::vector<T> data_;
};
+/*
+ * We'll use the term SparseArray for the large sparse matrices that are
+ * XY tall but have only 4 non-zero elements on each row.
+ */
+
+template<typename T>
+using SparseArray = std::vector<std::array<T, 4>>;
+
struct AlscCalibration {
double ct;
Array2D<double> table;
@@ -160,7 +168,7 @@ private:
/* Temporaries for the computations */
std::array<Array2D<double>, 5> tmpC_;
- std::array<std::vector<std::array<double, 4>>, 3> tmpM_;
+ std::array<SparseArray<double>, 3> tmpM_;
};
} /* namespace RPiController */