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ipa: raspberrypi: alsc: Use a better type name for sparse arrays

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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>
This commit is contained in:
David Plowman 2023-03-27 13:20:25 +01:00 • committed by Kieran Bingham
parent c557de126e
commit a82d08973f
2 changed files with 21 additions and 13 deletions
Download patch file Download diff file Expand all files Collapse all files

24
src/ipa/raspberrypi/controller/rpi/alsc.cpp
View file

@ -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

10
src/ipa/raspberrypi/controller/rpi/alsc.h
View file

@ -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 */