[libcamera-devel] [PATCH v1 05/10] ipa: raspberrypi: alsc: Use a better type name for sparse arrays
Jacopo Mondi
jacopo.mondi at ideasonboard.com
Fri Mar 24 09:55:20 CET 2023
Hi David
On Wed, Mar 22, 2023 at 01:06:07PM +0000, Naushir Patuck via libcamera-devel wrote:
> From: David Plowman <david.plowman at raspberrypi.com>
>
> 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>.
Nicer to read indeed
Reviewed-by: Jacopo Mondi <jacopo.mondi at ideasonboard.com>
Thanks
j
>
> No functional changes.
>
> Signed-off-by: David Plowman <david.plowman at raspberrypi.com>
> Reviewed-by: Naushir Patuck <naush at raspberrypi.com>
> ---
> src/ipa/raspberrypi/controller/rpi/alsc.cpp | 24 ++++++++++-----------
> src/ipa/raspberrypi/controller/rpi/alsc.h | 10 ++++++++-
> 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 524c48093590..3a2e8fe00ca6 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 1ab61299c4cd..0b6d9478073c 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 */
> --
> 2.34.1
>
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