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>

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 */