[PATCH v2 06/17] test: Add minimal test for Matrix

Laurent Pinchart laurent.pinchart at ideasonboard.com
Mon Mar 31 15:15:11 CEST 2025


I'm not sure what happens, this is a review of 05/17 but it cames as a
reply to 06/17.

On Wed, Mar 19, 2025 at 06:12:21PM +0100, Barnabás Pőcze wrote:
> 2025. 03. 19. 17:11 keltezéssel, Stefan Klug írta:
> > For calculations in upcoming algorithm patches, the inverse of a matrix
> > is required. Add an implementation of the inverse() function for square
> > matrices.
> > 
> > Signed-off-by: Stefan Klug <stefan.klug at ideasonboard.com>
> > Signed-off-by: Laurent Pinchart <laurent.pinchart at ideasonboard.com>
> > 
> > ---
> > 
> > Changes in v2:
> > - Replaced the implementation by a generic one provided by Laurent that
> >    supports arbitrary square matrices instead of 2x2 and 3x3 only.
> > - Moved the implementation into the cpp file.
> > ---
> >   include/libcamera/internal/matrix.h |  16 +++
> >   src/libcamera/matrix.cpp            | 160 ++++++++++++++++++++++++++++
> >   2 files changed, 176 insertions(+)
> > 
> > diff --git a/include/libcamera/internal/matrix.h b/include/libcamera/internal/matrix.h
> > index 9b80521e3cb0..6e3c190286fe 100644
> > --- a/include/libcamera/internal/matrix.h
> > +++ b/include/libcamera/internal/matrix.h
> > @@ -19,6 +19,11 @@ namespace libcamera {
> >   
> >   LOG_DECLARE_CATEGORY(Matrix)
> >   
> > +#ifndef __DOXYGEN__
> > +template<typename T>
> > +bool matrixInvert(Span<const T> dataIn, Span<T> dataOut, unsigned int dim);
> > +#endif /* __DOXYGEN__ */
> > +
> >   template<typename T, unsigned int Rows, unsigned int Cols>
> >   class Matrix
> >   {
> > @@ -88,6 +93,17 @@ public:
> >   		return *this;
> >   	}
> >   
> > +	Matrix<T, Rows, Cols> inverse(bool *ok = nullptr) const
> 
> Returning `std::optional<...>`? Or `std::pair<Matrix<...>, bool>` if the
> returned matrix is in any way useful?
> 
> 
> > +	{
> > +		static_assert(Rows == Cols, "Matrix must be square");
> > +
> > +		Matrix<T, Rows, Cols> inverse;
> > +		bool res = matrixInvert(Span<const T>(data_), Span<T>(inverse.data_), Rows);
> > +		if (ok)
> > +			*ok = res;
> > +		return inverse;
> > +	}
> > +
> >   private:
> >   	std::array<T, Rows * Cols> data_{};
> >   };
> > diff --git a/src/libcamera/matrix.cpp b/src/libcamera/matrix.cpp
> > index 6dca7498cab3..8590f8efeff3 100644
> > --- a/src/libcamera/matrix.cpp
> > +++ b/src/libcamera/matrix.cpp
> > @@ -7,6 +7,12 @@
> >   
> >   #include "libcamera/internal/matrix.h"
> >   
> > +#include <algorithm>
> > +#include <assert.h>
> > +#include <cmath>
> > +#include <numeric>
> > +#include <vector>
> > +
> >   #include <libcamera/base/log.h>
> >   
> >   /**
> > @@ -87,6 +93,20 @@ LOG_DEFINE_CATEGORY(Matrix)
> >    * \return Row \a i from the matrix, as a Span
> >    */
> >   
> > +/**
> > + * \fn Matrix::inverse(bool *ok) const
> > + * \param[out] ok Indicate if the matrix was successfully inverted
> > + * \brief Compute the inverse of the matrix
> > + *
> > + * This function computes the inverse of the matrix. It is only implemented for
> > + * matrices of float and double types. If \a ok is provided it will be set to a
> > + * boolean value to indicate of the inversion was successful. This can be used
> > + * to check if the matrix is singular, in which case the function will return
> > + * an identity matrix.
> > + *
> > + * \return The inverse of the matrix
> > + */
> > +
> >   /**
> >    * \fn Matrix::operator[](size_t i)
> >    * \copydoc Matrix::operator[](size_t i) const
> > @@ -142,6 +162,146 @@ LOG_DEFINE_CATEGORY(Matrix)
> >    */
> >   
> >   #ifndef __DOXYGEN__
> > +template<typename T>
> > +bool matrixInvert(Span<const T> dataIn, Span<T> dataOut, unsigned int dim)
> 
> Sorry, but I think this is a bit of an overkill:
> 
>    (1) it is (most likely) slower than hard-coding the inversion of a 3x3 matrix
>        (where it would be used);

Is this a real concern, given our usage patterns ? If so we could use
template specialization for 3x3 matrices.

>    (2) it adds dynamic memory allocations.
> 
> Or am I missing something? I suppose (2) could be addressed by providing a
> "scratch buffer" as well, but I think (1) still stands.
> 
> > +{
> > +	/*
> > +	 * Convenience class to access matrix data, providing a row-major (i,j)
> > +	 * element accessor through the call operator, and the ability to swap
> > +	 * rows without modifying the backing storage.
> > +	 */
> > +	class MatrixAccessor
> > +	{
> > +	public:
> > +		MatrixAccessor(Span<T> data, unsigned int rows, unsigned int cols)
> > +			: data_(data), swap_(rows), rows_(rows), cols_(cols)
> > +		{
> > +			std::iota(swap_.begin(), swap_.end(), T{ 0 });
> > +		}
> > +
> > +		T &operator()(unsigned int row, unsigned int col)
> > +		{
> > +			assert(row < rows_ && col < cols_);
> > +			return data_[index(row, col)];
> > +		}
> > +
> > +		void swap(unsigned int a, unsigned int b)
> > +		{
> > +			assert(a < rows_ && a < cols_);
> > +			std::swap(swap_[a], swap_[b]);
> > +		}
> > +
> > +	private:
> > +		unsigned int index(unsigned int row, unsigned int col) const
> > +		{
> > +			return swap_[row] * cols_ + col;
> > +		}
> > +
> > +		Span<T> data_;
> > +		std::vector<unsigned int> swap_;
> > +		unsigned int rows_;
> > +		unsigned int cols_;
> > +	};
> > +
> > +	/*
> > +	 * Matrix inversion using Gaussian elimination.
> > +	 *
> > +	 * Start by augmenting the original matrix with an identiy matrix of
> > +	 * the same size.
> > +	 */
> > +	std::vector<T> data(dim * dim * 2);
> > +	MatrixAccessor matrix(data, dim, dim * 2);
> > +
> > +	for (unsigned int i = 0; i < dim; ++i) {
> > +		for (unsigned int j = 0; j < dim; ++j)
> > +			matrix(i, j) = dataIn[i * dim + j];
> > +		matrix(i, i + dim) = T{ 1 };
> > +	}
> > +
> > +	/* Start by triangularizing the input . */
> > +	for (unsigned int pivot = 0; pivot < dim; ++pivot) {
> > +		/*
> > +		 * Locate the next pivot. To improve numerical stability, use
> > +		 * the row with the largest value in the pivot's column.
> > +		 */
> > +		unsigned int row;
> > +		T maxValue{ 0 };
> > +
> > +		for (unsigned int i = pivot; i < dim; ++i) {
> > +			T value = std::abs(matrix(i, pivot));
> > +			if (maxValue < value) {
> > +				maxValue = value;
> > +				row = i;
> > +			}
> > +		}
> > +
> > +		/*
> > +		 * If no pivot is found in the column, the matrix is not
> > +		 * invertible. Return an identity matrix.
> > +		 */
> > +		if (maxValue == 0) {
> > +			std::fill(dataOut.begin(), dataOut.end(), T{ 0 });
> > +			for (unsigned int i = 0; i < dim; ++i)
> > +				dataOut[i * dim + i] = T{ 1 };
> > +			return false;
> > +		}
> > +
> > +		/* Swap rows to bring the pivot in the right location. */
> > +		matrix.swap(pivot, row);
> > +
> > +		/* Process all rows below the pivot to zero the pivot column. */
> > +		const T pivotValue = matrix(pivot, pivot);
> > +
> > +		for (unsigned int i = pivot + 1; i < dim; ++i) {
> > +			const T factor = matrix(i, pivot) / pivotValue;
> > +
> > +			/*
> > +			 * We know the element in the pivot column will be 0,
> > +			 * hardcode it instead of computing it.
> > +			 */
> > +			matrix(i, pivot) = T{ 0 };
> > +
> > +			for (unsigned int j = pivot + 1; j < dim * 2; ++j)
> > +				matrix(i, j) -= matrix(pivot, j) * factor;
> > +		}
> > +	}
> > +
> > +	/*
> > +	 * Then diagonalize the input, walking the diagonal backwards. There's
> > +	 * no need to update the input matrix, as all the values we would write
> > +	 * in the top-right triangle aren't used in further calculations (and
> > +	 * would all by definition be zero).
> > +	 */
> > +	for (unsigned int pivot = dim - 1; pivot > 0; --pivot) {
> > +		const T pivotValue = matrix(pivot, pivot);
> > +
> > +		for (unsigned int i = 0; i < pivot; ++i) {
> > +			const T factor = matrix(i, pivot) / pivotValue;
> > +
> > +			for (unsigned int j = dim; j < dim * 2; ++j)
> > +				matrix(i, j) -= matrix(pivot, j) * factor;
> > +		}
> > +	}
> > +
> > +	/*
> > +	 * Finally, normalize the diagonal and store the result in the output
> > +	 * data.
> > +	 */
> > +	for (unsigned int i = 0; i < dim; ++i) {
> > +		const T factor = matrix(i, i);
> > +
> > +		for (unsigned int j = 0; j < dim; ++j)
> > +			dataOut[i * dim + j] = matrix(i, j + dim) / factor;
> > +	}
> > +
> > +	return true;
> > +}
> > +
> > +template bool matrixInvert<float>(Span<const float> dataIn, Span<float> dataOut,
> > +				  unsigned int dim);
> > +template bool matrixInvert<double>(Span<const double> data, Span<double> dataOut,
> > +				   unsigned int dim);
> > +
> >   /*
> >    * The YAML data shall be a list of numerical values. Its size shall be equal
> >    * to the product of the number of rows and columns of the matrix (Rows x

-- 
Regards,

Laurent Pinchart


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