Add custom kernels to image solver (8a23431f) · Commits · TNL / tnl-dev

src/Benchmarks/Convolution/support/DummyTask.h

+1 −1

Original line number	Diff line number	Diff line
		@@ -118,7 +118,7 @@ public:
		{
		auto index = i + j * dimensions.x();

		result[ index ] = TNL::max(TNL::min(resultValue, 1.), 0.);
		result[ index ] = resultValue;
		};

		ConvolutionLauncher::execute< Index, Real >( dimensions,

src/Benchmarks/Convolution/support/ImageSolver.h

+105 −30

Original line number	Diff line number	Diff line
		@@ -14,6 +14,8 @@

		static std::vector< TNL::String > dimensionIds = { "x-dimension", "y-dimension", "z-dimension" };
		static std::vector< TNL::String > kernelSizeIds = { "x-kernel-size", "y-kernel-size", "z-kernel-size" };
		static std::vector< TNL::String > kernels = { "identity", "gauss3x3", "gauss5x5",
		"sobelHorizontal", "sobelVertical", "edgeDetection" };

		class ImageSolver : public Solver< 2, TNL::Devices::Cuda >
		{
		@@ -42,18 +44,19 @@ public:

		auto output = parameters.getParameter< TNL::String >( "output" );

		if (!this -> readImage(parameters, grid, meshFunction, image, roi) \|\|
		!this -> convolve(parameters, meshFunction) \|\|
		!this -> write(parameters, image, meshFunction))
		if( ! this->readImage( parameters, grid, meshFunction, image, roi ) \|\| ! this->convolve( parameters, meshFunction )
		\|\| ! this->write( parameters, image, meshFunction ) )
		return;
		}

		template< typename Image >
		bool readImage(const TNL::Config::ParameterContainer& parameters,
		bool
		readImage( const TNL::Config::ParameterContainer& parameters,
		GridPointer& grid,
		MeshFunctionType& meshFunction,
		Image& image,
		TNL::Images::RegionOfInterest< int >& roi) const {
		TNL::Images::RegionOfInterest< int >& roi ) const
		{
		auto input = parameters.getParameter< TNL::String >( "input" );

		if( image.openForRead( input ) ) {
		@@ -72,7 +75,8 @@ public:
		meshFunction.setMesh( meshPointer );

		if( ! image.read( roi, meshFunction ) ) {
		std::cout << "Invalid image size" << std::endl;;
		std::cout << "Invalid image size" << std::endl;

		image.close();
		return false;
		}
		@@ -83,12 +87,14 @@ public:
		return true;
		}

		std::cout << "Image open for read failed. Please check file path" << std::endl;;
		std::cout << "Image open for read failed. Please check file path" << std::endl;

		return false;
		}

		bool convolve(const TNL::Config::ParameterContainer& parameters, MeshFunctionType& meshFunction) const {
		bool
		convolve( const TNL::Config::ParameterContainer& parameters, MeshFunctionType& meshFunction ) const
		{
		auto imageData = meshFunction.getData().getConstView();

		Vector kernelSize;
		@@ -107,14 +113,17 @@ public:

		std::cout << imageData.getSize() << " " << result.getSize() << std::endl;

		launchConvolution( imageData,
		kernel.getConstView(),
		result.getView(),
		meshFunction.getMeshPointer() -> getDimensions(),
		kernelSize );
		launchConvolution(
		imageData, kernel.getConstView(), result.getView(), meshFunction.getMeshPointer()->getDimensions(), kernelSize );

		timer.stop();

		result.forAllElements(
		[] __cuda_callable__( int i, float& value )
		{
		value = TNL::max( TNL::min( value, 1.0 ), 0.0 );
		} );

		meshFunction.getData() = result;

		std::cout << "Image convolution was successful. Time: " << timer.getRealTime() << " sec" << std::endl;
		@@ -123,13 +132,16 @@ public:
		}

		template< typename Image >
		bool write(const TNL::Config::ParameterContainer& parameters, Image& image, MeshFunctionType& meshFunction) const {
		bool
		write( const TNL::Config::ParameterContainer& parameters, Image& image, MeshFunctionType& meshFunction ) const
		{
		auto output = parameters.getParameter< TNL::String >( "output" );
		GridType grid = meshFunction.getMesh();

		if( image.openForWrite( output, grid ) ) {
		if( ! image.write( meshFunction ) ) {
		std::cout << "Image write failed" << std::endl;;
		std::cout << "Image write failed" << std::endl;

		image.close();
		return false;
		}
		@@ -144,7 +156,62 @@ public:
		return false;
		}

		HostDataStore getKernel( const TNL::Config::ParameterContainer& parameters, Vector& kernelDimension ) const {
		HostDataStore
		getKernel( const TNL::Config::ParameterContainer& parameters, Vector& kernelDimension ) const
		{
		auto kernel = parameters.getParameter< TNL::String >( "kernel" );

		if( kernel == "identity" ) {
		kernelDimension = { 3, 3 };

		return { 0, 0, 0,
		0, 1, 0,
		0, 0, 0 };
		}

		if( kernel == "gauss3x3" ) {
		kernelDimension = { 3, 3 };

		HostDataStore kernel = { 1, 2, 1,
		2, 4, 2,
		1, 2, 1 };

		kernel /= 16;

		return kernel;
		}

		if( kernel == "gauss5x5" ) {
		kernelDimension = { 5, 5 };

		HostDataStore kernel = { 1, 4, 7, 4, 1,
		4, 16, 26, 16, 4,
		7, 26, 41, 26, 7,
		4, 16, 26, 16, 4,
		1, 4, 7, 4, 1 };

		kernel /= 273;

		return kernel;
		}

		if( kernel == "sobelHorizontal" ) {
		kernelDimension = { 3, 3 };

		return { 1, 2, 1,
		0, 0, 0,
		-1, -2, -1 };
		}

		if( kernel == "sobelVertical" ) {
		kernelDimension = { 3, 3 };

		return { 1, 0, -1,
		2, 0, -2,
		1, 0, -1 };
		}

		if( kernel == "edgeDetection" ) {
		kernelDimension = { 3, 3 };

		return { -1, -1, -1,
		@@ -152,6 +219,10 @@ public:
		-1, -1, -1 };
		}

		std::cout << "Unknown kernel " << kernel << ". Exit" << std::endl;
		exit(1);
		}

		void
		launchConvolution( DataStore::ConstViewType image,
		DataStore::ConstViewType kernel,
		@@ -171,6 +242,10 @@ public:

		config.addEntry< TNL::String >( "input", "PNG image" );
		config.addEntry< TNL::String >( "output", "PNG image" );
		config.addEntry< TNL::String >( "kernel", "A kernel to apply", kernels[ 0 ] );

		for( const auto& kernel : kernels )
		config.addEntryEnum( kernel);

		config.addDelimiter( "Roi settings:" );