ImageDev

ConvolutionWithImage2d

Performs a two-dimensional convolution between an image and another image used as a convolution kernel.

Access to parameter description

This filter produces a convolution between an image $I$ and another image $k$. This image $k$ is converted into a 1D-array and convolution by this general kernel is performed. This algorithm only supports kernel images with odd X and Y dimensions.

If image $k$ has a size $(2p+1)\times(2q+1)$, it is in fact a 2D-array with $(2p+1)$ columns and $(2q+1)$ lines. It is converted in a 1D-array of $(2p+1)\times(2q+1)$ elements by placing side by side the $(2q+1)$ lines of the $k$ array. $$ O(n,m)=\frac{1}{K}\sum_{i=-p}^{p}\sum_{j=-q}^{q}k(i,j)\times I(n-i,m-j) $$ Where $K$ is the normalization or automatic scaling coefficient. If normalization is disabled $K=1$, else it is given by: $$K=\sum_{i=-p}^{p}\sum_{j=-q}^{q}|k(i,j)|$$
Note: Since the original data range may not be preserved by this filter, the output image type is upgraded according to the Image type basic rule.

See also

Function Syntax

This function returns the outputImage output parameter. 
// Function prototype.
std::shared_ptr< iolink::ImageView >
convolutionWithImage2d( std::shared_ptr< iolink::ImageView > inputImage,
                        std::shared_ptr< iolink::ImageView > inputKernelImage,
                        ConvolutionWithImage2d::AutoScale autoScale,
                        std::shared_ptr< iolink::ImageView > outputImage = NULL );
This function returns the outputImage output parameter. 
// Function prototype.
convolution_with_image_2d( input_image,
                           input_kernel_image,
                           auto_scale = ConvolutionWithImage2d.AutoScale.YES,
                           output_image = None )
This function returns the outputImage output parameter. 
// Function prototype.
public static IOLink.ImageView
ConvolutionWithImage2d( IOLink.ImageView inputImage,
                        IOLink.ImageView inputKernelImage,
                        ConvolutionWithImage2d.AutoScale autoScale = ImageDev.ConvolutionWithImage2d.AutoScale.YES,
                        IOLink.ImageView outputImage = null );

Class Syntax

Parameters

Class Name ConvolutionWithImage2d

Parameter Name Description Type Supported Values Default Value
input
inputImage
The input image. Image Binary, Label, Grayscale or Multispectral nullptr
input
inputKernelImage
The convolution kernel image. It must have odd X and Y dimensions. Image Binary, Label, Grayscale or Multispectral nullptr
input
autoScale
The automatic intensity scaling mode.
NO The result is not normalized.
YES The result is automatically normalized by the sum of the kernel elements.
Enumeration YES
output
outputImage
The output image. Its dimensions are forced to the same values as the input. Its data type is promoted. Image nullptr

Object Examples

std::shared_ptr< iolink::ImageView > polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );
auto kernel_2d = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "kernel_2d.vip" );

ConvolutionWithImage2d convolutionWithImage2dAlgo;
convolutionWithImage2dAlgo.setInputImage( polystyrene );
convolutionWithImage2dAlgo.setInputKernelImage( kernel_2d );
convolutionWithImage2dAlgo.setAutoScale( ConvolutionWithImage2d::AutoScale::YES );
convolutionWithImage2dAlgo.execute();

std::cout << "outputImage:" << convolutionWithImage2dAlgo.outputImage()->toString();

polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))
kernel__2d = imagedev.read_vip_image(imagedev_data.get_image_path("kernel_2d.vip"))

convolution_with_image_2d_algo = imagedev.ConvolutionWithImage2d()
convolution_with_image_2d_algo.input_image = polystyrene
convolution_with_image_2d_algo.input_kernel_image = kernel__2d
convolution_with_image_2d_algo.auto_scale = imagedev.ConvolutionWithImage2d.YES
convolution_with_image_2d_algo.execute()

print( "output_image:", str( convolution_with_image_2d_algo.output_image ) )

ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );
ImageView kernel_2d = Data.ReadVipImage( @"Data/images/kernel_2d.vip" );

ConvolutionWithImage2d convolutionWithImage2dAlgo = new ConvolutionWithImage2d
{
    inputImage = polystyrene,
    inputKernelImage = kernel_2d,
    autoScale = ConvolutionWithImage2d.AutoScale.YES
};
convolutionWithImage2dAlgo.Execute();

Console.WriteLine( "outputImage:" + convolutionWithImage2dAlgo.outputImage.ToString() );

Function Examples

std::shared_ptr< iolink::ImageView > polystyrene = ioformat::readImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "polystyrene.tif" );
auto kernel_2d = readVipImage( std::string( IMAGEDEVDATA_IMAGES_FOLDER ) + "kernel_2d.vip" );

auto result = convolutionWithImage2d( polystyrene, kernel_2d, ConvolutionWithImage2d::AutoScale::YES );

std::cout << "outputImage:" << result->toString();

polystyrene = ioformat.read_image(imagedev_data.get_image_path("polystyrene.tif"))
kernel__2d = imagedev.read_vip_image(imagedev_data.get_image_path("kernel_2d.vip"))

result = imagedev.convolution_with_image_2d( polystyrene, kernel__2d, imagedev.ConvolutionWithImage2d.YES )

print( "output_image:", str( result ) )

ImageView polystyrene = ViewIO.ReadImage( @"Data/images/polystyrene.tif" );
ImageView kernel_2d = Data.ReadVipImage( @"Data/images/kernel_2d.vip" );

IOLink.ImageView result = Processing.ConvolutionWithImage2d( polystyrene, kernel_2d, ConvolutionWithImage2d.AutoScale.YES );

Console.WriteLine( "outputImage:" + result.ToString() );