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
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 outputImage.
// 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 = nullptr );
This function returns outputImage.
// Function prototype.
convolution_with_image_2d(input_image: idt.ImageType,
input_kernel_image: idt.ImageType,
auto_scale: ConvolutionWithImage2d.AutoScale = ConvolutionWithImage2d.AutoScale.YES,
output_image: idt.ImageType = None) -> idt.ImageType
This function returns outputImage.
// 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
| Parameter Name | Description | Type | Supported Values | Default Value | |||||
|---|---|---|---|---|---|---|---|---|---|
 |
inputImage |
The input image. | Image | Binary, Label, Grayscale or Multispectral | nullptr | ||||
|
inputKernelImage |
The convolution kernel image. It must have odd X and Y dimensions. | Image | Binary, Label, Grayscale or Multispectral | nullptr | ||||
|
autoScale |
The automatic intensity scaling mode.
|
Enumeration | YES | |||||
 |
outputImage |
The output image. Its dimensions are forced to the same values as the input. Its data type is promoted. | Image | nullptr | |||||
| Parameter Name | Description | Type | Supported Values | Default Value | |||||
|---|---|---|---|---|---|---|---|---|---|
|
input_image |
The input image. | image | Binary, Label, Grayscale or Multispectral | None | ||||
|
input_kernel_image |
The convolution kernel image. It must have odd X and Y dimensions. | image | Binary, Label, Grayscale or Multispectral | None | ||||
|
auto_scale |
The automatic intensity scaling mode.
|
enumeration | YES | |||||
|
output_image |
The output image. Its dimensions are forced to the same values as the input. Its data type is promoted. | image | None | |||||
| Parameter Name | Description | Type | Supported Values | Default Value | |||||
|---|---|---|---|---|---|---|---|---|---|
|
inputImage |
The input image. | Image | Binary, Label, Grayscale or Multispectral | null | ||||
|
inputKernelImage |
The convolution kernel image. It must have odd X and Y dimensions. | Image | Binary, Label, Grayscale or Multispectral | null | ||||
|
autoScale |
The automatic intensity scaling mode.
|
Enumeration | YES | |||||
|
outputImage |
The output image. Its dimensions are forced to the same values as the input. Its data type is promoted. | Image | null | |||||
Object Examples
auto 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
auto 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() );