SmoothImage_Mean


Smooths an image by averaging pixels within a rectangular kernel.

Applications:Usually used for computing features related to local image "windows". Can be also used for noise removal, but Gauss is superior here.

Syntax

C++
Python
 
def SmoothImage_Mean(
	inImage: Image,
	outImage: Image,
	/,
	*,
	inRoi: Region | None = None,
	inSourceRoi: Region | None = None,
	inBorderColor: Pixel | None = None,
	inKernel: KernelShape = KernelShape.Box,
	inRadiusX: int = 1,
	inRadiusY: int | None = None
)
-> None

Parameters

Name Type Range Default Description
Input value inImage Image Input image
Input value inRoi Region | None None Range of outImage pixels to be computed
Input value inSourceRoi Region | None None Range of inImage pixels to be read
Input value inBorderColor Pixel | None None Color of the imaginary pixels outside the image boundaries
Input value inKernel KernelShape KernelShape.Box Kernel shape
Input value inRadiusX int 0 - 1 Nearly half of the kernel's width (2*R+1)
Input value inRadiusY int | None 0 - None Nearly half of the kernel's height (2*R+1), or same as inRadiusX
Output value outImage Image Output image

Hardware Acceleration

This operation is optimized for PARALLEL SSE2 technology for pixels of types: UINT8, SINT8, SINT16, SINT32, REAL.

This operation is optimized for SSE41 technology for pixels of type: UINT16.

This operation is optimized for AVX2 technology for pixels of types: UINT8, SINT8, SINT16, SINT32, REAL, UINT16.

This operation is optimized for NEON technology for pixels of types: UINT8, UINT16.