CreateGoldenTemplate2


Create a model to be used with CompareGoldenTemplate2 filter.

Syntax

C++
C#
Python
 
def CreateGoldenTemplate2(
	inImages: list[Image],
	outModel: GoldenTemplate2Model,
	/,
	*,
	inObjectMask: Region | None = None,
	inDownscale: int = 2,
	inMaxDisplacement: int = 2,
	inLargeDefectSize: int = 50,
	inBrightnessAugmentation: int = 0,
	inNoiseAugmentation: int = 0,
	inSmoothingAugmentationStdDev: float = 0.0
)
-> None

Parameters

Name Type Range Default Description
Input value inImages list[Image] List of input images that has to be uniform in terms of size and format.
Input value inObjectMask Region | None None
Input value inDownscale int 1 - 2 Shrink the input for processing by dividing by specified value. Reduces sensitivity to minuscule (pixel-size) defects. Greatly improves processing speed.
Input value inMaxDisplacement int 0 - 2 Error in object positioning. If in doubt, it is better to set this value too high. If set too low, subtle defects won't be detected, or no defects may not be detected at all. High values may impair detection of small defects, especially near edges.
Input value inLargeDefectSize int 0 - 50 Expected size (diameter) of largest, extensive defects.
Input value inBrightnessAugmentation int 0 Allows for greater (additional to the value inferred from inImages training set) brightness deviation in inspected images.
Input value inNoiseAugmentation int 0 Allows for greater (additional to the value inferred from inImages training set) noise presence in inspected images. Uses a uniform noise with specified distribution width.
Input value inSmoothingAugmentationStdDev float 0.0 - 0.0 Allows for greater (additional to the value inferred from inImages training set) image smoothing in inspected images. Uses gaussian smoothing with specified standard deviation.
Output value outModel GoldenTemplate2Model