BottomHatImage
Performs a morphological black top hat (bottom hat) operation on a image using a predefined kernel.
Syntax
C++
Python
def BottomHatImage( 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, diagKernel: Region | None = None ) -> None
Parameters
| Name | Type | Range | Default | Description | |
|---|---|---|---|---|---|
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inImage | Image | Input image | ||
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inRoi | Region | None | None | Range of outImage pixels to be computed | |
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inSourceRoi | Region | None | None | Range of inImage pixels to be considered in computations | |
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inBorderColor | Pixel | None | None | Color of the imaginary pixels outside the image boundaries | |
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inKernel | KernelShape | KernelShape.Box | Selects kernel shape | |
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inRadiusX | int | 0 - ![]() |
1 | Nearly half of the kernel's width (2*R+1) |
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inRadiusY | int | None | 0 - ![]() |
None | Nearly half of the kernel's height |
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outImage | Image | Output image | ||
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diagKernel | Region | None | None | Kernel shape |
Hardware Acceleration
This operation is optimized for SSE2 technology for pixels of types: all formats (when inSourceRoi = NIL and inBorderColor = NIL).
This operation is optimized for NEON technology for pixels of types: all formats (when inSourceRoi = NIL and inBorderColor = NIL).
This operation supports automatic parallelization for multicore and multiprocessor systems.




