Comments (5)
I don't know if there is a particular reason, besides this originally being written to replicate other integer-only code that was much slower. Looking at the code, it may even just work well as is by removing the current limitations
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When getting accustomed to this library's code base, I ran into something interesting on line 117. To my assumption, this is the cause of unexpected results on the function erode
. Is there a particular reason why erode_sub
is written the way it is?
from mahotas.
The original reason was probably to make it behave like pymorph: at the time, pymorph did a lot of what I wanted, but was Python-only and 2D only, so a lot of mahotas was written to be a C++ version of that
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Should I implement dilate and erode functions that use a boolean footprint like what is implemented in SciPy? Currently, the phase separation algorithm I am implementing does not work with the current dilate and erode functions but works for SciPy and my own implementation.
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Yes, I'd be happy to review/help.
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Related Issues (20)
- different shape has the same Zernike moments value HOT 4
- Can't install package at conda HOT 1
- Try installing and then changing to another directory before importing mahotas. HOT 2
- Installation of mahotas fails in a clean conda environment HOT 1
- Not Able To import mahotas HOT 2
- Can't import mahotas.polygon on AWS Ubuntu 20.04 when inside CellProfiler HOT 3
- Find the the haralick feature name from the value using Mahotas HOT 1
- Proposing a PR to fix a few small typos
- "Input data has unexpected types" - Bug in mahotas.features.lbp.lbp HOT 1
- Publish wheels for python 3.9 HOT 3
- Error in using the Zernike moments. HOT 6
- Add support to release linux aarch64 wheels
- freeimage tests fail due to use of `tmpdir` HOT 1
- Latest version (1.4.12) is without a source distribution HOT 2
- make fill_polygon accept floats HOT 2
- Pip install error HOT 3
- Python 3.11 wheels HOT 1
- Is it possible to *not* release the GIL? HOT 4
- RuntimeError: module compiled against API version 0x10 but this version of numpy is 0xf HOT 1
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