Comments (5)
With regards to allowing binary images represented as std::vector<bool>
, this is simple to do through the C++ interface, but may require copy-pasting a lot of code (ugly). It's not so simple to do through the Cython interface. If there's demand for this, let me know, but it should be pretty easy to just change some declarations to const std::vector<bool> &in_labels
in a few places if that's what you need.
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https://pypi.org/project/bitarray/
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Previously, playing around with bit packing was sure to reduce performance and not really significantly reduce memory because of the output image and union find. However, after zeroth pass was implemented bit packing can save 20% (uint32) to 40% (for uint16) on peak memory. Might be worth looking into.
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Looks like std::bitset and std::vector would be annoying to work with in different respects. Will have to write own bit accessor class that accepts a byte string with zero parsing.
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Might be able to do something with np.packbits
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Related Issues (20)
- Applying Dust and largest_k dtype output option HOT 2
- dust sugnature HOT 1
- Massive memory Leak HOT 7
- 1D Array of 4 Elements Incorrect HOT 5
- Cannot find reference 'dust' in 'cc3d.py' HOT 2
- Question on comparing individual lesions between two masks based on the cc3d.statistics output. HOT 1
- Additional metrics support HOT 2
- Does cc3d also work with memmory-mapped numpy arrays and array-like data? HOT 14
- cc3d.statistics["bounding_boxes"] are wrong HOT 1
- largest_k fails for transposed arrays HOT 6
- About the lastest_k function HOT 4
- Statistics output HOT 7
- Question on the output of contacts HOT 9
- Periodic Boundary Conditions HOT 4
- Is the output label of largest_k ordered? HOT 2
- Add a better error for type support. HOT 4
- Any way to make this GPU Compatible? HOT 6
- Applying dust to labels does not do anything HOT 3
- Support for Numpy 2.0 HOT 3
- Supporting V1 dtype HOT 4
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