Metadata-Version: 2.1
Name: fastlbp-imbg
Version: 0.0.dev12
Summary: Parallel multiradial LBP features
Author-email: Mykhailo Koreshkov <koreshov.m@gmail.com>
Project-URL: Homepage, https://github.com/imbg-ua/fastLBP
Project-URL: Bug Tracker, https://github.com/imbg-ua/fastLBP/issues
Keywords: lbp,texture,features,image processing,parallel
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: wheel
Requires-Dist: scikit-image>=0.22.0
Requires-Dist: numpy>=1.26.0
Requires-Dist: Pillow>=10.0.1
Requires-Dist: pandas>=2.1.1

# fastLBP
Highly parallel LBP implementation

## Implemented modules
### run_skimage
Computes multiradial LBP of a single multichannel image in a parallel fashion.  
This is a quick sample implementation that could be a baseline for further benchmarking.

Features:
- Powered by `skimage.feature.local_binary_pattern`
- Concurrency is managed by Python's [`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html) module
- Parallel computation via `multiprocessing.Pool` of size `ncpus`
- Efficient memory usage via  `multiprocessing.shared_memory` to make sure processes do not create redundant copies of data
- It computes everything in RAM, no filesystem usage

TODO: 
- Use `max_ram` parameter to estimate optimal number of sub-processes and collect memory stats. Now `max_ram` is ignored.

## Planned modules
### run_chunked_skimage
Similar to [1. run_skimage](#1-run_skimage), but each subprocess should compute LBP for its image chunk, not the whole image.

### run_dask and run_chunked_dask
Similar to [1. run_skimage](#1-run_skimage), but use [Dask](https://docs.dask.org/en/stable/) and [`dask.array.map_overlap`](https://docs.dask.org/en/stable/generated/dask.array.map_overlap.html#dask.array.map_overlap) for parallelisation instead of `multiprocessing` and manual data wrangling

## Other notable things to try
- Perform benchmarking of an in-house optimised cython version of `skimage.feature.local_binary_pattern` (see [skimage_lbp.pyx at imbg-ua/nf-img-benchmarking](https://github.com/imbg-ua/nf-img-benchmarking/blob/main/skimage_lbp/skimage_lbp.pyx))
- Do some research on Numba - is it applicable here?
- Add annotations to run_skimage results using [`anndata`](https://anndata.readthedocs.io/en/latest/)
