fastlbp-imbg 0.2.0

Parallel multiradial LBP features

fastLBP

Highly parallel LBP implementation

Installation

Note: it is not recommended to proceed on a head node; consider starting an ijob or a jupyter instance.

• Activate a Python 3.11 environment (e.g. using conda create -y -n p11 python=3.11 && conda activate p11)
• Verify you are using the right env
  • python --version та pip --version
• Install a stable version from PyPI
  • pip install fastlbp-imbg
• Or build the latest version from sources

git clone [e-mail]:imbg-ua/fastLBP.git
cd fastLBP
# git checkout <branchname> # if you need a specific branch
pip install . # this will install the fastlbp_imbg package in the current env

• You can use import fastlbp_imbg now

Testing

# in repo root
conda activate fastlbp
pip install -e .
python -m unittest

Implemented modules

run_fastlbp

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 fastlbp_imbg.lbp, our implementation of skimage.feature.local_binary_pattern
• Concurrency is managed by Python's multiprocessing 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
• If save_intermediate_results=False then 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_fastlbp, but each subprocess should compute LBP for its image chunk, not the whole image.

run_dask and run_chunked_dask

Similar to 1. run_fastlbp, but use Dask and dask.array.map_overlap for parallelisation instead of multiprocessing and manual data wrangling