# fastLBP
Highly parallel LBP implementation
> **Important pre-release warning**:
> If aborted mid-execution, this software sometimes create a lot of orphan processes that needs to be killed manually.
> Please, note down the name of your python script, search for `Python` in your task manager and look for the processes that correspond to your python script.
## Requirements
FastLBP is tested with Python 3.11 on Windows 10, Debian 11, and Ubuntu 22.04
Python requirements are:
- numpy >= 1.26.0
- Cython (to build the binary modules, will be optional in the future)
- scikit-image >= 0.22.0 (mostly for testing, we plan making this requirement optional in the future)
- pandas >= 2.1.1
- psutil
## Installation
- Activate or create 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` and `pip --version`
- Install a stable version from PyPI
`pip install fastlbp-imbg`
- Or build the latest version from sources
```
git clone git@github.com: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 as fastlbp` now
## Testing
```
# in repo root
conda activate fastlbp
pip install -e .
python -m unittest
```
## Bug reporting
You can report a bug or suggest an improvement using [our github issues](https://github.com/imbg-ua/fastLBP/issues)
## Implemented modules
### run_fastlbp
Computes multiradial LBP of a single multichannel image in a parallel fashion.
Features:
- Powered by `fastlbp_imbg.lbp`, our implementation of `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
- 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](#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](#1-run_fastlbp), 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
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"description": "# fastLBP\r\nHighly parallel LBP implementation\r\n\r\n> **Important pre-release warning**:\r\n> If aborted mid-execution, this software sometimes create a lot of orphan processes that needs to be killed manually.\r\n> Please, note down the name of your python script, search for `Python` in your task manager and look for the processes that correspond to your python script.\r\n\r\n## Requirements\r\nFastLBP is tested with Python 3.11 on Windows 10, Debian 11, and Ubuntu 22.04\r\n\r\nPython requirements are:\r\n- numpy >= 1.26.0\r\n- Cython (to build the binary modules, will be optional in the future)\r\n- scikit-image >= 0.22.0 (mostly for testing, we plan making this requirement optional in the future)\r\n- pandas >= 2.1.1\r\n- psutil\r\n\r\n## Installation\r\n\r\n- Activate or create a Python 3.11 environment (e.g. using `conda create -y -n p11 python=3.11 && conda activate p11`)\r\n- Verify you are using the right env\r\n\t- `python --version` and `pip --version`\r\n- Install a stable version from PyPI \r\n\t`pip install fastlbp-imbg`\r\n- Or build the latest version from sources \r\n\t```\r\n\tgit clone git@github.com:imbg-ua/fastLBP.git\r\n\tcd fastLBP\r\n\t# git checkout <branchname> # if you need a specific branch\r\n\tpip install . # this will install the fastlbp_imbg package in the current env\r\n\t```\r\n- You can use `import fastlbp_imbg as fastlbp` now\r\n\r\n## Testing\r\n```\r\n# in repo root\r\nconda activate fastlbp\r\npip install -e .\r\npython -m unittest\r\n```\r\n\r\n## Bug reporting\r\nYou can report a bug or suggest an improvement using [our github issues](https://github.com/imbg-ua/fastLBP/issues)\r\n\r\n## Implemented modules\r\n### run_fastlbp\r\nComputes multiradial LBP of a single multichannel image in a parallel fashion.\r\n\r\nFeatures:\r\n- Powered by `fastlbp_imbg.lbp`, our implementation of `skimage.feature.local_binary_pattern`\r\n- Concurrency is managed by Python's [`multiprocessing`](https://docs.python.org/3/library/multiprocessing.html) module\r\n- Parallel computation via `multiprocessing.Pool` of size `ncpus`\r\n- Efficient memory usage via `multiprocessing.shared_memory` to make sure processes do not create redundant copies of data\r\n- If `save_intermediate_results=False` then computes everything in RAM, no filesystem usage\r\n\r\nTODO: \r\n- Use `max_ram` parameter to estimate optimal number of sub-processes and collect memory stats. Now `max_ram` **is ignored**.\r\n\r\n## Planned modules\r\n### run_chunked_skimage\r\nSimilar to [1. run_fastlbp](#1-run_fastlbp), but each subprocess should compute LBP for its image chunk, not the whole image.\r\n\r\n### run_dask and run_chunked_dask\r\nSimilar to [1. run_fastlbp](#1-run_fastlbp), 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\r\n",
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