Metadata-Version: 2.1
Name: histoprep
Version: 0.0.1.dev3
Summary: Preprocessing module for large histological images.
Home-page: https://github.com/jopo666/HistoPrep
Author: jopo666
Author-email: jopo@birdlover.com
License: UNKNOWN
Description: <div align="center">
        
        # HistoPrep
        Preprocessing large histological slides for machine learning made easy!
        </div>
        
        ## Description
        
        This module allows you to cut and preprocess large histological slides. Some of the features include:
        
        - Cut large whole slide image (WSI) into tiles of desired size.
        - Dearray individual tissue microarray (TMA) spots from a large slide image.
        - **Easily** detect and discard blurry images or images with artifacts after cutting.
        - Save a lot of tears while preprocessing images.
        
        ## Installation
        ```bash
        # install as a module   
        pip install histoprep
        
        # install as an executable
        git clone https://github.com/jopo666/HistoPrep
        ```
        
        ## Requirements
        
        `python >= 3.8` and `openslide`
        
        ```bash
        sudo apt-get install openslide-tools
        ```
        
        
        ## Usage
        
        HistoPrep can be used either as a module...
        
        ```python
        import histoprep as hp
        cutter = hp.Cutter('/path/to/slide', width=512, overlap=0.25)
        cutter.save('/path/to/output_folder')
        ```
        
        or as an excecutable!
        
        ```bash
        python3 HistoPrep cut ./input_dir ./output_dir --width 512 --overlap 0.25 --img_type jpeg
        ```
        
        ## Examples
        
        Detailed examples with best practices:
        
        - [Cutting and preprocessing a whole slide image.](https://github.com/jopo666/HistoPrep/examples/cut.ipynb)
        - [Cutting individual TMA spots from a slide.](https://github.com/jopo666/HistoPrep/examples/dearray.ipynb)
        
        ## Documentation
        
        Work in progress! Each function does have a detailed `__doc__` explaining the use of each argument.
        
Keywords: image-analysis preprocessing histology openslide
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.8
Description-Content-Type: text/markdown
