Metadata-Version: 2.1
Name: histoprep
Version: 0.0.2.3
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 medical images for machine learning made easy!
        
        <p align="center">
            <a href="#version" alt="Version">
                <img src="https://img.shields.io/pypi/v/histoprep"/></a>
            <a href="#licence" alt="Licence">
                <img src="https://img.shields.io/github/license/jopo666/HistoPrep"/></a>
            <a href="#docs" alt="Docs">
                <img src="https://img.shields.io/readthedocs/histoprep"/></a>
            <a href="#issues" alt="Issues">
                <img src="https://img.shields.io/github/issues/jopo666/HistoPrep"/></a>
            <a href="#activity" alt="Activity">
                <img src="https://img.shields.io/github/last-commit/jopo666/HistoPrep"/></a>
        </p>
        
        <p align="center">
          <a href="#description">Description</a> •
          <a href="#installation">Installation</a> •
          <a href="https://histoprep.readthedocs.io/en/latest/">Documentation</a> •
          <a href="#how-to-use">How To Use</a> •
          <a href="#examples">Examples</a> •
          <a href="#whats-coming">What's coming?</a>
        </p>
        
        </div>
        
        
        ## Description
        
        This module allows you to easily **cut** and **preprocess** large histological slides.
        
        - Cut tiles from large slide images.
        - Dearray TMA spots (and cut tiles from individual spots).
        - Preprocess extracted tiles **easily**.
        
        ![workflow](./docs/_static/workflow.jpeg)
        
        
        ## Installation
        
        First install `OpenCV` and `OpenSlide` on your system (instructions [here](https://docs.opencv.org/master/d0/d3d/tutorial_general_install.html) and [here](https://openslide.org/download/)).
        
        ```bash 
        pip install histoprep
        ```
        
        Detailed installation instructions can be found from `HistoPrep` [docs](https://histoprep.readthedocs.io/en/latest/install.html). 
        
        ## How To Use
        
        ``HistoPrep`` has a few simple commands that do most of the heavy lifting.
        
        ```python
        import histoprep as hp
        
        # Cutting tiles is done with two lines of
        cutter = hp.Cutter('/path/to/slide', width=512, overlap=0.25, max_background=0.7)
        metadata = cutter.save('/path/to/output_folder')
        ```
        
        If you have many slides to process, you can also use `HistoPrep` as an excecutable for easy cutting.
        
        ```bash
        python3 path/to/HistoPrep cut ./input_dir ./output_dir --width 512 --overlap 0.25 --img_type jpeg
        ```
        
        
        After the tiles have been saved, preprocessing is just a simple outlier detection from the preprocessing metrics saved in `metadata`!
        
        ```python
        from histoprep import preprocess
        
        all_metadata = preprocess.collect_metadata('/path/to/output_folder')
        
        blurry_tiles = all_metadata['sharpness_max'] < 10
        pen_markings = all_metadata['hue_0.1'] < 120
        weird_blue_shit = all_metadata['blue_0.05'] > 160
        ```
        
        ## Examples
        
        Detailed examples can be found in the [docs](https://histoprep.readthedocs.io/en/latest/) or the [examples](./examples) folder.
        
        ## What's coming?
        
        `HistoPrep` is under constant development. If there are some features you would like to be added, just submit an [issue](https://github.com/jopo666/HistoPrep/issues) and we'll start working on the feature!
        
        #### Requested features:
        
        - [ ] Cutting and preprocessing for multichannel images (currently supports only `RGB`-images).
        - [ ] Add automatic detection of outliers from `metadata`.
          - This could be implemented with dimensionality reduction.
        
        
        ## Citation
        
        If you use `HistoPrep` in a publication, please cite the github repository.
        
        ```
        @misc{histoprep2021,
          author = {Pohjonen J. and Ariotta. V},
          title = {HistoPrep: Preprocessing large medical images for machine learning made easy!},
          year = {2021},
          publisher = {GitHub},
          journal = {GitHub repository},
          howpublished = {\url{https://github.com/jopo666/HistoPrep}},
        }
        ```
        
        ## Changelog
        
        Can be found [here](CHANGELOG.md).
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
