Metadata-Version: 2.1
Name: imagedataextractor
Version: 2.0.3
Summary: imagedataextractor is a Python library for electron microscopy image quantification.
Home-page: https://github.com/by256/imagedataextractor
Author: Batuhan Yildirim
Author-email: by256@cam.ac.uk
License: MIT
Description: <img src="./logo.png" height="100">
        
        **imagedataextractor** is a Python library for nanoparticle electron microscopy image quantification.
        
        Try the online [Demo](https://imagedataextractor.org/demo).
        
        ## Features
        
        - Automatic detection and download of microscopy images from scientific articles.
        - Particle segmentation with uncertainty quantification.
        - Particle localization.
        - Automatic scalebar detection (reading and measurement).
        - Particle size distributions.
        - Locations, sizes and aspect ratios of all particles in an image (in the form of a .csv file).
        - Radial distribution functions of extracted particle systems.
        
        ## Installation
        
        **imagedataextractor** requires Python 3.7 or above. We strongly recommend the use of a [virtual environment](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/) for installation, as **imagedataextractor** requires specific versions of its requirements to be installed in order to function as intended.
        
        #### Install Tesseract
        
        **imagedataextractor** requires [Tesseract 4](https://tesseract-ocr.github.io/tessdoc/Installation.html). Installation instructions for Tesseract can be found [here](https://tesseract-ocr.github.io/tessdoc/Installation.html). On Linux, this is very simple:
        
        ```bash
        sudo apt-get install tesseract-ocr libtesseract-dev
        ```
        
        #### Installation with `pip` (recommended)
        ```bash
        pip install imagedataextractor
        ```
        
        #### Installation from source
        
        1. Clone the repo and move into the directory:
        
        
        ```bash
        git clone https://github.com/by256/imagedataextractor.git
        cd imagedataextractor
        ```
        
        2. Activate your virtual environment.
        
        3. Install:
        
        ```bash
        python setup.py install
        ```
        
        ## Usage
        
        Simply provide as input a path to an image or a document, or a path to a directory of images and/or documents.
        
        ```python
        import imagedataextractor as ide
        
        image_path = '<path/to/image>'
        data = ide.extract(image_path)
        
        # view extracted data as a pandas DataFrame
        df = data.to_pandas()
        
        # retrieve extracted scalebar data
        sb_text = data.scalebar.text
        conversion = data.scalebar.conversion  # pixels to meters
        
        # resulting particle segmentations
        seg = data.segmentation
        ```
        
        If the input image is a figure containing a panel of images, these will be split and extraction will be performed on each sub-image separately.
        
        See the [example notebook](https://github.com/by256/imagedataextractor/blob/master/examples/example-notebook.ipynb). A more detailed usage guide can be found in the [Documentation](https://imagedataextractor.org/docs/usage).
        
        ## Citing
        
        If you use **imagedataextractor** in your work, please cite the following works:
        
        
        B. Yildirim, J. M. Cole, "Bayesian Particle Instance Segmentation for Electron Microscopy Image Quantification", *J. Chem. Inf. Model.* (2021)  https://doi.org/10.1021/acs.jcim.0c01455
        
        K. T. Mukaddem, E. J. Beard, B. Yildirim, J. M. Cole, "ImageDataExtractor: A Tool to Extract and Quantify Data from Microscopy Images", *J. Chem. Inf. Model.* (2019) https://doi.org/10.1021/acs.jcim.9b00734
        
        ## Funding
        
        This project was financially supported by the [Science and Technology Facilities Council (STFC)](https://stfc.ukri.org/) and the [Royal Academy of Engineering](https://www.raeng.org.uk/) (RCSRF1819\7\10).
        
        ## License
        
        [![License](http://img.shields.io/:license-mit-blue.svg?style=flat-square)](http://badges.mit-license.org)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
