gwel 0.0.4a0

A computer vision package for handling large image datasets and using neural networks

Gwel

The gwel Python module provides a framework for handling large image datasets and using neural networks for computer vision tasks in crop science research. The module supports object instance detection and semantic segmentation. The flowchart below outlines the workflows that can be achieved using this module.

Maintained and created by Jack Rich, Department of Crop Science, School of Agriculture, Policy, and Development; University of Reading as part of my PhD research.

0 Install via PyPI

For a quick installation of the latest stable version ( conda package manager recomended):

pip install gwel

If installing from source following these directions:

1 Clone this repo

git clone https://github.com/jbr819/gwel.git
cd gwel

2 Create Virtual Environment

With conda (recommended):

conda env create -f environment.yml
conda activate gwel

With venv (Linux and macOS):

python3.10 -m venv gwel 
source gwel/bin/activate
pip install -r requirements.txt

With venv (Windows):

python3.10 -m venv gwel 
gwel\Scripts\activate 
pip install -r requirements.txt

3 Install gwel

pip install -e .

Command Line Interface

Verify gwel installation:

gwel --version
GWEL CLI version x.y.z

To see gwel subcommands:

gwel --help

Navigate to images directory and view images with:

gwel view
#to navigate to the next or previous images use the 'n' and 'p' keys respectively.
#press the 'q' key to quit. 
#pressing the 'f' key will flag images.

For detailed tutorials, visit the wiki.

Acknowledgments

This research is funded by the Biotechnology and Biological Sciences Research Council (BBSRC), part of UK Research and Innovation (UKRI), through the FoodBioSystems Doctoral Training Partnership (DTP) as part of my PhD project at the University of Reading.