bioimage_embed is an all-in-one Python package designed to cater to the needs of computational biologists, data scientists, and researchers working on biological image data. With specialized functions to handle, preprocess, and visualize microscopy datasets, this tool is tailored to streamline the embedding process for biological imagery.
- Seamless loading of microscopy datasets, compatible with the BioImage Data Resource and Cell Image Library.
- Built-in preprocessing functions to ensure your images are primed for encoding.
- Visual tools to dive deep into the encoding and decoding processes of your autoencoders.
To get started with bioimage_embed, you can install it directly via pip or from the GitHub repository.
pip install bioimage_embed
pip install git+https://github.com/ctr26/bioimage_embed
pip install -e .
To get a list of all commands and functions:
bioimage_embed --help
OR
bie --help
This utility makes it simple to fetch the necessary datasets:
make download.data
If you don't have a Kaggle account you must create one and then follow the next steps:
- Install the Kaggle API package so you can download the data from the Makefile you have all the information in their Github repository.
- To use the Kaggle API you need also to create an API token. You can found how to do it in their documentation
- After that you will need to add your user and key in a file called
kaggle.json
in this location in your home directorychmod 600 ~/.kaggle/kaggle.json
- Don't forget to accept the conditions for the "2018 Data Science Bowl" on the Kaggle website. Otherwise you would not be able to pull this data from the command line.
For those intending to contribute or looking for a deeper dive into the codebase, we use poetry
to manage our dependencies and virtual environments:
poetry env use python
poetry install
poetry shell
For any issues, please refer to our issues page. Contributions are more than welcome! Please submit pull requests to the master branch.
bioimage_embed is licensed under the MIT License. Please refer to the LICENSE for more details.
Happy Embedding! 🧬🔬