Skip to main content

RootPainter GUI and trainer packaged as a single CLI

Project description

RootPainter

DOI Documentation

RootPainter is a GUI-based software tool for the rapid training of deep neural networks for use in image analysis. RootPainter uses a client-server architecture, allowing users with a typical laptop to utilise a GPU on a more computationally powerful server.

A detailed description is available in the paper published in the New Phytologist RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation

RootPainter Interface

To see a list of work using (or citing) the RootPainter paper, please see the google scholar page

A BioRxiv Pre-print (earlier version of the paper) is available at: https://www.biorxiv.org/content/10.1101/2020.04.16.044461v2

Getting started quickly

I suggest the colab tutorial.

A shorter quickstart is available including more concise instruction, that could be used as reference. I suggest the paper, videos and then colab tutorial to get an idea of how the software interface could be used and then this quickstart for reference to help remember each of the key steps to get from raw data to final measurements.

Videos

A 14 minute video showing how to install RootPainter on windows 11 with google drive and google colab is available on youtube. A similar video for macOS is also now available on youtube. I suggest watching these videos to help with the installation part of the colab tutorial.

A video demonstrating how to train and use a model is available to download

There is a youtube video of a workshop explaining the background behind the software and covering using the colab notebook to train and use a root segmentation model.


Installation

Server

The server needs to be installed on the machine that will run the training:

# with uv
uvx root-painter[trainer] trainer

# with pip
pip install root-painter[trainer]
root-painter trainer

Client

Go to releases, download, and install the client for your platform, or install directly with python

# run directly with uv
uvx root-painter

# similarly with pip - you need to figure out how to install the correct python yourself - uv does this for you.
pip install root-painter
root-painter

If you are not confident installing and running python applications on the command line then to get started quickly I suggest the colab tutorial.

Documentation

You can find comprehensive documentation, including setup guides, tutorials, and developer information here. Some quick links to the documentation:

For Developers

Developer-focused documentation, including build instructions and contribution guidelines, can found in the developer section

Questions and Problems

The FAQ may be worth checking before reaching out with any questions you have. If you do have a question you can either email me or post in the discussions. If you have an issue/ have identified a problem with the software then you can post an issue.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

root_painter-0.1.6.tar.gz (116.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

root_painter-0.1.6-py3-none-any.whl (161.8 kB view details)

Uploaded Python 3

File details

Details for the file root_painter-0.1.6.tar.gz.

File metadata

  • Download URL: root_painter-0.1.6.tar.gz
  • Upload date:
  • Size: 116.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.7.19

File hashes

Hashes for root_painter-0.1.6.tar.gz
Algorithm Hash digest
SHA256 2e5717466a353f21c3256c32fbb2ba9727e42408b26a762c2958bec74f23e2d0
MD5 0ab617a7bb9b4d6effcc8d58484bebda
BLAKE2b-256 774351020de180b21f79fe4e8c98a44b74c23bbe9f7592a396a3f61030c98fdc

See more details on using hashes here.

File details

Details for the file root_painter-0.1.6-py3-none-any.whl.

File metadata

File hashes

Hashes for root_painter-0.1.6-py3-none-any.whl
Algorithm Hash digest
SHA256 34e241d2d8cf9bbcb71246c267138111c54205e60ebd0f08367502b2b0dc33a6
MD5 28597bcba3b82a524cf2ee8dca889bf6
BLAKE2b-256 704f5211f2d3c5ff20a2e617f1ce71d675e412d66031aedfedc69d5eb2409b0f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page