Skip to main content

Static and Dynamic classification tool.

Project description

Oneat

ONEAT = Otherwise Not Even Accurate Tracks

PyPI version

This project provides static and action classification networks for LSTM based networks to recoganize cell events such as division, apoptosis, cell rearrangement for various imaging modalities.

Installation & Usage

Installation

This package can be installed by

pip install --user oneat

If you are building this from the source, clone the repository and install via

git clone https://github.com/Kapoorlabs-CAPED/CAPED-AI-oneat/

cd CAPED-AI-oneat

pip install --user -e .

# or, to install in editable mode AND grab all of the developer tools
# (this is required if you want to contribute code back to NapaTrackMater)
pip install --user -r requirements.txt

Pipenv install

Pipenv allows you to install dependencies in a virtual environment.

# install pipenv if you don't already have it installed
pip install --user pipenv

# clone the repository and sync the dependencies
git clone https://github.com/Kapoorlabs-CAPED/CAPED-AI-oneat/
cd CAPED-AI-oneat
pipenv sync

# make the current package available
pipenv run python setup.py develop

# you can run the example notebooks by starting the jupyter notebook inside the virtual env
pipenv run jupyter notebook

Examples

oneat comes with different options to combine segmentation with classification or to just use classification independently of any segmentation during the model prediction step. We summarize this in the table below:

Example Dataset DataSet Trained Model Notebook Code
Example timelapse Oneat model Napari notebook
Example timelapse Oneat model Napari notebook
Example timelapse Oneat model Napari notebook

Troubleshooting & Support

  • The image.sc forum is the best place to start getting help and support. Make sure to use the tag oneat, since we are monitoring all questions with this tag.
  • If you have technical questions or found a bug, feel free to open an issue.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distribution

oneat-3.6.5.tar.gz (92.6 kB view details)

Uploaded Source

Built Distribution

oneat-3.6.5-py3-none-any.whl (103.6 kB view details)

Uploaded Python 3

File details

Details for the file oneat-3.6.5.tar.gz.

File metadata

  • Download URL: oneat-3.6.5.tar.gz
  • Upload date:
  • Size: 92.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.0

File hashes

Hashes for oneat-3.6.5.tar.gz
Algorithm Hash digest
SHA256 aa90a7f202c57ba51a386b0f2c37cc7db38d7147d27a332831970aacde3facbb
MD5 85813206352b16e8aa3e099ef7e025a3
BLAKE2b-256 4fd86e9509fb7bf5083c853a118597c9db9e2351f55cf6bf5308fb51446bc89a

See more details on using hashes here.

File details

Details for the file oneat-3.6.5-py3-none-any.whl.

File metadata

  • Download URL: oneat-3.6.5-py3-none-any.whl
  • Upload date:
  • Size: 103.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.9.0

File hashes

Hashes for oneat-3.6.5-py3-none-any.whl
Algorithm Hash digest
SHA256 4612735fd3ee9ae15759124e7201de8e84fcbd0d07fe31588759c7724e089c98
MD5 9e11ea52b179310c4852aa70344bcb01
BLAKE2b-256 5ee97c0f079210371b545f708ac1c9641a9346561c3ba14eb8432b44645f359d

See more details on using hashes here.

Supported by

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