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-3.5.2.tar.gz (82.1 kB view details)

Uploaded Source

Built Distribution

oneat-3.5.2-py3-none-any.whl (91.5 kB view details)

Uploaded Python 3

File details

Details for the file oneat-3.5.2.tar.gz.

File metadata

  • Download URL: oneat-3.5.2.tar.gz
  • Upload date:
  • Size: 82.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.0

File hashes

Hashes for oneat-3.5.2.tar.gz
Algorithm Hash digest
SHA256 62b28cffa5352f0de78538b942ece8aaf656013ddb03785ee8a17739c5929f33
MD5 fc11a14a1c21b61c164f96f1adf4c5e5
BLAKE2b-256 4662b6ae438546ddf1fa41ff1e7f130e0331b4330dfffcae38db255bce1e4420

See more details on using hashes here.

File details

Details for the file oneat-3.5.2-py3-none-any.whl.

File metadata

  • Download URL: oneat-3.5.2-py3-none-any.whl
  • Upload date:
  • Size: 91.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.7.1 importlib_metadata/4.10.0 pkginfo/1.8.2 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.9.0

File hashes

Hashes for oneat-3.5.2-py3-none-any.whl
Algorithm Hash digest
SHA256 490c34df27c97aa777beb1e4dda917fffd3dea1aef5bbad5a2e1b0d899b4e995
MD5 b7090c8aa29190031f4b5d3984136518
BLAKE2b-256 e5437e2403f2c03127f44fbd12eafb1895333dd55776e74cbbf06d5ac1f98c5d

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