Skip to main content

Static and Dynamic classification tool.

Project description

Oneat

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

additionally ensure that your installed tensorflow version is not over 2.3.4

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 Heat Map Csv output Visualization Notebook
Example timelapse Oneat model Colab Notebook Heat Map Csv File [Napari notebook] ()
Example timelapse Oneat model Colab Notebook Heat Map Csv File [Napari notebook] ()
Example timelapse Oneat model Colab Notebook Heat Map Csv File [Napari notebook] ()
Example timelapse Oneat model Colab Notebook Heat Map Csv File [Napari notebook] ()
Example timelapse Oneat model Colab Notebook Heat Map Csv File [Napari notebook] ()
Example timelapse Oneat model Colab Notebook Heat Map Csv File [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-2.2.7.tar.gz (77.8 kB view details)

Uploaded Source

Built Distribution

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

oneat-2.2.7-py3-none-any.whl (87.2 kB view details)

Uploaded Python 3

File details

Details for the file oneat-2.2.7.tar.gz.

File metadata

  • Download URL: oneat-2.2.7.tar.gz
  • Upload date:
  • Size: 77.8 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-2.2.7.tar.gz
Algorithm Hash digest
SHA256 7cbb995ebb8a38ccf82608c739cdd89047b29e39a41fec3b4f050d07cf677f22
MD5 734f35c1663d31b1fdd2a34bdf179720
BLAKE2b-256 a384307044d224da1959f72027c387b394e9e4f24173b4fe0c1f16d20375d149

See more details on using hashes here.

File details

Details for the file oneat-2.2.7-py3-none-any.whl.

File metadata

  • Download URL: oneat-2.2.7-py3-none-any.whl
  • Upload date:
  • Size: 87.2 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-2.2.7-py3-none-any.whl
Algorithm Hash digest
SHA256 13803256db50108d99b4288d06b77b81d4029eacf5205055d9f50b06b782e4de
MD5 3279b8b7cf071c5f2e92c1739dbcceac
BLAKE2b-256 f540d2ecea038a33a4ab2da850a600d416f78f0696427ce894eba938f2b6727d

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