Version:

  • 0.0.9

Release date:

  • 13 May 2022

First released:

  • 04 May 2022

License:

  • BSD-3-Clause

Supported data:

  • Time series

GitHub activity:

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1

GitHub activity:

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1

Python versions supported:

  • >=3.8

Operating system:

  • OS Independent

Requirements:

  • numpy
  • magicgui
  • qtpy
  • scikit-image
  • opencv-python

napari-roi-registration

A plugin to perform roi registration.


Authors:

  • Andrea Bassi and Giorgia Tortora

Authors:

  • Andrea Bassi and Giorgia Tortora

License PyPI Python Version tests codecov napari hub

A Napari plugin for the registration of regions of interests (ROI) in a time lapse acquistion and processing of the intensity of the registered data.

The ROI are defined using a Labels layer. Registration of multiple ROIs is supported.

The Registration widget uses the user-defined labels, constructs a rectangular ROI around each of them and registers the ROIs in each time frame. The Processing widget measures the ROI displacements and extract the average intensity of the ROI, calculated in the label area. The Subtract background widget subtracts a background on each frame, calculated as the mean intensity on a Labels layer.

Tipically useful when ambient light affects the measurement.


This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

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Installation

You can install napari-roi-registration via pip:

pip install napari-roi-registration

To install latest development version :

pip install git+https://github.com/GiorgiaTortora/napari-roi-registration.git

Usage

Registration Widget

  1. Create a new Labels layer and draw one or more labels where you want to select a ROI (Region Of Interest). Each color in the same Labels layer represent a different label which is a different ROI.

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  1. Push the Register ROIs button: registration of the entire stack will be performed. When the registration is finished two new layers will appear in the viewer. One layer contains the centroids of the drawn labels while the other contains the bounding boxes encloding the ROIs.

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Processing Widget

Pushing the Process registered ROIs button processing of the registered ROIs will be performed. Information about the intensity of the registered data and the displacement of the ROIs will be given. In the IPhyton console the displacement vs time index and the mean intensity vs time index plots will appear. Choosing the save results option an excel file containg information about the ROIs positions, displacement and intensity at each frame will be generated.

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Background Widget

  1. Create a new Labels layer and draw a label on the area from which to get the background.

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  1. Push the Subtract background button. A new image layer will appear in the viewer. This layer contains the image to which the background was subtracted.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-roi-registration" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

Version:

  • 0.0.9

Release date:

  • 13 May 2022

First released:

  • 04 May 2022

License:

  • BSD-3-Clause

Supported data:

  • Time series

GitHub activity:

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1

Python versions supported:

  • >=3.8

Operating system:

  • OS Independent

Requirements:

  • numpy
  • magicgui
  • qtpy
  • scikit-image
  • opencv-python

Version:

  • 0.0.9

Release date:

  • 13 May 2022

First released:

  • 04 May 2022

License:

  • BSD-3-Clause

Supported data:

  • Time series

GitHub activity:

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1

GitHub activity:

  • Stars: 2
  • Forks: 0
  • Issues + PRs: 1

Python versions supported:

  • >=3.8

Operating system:

  • OS Independent

Requirements:

  • numpy
  • magicgui
  • qtpy
  • scikit-image
  • opencv-python

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