A regionprops table widget plugin for napari
Project description
napari-skimage-regionprops
A napari plugin for measuring properties of labeled objects based on scikit-image
Usage
From the menu Tools > Measurement > Regionprops
you can open a dialog where you can choose an intensity image, a corresponding label image and the features you want to measure:
If you want to interace with the labels and see which table row corresponds to which labeled object, use the label picker and
activate the show selected
checkbox.
Features
The user can select categories of features for feature extraction in the user interface. These categories contain measurements from the scikit-image regionprops list of measurements library:
-
size:
-
area
-
bbox_area
-
convex_area
-
equivalent_diameter
-
-
intensity:
-
max_intensity
-
mean_intensity
-
min_intensity
-
standard_deviation_intensity (
extra_properties
implementation using numpy)
-
-
perimeter:
-
perimeter
-
perimeter_crofton
-
-
shape
-
major_axis_length
-
minor_axis_length
-
orientation
-
solidity
-
eccentricity
-
extent
-
feret_diameter_max
-
local_centroid
-
-
position:
-
centroid
-
bbox
-
weighted_centroid
-
-
moments:
-
moments
-
moments_central
-
moments_hu
-
moments_normalized
-
This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.
Installation
You can install napari-skimage-regionprops
via pip:
pip install napari-skimage-regionprops
Or if you plan to develop it:
git clone https://github.com/haesleinhuepf/napari-skimage-regionprops
cd napari-skimage-regionprops
pip install -e .
If there is an error message suggesting that git is not installed, run conda install git
.
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-skimage-regionprops" is free and open source software
Issues
If you encounter any problems, please create a thread on image.sc along with a detailed description and tag @haesleinhuepf.
Project details
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Source Distribution
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