Python package to decode Amira 3D coordinate spatial graphs into semantic label mask
Project description
Semantic Label Creator [slcpy]
Python package to decode Amira 3D coordinate spatial graphs into semantic label mask.
Documentation: https://semantic-label-creator.readthedocs.io/en/latest/
Features
Load Amira spatial graph to transform 3D coordinates into semantic label mask
Installation
Stable release
To install Semantic_Label_Creator, run this command in your terminal:
$ pip install slcpy
This is the preferred method to install Semantic_Label_Creator, as it will always install the most recent stable release.
From sources
The sources for Semantic_Label_Creator can be downloaded from the Github repo.
You can either clone the public repository:
$ git clone git://github.com/RRobert92/Semantic_Label_Creator
Once you have a copy of the source, you can install it with:
$ python setup.py install
Usage
To use Semantic_Label_Creator in a project:
from slcpy.main import slcpy label_mask = slcpy(dir_path, pixel_size=None, circle_size=250)
or with terminal:
slcpy -dir -o -px -d string [-dir] directory of the folder that contain data. [-default] os.getcwd() + r'\data' string [-o] output directory to the folder where all of converted filed are stored. [-default] os.getcwd() + r'\data' + r'\output' int [-px] anisotropic images pixel size in Angstrom. [-default] None int [-d] diameter in Angstrom of a circle that would be drawn a semantic mask [-default] 250 int [-l] specified if lines should have independent labeling [-default] False int [-t] specified if the input image has to be trim to fit labels. [-default] True
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
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