Metadata-Version: 2.1
Name: surfcut
Version: 0.0.3
Summary: Cutting confocal stacks at various depths relative to surface signal 
Home-page: UNKNOWN
License: UNKNOWN
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: napari (>=0.2.12)
Requires-Dist: tifffile
Requires-Dist: pillow
Requires-Dist: scikit-image

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Surfcut for Python & Matlab
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Surfcut for Python & Matlab is based on Surfcut for ImageJ

Paper:
Erguvan, O., Louveaux, M., Hamant, O., Verger, S. (2019) ImageJ SurfCut: a user-friendly pipeline for high-throughput extraction of cell contours from 3D image stacks. BMC Biology, 17:38. https://doi.org/10.1186/s12915-019-0657-1

Software:
Verger Stéphane. (2019, April 10). sverger/SurfCut: SurfCut (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.2635737

Example Data:
Erguvan Özer, & Verger Stéphane. (2019). Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2577053

Headless Mode
=============

If you are a Python user, this package is available on pip:

::

    pip install surfcut

To run from the command line:

::

    surfcut <image name>.tif

This version implements Surfcut exactly as in the original paper.

A version including morphological operations (erode and dilate) is available for surfaces which are very curved, but is much slower than the original approach.

::

    surfcut <image name>.tif -m

.. image:: resources/morph.gif

For more options:

::

    surfcut --help


