edt
pypi i edt

edt

Euclidean distance & signed distance transform for multi-label 3D anisotropic images using marching parabolas.

by seung-lab

2.3.0 (see all)
pypi i edt
Readme

Python Instructions for MLAEDT-3D

Compute the Euclidean Distance Transform of a 1d, 2d, or 3d labeled image containing multiple labels in a single pass with support for anisotropic dimensions.

Python Installation

Requires a C++ compiler

The installation process depends on edt.cpp for the Python bindings derived from edt.pyx. edt.hpp contains the algorithm implementation.

pip install numpy
pip install edt

Recompiling edt.pyx

Requires Cython and a C++ compiler

cd python
cython -3 --cplus edt.pyx # generates edt.cpp
python setup.py develop # compiles edt.cpp and edt.hpp 
                        # together into a shared binary e.g. edt.cpython-36m-x86_64-linux-gnu.so

Python Usage

Consult help(edt) after importing. The edt module contains: edt and edtsq which compute the euclidean and squared euclidean distance respectively. Both functions select dimension based on the shape of the numpy array fed to them. 1D, 2D, and 3D volumes are supported. 1D processing is extremely fast. Numpy boolean arrays are handled specially for faster processing.

If for some reason you'd like to use a specific 'D' function, edt1d, edt1dsq, edt2d, edt2dsq, edt3d, and edt3dsq are available.

The three optional parameters are anisotropy, black_border, and order. Anisotropy is used to correct for distortions in voxel space, e.g. if X and Y were acquired with a microscope, but the Z axis was cut more corsely.

black_border allows you to specify that the edges of the image should be considered in computing pixel distances (it's also slightly faster).

order allows the programmer to determine how the underlying array should be interpreted. 'C' (C-order, XYZ, row-major) and 'F' (Fortran-order, ZYX, column major) are supported. 'C' order is the default.

parallel controls the number of threads. Set it <= 0 to automatically determine your CPU count.

import edt
import numpy as np

# e.g. 6nm x 6nm x 30nm for the S1 dataset by Kasthuri et al., 2014
labels = np.ones(shape=(512, 512, 512), dtype=np.uint32, order='F')
dt = edt.edt(labels, anisotropy=(6, 6, 30), black_border=True, order='F', parallel=1) 

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