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
python setup.py develop
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
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)