Search ANTsPy documentation at read the docs.
ANTsPy is a Python library which wraps the C++ biomedical image processing library ANTs, matches much of the statistical capabilities of ANTsR, and allows seamless integration with numpy, scikit-learn, and the greater Python community.
ANTsPy includes blazing-fast IO (~40% faster than nibabel for loading Nifti images and converting them to numpy arrays), registration, segmentation, statistical learning, visualization, and other useful utility functions.
ANTsPy also provides a low-barrier opportunity for users to quickly wrap their ITK (or general C++) code in Python without having to build an entire IO/plotting/wrapping code base from scratch - see C++ Wrap Guide for a succinct tutorial.
If you want to contribute to ANTsPy or simply want to learn about the package architecture and wrapping process, please read the extensive contributors guide.
If you have any questions or feature requests, feel free to open an issue or email Nick (ncullen at pennmedicine dot upenn dot edu).
We recommend that users install the latest pre-compiled binaries, which takes ~1 minute. Note that ANTsPy is not currently tested for Python 2.7 support. Copy the following command and paste it into your bash terminal:
For MacOS and Linux:
pip install antspyxIf we do not have releases for your platform, then use:
git clone https://github.com/ANTsX/ANTsPy
cd ANTsPy
python3 setup.py install
if you want more detailed instructions on compiling ANTsPy from source, you can read the installation tutorial.
NOTE: we are hoping to relatively soon release windows wheels via pip.
If they are not yet available, please check the discussion in the issues
for how to build from source on windows machines.
Here is a quick example to show the similarity with ANTsR:
ANTsR code:
library(ANTsR)
img <- antsImageRead(getANTsRData("r16"))
img <- resampleImage(img, c(64,64), 1, 0 )
mask <- getMask(img)
segs1 <- atropos(a=img, m='[0.2,1x1]', c='[2,0]', i='kmeans[3]', x=mask )ANTsPy code:
import ants
img = ants.image_read(ants.get_ants_data("r16"))
img = ants.resample_image(img, (64,64), 1, 0 )
mask = ants.get_mask(img)
segs1 = ants.atropos(a=img, m='[0.2,1x1]', c='[2,0]', i='kmeans[3]', x=mask )We provide numerous tutorials for new users: https://github.com/ANTsX/ANTsPy/tree/master/tutorials
cd docs
sphinx-apidoc -o source/ ../
make html