Advanced Normalization Tools in Python
About ANTsPy
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).
Installation
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 antspyxBecause of limited storage space, pip binaries are not available for every combination of python version and platform. If we do not have releases for your platform, you can check the Github Releases page or build from source with:
git clone https://github.com/ANTsX/ANTsPy
cd ANTsPy
python3 setup.py install
or see below for an alternative strategy using pip. If you want more detailed instructions
on compiling ANTsPy from source, you can read the
installation tutorial.
Installing older versions
We cannot store the entire history of releases because storage space on pip is limited.
if you need an older release, you can check the Github Releases page or build from source.
Try something like:
pip install 'antspyx @ git+https://github.com/ANTsX/[email protected]'which will attempt to build from source (requires a machine with developer tools).
Recent wheels
Non-release commits have wheels built automatically, which are available for download for a limited period. Look under the Actions tab. Then click on the commit for the software version you want. Recent commits will have wheels stored as "artifacts".
Docker images
Available on Docker Hub. To build ANTsPy docker images, see the (installation tutorial)(https://github.com/ANTsX/ANTsPy/blob/master/tutorials/InstallingANTsPy.md#docker-installation).
ANTsR Comparison
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:
from ants import atropos, get_ants_data, image_read, resample_image, get_mask
img = image_read(get_ants_data("r16"))
img = resample_image(img, (64,64), 1, 0 )
mask = get_mask(img)
segs1 = atropos(a=img, m='[0.2,1x1]', c='[2,0]', i='kmeans[3]', x=mask )Tutorials
We provide numerous tutorials for new users: https://github.com/ANTsX/ANTsPy/tree/master/tutorials
other notes on compilation
in some cases, you may need some other libraries if they are not already installed eg if cmake says something about
a missing png library or a missing Python.h file.
sudo apt-get install libblas-dev liblapack-dev
sudo apt-get install gfortran
sudo apt-get install libpng-dev
sudo apt-get install python3-dev # for python3.x installs
Build documentation
cd docs
sphinx-apidoc -o source/ ../
make html