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%global _empty_manifest_terminate_build 0
Name: python-elasticdeform
Version: 0.5.0
Release: 1
Summary: Elastic deformations for N-D images.
License: BSD
URL: https://github.com/gvtulder/elasticdeform
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/ee/562d0bc43151411ffcfbac1cb1419748df9fbffb2a2685c9735f3e7e6352/elasticdeform-0.5.0.tar.gz
Requires: python3-numpy
Requires: python3-scipy
%description
[](https://elasticdeform.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/gvtulder/elasticdeform/actions/workflows/test.yml)
[](https://github.com/gvtulder/elasticdeform/actions/workflows/wheels.yml)
[](https://zenodo.org/badge/latestdoi/145003699)
This library implements elastic grid-based deformations for N-dimensional images.
The elastic deformation approach is described in
* Ronneberger, Fischer, and Brox, "U-Net: Convolutional Networks for Biomedical
Image Segmentation" (<https://arxiv.org/abs/1505.04597>)
* Çiçek et al., "3D U-Net: Learning Dense Volumetric
Segmentation from Sparse Annotation" (<https://arxiv.org/abs/1606.06650>)
The procedure generates a coarse displacement grid with a random displacement
for each grid point. This grid is then interpolated to compute a displacement for
each pixel in the input image. The input image is then deformed using the
displacement vectors and a spline interpolation.
In addition to the normal, forward deformation, this package also provides a
function that can backpropagate the gradient through the deformation. This makes
it possible to use the deformation as a layer in a convolutional neural network.
For convenience, TensorFlow and PyTorch wrappers are provided in `elasticdeform.tf`
and `elasticdeform.torch`.
%package -n python3-elasticdeform
Summary: Elastic deformations for N-D images.
Provides: python-elasticdeform
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-elasticdeform
[](https://elasticdeform.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/gvtulder/elasticdeform/actions/workflows/test.yml)
[](https://github.com/gvtulder/elasticdeform/actions/workflows/wheels.yml)
[](https://zenodo.org/badge/latestdoi/145003699)
This library implements elastic grid-based deformations for N-dimensional images.
The elastic deformation approach is described in
* Ronneberger, Fischer, and Brox, "U-Net: Convolutional Networks for Biomedical
Image Segmentation" (<https://arxiv.org/abs/1505.04597>)
* Çiçek et al., "3D U-Net: Learning Dense Volumetric
Segmentation from Sparse Annotation" (<https://arxiv.org/abs/1606.06650>)
The procedure generates a coarse displacement grid with a random displacement
for each grid point. This grid is then interpolated to compute a displacement for
each pixel in the input image. The input image is then deformed using the
displacement vectors and a spline interpolation.
In addition to the normal, forward deformation, this package also provides a
function that can backpropagate the gradient through the deformation. This makes
it possible to use the deformation as a layer in a convolutional neural network.
For convenience, TensorFlow and PyTorch wrappers are provided in `elasticdeform.tf`
and `elasticdeform.torch`.
%package help
Summary: Development documents and examples for elasticdeform
Provides: python3-elasticdeform-doc
%description help
[](https://elasticdeform.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/gvtulder/elasticdeform/actions/workflows/test.yml)
[](https://github.com/gvtulder/elasticdeform/actions/workflows/wheels.yml)
[](https://zenodo.org/badge/latestdoi/145003699)
This library implements elastic grid-based deformations for N-dimensional images.
The elastic deformation approach is described in
* Ronneberger, Fischer, and Brox, "U-Net: Convolutional Networks for Biomedical
Image Segmentation" (<https://arxiv.org/abs/1505.04597>)
* Çiçek et al., "3D U-Net: Learning Dense Volumetric
Segmentation from Sparse Annotation" (<https://arxiv.org/abs/1606.06650>)
The procedure generates a coarse displacement grid with a random displacement
for each grid point. This grid is then interpolated to compute a displacement for
each pixel in the input image. The input image is then deformed using the
displacement vectors and a spline interpolation.
In addition to the normal, forward deformation, this package also provides a
function that can backpropagate the gradient through the deformation. This makes
it possible to use the deformation as a layer in a convolutional neural network.
For convenience, TensorFlow and PyTorch wrappers are provided in `elasticdeform.tf`
and `elasticdeform.torch`.
%prep
%autosetup -n elasticdeform-0.5.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-elasticdeform -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.0-1
- Package Spec generated
|
