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%global _empty_manifest_terminate_build 0
Name: python-DicomRTTool
Version: 2.0.8
Release: 1
Summary: Services for reading dicom files, RT structures, and dose files, as well as tools for converting numpy prediction masks back to an RT structure
License: GNU Affero General Public License v3
URL: https://github.com/brianmanderson/Dicom_RT_and_Images_to_Mask
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/fd/68/778eac11a2991c66bb6e23ff80cf5b91717b125d0d282c016b868640d7cb/DicomRTTool-2.0.8.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-matplotlib
Requires: python3-opencv-python
Requires: python3-openpyxl
Requires: python3-pandas
Requires: python3-Pillow
Requires: python3-pydicom
Requires: python3-scikit-image
Requires: python3-scipy
Requires: python3-SimpleITK
Requires: python3-six
Requires: python3-xlrd
Requires: python3-check-manifest
Requires: python3-tqdm
Requires: python3-pytest
Requires: python3-PlotScrollNumpyArrays
Requires: python3-setuptools
%description
# We're published! Please check out the Technical Note here: https://www.sciencedirect.com/science/article/abs/pii/S1879850021000485 and reference this work if you find it useful
### DOI:https://doi.org/10.1016/j.prro.2021.02.003
## This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures
## Installation guide
pip install DicomRTTool
### Highly recommend to go through the jupyter notebook in the Examples folder and to read the Wiki
### Quick use guide
from DicomRTTool.ReaderWriter import DicomReaderWriter, ROIAssociationClass
Dicom_path = r'.some_path_to_dicom'
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True)
Dicom_reader.walk_through_folders(Dicom_path) # This will parse through all DICOM present in the folder and subfolders
all_rois = Dicom_reader.return_rois(print_rois=True) # Return a list of all rois present
Contour_names = ['tumor'] # Define what rois you want
associations = [ROIAssociationClass('tumor', ['tumor_mr', 'tumor_ct'])] # Any list of roi associations
Dicom_reader.set_contour_names_and_assocations(contour_names=Contour_names, associations=associations)
Dicom_reader.get_images_and_mask()
image_numpy = Dicom_reader.ArrayDicom
mask_numpy = Dicom_reader.mask
image_sitk_handle = Dicom_reader.dicom_handle
mask_sitk_handle = Dicom_reader.annotation_handle
### Other interesting additions
### Adding information to the Dicom_reader.series_instances_dictionary
from DicomRTTool.ReaderWriter import Tag
plan_pydicom_string_keys = {"MyNamedRTPlan": Tag((0x300a, 0x002))}
image_sitk_string_keys = {"MyPatientName": "0010|0010"}
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True, plan_pydicom_string_keys=plan_pydicom_string_keys, image_sitk_string_keys=image_sitk_string_keys)
##### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!
###### Ring update allows for multiple rings to be represented correctly
#### Works on oblique images for masks and predictions*
%package -n python3-DicomRTTool
Summary: Services for reading dicom files, RT structures, and dose files, as well as tools for converting numpy prediction masks back to an RT structure
Provides: python-DicomRTTool
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-DicomRTTool
# We're published! Please check out the Technical Note here: https://www.sciencedirect.com/science/article/abs/pii/S1879850021000485 and reference this work if you find it useful
### DOI:https://doi.org/10.1016/j.prro.2021.02.003
## This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures
## Installation guide
pip install DicomRTTool
### Highly recommend to go through the jupyter notebook in the Examples folder and to read the Wiki
### Quick use guide
from DicomRTTool.ReaderWriter import DicomReaderWriter, ROIAssociationClass
Dicom_path = r'.some_path_to_dicom'
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True)
Dicom_reader.walk_through_folders(Dicom_path) # This will parse through all DICOM present in the folder and subfolders
all_rois = Dicom_reader.return_rois(print_rois=True) # Return a list of all rois present
Contour_names = ['tumor'] # Define what rois you want
associations = [ROIAssociationClass('tumor', ['tumor_mr', 'tumor_ct'])] # Any list of roi associations
Dicom_reader.set_contour_names_and_assocations(contour_names=Contour_names, associations=associations)
Dicom_reader.get_images_and_mask()
image_numpy = Dicom_reader.ArrayDicom
mask_numpy = Dicom_reader.mask
image_sitk_handle = Dicom_reader.dicom_handle
mask_sitk_handle = Dicom_reader.annotation_handle
### Other interesting additions
### Adding information to the Dicom_reader.series_instances_dictionary
from DicomRTTool.ReaderWriter import Tag
plan_pydicom_string_keys = {"MyNamedRTPlan": Tag((0x300a, 0x002))}
image_sitk_string_keys = {"MyPatientName": "0010|0010"}
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True, plan_pydicom_string_keys=plan_pydicom_string_keys, image_sitk_string_keys=image_sitk_string_keys)
##### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!
###### Ring update allows for multiple rings to be represented correctly
#### Works on oblique images for masks and predictions*
%package help
Summary: Development documents and examples for DicomRTTool
Provides: python3-DicomRTTool-doc
%description help
# We're published! Please check out the Technical Note here: https://www.sciencedirect.com/science/article/abs/pii/S1879850021000485 and reference this work if you find it useful
### DOI:https://doi.org/10.1016/j.prro.2021.02.003
## This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures
## Installation guide
pip install DicomRTTool
### Highly recommend to go through the jupyter notebook in the Examples folder and to read the Wiki
### Quick use guide
from DicomRTTool.ReaderWriter import DicomReaderWriter, ROIAssociationClass
Dicom_path = r'.some_path_to_dicom'
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True)
Dicom_reader.walk_through_folders(Dicom_path) # This will parse through all DICOM present in the folder and subfolders
all_rois = Dicom_reader.return_rois(print_rois=True) # Return a list of all rois present
Contour_names = ['tumor'] # Define what rois you want
associations = [ROIAssociationClass('tumor', ['tumor_mr', 'tumor_ct'])] # Any list of roi associations
Dicom_reader.set_contour_names_and_assocations(contour_names=Contour_names, associations=associations)
Dicom_reader.get_images_and_mask()
image_numpy = Dicom_reader.ArrayDicom
mask_numpy = Dicom_reader.mask
image_sitk_handle = Dicom_reader.dicom_handle
mask_sitk_handle = Dicom_reader.annotation_handle
### Other interesting additions
### Adding information to the Dicom_reader.series_instances_dictionary
from DicomRTTool.ReaderWriter import Tag
plan_pydicom_string_keys = {"MyNamedRTPlan": Tag((0x300a, 0x002))}
image_sitk_string_keys = {"MyPatientName": "0010|0010"}
Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True, plan_pydicom_string_keys=plan_pydicom_string_keys, image_sitk_string_keys=image_sitk_string_keys)
##### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!
###### Ring update allows for multiple rings to be represented correctly
#### Works on oblique images for masks and predictions*
%prep
%autosetup -n DicomRTTool-2.0.8
%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-DicomRTTool -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.8-1
- Package Spec generated
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