PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. It can translate bounding box annotations between different formats. (For example, COCO to YOLO.) And it includes an AI-assisted labeling tool that runs in a Jupyter notebook.
- **Translate:** Convert annotation formats with a single line of code:
```
importer.ImportCoco(path_to_annotations).export.ExportToYoloV5()
```
- **Analyze:** PyLabel stores annotatations in a pandas dataframe so you can easily perform analysis on image datasets.
- **Split:** Divide image datasets into train, test, and val with stratification to get consistent class distribution. 
- **Label:** PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model.
- **Visualize:** Render images from your dataset with bounding boxes overlaid so you can confirm the accuracy of the annotations.
## Tutorial Notebooks
See PyLabel in action in these [sample Jupyter notebooks](https://github.com/pylabel-project/samples/):
- [Convert COCO to YOLO](https://github.com/pylabel-project/samples/blob/main/coco2yolov5.ipynb)
- [Convert COCO to VOC](https://github.com/pylabel-project/samples/blob/main/coco2voc.ipynb)
- [Convert VOC to COCO](https://github.com/pylabel-project/samples/blob/main/voc2coco.ipynb)
- [Convert YOLO to COCO](https://github.com/pylabel-project/samples/blob/main/yolo2coco.ipynb)
- [Convert YOLO to VOC](https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb)
- [Import a YOLO YAML File](https://github.com/pylabel-project/samples/blob/main/yolo_with_yaml_importer.ipynb)
- [Splitting Images Datasets into Train, Test, Val](https://github.com/pylabel-project/samples/blob/main/dataset_splitting.ipynb)
- [Labeling Tool Demo with AI Assisted Labeling](https://github.com/pylabel-project/samples/blob/main/pylabeler.ipynb)
Find more docs at https://pylabel.readthedocs.io.
## About PyLabel
PyLabel was developed by Jeremy Fraenkel, Alex Heaton, and Derek Topper as the Capstope project for the Master of Information and Data Science (MIDS) at the UC Berkeley School of Information. If you have any questions or feedback please [create an issue](https://github.com/pylabel-project/pylabel/issues). Please let us know how we can make PyLabel more useful.
%package -n python3-pylabel
Summary: Transform, analyze, and visualize computer vision annotations.
Provides: python-pylabel
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-pylabel
# PyLabel
PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. It can translate bounding box annotations between different formats. (For example, COCO to YOLO.) And it includes an AI-assisted labeling tool that runs in a Jupyter notebook.
- **Translate:** Convert annotation formats with a single line of code:
```
importer.ImportCoco(path_to_annotations).export.ExportToYoloV5()
```
- **Analyze:** PyLabel stores annotatations in a pandas dataframe so you can easily perform analysis on image datasets.
- **Split:** Divide image datasets into train, test, and val with stratification to get consistent class distribution.
- **Label:** PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model.
- **Visualize:** Render images from your dataset with bounding boxes overlaid so you can confirm the accuracy of the annotations.
## Tutorial Notebooks
See PyLabel in action in these [sample Jupyter notebooks](https://github.com/pylabel-project/samples/):
- [Convert COCO to YOLO](https://github.com/pylabel-project/samples/blob/main/coco2yolov5.ipynb)
- [Convert COCO to VOC](https://github.com/pylabel-project/samples/blob/main/coco2voc.ipynb)
- [Convert VOC to COCO](https://github.com/pylabel-project/samples/blob/main/voc2coco.ipynb)
- [Convert YOLO to COCO](https://github.com/pylabel-project/samples/blob/main/yolo2coco.ipynb)
- [Convert YOLO to VOC](https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb)
- [Import a YOLO YAML File](https://github.com/pylabel-project/samples/blob/main/yolo_with_yaml_importer.ipynb)
- [Splitting Images Datasets into Train, Test, Val](https://github.com/pylabel-project/samples/blob/main/dataset_splitting.ipynb)
- [Labeling Tool Demo with AI Assisted Labeling](https://github.com/pylabel-project/samples/blob/main/pylabeler.ipynb)
Find more docs at https://pylabel.readthedocs.io.
## About PyLabel
PyLabel was developed by Jeremy Fraenkel, Alex Heaton, and Derek Topper as the Capstope project for the Master of Information and Data Science (MIDS) at the UC Berkeley School of Information. If you have any questions or feedback please [create an issue](https://github.com/pylabel-project/pylabel/issues). Please let us know how we can make PyLabel more useful.
%package help
Summary: Development documents and examples for pylabel
Provides: python3-pylabel-doc
%description help
# PyLabel
PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. It can translate bounding box annotations between different formats. (For example, COCO to YOLO.) And it includes an AI-assisted labeling tool that runs in a Jupyter notebook.
- **Translate:** Convert annotation formats with a single line of code:
```
importer.ImportCoco(path_to_annotations).export.ExportToYoloV5()
```
- **Analyze:** PyLabel stores annotatations in a pandas dataframe so you can easily perform analysis on image datasets.
- **Split:** Divide image datasets into train, test, and val with stratification to get consistent class distribution.
- **Label:** PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model.
- **Visualize:** Render images from your dataset with bounding boxes overlaid so you can confirm the accuracy of the annotations.
## Tutorial Notebooks
See PyLabel in action in these [sample Jupyter notebooks](https://github.com/pylabel-project/samples/):
- [Convert COCO to YOLO](https://github.com/pylabel-project/samples/blob/main/coco2yolov5.ipynb)
- [Convert COCO to VOC](https://github.com/pylabel-project/samples/blob/main/coco2voc.ipynb)
- [Convert VOC to COCO](https://github.com/pylabel-project/samples/blob/main/voc2coco.ipynb)
- [Convert YOLO to COCO](https://github.com/pylabel-project/samples/blob/main/yolo2coco.ipynb)
- [Convert YOLO to VOC](https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb)
- [Import a YOLO YAML File](https://github.com/pylabel-project/samples/blob/main/yolo_with_yaml_importer.ipynb)
- [Splitting Images Datasets into Train, Test, Val](https://github.com/pylabel-project/samples/blob/main/dataset_splitting.ipynb)
- [Labeling Tool Demo with AI Assisted Labeling](https://github.com/pylabel-project/samples/blob/main/pylabeler.ipynb)
Find more docs at https://pylabel.readthedocs.io.
## About PyLabel
PyLabel was developed by Jeremy Fraenkel, Alex Heaton, and Derek Topper as the Capstope project for the Master of Information and Data Science (MIDS) at the UC Berkeley School of Information. If you have any questions or feedback please [create an issue](https://github.com/pylabel-project/pylabel/issues). Please let us know how we can make PyLabel more useful.
%prep
%autosetup -n pylabel-0.1.50
%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-pylabel -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot