%global _empty_manifest_terminate_build 0 Name: python-vipy Version: 1.14.4 Release: 1 Summary: Python Tools for Visual Dataset Transformation License: MIT License URL: https://github.com/visym/vipy Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b6/d0/380f52b8ce9592c492a4be369ad25c0ce31d6de7e1ef3e0fe29ab70b3187/vipy-1.14.4.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-matplotlib Requires: python3-dill Requires: python3-pillow Requires: python3-ffmpeg-python Requires: python3-scikit-build Requires: python3-scipy Requires: python3-opencv-python Requires: python3-torch Requires: python3-ipython Requires: python3-scikit-learn Requires: python3-boto3 Requires: python3-youtube-dl Requires: python3-dask Requires: python3-distributed Requires: python3-h5py Requires: python3-nltk Requires: python3-bs4 Requires: python3-pyyaml Requires: python3-pytest Requires: python3-paramiko Requires: python3-scp Requires: python3-ujson Requires: python3-pdoc3 Requires: python3-dill Requires: python3-pillow Requires: python3-numpy Requires: python3-matplotlib Requires: python3-ffmpeg-python Requires: python3-heyvi Requires: python3-scikit-build Requires: python3-scipy Requires: python3-opencv-python Requires: python3-torch Requires: python3-ipython Requires: python3-scikit-learn Requires: python3-boto3 Requires: python3-youtube-dl Requires: python3-dask Requires: python3-distributed Requires: python3-h5py Requires: python3-nltk Requires: python3-bs4 Requires: python3-pyyaml Requires: python3-pytest Requires: python3-paramiko Requires: python3-scp Requires: python3-ujson Requires: python3-numba Requires: python3-pdoc3 Requires: python3-dill Requires: python3-pillow Requires: python3-numpy Requires: python3-matplotlib Requires: python3-ffmpeg-python Requires: python3-heyvi Requires: python3-ujson Requires: python3-numba %description VIPY: Python Tools for Visual Dataset Transformation Documentation: https://visym.github.io/vipy VIPY is a python package for representation, transformation and visualization of annotated videos and images. Annotations are the ground truth provided by labelers (e.g. object bounding boxes, face identities, temporal activity clips), suitable for training computer vision systems. VIPY provides tools to easily edit videos and images so that the annotations are transformed along with the pixels. This enables a clean interface for transforming complex datasets for input to your computer vision training and testing pipeline. VIPY provides: * Representation of videos with labeled activities that can be resized, clipped, rotated, scaled, padded, cropped and resampled * Representation of images with object bounding boxes that can be manipulated as easily as editing an image * Clean visualization of annotated images and videos * Lazy loading of images and videos suitable for distributed processing (e.g. dask, spark) * Straightforward integration into machine learning toolchains (e.g. torch, numpy) * Fluent interface for chaining operations on videos and images * Dataset download, unpack and import (e.g. Charades, AVA, ActivityNet, Kinetics, Moments in Time) * Minimum dependencies for easy installation (e.g. AWS Lambda, Flask) [![VIPY MEVA dataset visualization](http://i3.ytimg.com/vi/_jixHQr5dK4/maxresdefault.jpg)](https://youtu.be/_jixHQr5dK4) %package -n python3-vipy Summary: Python Tools for Visual Dataset Transformation Provides: python-vipy BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-vipy VIPY: Python Tools for Visual Dataset Transformation Documentation: https://visym.github.io/vipy VIPY is a python package for representation, transformation and visualization of annotated videos and images. Annotations are the ground truth provided by labelers (e.g. object bounding boxes, face identities, temporal activity clips), suitable for training computer vision systems. VIPY provides tools to easily edit videos and images so that the annotations are transformed along with the pixels. This enables a clean interface for transforming complex datasets for input to your computer vision training and testing pipeline. VIPY provides: * Representation of videos with labeled activities that can be resized, clipped, rotated, scaled, padded, cropped and resampled * Representation of images with object bounding boxes that can be manipulated as easily as editing an image * Clean visualization of annotated images and videos * Lazy loading of images and videos suitable for distributed processing (e.g. dask, spark) * Straightforward integration into machine learning toolchains (e.g. torch, numpy) * Fluent interface for chaining operations on videos and images * Dataset download, unpack and import (e.g. Charades, AVA, ActivityNet, Kinetics, Moments in Time) * Minimum dependencies for easy installation (e.g. AWS Lambda, Flask) [![VIPY MEVA dataset visualization](http://i3.ytimg.com/vi/_jixHQr5dK4/maxresdefault.jpg)](https://youtu.be/_jixHQr5dK4) %package help Summary: Development documents and examples for vipy Provides: python3-vipy-doc %description help VIPY: Python Tools for Visual Dataset Transformation Documentation: https://visym.github.io/vipy VIPY is a python package for representation, transformation and visualization of annotated videos and images. Annotations are the ground truth provided by labelers (e.g. object bounding boxes, face identities, temporal activity clips), suitable for training computer vision systems. VIPY provides tools to easily edit videos and images so that the annotations are transformed along with the pixels. This enables a clean interface for transforming complex datasets for input to your computer vision training and testing pipeline. VIPY provides: * Representation of videos with labeled activities that can be resized, clipped, rotated, scaled, padded, cropped and resampled * Representation of images with object bounding boxes that can be manipulated as easily as editing an image * Clean visualization of annotated images and videos * Lazy loading of images and videos suitable for distributed processing (e.g. dask, spark) * Straightforward integration into machine learning toolchains (e.g. torch, numpy) * Fluent interface for chaining operations on videos and images * Dataset download, unpack and import (e.g. Charades, AVA, ActivityNet, Kinetics, Moments in Time) * Minimum dependencies for easy installation (e.g. AWS Lambda, Flask) [![VIPY MEVA dataset visualization](http://i3.ytimg.com/vi/_jixHQr5dK4/maxresdefault.jpg)](https://youtu.be/_jixHQr5dK4) %prep %autosetup -n vipy-1.14.4 %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-vipy -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Tue May 30 2023 Python_Bot - 1.14.4-1 - Package Spec generated