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@@ -0,0 +1 @@ +/datumaro-1.2.1.tar.gz diff --git a/python-datumaro.spec b/python-datumaro.spec new file mode 100644 index 0000000..ef9185f --- /dev/null +++ b/python-datumaro.spec @@ -0,0 +1,402 @@ +%global _empty_manifest_terminate_build 0 +Name: python-datumaro +Version: 1.2.1 +Release: 1 +Summary: Dataset Management Framework (Datumaro) +License: MIT License +URL: https://github.com/openvinotoolkit/datumaro +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/31/63/1ef1d9e53b19a459827c8ef5208d91afc88cfdd722329b331a5c76c51fd2/datumaro-1.2.1.tar.gz +BuildArch: noarch + + +%description +# Dataset Management Framework (Datumaro) + +[](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml) +[](https://codecov.io/gh/openvinotoolkit/datumaro) + +A framework and CLI tool to build, transform, and analyze datasets. + +<!--lint disable fenced-code-flag--> +``` +VOC dataset ---> Annotation tool + + / +COCO dataset -----> Datumaro ---> dataset ------> Model training + + \ +CVAT annotations ---> Publication, statistics etc. +``` +<!--lint enable fenced-code-flag--> + +- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started) +- [Features](#features) +- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual) +- [Developer manual](https://openvinotoolkit.github.io/datumaro/api) +- [Contributing](#contributing) + +## Features + +[(Back to top)](#dataset-management-framework-datumaro) + +- Dataset reading, writing, conversion in any direction. + - [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`) + - [Cityscapes](https://www.cityscapes-dataset.com/) + - [COCO](http://cocodataset.org/#format-data) (`image_info`, `instances`, `person_keypoints`, + `captions`, `labels`, `panoptic`, `stuff`) + - [CVAT](https://openvinotoolkit.github.io/cvat/docs/manual/advanced/xml_format) + - [ImageNet](http://image-net.org/) + - [Kitti](http://www.cvlibs.net/datasets/kitti/index.php) (`segmentation`, `detection`, + `3D raw` / `velodyne points`) + - [LabelMe](http://labelme.csail.mit.edu/Release3.0) + - [LFW](http://vis-www.cs.umass.edu/lfw/) (`classification`, `person re-identification`, + `landmarks`) + - [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`) + - [Open Images](https://storage.googleapis.com/openimages/web/download.html) + - [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html) + (`classification`, `detection`, `segmentation`, `action_classification`, `person_layout`) + - [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md) + (`bboxes`, `masks`) + - [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`) + + Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats). +- Dataset building + - Merging multiple datasets into one + - Dataset filtering by a custom criteria: + - remove polygons of a certain class + - remove images without annotations of a specific class + - remove `occluded` annotations from images + - keep only vertically-oriented images + - remove small area bounding boxes from annotations + - Annotation conversions, for instance: + - polygons to instance masks and vice-versa + - apply a custom colormap for mask annotations + - rename or remove dataset labels + - Splitting a dataset into multiple subsets like `train`, `val`, and `test`: + - random split + - task-specific splits based on annotations, + which keep initial label and attribute distributions + - for classification task, based on labels + - for detection task, based on bboxes + - for re-identification task, based on labels, + avoiding having same IDs in training and test splits + - Sampling a dataset + - analyzes inference result from the given dataset + and selects the ‘best’ and the ‘least amount of’ samples for annotation. + - Select the sample that best suits model training. + - sampling with Entropy based algorithm +- Dataset quality checking + - Simple checking for errors + - Comparison with model inference + - Merging and comparison of multiple datasets + - Annotation validation based on the task type(classification, etc) +- Dataset comparison +- Dataset statistics (image mean and std, annotation statistics) +- Model integration + - Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.) + - Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421)) + - RISE for classification + - RISE for object detection + +> Check + [the design document](https://openvinotoolkit.github.io/datumaro/docs/design) + for a full list of features. +> Check + [the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual) + for usage instructions. + +## Contributing + +[(Back to top)](#dataset-management-framework-datumaro) + +Feel free to +[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you +think something needs to be changed. You are welcome to participate in +development, instructions are available in our +[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing). + +## Telemetry data collection note + +The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/) +is used to collect basic information about Datumaro usage. + +To enable/disable telemetry data collection please see the +[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/). + + +%package -n python3-datumaro +Summary: Dataset Management Framework (Datumaro) +Provides: python-datumaro +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-datumaro +# Dataset Management Framework (Datumaro) + +[](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml) +[](https://codecov.io/gh/openvinotoolkit/datumaro) + +A framework and CLI tool to build, transform, and analyze datasets. + +<!--lint disable fenced-code-flag--> +``` +VOC dataset ---> Annotation tool + + / +COCO dataset -----> Datumaro ---> dataset ------> Model training + + \ +CVAT annotations ---> Publication, statistics etc. +``` +<!--lint enable fenced-code-flag--> + +- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started) +- [Features](#features) +- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual) +- [Developer manual](https://openvinotoolkit.github.io/datumaro/api) +- [Contributing](#contributing) + +## Features + +[(Back to top)](#dataset-management-framework-datumaro) + +- Dataset reading, writing, conversion in any direction. + - [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`) + - [Cityscapes](https://www.cityscapes-dataset.com/) + - [COCO](http://cocodataset.org/#format-data) (`image_info`, `instances`, `person_keypoints`, + `captions`, `labels`, `panoptic`, `stuff`) + - [CVAT](https://openvinotoolkit.github.io/cvat/docs/manual/advanced/xml_format) + - [ImageNet](http://image-net.org/) + - [Kitti](http://www.cvlibs.net/datasets/kitti/index.php) (`segmentation`, `detection`, + `3D raw` / `velodyne points`) + - [LabelMe](http://labelme.csail.mit.edu/Release3.0) + - [LFW](http://vis-www.cs.umass.edu/lfw/) (`classification`, `person re-identification`, + `landmarks`) + - [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`) + - [Open Images](https://storage.googleapis.com/openimages/web/download.html) + - [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html) + (`classification`, `detection`, `segmentation`, `action_classification`, `person_layout`) + - [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md) + (`bboxes`, `masks`) + - [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`) + + Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats). +- Dataset building + - Merging multiple datasets into one + - Dataset filtering by a custom criteria: + - remove polygons of a certain class + - remove images without annotations of a specific class + - remove `occluded` annotations from images + - keep only vertically-oriented images + - remove small area bounding boxes from annotations + - Annotation conversions, for instance: + - polygons to instance masks and vice-versa + - apply a custom colormap for mask annotations + - rename or remove dataset labels + - Splitting a dataset into multiple subsets like `train`, `val`, and `test`: + - random split + - task-specific splits based on annotations, + which keep initial label and attribute distributions + - for classification task, based on labels + - for detection task, based on bboxes + - for re-identification task, based on labels, + avoiding having same IDs in training and test splits + - Sampling a dataset + - analyzes inference result from the given dataset + and selects the ‘best’ and the ‘least amount of’ samples for annotation. + - Select the sample that best suits model training. + - sampling with Entropy based algorithm +- Dataset quality checking + - Simple checking for errors + - Comparison with model inference + - Merging and comparison of multiple datasets + - Annotation validation based on the task type(classification, etc) +- Dataset comparison +- Dataset statistics (image mean and std, annotation statistics) +- Model integration + - Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.) + - Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421)) + - RISE for classification + - RISE for object detection + +> Check + [the design document](https://openvinotoolkit.github.io/datumaro/docs/design) + for a full list of features. +> Check + [the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual) + for usage instructions. + +## Contributing + +[(Back to top)](#dataset-management-framework-datumaro) + +Feel free to +[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you +think something needs to be changed. You are welcome to participate in +development, instructions are available in our +[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing). + +## Telemetry data collection note + +The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/) +is used to collect basic information about Datumaro usage. + +To enable/disable telemetry data collection please see the +[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/). + + +%package help +Summary: Development documents and examples for datumaro +Provides: python3-datumaro-doc +%description help +# Dataset Management Framework (Datumaro) + +[](https://github.com/openvinotoolkit/datumaro/actions/workflows/health_check.yml) +[](https://codecov.io/gh/openvinotoolkit/datumaro) + +A framework and CLI tool to build, transform, and analyze datasets. + +<!--lint disable fenced-code-flag--> +``` +VOC dataset ---> Annotation tool + + / +COCO dataset -----> Datumaro ---> dataset ------> Model training + + \ +CVAT annotations ---> Publication, statistics etc. +``` +<!--lint enable fenced-code-flag--> + +- [Getting started](https://openvinotoolkit.github.io/datumaro/docs/getting_started) +- [Features](#features) +- [User manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual) +- [Developer manual](https://openvinotoolkit.github.io/datumaro/api) +- [Contributing](#contributing) + +## Features + +[(Back to top)](#dataset-management-framework-datumaro) + +- Dataset reading, writing, conversion in any direction. + - [CIFAR-10/100](https://www.cs.toronto.edu/~kriz/cifar.html) (`classification`) + - [Cityscapes](https://www.cityscapes-dataset.com/) + - [COCO](http://cocodataset.org/#format-data) (`image_info`, `instances`, `person_keypoints`, + `captions`, `labels`, `panoptic`, `stuff`) + - [CVAT](https://openvinotoolkit.github.io/cvat/docs/manual/advanced/xml_format) + - [ImageNet](http://image-net.org/) + - [Kitti](http://www.cvlibs.net/datasets/kitti/index.php) (`segmentation`, `detection`, + `3D raw` / `velodyne points`) + - [LabelMe](http://labelme.csail.mit.edu/Release3.0) + - [LFW](http://vis-www.cs.umass.edu/lfw/) (`classification`, `person re-identification`, + `landmarks`) + - [MNIST](http://yann.lecun.com/exdb/mnist/) (`classification`) + - [Open Images](https://storage.googleapis.com/openimages/web/download.html) + - [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/index.html) + (`classification`, `detection`, `segmentation`, `action_classification`, `person_layout`) + - [TF Detection API](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md) + (`bboxes`, `masks`) + - [YOLO](https://github.com/AlexeyAB/darknet#how-to-train-pascal-voc-data) (`bboxes`) + + Other formats and documentation for them can be found [here](https://openvinotoolkit.github.io/datumaro/docs/user-manual/supported_formats). +- Dataset building + - Merging multiple datasets into one + - Dataset filtering by a custom criteria: + - remove polygons of a certain class + - remove images without annotations of a specific class + - remove `occluded` annotations from images + - keep only vertically-oriented images + - remove small area bounding boxes from annotations + - Annotation conversions, for instance: + - polygons to instance masks and vice-versa + - apply a custom colormap for mask annotations + - rename or remove dataset labels + - Splitting a dataset into multiple subsets like `train`, `val`, and `test`: + - random split + - task-specific splits based on annotations, + which keep initial label and attribute distributions + - for classification task, based on labels + - for detection task, based on bboxes + - for re-identification task, based on labels, + avoiding having same IDs in training and test splits + - Sampling a dataset + - analyzes inference result from the given dataset + and selects the ‘best’ and the ‘least amount of’ samples for annotation. + - Select the sample that best suits model training. + - sampling with Entropy based algorithm +- Dataset quality checking + - Simple checking for errors + - Comparison with model inference + - Merging and comparison of multiple datasets + - Annotation validation based on the task type(classification, etc) +- Dataset comparison +- Dataset statistics (image mean and std, annotation statistics) +- Model integration + - Inference (OpenVINO, Caffe, PyTorch, TensorFlow, MxNet, etc.) + - Explainable AI ([RISE algorithm](https://arxiv.org/abs/1806.07421)) + - RISE for classification + - RISE for object detection + +> Check + [the design document](https://openvinotoolkit.github.io/datumaro/docs/design) + for a full list of features. +> Check + [the user manual](https://openvinotoolkit.github.io/datumaro/docs/user-manual) + for usage instructions. + +## Contributing + +[(Back to top)](#dataset-management-framework-datumaro) + +Feel free to +[open an Issue](https://github.com/openvinotoolkit/datumaro/issues/new), if you +think something needs to be changed. You are welcome to participate in +development, instructions are available in our +[contribution guide](https://openvinotoolkit.github.io/datumaro/docs/contributing). + +## Telemetry data collection note + +The [OpenVINO™ telemetry library](https://github.com/openvinotoolkit/telemetry/) +is used to collect basic information about Datumaro usage. + +To enable/disable telemetry data collection please see the +[guide](https://openvinotoolkit.github.io/datumaro/docs/user-manual/how_to_control_tm_data_collection/). + + +%prep +%autosetup -n datumaro-1.2.1 + +%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-datumaro -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2.1-1 +- Package Spec generated @@ -0,0 +1 @@ +d5008ddacf7fb04e5e7fcc1edbdf237b datumaro-1.2.1.tar.gz |