From 6fe3d64d708c3fe73bc9e11bef9ddff73ea36afc Mon Sep 17 00:00:00 2001 From: CoprDistGit Date: Fri, 5 May 2023 13:49:29 +0000 Subject: automatic import of python-labelme2coco --- .gitignore | 1 + python-labelme2coco.spec | 329 +++++++++++++++++++++++++++++++++++++++++++++++ sources | 1 + 3 files changed, 331 insertions(+) create mode 100644 python-labelme2coco.spec create mode 100644 sources diff --git a/.gitignore b/.gitignore index e69de29..15e52e8 100644 --- a/.gitignore +++ b/.gitignore @@ -0,0 +1 @@ +/labelme2coco-0.2.4.tar.gz diff --git a/python-labelme2coco.spec b/python-labelme2coco.spec new file mode 100644 index 0000000..62eb5ce --- /dev/null +++ b/python-labelme2coco.spec @@ -0,0 +1,329 @@ +%global _empty_manifest_terminate_build 0 +Name: python-labelme2coco +Version: 0.2.4 +Release: 1 +Summary: Convert labelme annotations into coco format in one step +License: MIT License +URL: https://github.com/fcakyon/labelme2coco +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ad/ee/d2b7186eeb04007794edf49f3105ce3e128add0cb9cbf0ebeb42638fd614/labelme2coco-0.2.4.tar.gz +BuildArch: noarch + +Requires: python3-sahi +Requires: python3-jsonschema + +%description +
+

+ labelme2coco +

+ +downloads +pypi version +ci +fcakyon twitter + +

+ A lightweight package for converting your labelme annotations into COCO object detection format. +

+ +

+ teaser +

+
+ +## Convert LabelMe annotations to COCO format in one step +[labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats. +However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations. + +You can use this package to convert labelme annotations to COCO format. + +## Getting started +### Installation +``` +pip install -U labelme2coco +``` + +### Basic Usage + +```python +labelme2coco path/to/labelme/dir +``` + +```python +labelme2coco path/to/labelme/dir --train_split_rate 0.85 +``` + +### Advanced Usage + +```python +# import package +import labelme2coco + +# set directory that contains labelme annotations and image files +labelme_folder = "tests/data/labelme_annot" + +# set export dir +export_dir = "tests/data/" + +# set train split rate +train_split_rate = 0.85 + +# convert labelme annotations to coco +labelme2coco.convert(labelme_folder, export_dir, train_split_rate) +``` + +```python +# import functions +from labelme2coco import get_coco_from_labelme_folder, save_json + +# set labelme training data directory +labelme_train_folder = "tests/data/labelme_annot" + +# set labelme validation data directory +labelme_val_folder = "tests/data/labelme_annot" + +# set path for coco json to be saved +export_dir = "tests/data/" + +# create train coco object +train_coco = get_coco_from_labelme_folder(labelme_train_folder) + +# export train coco json +save_json(train_coco.json, export_dir+"train.json") + +# create val coco object +val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories) + +# export val coco json +save_json(val_coco.json, export_dir+"val.json") +``` + + +%package -n python3-labelme2coco +Summary: Convert labelme annotations into coco format in one step +Provides: python-labelme2coco +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-labelme2coco +
+

+ labelme2coco +

+ +downloads +pypi version +ci +fcakyon twitter + +

+ A lightweight package for converting your labelme annotations into COCO object detection format. +

+ +

+ teaser +

+
+ +## Convert LabelMe annotations to COCO format in one step +[labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats. +However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations. + +You can use this package to convert labelme annotations to COCO format. + +## Getting started +### Installation +``` +pip install -U labelme2coco +``` + +### Basic Usage + +```python +labelme2coco path/to/labelme/dir +``` + +```python +labelme2coco path/to/labelme/dir --train_split_rate 0.85 +``` + +### Advanced Usage + +```python +# import package +import labelme2coco + +# set directory that contains labelme annotations and image files +labelme_folder = "tests/data/labelme_annot" + +# set export dir +export_dir = "tests/data/" + +# set train split rate +train_split_rate = 0.85 + +# convert labelme annotations to coco +labelme2coco.convert(labelme_folder, export_dir, train_split_rate) +``` + +```python +# import functions +from labelme2coco import get_coco_from_labelme_folder, save_json + +# set labelme training data directory +labelme_train_folder = "tests/data/labelme_annot" + +# set labelme validation data directory +labelme_val_folder = "tests/data/labelme_annot" + +# set path for coco json to be saved +export_dir = "tests/data/" + +# create train coco object +train_coco = get_coco_from_labelme_folder(labelme_train_folder) + +# export train coco json +save_json(train_coco.json, export_dir+"train.json") + +# create val coco object +val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories) + +# export val coco json +save_json(val_coco.json, export_dir+"val.json") +``` + + +%package help +Summary: Development documents and examples for labelme2coco +Provides: python3-labelme2coco-doc +%description help +
+

+ labelme2coco +

+ +downloads +pypi version +ci +fcakyon twitter + +

+ A lightweight package for converting your labelme annotations into COCO object detection format. +

+ +

+ teaser +

+
+ +## Convert LabelMe annotations to COCO format in one step +[labelme](https://github.com/wkentaro/labelme) is a widely used is a graphical image annotation tool that supports classification, segmentation, instance segmentation and object detection formats. +However, widely used frameworks/models such as Yolact/Solo, Detectron, MMDetection etc. requires COCO formatted annotations. + +You can use this package to convert labelme annotations to COCO format. + +## Getting started +### Installation +``` +pip install -U labelme2coco +``` + +### Basic Usage + +```python +labelme2coco path/to/labelme/dir +``` + +```python +labelme2coco path/to/labelme/dir --train_split_rate 0.85 +``` + +### Advanced Usage + +```python +# import package +import labelme2coco + +# set directory that contains labelme annotations and image files +labelme_folder = "tests/data/labelme_annot" + +# set export dir +export_dir = "tests/data/" + +# set train split rate +train_split_rate = 0.85 + +# convert labelme annotations to coco +labelme2coco.convert(labelme_folder, export_dir, train_split_rate) +``` + +```python +# import functions +from labelme2coco import get_coco_from_labelme_folder, save_json + +# set labelme training data directory +labelme_train_folder = "tests/data/labelme_annot" + +# set labelme validation data directory +labelme_val_folder = "tests/data/labelme_annot" + +# set path for coco json to be saved +export_dir = "tests/data/" + +# create train coco object +train_coco = get_coco_from_labelme_folder(labelme_train_folder) + +# export train coco json +save_json(train_coco.json, export_dir+"train.json") + +# create val coco object +val_coco = get_coco_from_labelme_folder(labelme_val_folder, coco_category_list=train_coco.json_categories) + +# export val coco json +save_json(val_coco.json, export_dir+"val.json") +``` + + +%prep +%autosetup -n labelme2coco-0.2.4 + +%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-labelme2coco -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot - 0.2.4-1 +- Package Spec generated diff --git a/sources b/sources new file mode 100644 index 0000000..5709710 --- /dev/null +++ b/sources @@ -0,0 +1 @@ +16d753f35932612564ac40065891bd1a labelme2coco-0.2.4.tar.gz -- cgit v1.2.3