automatic import of python-labelme2cocoopeneuler20.03

3 files changed, 331 insertions, 0 deletions

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
+<div align="center">
+<h1>
+  labelme2coco
+</h1>
+
+<a href="https://pepy.tech/project/labelme2coco"><img src="https://pepy.tech/badge/labelme2coco" alt="downloads"></a>
+<a href="https://badge.fury.io/py/labelme2coco"><img src="https://badge.fury.io/py/labelme2coco.svg" alt="pypi version"></a>
+<a href="https://github.com/fcakyon/labelme2coco/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/labelme2coco/workflows/CI/badge.svg" alt="ci"></a>
+<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/badge/twitter-fcakyon_-blue?logo=twitter&style=flat" alt="fcakyon twitter">
+
+<h4>
+  A lightweight package for converting your <a href="https://github.com/wkentaro/labelme">labelme</a> annotations into COCO object detection format.
+</h4>
+
+<h4>
+    <img width="700" alt="teaser" src="https://user-images.githubusercontent.com/34196005/148746639-9a7b9c08-2156-42ca-abae-a4e6aad095dd.gif">
+</h4>
+</div>
+
+## 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
+<div align="center">
+<h1>
+  labelme2coco
+</h1>
+
+<a href="https://pepy.tech/project/labelme2coco"><img src="https://pepy.tech/badge/labelme2coco" alt="downloads"></a>
+<a href="https://badge.fury.io/py/labelme2coco"><img src="https://badge.fury.io/py/labelme2coco.svg" alt="pypi version"></a>
+<a href="https://github.com/fcakyon/labelme2coco/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/labelme2coco/workflows/CI/badge.svg" alt="ci"></a>
+<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/badge/twitter-fcakyon_-blue?logo=twitter&style=flat" alt="fcakyon twitter">
+
+<h4>
+  A lightweight package for converting your <a href="https://github.com/wkentaro/labelme">labelme</a> annotations into COCO object detection format.
+</h4>
+
+<h4>
+    <img width="700" alt="teaser" src="https://user-images.githubusercontent.com/34196005/148746639-9a7b9c08-2156-42ca-abae-a4e6aad095dd.gif">
+</h4>
+</div>
+
+## 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
+<div align="center">
+<h1>
+  labelme2coco
+</h1>
+
+<a href="https://pepy.tech/project/labelme2coco"><img src="https://pepy.tech/badge/labelme2coco" alt="downloads"></a>
+<a href="https://badge.fury.io/py/labelme2coco"><img src="https://badge.fury.io/py/labelme2coco.svg" alt="pypi version"></a>
+<a href="https://github.com/fcakyon/labelme2coco/actions/workflows/ci.yml"><img src="https://github.com/fcakyon/labelme2coco/workflows/CI/badge.svg" alt="ci"></a>
+<a href="https://twitter.com/fcakyon"><img src="https://img.shields.io/badge/twitter-fcakyon_-blue?logo=twitter&style=flat" alt="fcakyon twitter">
+
+<h4>
+  A lightweight package for converting your <a href="https://github.com/wkentaro/labelme">labelme</a> annotations into COCO object detection format.
+</h4>
+
+<h4>
+    <img width="700" alt="teaser" src="https://user-images.githubusercontent.com/34196005/148746639-9a7b9c08-2156-42ca-abae-a4e6aad095dd.gif">
+</h4>
+</div>
+
+## 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 <Python_Bot@openeuler.org> - 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