update

README.md

@@ -1,7 +1,7 @@
-# YOLO Prediction to Labelme and Anylabeling Json
+# YOLO Predictions to Labelme and Anylabeling-Compatible JSON
 
 <p align="center">
-  <img alt="yolosegment2labelme" style="width: 128px; max-width: 100%; height: auto;" src="images/labelme_test/logo.png"/>
+  <img alt="yolosegment2labelme" style="width: 128px; max-width: 100%; height: auto;" src="https://raw.githubusercontent.com/Abonia1/yolosegment2labelme/main/images/labelme_test/logo.png"/>
   <h1 align="center">🌟 yolosegment2labelme 🌟</h1>
   <p align="center">Convert your yolo model prediction results to json to view and edit in Labelme and Anylabeling. <b>YOLO Result to Json with single line cmd</b>!</p>
   <p align="center"><b>yolosegment2labelme = Easy Coversion + Predicted to Json  + Auto-labeling</b></p>
@@ -12,12 +12,16 @@
 [![PyPI](https://img.shields.io/pypi/v/yolosegment2labelme)](https://pypi.org/project/yolosegment2labelme/)
 [![license](https://img.shields.io/github/license/abonia1/yolosegment2labelme.svg)](https://github.com/Abonia1/yolosegment2labelme/blob/main/LICENSE)
 [![open issues](https://isitmaintained.com/badge/open/abonia1/yolosegment2labelme.svg)](https://github.com/abonia1/yolosegment2labelme/issues)
-[![Article](https://img.shields.io/badge/Read-Documentation-green)](https://abonia1.github.io/)
+[![Website](https://img.shields.io/website?url=https%3A//abonia1.github.io&style=flat&logo=github&logoColor=white)](https://abonia1.github.io/)
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+[![YouTube](https://img.shields.io/badge/-YouTube-red?style=flat-square&logo=youtube)](https://www.youtube.com/channel/UCGphGM_oeR4r9dqVs71Jc5w)
+[![Medium](https://img.shields.io/badge/-Medium-black?style=flat-square&logo=medium)](https://medium.com/@abonia)
+[![GitHub](https://img.shields.io/badge/-GitHub-black?style=flat-square&logo=github)](https://github.com/Abonia1)
+
 
 > ⭐ Follow [AboniaSojasingarayar](https://www.linkedin.com/in/aboniasojasingarayar) for project updates.
 
-**yolosegment2labelme** is a Python package that allows you to convert YOLO segmentation prediction results to LabelMe JSON format. This tool facilitates the annotation process by generating JSON files that are compatible with LabelMe and other labeling annotation tools.
+**yolosegment2labelme** is a Python package that allows you to convert YOLO segmentation prediction results to LabelMe JSON format. This tool facilitates the annotation process by generating JSON files that are compatible with [Labelme](https://github.com/labelmeai/labelme) and [Anylabeling](https://github.com/vietanhdev/anylabeling) annotation tools.
 
 ## Features
 
@@ -53,18 +57,18 @@ yolosegment2labelme --model yolov8n-seg.pt --images /path/to/images --conf 0.3
 This command will process the images located in the specified directory (`/path/to/images`), using the YOLO model weights file `yolov8n-seg.pt` and here you can add path/to/your/customYOLOModel, and generate LabelMe JSON files with a confidence threshold of your choice and here it is of 0.3.
 
 
-## Sample Images
-The table below displays sample images along with their corresponding annotations generated using yolosegment2labelme:
+## Sample Output in Anylabeling Annotation Tool
 
+Below are examples of image annotations created using yolosegment2labelme and viewed in the [Anylabeling](https://github.com/vietanhdev/anylabeling) annotation tool:
 
 | Sample Image 1                                      | Sample Image 2                                      |
 |-----------------------------------------------------|-----------------------------------------------------|
-| ![Sample Image 1](images/labelme_test/sample1.png)      | ![Sample Image 2](images/labelme_test/sample2.png)      |
+| ![Sample Image 1](https://raw.githubusercontent.com/Abonia1/yolosegment2labelme/main/images/labelme_test/sample1.png)      | ![Sample Image 2](https://raw.githubusercontent.com/Abonia1/yolosegment2labelme/main/images/labelme_test/sample2.png)      |
 | Sample Annotation for Image 1                      | Sample Annotation for Image 2                      |
 
 | Sample Image 3                                      | Sample Image 4                                      |
 |-----------------------------------------------------|-----------------------------------------------------|
-| ![Sample Image 3](images/labelme_test/sample3.png)      | ![Sample Image 4](images/labelme_test/sample4.png)      |
+| ![Sample Image 3](https://raw.githubusercontent.com/Abonia1/yolosegment2labelme/main/images/labelme_test/sample3.png)      | ![Sample Image 4](https://raw.githubusercontent.com/Abonia1/yolosegment2labelme/main/images/labelme_test/sample4.png)      |
 | Sample Annotation for Image 3                      | Sample Annotation for Image 4                      |
 
 
@@ -74,6 +78,9 @@ The documentation for **yolosegment2labelme** can be found on GitHub: [yolosegme
 
 ## Contributing
 
+#### If you like this work do star to this repo ⭐ and contribute...💁💁💁
+
+---
 Contributions are welcome! If you'd like to contribute to **yolosegment2labelme**, please check out the [Contribution Guidelines](CONTRIBUTING.md).
 
 ## License

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "yolosegment2labelme"
-version = "0.0.2"
+version = "0.0.3"
 authors = [
   { name="Abonia Sojasingarayar", email="[email protected]" },
 ]

setup.py

@@ -7,7 +7,7 @@
 with codecs.open(os.path.join(here, "README.md"), encoding="utf-8") as fh:
     long_description = "\n" + fh.read()
 
-VERSION = '0.0.2'
+VERSION = '0.0.3'
 DESCRIPTION = 'Yolo segmentation prediction to labelme json'
 LONG_DESCRIPTION = 'A package that allows generating JSON from YOLO prediction results, compatible with LabelMe/any labeling annotation tool'