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labelme2yolo
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Wang Xin 2024-02-18 09:32:25 +08:00
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2
.github/workflows/pylint.yml vendored
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@ -7,7 +7,7 @@ jobs:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.8", "3.9", "3.10"]
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}

55
README.md
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@ -1,7 +1,5 @@
# Labelme2YOLO
**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
[![PyPI - Version](https://img.shields.io/pypi/v/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
![PyPI - Downloads](https://img.shields.io/pypi/dm/labelme2yolo?style=flat)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
@ -11,7 +9,7 @@ Labelme2YOLO is a powerful tool for converting LabelMe's JSON format to [YOLOv5]
## New Features
* export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
* export data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)
* Now you can choose the output format of the label text. The two available alternatives are `polygon` and bounding box (`bbox`).
## Installation
@ -26,7 +24,7 @@ pip install labelme2yolo
**--val\_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
**--test\_size (Optional)** Test dataset size, for example 0.2 means 20% for Test.
**--test\_size (Optional)** Test dataset size, for example 0.1 means 10% for Test.
**--json\_name (Optional)** Convert single LabelMe JSON file.
@ -36,7 +34,25 @@ pip install labelme2yolo
## How to Use
### 1. Converting JSON files and splitting training, validation, and test datasets with --val\_size and --test\_size
### 1. Converting JSON files and splitting training, validation datasets
You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
```shell
labelme2yolo --json_dir /path/to/labelme_json_dir/
```
This tool will generate dataset labels and images with YOLO format in different folders, such as
```plaintext
/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml
```
### 2. Converting JSON files and splitting training, validation, and test datasets with --val\_size and --test\_size
You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
@ -53,33 +69,6 @@ This tool will generate dataset labels and images with YOLO format in different
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/test/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml
```
### 2. Converting JSON files and splitting training and validation datasets by folders
If you have split the LabelMe training dataset and validation dataset on your own, please put these folders under **labelme\_json\_dir** as shown below:
```plaintext
/path/to/labelme_json_dir/train/
/path/to/labelme_json_dir/val/
```
This tool will read the training and validation datasets by folder. You may run the following command to do this:
```shell
labelme2yolo --json_dir /path/to/labelme_json_dir/
```
This tool will generate dataset labels and images with YOLO format in different folders, such as
```plaintext
/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml
```
@ -94,4 +83,6 @@ hatch build
## License
**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
`labelme2yolo` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

12
pyproject.toml
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@ -13,18 +13,20 @@ authors = [
{ name = "GreatV(Wang Xin)", email = "xinwang614@gmail.com" },
]
classifiers = [
"Development Status :: 4 - Beta",
"Development Status :: 5 - Production/Stable",
"Programming Language :: Python",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"License :: OSI Approved :: MIT License",
]
dependencies = [
"opencv-python>=4.1.2",
"Pillow>=9.2,<10.3",
"numpy>=1.23.1,<1.27.0"
"numpy>=1.23.1,<1.27.0",
"tqdm"
]
dynamic = ["version"]
@ -52,7 +54,7 @@ cov = "pytest --cov-report=term-missing --cov-config=pyproject.toml --cov=labelm
no-cov = "cov --no-cov"
[[tool.hatch.envs.test.matrix]]
python = ["38", "39", "310"]
python = ["38", "39", "310", "311", "312"]
[tool.coverage.run]
branch = true