https://github.com/pd-mera/mlabelimg
Modified version of labelImg, fix various errors
https://github.com/pd-mera/mlabelimg
annotations object-detection tools
Last synced: about 1 month ago
JSON representation
Modified version of labelImg, fix various errors
- Host: GitHub
- URL: https://github.com/pd-mera/mlabelimg
- Owner: PD-Mera
- License: mit
- Created: 2024-05-10T03:46:36.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-09T07:48:43.000Z (5 months ago)
- Last Synced: 2025-03-29T09:33:50.675Z (2 months ago)
- Topics: annotations, object-detection, tools
- Language: Python
- Homepage: https://github.com/HumanSignal/labelImg
- Size: 6.15 MB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
Awesome Lists containing this project
README
# Adapted LabelImg for Enhanced User Experience
This repository is a copy from [HumanSignal/labelImg](https://github.com/HumanSignal/labelImg). The original repository is archived and no longer being maintained. So I make a copy from the latest version (1.8.6) to modify some function and fix some error for personal use.
[Original README](./original_README.rst)
## Installation
### Install with pip
``` bash
pip install -U mlabelImg
```
### Install from source
``` bash
git clone https://github.com/PD-Mera/mlabelImg
pip install pyqt5 lxml
pyrcc5 -o mlabelImg/libs/resources.py mlabelImg/resources.qrc
pip install -e mlabelImg
```
## Usage
### Setup directory
Create a folder structure same as below
```
├── data
├── images
└── labels
```
Put all of your image in `images` directory. And create a `classes.txt` contain all class you want to label. Example of `classes.txt` as below
``` txt
dog
cat
pig
```
Put `classes.txt` in 2 place, in `labels` directory and same level as `labels` directory
Full structure of workspace as below
```
├── data
├── images
│ ├── img1.jpg
│ ├── img2.jpg
│ └── ...
├── labels
│ └── classes.txt
└── classes.txt
```
### Run mlabelImg
Run mlabelImg with
``` shell
# mlabelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
mlabelImg .\data\images\ .\data\classes.txt
```
On GUI of labelImg:
- `File -> Change Save Dir -> (save label directory)`
- Choose `YOLO` format on the left tray
Next and previous image with `D -> A`
Label with `W`
Delete `.\data\classes.txt` after labeling
### Label format
With `YOLO` format, label will be saved with format `label_index x_center y_center w h` and normalize to scale `[0, 1]`
``` txt
1 0.415842 0.863095 0.102970 0.101190
1 0.228713 0.315476 0.077228 0.053571
1 0.756436 0.328869 0.114851 0.050595
```
## Reference
- Author: TzuTa Lin
- Author Email: [email protected]
- [tzutalin/labelImg](https://github.com/tzutalin/labelImg)