Metadata-Version: 2.1
Name: mlabelImg
Version: 1.8.8
Summary: LabelImg is a graphical image annotation tool and label object bounding boxes in images (Modified Version)
Home-page: https://github.com/PD-Mera/mlabelImg
Author: PD-Mera
Author-email: phuongdong1772000@gmail.com
License: MIT license
Keywords: labelImg labelTool development annotation deeplearning
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Requires-Python: >=3.0.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pyqt5
Requires-Dist: lxml

# 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: tzu.ta.lin@gmail.com

- [tzutalin/labelImg](https://github.com/tzutalin/labelImg)

