Introduction to LabelImg
LabelImg is a graphical image annotation tool designed to make the process of labeling images easy and efficient. Developed primarily by Tzutalin with contributions from the open-source community, LabelImg has been a favored choice for many users needing a reliable annotation solution. Although it's no longer under active development as it transitions into the Label Studio community, it remains a powerful tool for specific annotation needs.
Core Features
LabelImg is built using Python and employs the Qt framework for its graphical user interface. It supports a variety of annotation formats, crucial for different machine learning processes. The primary formats include:
- PASCAL VOC: Known for its use in the ImageNet framework, this is one of the most widely recognized formats.
- YOLO: A format appreciated among real-time object detection projects.
- CreateML: Useful for Apple’s machine learning models.
Installation Methods
LabelImg can be installed via several methods across different operating systems:
Installation via PyPI
For Linux distributions such as Ubuntu and Fedora, LabelImg can be installed easily using Python's package manager:
pip3 install labelImg
labelImg
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
Building from Source
Extended instructions are provided for Linux, macOS, and Windows users who prefer or need to build LabelImg from source. Recommendations include using Python 3 and PyQt5 for the latest features and stability.
Example for Ubuntu/Linux
sudo apt-get install pyqt5-dev-tools
sudo pip3 install -r requirements/requirements-linux-python3.txt
make qt5py3
python3 labelImg.py
Using Docker
For a smoother installation process that avoids dependency conflicts, users can opt to use Docker with a pre-built image that includes all necessary components.
docker run -it --rm tzutalin/py2qt4
make qt4py2; ./labelImg.py
Usage Instructions
Annotating Images in PASCAL VOC Format
- Launch LabelImg using your preferred method.
- Open the directory containing the images you wish to annotate.
- Begin creating rectangular annotation boxes around objects of interest.
Annotating Using YOLO Format
- Define classes in the
data/predefined_classes.txt. - Switch the interface to YOLO format via the settings.
- Annotate images and save them in YOLO's unique text format.
Important Hotkeys
LabelImg simplifies the annotation process through a series of hotkeys:
- Ctrl + S: Save the current state of your work.
- W: Create a new rectangular box for annotation.
- Delete: Remove the selected annotation box.
Community and Contribution
LabelImg continues to thrive thanks to its open-source community. Developers and users are encouraged to contribute by submitting pull requests on the GitHub repository. The tool is distributed under the MIT license, allowing for widespread use and modification.
Interested individuals can also explore additional tools and resources related to LabelImg, including Label Studio for multi-modal data annotation and conversion scripts for various data formats.
By transitioning into the Label Studio ecosystem, LabelImg aligns itself with a broader suite of annotation tools, paving the way for future integrations and advancements in data labeling. As a comprehensive and flexible solution, LabelImg remains an excellent choice for those involved in image-based machine learning projects.