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LabelImg_OBB

LabelImg is a graphical image annotation tool. In this LabelImg_OBB fork, mouse right clicks can be used to rotate objects to annotate oriented bounding boxes (OBB).

Used to annotate the date used in this work: Hesham M. Eraqi et al. Automatic Roadway Features Detection with Oriented Object Detection. Journal of Intelligent Transportation Systems. Applied Sciences (ISSN 2076-3417), 11(8), 2021. https://www.mdpi.com/2076-3417/11/8/3531

Dataset download link: https://drive.google.com/file/d/1N3Q8Rehgyxjc7wwpKR3UA9MXndhs80o0/

It is written in Python and uses Qt for its graphical interface.

Annotations are saved as XML files in PASCAL VOC format, the format used by ImageNet. Besdies, it also supports YOLO and YOLO OBB format

Demo Image

Demo Image

Demo Image

Watch a demo video

Installation

Download prebuilt binaries

  • Windows
  • macOS. Binaries for macOS are not yet available. Help would be appreciated. At present, it must be built from source.

Build from source

Linux/Ubuntu/Mac requires at least Python 2.6 and has been tested with PyQt 4.8.

Ubuntu Linux

Python 2 + Qt4

sudo apt-get install pyqt4-dev-tools
sudo pip install lxml
make qt4py2
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 + Qt5

sudo apt-get install pyqt5-dev-tools python3-pip python3-examples python3-tk
sudo pip3 install -r requirements/requirements-linux-python3.txt
make qt5py3
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

macOS

Python 2 + Qt4

brew install qt qt4
brew install libxml2
make qt4py2
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Python 3 + Qt5 (Works on macOS High Sierra)

brew install qt  # will install qt-5.x.x
brew install libxml2
make qt5py3
python3 labelImg.py
python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

As a side note, if mssing pyrcc5 or lxml, try
pip3 install pyqt5 lxml

NEW Python 3 Virtualenv + Binary This avoids a lot of the QT / Python version issues, and gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider this script: build-tools/build-for-macos.sh

brew install python3
pip install pipenv
pipenv --three
pipenv shell
pip install py2app
pip install PyQt5 lxml
make qt5py3
rm -rf build dist
python setup.py py2app -A
mv "dist/labelImg.app" /Applications

Windows

Download and setup Python 2.6 or later, PyQt4 and install lxml.

Open cmd and go to the labelImg directory

pyrcc4 -o resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Windows + Anaconda

Download and install Anaconda (Python 3+)

Open the Anaconda Prompt and go to the labelImg directory

conda install pyqt=5
pyrcc5 -o resources.py resources.qrc
python labelImg.py
python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

Get from PyPI

pip install labelImg
labelImg
labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]

I tested pip on Ubuntu 14.04 and 16.04. However, I didn't test pip on macOS and Windows

Use Docker

docker run -it \
--user $(id -u) \
-e DISPLAY=unix$DISPLAY \
--workdir=$(pwd) \
--volume="/home/$USER:/home/$USER" \
--volume="/etc/group:/etc/group:ro" \
--volume="/etc/passwd:/etc/passwd:ro" \
--volume="/etc/shadow:/etc/shadow:ro" \
--volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
-v /tmp/.X11-unix:/tmp/.X11-unix \
tzutalin/py2qt4

make qt4py2;./labelImg.py

You can pull the image which has all of the installed and required dependencies. Watch a demo video

Usage

Steps (PascalVOC)

  1. Build and launch using the instructions above.
  2. Click 'Change default saved annotation folder' in Menu/File
  3. Click 'Open Dir'
  4. Click 'Create RectBox'
  5. Click and release left mouse button to select a region to annotate the rect box and set its orientation
  6. You can use right mouse button to drag the rect box to copy or move it. To rotate drag using right mouse button from box vertices (red dots).

The annotation will be saved to the folder you specify.

You can refer to the below hotkeys to speed up your workflow.

Steps (YOLO or YOLO_OBB)

  1. In data/predefined_classes.txt define the list of classes that will be used for your training.
  2. Build and launch using the instructions above.
  3. Right below "Save" button in toolbar, click "PascalVOC" button to switch to YOLO format.
  4. You may use Open/OpenDIR to process single or multiple images. When finished with single image, click save.

A txt file of yolo format will be saved in the same folder as your image with same name. A file named "classes.txt" is saved to that folder too. "classes.txt" defines the list of class names that your yolo label refers to.

Note:

  • Your label list shall not change in the middle of processing a list of images. When you save a image, classes.txt will also get updated, while previous annotations will not be updated.
  • You shouldn't use "default class" function when saving to YOLO format, it will not be referred.
  • When saving as YOLO format, "difficult" flag is discarded.

Create pre-defined classes

You can edit the data/predefined_classes.txt to load pre-defined classes

Hotkeys

Ctrl + u Load all of the images from a directory
Ctrl + r Change the default annotation target dir
Ctrl + s Save
Ctrl + d Copy the current label and rect box
Space Flag the current image as verified
w Create a rect box
d Next image
a Previous image
del Delete the selected rect box
Ctrl++ Zoom in
Ctrl-- Zoom out
  Keyboard arrows to move selected rect box

How to contribute

Send a pull request

License

Free software: MIT license

Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg

Related

  1. ImageNet Utils to download image, create a label text for machine learning, etc
  2. Use Docker to run labelImg
  3. Generating the PASCAL VOC TFRecord files
  4. App Icon based on Icon by Nick Roach (GPL) <https://www.elegantthemes.com/> <https://www.iconfinder.com/icons/1054978/shop_tag_icon> __