LabelImg ======== .. image:: https://img.shields.io/pypi/v/labelimg.svg :target: https://pypi.python.org/pypi/labelimg .. image:: https://img.shields.io/travis/tzutalin/labelImg.svg :target: https://travis-ci.org/tzutalin/labelImg LabelImg is a graphical image annotation tool. 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 `__. .. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo3.jpg :alt: Demo Image `Watch a demo video by author tzutalin `__ Installation ------------------ Download prebuilt binaries ~~~~~~~~~~~~~~~~~~~~~~~~~~ - `Windows & Linux `__ - OS X. Binaries for OS X are not yet available. Help would be appreciated. At present, it must be `built from source <#os-x>`__. Build from source ~~~~~~~~~~~~~~~~~ Linux/Ubuntu/Mac requires at least `Python 2.6 `__ and has been tested with `PyQt 4.8 `__. Ubuntu Linux ^^^^^^^^^^^^ .. code:: sudo apt-get install pyqt4-dev-tools sudo pip install lxml make all ./labelImg.py ./labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] OS X ^^^^ .. code:: brew install qt qt4 brew install libxml2 make all ./labelImg.py ./labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Windows ^^^^^^^ Download and setup `Python 2.6 or later `__, `PyQt4 `__ and `install lxml `__. Open cmd and go to `labelImg <#labelimg>`__ directory .. code:: pyrcc4 -o resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Get from PyPI ~~~~~~~~~~~~~~~~~ .. code:: pip install labelImg labelImg labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] I tested pip on Ubuntu14.04 and 16.04. However, I didn't test pip on MacOS and windows Use Docker ~~~~~~~~~~~~~~~~~ .. code:: docker pull tzutalin/py2qt4 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 You can pull the image which has all of the installed and required dependencies. Usage ----- Steps ~~~~~ 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 to select a region to annotate the rect box 6. You can use right mouse to drag the rect box to copy or move it The annotation will be saved to the folder you specify. You can refer to the below hotkeys to speed up your workflow. 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 `_ Related ~~~~~~~ 1. `ImageNet Utils `__ to download image, create a label text for machine learning, etc 2. `Docker hub to run it `__