# labelImg **Repository Path**: rukit/labelImg ## Basic Information - **Project Name**: labelImg - **Description**: ️ LabelImg is a graphical image annotation tool and label object bounding boxes in images - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-05-10 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README 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 .. image:: /resources/icons/app.png :width: 200px :align: center 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 `__. Besides, it also supports YOLO format .. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo3.jpg :alt: Demo Image .. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo.jpg :alt: Demo Image `Watch a demo video `__ Installation ------------------ Build from source ~~~~~~~~~~~~~~~~~ Linux/Ubuntu/Mac requires at least `Python 2.6 `__ and has been tested with `PyQt 4.8 `__. However, `Python 3 or above `__ and `PyQt5 `__ are strongly recommended. Ubuntu Linux ^^^^^^^^^^^^ Python 2 + Qt4 .. code:: shell 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 (Recommended) .. code:: shell sudo apt-get install pyqt5-dev-tools 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 .. code:: shell brew install qt qt4 brew install libxml2 make qt4py2 python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Python 3 + Qt5 (Recommended) .. code:: shell brew install qt # Install qt-5.x.x by Homebrew brew install libxml2 or using pip pip3 install pyqt5 lxml # Install qt and lxml by pip make qt5py3 python3 labelImg.py python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Python 3 Virtualenv (Recommended) Virtualenv can avoid a lot of the QT / Python version issues .. code:: shell brew install python3 pip3 install pipenv pipenv run pip install pyqt5==5.13.2 lxml pipenv run make qt5py3 python3 labelImg.py [Optional] rm -rf build dist; python setup.py py2app -A;mv "dist/labelImg.app" /Applications Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/build-for-macos.sh Windows ^^^^^^^ Install `Python `__, `PyQt5 `__ and `install lxml `__. Open cmd and go to the `labelImg <#labelimg>`__ directory .. code:: shell pyrcc4 -o lib/resources.py resources.qrc For pyqt5, pyrcc5 -o libs/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 <#labelimg>`__ directory .. code:: shell conda install pyqt=5 pyrcc5 -o libs/resources.py resources.qrc python labelImg.py python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Get from PyPI but only python3.0 or above ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. code:: shell pip3 install labelImg labelImg labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE] Use Docker ~~~~~~~~~~~~~~~~~ .. code:: shell 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 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. Steps (YOLO) ~~~~~~~~~~~~ 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 the toolbar, click "PascalVOC" button to switch to YOLO format. 4. You may use Open/OpenDIR to process single or multiple images. When finished with a 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 an 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 | +------------+--------------------------------------------+ **Verify Image:** When pressing space, the user can flag the image as verified, a green background will appear. This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them. **Difficult:** The difficult field is set to 1 indicates that the object has been annotated as "difficult", for example, an object which is clearly visible but difficult to recognize without substantial use of context. According to your deep neural network implementation, you can include or exclude difficult objects during training. How to reset the settings ~~~~~~~~~~~~~~~~~~~~~~~~~ In case there are issues with loading the classes, you can either: 1. From the top menu of the labelimg click on Menu/File/Reset All 2. Remove the `.labelImgSettings.pkl` from your home directory. In Linux and Mac you can do: `rm ~/.labelImgSettings.pkl` 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) `__ 5. `Setup python development in vscode `__ 6. `The link of this project on iHub platform `__