Metadata-Version: 1.2
Name: labelImg
Version: 1.8.5
Summary: LabelImg is a graphical image annotation tool and label object bounding boxes in images
Home-page: https://github.com/tzutalin/labelImg
Author: TzuTa Lin
Author-email: tzu.ta.lin@gmail.com
License: MIT license
Description: 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:: https://img.shields.io/badge/lang-en-blue.svg
                :target: https://github.com/tzutalin/labelImg/blob/master/README.zh.rst
        
        .. image:: https://img.shields.io/badge/lang-zh-green.svg
                :target: https://github.com/tzutalin/labelImg/blob/master/readme/README.zh.rst
        
        .. image:: https://img.shields.io/badge/lang-zh--TW-green.svg
            :target: (https://github.com/jonatasemidio/multilanguage-readme-pattern/blob/master/README.pt-br.md
        
        .. 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 <http://www.image-net.org/>`__.  Besides, it also supports YOLO and CreateML formats.
        
        .. 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 <https://youtu.be/p0nR2YsCY_U>`__
        
        Installation
        ------------------
        
        
        Build from source
        ~~~~~~~~~~~~~~~~~
        
        Linux/Ubuntu/Mac requires at least `Python
        2.6 <https://www.python.org/getit/>`__ and has been tested with `PyQt
        4.8 <https://www.riverbankcomputing.com/software/pyqt/intro>`__. However, `Python
        3 or above <https://www.python.org/getit/>`__ and  `PyQt5 <https://pypi.org/project/PyQt5/>`__ are strongly recommended.
        
        
        Ubuntu Linux
        ^^^^^^^^^^^^
        
        Python 3 + Qt5
        
        .. 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 3 + Qt5
        
        .. 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==5.12.1 lxml
            pipenv run make qt5py3
            pipenv run 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 <https://www.python.org/downloads/windows/>`__,
        `PyQt5 <https://www.riverbankcomputing.com/software/pyqt/download5>`__
        and `install lxml <http://lxml.de/installation.html>`__.
        
        Open cmd and go to the `labelImg <#labelimg>`__ directory
        
        .. code:: shell
        
            pyrcc4 -o libs/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 <https://www.anaconda.com/download/#download>`__ (Python 3+)
        
        Open the Anaconda Prompt and go to the `labelImg <#labelimg>`__ directory
        
        .. code:: shell
        
            conda install pyqt=5
            conda install -c anaconda lxml
            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
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        This is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora.
        
        .. 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 <https://youtu.be/nw1GexJzbCI>`__
        
        
        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 <https://github.com/tzutalin/labelImg/blob/master/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        |
        +--------------------+--------------------------------------------+
        | Ctrl + Shift + d   | Delete the current image                   |
        +--------------------+--------------------------------------------+
        | 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 <https://github.com/tzutalin/labelImg/blob/master/LICENSE>`_
        
        Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
        
        Related and additional tools
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        1. `ImageNet Utils <https://github.com/tzutalin/ImageNet_Utils>`__ to
           download image, create a label text for machine learning, etc
        2. `Use Docker to run labelImg <https://hub.docker.com/r/tzutalin/py2qt4>`__
        3. `Generating the PASCAL VOC TFRecord files <https://github.com/tensorflow/models/blob/4f32535fe7040bb1e429ad0e3c948a492a89482d/research/object_detection/g3doc/preparing_inputs.md#generating-the-pascal-voc-tfrecord-files>`__
        4. `App Icon based on Icon by Nick Roach (GPL) <https://www.elegantthemes.com/>`__
        5. `Setup python development in vscode <https://tzutalin.blogspot.com/2019/04/set-up-visual-studio-code-for-python-in.html>`__
        6. `The link of this project on iHub platform <https://code.ihub.org.cn/projects/260/repository/labelImg>`__
        7. `Convert annotation files to CSV format or format for Google Cloud AutoML <https://github.com/tzutalin/labelImg/tree/master/tools>`__
        
        
        
        Stargazers over time
        ~~~~~~~~~~~~~~~~~~~~
        
        .. image:: https://starchart.cc/tzutalin/labelImg.svg
        
        
        
        History
        =======
        
        1.8.5 (2021-04-11)
        ------------------
        
        * Merged a couple of PRs
        * Fixed issues
        * Support CreateML format
        
        
        1.8.4 (2020-11-04)
        ------------------
        
        * Merged a couple of PRs
        * Fixed issues
        
        1.8.2 (2018-12-02)
        ------------------
        
        * Fix pip depolyment issue
        
        
        1.8.1 (2018-12-02)
        ------------------
        
        * Fix issues
        * Support zh-Tw strings
        
        
        1.8.0 (2018-10-21)
        ------------------
        
        * Support drawing sqaure rect
        * Add item single click slot
        * Fix issues
        
        1.7.0 (2018-05-18)
        ------------------
        
        * Support YOLO
        * Fix minor issues
        
        
        1.6.1 (2018-04-17)
        ------------------
        
        * Fix issue
        
        1.6.0 (2018-01-29)
        ------------------
        
        * Add more pre-defined labels
        * Show cursor pose in status bar
        * Fix minor issues
        
        1.5.2 (2017-10-24)
        ------------------
        
        * Assign different colors to different lablels
        
        1.5.1 (2017-9-27)
        ------------------
        
        * Show a autosaving dialog
        
        1.5.0 (2017-9-14)
        ------------------
        
        * Fix the issues
        * Add feature: Draw a box easier
        
        
        1.4.3 (2017-08-09)
        ------------------
        
        * Refactor setting
        * Fix the issues
        
        
        1.4.0 (2017-07-07)
        ------------------
        
        * Add feature: auto saving
        * Add feature: single class mode
        * Fix the issues
        
        1.3.4 (2017-07-07)
        ------------------
        
        * Fix issues and improve zoom-in
        
        1.3.3 (2017-05-31)
        ------------------
        
        * Fix issues
        
        1.3.2 (2017-05-18)
        ------------------
        
        * Fix issues
        
        
        1.3.1 (2017-05-11)
        ------------------
        
        * Fix issues
        
        1.3.0 (2017-04-22)
        ------------------
        
        * Fix issues
        * Add difficult tag
        * Create new files for pypi
        
        1.2.3 (2017-04-22)
        ------------------
        
        * Fix issues
        
        1.2.2 (2017-01-09)
        ------------------
        
        * Fix issues
        
Keywords: labelImg labelTool development annotation deeplearning
Platform: UNKNOWN
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
