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https://github.com/mfl28/boundingboxeditor

A JavaFX desktop application for creating image-object-annotations with bounding boxes.
https://github.com/mfl28/boundingboxeditor

annotation-tool bounding-boxes computer-vision-annotation desktop-application detection image-annotation image-labeling java javafx javafx-application javafx-desktop-apps labeling tools

Last synced: 13 days ago
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A JavaFX desktop application for creating image-object-annotations with bounding boxes.

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  Bounding Box Editor






  

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This is an image annotation desktop-application written in Java using the JavaFX application platform. It allows you to create bounding box annotations using rectangular and polygonal shapes. 

Annotations can be imported and saved from/to JSON files, [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) format XML-files or [YOLO](https://pjreddie.com/darknet/yolo/) format TXT-files.





  

  

  Demo screenshot of release v2.0.0.




## Main Features

* Create rectangular and polygonal ("vertices-clicking" and "freehand-drawing" modes) bounding box annotations for objects in images

* Export and import  rectangular and polygonal bounding box annotations to and from JSON and XML files (using [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) format)

* Export and import rectangular bounding box annotations using the [YOLO](https://pjreddie.com/darknet/yolo/) format

* Connect your own [Torch Serve](https://pytorch.org/serve/) prediction endpoint and use bounding box predictions as annotation hints

* Format validation and error reporting when importing annotations

* Nest bounding box labels (which is then reflected in the output XML-file if using Pascal VOC format)

* Easily and swiftly navigate and search the loaded image files via a side-panel with thumbnails

* Tag bounding boxes using tags defined in the Pascal VOC format (truncated, difficult, occluded, pose: *, action: *)

* Color-coded, searchable and fully dynamic object categories



## Latest Release 

[![GitHub release (latest by date)](https://img.shields.io/github/v/release/mfl28/BoundingBoxEditor?label=release&style=for-the-badge)](https://github.com/mfl28/BoundingBoxEditor/releases/latest)

![platform](https://img.shields.io/static/v1.svg?label=Platform&message=Linux%20|%20macOS%20|%20Win%20&style=for-the-badge)



Download the latest release installer or portable image (no installation required) of *Bounding Box Editor* for your operating system from the links below. These files were created using the

[jpackage](https://openjdk.java.net/jeps/343) packaging tool, the [Badass JLink Gradle plugin](https://github.com/beryx/badass-jlink-plugin) and [github-actions](.github/workflows/workflow.yml).



| OS            | Installer                                                                                                                                                                                                                       | Portable | Stats                                                                                                                                                      |

| ------------- |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -------- |------------------------------------------------------------------------------------------------------------------------------------------------------------|

| Linux | [deb](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor_2.8.0-1_amd64.deb), [rpm](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor-2.8.0-1.x86_64.rpm) | [image](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor-portable-linux.zip)| ![GitHub release (latest by SemVer and asset)](https://img.shields.io/github/downloads/mfl28/boundingboxeditor/latest/boundingboxeditor_2.8.0-1_amd64.deb) |

| macOS | [dmg](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor-2.8.0.dmg)                                                                                                                          | [image](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor-portable-macos.zip) | ![GitHub release (latest by SemVer and asset)](https://img.shields.io/github/downloads/mfl28/boundingboxeditor/latest/boundingboxeditor-2.8.0.dmg)         |

| Windows | [exe](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor-2.8.0.exe)                                                                                                                          | [image](https://github.com/mfl28/BoundingBoxEditor/releases/latest/download/boundingboxeditor-portable-windows.zip) | ![GitHub release (latest by SemVer and asset)](https://img.shields.io/github/downloads/mfl28/boundingboxeditor/latest/boundingboxeditor-2.8.0.exe)         | 



### Alternative installation methods

#### Windows

[![Chocolatey Version (including pre-releases)](https://img.shields.io/chocolatey/v/boundingboxeditor?style=flat-square)](https://chocolatey.org/packages/boundingboxeditor)

```

choco install boundingboxeditor

```



## How to use the application

Please refer to the [User Manual](https://github.com/mfl28/BoundingBoxEditor/wiki#user-manual) in the Wiki for a detailed usage guide and presentation (including gifs) of the application's main functions.



## Using annotations for object detection

After having created annotations for your images, you can use the saved bounding boxes as ground-truths in the training and evaluation of neural networks in order to perform object-detection tasks. How this can be done for any kind of labeled objects using Python and the [Pytorch](https://pytorch.org/) deep learning library is shown exemplarily in the [Humpback Whale Fluke Detection - Jupyter notebook](https://nbviewer.jupyter.org/github/mfl28/MachineLearning/blob/master/notebooks/Humpback_Whale_Fluke_Detection.ipynb) which you can find in my [Machine Learning repo](https://github.com/mfl28/MachineLearning).



## How to build the application

The project uses [Gradle](https://gradle.org/) as build-system.

You will need to have Gradle version 5+ and a Java JDK version 11+ installed on your system, e.g. from [OpenJDK](https://openjdk.java.net/). 

After cloning the repository into a folder on your machine you may build the application from the root folder by opening a command line and using:

```bash

gradlew build # Add "-x test" to skip the UI-tests.

```

*Note:* The concrete way of invoking `gradlew` depends on your OS and used command line: 

* __Linux & MacOs__: `./gradlew ...`

* __Windows__:

  - Command Prompt: `gradlew ...`

  - Powershell: `.\gradlew ...`



## How to run the application

To run the app using Gradle, use:

```bash

gradlew run

```



## How to run the tests

The project comes equipped with automatic UI-tests which use [TestFX](https://github.com/TestFX/TestFX) and the [JUnit 5](https://junit.org/junit5/) testing frameworks. Due to some used functionality in the implemented tests it is (currently) not possible to run the tests in headless mode.



To run the tests, use :

```bash

gradlew test

```



## How to build the latest Linux image and installers using Docker

First build the Docker image from the cloned repo's root directory using:

```bash

docker image build -t bbeditor .

```

Then create a writable container layer over the image (without starting a container):

```bash

docker container create --name bbeditor bbeditor

```

Finally, copy the directory containing the build artifacts to the host:

```bash

docker container cp bbeditor:/artifacts .

```

> **Alternative**:  

> If you have a recent Docker version that supports BuildKit engine (version >= 19.03) you can do 

> the whole build using a one-line command:

>```bash

> DOCKER_BUILDKIT=1 docker image build --target artifacts --output type=local,dest=. . 

>```



## Acknowledgements

* [OpenJDK](https://openjdk.java.net/) (open-source implementation of the Java platform)

* [OpenJFX](https://openjfx.io/) (open-source implementation of the JavaFX platform)

* [ControlsFX](https://github.com/controlsfx/controlsfx) (used for progress dialogs)

* [Caffeine](https://github.com/ben-manes/caffeine) (used for caching of images)

* [Gson](https://github.com/google/gson) (used for JSON serialization & deserialization)

* [Apache Commons](https://commons.apache.org/) (used for ListOrderedMap data structure and String/Iterator utilities)

* [TestFX](https://github.com/TestFX/TestFX) (used for the tests)

* [JUnit 5](https://junit.org/junit5/) (used for the tests)

* [Jacoco](https://www.jacoco.org/jacoco/) (used for creating code coverage results)

* [sass-gradle-plugin](https://github.com/EtienneMiret/sass-gradle-plugin) (used to compile .scss style-files into [JavaFX supported] .css files)

* [Badass JLink Plugin](https://github.com/beryx/badass-jlink-plugin) (used to create modular runtime images of the application)

* [Gradle Modules Plugin](https://github.com/java9-moduqlarity/gradle-modules-plugin) (used to run the tests on the classpath)

* [Feather Icons](https://feathericons.com/)

* [Nord Color-Palette](https://github.com/arcticicestudio/nord)

* [Unsplash](https://unsplash.com/) (used as source for test- & demo-images)



## License

This project is licensed under GPL v3. See [LICENSE](LICENSE).