Manual

Welcome to BoofCV! BoofCV is an open source Java computer vision library intended for developers. The following manual provides an introduction to development with BoofCV. It is assumed that the reader is familiar with development in the Java programming language and the basics of computer vision. This manual primarily takes the form of example code and tutorials.

Getting Started

Before you can do anything with BoofCV you will need to download it. The download page provides instructions on how to download pre-compiled jars, source code, add a Maven dependency, and checkout the latest source code. After you obtain the jar files browse through the examples below to get ideas on how you can use BoofCV.

• Video tutorial on creating a new Desktop project

Pre-Build Applications

Want to try out BoofCV without needing to build it? Check out the available applications!

Building

To use BoofCV in your project you do NOT need to build it. The best way to add BoofCV to you project is by using the jars uploaded to Maven Central and referencing them in your Gradle or Maven project. See Download for more instructions on adding BoofCV to your project.

Building BoofCV is very easy. The only bit that's tricky is knowing that you need to first run the auto code generator as is shown below. Note that it can take a minute or two the first time you build to download all the dependencies.

cd boofcv
git clean -fd main
./gradlew autogenerate
./gradlew publishToMavenLocal

The latest build script should even download the latest JDK for you. If there are any issues please post a question to the message board. If you are building from an IDE such as IntelliJ or Eclipse you will probably need to point it towards the correct JDK manually. The readme.md will have the latest info on JDK compatibility, but Java 15 is what it needs to be built with.

Support

Support is provided in the form of the documentation on this website and through its message board. See the left navigation board for a link to the message board. Please read through the documentation and FAQ (see below) first before posting a question there. You are much more likely to get a good response if you demonstrate due diligence.

http://boofcv.org/index.php?title=FAQ

If you find any mistakes in the documentation or library itself please submit a bug report or post a message about it! If you don't let us know about it we can't fix it.

Propaganda

Did you find BoofCV useful and use it on your project/work/research/thesis? Well let others know about it through your blog, twitter, or status message! Academics, please cite BoofCV in your papers and checkout the papers page to see if the specific algorithm you are using is mentioned there.

BoofCV Papers and Tech Report

Examples and Tutorial

A few tutorials and examples are provided to provide the basic concepts of development with BoofCV. Data files used in these examples are stored in a separate GIT repository from the main code. See boofcv/examples/readme.txt or https://github.com/lessthanoptimal/BoofCV-Data

Want to quickly explore all the examples and run all the demonstrations? Checkout the source code and run the following applications:

cd boofcv
./gradlew examples
java -jar examples/examples.jar
./gradlew demonstrations
java -jar demonstrations/demonstrations.jar

YouTube Video showing the above applications being built and run.

Alternatively you can just download a pre-compiled application and explore these examples that way. See Applications.

Tutorials

Topics	Languages
Quick Start Images in BoofCV Image Segmentation Fiducials QR Codes Videos and Webcams Camera Calibration 3D Computer Vision / Structure from Motion Photogrammetry / 3D Reconstruction Kinect RGB-D Sensor Concurrency / Multi Threading Visualization	Kotlin PyBoof (Python) Processing
Devices
Android Support Raspberry PI

Example Code

List of simple examples which demonstrate a single capability of BoofCV.

Image Processing Binary Images Color Space Convolution Discrete Fourier Transform Image Blur Converting Images Image Derivative Image Filters Interpolation Morphological Thinning Using Planar Images Point Based Deformation Image Pyramid Rgb To Gray Simulate Motion Blur Multi Threading	Feature Associate Interest Points Associate Three Views Canny Edge Detector Corner Features Dense Image Features Dense Optical Flow Detecting Black Ellipses Detecting Black Polygons Detect Describe Interface Computing SURF Features Fitting Ellipses Fitting Polygons Detecting Interest Points Detecting Lines and Line Segments Non Maximum Suppression
Image Enhancement Image Enhancement Wavelet Noise Removal	Segmentation Color Segmentation Superpixels Thresholding Watershed with Seeds
Stereo Vision Rectify Calibrated Stereo Stereo Disparity Fully Calibrated Disparity to 3D Cloud Disparity Smoothing Stereo Calibrated Single Camera Stereo Uncalibrated Single Camera Stereo Uncalibrated 3 Views	Geometry Point Cloud from Depth/RGB-D Image Stitching Image to Overhead View Remove Perspective Distortion Video Mosaic Video Stabilization
Structure from Motion Sparse Bundle Adjustment Computing Fundamental Matrix Computing Trifocal Tensor Perspective-n-Point Using Trifocal Tensor Visual Odometry: Depth/RGB-D Visual Odometry: Monocular Plane Visual Odometry: Stereo	Calibration Calibrate Fisheye Camera Calibrate Monocular Camera Calibrate Stereo Camera Detecting Calibration Targets Equirectangular To Pinhole Fisheye To Equirectangular Fisheye To Pinhole Remove Lens Distortion
Tracking Background Moving Camera Background Stationary Camera Track Point Features Mean Shift Likelihood Tracker Object Tracker	Reconstruction Multi Baseline Stereo Multiview Dense Reconstruction Uncalibrated Multiview Sparse
Fiducials Micro QR Code Detector QR Code Detector Square Binary Square Hamming Square Image Random Dots Calibration Target QR Code with Binary Data Micro QR Code Rendering QR Code Rendering	Recognition KNN Classification Color Histogram Lookup Image Classification Scene Recognition Template Matching
Point Clouds Load and Save Clouds View Point Cloud	Integration Android Video Webcam Capture