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.

Build

Gradle is the preferred way to build BoofCV. Complete instructions are contained in the boofcv/README.md. To compile BoofCV and output all of its jars simply do the following:

cd boofcv
gradle createLibraryDirectory

It will download dependencies, compile, and save the jars to to boofcv/libraries directory. Dependencies for 'integration' are omitted since they can be quite large. If you don't have Gradle installed you can invoke the boofcv/gradlew or boofcv/gradle.bat scripts instead, depending if you're on Linux/MacOS or Windows.

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
gradle examples
java -jar examples/examples.jar
gradle demonstrations
java -jar demonstrations/demonstrations.jar

Tutorials

1. Quick Start
2. Images in BoofCV
3. Image Segmentation
4. Fiducials
5. Videos and Webcams
6. Android Support
7. Camera Calibration
8. 3D Computer Vision / Structure from Motion
9. Kinect RGB-D Sensor
10. Processing

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 Image Pyramid Rgb To Gray	Feature Associate Interest Points Canny Edge Detector 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 Template Matching
Image Enhancement Image Enhancement Wavelet Noise Removal	Segmentation Color Segmentation Superpixels Thresholding Watershed with Seeds
Stereo Vision Compute Fundamental Matrix Rectify Calibrated Stereo Rectify Uncalibrated Stereo Dense Stereo Disparity Dense Stereo Disparity 3D Dense Stereo from Monocular Camera	Geometry Point Cloud from Depth/RGB-D Image Stitching Image to Overhead View Remove Perspective Distortion Video Mosaic Video Stabilization
Structure from Motion Multiview Scene Reconstruction Perspective-n-Point Visual Odometry: Depth/RGB-D Visual Odometry: Monocular Plane Visual Odometry: Stereo	Calibration 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	Integration Android Video Webcam Capture
Fiducials Square Binary Square Image Calibration Target	Recognition KNN Classification Color Histogram Lookup Image Classification

Other resources

• List of Applets