Core Technologies

• JavaCPP [API] – A tool that can not only generate JNI code but also build native wrapper library files from an appropriate interface file written entirely in Java. It can also parse automatically C/C++ header files to produce the required Java interface files.

Prebuilt Java Bindings to C/C++ Libraries

These are part of a project that we call the JavaCPP Presets. Many coexist in the same GitHub repository, and all use JavaCPP to wrap predefined C/C++ libraries from open-source land. The bindings expose almost all of the relevant APIs and make them available in a portable and user-friendly fashion to any Java virtual machine (including Android), as if they were like any other normal Java libraries. We have presets for the following C/C++ libraries:

• OpenCV – [sample usage] [API] – More than 2500 optimized computer vision and machine learning algorithms
• FFmpeg – [sample usage] [API] – A complete, cross-platform solution to record, convert and stream audio and video
• FlyCapture – [sample usage] [API] – Image acquisition and camera control software from PGR
• Spinnaker – [sample usage] [API] – Image acquisition and camera control software from FLIR
• libdc1394 – [sample usage] [API] – A high-level API for DCAM/IIDC cameras
• OpenKinect – [sample usage] [API] [API 2] – Open source library to use Kinect for Xbox and for Windows sensors
• librealsense – [sample usage] [API] [API 2] – Cross-platform library for Intel RealSense depth and tracking cameras
• videoInput – [sample usage] [API] – A free Windows video capture library
• ARToolKitPlus – [sample usage] [API] – Marker-based augmented reality tracking library
• Chilitags – [sample usage] [API] – Robust fiducial markers for augmented reality and robotics
• flandmark – [sample usage] [API] – Open-source implementation of facial landmark detector
• Arrow – [sample usage] [API] – A cross-language development platform for in-memory data
• HDF5 – [sample usage] [API] – Makes possible the management of extremely large and complex data collections
• Hyperscan – [sample usage] [API] – High-performance regular expression matching library
• LZ4 – [sample usage] [API] – Extremely fast compression algorithm
• MKL – [sample usage] [API] – The fastest and most-used math library for Intel-based systems
• oneDNN – [sample usage] [API] [API 2] – Intel Math Kernel Library for Deep Neural Networks (DNNL)
• OpenBLAS – [sample usage] [API] – An optimized BLAS library based on GotoBLAS2 1.13 BSD version, plus LAPACK
• ARPACK-NG – [sample usage] [API] – Collection of subroutines designed to solve large scale eigenvalue problems
• CMINPACK – [sample usage] [API] – For solving nonlinear equations and nonlinear least squares problems
• FFTW – [sample usage] [API] – Fast computing of the discrete Fourier transform (DFT) in one or more dimensions
• GSL – [sample usage] [API] – The GNU Scientific Library, a numerical library for C and C++ programmers
• CPython – [sample usage] [API] – The standard runtime of the Python programming language
• NumPy – [sample usage] [API] – Base N-dimensional array package
• SciPy – [sample usage] [API] – Fundamental library for scientific computing
• Gym – [sample usage] [API] – A toolkit for developing and comparing reinforcement learning algorithms
• LLVM – [sample usage] [API] – A collection of modular and reusable compiler and toolchain technologies
• libffi – [sample usage] [API] – A portable foreign-function interface library
• libpostal – [sample usage] [API] – For parsing/normalizing street addresses around the world
• LibRaw – [sample usage] [API] – A simple and unified interface for RAW files generated by digital photo cameras
• Leptonica – [sample usage] [API] – Software useful for image processing and image analysis applications
• Tesseract – [sample usage] [API] – Probably the most accurate open source OCR engine available
• Caffe – [sample usage] [API] – A fast open framework for deep learning
• OpenPose – [sample usage] [API] – Real-time multi-person keypoint detection for body, face, hands, and foot estimation
• CUDA – [sample usage] [API] – Arguably the most popular parallel computing platform for GPUs
• NVIDIA Video Codec SDK – [sample usage] [API] – An API for hardware accelerated video encode and decode
• OpenCL – [sample usage] [API] – Open standard for parallel programming of heterogeneous systems
• MXNet – [sample usage] [API] – Flexible and efficient library for deep learning
• PyTorch – [sample usage] [API] – Tensors and dynamic neural networks with strong GPU acceleration
• SentencePiece – [sample usage] [API] – Unsupervised text tokenizer for neural-network-based text generation
• TensorFlow – [sample usage] [API] – Computation using data flow graphs for scalable machine learning
• TensorFlow Lite – [sample usage] [API] – An open source deep learning framework for on-device inference
• TensorRT – [sample usage] [API] – High-performance deep learning inference optimizer and runtime
• Triton Inference Server – [sample usage] [API] – An optimized cloud and edge inferencing solution
• ALE – [sample usage] [API] – The Arcade Learning Environment to develop AI agents for Atari 2600 games
• DepthAI – [sample usage] [API] – An embedded spatial AI platform built around Intel Myriad X
• ONNX – [sample usage] [API] – Open Neural Network Exchange, an open source format for AI models
• nGraph – [sample usage] [API] – An open source C++ library, compiler, and runtime for deep learning frameworks
• ONNX Runtime – [sample usage] [API] – Cross-platform, high performance scoring engine for ML models
• TVM – [sample usage] [API] – An end to end machine learning compiler framework for CPUs, GPUs and accelerators
• Bullet Physics SDK – [sample usage] [API] – Real-time collision detection and multi-physics simulation
• LiquidFun – [sample usage] [API] – 2D physics engine for games
• Qt – [sample usage] [API] – A cross-platform framework that is usually used as a graphical toolkit
• Skia – [sample usage] [API] – A complete 2D graphic library for drawing text, geometries, and images
• cpu_features – [sample usage] [API] – A cross platform C99 library to get cpu features at runtime
• ModSecurity – [sample usage] [API] – A cross platform web application firewall (WAF) engine for Apache, IIS and Nginx
• Systems – [sample usage] [API] – To call native functions of operating systems (glibc, XNU libc, Win32, etc)
• Add here your favorite C/C++ library, for example: Caffe2, OpenNI, OpenMesh, PCL, etc. Read about how to do that.

We will add more to this list as they are made, including those from outside the bytedeco/javacpp-presets repository.

Projects Leveraging the Presets Bindings

• JavaCV [API] – Library based on the JavaCPP Presets that depends on commonly used native libraries in the field of computer vision to facilitate the development of those applications on the Java platform. It provides easy-to-use interfaces to grab frames from cameras and audio/video streams, process them, and record them back on disk or send them over the network.
• JavaCV Examples – Collection of examples originally written in C++ for the book entitled OpenCV 2 Computer Vision Application Programming Cookbook by Robert Laganière, but ported to JavaCV and written in Scala.
• ProCamCalib – Sample JavaCV application that can perform geometric and photometric calibration of a set of video projectors and color cameras.
• ProCamTracker – Another sample JavaCV application that uses the calibration from ProCamCalib to implement a vision method that tracks a textured planar surface and realizes markerless interactive augmented reality with projection mapping.

To facilitate its adoption in commercial applications such as Deeplearning4j, the core software of JavaCPP, JavaCPP Presets, and JavaCV has been relicensed to offer users the Apache License, Version 2.0 as a new choice of license. No large technical changes has otherwise occurred, but we believe these core components have acquired a stable enough existence to merit an increment in their major version number to 1.0. Moreover, this way we keep a meaningful relationship between our version numbers and the ones of OpenCV, which has recently seen large changes in its API and has been updated to version 3.0.

In other news, we now provide presets for CUDA, mapping close to the entirety of its API, including cuDNN, for which no other wrappers that we are aware of currently exist. We hope that, along with the presets for Caffe, which are as far as we know also still a unique offering of ours, this will provide some useful tools to the new exciting world of deep learning.

As usual, if you have any questions, problems, or would like to contribute, but are unsure how to proceed, please feel free to share your concerns on the mailing list of JavaCPP, on the one for JavaCV, or via “issues” on GitHub. We are looking forward to work with you in the future. Have fun!