Bytedeco makes native libraries available to the Java platform by offering ready-to-use bindings generated with the codeveloped JavaCPP technology. This, we hope, is the missing bridge between Java and C/C++, bringing compute-intensive science, multimedia, computer vision, deep learning, etc to the Java platform.
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.
More Project Information
Please refer to the contribute and download pages for more information about how to help out or obtain this software.
See the developer site on GitHub for more general information about the Bytedeco projects.
Latest News
Java as a system programming language
Happy New Year! After spending over a year in (part-time) preparation for the next major release here at Bytedeco, version 1.4 has finally been released! A lot has been happening, so let me summarize the most important items. First, a million thanks to Vincent Baines for all the hard work getting builds to pass. We now have a proper continuous integration (CI) infrastructure based on AppVeyor and Travis CI testing builds for pull requests as well as publishing SNAPSHOT artifacts for all platforms at each commit to the source code repositories. More information about that on the builds page. Next, we have introduced the concept of “extension” to JavaCPP, letting us provide separate but optional CUDA builds for OpenCV, Caffe, and TensorFlow. To enable them, one simply needs to add to the class path the JAR files containing “-gpu” in their names, and JavaCPP will automatically pick them up based on their contents. On load error, it also gracefully fall backs on non-CUDA binaries. The list of currently available CUDA artifacts is given at the bottom of the download page. Finally, we have been busy closing the gap that prevents Java from being usable as a system programming language by introducing the JavaCPP Presets for Systems to access system APIs such as libc and Win32.
Before going into more details on the latter topic at hand, please find a complete list of all changes in the CHANGELOG.md
files for JavaCPP, JavaCPP Presets, JavaCV, ProCamCalib, and ProCamTracker. Binaries can be obtained as usual from the Maven Central Repository. New presets that were contributed include libfreenect2, MKL, libpostal, The Arcade Learning Environment (ALE), LiquidFun, and Skia, in addition to the system APIs.
Although Java is still the most widely used programming language, according to the TIOBE Index, among many other sources, it is still not considered a system programming language and is conspicuously missing from the list of system programming languages on Wikipedia. However, things are starting to change. Beginning with Java 9, the JDK supports ahead-of-time (AOT) compilation of Java classes, something otherwise supported by Avian and many others for a while already, thus allowing developers to create native executable programs that can be integrated into operating systems. Finally, thanks to JNI, JavaCPP, and now the systems presets, we can benefit easily from all the features (and suffer from all the pitfalls) of C++ right from the Java platform.
Having access to systems APIs allows us to perform any operation supported by the underlying platform that is not otherwise mapped to a high-level Java API. Before the process API updates included in Java 9, it was not possible to query all children and descendants of a process, or to kill forcibly an arbitrary process on the system, but it could still have been done with native APIs. However, it is still not possible, for example, to set the priority of a process on the system without resorting to external tools. With the systems presets, we can accomplish this with just a few lines of code, such as the following to set the current process priority to the lowest level, code that can also be executed interactively in a JShell session:
import org.bytedeco.javacpp.*;
String platform = Loader.getPlatform();
if (platform.startsWith("linux")) {
linux.setpriority(linux.PRIO_PROCESS, linux.getpid(), 20);
} else if (platform.startsWith("macosx")) {
macosx.setpriority(macosx.PRIO_PROCESS, macosx.getpid(), 20);
} else if (platform.startsWith("windows")) {
windows.SetPriorityClass(windows.GetCurrentProcess(), windows.IDLE_PRIORITY_CLASS);
}
We can perform the same calls with other tools such as JNA or JNR, but with JavaCPP we get a uniform layer that also supports C++ libraries, providing a level of integration as yet unmatched by any other solutions that we are aware of on any platform. In a nutshell, any language targeting Java bytecode is now in a good position to become a system programming language, on the same level as the Go language!
We hope that you are excited as we are in participating in this grand experiment, so please do not hesitate to contact us via the mailing list from Google Groups, issues on GitHub, or the chat room at Gitter, for any questions that you may have. We hope to hear from all of you soon!