Programming language: Java
License: GNU General Public License v3.0 or later
Tags: Miscellaneous     Projects    
Latest version: v1.5.7

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Gitter Maven Central Sonatype Nexus (Snapshots) Build Status Commercial support: xscode


JavaCV uses wrappers from the JavaCPP Presets of commonly used libraries by researchers in the field of computer vision (OpenCV, FFmpeg, libdc1394, FlyCapture, Spinnaker, OpenKinect, librealsense, CL PS3 Eye Driver, videoInput, ARToolKitPlus, flandmark, Leptonica, and Tesseract) and provides utility classes to make their functionality easier to use on the Java platform, including Android.

JavaCV also comes with hardware accelerated full-screen image display (CanvasFrame and GLCanvasFrame), easy-to-use methods to execute code in parallel on multiple cores (Parallel), user-friendly geometric and color calibration of cameras and projectors (GeometricCalibrator, ProCamGeometricCalibrator, ProCamColorCalibrator), detection and matching of feature points (ObjectFinder), a set of classes that implement direct image alignment of projector-camera systems (mainly GNImageAligner, ProjectiveTransformer, ProjectiveColorTransformer, ProCamTransformer, and ReflectanceInitializer), a blob analysis package (Blobs), as well as miscellaneous functionality in the JavaCV class. Some of these classes also have an OpenCL and OpenGL counterpart, their names ending with CL or starting with GL, i.e.: JavaCVCL, GLCanvasFrame, etc.

To learn how to use the API, since documentation currently lacks, please refer to the Sample Usage section below as well as the sample programs, including two for Android (FacePreview.java and RecordActivity.java), also found in the samples directory. You may also find it useful to refer to the source code of ProCamCalib and ProCamTracker as well as examples ported from OpenCV2 Cookbook and the associated wiki pages.

Please keep me informed of any updates or fixes you make to the code so that I may integrate them into the next release. Thank you! And feel free to ask questions on the mailing list or the discussion forum if you encounter any problems with the software! I am sure it is far from perfect...


Archives containing JAR files are available as releases. The binary archive contains builds for Android, iOS, Linux, Mac OS X, and Windows. The JAR files for specific child modules or platforms can also be obtained individually from the Maven Central Repository.

To install manually the JAR files, follow the instructions in the Manual Installation section below.

We can also have everything downloaded and installed automatically with:

  • Maven (inside the pom.xml file)

  • Gradle (inside the build.gradle file)

    dependencies {
    implementation group: 'org.bytedeco', name: 'javacv-platform', version: '1.5.7'
  • Leiningen (inside the project.clj file)

    :dependencies [
    [org.bytedeco/javacv-platform "1.5.7"]
  • sbt (inside the build.sbt file)

    libraryDependencies += "org.bytedeco" % "javacv-platform" % "1.5.7"

This downloads binaries for all platforms, but to get binaries for only one platform we can set the javacpp.platform system property (via the -D command line option) to something like android-arm, linux-x86_64, macosx-x86_64, windows-x86_64, etc. Please refer to the README.md file of the JavaCPP Presets for details. Another option available to Gradle users is Gradle JavaCPP, and similarly for Scala users there is SBT-JavaCV.

Required Software

To use JavaCV, you will first need to download and install the following software:

Further, although not always required, some functionality of JavaCV also relies on:

Finally, please make sure everything has the same bitness: 32-bit and 64-bit modules do not mix under any circumstances.

Manual Installation

Simply put all the desired JAR files (opencv*.jar, ffmpeg*.jar, etc.), in addition to javacpp.jar and javacv.jar, somewhere in your class path. Here are some more specific instructions for common cases:

NetBeans (Java SE 7 or newer):

  1. In the Projects window, right-click the Libraries node of your project, and select "Add JAR/Folder...".
  2. Locate the JAR files, select them, and click OK.

Eclipse (Java SE 7 or newer):

  1. Navigate to Project > Properties > Java Build Path > Libraries and click "Add External JARs...".
  2. Locate the JAR files, select them, and click OK.

Visual Studio Code (Java SE 7 or newer):

  1. Navigate to Java Projects > Referenced Libraries, and click +.
  2. Locate the JAR files, select them, and click OK.

IntelliJ IDEA (Android 7.0 or newer):

  1. Follow the instructions on this page: http://developer.android.com/training/basics/firstapp/
  2. Copy all the JAR files into the app/libs subdirectory.
  3. Navigate to File > Project Structure > app > Dependencies, click +, and select "2 File dependency".
  4. Select all the JAR files from the libs subdirectory.

After that, the wrapper classes for OpenCV and FFmpeg, for example, can automatically access all of their C/C++ APIs:

Sample Usage

The class definitions are basically ports to Java of the original header files in C/C++, and I deliberately decided to keep as much of the original syntax as possible. For example, here is a method that tries to load an image file, smooth it, and save it back to disk:

import org.bytedeco.opencv.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_imgcodecs.*;

public class Smoother {
    public static void smooth(String filename) {
        Mat image = imread(filename);
        if (image != null) {
            GaussianBlur(image, image, new Size(3, 3), 0);
            imwrite(filename, image);

JavaCV also comes with helper classes and methods on top of OpenCV and FFmpeg to facilitate their integration to the Java platform. Here is a small demo program demonstrating the most frequently useful parts:

import java.io.File;
import java.net.URL;
import org.bytedeco.javacv.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.indexer.*;
import org.bytedeco.opencv.opencv_core.*;
import org.bytedeco.opencv.opencv_imgproc.*;
import org.bytedeco.opencv.opencv_calib3d.*;
import org.bytedeco.opencv.opencv_objdetect.*;
import static org.bytedeco.opencv.global.opencv_core.*;
import static org.bytedeco.opencv.global.opencv_imgproc.*;
import static org.bytedeco.opencv.global.opencv_calib3d.*;
import static org.bytedeco.opencv.global.opencv_objdetect.*;

public class Demo {
    public static void main(String[] args) throws Exception {
        String classifierName = null;
        if (args.length > 0) {
            classifierName = args[0];
        } else {
            URL url = new URL("https://raw.github.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_alt.xml");
            File file = Loader.cacheResource(url);
            classifierName = file.getAbsolutePath();

        // We can "cast" Pointer objects by instantiating a new object of the desired class.
        CascadeClassifier classifier = new CascadeClassifier(classifierName);
        if (classifier == null) {
            System.err.println("Error loading classifier file \"" + classifierName + "\".");

        // The available FrameGrabber classes include OpenCVFrameGrabber (opencv_videoio),
        // DC1394FrameGrabber, FlyCapture2FrameGrabber, OpenKinectFrameGrabber, OpenKinect2FrameGrabber,
        // RealSenseFrameGrabber, RealSense2FrameGrabber, PS3EyeFrameGrabber, VideoInputFrameGrabber, and FFmpegFrameGrabber.
        FrameGrabber grabber = FrameGrabber.createDefault(0);

        // CanvasFrame, FrameGrabber, and FrameRecorder use Frame objects to communicate image data.
        // We need a FrameConverter to interface with other APIs (Android, Java 2D, JavaFX, Tesseract, OpenCV, etc).
        OpenCVFrameConverter.ToMat converter = new OpenCVFrameConverter.ToMat();

        // FAQ about IplImage and Mat objects from OpenCV:
        // - For custom raw processing of data, createBuffer() returns an NIO direct
        //   buffer wrapped around the memory pointed by imageData, and under Android we can
        //   also use that Buffer with Bitmap.copyPixelsFromBuffer() and copyPixelsToBuffer().
        // - To get a BufferedImage from an IplImage, or vice versa, we can chain calls to
        //   Java2DFrameConverter and OpenCVFrameConverter, one after the other.
        // - Java2DFrameConverter also has static copy() methods that we can use to transfer
        //   data more directly between BufferedImage and IplImage or Mat via Frame objects.
        Mat grabbedImage = converter.convert(grabber.grab());
        int height = grabbedImage.rows();
        int width = grabbedImage.cols();

        // Objects allocated with `new`, clone(), or a create*() factory method are automatically released
        // by the garbage collector, but may still be explicitly released by calling deallocate().
        // You shall NOT call cvReleaseImage(), cvReleaseMemStorage(), etc. on objects allocated this way.
        Mat grayImage = new Mat(height, width, CV_8UC1);
        Mat rotatedImage = grabbedImage.clone();

        // The OpenCVFrameRecorder class simply uses the VideoWriter of opencv_videoio,
        // but FFmpegFrameRecorder also exists as a more versatile alternative.
        FrameRecorder recorder = FrameRecorder.createDefault("output.avi", width, height);

        // CanvasFrame is a JFrame containing a Canvas component, which is hardware accelerated.
        // It can also switch into full-screen mode when called with a screenNumber.
        // We should also specify the relative monitor/camera response for proper gamma correction.
        CanvasFrame frame = new CanvasFrame("Some Title", CanvasFrame.getDefaultGamma()/grabber.getGamma());

        // Let's create some random 3D rotation...
        Mat randomR    = new Mat(3, 3, CV_64FC1),
            randomAxis = new Mat(3, 1, CV_64FC1);
        // We can easily and efficiently access the elements of matrices and images
        // through an Indexer object with the set of get() and put() methods.
        DoubleIndexer Ridx = randomR.createIndexer(),
                   axisIdx = randomAxis.createIndexer();
        axisIdx.put(0, (Math.random() - 0.5) / 4,
                       (Math.random() - 0.5) / 4,
                       (Math.random() - 0.5) / 4);
        Rodrigues(randomAxis, randomR);
        double f = (width + height) / 2.0;  Ridx.put(0, 2, Ridx.get(0, 2) * f);
                                            Ridx.put(1, 2, Ridx.get(1, 2) * f);
        Ridx.put(2, 0, Ridx.get(2, 0) / f); Ridx.put(2, 1, Ridx.get(2, 1) / f);

        // We can allocate native arrays using constructors taking an integer as argument.
        Point hatPoints = new Point(3);

        while (frame.isVisible() && (grabbedImage = converter.convert(grabber.grab())) != null) {
            // Let's try to detect some faces! but we need a grayscale image...
            cvtColor(grabbedImage, grayImage, CV_BGR2GRAY);
            RectVector faces = new RectVector();
            classifier.detectMultiScale(grayImage, faces);
            long total = faces.size();
            for (long i = 0; i < total; i++) {
                Rect r = faces.get(i);
                int x = r.x(), y = r.y(), w = r.width(), h = r.height();
                rectangle(grabbedImage, new Point(x, y), new Point(x + w, y + h), Scalar.RED, 1, CV_AA, 0);

                // To access or pass as argument the elements of a native array, call position() before.
                hatPoints.position(0).x(x - w / 10     ).y(y - h / 10);
                hatPoints.position(1).x(x + w * 11 / 10).y(y - h / 10);
                hatPoints.position(2).x(x + w / 2      ).y(y - h / 2 );
                fillConvexPoly(grabbedImage, hatPoints.position(0), 3, Scalar.GREEN, CV_AA, 0);

            // Let's find some contours! but first some thresholding...
            threshold(grayImage, grayImage, 64, 255, CV_THRESH_BINARY);

            // To check if an output argument is null we may call either isNull() or equals(null).
            MatVector contours = new MatVector();
            findContours(grayImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
            long n = contours.size();
            for (long i = 0; i < n; i++) {
                Mat contour = contours.get(i);
                Mat points = new Mat();
                approxPolyDP(contour, points, arcLength(contour, true) * 0.02, true);
                drawContours(grabbedImage, new MatVector(points), -1, Scalar.BLUE);

            warpPerspective(grabbedImage, rotatedImage, randomR, rotatedImage.size());

            Frame rotatedFrame = converter.convert(rotatedImage);

Furthermore, after creating a pom.xml file with the following content:


        <!-- Additional dependencies required to use CUDA and cuDNN -->

        <!-- Optional GPL builds with (almost) everything enabled -->

And by placing the source code above in Demo.java, or similarly for other classes found in the [samples](samples), we can use the following command to have everything first installed automatically and then executed by Maven:

 $ mvn compile exec:java -Dexec.mainClass=Demo

Note: In case of errors, please make sure that the artifactId in the pom.xml file reads javacv-platform, not javacv only, for example. The artifact javacv-platform adds all the necessary binary dependencies.

Build Instructions

If the binary files available above are not enough for your needs, you might need to rebuild them from the source code. To this end, the project files were created for:

Once installed, simply call the usual mvn install command for JavaCPP, its Presets, and JavaCV. By default, no other dependencies than a C++ compiler for JavaCPP are required. Please refer to the comments inside the pom.xml files for further details.

Instead of building the native libraries manually, we can run mvn install for JavaCV only and rely on the snapshot artifacts from the CI builds:

Project lead: Samuel Audet samuel.audet at gmail.com
Developer site: https://github.com/bytedeco/javacv
Discussion group: http://groups.google.com/group/javacv