OpenCV  4.0.0-pre
Open Source Computer Vision
Introduction to Java Development

As of OpenCV 2.4.4, OpenCV supports desktop Java development using nearly the same interface as for Android development. This guide will help you to create your first Java (or Scala) application using OpenCV. We will use either Apache Ant or Simple Build Tool (SBT) to build the application.

If you want to use Eclipse head to Using OpenCV Java with Eclipse. For further reading after this guide, look at the Introduction into Android Development tutorials.

What we'll do in this guide

In this guide, we will:

The same process was used to create the samples in the samples/java folder of the OpenCV repository, so consult those files if you get lost.

Get proper OpenCV

Starting from version 2.4.4 OpenCV includes desktop Java bindings.

Download

The most simple way to get it is downloading the appropriate package of version 2.4.4 or higher from the OpenCV SourceForge repository.

Note
Windows users can find the prebuilt files needed for Java development in the opencv/build/java/ folder inside the package. For other OSes it's required to build OpenCV from sources.

Another option to get OpenCV sources is to clone OpenCV git repository. In order to build OpenCV with Java bindings you need JDK (Java Development Kit) (we recommend Oracle/Sun JDK 6 or 7), Apache Ant and Python v2.6 or higher to be installed.

Build

Let's build OpenCV:

git clone git://github.com/opencv/opencv.git
cd opencv
git checkout 2.4
mkdir build
cd build

Generate a Makefile or a MS Visual Studio* solution, or whatever you use for building executables in your system:

cmake -DBUILD_SHARED_LIBS=OFF ..

or

cmake -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 10" ..
Note
When OpenCV is built as a set of static libraries (-DBUILD_SHARED_LIBS=OFF option) the Java bindings dynamic library is all-sufficient, i.e. doesn't depend on other OpenCV libs, but includes all the OpenCV code inside.

Examine the output of CMake and ensure java is one of the modules "To be built". If not, it's likely you're missing a dependency. You should troubleshoot by looking through the CMake output for any Java-related tools that aren't found and installing them.

cmake_output.png
Note
If CMake can't find Java in your system set the JAVA_HOME environment variable with the path to installed JDK before running it. E.g.:
export JAVA_HOME=/usr/lib/jvm/java-6-oracle
cmake -DBUILD_SHARED_LIBS=OFF ..

Now start the build:

make -j8

or

msbuild /m OpenCV.sln /t:Build /p:Configuration=Release /v:m

Besides all this will create a jar containing the Java interface (bin/opencv-244.jar) and a native dynamic library containing Java bindings and all the OpenCV stuff (lib/libopencv_java244.so or bin/Release/opencv_java244.dll respectively). We'll use these files later.

Java sample with Ant

Note
The described sample is provided with OpenCV library in the opencv/samples/java/ant folder.

SBT project for Java and Scala

Now we'll create a simple Java application using SBT. This serves as a brief introduction to those unfamiliar with this build tool. We're using SBT because it is particularly easy and powerful.

First, download and install SBT using the instructions on its web site.

Next, navigate to a new directory where you'd like the application source to live (outside opencv dir). Let's call it "JavaSample" and create a directory for it:

cd <somewhere outside opencv>
mkdir JavaSample

Now we will create the necessary folders and an SBT project:

cd JavaSample
mkdir -p src/main/java # This is where SBT expects to find Java sources
mkdir project # This is where the build definitions live

Now open project/build.scala in your favorite editor and paste the following. It defines your project:

import sbt._
import Keys._
object JavaSampleBuild extends Build {
def scalaSettings = Seq(
scalaVersion := "2.10.0",
scalacOptions ++= Seq(
"-optimize",
"-unchecked",
"-deprecation"
)
)
def buildSettings =
Project.defaultSettings ++
scalaSettings
lazy val root = {
val settings = buildSettings ++ Seq(name := "JavaSample")
Project(id = "JavaSample", base = file("."), settings = settings)
}
}

Now edit project/plugins.sbt and paste the following. This will enable auto-generation of an Eclipse project:

addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.1.0")

Now run sbt from the JavaSample root and from within SBT run eclipse to generate an eclipse project:

sbt # Starts the sbt console
eclipse # Running "eclipse" from within the sbt console

You should see something like this:

sbt_eclipse.png

You can now import the SBT project to Eclipse using Import ... -> Existing projects into workspace. Whether you actually do this is optional for the guide; we'll be using SBT to build the project, so if you choose to use Eclipse it will just serve as a text editor.

To test that everything is working, create a simple "Hello OpenCV" application. Do this by creating a file src/main/java/HelloOpenCV.java with the following contents:

public class HelloOpenCV {
public static void main(String[] args) {
System.out.println("Hello, OpenCV");
}

}

Now execute run from the sbt console, or more concisely, run sbt run from the command line:

sbt run

You should see something like this:

sbt_run.png

Running SBT samples

Now we'll create a simple face detection application using OpenCV.

First, create a lib/ folder and copy the OpenCV jar into it. By default, SBT adds jars in the lib folder to the Java library search path. You can optionally rerun sbt eclipse to update your Eclipse project.

mkdir lib
cp <opencv_dir>/build/bin/opencv_<version>.jar lib/
sbt eclipse

Next, create the directory src/main/resources and download this Lena image into it:

lena.png

Make sure it's called "lena.png". Items in the resources directory are available to the Java application at runtime.

Next, copy lbpcascade_frontalface.xml from opencv/data/lbpcascades/ into the resources directory:

cp <opencv_dir>/data/lbpcascades/lbpcascade_frontalface.xml src/main/resources/

Now modify src/main/java/HelloOpenCV.java so it contains the following Java code:

import org.opencv.core.Core;
import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.objdetect.CascadeClassifier;
//
// Detects faces in an image, draws boxes around them, and writes the results
// to "faceDetection.png".
//
class DetectFaceDemo {
public void run() {
System.out.println("\nRunning DetectFaceDemo");
// Create a face detector from the cascade file in the resources
// directory.
CascadeClassifier faceDetector = new CascadeClassifier(getClass().getResource("/lbpcascade_frontalface.xml").getPath());
Mat image = Imgcodecs.imread(getClass().getResource("/lena.png").getPath());
// Detect faces in the image.
// MatOfRect is a special container class for Rect.
MatOfRect faceDetections = new MatOfRect();
faceDetector.detectMultiScale(image, faceDetections);
System.out.println(String.format("Detected %s faces", faceDetections.toArray().length));
// Draw a bounding box around each face.
for (Rect rect : faceDetections.toArray()) {
Imgproc.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(0, 255, 0));
}
// Save the visualized detection.
String filename = "faceDetection.png";
System.out.println(String.format("Writing %s", filename));
Imgcodecs.imwrite(filename, image);
}
}
public class HelloOpenCV {
public static void main(String[] args) {
System.out.println("Hello, OpenCV");
// Load the native library.
System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
new DetectFaceDemo().run();
}
}

Note the call to System.loadLibrary(Core.NATIVE_LIBRARY_NAME). This command must be executed exactly once per Java process prior to using any native OpenCV methods. If you don't call it, you will get UnsatisfiedLink errors. You will also get errors if you try to load OpenCV when it has already been loaded.

Now run the face detection app using `sbt run`:

sbt run

You should see something like this:

sbt_run_face.png

It should also write the following image to faceDetection.png:

faceDetection.png

You're done! Now you have a sample Java application working with OpenCV, so you can start the work on your own. We wish you good luck and many years of joyful life!