opencv/doc/tutorials/introduction/desktop_java/java_dev_intro.markdown

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 @ref tutorial_java_eclipse. For further reading after this guide, look at the @ref tutorial_android_dev_intro tutorials.

What we'll do in this guide

In this guide, we will:

• Get OpenCV with desktop Java support
• Create an Ant or SBT project
• Write a simple OpenCV application in Java or Scala

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: @code{.bash} git clone git://github.com/Itseez/opencv.git cd opencv git checkout 2.4 mkdir build cd build @endcode Generate a Makefile or a MS Visual Studio* solution, or whatever you use for building executables in your system: @code{.bash} cmake -DBUILD_SHARED_LIBS=OFF .. @endcode or @code{.bat} cmake -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 10" .. @endcode

@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.

@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.: @code{.bash} export JAVA_HOME=/usr/lib/jvm/java-6-oracle cmake -DBUILD_SHARED_LIBS=OFF .. @endcode

Now start the build: @code{.bash} make -j8 @endcode or @code{.bat} msbuild /m OpenCV.sln /t:Build /p:Configuration=Release /v:m @endcode 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.

• Create a folder where you'll develop this sample application.

• In this folder create the build.xml file with the following content using any text editor: @include samples/java/ant/build.xml @note This XML file can be reused for building other Java applications. It describes a common folder structure in the lines 3 - 12 and common targets for compiling and running the application. When reusing this XML don't forget to modify the project name in the line 1, that is also the name of the main class (line 14). The paths to OpenCV jar and jni lib are expected as parameters ("${ocvJarDir}" in line 5 and "${ocvLibDir}" in line 37), but you can hardcode these paths for your convenience. See Ant documentation for detailed description of its build file format.

• Create an src folder next to the build.xml file and a SimpleSample.java file in it.

• Put the following Java code into the SimpleSample.java file: @code{.java} import org.opencv.core.Core; import org.opencv.core.Mat; import org.opencv.core.CvType; import org.opencv.core.Scalar;

  class SimpleSample {
  
    static{ System.loadLibrary(Core.NATIVE_LIBRARY_NAME); }
  
    public static void main(String[] args) {
      System.out.println("Welcome to OpenCV " + Core.VERSION);
      Mat m = new Mat(5, 10, CvType.CV_8UC1, new Scalar(0));
      System.out.println("OpenCV Mat: " + m);
      Mat mr1 = m.row(1);
      mr1.setTo(new Scalar(1));
      Mat mc5 = m.col(5);
      mc5.setTo(new Scalar(5));
      System.out.println("OpenCV Mat data:\n" + m.dump());
    }
  
  }
  @endcode
  

• Run the following command in console in the folder containing build.xml: @code{.bash} ant -DocvJarDir=path/to/dir/containing/opencv-244.jar -DocvLibDir=path/to/dir/containing/opencv_java244/native/library @endcode For example: @code{.bat} ant -DocvJarDir=X:\opencv-2.4.4\bin -DocvLibDir=X:\opencv-2.4.4\bin\Release @endcode The command should initiate [re]building and running the sample. You should see on the screen something like this:

  

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: @code{.bash} cd mkdir JavaSample @endcode Now we will create the necessary folders and an SBT project: @code{.bash} 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 @endcode Now open project/build.scala in your favorite editor and paste the following. It defines your project: @code{.scala} 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) } } @endcode Now edit project/plugins.sbt and paste the following. This will enable auto-generation of an Eclipse project: @code{.scala} addSbtPlugin("com.typesafe.sbteclipse" % "sbteclipse-plugin" % "2.1.0") @endcode Now run sbt from the JavaSample root and from within SBT run eclipse to generate an eclipse project: @code{.bash} sbt # Starts the sbt console eclipse # Running "eclipse" from within the sbt console @endcode You should see something like this:

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

Now execute run from the sbt console, or more concisely, run sbt run from the command line: @code{.bash} sbt run @endcode You should see something like this:

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. @code{.bash} mkdir lib cp <opencv_dir>/build/bin/opencv_.jar lib/ sbt eclipse @endcode Next, create the directory src/main/resources and download this Lena image into it:

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: @code{.bash} cp <opencv_dir>/data/lbpcascades/lbpcascade_frontalface.xml src/main/resources/ @endcode Now modify src/main/java/HelloOpenCV.java so it contains the following Java code: @code{.java} 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();

} } @endcode 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`: @code{.bash} sbt run @endcode You should see something like this:

It should also write the following image to 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!