Getting started with Deep Java Library (DJL)

Posted by Hayri Cicek on December 30, 2019

In this tutorial, I will show you how to get started with this new open source library from Amazon to build and deploy Deep Learning in Java.
Open IntelliJ and create a new Maven project and add the following to the pom.xml file.


<properties>
    <maven.compiler.source>11</maven.compiler.source>
    <maven.compiler.target>11</maven.compiler.target>
</properties>

<repositories>
    <repository>
        <id>djl.ai</id>
        <url>https://oss.sonatype.org/content/repositories/snapshots/</url>
    </repository>
</repositories>

<dependencies>
    <dependency>
        <groupId>ai.djl</groupId>
        <artifactId>api</artifactId>
        <version>0.3.0-SNAPSHOT</version>
    </dependency>
    <dependency>
        <groupId>ai.djl.mxnet</groupId>
        <artifactId>mxnet-model-zoo</artifactId>
        <version>0.3.0-SNAPSHOT</version>
    </dependency>
    <dependency>
        <groupId>ai.djl.mxnet</groupId>
        <artifactId>mxnet-native-mkl</artifactId>
        <version>1.6.0-b-SNAPSHOT</version>
        <classifier>osx-x86_64</classifier>
<!--            <classifier>linux-x86_64</classifier>-->
<!--            <classifier>win-x86_64</classifier>-->
        <scope>runtime</scope>
    </dependency>
</dependencies>

I'm using the osx-x86_64 classifier because I'm on Mac, if you are using Linux or Windows uncomment one of the other classifiers.




Now create the DetectObject.java file and make it look like following:


package com.kodnito.djl;

import ai.djl.MalformedModelException;
import ai.djl.modality.cv.DetectedObjects;
import ai.djl.modality.cv.ImageVisualization;
import ai.djl.modality.cv.util.BufferedImageUtils;
import ai.djl.mxnet.zoo.MxModelZoo;
import ai.djl.repository.zoo.ModelNotFoundException;
import ai.djl.repository.zoo.ZooModel;
import ai.djl.training.util.ProgressBar;
import ai.djl.translate.TranslateException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import javax.imageio.ImageIO;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.nio.file.Paths;

public class DetectObject {

    private static final Logger logger = LoggerFactory.getLogger(DetectObject.class);

    public static void main(String[] args) throws MalformedModelException, ModelNotFoundException, TranslateException, IOException {
        var detectedObjects = new DetectObject().predict();
        logger.info("{}", detectedObjects);
    }

    public DetectedObjects predict() throws MalformedModelException, ModelNotFoundException, IOException, TranslateException {
        var imageFile = Paths.get("src/main/resources/new-york.jpg");
        var img = BufferedImageUtils.fromFile(imageFile);

        ZooModel<BufferedImage, DetectedObjects> model =
                MxModelZoo.SSD.loadModel(new ProgressBar());

        var predictor = model.newPredictor().predict(img);
        ImageVisualization.drawBoundingBoxes(img, predictor);
        ImageIO.write(img, "png", new File("new-york.png"));
        model.close();
        return predictor;
    }

}

First we load image from disk.


var imageFile = Paths.get("src/main/resources/new-york.jpg");
var img = BufferedImageUtils.fromFile(imageFile);

Now, we load a SSD (Single Shot MultiBox Detector) model from the MXNet model zoo.
The Model Zoo is a collection of pre-trained models, which are ready to use out of the box.


ZooModel<BufferedImage, DetectedObjects> model =
                MxModelZoo.SSD.loadModel(new ProgressBar());

We create predictor and detect an object in the image.


var predictResult = model.newPredictor().predict(img);

We check detected result.


ImageVisualization.drawBoundingBoxes(img, predictResult);

Saving the result.


ImageIO.write(img, "png", new File("new-york.png"));

Image we loaded:

Detected result

Useful links:

Download source code: GitHub
DJL Website
DJL GitHub
MXNet

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Getting started with Deep Java Library (DJL)

Detected result

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