18

使用Java语言开发人工智能服务应用 - 简书

 3 years ago
source link: https://www.jianshu.com/p/49c58c442f8e?
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.

使用Java语言开发人工智能服务应用

32020.06.08 18:38:20字数 226阅读 498

目前市面上主流的深度学习框架 TensorFlow、pytorch、MxNet都是以Python语言为主，Java工程师们想要利用自己的优势开发一款深度学习应用绝非易事；通过本篇文章我们将解决这个问题，用极少的代码实现一个图片分类服务

【物体分类】
通过Http请求，向后端服务传入一张图片地址，后端服务调用深度学习模型对图片进行处理，给出分类预测结果

使用idea或者eclipse构建maven工程，并导入以下maven依赖

        <dependency>
            <groupId>commons-cli</groupId>
            <artifactId>commons-cli</artifactId>
            <version>1.4</version>
        </dependency>
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j-impl</artifactId>
            <version>2.12.1</version>
        </dependency>
        <dependency>
            <groupId>com.google.code.gson</groupId>
            <artifactId>gson</artifactId>
            <version>2.8.5</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>api</artifactId>
            <version>${djl.version}</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>basicdataset</artifactId>
            <version>${djl.version}</version>
        </dependency>
        <dependency>
            <groupId>ai.djl</groupId>
            <artifactId>model-zoo</artifactId>
            <version>${djl.version}</version>
        </dependency>

         <dependency>
            <groupId>com.sparkjava</groupId>
            <artifactId>spark-core</artifactId>
            <version>2.8.0</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.mxnet</groupId>
            <artifactId>mxnet-model-zoo</artifactId>
            <version>${djl.version}</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.mxnet</groupId>
            <artifactId>mxnet-engine</artifactId>
            <version>${djl.version}</version>
        </dependency>

        <dependency>
            <groupId>ai.djl.mxnet</groupId>
            <artifactId>mxnet-native-auto</artifactId>
            <version>1.7.0-a</version>
            <scope>runtime</scope>
        </dependency>

使用djl ModelZoo加载ImageNet模型，并对输入图片进行分类预测

 
public class ImageNetTest {

    private static Predictor<BufferedImage, Classifications> predictor = null;

    static {
        load();
    }

    private static void load(){
        Criteria<BufferedImage, Classifications> criteria =
                Criteria.builder()
                        .optApplication(Application.CV.IMAGE_CLASSIFICATION)
                        .setTypes(BufferedImage.class, Classifications.class)
                        .optFilter("multiplier", "0.75")
                        .optFilter("flavor", "v1")
                        .optFilter("dataset", "imagenet")
                        .optArtifactId("mobilenet")
                        .optProgress(new ProgressBar())
                        .build();

        try {
            ZooModel<BufferedImage, Classifications> model = ModelZoo.loadModel(criteria);
            predictor =  model.newPredictor();
        } catch (IOException e) {
            e.printStackTrace();
        } catch (ModelNotFoundException e) {
            e.printStackTrace();
        } catch (MalformedModelException e) {
            e.printStackTrace();
        }
    }
    public static String predict(String imagePath) throws Exception {
        BufferedImage image;
        if (imagePath.startsWith("http")) {
            image = BufferedImageUtils.fromUrl(new URL(imagePath));
        } else {
            image = BufferedImageUtils.fromFile(Paths.get(imagePath));
        }
        return new Gson().toJson(predictor.predict(image).topK(3));
    }

    public static void main(String[] args)throws Exception  {
        System.out.println(predict("src/test/resources/dog-cat.jpg"));
    }
}

模型下载地址

下载模型 将文件解压到


{your_os_user_root}\.djl.ai\cache\repo\model\cv\image_classification\ai\djl\mxnet\mobilenet\v1\0.75

SimpleHttp

通过web spark 快速实现restful api


 public static void main(String[] args) {
        port(8899);
        get("/img_classes/predict", (request, response) -> {
            return ImageNetTest.predict(request.queryParams("img_url"));
        });
    }
image.png

report

Recommend

About Joyk


Aggregate valuable and interesting links.
Joyk means Joy of geeK