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Java调用Pytorch模型实现图像识别

作者：欧内的手好汗

这篇文章主要为大家详细介绍了Java如何调用Pytorch实现图像识别功能，文中的示例代码讲解详细，具有一定的学习价值，感兴趣的小伙伴可以了解一下

之前写了个输入是1x2向量的模型的调用文章，后来有了个需要用到图像识别的项目，因此写下此文记录一下在java中如何借助DJL调用自己写的pytorch模型进行图像识别。

官网例子

我具体模型用的什么模型就不介绍了，输入图片是3*224*224，放入图片前需要看一下横纵比是否合理，不合理的话会进行下面这样的操作：

1. 依赖

<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-engine</artifactId>
    <version>0.16.0</version>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-native-auto</artifactId>
    <version>1.9.1</version>
    <scope>runtime</scope>
</dependency>
<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-jni</artifactId>
    <version>1.9.1-0.16.0</version>
    <scope>runtime</scope>
</dependency>

2. 准备模型

1.首先将模型按下面方法保存，放到项目resources中

import torch
# An instance of your model.
model = MyModel(num_classes = 80)
# Switch the model to eval model
model.eval()
# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224)
# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)
# Save the TorchScript model
traced_script_module.save("model.pt")

2.编写工具类，用于完成识别功能

public class HerbUtil {
    //规定输入尺寸
    private static final int INPUT_SIZE = 224;
    //标签文件 一种类别名字占一行
    private List<String> herbNames;
    //用于识别
    Predictor<Image, Classifications> predictor;
    //模型
    private Model model;
    public HerbUtil() {
        //加载标签到herbNames中
        this.loadHerbNames();
        //初始化模型工作
        this.init();
    }
}

3.将标签文件放到resources中，载入标签

   private void loadHerbNames() {
        BufferedReader reader = null;
        herbNames = new ArrayList<>();
        try {
            InputStream in = HerbUtil.class.getClassLoader().getResourceAsStream("names.txt");
            reader = new BufferedReader(new InputStreamReader(in));
            String name = null;
            while ((name = reader.readLine()) != null) {
                herbNames.add(name);
            }
            System.out.println(herbNames);
        } catch (Exception e) {
            e.printStackTrace();
        } finally {
            if (reader != null) {
                try {
                    reader.close();
                } catch (IOException e) {
                    e.printStackTrace();
                }
            }
        }
    }

4.初始化模型

   private void init() {
        Translator<Image, Classifications> translator = ImageClassificationTranslator.builder()
                //下面的transform根据自己的改
                .addTransform(new RandomResizedCrop(INPUT_SIZE, INPUT_SIZE, 0.6, 1,
                        3. / 4, 4. / 3))
                .addTransform(new ToTensor())
                .addTransform(new Normalize(
                        new float[] {0.5f, 0.5f, 0.5f},
                        new float[] {0.5f, 0.5f, 0.5f}))
                //如果你的模型最后一层没有经过softmax就启用它
                .optApplySoftmax(true)
                //载入所有标签进去
                .optSynset(herbNames)
                //最终显示概率最高的5个
                .optTopK(5)
                .build();
        //随便起名
        Model model = Model.newInstance("model", Device.cpu());
        try {
            InputStream inputStream = HerbUtil.class.getClassLoader().getResourceAsStream("model.pt");
            if (inputStream == null) {
                throw new RuntimeException("找不到模型文件");
            }
            model.load(inputStream);
            predictor = model.newPredictor(translator);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

5.我开头提到的图片预处理 的代码

   private Image resizeImage(InputStream inputStream) {
        BufferedImage input = null;
        try {
            input = ImageIO.read(inputStream);
        } catch (IOException e) {
            e.printStackTrace();
        }
        int iw = input.getWidth(), ih = input.getHeight();
        int w = 224, h = 224;
        double scale = Math.min(1. *  w / iw, 1. * h / ih);
        int nw = (int) (iw * scale), nh = (int) (ih * scale);
        java.awt.Image img;
        //只有太长或太宽才会保留横纵比，填充颜色
        boolean needResize = 1. * iw / ih > 1.4 || 1. * ih / iw > 1.4;
        if (needResize) {
            img = input.getScaledInstance(nw, nh, BufferedImage.SCALE_SMOOTH);
        } else {
            img = input.getScaledInstance(INPUT_SIZE, INPUT_SIZE, BufferedImage.SCALE_SMOOTH);
        }
        BufferedImage out = new BufferedImage(INPUT_SIZE, INPUT_SIZE, BufferedImage.TYPE_INT_RGB);
        Graphics g = out.getGraphics();
        //先将整个224*224区域填充128 128 128颜色
        g.setColor(new Color(128, 128, 128));
        g.fillRect(0, 0, INPUT_SIZE, INPUT_SIZE);
        out.getGraphics().drawImage(img, 0, needResize ? (INPUT_SIZE - nh) / 2 : 0, null);
        ByteArrayOutputStream outputStream = new ByteArrayOutputStream();
        try {
            ImageOutputStream imageOutputStream = ImageIO.createImageOutputStream(outputStream);
            ImageIO.write(out, "jpg", imageOutputStream);
            //去D盘看效果
            //ImageIO.write(out, "jpg", new File("D:\\out.jpg"));
            InputStream is = new ByteArrayInputStream(outputStream.toByteArray());
            return ImageFactory.getInstance().fromInputStream(is);
        } catch (IOException e) {
            e.printStackTrace();
            throw new RuntimeException("图片转换失败");
        }
    }

6.识别功能

    public List<Classifications.Classification> predict(InputStream inputStream) {
        List<Classifications.Classification> result = new ArrayList<>();
        Image input = this.resizeImage(inputStream);
        try {
            Classifications output = predictor.predict(input);
            System.out.println("推测为：" + output.best().getClassName()
                    + ", 概率：" + output.best().getProbability());
            System.out.println(output);
            result = output.topK();
        } catch (Exception e) {
            e.printStackTrace();
        }
        return result;
    }

3. 测试

    @Test
    public void test7() {
        HerbUtil herbUtil = new HerbUtil();
        String path = "E:\\深度学习专用\\data\\train\\当归\\24.jpeg";
        try {
            File file = new File(path);
            InputStream inputStream = new FileInputStream(file);
            herbUtil.predict(inputStream);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }

 输出：

加入到项目中后，工具类直接Autowire注入或者方法都写static的，Controller接收前端MultipartFile，将其inputstream用于推测

如果你想加载网络图片，那就去网上搜索怎么把它转成inputstream吧

测试多线程一起predict时报错了

4.更新

当我打包成jar到centos7的linux中运行时，报错UnsatisfiedLinkError，经过大神的指导，问题出来我引的依赖。

修改后的依赖：

    <properties>
        <java.version>8</java.version>
        <jna.version>5.3.0</jna.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-engine</artifactId>
            <version>0.16.0</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-native-cpu-precxx11</artifactId>
            <classifier>linux-x86_64</classifier>
            <version>1.9.1</version>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-jni</artifactId>
            <version>1.9.1-0.16.0</version>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
    </dependencies>

到此这篇关于Java调用Pytorch模型实现图像识别的文章就介绍到这了,更多相关Java Pytorch图像识别内容请搜索脚本之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持脚本之家！

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