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SpringBoot集成tensorflow實現(xiàn)圖片檢測功能

更新時間：2024年06月20日 11:30:28 作者：HBLOG

TensorFlow名字的由來就是張量(Tensor)在計算圖(Computational?Graph)里的流動(Flow),它的基礎(chǔ)就是前面介紹的基于計算圖的自動微分,本文將給大家介紹Spring?Boot集成tensorflow實現(xiàn)圖片檢測功能,需要的朋友可以參考下

1.什么是tensorflow？

TensorFlow名字的由來就是張量(Tensor)在計算圖(Computational Graph)里的流動(Flow)，如圖。它的基礎(chǔ)就是前面介紹的基于計算圖的自動微分，除了自動幫你求梯度之外，它也提供了各種常見的操作(op，也就是計算圖的節(jié)點)，常見的損失函數(shù)，優(yōu)化算法。

TensorFlow 是一個開放源代碼軟件庫，用于進行高性能數(shù)值計算。借助其靈活的架構(gòu)，用戶可以輕松地將計算工作部署到多種平臺（CPU、GPU、TPU）和設(shè)備（桌面設(shè)備、服務(wù)器集群、移動設(shè)備、邊緣設(shè)備等）。
TensorFlow 是一個用于研究和生產(chǎn)的開放源代碼機器學習庫。TensorFlow 提供了各種 API，可供初學者和專家在桌面、移動、網(wǎng)絡(luò)和云端環(huán)境下進行開發(fā)。
TensorFlow是采用數(shù)據(jù)流圖（data　flow　graphs）來計算,所以首先我們得創(chuàng)建一個數(shù)據(jù)流流圖,然后再將我們的數(shù)據(jù)（數(shù)據(jù)以張量(tensor)的形式存在）放在數(shù)據(jù)流圖中計算. 節(jié)點（Nodes）在圖中表示數(shù)學操作,圖中的邊（edges）則表示在節(jié)點間相互聯(lián)系的多維數(shù)據(jù)數(shù)組, 即張量（tensor)。訓(xùn)練模型時tensor會不斷的從數(shù)據(jù)流圖中的一個節(jié)點flow到另一節(jié)點, 這就是TensorFlow名字的由來。 張量（Tensor):張量有多種. 零階張量為純量或標量 (scalar) 也就是一個數(shù)值. 比如 [1],一階張量為向量 (vector), 比如一維的 [1, 2, 3],二階張量為矩陣 (matrix), 比如二維的 [[1, 2, 3],[4, 5, 6],[7, 8, 9]],以此類推, 還有三階三維的 … 張量從流圖的一端流動到另一端的計算過程。它生動形象地描述了復(fù)雜數(shù)據(jù)結(jié)構(gòu)在人工神經(jīng)網(wǎng)中的流動、傳輸、分析和處理模式。

在機器學習中，數(shù)值通常由4種類型構(gòu)成：（1）標量（scalar）：即一個數(shù)值，它是計算的最小單元，如“1”或“3.2”等。（2）向量（vector）：由一些標量構(gòu)成的一維數(shù)組，如[1, 3.2, 4.6]等。（3）矩陣（matrix）：是由標量構(gòu)成的二維數(shù)組。（4）張量（tensor）：由多維（通常）數(shù)組構(gòu)成的數(shù)據(jù)集合，可理解為高維矩陣。

tensorflow的基本概念

圖：描述了計算過程，Tensorflow用圖來表示計算過程
張量：Tensorflow 使用tensor表示數(shù)據(jù)，每一個tensor是一個多維化的數(shù)組
操作：圖中的節(jié)點為op，一個op獲得/輸入0個或者多個Tensor，執(zhí)行并計算，產(chǎn)生0個或多個Tensor
會話：session tensorflow的運行需要再繪話里面運行

tensorflow寫代碼流程

定義變量占位符
根據(jù)數(shù)學原理寫方程
定義損失函數(shù)cost
定義優(yōu)化梯度下降 GradientDescentOptimizer
session 進行訓(xùn)練，for循環(huán)
保存saver

2.環(huán)境準備

整合步驟

模型構(gòu)建：首先，我們需要在TensorFlow中定義并訓(xùn)練深度學習模型。這可能涉及選擇合適的網(wǎng)絡(luò)結(jié)構(gòu)、優(yōu)化器和損失函數(shù)等。
訓(xùn)練數(shù)據(jù)準備：接下來，我們需要準備用于訓(xùn)練和驗證模型的數(shù)據(jù)。這可能包括數(shù)據(jù)清洗、標注和預(yù)處理等步驟。
REST API設(shè)計：為了與TensorFlow模型進行交互，我們需要在SpringBoot中創(chuàng)建一個REST API。這可以使用SpringBoot的內(nèi)置功能來實現(xiàn)，例如使用Spring MVC或Spring WebFlux。
模型部署：在模型訓(xùn)練完成后，我們需要將其部署到SpringBoot應(yīng)用中。為此，我們可以使用TensorFlow的Java API將模型導(dǎo)出為ONNX或SavedModel格式，然后在SpringBoot應(yīng)用中加載并使用。

在整合過程中，有幾個關(guān)鍵點需要注意。首先，防火墻設(shè)置可能會影響TensorFlow訓(xùn)練過程中的網(wǎng)絡(luò)通信。確保你的防火墻允許TensorFlow訪問其所需的網(wǎng)絡(luò)資源，以免出現(xiàn)訓(xùn)練中斷或模型性能下降的問題。其次，要關(guān)注版本兼容性。SpringBoot和TensorFlow都有各自的版本更新周期，確保在整合時使用兼容的版本可以避免很多不必要的麻煩。

3.代碼工程

實驗?zāi)康?/h3>
實現(xiàn)圖片檢測

pom.xml

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <parent>
        <artifactId>springboot-demo</artifactId>
        <groupId>com.et</groupId>
        <version>1.0-SNAPSHOT</version>
    </parent>
    <modelVersion>4.0.0</modelVersion>

    <artifactId>Tensorflow</artifactId>

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

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-autoconfigure</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <dependency>
            <groupId>org.tensorflow</groupId>
            <artifactId>tensorflow-core-platform</artifactId>
            <version>0.5.0</version>
        </dependency>
        <dependency>
            <groupId>org.projectlombok</groupId>
            <artifactId>lombok</artifactId>
        </dependency>

        <dependency>
            <groupId>jmimemagic</groupId>
            <artifactId>jmimemagic</artifactId>
            <version>0.1.2</version>
        </dependency>
        <dependency>
            <groupId>jakarta.platform</groupId>
            <artifactId>jakarta.jakartaee-api</artifactId>
            <version>9.0.0</version>
        </dependency>
        <dependency>
            <groupId>commons-io</groupId>
            <artifactId>commons-io</artifactId>
            <version>2.16.1</version>
        </dependency>
        <dependency>
            <groupId>org.springframework.restdocs</groupId>
            <artifactId>spring-restdocs-mockmvc</artifactId>
            <scope>test</scope>
        </dependency>

    </dependencies>
</project>

controller

package com.et.tf.api;

import java.io.IOException;

import com.et.tf.service.ClassifyImageService;
import net.sf.jmimemagic.Magic;
import net.sf.jmimemagic.MagicMatch;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.CrossOrigin;
import org.springframework.web.bind.annotation.PostMapping;
import org.springframework.web.bind.annotation.RequestMapping;
import org.springframework.web.bind.annotation.RequestParam;
import org.springframework.web.bind.annotation.RestController;
import org.springframework.web.multipart.MultipartFile;

@RestController
@RequestMapping("/api")
public class AppController {
    @Autowired
    ClassifyImageService classifyImageService;


    @PostMapping(value = "/classify")
    @CrossOrigin(origins = "*")
    public ClassifyImageService.LabelWithProbability classifyImage(@RequestParam MultipartFile file) throws IOException {
        checkImageContents(file);
        return classifyImageService.classifyImage(file.getBytes());
    }

    @RequestMapping(value = "/")
    public String index() {
        return "index";
    }

    private void checkImageContents(MultipartFile file) {
        MagicMatch match;
        try {
            match = Magic.getMagicMatch(file.getBytes());
        } catch (Exception e) {
            throw new RuntimeException(e);
        }
        String mimeType = match.getMimeType();
        if (!mimeType.startsWith("image")) {
            throw new IllegalArgumentException("Not an image type: " + mimeType);
        }
    }

}

service

package com.et.tf.service;

import jakarta.annotation.PreDestroy;
import java.util.Arrays;
import java.util.List;
import lombok.AllArgsConstructor;
import lombok.Data;
import lombok.NoArgsConstructor;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.stereotype.Service;
import org.tensorflow.Graph;
import org.tensorflow.Output;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.ndarray.NdArrays;
import org.tensorflow.ndarray.Shape;
import org.tensorflow.ndarray.buffer.FloatDataBuffer;
import org.tensorflow.op.OpScope;
import org.tensorflow.op.Scope;
import org.tensorflow.proto.framework.DataType;
import org.tensorflow.types.TFloat32;
import org.tensorflow.types.TInt32;
import org.tensorflow.types.TString;
import org.tensorflow.types.family.TType;

//Inspired from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/java/src/main/java/org/tensorflow/examples/LabelImage.java
@Service
@Slf4j
public class ClassifyImageService {

    private final Session session;
    private final List<String> labels;
    private final String outputLayer;

    private final int W;
    private final int H;
    private final float mean;
    private final float scale;

    public ClassifyImageService(
        Graph inceptionGraph, List<String> labels, @Value("${tf.outputLayer}") String outputLayer,
        @Value("${tf.image.width}") int imageW, @Value("${tf.image.height}") int imageH,
        @Value("${tf.image.mean}") float mean, @Value("${tf.image.scale}") float scale
    ) {
        this.labels = labels;
        this.outputLayer = outputLayer;
        this.H = imageH;
        this.W = imageW;
        this.mean = mean;
        this.scale = scale;
        this.session = new Session(inceptionGraph);
    }

    public LabelWithProbability classifyImage(byte[] imageBytes) {
        long start = System.currentTimeMillis();
        try (Tensor image = normalizedImageToTensor(imageBytes)) {
            float[] labelProbabilities = classifyImageProbabilities(image);
            int bestLabelIdx = maxIndex(labelProbabilities);
            LabelWithProbability labelWithProbability =
                new LabelWithProbability(labels.get(bestLabelIdx), labelProbabilities[bestLabelIdx] * 100f, System.currentTimeMillis() - start);
            log.debug(String.format(
                    "Image classification [%s %.2f%%] took %d ms",
                    labelWithProbability.getLabel(),
                    labelWithProbability.getProbability(),
                    labelWithProbability.getElapsed()
                )
            );
            return labelWithProbability;
        }
    }

    private float[] classifyImageProbabilities(Tensor image) {
        try (Tensor result = session.runner().feed("input", image).fetch(outputLayer).run().get(0)) {
            final Shape resultShape = result.shape();
            final long[] rShape = resultShape.asArray();
            if (resultShape.numDimensions() != 2 || rShape[0] != 1) {
                throw new RuntimeException(
                    String.format(
                        "Expected model to produce a [1 N] shaped tensor where N is the number of labels, instead it produced one with shape %s",
                        Arrays.toString(rShape)
                    ));
            }
            int nlabels = (int) rShape[1];
            FloatDataBuffer resultFloatBuffer = result.asRawTensor().data().asFloats();
            float[] dst = new float[nlabels];
            resultFloatBuffer.read(dst);
            return dst;
        }
    }

    private int maxIndex(float[] probabilities) {
        int best = 0;
        for (int i = 1; i < probabilities.length; ++i) {
            if (probabilities[i] > probabilities[best]) {
                best = i;
            }
        }
        return best;
    }

    private Tensor normalizedImageToTensor(byte[] imageBytes) {
        try (Graph g = new Graph();
             TInt32 batchTensor = TInt32.scalarOf(0);
             TInt32 sizeTensor = TInt32.vectorOf(H, W);
             TFloat32 meanTensor = TFloat32.scalarOf(mean);
             TFloat32 scaleTensor = TFloat32.scalarOf(scale);
        ) {
            GraphBuilder b = new GraphBuilder(g);
            //Tutorial python here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/label_image
            // Some constants specific to the pre-trained model at:
            // https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz
            //
            // - The model was trained with images scaled to 299x299 pixels.
            // - The colors, represented as R, G, B in 1-byte each were converted to
            //   float using (value - Mean)/Scale.

            // Since the graph is being constructed once per execution here, we can use a constant for the
            // input image. If the graph were to be re-used for multiple input images, a placeholder would
            // have been more appropriate.
            final Output input = b.constant("input", TString.tensorOfBytes(NdArrays.scalarOfObject(imageBytes)));
            final Output output =
                b.div(
                    b.sub(
                        b.resizeBilinear(
                            b.expandDims(
                                b.cast(b.decodeJpeg(input, 3), DataType.DT_FLOAT),
                                b.constant("make_batch", batchTensor)
                            ),
                            b.constant("size", sizeTensor)
                        ),
                        b.constant("mean", meanTensor)
                    ),
                    b.constant("scale", scaleTensor)
                );
            try (Session s = new Session(g)) {
                return s.runner().fetch(output.op().name()).run().get(0);
            }
        }
    }

    static class GraphBuilder {
        final Scope scope;

        GraphBuilder(Graph g) {
            this.g = g;
            this.scope = new OpScope(g);
        }

        Output div(Output x, Output y) {
            return binaryOp("Div", x, y);
        }

        Output sub(Output x, Output y) {
            return binaryOp("Sub", x, y);
        }

        Output resizeBilinear(Output images, Output size) {
            return binaryOp("ResizeBilinear", images, size);
        }

        Output expandDims(Output input, Output dim) {
            return binaryOp("ExpandDims", input, dim);
        }

        Output cast(Output value, DataType dtype) {
            return g.opBuilder("Cast", "Cast", scope).addInput(value).setAttr("DstT", dtype).build().output(0);
        }

        Output decodeJpeg(Output contents, long channels) {
            return g.opBuilder("DecodeJpeg", "DecodeJpeg", scope)
                .addInput(contents)
                .setAttr("channels", channels)
                .build()
                .output(0);
        }

        Output<? extends TType> constant(String name, Tensor t) {
            return g.opBuilder("Const", name, scope)
                .setAttr("dtype", t.dataType())
                .setAttr("value", t)
                .build()
                .output(0);
        }

        private Output binaryOp(String type, Output in1, Output in2) {
            return g.opBuilder(type, type, scope).addInput(in1).addInput(in2).build().output(0);
        }

        private final Graph g;
    }

    @PreDestroy
    public void close() {
        session.close();
    }

    @Data
    @NoArgsConstructor
    @AllArgsConstructor
    public static class LabelWithProbability {
        private String label;
        private float probability;
        private long elapsed;
    }
}

application.yaml

tf:
    frozenModelPath: inception-v3/inception_v3_2016_08_28_frozen.pb
    labelsPath: inception-v3/imagenet_slim_labels.txt
    outputLayer: InceptionV3/Predictions/Reshape_1
    image:
        width: 299
        height: 299
        mean: 0
        scale: 255

logging.level.net.sf.jmimemagic: WARN
spring:
  servlet:
    multipart:
      max-file-size: 5MB

Application.java

package com.et.tf;

import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.util.List;
import java.util.stream.Collectors;
import lombok.extern.slf4j.Slf4j;
import org.apache.commons.io.IOUtils;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.context.annotation.Bean;
import org.springframework.core.io.ClassPathResource;
import org.springframework.core.io.FileSystemResource;
import org.springframework.core.io.Resource;
import org.tensorflow.Graph;
import org.tensorflow.proto.framework.GraphDef;

@SpringBootApplication
@Slf4j
public class Application {

    public static void main(String[] args) {
        SpringApplication.run(Application.class, args);
    }

    @Bean
    public Graph tfModelGraph(@Value("${tf.frozenModelPath}") String tfFrozenModelPath) throws IOException {
        Resource graphResource = getResource(tfFrozenModelPath);

        Graph graph = new Graph();
        graph.importGraphDef(GraphDef.parseFrom(graphResource.getInputStream()));
        log.info("Loaded Tensorflow model");
        return graph;
    }

    private Resource getResource(@Value("${tf.frozenModelPath}") String tfFrozenModelPath) {
        Resource graphResource = new FileSystemResource(tfFrozenModelPath);
        if (!graphResource.exists()) {
            graphResource = new ClassPathResource(tfFrozenModelPath);
        }
        if (!graphResource.exists()) {
            throw new IllegalArgumentException(String.format("File %s does not exist", tfFrozenModelPath));
        }
        return graphResource;
    }

    @Bean
    public List<String> tfModelLabels(@Value("${tf.labelsPath}") String labelsPath) throws IOException {
        Resource labelsRes = getResource(labelsPath);
        log.info("Loaded model labels");
        return IOUtils.readLines(labelsRes.getInputStream(), StandardCharsets.UTF_8).stream()
            .map(label -> label.substring(label.contains(":") ? label.indexOf(":") + 1 : 0)).collect(Collectors.toList());
    }
}

以上只是一些關(guān)鍵代碼，所有代碼請參見下面代碼倉庫

代碼倉庫

https://github.com/Harries/springboot-demo

4.測試

啟動 Spring Boot應(yīng)用程序

測試圖片分類

訪問http://127.0.0.1:8080/，上傳一張圖片，點擊分類

5.總結(jié)

以上就是SpringBoot集成tensorflow實現(xiàn)圖片檢測功能的詳細內(nèi)容，更多關(guān)于SpringBoot tensorflow圖片檢測的資料請關(guān)注腳本之家其它相關(guān)文章！

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SpringBoot集成tensorflow實現(xiàn)圖片檢測功能

目錄

1.什么是tensorflow？

tensorflow的基本概念

tensorflow寫代碼流程

2.環(huán)境準備

整合步驟

3.代碼工程

實驗?zāi)康?/h3>
實現(xiàn)圖片檢測

pom.xml

controller

service

application.yaml

Application.java

代碼倉庫

4.測試

測試圖片分類

5.總結(jié)

相關(guān)文章

最新評論

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SpringBoot集成tensorflow實現(xiàn)圖片檢測功能

目錄

1.什么是tensorflow？

tensorflow的基本概念

tensorflow寫代碼流程

2.環(huán)境準備

整合步驟

3.代碼工程

實驗?zāi)康?/h3> 實現(xiàn)圖片檢測

pom.xml

controller

service

application.yaml

Application.java

代碼倉庫

4.測試

測試圖片分類

5.總結(jié)

相關(guān)文章

最新評論

大家感興趣的內(nèi)容

最近更新的內(nèi)容

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1.什么是tensorflow？

實驗?zāi)康?/h3>
實現(xiàn)圖片檢測