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Java+ElasticSearch+Pytorch实现以图搜图功能

 更新时间：2023年06月12日 15:02:02   作者：欧内的手好汗  
这篇文章主要为大家详细介绍了Java如何利用ElasticSearch和Pytorch实现以图搜图功能，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

以图搜图，涉及两大功能：

1、提取图像特征向量。

2、相似向量检索。

第一个功能我通过编写pytorch模型并在java端借助djl调用实现，第二个功能通过elasticsearch7.6.2的dense_vector、cosineSimilarity实现。

一、准备模型

创建demo.py，输入代码，借助resnet提取图像特征

import torch
import torch.nn as nn
import torchvision.models as models
class ImageFeatureExtractor(nn.Module):
    def __init__(self):
        super(ImageFeatureExtractor, self).__init__()
        self.resnet = models.resnet50(pretrained=True)
        #最终输出维度1024的向量，下文elastic search要设置dims为1024
        self.resnet.fc = nn.Linear(2048, 1024)
    def forward(self, x):
        x = self.resnet(x)
        return x
if __name__ == '__main__':
    model = ImageFeatureExtractor()
    model.eval()
    #根据模型随便创建一个输入
    input = torch.rand([1, 3, 224, 224])
    output = model(input)
    #以这种方式保存
    script = torch.jit.trace(model, input)
    script.save("model.pt")

保存好的model.pt文件放入java项目的resources中，可以在java中引入Deep-Java-Library来调用

二、创建Java项目

创建项目，引入djl和elasticsearch的依赖

<dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-engine</artifactId>
            <version>0.19.0</version>
        </dependency>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-native-cpu</artifactId>
            <version>1.10.0</version>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>ai.djl.pytorch</groupId>
            <artifactId>pytorch-jni</artifactId>
            <version>1.10.0-0.19.0</version>
        </dependency>
        <dependency>
            <groupId>org.elasticsearch.client</groupId>
            <artifactId>elasticsearch-rest-high-level-client</artifactId>
            <version>7.6.2</version>
        </dependency>

然后随便从网上下载点图片，比如猫5张图狗5张图什么的，放到项目"resources/随便" 路径下，一会要提取他们的特征向量并上传至elasticsearch

三、es创建文档

需要在elastic search中创建一个新文档。localhost:9200/isi   (img search img)

PUT /isi
{
  "mappings": {
    "properties": {
      "vector": {
        "type": "dense_vector",
        "dims": 1024
      },
      "url" : {
        "type" : "keyword"
      },
      "user_id": {
          "type": "keyword"
      }
    }
  }
}

完成下文上传操作后测试搜索（params中queryVector为随便选了个图像提取的特征向量）：

POST /isi/_search
{
  "query": {
    "function_score": {
      "query": {
        "match_all": {}
      },
      "script_score": {
        "script": {
          "source": "cosineSimilarity(params.queryVector, 'vector') ",
          "params": {
            "queryVector": [-0.21950562, 0.0979692, 0.30605257, -0.04246464, 0.3086218, 0.2133326, -0.13531154, 0.16382562, 0.2505685, 0.35654455, 0.50346404, -0.2031727, -0.4501943, 0.23117387, 0.39451313, 0.044487886, -0.11032343, 0.47252116, 0.24667346, -0.2052311, -0.10872754, 0.22328046, 0.13366169, -0.5555884, 0.23139203, 0.024292288, 0.3071902, 0.23381571, -0.14484097, -0.80570614, 0.096950606, -0.034106746, 0.3221968, 0.35980088, -0.24408965, 0.10010342, 0.34878045, 0.25403115, 0.8813986, -0.23978959, -0.101492174, -0.34241566, -0.258092, 0.38593173, 0.24993907, -0.6891467, 0.5723483, -0.4987241, -0.46613082, 0.07435644, -0.32876882, 0.1923833, 0.41619772, 0.006919967, -0.35519657, -0.2463252, -0.07216969, -0.10412077, -0.3964988, -0.43174505, 0.6576338, -0.09753291, 0.058324523, -0.366405, -0.08003934, -0.41232625, -0.59834087, 0.35432702, -0.33971205, -0.695481, -0.38738084, -0.08746443, 0.37581405, 0.5092232, 0.26168102, 0.33873072, 0.3769325, 0.5525994, -0.018578911, 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-0.09063532, 0.5011331, -0.5051317, -0.054662913, -0.26086497, -0.53341925, 0.9624672, 0.08449669, -0.21910548, 0.36410314, -0.24794322, 0.16658492, 0.7944018, -0.058724128, -0.22618303, 0.5062074, -0.516353, 0.69395834, -0.23764399, -0.13169304, 0.51044196, -0.042955525, -0.42410484, -0.4293069, 0.13401544, 0.80136365, 0.30296534, -0.06788176, 0.16880289, 0.27950272, -0.37403736, 0.11813866, -0.41821468, 0.0033562258, -0.53348655, -0.22950119, 0.3889678, 0.10558852, -0.25912097, -0.03190498, 0.028149713, 0.36284888, -0.63619995, 0.8380439, 0.6589971, 0.6046954, -0.2093836, 0.08808039, 0.48332697, -0.010615652, -0.40519536, 0.011716956, 0.096273005, -0.27340046, -0.19237258, -0.2970637, -0.44011658, 0.17786184, 0.0071578454, 0.23985118, -0.040508576]
          }
        }
      }
    }
  },
  "_source": ["url"],
  "size": 100
}

四、调用pytorch模型代码

创建Test类，copy一下我的，感兴趣可以去djl的官网学习更多内容。写完后，就可以获取“随便”文件夹中的图像的特征向量，上传到es里了。

package org.gwen;
import ai.djl.Device;
import ai.djl.Model;
import ai.djl.inference.Predictor;
import ai.djl.modality.cv.Image;
import ai.djl.modality.cv.ImageFactory;
import ai.djl.modality.cv.transform.Normalize;
import ai.djl.modality.cv.transform.Resize;
import ai.djl.modality.cv.transform.ToTensor;
import ai.djl.modality.cv.util.NDImageUtils;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.translate.Transform;
import ai.djl.translate.Translator;
import ai.djl.translate.TranslatorContext;
import org.apache.http.HttpHost;
import org.elasticsearch.action.bulk.BulkRequest;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.action.search.SearchRequest;
import org.elasticsearch.action.search.SearchResponse;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestClient;
import org.elasticsearch.client.RestClientBuilder;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.client.transport.TransportClient;
import org.elasticsearch.common.settings.Settings;
import org.elasticsearch.common.xcontent.XContentType;
import org.elasticsearch.index.query.QueryBuilders;
import org.elasticsearch.index.query.ScriptQueryBuilder;
import org.elasticsearch.index.query.functionscore.FunctionScoreQueryBuilder;
import org.elasticsearch.index.query.functionscore.ScoreFunctionBuilders;
import org.elasticsearch.script.Script;
import org.elasticsearch.script.ScriptType;
import org.elasticsearch.search.SearchHit;
import org.elasticsearch.search.SearchHits;
import org.elasticsearch.search.builder.SearchSourceBuilder;
import org.gwen.entity.SearchResult;
import java.io.File;
import java.io.IOException;
import java.io.InputStream;
import java.net.URI;
import java.net.URL;
import java.nio.file.Paths;
import java.util.*;
public class Test {
    private static final String INDEX = "isi";
    private static final int IMAGE_SIZE = 224;
    private static Model model; //模型
    private static Predictor<Image, float[]> predictor; //predictor.predict(input)相当于python中model(input)
    static {
        try {
            model = Model.newInstance("model");
            //这里的model.pt是上面代码展示的那种方式保存的
            model.load(Test.class.getClassLoader().getResourceAsStream("model.pt"));
            Transform resize = new Resize(IMAGE_SIZE);
            Transform toTensor = new ToTensor();
            Transform normalize = new Normalize(new float[]{0.485f, 0.456f, 0.406f}, new float[]{0.229f, 0.224f, 0.225f});
            //Translator处理输入Image转为tensor、输出转为float[]
            Translator<Image, float[]> translator = new Translator<Image, float[]>() {
                @Override
                public NDList processInput(TranslatorContext ctx, Image input) throws Exception {
                    NDManager ndManager = ctx.getNDManager();
                    System.out.println("input: " + input.getWidth() + ", " + input.getHeight());
                    NDArray transform = normalize.transform(toTensor.transform(resize.transform(input.toNDArray(ndManager))));
                    System.out.println(transform.getShape());
                    NDList list = new NDList();
                    list.add(transform);
                    return list;
                }
                @Override
                public float[] processOutput(TranslatorContext ctx, NDList ndList) throws Exception {
                    return ndList.get(0).toFloatArray();
                }
            };
            predictor = new Predictor<>(model, translator, Device.cpu(), true);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

五、es上传和搜索

上传：遍历每张图片，获取每张图片的特征，上传到es

搜索：获取输入图像的特征，创建SearchRequest在es中通过painless脚本进行余弦相似度对比检索。

首先创建SearchResult类表示es搜索的结果，包括图像url和相关度评分score

@Data
@AllArgsConstructor
public class SearchResult {
    private String url;
    private Float score;
}

然后在Test里实现upload和search

    public static void upload() throws Exception {
        RestHighLevelClient client = new RestHighLevelClient(
                RestClient.builder(new HttpHost("localhost", 9200, "http")));
        //批量上传请求
        BulkRequest bulkRequest = new BulkRequest(INDEX);
        File file = new File("C:\\Users\\Administrator\\IdeaProjects\\img_search_img\\src\\main\\resources\\随便");
        for (File listFile : file.listFiles()) {
            float[] vector = predictor.predict(ImageFactory.getInstance().fromInputStream(Test.class.getClassLoader().getResourceAsStream("随便/" + listFile.getName())));
            // 构建文档
            Map<String, Object> jsonMap = new HashMap<>();
            jsonMap.put("url", listFile.getAbsolutePath());
            jsonMap.put("vector", vector);
            jsonMap.put("user_id", "user123");
            IndexRequest request = new IndexRequest(INDEX).source(jsonMap, XContentType.JSON);
            bulkRequest.add(request);
        }
        client.bulk(bulkRequest, RequestOptions.DEFAULT);
        client.close();
    }
    //接收待搜索图片的inputstream，搜索与其相似的图片
    public static List<SearchResult> search(InputStream input) throws Throwable {
        float[] vector = predictor.predict(ImageFactory.getInstance().fromInputStream(input));
        System.out.println(Arrays.toString(vector));
        //展示k个结果
        int k = 100;
        // 连接Elasticsearch服务器
        RestHighLevelClient client = new RestHighLevelClient(
                RestClient.builder(new HttpHost("localhost", 9200, "http")));
        SearchRequest searchRequest = new SearchRequest(INDEX);
        Script script = new Script(
                ScriptType.INLINE,
                "painless",
                "cosineSimilarity(params.queryVector, doc['vector'])",
                Collections.singletonMap("queryVector", vector));
        FunctionScoreQueryBuilder functionScoreQueryBuilder = QueryBuilders.functionScoreQuery(
                QueryBuilders.matchAllQuery(),
                ScoreFunctionBuilders.scriptFunction(script));
        SearchSourceBuilder searchSourceBuilder = new SearchSourceBuilder();
        searchSourceBuilder.query(functionScoreQueryBuilder)
                .fetchSource(null, "vector") //不返回vector字段，太多了没用还耗时
                .size(k);
        searchRequest.source(searchSourceBuilder);
        SearchResponse searchResponse = client.search(searchRequest, RequestOptions.DEFAULT);
        SearchHits hits = searchResponse.getHits();
        List<SearchResult> list = new ArrayList<>();
        for (SearchHit hit : hits) {
            // 处理搜索结果
            System.out.println(hit.toString());
            SearchResult result = new SearchResult((String) hit.getSourceAsMap().get("url"), hit.getScore());
            list.add(result);
        }
        client.close();
        return list;
    }

六、测试

@RestController
@CrossOrigin
public class SearchController {
    @PostMapping("search")
    public ResponseEntity search(MultipartFile file) {
        try {
            List<SearchResult> list = Test.search(file.getInputStream());
            return ResponseEntity.ok(list);
        } catch (Throwable e) {
            return ResponseEntity.status(400).body(null);
        }
    }
}

页面：

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Title</title>
    <style>
        body {
            background: url("/img/bg.jpg");
            background-attachment: fixed;
            background-size: 100% 100%;
        }
        body > div {
            width: 1000px;
            margin: 50px auto;
            padding: 10px 20px;
            border: 1px solid lightgray;
            border-radius: 20px;
            box-sizing: border-box;
            background: rgba(255, 255, 255, 0.7);
        }
        .upload {
            display: inline-block;
            width: 300px;
            height: 280px;
            border: 1px dashed lightcoral;
            vertical-align: top;
        }
        .upload .cover {
            width: 200px;
            height: 200px;
            margin: 10px 50px;
            border: 1px solid black;
            box-sizing: border-box;
            text-align: center;
            line-height: 200px;
            position: relative;
        }
        .upload img {
            width: 198px;
            height: 198px;
            position: absolute;
            left:0;
            top: 0;
        }
        .upload input {
            margin-left: 50px;
        }
        .upload button {
            width: 80px;
            height: 30px;
            margin-left: 110px;
        }
        .result-block {
            display:  inline-block;
            margin-left: 40px;
            border: 1px solid lightgray;
            border-radius: 10px;
            min-height: 500px;
            width: 600px;
        }
        .result-block h1 {
            text-align: center;
            margin-top: 100px;
        }
        .result {
            padding: 10px;
            cursor: pointer;
            display: inline-block;
        }
        .result:hover {
            background: rgb(240, 240, 240);
        }
        .result p {
            width: 110px;
            overflow: hidden;
            white-space: nowrap;
            text-overflow: ellipsis;
        }
        .result img {
            width: 160px;
            height: 160px;
        }
        .result .prob {
            color: rgb(37, 147, 60)
        }
    </style>
    <script src="js/jquery-3.6.0.js"></script>
</head>
<body>
<div>
    <div class="upload">
        <div class="cover">
            请选择图片
            <img id="image" src=""/>
        </div>
        <input id="file" type="file">
    </div>
    <div class="result-block">
        <h1>请选择图片</h1>
    </div>
</div>
    <ul id="box">
    </ul>
<script>
    var file = $('#file')
    file.change(function () {
        let f = this.files[0]
        let index = f.name.lastIndexOf('.')
        let fileText = f.name.substring(index,f.name.length)
        let ext = fileText.toLowerCase() //文件类型
        console.log(ext)
        if(ext != '.png' && ext != '.jpg' && ext != '.jpeg'){
            alert('系统仅支持 JPG、PNG、JPEG 格式的图片，请您调整格式后重新上传')
            return
        }
        $('.result-block').empty().append($('<h1>正在识别中...</h1>'))
        $("#image").attr("src",getObjectURL(f));
        let formData = new FormData()
        formData.append('file',f)
        $.ajax({
            url: 'http://localhost:8080/search',
            method: 'post',
            data: formData,
            processData: false,
            contentType: false,
            success: res => {
                console.log('shibie', res)
                $('.result-block').empty()
                for (let item of res) {
                    console.log(item)
                    let html = `<div class="result">
                                    <img src="file:///${item.url}"/>
                                    <div style="display: inline-block;vertical-align: top">
                                        <p class="prob">得分：${item.score.toFixed(4)}</p>
                                    </div>
                                </div>`
                    $('.result-block').append($(html))
                }
            }
        })
    });
    $('#button').click(function(e) {
        var file = $('#file')[0].files[0] //单个
        console.log(file)
    })
    function getObjectURL(file) {
        var url = null;
        if (window.createObjcectURL != undefined) {
            url = window.createOjcectURL(file);
        } else if (window.URL != undefined) {
            url = window.URL.createObjectURL(file);
        } else if (window.webkitURL != undefined) {
            url = window.webkitURL.createObjectURL(file);
        }
        return url;
    }
    function detect() {
    }
</script>
</body>
</html>

到此这篇关于Java+ElasticSearch+Pytorch实现以图搜图功能的文章就介绍到这了,更多相关Java 以图搜图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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