deeplearning4j使用vgg19图片向量比对springboot+es环境

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一、桌面创建两个目录读图

二、POM

<dependency><groupId>org.springframework.data</groupId><artifactId>spring-data-elasticsearch</artifactId>
</dependency><dependency><groupId>org.deeplearning4j</groupId><artifactId>deeplearning4j-core</artifactId><version>1.0.0-beta7</version>
</dependency>
<dependency><groupId>org.deeplearning4j</groupId><artifactId>deeplearning4j-zoo</artifactId><version>1.0.0-beta7</version>
</dependency>
<dependency><groupId>org.elasticsearch</groupId><artifactId>elasticsearch</artifactId>
</dependency>
<dependency><groupId>org.elasticsearch.client</groupId><artifactId>transport</artifactId>
</dependency>
<dependency><groupId>org.elasticsearch.client</groupId><artifactId>elasticsearch-rest-client</artifactId>
</dependency>
<dependency><groupId>org.elasticsearch.plugin</groupId><artifactId>transport-netty4-client</artifactId>
</dependency>

三、code

import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.zoo.model.VGG19;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import org.springframework.web.multipart.MultipartFile;import javax.annotation.PostConstruct;
import java.io.File;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;@Service("vgg19Service")
public class Vgg19ServiceImpl implements Vgg19Service {private static ComputationGraph vgg19Model;@PostConstructpublic void init() throws IOException {VGG19 vgg19 = VGG19.builder().build();vgg19Model = (ComputationGraph) vgg19.initPretrained();}@Autowiredprivate INDArrayPojoRepository indArrayPojoRepository;@Overridepublic String find(MultipartFile file) throws IOException {//        VGG19 vgg19 = VGG19.builder().build();
//         vgg19Model = (ComputationGraph) vgg19.initPretrained();String templateImagePath = "C:\\Users\\Administrator\\Desktop\\template\\1.png";// 图像文件夹路径String imageFolder = "C:\\Users\\Administrator\\Desktop\\target";// 加载模板图像NativeImageLoader imageLoader = new NativeImageLoader(224, 224, 3);INDArray templateImage = imageLoader.asMatrix(new File(templateImagePath));// 提取模板图像的特征向量INDArray templateFeatures = vgg19Model.outputSingle(templateImage);// 存储图像相似度的映射Map<String, Double> similarityMap = new HashMap<>();// 遍历图像文件夹File folder = new File(imageFolder);File[] imageFiles = folder.listFiles();long i = 1L;indArrayPojoRepository.deleteAll();if (imageFiles != null) {for (File imageFile : imageFiles) {// 加载当前图像
//                INDArray currentImage = imageLoader.asMatrix(imageFile);
//                // 提取当前图像的特征向量
//                INDArray currentFeatures = vgg19Model.outputSingle(currentImage);
//                long[] longVector = currentFeatures.toLongVector();
//                System.out.println(longVector);
//                double[] doubleVector = currentFeatures.toDoubleVector();
//                System.out.println(new ImagesArrayPojo(i,doubleVector));indArrayPojoRepository.save( new ImagesArrayPojo(i,new double[]{1,11.11,1}));
//                indArrayPojoRepository.findBySimilarity(templateFeatures.toDoubleVector(), PageRequest.of(1, 20));
//                System.out.println(currentFeatures);
//                // 计算余弦相似度
//                double similarityScore = Transforms.cosineSim(templateFeatures, currentFeatures);
//
//                // 将图像名称和相似度存储到映射中
//                similarityMap.put(imageFile.getName(), similarityScore);i ++;}}// 打印相似度最高的三张图像名称
//        similarityMap.entrySet().stream()
//                .sorted(Map.Entry.<String, Double>comparingByValue().reversed())
//                .limit(3)
//                .forEach(entry -> System.out.println("Image: " + entry.getKey() + ", Similarity: " + entry.getValue()));
return null;}
}

java实体类

@Data
@AllArgsConstructor
@NoArgsConstructor
@Document(indexName = "images_double")
public class ImagesArrayPojo {@Idprivate Long id;@Field(type = FieldType.Dense_Vector,dims = 1000)private double[] ndDoubleArray;
}

搭配

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

四、es查询脚本

这里注意查看官方文档,不同的es脚本写法稍有不同,这里使用的是7.4.2

docker run -d -e ES_JAVA_OPTS="-Xms128m -Xmx128m" -e "discovery.type=single-node" -e "script.disable_dynamic: false" -p 9200:9200 -p 9300:9300 -e ES_MIN_MEM=128m -e ES_MAX_MEM=4096m --name es elasticsearch:7.4.2 

{"query": {"script_score": {"query": {"match_all": {}},"script": {"source": "cosineSimilarity(params.query_vector,doc['ndDoubleArray']) + 1.0","params": {"query_vector": [维度数组]}}}}
}

五、没测试的代码

import org.springframework.data.domain.Page;
import org.springframework.data.domain.Pageable;
import org.springframework.data.elasticsearch.annotations.Query;
import org.springframework.data.elasticsearch.repository.ElasticsearchRepository;
import org.springframework.data.repository.query.Param;
import org.springframework.stereotype.Repository;@Repository
public interface INDArrayPojoRepository extends ElasticsearchRepository<ImagesArrayPojo,Long> {@Query("{\n" +"  \"size\": 10,\n" +"  \"from\": 0,\n" +"  \"query\": {\n" +"    \"script_score\": {\n" +"      \"query\": {\n" +"        \"match_all\": {}\n" +"      },\n" +"      \"script\": {\n" +"        \"source\": \"cosineSimilarity(params.query_vector,doc['ndDoubleArray']) + 1.0\",\n" +"        \"params\": {\n" +"          \"query_vector\": [?1]\n" +"        }\n" +"      }\n" +"    }\n" +"  }\n" +"}")Page<ImagesArrayPojo> findBySimilarity(@Param("queryVector") double[] queryVector, Pageable pageable);
}

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总结

思路:首先使用deeplearning4j加载vgg19采集图片的向量值,然后将向量值存储到es中,然后后续搜索使用es的余弦脚本查询

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