Java【代码 16】Milvus向量库工具类和SeetaSDK获取人像向量和属性的工具类分享

比眉伴天荒 2023-10-13 13:25 20阅读 0赞

### Milvus向量库和SeetaSDK工具类分享 ###

*  1.Milvus向量库工具类
 *  2.SeetaSDK工具类

# 1.Milvus向量库工具类 #

Milvus的Maven依赖：

<dependency>
                <groupId>io.milvus</groupId>
                <artifactId>milvus-sdk-java</artifactId>
                <version>2.1.0</version>
                <exclusions>
                    <exclusion>
                        <artifactId>log4j-slf4j-impl</artifactId>
                        <groupId>org.apache.logging.log4j</groupId>
                    </exclusion>
                </exclusions>
            </dependency>

向量库的配置类：

@Data
    @Component
    @ConfigurationProperties(MilvusConfiguration.PREFIX)
    public class MilvusConfiguration {
        
        public static final String PREFIX = "milvus-config";
    
        public String host;
        public int port;
        public String collectionName;
    
    }

工具类主类：

@Slf4j
    @Component
    public class MilvusUtil {
        
    
        @Resource
        private MilvusConfiguration milvusConfiguration;
    
        private MilvusServiceClient milvusServiceClient;
    
        @PostConstruct
        private void connectToServer() {
        
            milvusServiceClient = new MilvusServiceClient(
                    ConnectParam.newBuilder()
                            .withHost(milvusConfiguration.host)
                            .withPort(milvusConfiguration.port)
                            .build());
            // 加载数据
            LoadCollectionParam faceSearchNewLoad = LoadCollectionParam.newBuilder()
                    .withCollectionName(milvusConfiguration.collectionName).build();
            R<RpcStatus> rpcStatusR = milvusServiceClient.loadCollection(faceSearchNewLoad);
            log.info("Milvus LoadCollection [{}]", rpcStatusR.getStatus() == 0 ? "Successful!" : "Failed!");
        }
    }

主类里的数据入库方法：

public int insertDataToMilvus(String id, String path, float[] feature) {
        
            List<InsertParam.Field> fields = new ArrayList<>();
            List<Float> featureList = new ArrayList<>(feature.length);
            for (float v : feature) {
        
                featureList.add(v);
            }
            fields.add(new InsertParam.Field("id", Collections.singletonList(id)));
            fields.add(new InsertParam.Field("image_path", Collections.singletonList(path)));
            fields.add(new InsertParam.Field("image_feature", Collections.singletonList(featureList)));
            InsertParam insertParam = InsertParam.newBuilder()
                    .withCollectionName(milvusConfiguration.collectionName)
                    //.withPartitionName("novel")
                    .withFields(fields)
                    .build();
            R<MutationResult> insert = milvusServiceClient.insert(insertParam);
            return insert.getStatus();
        }

主类类的数据查询方法：

*  这里的topK没有进行参数化。

public List<MilvusRes> searchImageByFeatureVector(float[] feature) {
        
            List<Float> featureList = new ArrayList<>(feature.length);
            for (float v : feature) {
        
                featureList.add(v);
            }
            List<String> queryOutputFields = Arrays.asList("image_path");
    
            SearchParam faceSearch = SearchParam.newBuilder()
                    .withCollectionName(milvusConfiguration.collectionName)
                    .withMetricType(MetricType.IP)
                    .withVectorFieldName("image_feature")
                    .withVectors(Collections.singletonList(featureList))
                    .withOutFields(queryOutputFields)
                    .withRoundDecimal(3)
                    .withTopK(10).build();
            // 执行搜索
            long l = System.currentTimeMillis();
            R<SearchResults> respSearch = milvusServiceClient.search(faceSearch);
            log.info("MilvusServiceClient.search cost [{}]", System.currentTimeMillis() - l);
            // 解析结果数据
            SearchResultData results = respSearch.getData().getResults();
            int scoresCount = results.getScoresCount();
            SearchResultsWrapper wrapperSearch = new SearchResultsWrapper(results);
            List<MilvusRes> milvusResList = new ArrayList<>();
            for (int i = 0; i < scoresCount; i++) {
        
                float score = wrapperSearch.getIDScore(0).get(i).getScore();
                Object imagePath = wrapperSearch.getFieldData("image_path", 0).get(i);
                MilvusRes milvusRes = MilvusRes.builder().score(score).imagePath(imagePath.toString()).build();
                milvusResList.add(milvusRes);
            }
            return milvusResList;
        }

# 2.SeetaSDK工具类 #

SeetaSDK的Maven依赖：

<dependency>
                <groupId>com.seeta</groupId>
                <artifactId>sdk</artifactId>
                <version>1.2.1</version>
                <scope>system</scope>
                <systemPath>${project.basedir}/lib/seeta-sdk-platform-1.2.1.jar</systemPath>
            </dependency>
           		
                <plugin>
                    <groupId>org.springframework.boot</groupId>
                    <artifactId>spring-boot-maven-plugin</artifactId>
                    <configuration>
                        <includeSystemScope>true</includeSystemScope>
                    </configuration>
                </plugin>

jar是从官网下的源码进行的打包：

![在这里插入图片描述][2086a16af7e84be59cfa11d8545b5cdf.png_pic_center]  
工具类主类：

@Slf4j
    @Component
    public class FaceUtil {
        
    
        static {
        
            // 加载本地方法
            LoadNativeCore.LOAD_NATIVE(SeetaDevice.SEETA_DEVICE_AUTO);
        }
    
        @Resource
        private SeetaModelConfiguration seetaModelConfiguration;
    
        private FaceDetectorProxy faceDetectorProxy;
        private FaceLandmarkerProxy faceLandmarkerProxy;
        private FaceRecognizerProxy faceRecognizerProxy;
        private AgePredictorProxy agePredictorProxy;
        private GenderPredictorProxy genderPredictorProxy;
        private MaskDetectorProxy maskDetectorProxy;
        private EyeStateDetectorProxy eyeStateDetectorProxy;
    
    }

主类里的初始方法：

@PostConstruct
        private void inti() {
        
            String basePath = seetaModelConfiguration.basePath;
            try {
        
                // 人脸识别检测器对象池配置
                SeetaConfSetting detectorPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(0, new String[]{
        basePath + seetaModelConfiguration.faceDetectorModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                faceDetectorProxy = new FaceDetectorProxy(detectorPoolSetting);
                // 关键点定位器【默认使用5点可通过配置切换为68点】
                SeetaConfSetting faceLandmarkerPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(1, new String[]{
        basePath + seetaModelConfiguration.faceLandmarkerModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                faceLandmarkerProxy = new FaceLandmarkerProxy(faceLandmarkerPoolSetting);
                // 人脸向量特征提取和对比器
                SeetaConfSetting faceRecognizerPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(2, new String[]{
        basePath + seetaModelConfiguration.faceRecognizerModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                faceRecognizerProxy = new FaceRecognizerProxy(faceRecognizerPoolSetting);
                // 年龄评估器
                SeetaConfSetting agePredictorPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(3, new String[]{
        basePath + seetaModelConfiguration.agePredictorModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                agePredictorProxy = new AgePredictorProxy(agePredictorPoolSetting);
                // 性别识别器
                SeetaConfSetting genderPredictorPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(4, new String[]{
        basePath + seetaModelConfiguration.genderPredictorModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                genderPredictorProxy = new GenderPredictorProxy(genderPredictorPoolSetting);
                // 口罩检测器
                SeetaConfSetting maskDetectorPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(5, new String[]{
        basePath + seetaModelConfiguration.maskDetectorModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                maskDetectorProxy = new MaskDetectorProxy(maskDetectorPoolSetting);
                // 眼睛状态检测
                SeetaConfSetting eyeStaterPoolSetting = new SeetaConfSetting(
                        new SeetaModelSetting(5, new String[]{
        basePath + seetaModelConfiguration.eyeStateModelFileName},
                                SeetaDevice.SEETA_DEVICE_AUTO));
                eyeStateDetectorProxy = new EyeStateDetectorProxy(eyeStaterPoolSetting);
            } catch (Exception e) {
        
                e.printStackTrace();
            }
        }

主类里的根据图片路径获取脸部特征向量方法：

/**
         * 根据图片路径获取脸部特征向量
         *
         * @param imagePath 图片路径
         * @return 脸部特征向量
         */
        public float[] getFaceFeaturesByPath(String imagePath) {
        
            try {
        
                // 照片人脸识别
                SeetaImageData image = SeetafaceUtil.toSeetaImageData(imagePath);
                SeetaRect[] detects = faceDetectorProxy.detect(image);
                // 人脸关键点定位【主驾或副驾仅有一个人脸，多个人脸仅取第一个】
                if (detects.length > 0) {
        
                    SeetaPointF[] pointFace = faceLandmarkerProxy.mark(image, detects[0]);
                    // 人脸向量特征提取features
                    return faceRecognizerProxy.extract(image, pointFace);
                }
            } catch (Exception e) {
        
                e.printStackTrace();
            }
            return null;
        }

主类里的根据人像图片的路径获取其属性【年龄、性别、是否戴口罩、眼睛状态】方法：

/**
         * 根据人像图片的路径获取其属性【年龄、性别、是否戴口罩、眼睛状态】
         *
         * @param imagePath 图片路径
         * @return 图片属性 MAP 对象
         */
        public Map<String, Object> getAttributeByPath(String imagePath) {
        
            long l = System.currentTimeMillis();
            Map<String, Object> attributeMap = new HashMap<>(4);
            try {
        
                // 监测人脸
                SeetaImageData image = SeetafaceUtil.toSeetaImageData(imagePath);
                SeetaRect[] detects = faceDetectorProxy.detect(image);
                if (detects.length > 0) {
        
                    SeetaPointF[] pointFace = faceLandmarkerProxy.mark(image, detects[0]);
                    // 获取年龄
                    int age = agePredictorProxy.predictAgeWithCrop(image, pointFace);
                    attributeMap.put("age", age);
                    // 性别
                    GenderPredictor.GENDER gender = genderPredictorProxy.predictGenderWithCrop(image, pointFace).getGender();
                    attributeMap.put("gender", gender);
                    // 口罩
                    boolean mask = maskDetectorProxy.detect(image, detects[0]).getMask();
                    attributeMap.put("mask", mask);
                    // 眼睛
                    EyeStateDetector.EYE_STATE[] eyeStates = eyeStateDetectorProxy.detect(image, pointFace);
                    attributeMap.put("eye", Arrays.toString(eyeStates));
                    log.info("getAttributeByPath [{}] cost [{}]", imagePath, System.currentTimeMillis() - l);
                }
            } catch (Exception e) {
        
                e.printStackTrace();
                return attributeMap;
            }
            return attributeMap;
        }

[2086a16af7e84be59cfa11d8545b5cdf.png_pic_center]: https://img-blog.csdnimg.cn/2086a16af7e84be59cfa11d8545b5cdf.png#pic_center