examples

examples/.gitignore

@@ -0,0 +1,7 @@
+.idea
+.idea/
+target
+log
+*.iml
+/.settings/
+/logging.file_IS_UNDEFINED/

examples/pom.xml

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+<?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">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>cn.smartjavaai</groupId>
+    <artifactId>examples</artifactId>
+    <version>1.0.0-SNAPSHOT</version>
+
+    <properties>
+        <maven.compiler.source>11</maven.compiler.source>
+        <maven.compiler.target>11</maven.compiler.target>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <smartjavaai.version>1.0.12</smartjavaai.version>
+        <exec.mainClass>smartai.examples.face.FaceDemo2</exec.mainClass>
+
+        <javacv.version>1.5.10</javacv.version>
+
+        <javacv.platform.macosx-arm64>macosx-arm64</javacv.platform.macosx-arm64>
+        <javacv.platform.linux-x86_64>linux-x86_64</javacv.platform.linux-x86_64>
+        <javacv.platform.linux-arm64>linux-arm64</javacv.platform.linux-arm64>
+        <javacv.platform.windows-x86_64>windows-x86_64</javacv.platform.windows-x86_64>
+
+
+        <djl.platform.windows-x86_64>win-x86_64</djl.platform.windows-x86_64>
+        <djl.platform.linux-x86_64>linux-x86_64</djl.platform.linux-x86_64>
+        <djl.platform.linux-aarch64>linux-aarch64</djl.platform.linux-aarch64>
+        <djl.platform.osx-aarch64>osx-aarch64</djl.platform.osx-aarch64>
+    </properties>
+
+    <dependencyManagement>
+        <dependencies>
+            <dependency>
+                <groupId>cn.smartjavaai</groupId>
+                <artifactId>smartjavaai-bom</artifactId>
+                <version>${smartjavaai.version}</version>
+                <type>pom</type>
+                <!-- 注意这里是import -->
+                <scope>import</scope>
+            </dependency>
+        </dependencies>
+    </dependencyManagement>
+
+
+    <dependencies>
+        <dependency>
+            <groupId>commons-cli</groupId>
+            <artifactId>commons-cli</artifactId>
+            <version>1.9.0</version>
+        </dependency>
+        <dependency>
+            <groupId>commons-io</groupId>
+            <artifactId>commons-io</artifactId>
+            <version>2.17.0</version>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.logging.log4j</groupId>
+            <artifactId>log4j-slf4j2-impl</artifactId>
+            <version>2.24.1</version>
+        </dependency>
+        <dependency>
+            <groupId>org.testng</groupId>
+            <artifactId>testng</artifactId>
+            <version>7.10.2</version>
+            <scope>test</scope>
+        </dependency>
+
+
+        <dependency>
+            <groupId>ch.qos.logback</groupId>
+            <artifactId>logback-classic</artifactId>
+            <version>1.2.3</version>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>1.7.30</version>
+        </dependency>
+
+        <dependency>
+            <groupId>com.alibaba</groupId>
+            <artifactId>fastjson</artifactId>
+            <version>1.2.83</version>
+        </dependency>
+
+        <dependency>
+            <groupId>junit</groupId>
+            <artifactId>junit</artifactId>
+            <version>4.13.2</version>
+        </dependency>
+
+        <!--人脸识别模块-->
+        <dependency>
+            <groupId>cn.smartjavaai</groupId>
+            <artifactId>smartjavaai-face</artifactId>
+        </dependency>
+
+        <!--目标检测模块-->
+        <dependency>
+            <groupId>cn.smartjavaai</groupId>
+            <artifactId>smartjavaai-objectdetection</artifactId>
+        </dependency>
+
+
+        <!-- windows平台 (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.windows-x86_64}</classifier>
+        </dependency>
+
+
+        <!-- linux x86 平台 (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.linux-x86_64}</classifier>
+        </dependency>
+
+
+        <!-- macOS M系列 平台  (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.macosx-arm64}</classifier>
+        </dependency>
+
+
+        <!-- linux aarch64 平台 (保留对应平台的配置，可以减小包大小)-->
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>javacpp</artifactId>
+            <version>${javacv.version}</version>
+            <classifier>${javacv.platform.linux-arm64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>ffmpeg</artifactId>
+            <version>6.1.1-1.5.10</version>
+            <classifier>${javacv.platform.linux-arm64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>openblas</artifactId>
+            <version>0.3.26-1.5.10</version>
+            <classifier>${javacv.platform.linux-arm64}</classifier>
+        </dependency>
+
+        <dependency>
+            <groupId>org.bytedeco</groupId>
+            <artifactId>opencv</artifactId>
+            <version>4.9.0-1.5.10</version>
+            <classifier>${javacv.platform.linux-arm64}</classifier>
+        </dependency>
+
+
+
+
+
+
+    </dependencies>
+
+    <build>
+        <finalName>example</finalName>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-assembly-plugin</artifactId>
+                <version>2.3</version>
+                <configuration>
+                    <!--如果不想在打包的后缀加上assembly.xml中设置的id，可以加上下面的配置-->
+                    <appendAssemblyId>false</appendAssemblyId>
+                    <descriptorRefs>
+                        <descriptorRef>jar-with-dependencies</descriptorRef>
+                    </descriptorRefs>
+                    <archive>
+                        <manifest>
+                            <!-- 是否绑定依赖，将外部jar包依赖加入到classPath中 -->
+                            <addClasspath>true</addClasspath>
+                            <!-- 依赖前缀，与之前设置的文件夹路径要匹配 -->
+                            <classpathPrefix>lib/</classpathPrefix>
+                            <!-- 执行的主程序入口 -->
+                            <mainClass>smartai.examples.face.FaceDemo2</mainClass>
+                        </manifest>
+                    </archive>
+                </configuration>
+                <executions>
+                    <execution>
+                        <id>make-assembly</id>
+                        <!--绑定的maven操作-->
+                        <phase>package</phase>
+                        <goals>
+                            <goal>assembly</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+        </plugins>
+    </build>
+
+    <repositories>
+        <repository>
+            <id>aliyunmaven</id>
+            <name>阿里云公共仓库</name>
+            <url>https://maven.aliyun.com/repository/public</url>
+            <releases>
+                <enabled>true</enabled>
+            </releases>
+            <snapshots>
+                <enabled>false</enabled>
+            </snapshots>
+        </repository>
+    </repositories>
+
+
+
+
+
+
+
+</project>

examples/src/main/java/smartai/examples/face/attribute/FaceAttributeDetDemo.java

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+package smartai.examples.face.attribute;
+
+import cn.smartjavaai.common.entity.*;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.config.FaceAttributeConfig;
+import cn.smartjavaai.face.constant.LivenessConstant;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.enums.FaceAttributeModelEnum;
+import cn.smartjavaai.face.exception.FaceException;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.factory.FaceAttributeModelFactory;
+import cn.smartjavaai.face.model.attribute.FaceAttributeModel;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import cn.smartjavaai.face.utils.FaceUtils;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.bytedeco.javacv.FFmpegFrameGrabber;
+import org.bytedeco.javacv.Frame;
+import org.bytedeco.javacv.Java2DFrameUtils;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Paths;
+import java.util.List;
+
+/**
+ * 人脸属性检测demo
+ * @author dwj
+ * @date 2025/5/1
+ */
+@Slf4j
+public class FaceAttributeDetDemo {
+
+
+    /**
+     * 人脸属性检测（多人脸）
+     */
+    @Test
+    public void testFaceAttributeDetect(){
+        FaceAttributeConfig config = new FaceAttributeConfig();
+        config.setModelEnum(FaceAttributeModelEnum.SEETA_FACE6_MODEL);
+        //需替换为实际模型存储路径
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        FaceAttributeModel faceAttributeModel = FaceAttributeModelFactory.getInstance().getModel(config);
+        DetectionResponse detectionResponse = faceAttributeModel.detect("src/main/resources/double_person.png");
+        try {
+            //绘制并导出人脸属性图片，小人脸仅有人脸框
+            BufferedImage image = ImageIO.read(new File(Paths.get("src/main/resources/double_person.png").toAbsolutePath().toString()));
+            FaceUtils.drawBoxesWithFaceAttribute(image, detectionResponse,"C:/Users/Administrator/Downloads/double_person_.png");
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+        log.info("人脸属性检测结果：{}", JSONObject.toJSONString(detectionResponse));
+    }
+
+    /**
+     * 图片人脸属性检测（分数最高人脸）
+     */
+    @Test
+    public void testFaceAttributeDetect2(){
+        FaceAttributeConfig config = new FaceAttributeConfig();
+        config.setModelEnum(FaceAttributeModelEnum.SEETA_FACE6_MODEL);
+        //需替换为实际模型存储路径
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        FaceAttributeModel faceAttributeModel = FaceAttributeModelFactory.getInstance().getModel(config);
+        FaceAttribute faceAttribute = faceAttributeModel.detectTopFace("src/main/resources/double_person.png");
+        log.info("人脸属性检测结果：{}", JSONObject.toJSONString(faceAttribute));
+    }
+
+    /**
+     * 图片多人脸属性检测（基于已检测出的人脸区域和关键点）
+     */
+    @Test
+    public void testFaceAttributeDetect3(){
+        //人脸检测
+        //需替换为实际模型存储路径
+        String modelPath = "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models";
+        FaceModelConfig faceDetectModelConfig = new FaceModelConfig();
+        faceDetectModelConfig.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);
+        faceDetectModelConfig.setModelPath(modelPath);
+        FaceModel faceDetectModel = FaceModelFactory.getInstance().getModel(faceDetectModelConfig);
+        DetectionResponse detectionResponse = faceDetectModel.detect("src/main/resources/double_person.png");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectionResponse));
+        //检测到人脸
+        if(detectionResponse != null && detectionResponse.getDetectionInfoList() != null && detectionResponse.getDetectionInfoList().size() > 0){
+            //人脸属性检测
+            FaceAttributeConfig config = new FaceAttributeConfig();
+            config.setModelEnum(FaceAttributeModelEnum.SEETA_FACE6_MODEL);
+            config.setModelPath(modelPath);
+            FaceAttributeModel faceAttributeModel = FaceAttributeModelFactory.getInstance().getModel(config);
+            List<FaceAttribute> livenessStatusList = faceAttributeModel.detect("src/main/resources/double_person.png",detectionResponse);
+            log.info("人脸属性检测结果：{}", JSONObject.toJSONString(livenessStatusList));
+        }
+    }
+
+    /**
+     * 图片单人脸人脸属性检测（基于已检测出的人脸区域和关键点）
+     */
+    @Test
+    public void testFaceAttributeDetect4(){
+        try {
+            //人脸检测
+            //需替换为实际模型存储路径
+            String modelPath = "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models";
+            String imagePath = "src/main/resources/double_person.png";
+            FaceModelConfig faceDetectModelConfig = new FaceModelConfig();
+            faceDetectModelConfig.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);
+            faceDetectModelConfig.setModelPath(modelPath);
+            FaceModel faceDetectModel = FaceModelFactory.getInstance().getModel(faceDetectModelConfig);
+            DetectionResponse detectionResponse = faceDetectModel.detect(imagePath);
+            log.info("人脸检测结果：{}", JSONObject.toJSONString(detectionResponse));
+            //检测到人脸
+            if(detectionResponse != null && detectionResponse.getDetectionInfoList() != null && detectionResponse.getDetectionInfoList().size() > 0){
+                //人脸属性检测
+                FaceAttributeConfig config = new FaceAttributeConfig();
+                config.setModelEnum(FaceAttributeModelEnum.SEETA_FACE6_MODEL);
+                config.setModelPath(modelPath);
+                FaceAttributeModel faceAttributeModel = FaceAttributeModelFactory.getInstance().getModel(config);
+                BufferedImage image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
+                for (DetectionInfo detectionInfo : detectionResponse.getDetectionInfoList()){
+                    FaceInfo faceInfo = detectionInfo.getFaceInfo();
+                    FaceAttribute faceAttribute = faceAttributeModel.detect(image, detectionInfo.getDetectionRectangle(), faceInfo.getKeyPoints());
+                    log.info("人脸属性检测结果：{}", JSONObject.toJSONString(faceAttribute));
+                }
+            }
+        } catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+}

examples/src/main/java/smartai/examples/face/facerec/FaceNetDemo.java

@@ -0,0 +1,155 @@
+package smartai.examples.face.facerec;
+
+import cn.smartjavaai.face.config.FaceExtractConfig;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.entity.FaceResult;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Assert;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.List;
+
+/**
+ * FaceNet人脸算法模型demo
+ * 支持功能：人脸特征提取、人脸比对（1：1）
+ * @author dwj
+ * @date 2025/4/11
+ */
+@Slf4j
+public class FaceNetDemo {
+
+    /**
+     * 提取人脸特征(支持多人脸)
+     * 默认使用检测模型：FACENET_FEATURE_EXTRACTION
+     * 自动裁剪人脸 + 人脸对齐
+     */
+    @Test
+    public void testExtractFeatures(){
+        try {
+            //人脸特征提取模型
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.FACENET_FEATURE_EXTRACTION);
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            List<float[]> faceResult = faceModel.extractFeatures("src/main/resources/kana1.jpg");
+            log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
+        }catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+    /**
+     * 提取人脸特征(支持多人脸，自定义配置)
+     * 自动裁剪人脸 + 人脸对齐
+     */
+    @Test
+    public void testExtractFeaturesWithCustomConfig(){
+        try {
+            //人脸特征提取模型
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(
+                    new FaceModelConfig(FaceModelEnum.FACENET_FEATURE_EXTRACTION));
+            //人脸特征提取参数
+            FaceExtractConfig extractConfig = new FaceExtractConfig();
+            //人脸检测模型配置
+            extractConfig.setDetectModelConfig(new FaceModelConfig(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE));
+            List<float[]> faceResult = faceModel.extractFeatures("src/main/resources/kana1.jpg",extractConfig);
+            log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
+        }catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+    /**
+     * 提取人脸特征(分数最高人脸)
+     * 默认使用检测模型：FACENET_FEATURE_EXTRACTION
+     * 自动裁剪人脸 + 人脸对齐
+     */
+    @Test
+    public void testExtractTopFaceFeature(){
+        try {
+            //人脸特征提取模型
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.FACENET_FEATURE_EXTRACTION);
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            float[] faceResult = faceModel.extractTopFaceFeature("src/main/resources/kana1.jpg");
+            log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
+        }catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+    /**
+     * 提取人脸特征(分数最高人脸，自定义配置)
+     * 自动裁剪人脸 + 人脸对齐
+     */
+    @Test
+    public void testExtractTopFaceFeatureWithCustomConfig(){
+        try {
+            //人脸特征提取模型
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(
+                    new FaceModelConfig(FaceModelEnum.FACENET_FEATURE_EXTRACTION));
+            //人脸特征提取参数
+            FaceExtractConfig extractConfig = new FaceExtractConfig();
+            //人脸检测模型配置
+            extractConfig.setDetectModelConfig(new FaceModelConfig(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE));
+            float[] faceResult = faceModel.extractTopFaceFeature("src/main/resources/kana1.jpg");
+            log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
+        }catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+    /**
+     * 人脸比对（1：1）-在线模型
+     * 图片参数：图片路径
+     * @throws Exception
+     */
+    @Test
+    public void featureComparison(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.FACENET_FEATURE_EXTRACTION);//人脸模型
+            //config.setModelPath("/Users/xxx/Documents/develop/face_model/model_ir_se50.pth");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //自动裁剪人脸并比对人脸特征
+            float similar = faceModel.featureComparison("src/main/resources/kana1.jpg","src/main/resources/kana2.jpg");
+            log.info("相似度：{}", similar);
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+    /**
+     * 人脸比对（1：1）- 使用离线模型
+     * 图片参数：图片路径
+     * @throws Exception
+     */
+    @Test
+    public void featureComparisonOffline(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.FACENET_FEATURE_EXTRACTION);//人脸模型
+            config.setModelPath("/Users/xxx/Documents/develop/face_model/face_feature.pt");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //自动裁剪人脸并比对人脸特征
+            float similar = faceModel.featureComparison("src/main/resources/kana1.jpg","src/main/resources/kana2.jpg");
+            log.info("相似度：{}", similar);
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+}

examples/src/main/java/smartai/examples/face/facerec/GpuFaceDemo.java

@@ -0,0 +1,35 @@
+package smartai.examples.face.facerec;
+
+import cn.smartjavaai.common.entity.DetectionResponse;
+import cn.smartjavaai.common.enums.DeviceEnum;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Test;
+
+/**
+ * GPU 人脸检测
+ * @author dwj
+ * @date 2025/4/14
+ */
+@Slf4j
+public class GpuFaceDemo {
+
+    /**
+     * 人脸检测(GPU)
+     * 图片参数：图片路径
+     */
+    @Test
+    public void testFaceGpu(){
+        FaceModelConfig config = new FaceModelConfig();
+        config.setModelEnum(FaceModelEnum.RETINA_FACE);//人脸模型
+        config.setDevice(DeviceEnum.GPU);
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+        DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+    }
+
+}

examples/src/main/java/smartai/examples/face/facerec/LightFaceDemo.java

@@ -0,0 +1,98 @@
+package smartai.examples.face.facerec;
+
+import cn.smartjavaai.common.entity.DetectionResponse;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Assert;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+
+/**
+ * UltraLightFastGenericFaceModel 轻量人脸算法模型demo
+ * 支持功能：人脸检测（不支持人脸特征提取）
+ * @author dwj
+ * @date 2025/4/11
+ */
+@Slf4j
+public class LightFaceDemo {
+
+
+    /**
+     * 人脸检测-自定义参数
+     * 图片参数：图片路径
+     */
+    @Test
+    public void testFaceDetectCustomConfig(){
+        FaceModelConfig config = new FaceModelConfig();
+        config.setModelEnum(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE);//人脸模型
+        //config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
+        //config.setNmsThresh(FaceConfig.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+        DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+    }
+
+
+    /**
+     * 人脸检测并绘制人脸框
+     */
+    @Test
+    public void testFaceDetectAndDraw(){
+        FaceModelConfig config = new FaceModelConfig();
+        config.setModelEnum(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE);//人脸模型
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+        faceModel.detectAndDraw("src/main/resources/largest_selfie.jpg","output/largest_selfie_detected.png");
+    }
+
+    /**
+     * 人脸检测并绘制人脸框,返回BufferedImage
+     *
+     */
+    @Test
+    public void testFaceDetectAndDraw2(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE);//人脸模型
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            BufferedImage image = null;
+            String imagePath = "src/main/resources/largest_selfie.jpg";
+            image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
+            //可以根据后续业务场景使用detectedImage
+            BufferedImage detectedImage = faceModel.detectAndDraw(image);
+            Assert.assertNotNull("detectedImage null", detectedImage);
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+
+    }
+
+    /**
+     * 人脸检测（离线模型）
+     */
+    @Test
+    public void testDetectFaceOffine(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.ULTRA_LIGHT_FAST_GENERIC_FACE);//人脸模型
+            //模型路径,不同模型下载路径请参看文档
+            config.setModelPath("/Users/xxx/Documents/develop/face_model/ultranet.pt");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+            log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+
+}

examples/src/main/java/smartai/examples/face/facerec/RetinaFaceDemo.java

@@ -0,0 +1,107 @@
+package smartai.examples.face.facerec;
+
+import cn.smartjavaai.common.entity.DetectionResponse;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.constant.FaceDetectConstant;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.exception.FaceException;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Assert;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+
+/**
+ * RetinaFace人脸算法模型demo
+ * 支持功能：人脸检测（不支持人脸特征提取）
+ * @author dwj
+ * @date 2025/4/11
+ */
+@Slf4j
+public class RetinaFaceDemo {
+
+    /**
+     * 人脸检测(默认配置)
+     * 使用默认模型参数检测，默认模型：retinaface，需联网，会自动下载模型
+     * 图片参数：图片路径
+     */
+    @Test
+    public void testFaceDetect(){
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel();
+        DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+    }
+
+    /**
+     * 人脸检测(自定义模型参数)
+     * 图片参数：图片路径
+     */
+    @Test
+    public void testFaceDetectCustomConfig(){
+        FaceModelConfig config = new FaceModelConfig();
+        config.setModelEnum(FaceModelEnum.RETINA_FACE);//人脸模型
+        config.setConfidenceThreshold(FaceDetectConstant.DEFAULT_CONFIDENCE_THRESHOLD);//只返回相似度大于该值的人脸
+        config.setNmsThresh(FaceDetectConstant.NMS_THRESHOLD);//用于去除重复的人脸框，当两个框的重叠度超过该值时，只保留一个
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+        DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+    }
+
+
+    /**
+     * 人脸检测并绘制人脸框
+     */
+    @Test
+    public void testFaceDetectAndDraw(){
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel();
+        faceModel.detectAndDraw("src/main/resources/largest_selfie.jpg","output/largest_selfie_detected.png");
+    }
+
+    /**
+     * 人脸检测并绘制人脸框,返回BufferedImage
+     *
+     */
+    @Test
+    public void testFaceDetectAndDraw2(){
+        try {
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel();
+            BufferedImage image = null;
+            String imagePath = "src/main/resources/largest_selfie.jpg";
+            image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
+            //可以根据后续业务场景使用detectedImage
+            BufferedImage detectedImage = faceModel.detectAndDraw(image);
+            Assert.assertNotNull("detectedImage null", detectedImage);
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+
+    }
+
+    /**
+     * 人脸检测（离线模型）
+     */
+    @Test
+    public void testDetectFaceOffine(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.RETINA_FACE);//人脸模型
+            //模型路径,不同模型下载路径请参看文档
+            config.setModelPath("/Users/xxx/Documents/develop/face_model/retinaface.pt");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+            log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+
+}

examples/src/main/java/smartai/examples/face/facerec/SeetaFace6Demo.java

@@ -0,0 +1,251 @@
+package smartai.examples.face.facerec;
+
+import cn.smartjavaai.common.entity.DetectionResponse;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.entity.FaceResult;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Assert;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Paths;
+import java.util.List;
+
+/**
+ * SeetaFace6人脸算法模型demo
+ * 支持系统：windows 64位
+ * 支持功能：人脸检测、人脸特征提取、人脸比对（1：1）、人脸比对（1：N）、人脸注册
+ * @author dwj
+ * @date 2025/4/11
+ */
+@Slf4j
+public class SeetaFace6Demo {
+
+
+    /**
+     * 人脸检测(自定义模型参数)
+     * 图片参数：图片路径
+     */
+    @Test
+    public void testFaceDetectCustomConfig(){
+        FaceModelConfig config = new FaceModelConfig();
+        config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+        DetectionResponse detectedResult = faceModel.detect("src/main/resources/largest_selfie.jpg");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectedResult));
+    }
+
+
+    /**
+     * 人脸检测并绘制人脸框
+     */
+    @Test
+    public void testFaceDetectAndDraw(){
+        FaceModelConfig config = new FaceModelConfig();
+        config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+        faceModel.detectAndDraw("src/main/resources/largest_selfie.jpg","output/largest_selfie_detected.png");
+    }
+
+    /**
+     * 人脸检测并绘制人脸框,返回BufferedImage
+     *
+     */
+    @Test
+    public void testFaceDetectAndDraw2(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+            config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            BufferedImage image = null;
+            String imagePath = "src/main/resources/largest_selfie.jpg";
+            image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
+            //可以根据后续业务场景使用detectedImage
+            BufferedImage detectedImage = faceModel.detectAndDraw(image);
+            Assert.assertNotNull("detectedImage null", detectedImage);
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+
+    }
+
+
+    /**
+     * 提取人脸特征(支持多人脸)
+     * 自动裁剪人脸 + 人脸对齐
+     */
+    @Test
+    public void testExtractFeatures(){
+        try {
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(new FaceModelConfig(FaceModelEnum.SEETA_FACE6_MODEL,
+                    "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models"));
+            List<float[]> faceResult = faceModel.extractFeatures("src/main/resources/kana1.jpg");
+            log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
+        }catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+    /**
+     * 提取人脸特征(分数最高人脸)
+     * 自动裁剪人脸 + 人脸对齐
+     */
+    @Test
+    public void testExtractTopFaceFeature(){
+        try {
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(new FaceModelConfig(FaceModelEnum.SEETA_FACE6_MODEL,
+                    "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models"));
+            float[] faceResult = faceModel.extractTopFaceFeature("src/main/resources/kana1.jpg");
+            log.info("人脸特征提取结果：{}", JSONObject.toJSONString(faceResult));
+        }catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+
+
+    /**
+     * 人脸比对（1：1）
+     * 图片参数：图片路径
+     * @throws Exception
+     */
+    @Test
+    public void featureComparison(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+            config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //自动裁剪人脸并比对人脸特征
+            float similar = faceModel.featureComparison("src/main/resources/kana1.jpg","src/main/resources/kana2.jpg");
+            log.info("相似度：{}", similar);
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+    /**
+     * 人脸比对（1：1）
+     * 先特征提取，后比对人脸特征
+     * 提取人脸特征图片参数：图片路径
+     */
+    @Test
+    public void featureExtractionAndCompare(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+            config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //提取图像中最大人脸的特征
+            float[] feature1 = faceModel.extractTopFaceFeature("src/main/resources/kana1.jpg");
+            float[] feature2 = faceModel.extractTopFaceFeature("src/main/resources/kana2.jpg");
+            if(feature1 != null && feature2 != null){
+                float similar = faceModel.calculSimilar(feature1, feature2);
+                log.info("相似度：{}", similar);
+            }else{
+                log.warn("人脸特征提取失败");
+            }
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+
+    /**
+     * 注册人脸
+     * 图片参数：图片路径
+     */
+    @Test
+    public void registerFace(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+            //人脸库路径，从项目中 db/faces-data.db下载到本地
+            config.setFaceDbPath("C:/Users/Administrator/Downloads/faces-data.db");
+            config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //等待人脸库加载完毕
+            Thread.sleep(1000);
+            //注册kana1人脸，参数key建议设置为人名
+            boolean isSuccss = faceModel.register("kana1","src/main/resources/kana1.jpg");
+            log.info("注册结果：{}", isSuccss);
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+    /**
+     * 搜索人脸（1：N）
+     * 图片参数：图片路径
+     * 注意事项：请先注册人脸
+     */
+    @Test
+    public void searchFace(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+            //人脸库路径，从项目中 db/faces-data.db下载到本地
+            config.setFaceDbPath("C:/Users/Administrator/Downloads/faces-data.db");
+            config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //等待人脸库加载完毕
+            Thread.sleep(1000);
+            FaceResult faceResult = faceModel.search("src/main/resources/kana1.jpg");
+            if(faceResult != null){
+                log.info("查询到人脸：{}", faceResult.toString());
+            }else{
+                log.info("未查询到人脸");
+            }
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+    /**
+     * 删除已注册人脸
+     * 注意事项：请先注册人脸
+     */
+    @Test
+    public void removeRegisterFace(){
+        try {
+            FaceModelConfig config = new FaceModelConfig();
+            config.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);//人脸模型
+            //人脸库路径，从项目中 db/faces-data.db下载到本地
+            config.setFaceDbPath("C:/Users/Administrator/Downloads/faces-data.db");
+            config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+            FaceModel faceModel = FaceModelFactory.getInstance().getModel(config);
+            //等待人脸库加载完毕
+            Thread.sleep(1000);
+            //使用注册人脸时的key值删除，可一次性删除单个
+            long num = faceModel.removeRegister("kana1");
+            //删除全部人脸
+            //long num = currentAlgorithm.clearFace();
+            log.info("删除成功数量：" + num);
+        }
+        catch (Exception e){
+            e.printStackTrace();
+        }
+    }
+
+
+}

examples/src/main/java/smartai/examples/face/liveness/LivenessDetDemo.java

@@ -0,0 +1,283 @@
+package smartai.examples.face.liveness;
+
+import cn.smartjavaai.common.entity.DetectionInfo;
+import cn.smartjavaai.common.entity.DetectionRectangle;
+import cn.smartjavaai.common.entity.DetectionResponse;
+import cn.smartjavaai.common.entity.FaceInfo;
+import cn.smartjavaai.common.enums.DeviceEnum;
+import cn.smartjavaai.common.enums.LivenessStatus;
+import cn.smartjavaai.face.config.FaceModelConfig;
+import cn.smartjavaai.face.config.LivenessConfig;
+import cn.smartjavaai.face.constant.LivenessConstant;
+import cn.smartjavaai.face.enums.FaceModelEnum;
+import cn.smartjavaai.face.enums.LivenessModelEnum;
+import cn.smartjavaai.face.exception.FaceException;
+import cn.smartjavaai.face.factory.FaceModelFactory;
+import cn.smartjavaai.face.factory.LivenessModelFactory;
+import cn.smartjavaai.face.model.facerec.FaceModel;
+import cn.smartjavaai.face.model.liveness.LivenessDetModel;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.bytedeco.javacv.FFmpegFrameGrabber;
+import org.bytedeco.javacv.Frame;
+import org.bytedeco.javacv.Java2DFrameUtils;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.nio.file.Paths;
+import java.util.List;
+
+/**
+ * 静态活体检测demo
+ * @author dwj
+ * @date 2025/5/1
+ */
+@Slf4j
+public class LivenessDetDemo {
+
+
+
+    /**
+     * 图片活体检测（多人脸）
+     */
+    @Test
+    public void testLivenessDetect(){
+        LivenessConfig config = new LivenessConfig();
+        config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+        config.setDevice(DeviceEnum.GPU);
+        //需替换为实际模型存储路径
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+        config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+        //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+        config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+        LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+        DetectionResponse livenessStatusList = livenessDetModel.detect("src/main/resources/double_person.png");
+        log.info("活体检测结果：{}", JSONObject.toJSONString(livenessStatusList));
+    }
+
+    /**
+     * 图片活体检测（分数最高人脸）
+     */
+    @Test
+    public void testLivenessDetect2(){
+        LivenessConfig config = new LivenessConfig();
+        config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+        //需替换为实际模型存储路径
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+        config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+        //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+        config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+        LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+        LivenessStatus livenessStatus = livenessDetModel.detectTopFace("src/main/resources/double_person.png");
+        log.info("活体检测结果：{}", JSONObject.toJSONString(livenessStatus));
+    }
+
+    /**
+     * 图片多人脸活体检测（基于已检测出的人脸区域和关键点）
+     */
+    @Test
+    public void testLivenessDetect3(){
+        //人脸检测
+        //需替换为实际模型存储路径
+        String modelPath = "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models";
+        FaceModelConfig faceDetectModelConfig = new FaceModelConfig();
+        faceDetectModelConfig.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);
+        faceDetectModelConfig.setModelPath(modelPath);
+        FaceModel faceDetectModel = FaceModelFactory.getInstance().getModel(faceDetectModelConfig);
+        DetectionResponse detectionResponse = faceDetectModel.detect("src/main/resources/double_person.png");
+        log.info("人脸检测结果：{}", JSONObject.toJSONString(detectionResponse));
+        //检测到人脸
+        if(detectionResponse != null && detectionResponse.getDetectionInfoList() != null && detectionResponse.getDetectionInfoList().size() > 0){
+            //活体检测
+            LivenessConfig config = new LivenessConfig();
+            config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+            config.setModelPath(modelPath);
+            //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+            config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+            //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+            config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+            LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+            List<LivenessStatus> livenessStatusList = livenessDetModel.detect("src/main/resources/double_person.png",detectionResponse);
+            log.info("活体检测结果：{}", JSONObject.toJSONString(livenessStatusList));
+        }
+    }
+
+    /**
+     * 图片单人脸活体检测（基于已检测出的人脸区域和关键点）
+     */
+    @Test
+    public void testLivenessDetect4(){
+        try {
+            //人脸检测
+            //需替换为实际模型存储路径
+            String modelPath = "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models";
+            String imagePath = "src/main/resources/double_person.png";
+            FaceModelConfig faceDetectModelConfig = new FaceModelConfig();
+            faceDetectModelConfig.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);
+            faceDetectModelConfig.setModelPath(modelPath);
+            FaceModel faceDetectModel = FaceModelFactory.getInstance().getModel(faceDetectModelConfig);
+            DetectionResponse detectionResponse = faceDetectModel.detect(imagePath);
+            log.info("人脸检测结果：{}", JSONObject.toJSONString(detectionResponse));
+            //检测到人脸
+            if(detectionResponse != null && detectionResponse.getDetectionInfoList() != null && detectionResponse.getDetectionInfoList().size() > 0){
+                //活体检测
+                LivenessConfig config = new LivenessConfig();
+                config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+                config.setModelPath(modelPath);
+                //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+                config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+                //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+                config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+                LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+                BufferedImage image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
+                for (DetectionInfo detectionInfo : detectionResponse.getDetectionInfoList()){
+                    FaceInfo faceInfo = detectionInfo.getFaceInfo();
+                    LivenessStatus livenessStatus = livenessDetModel.detect(image, detectionInfo.getDetectionRectangle(), faceInfo.getKeyPoints());
+                    log.info("活体检测结果：{}", JSONObject.toJSONString(livenessStatus));
+                }
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+    /**
+     * 视频活体检测
+     */
+    @Test
+    public void testLivenessDetectVideo(){
+        LivenessConfig config = new LivenessConfig();
+        config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+        //需替换为实际模型存储路径
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+        config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+        //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+        config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+        /*视频检测帧数，可选，默认10，输出帧数超过这个number之后，就可以输出识别结果。
+        这个数量相当于多帧识别结果融合的融合的帧数。当输入的帧数超过设定帧数的时候，会采用滑动窗口的方式，返回融合的最近输入的帧融合的识别结果。
+        一般来说，在10以内，帧数越多，结果越稳定，相对性能越好，但是得到结果的延时越高。*/
+        config.setFrameCount(LivenessConstant.DEFAULT_FRAME_COUNT);
+        LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+        LivenessStatus livenessStatus = livenessDetModel.detectVideo("src/main/resources/girl.mp4");
+        log.info("视频活体检测结果：{}", JSONObject.toJSONString(livenessStatus));
+    }
+
+
+
+
+    /**
+     * 视频活体检测（逐帧检测，基于已检测出的人脸区域和关键点）
+     */
+    @Test
+    public void testLivenessDetectVideo2(){
+        LivenessConfig config = new LivenessConfig();
+        config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+        //需替换为实际模型存储路径
+        config.setModelPath("C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models");
+        //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+        config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+        //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+        config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+        /* 视频检测帧数，可选，默认10，输出帧数超过这个number之后，就可以输出识别结果。
+        这个数量相当于多帧识别结果融合的融合的帧数。当输入的帧数超过设定帧数的时候，会采用滑动窗口的方式，返回融合的最近输入的帧融合的识别结果。
+        一般来说，在10以内，帧数越多，结果越稳定，相对性能越好，但是得到结果的延时越高。*/
+        config.setFrameCount(LivenessConstant.DEFAULT_FRAME_COUNT);
+        LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+        try {
+            FFmpegFrameGrabber grabber = new FFmpegFrameGrabber("src/main/resources/girl.mp4");
+            grabber.start();
+            // 获取视频总帧数
+            int totalFrames = grabber.getLengthInFrames();
+            log.info("视频总帧数：{}，检测帧数：{}", totalFrames, config.getFrameCount());
+            //活体检测结果
+            LivenessStatus livenessStatus = LivenessStatus.UNKNOWN;
+            // 逐帧处理视频
+            for (int frameIndex = 0; frameIndex < totalFrames; frameIndex++) {
+                // 获取当前帧
+                Frame frame = grabber.grabImage();
+                if (frame != null) {
+                    BufferedImage bufferedImage = Java2DFrameUtils.toBufferedImage(frame);
+                    LivenessStatus livenessStatusFrame = livenessDetModel.detectVideoByFrame(bufferedImage);
+                    //满足检测帧数之后停止检测
+                    if(livenessStatusFrame != LivenessStatus.DETECTING){
+                        livenessStatus = livenessStatusFrame;
+                    }
+                }
+            }
+            log.info("视频活体检测结果：{}", JSONObject.toJSONString(livenessStatus));
+            grabber.stop();
+        } catch (FFmpegFrameGrabber.Exception e) {
+            throw new FaceException(e);
+        }
+    }
+
+    /**
+     * 视频活体检测（逐帧检测）
+     */
+    @Test
+    public void testLivenessDetectVideo3(){
+        //获取活体检测模型
+        //需替换为实际模型存储路径
+        String modelPath = "C:/Users/Administrator/Downloads/sf3.0_models/sf3.0_models";
+        LivenessConfig config = new LivenessConfig();
+        config.setModelEnum(LivenessModelEnum.SEETA_FACE6_MODEL);
+        config.setModelPath(modelPath);
+        //人脸清晰度阈值,可选,默认0.3，活体识别时，如果清晰度低的话，就会直接返回FUZZY，清晰度满足阈值，则判断真实度
+        config.setFaceClarityThreshold(LivenessConstant.DEFAULT_FACE_CLARITY_THRESHOLD);
+        //人脸活体阈值,可选,默认0.8，超过阈值则认为是真人，低于阈值是非活体
+        config.setRealityThreshold(LivenessConstant.DEFAULT_REALITY_THRESHOLD);
+        /* 视频检测帧数，可选，默认10，输出帧数超过这个number之后，就可以输出识别结果。
+        这个数量相当于多帧识别结果融合的融合的帧数。当输入的帧数超过设定帧数的时候，会采用滑动窗口的方式，返回融合的最近输入的帧融合的识别结果。
+        一般来说，在10以内，帧数越多，结果越稳定，相对性能越好，但是得到结果的延时越高。*/
+        config.setFrameCount(LivenessConstant.DEFAULT_FRAME_COUNT);
+        LivenessDetModel livenessDetModel = LivenessModelFactory.getInstance().getModel(config);
+        //获取人脸检测模型
+        FaceModelConfig faceDetectModelConfig = new FaceModelConfig();
+        faceDetectModelConfig.setModelEnum(FaceModelEnum.SEETA_FACE6_MODEL);
+        faceDetectModelConfig.setModelPath(modelPath);
+        FaceModel faceDetectModel = FaceModelFactory.getInstance().getModel(faceDetectModelConfig);
+        try {
+            FFmpegFrameGrabber grabber = new FFmpegFrameGrabber("src/main/resources/girl.mp4");
+            grabber.start();
+            // 获取视频总帧数
+            int totalFrames = grabber.getLengthInFrames();
+            log.info("视频总帧数：{}，检测帧数：{}", totalFrames, config.getFrameCount());
+            //活体检测结果
+            LivenessStatus livenessStatus = LivenessStatus.UNKNOWN;
+            // 逐帧处理视频
+            for (int frameIndex = 0; frameIndex < totalFrames; frameIndex++) {
+                // 获取当前帧
+                Frame frame = grabber.grabImage();
+                if (frame != null) {
+                    BufferedImage bufferedImage = Java2DFrameUtils.toBufferedImage(frame);
+                    //检测视频帧人脸
+                    DetectionResponse detectionResponse = faceDetectModel.detect(bufferedImage);
+                    //检测到人脸
+                    if(detectionResponse != null && detectionResponse.getDetectionInfoList() != null && detectionResponse.getDetectionInfoList().size() > 0){
+                        DetectionRectangle detectionRectangle = detectionResponse.getDetectionInfoList().get(0).getDetectionRectangle();
+                        FaceInfo faceInfo = detectionResponse.getDetectionInfoList().get(0).getFaceInfo();
+                        //使用人脸检测结果 活体检测
+                        LivenessStatus livenessStatusFrame = livenessDetModel.detectVideoByFrame(bufferedImage, detectionRectangle, faceInfo.getKeyPoints());
+                        //满足检测帧数之后停止检测
+                        if(livenessStatusFrame != LivenessStatus.DETECTING){
+                            livenessStatus = livenessStatusFrame;
+                        }
+                    }else{
+                        log.info("未检测到人脸");
+                    }
+                }
+            }
+            log.info("视频活体检测结果：{}", JSONObject.toJSONString(livenessStatus));
+            grabber.stop();
+        } catch (FFmpegFrameGrabber.Exception e) {
+            throw new FaceException(e);
+        }
+    }
+
+
+}

examples/src/main/java/smartai/examples/objectdetection/ObjectDetection.java

@@ -0,0 +1,114 @@
+package smartai.examples.objectdetection;
+
+import ai.djl.Application;
+import ai.djl.MalformedModelException;
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.ImageFactory;
+import ai.djl.modality.cv.output.*;
+import ai.djl.modality.cv.output.Rectangle;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ModelNotFoundException;
+import ai.djl.repository.zoo.ModelZoo;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.training.util.ProgressBar;
+import cn.smartjavaai.common.entity.DetectionResponse;
+import cn.smartjavaai.common.enums.DeviceEnum;
+import cn.smartjavaai.objectdetection.DetectorModelConfig;
+import cn.smartjavaai.objectdetection.DetectorModelEnum;
+import cn.smartjavaai.objectdetection.exception.DetectionException;
+import cn.smartjavaai.objectdetection.model.DetectorModel;
+import cn.smartjavaai.objectdetection.model.ObjectDetectionModelFactory;
+import com.alibaba.fastjson.JSONObject;
+import lombok.extern.slf4j.Slf4j;
+import org.junit.Assert;
+import org.junit.Test;
+
+import javax.imageio.ImageIO;
+import java.awt.*;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Paths;
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
+import java.util.concurrent.Callable;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+import java.util.concurrent.Future;
+
+/**
+ * 目标检测模型demo
+ * 支持功能：目标检测
+ * @author dwj
+ * @date 2025/4/11
+ */
+@Slf4j
+public class ObjectDetection {
+
+    /**
+     * 使用默认模型检测：YOLO11N
+     */
+    @Test
+    public void objectDetection(){
+        DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel();
+        DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/object_detection.jpg");
+        log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
+    }
+
+    /**
+     * 指定模型检测(19种模型可选)
+     */
+    @Test
+    public void objectDetection2(){
+        DetectorModelConfig config = new DetectorModelConfig();
+        config.setModelEnum(DetectorModelEnum.SSD_300_RESNET50);//检测模型，目前支持19种模型
+        DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
+        DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
+        log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
+    }
+
+    /**
+     * 人脸检测并绘制检测结果
+     */
+    @Test
+    public void objectDetectionAndDraw(){
+        DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel();
+        detectorModel.detectAndDraw("src/main/resources/object_detection.jpg","output/object_detection_detected.png");
+    }
+
+    /**
+     * 人脸检测并绘制检测结果,返回BufferedImage
+     */
+    @Test
+    public void objectDetectionAndDraw2(){
+        try {
+            DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel();
+            BufferedImage image = null;
+            String imagePath = "src/main/resources/object_detection.jpg";
+            image = ImageIO.read(new File(Paths.get(imagePath).toAbsolutePath().toString()));
+            //可以根据后续业务场景使用detectedImage
+            BufferedImage detectedImage = detectorModel.detectAndDraw(image);
+            Assert.assertNotNull("detectedImage null", detectedImage);
+        } catch (IOException e) {
+            throw new RuntimeException(e);
+        }
+
+    }
+
+    /**
+     * GPU 目标检测
+     */
+    @Test
+    public void gpuObjectDetection(){
+        DetectorModelConfig config = new DetectorModelConfig();
+        config.setModelEnum(DetectorModelEnum.YOLO11N);//检测模型，目前支持19种模型
+        config.setDevice(DeviceEnum.GPU);
+        DetectorModel detectorModel = ObjectDetectionModelFactory.getInstance().getModel(config);
+        DetectionResponse detectionResponse = detectorModel.detect("src/main/resources/dog_bike_car.jpg");
+        log.info("目标检测结果：{}", JSONObject.toJSONString(detectionResponse));
+    }
+
+
+
+}

examples/src/main/resources/META-INF/MANIFEST.MF

@@ -0,0 +1,3 @@
+Manifest-Version: 1.0
+Main-Class: smartai.examples.face.SeetaFace6LinuxDemo
+

examples/src/main/resources/logback.xml

@@ -0,0 +1,14 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!-- 步骤2: 配置文件 (src/main/resources/logback.xml) -->
+<configuration scan="true" scanPeriod="30 seconds">
+    <!-- 控制台日志输出 -->
+    <appender name="CONSOLE" class="ch.qos.logback.core.ConsoleAppender">
+        <encoder>
+            <pattern>%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %highlight(%-5level) %cyan(%logger{36}) - %msg%n</pattern>
+        </encoder>
+    </appender>
+
+    <root level="INFO">
+        <appender-ref ref="CONSOLE" />
+    </root>
+</configuration>