支持离线下载模型

2026-09-10 03:28:49 +00:00 · 2025-02-26 11:23:39 +08:00
parent e1aebbf37d
commit 685727e67c
15 changed files with 447 additions and 309 deletions
--- a/20
+++ b/20
@@ -0,0 +1,20 @@
+The MIT License (MIT)
+
+Copyright (c) 2025 DengWenJie
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
--- a/README.md
+++ b/README.md
@@ -73,7 +73,7 @@
     <dependency>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-face</artifactId>
-        <version>1.0.1</version>
+        <version>1.0.2</version>
     </dependency>
 </dependencies>
 ```
@@ -134,8 +134,14 @@ if(realTimeFeature != null){
    }
 }
 ```
-## 快速体验指南

-▌**人脸识别快速运行**  
+### 6. 离线下载模型
+
+	**SmartJavaAI**如果未指定模型地址，系统将自动下载模型至本地。因此，无论模型是否通过离线方式下载，SmartJavaAI 最终都会在离线环境下运行模型。
+
+- [离线使用模型使用教程](example/face_offline.md)
+
+## 完整代码
+
 `📁 examples/src/main/java/smartai/examples/face`  
  └── 📄[FaceDemo.java](https://github.com/geekwenjie/SmartJavaAI/blob/master/examples/src/main/java/smartai/examples/face/FaceDemo.java)  <sub>*（基于JDK11构建的完整可执行示例）*</sub>
--- a/examples/face_offline.md
+++ b/examples/face_offline.md
@@ -0,0 +1,99 @@
+# SmartJavaAI离线下载模型案例
+
+**SmartJavaAI**如果未指定模型地址，系统将自动下载模型至本地。因此，无论模型是否通过离线方式下载，SmartJavaAI 最终都会在离线环境下运行模型。
+
+### 1. 安装人脸算法依赖
+
+在 Maven 项目的 `pom.xml` 中添加 SmartJavaAI的人脸算法依赖：
+
+```xml
+<dependencies>
+     <dependency>
+        <groupId>ink.numberone</groupId>
+        <artifactId>smartjavaai-face</artifactId>
+        <version>1.0.2</version>
+     </dependency>
+</dependencies>
+```
+
+### 2. 下载模型
+
+|         模型名称          |                           下载地址                           | 文件大小 |    适用场景    |
+| :-----------------------: | :----------------------------------------------------------: | :------: | :------------: |
+|        retinaface         | [下载](https://resources.djl.ai/test-models/pytorch/retinaface.zip) |  110MB   | 高精度人脸检测 |
+| ultralightfastgenericface | [下载](https://resources.djl.ai/test-models/pytorch/ultranet.zip) |  1.7MB   |  高速人脸检测  |
+|     featureExtraction     | [下载](https://resources.djl.ai/test-models/pytorch/face_feature.zip) |  104MB   |  人脸特征提取  |
+
+### 3. 人脸检测代码示例（离线下载模型）
+
+```java
+// 初始化配置
+ModelConfig config = new ModelConfig();
+config.setAlgorithmName("retinaface");//人脸算法模型，目前支持：retinaface及ultralightfastgenericface
+//config.setAlgorithmName("ultralightfastgenericface");//轻量模型
+config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);//置信度阈值
+config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);//每张特征图保留的最大候选框数量
+//nms阈值:控制重叠框的合并程度,取值越低，合并越多重叠框（减少误检但可能漏检）；取值越高，保留更多框（增加检出但可能引入冗余）
+config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
+//模型下载地址：
+//retinaface: https://resources.djl.ai/test-models/pytorch/retinaface.zip
+//ultralightfastgenericface: https://resources.djl.ai/test-models/pytorch/ultranet.zip
+//改为模型存放路径
+config.setModelPath("/Users/xxx/Documents/develop/face_model/retinaface.pt");
+//创建人脸算法
+FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm(config);
+//使用图片路径检测
+FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+//使用图片流检测
+File input = new File("src/main/resources/largest_selfie.jpg");
+//FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
+//logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+BufferedImage image = ImageIO.read(input);
+//创建保存路径
+Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
+//绘制人脸框
+ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+```
+
+### 3. 人证核验示例（离线下载模型）
+
+人证核验步骤：
+
+（1）提取身份证人脸特征，
+
+（2）提取实时人脸特征
+
+（3）特征比对
+
+```java
+// 初始化配置
+ModelConfig config = new ModelConfig();
+config.setAlgorithmName("featureExtraction");
+//模型下载地址：https://resources.djl.ai/test-models/pytorch/face_feature.zip
+//改为模型存放路径
+config.setModelPath("/Users/xxx/Documents/develop/face_model/face_feature.pt");
+//创建脸算法
+FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm(config);
+//提取身份证人脸特征（图片仅供测试）
+float[] featureIdCard = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
+//提取身份证人脸特征（从图片流获取）
+//File input = new File("src/main/resources/kana1.jpg");
+//float[] featureIdCard = currentAlgorithm.featureExtraction(new FileInputStream(input));
+logger.info("身份证人脸特征：{}", JSONObject.toJSONString(featureIdCard));
+//提取实时人脸特征（图片仅供测试）
+float[] realTimeFeature = currentAlgorithm.featureExtraction("src/main/resources/kana2.jpg");
+logger.info("实时人脸特征：{}", JSONObject.toJSONString(realTimeFeature));
+if(realTimeFeature != null){
+    if(currentAlgorithm.calculSimilar(featureIdCard, realTimeFeature) > 0.8){
+        logger.info("人脸核验通过");
+    }else{
+        logger.info("人脸核验不通过");
+    }
+}
+```
+
+## 完整代码
+
+`📁 examples/src/main/java/smartai/examples/face`  
+ └── 📄[FaceDemo.java](https://github.com/geekwenjie/SmartJavaAI/blob/master/examples/src/main/java/smartai/examples/face/FaceDemo.java)  <sub>*（基于JDK11构建的完整可执行示例）*</sub>
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -13,7 +13,7 @@
        <maven.compiler.target>11</maven.compiler.target>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <smartjavaai.version>1.0.0-SNAPSHOT</smartjavaai.version>
-        <exec.mainClass>ai.djl.examples.inference.cv.ObjectDetection</exec.mainClass>
+        <exec.mainClass>smartai.examples.face.FaceDemo</exec.mainClass>
    </properties>


@@ -43,7 +43,7 @@
        <dependency>
            <groupId>ink.numberone</groupId>
            <artifactId>smartjavaai-face</artifactId>
-            <version>1.0.1</version>
+            <version>1.0.2</version>
        </dependency>

        <dependency>
--- a/examples/src/main/java/smartai/examples/face/FaceDemo.java
+++ b/examples/src/main/java/smartai/examples/face/FaceDemo.java
@@ -1,10 +1,7 @@
 package smartai.examples.face;

 import cn.smartjavaai.common.entity.Rectangle;
-import cn.smartjavaai.face.FaceAlgorithm;
-import cn.smartjavaai.face.FaceAlgorithmFactory;
-import cn.smartjavaai.face.FaceDetectedResult;
-import cn.smartjavaai.face.ModelConfig;
+import cn.smartjavaai.face.*;
 import com.alibaba.fastjson.JSONObject;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
@@ -26,14 +23,13 @@ import java.nio.file.Paths;
 */
 public class FaceDemo {

-    // 创建 Logger 实例
    private static final Logger logger = LoggerFactory.getLogger(FaceDemo.class);


    public static void main(String[] args) {
        try {
-            detectFace();
-            //verifyIDCard();
+            //detectFace();
+            verifyIDCard();
        } catch (Exception e) {
            e.printStackTrace();
        }
@@ -52,7 +48,7 @@ public class FaceDemo {
        FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
        logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
        //使用图片流检测
-        //File input = new File("src/main/resources/largest_selfie.jpg");
+        File input = new File("src/main/resources/largest_selfie.jpg");
        //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
        //logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
        BufferedImage image = ImageIO.read(input);
@@ -92,7 +88,74 @@ public class FaceDemo {
     */
    public static void verifyIDCard() throws Exception {
        //创建脸算法
-        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm();
+        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm();
+        //提取身份证人脸特征（图片仅供测试）
+        float[] featureIdCard = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
+        //提取身份证人脸特征（从图片流获取）
+        //File input = new File("src/main/resources/kana1.jpg");
+        //float[] featureIdCard = currentAlgorithm.featureExtraction(new FileInputStream(input));
+        logger.info("身份证人脸特征：{}", JSONObject.toJSONString(featureIdCard));
+        //提取实时人脸特征（图片仅供测试）
+        float[] realTimeFeature = currentAlgorithm.featureExtraction("src/main/resources/kana2.jpg");
+        logger.info("实时人脸特征：{}", JSONObject.toJSONString(realTimeFeature));
+        if(realTimeFeature != null){
+            if(currentAlgorithm.calculSimilar(featureIdCard, realTimeFeature) > 0.8){
+                logger.info("人脸核验通过");
+            }else{
+                logger.info("人脸核验不通过");
+            }
+        }
+    }
+
+    /**
+     * 人脸检测（离线模型）
+     * 人脸模型：retinaface
+     * 特点：识别精度高，高速
+     * 应用场景：如监控摄像头、智能安防系统等需要高精度检测的场合
+     */
+    public static void detectFaceOffine() throws Exception {
+        // 初始化配置
+        ModelConfig config = new ModelConfig();
+        config.setAlgorithmName("retinaface");//人脸算法模型，目前支持：retinaface及ultralightfastgenericface
+        //config.setAlgorithmName("ultralightfastgenericface");//轻量模型
+        config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);//置信度阈值
+        config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);//每张特征图保留的最大候选框数量
+        //nms阈值:控制重叠框的合并程度,取值越低，合并越多重叠框（减少误检但可能漏检）；取值越高，保留更多框（增加检出但可能引入冗余）
+        config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
+        //模型下载地址：
+        //retinaface: https://resources.djl.ai/test-models/pytorch/retinaface.zip
+        //ultralightfastgenericface: https://resources.djl.ai/test-models/pytorch/ultranet.zip
+        //改为模型存放路径
+        config.setModelPath("/Users/xxx/Documents/develop/face_model/retinaface.pt");
+        //创建人脸算法
+        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm(config);
+        //使用图片路径检测
+        FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+        logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+        //使用图片流检测
+        File input = new File("src/main/resources/largest_selfie.jpg");
+        //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
+        //logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+        BufferedImage image = ImageIO.read(input);
+        //创建保存路径
+        Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
+        //绘制人脸框
+        ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+    }
+
+    /**
+     * 人证核验(离线模型)
+     * @throws Exception
+     */
+    public static void verifyIDCardOffine() throws Exception {
+        // 初始化配置
+        ModelConfig config = new ModelConfig();
+        config.setAlgorithmName("featureExtraction");
+        //模型下载地址：https://resources.djl.ai/test-models/pytorch/face_feature.zip
+        //改为模型存放路径
+        config.setModelPath("/Users/xxx/Documents/develop/face_model/face_feature.pt");
+        //创建脸算法
+        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm(config);
        //提取身份证人脸特征（图片仅供测试）
        float[] featureIdCard = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
        //提取身份证人脸特征（从图片流获取）
--- a/pom.xml
+++ b/pom.xml
@@ -6,7 +6,7 @@

    <groupId>ink.numberone</groupId>
    <artifactId>smartjavaai-parent</artifactId>
-    <version>1.0.1</version>
+    <version>1.0.2</version>
    <packaging>pom</packaging>
    <description>SmartJavaAI</description>
    <modules>
@@ -34,13 +34,13 @@
            <dependency>
                <groupId>ink.numberone</groupId>
                <artifactId>smartjavaai-common</artifactId>
-                <version>1.0.1</version>
+                <version>1.0.2</version>
            </dependency>

            <dependency>
                <groupId>ink.numberone</groupId>
                <artifactId>smartjavaai-face</artifactId>
-                <version>1.0.1</version>
+                <version>1.0.2</version>
            </dependency>
        </dependencies>

@@ -127,13 +127,19 @@
            <artifactId>commons-beanutils</artifactId>
            <version>1.9.4</version>
        </dependency>
+
+        <dependency>
+            <groupId>org.apache.commons</groupId>
+            <artifactId>commons-lang3</artifactId>
+            <version>3.9</version>
+        </dependency>
    </dependencies>

    <url>https://github.com/geekwenjie/SmartJavaAI</url>
    <licenses>
        <license>
-            <name>The Apache Software License, Version 2.0</name>
-            <url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
+            <name>MIT License</name>
+            <url>https://opensource.org/licenses/MIT</url>
        </license>
    </licenses>

--- a/smartjavaai-common/pom.xml
+++ b/smartjavaai-common/pom.xml
@@ -6,7 +6,7 @@
    <parent>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-parent</artifactId>
-        <version>1.0.1</version>
+        <version>1.0.2</version>
    </parent>

    <artifactId>smartjavaai-common</artifactId>
@@ -14,8 +14,8 @@
    <url>https://github.com/geekwenjie/SmartJavaAI</url>
    <licenses>
        <license>
-            <name>The Apache Software License, Version 2.0</name>
-            <url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
+            <name>MIT License</name>
+            <url>https://opensource.org/licenses/MIT</url>
        </license>
    </licenses>

--- a/smartjavaai-face/pom.xml
+++ b/smartjavaai-face/pom.xml
@@ -6,18 +6,18 @@
    <parent>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-parent</artifactId>
-        <version>1.0.1</version>
+        <version>1.0.2</version>
    </parent>

    <artifactId>smartjavaai-face</artifactId>
-    <version>1.0.1</version>
+    <version>1.0.2</version>
    <name>smartjavaai-face</name>
    <description>SmartJavaAI</description>
    <url>https://github.com/geekwenjie/SmartJavaAI</url>
    <licenses>
        <license>
-            <name>The Apache Software License, Version 2.0</name>
-            <url>http://www.apache.org/licenses/LICENSE-2.0.txt</url>
+            <name>MIT License</name>
+            <url>https://opensource.org/licenses/MIT</url>
        </license>
    </licenses>

--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/AbstractFaceAlgorithm.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/AbstractFaceAlgorithm.java
@@ -12,6 +12,11 @@ public abstract class AbstractFaceAlgorithm implements FaceAlgorithm{
        throw new UnsupportedOperationException("默认不支持该功能");
    }

+    @Override
+    public void loadFaceFeatureModel(ModelConfig config) throws Exception {
+        throw new UnsupportedOperationException("默认不支持该功能");
+    }
+
    @Override
    public FaceDetectedResult detect(String imagePath) throws Exception {
        throw new UnsupportedOperationException("默认不支持该功能");
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/FaceAlgorithm.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/FaceAlgorithm.java
@@ -20,6 +20,13 @@ public interface FaceAlgorithm {
     */
    void loadModel(ModelConfig config) throws Exception; // 加载模型

+    /**
+     * 加载人脸特征提取模型
+     * @param config
+     * @throws Exception
+     */
+    void loadFaceFeatureModel(ModelConfig config) throws Exception; // 加载模型
+
    /**
     * 人脸检测
     * @param imagePath 图片路径
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/FaceAlgorithmFactory.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/FaceAlgorithmFactory.java
@@ -1,5 +1,6 @@
 package cn.smartjavaai.face;

+import cn.smartjavaai.face.algo.FeatureExtractionAlgo;
 import cn.smartjavaai.face.algo.RetinaFace;
 import cn.smartjavaai.face.algo.UltraLightFastGenericFace;

@@ -55,13 +56,7 @@ public class FaceAlgorithmFactory {
        config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);
        config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);
        config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
-        Class<?> clazz = registry.get(config.getAlgorithmName().toLowerCase());
-        if(clazz == null){
-            throw new IllegalArgumentException("Unsupported algorithm");
-        }
-        FaceAlgorithm algorithm = (FaceAlgorithm) clazz.newInstance();
-        algorithm.loadModel(config);
-        return algorithm;
+        return createFaceAlgorithm(config);
    }

    /**
@@ -85,10 +80,40 @@ public class FaceAlgorithmFactory {
        return algorithm;
    }

+    /**
+     * 使用ModelConfig创建人脸特征提取算法
+     * @param config
+     * @return
+     * @throws Exception
+     */
+    public static FaceAlgorithm createFaceFeatureAlgorithm(ModelConfig config) throws Exception {
+        Class<?> clazz = registry.get(config.getAlgorithmName().toLowerCase());
+        if(clazz == null){
+            throw new IllegalArgumentException("Unsupported algorithm");
+        }
+        FaceAlgorithm algorithm = (FaceAlgorithm) clazz.newInstance();
+        algorithm.loadFaceFeatureModel(config);
+        return algorithm;
+    }
+
+    /**
+     * 创建人脸特征提取算法
+     * @return
+     * @throws Exception
+     */
+    public static FaceAlgorithm createFaceFeatureAlgorithm() throws Exception {
+        // 初始化配置
+        ModelConfig config = new ModelConfig();
+        config.setAlgorithmName("featureExtraction");
+        return createFaceFeatureAlgorithm(config);
+    }
+
    // 初始化默认算法
    static {
        registerAlgorithm("retinaface", RetinaFace.class);
        registerAlgorithm("ultralightfastgenericface", UltraLightFastGenericFace.class);
+        //人脸特征提取
+        registerAlgorithm("featureExtraction", FeatureExtractionAlgo.class);
    }

 }
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/ModelConfig.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/ModelConfig.java
@@ -27,6 +27,12 @@ public class ModelConfig {
     */
    private int maxFaceCount;

+    /**
+     * 模型路径
+     */
+    private String modelPath;
+
+
    public String getAlgorithmName() {
        return algorithmName;
    }
@@ -58,4 +64,12 @@ public class ModelConfig {
    public void setMaxFaceCount(int maxFaceCount) {
        this.maxFaceCount = maxFaceCount;
    }
+
+    public String getModelPath() {
+        return modelPath;
+    }
+
+    public void setModelPath(String modelPath) {
+        this.modelPath = modelPath;
+    }
 }
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/FeatureExtractionAlgo.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/FeatureExtractionAlgo.java
@@ -0,0 +1,159 @@
+package cn.smartjavaai.face.algo;
+
+import ai.djl.inference.Predictor;
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.ImageFactory;
+import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.modality.cv.translator.ImageFeatureExtractorFactory;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.training.util.ProgressBar;
+import cn.smartjavaai.common.entity.Point;
+import cn.smartjavaai.common.entity.Rectangle;
+import cn.smartjavaai.face.AbstractFaceAlgorithm;
+import cn.smartjavaai.face.FaceDetectedResult;
+import cn.smartjavaai.face.FaceDetectionTranslator;
+import cn.smartjavaai.face.ModelConfig;
+import org.apache.commons.lang3.StringUtils;
+
+import java.io.InputStream;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.stream.Collectors;
+
+/**
+ * RetinaFace实现
+ * @author dwj
+ */
+public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
+
+
+    private Criteria<Image, float[]> faceFeatureCriteria;
+
+    public static final List<Float> mean =
+            Arrays.asList(
+                    127.5f / 255.0f,
+                    127.5f / 255.0f,
+                    127.5f / 255.0f,
+                    128.0f / 255.0f,
+                    128.0f / 255.0f,
+                    128.0f / 255.0f);
+
+
+    /**
+     * 加载人脸特征提取模型
+     * @param config
+     * @throws Exception
+     */
+    @Override
+    public void loadFaceFeatureModel(ModelConfig config) throws Exception {
+        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
+        faceFeatureCriteria = Criteria.builder()
+                        .setTypes(Image.class, float[].class)
+                        .optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
+                                "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
+                        .optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
+                        .optModelName("face_feature") // specify model file prefix
+                        .optArgument("normalize", normalize)
+                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
+                        .optProgress(new ProgressBar())
+                        .optEngine("PyTorch") // Use PyTorch engine
+                        .build();
+    }
+
+
+    /**
+     * 特征提取
+     * @param imagePath 图片路径
+     * @return
+     * @throws Exception
+     */
+    @Override
+    public float[] featureExtraction(String imagePath) throws Exception {
+        Path imageFile = Paths.get(imagePath);
+        Image img = ImageFactory.getInstance().fromFile(imageFile);
+        img.getWrappedImage();
+        try (ZooModel<Image, float[]> model = faceFeatureCriteria.loadModel()) {
+            Predictor<Image, float[]> predictor = model.newPredictor();
+            return predictor.predict(img);
+        }
+    }
+
+    /**
+     * 特征提取
+     * @param inputStream 输入流
+     * @return
+     * @throws Exception
+     */
+    @Override
+    public float[] featureExtraction(InputStream inputStream) throws Exception {
+        Image img = ImageFactory.getInstance().fromInputStream(inputStream);
+        img.getWrappedImage();
+        try (ZooModel<Image, float[]> model = faceFeatureCriteria.loadModel()) {
+            Predictor<Image, float[]> predictor = model.newPredictor();
+            return predictor.predict(img);
+        }
+    }
+
+    /**
+     * 计算相似度
+     * @param feature1 图1特征
+     * @param feature2 图2特征
+     * @return
+     * @throws Exception
+     */
+    @Override
+    public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
+        float ret = 0.0f;
+        float mod1 = 0.0f;
+        float mod2 = 0.0f;
+        int length = feature1.length;
+        for (int i = 0; i < length; ++i) {
+            ret += feature1[i] * feature2[i];
+            mod1 += feature1[i] * feature1[i];
+            mod2 += feature2[i] * feature2[i];
+        }
+        return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
+    }
+
+    /**
+     * 特征比较
+     * @param imagePath1 图1路径
+     * @param imagePath2 图2路径
+     * @return
+     * @throws Exception
+     */
+    @Override
+    public float featureComparison(String imagePath1, String imagePath2) throws Exception {
+        float[] feature1 = featureExtraction(imagePath1);
+        float[] feature2 = featureExtraction(imagePath2);
+        return calculSimilar(feature1, feature2);
+    }
+
+    /**
+     * 特征比较
+     * @param inputStream1 图1输入流
+     * @param inputStream2 图2输入流
+     * @return
+     * @throws Exception
+     */
+    @Override
+    public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
+        float[] feature1 = featureExtraction(inputStream1);
+        float[] feature2 = featureExtraction(inputStream2);
+        return calculSimilar(feature1, feature2);
+    }
+
+    /*@Override
+    public float[] recognize(FaceRegion region) {
+        return new float[0];
+    }*/
+
+    /*@Override
+    public void loadModel(ModelConfig config) throws Exception {
+
+    }*/
+}
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/RetinaFace.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/RetinaFace.java
@@ -14,8 +14,8 @@ import ai.djl.translate.TranslateException;
 import cn.smartjavaai.common.entity.Point;
 import cn.smartjavaai.common.entity.Rectangle;
 import cn.smartjavaai.face.*;
-import org.apache.commons.beanutils.BeanUtils;
-import org.apache.commons.compress.utils.Lists;
+import org.apache.commons.lang3.StringUtils;
+

 import java.io.IOException;
 import java.io.InputStream;
@@ -36,6 +36,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {

    private Criteria<Image, DetectedObjects> criteria;

+    private Criteria<Image, float[]> faceFeatureCriteria;
+
    /**
     * 特征图层的基础缩放比例
     */
@@ -49,6 +51,7 @@ public class RetinaFace extends AbstractFaceAlgorithm {
     */
    public static final double[] variance = {0.1f, 0.2f};

+
    /**
     * 加载模型
     * @param config
@@ -60,16 +63,18 @@ public class RetinaFace extends AbstractFaceAlgorithm {
        criteria =
                Criteria.builder()
                        .setTypes(Image.class, DetectedObjects.class)
-                        .optModelUrls("https://resources.djl.ai/test-models/pytorch/retinaface.zip")
-                        //.optModelPath(modelPath)
+                        .optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null : "https://resources.djl.ai/test-models/pytorch/retinaface.zip")
                        // Load model from local file, e.g:
-                        .optModelName("retinaface") // specify model file prefix
+                        .optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
+                        .optModelName(StringUtils.isNotBlank(config.getAlgorithmName()) ? config.getAlgorithmName() : "retinaface") // specify model file prefix
                        .optTranslator(translator)
                        .optProgress(new ProgressBar())
                        .optEngine("PyTorch") // Use PyTorch engine
                        .build();
    }

+
+
    /**
     * 检测人脸
     * @param imagePath 图片路径
@@ -138,140 +143,4 @@ public class RetinaFace extends AbstractFaceAlgorithm {
        faceDetectedResult.setRectangles(RectangleList);
        return faceDetectedResult;
    }
-
-    /**
-     * 特征提取
-     * @param imagePath 图片路径
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float[] featureExtraction(String imagePath) throws Exception {
-        Path imageFile = Paths.get(imagePath);
-        Image img = ImageFactory.getInstance().fromFile(imageFile);
-        img.getWrappedImage();
-        List<Float> mean =
-                Arrays.asList(
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f);
-        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
-
-        Criteria<Image, float[]> criteria =
-                Criteria.builder()
-                        .setTypes(Image.class, float[].class)
-                        .optModelUrls(
-                                "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
-                        .optModelName("face_feature") // specify model file prefix
-                        .optArgument("normalize", normalize)
-                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
-                        .optProgress(new ProgressBar())
-                        .optEngine("PyTorch") // Use PyTorch engine
-                        .build();
-
-        try (ZooModel<Image, float[]> model = criteria.loadModel()) {
-            Predictor<Image, float[]> predictor = model.newPredictor();
-            return predictor.predict(img);
-        }
-    }
-
-    /**
-     * 特征提取
-     * @param inputStream 输入流
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float[] featureExtraction(InputStream inputStream) throws Exception {
-        Image img = ImageFactory.getInstance().fromInputStream(inputStream);
-        img.getWrappedImage();
-        List<Float> mean =
-                Arrays.asList(
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f);
-        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
-
-        Criteria<Image, float[]> criteria =
-                Criteria.builder()
-                        .setTypes(Image.class, float[].class)
-                        .optModelUrls(
-                                "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
-                        .optModelName("face_feature") // specify model file prefix
-                        .optArgument("normalize", normalize)
-                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
-                        .optProgress(new ProgressBar())
-                        .optEngine("PyTorch") // Use PyTorch engine
-                        .build();
-
-        try (ZooModel<Image, float[]> model = criteria.loadModel()) {
-            Predictor<Image, float[]> predictor = model.newPredictor();
-            return predictor.predict(img);
-        }
-    }
-
-    /**
-     * 计算相似度
-     * @param feature1 图1特征
-     * @param feature2 图2特征
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
-        float ret = 0.0f;
-        float mod1 = 0.0f;
-        float mod2 = 0.0f;
-        int length = feature1.length;
-        for (int i = 0; i < length; ++i) {
-            ret += feature1[i] * feature2[i];
-            mod1 += feature1[i] * feature1[i];
-            mod2 += feature2[i] * feature2[i];
-        }
-        return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
-    }
-
-    /**
-     * 特征比较
-     * @param imagePath1 图1路径
-     * @param imagePath2 图2路径
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float featureComparison(String imagePath1, String imagePath2) throws Exception {
-        float[] feature1 = featureExtraction(imagePath1);
-        float[] feature2 = featureExtraction(imagePath2);
-        return calculSimilar(feature1, feature2);
-    }
-
-    /**
-     * 特征比较
-     * @param inputStream1 图1输入流
-     * @param inputStream2 图2输入流
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
-        float[] feature1 = featureExtraction(inputStream1);
-        float[] feature2 = featureExtraction(inputStream2);
-        return calculSimilar(feature1, feature2);
-    }
-
-    /*@Override
-    public float[] recognize(FaceRegion region) {
-        return new float[0];
-    }*/
-
-    /*@Override
-    public void loadModel(ModelConfig config) throws Exception {
-
-    }*/
 }
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/UltraLightFastGenericFace.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/UltraLightFastGenericFace.java
@@ -131,139 +131,4 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
        return faceDetectedResult;
    }

-    /**
-     * 特征提取
-     * @param imagePath 图片路径
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float[] featureExtraction(String imagePath) throws Exception {
-        Path imageFile = Paths.get(imagePath);
-        Image img = ImageFactory.getInstance().fromFile(imageFile);
-        img.getWrappedImage();
-        List<Float> mean =
-                Arrays.asList(
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f);
-        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
-
-        Criteria<Image, float[]> criteria =
-                Criteria.builder()
-                        .setTypes(Image.class, float[].class)
-                        .optModelUrls(
-                                "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
-                        .optModelName("face_feature") // specify model file prefix
-                        .optArgument("normalize", normalize)
-                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
-                        .optProgress(new ProgressBar())
-                        .optEngine("PyTorch") // Use PyTorch engine
-                        .build();
-
-        try (ZooModel<Image, float[]> model = criteria.loadModel()) {
-            Predictor<Image, float[]> predictor = model.newPredictor();
-            return predictor.predict(img);
-        }
-    }
-
-    /**
-     * 特征提取
-     * @param inputStream 输入流
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float[] featureExtraction(InputStream inputStream) throws Exception {
-        Image img = ImageFactory.getInstance().fromInputStream(inputStream);
-        img.getWrappedImage();
-        List<Float> mean =
-                Arrays.asList(
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        127.5f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f,
-                        128.0f / 255.0f);
-        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
-
-        Criteria<Image, float[]> criteria =
-                Criteria.builder()
-                        .setTypes(Image.class, float[].class)
-                        .optModelUrls(
-                                "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
-                        .optModelName("face_feature") // specify model file prefix
-                        .optArgument("normalize", normalize)
-                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
-                        .optProgress(new ProgressBar())
-                        .optEngine("PyTorch") // Use PyTorch engine
-                        .build();
-
-        try (ZooModel<Image, float[]> model = criteria.loadModel()) {
-            Predictor<Image, float[]> predictor = model.newPredictor();
-            return predictor.predict(img);
-        }
-    }
-
-    /**
-     * 计算相似度
-     * @param feature1 图1特征
-     * @param feature2 图2特征
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
-        float ret = 0.0f;
-        float mod1 = 0.0f;
-        float mod2 = 0.0f;
-        int length = feature1.length;
-        for (int i = 0; i < length; ++i) {
-            ret += feature1[i] * feature2[i];
-            mod1 += feature1[i] * feature1[i];
-            mod2 += feature2[i] * feature2[i];
-        }
-        return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
-    }
-
-    /**
-     * 特征比较
-     * @param imagePath1 图1路径
-     * @param imagePath2 图2路径
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float featureComparison(String imagePath1, String imagePath2) throws Exception {
-        float[] feature1 = featureExtraction(imagePath1);
-        float[] feature2 = featureExtraction(imagePath2);
-        return calculSimilar(feature1, feature2);
-    }
-
-    /**
-     * 特征比较
-     * @param inputStream1 图1输入流
-     * @param inputStream2 图2输入流
-     * @return
-     * @throws Exception
-     */
-    @Override
-    public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
-        float[] feature1 = featureExtraction(inputStream1);
-        float[] feature2 = featureExtraction(inputStream2);
-        return calculSimilar(feature1, feature2);
-    }
-
-    /*@Override
-    public float[] recognize(FaceRegion region) {
-        return new float[0];
-    }*/
-
-    /*@Override
-    public void loadModel(ModelConfig config) throws Exception {
-
-    }*/
 }