支持离线下载模型

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）