优化人脸检测速度

examples/src/main/java/smartai/examples/face/FaceDemo.java

@@ -3,6 +3,7 @@ package smartai.examples.face;
 import cn.smartjavaai.common.entity.Rectangle;
 import cn.smartjavaai.face.*;
 import com.alibaba.fastjson.JSONObject;
+import org.apache.commons.lang3.time.StopWatch;
 import org.slf4j.Logger;
 import org.slf4j.LoggerFactory;
 import smartai.examples.utils.ImageUtils;
@@ -28,7 +29,7 @@ public class FaceDemo {
 
     public static void main(String[] args) {
         try {
-            //detectFace();
+            //detectFace2();
             verifyIDCard();
         } catch (Exception e) {
             e.printStackTrace();
@@ -42,10 +43,18 @@ public class FaceDemo {
      * 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 detectFace() throws Exception {
+        // 创建并启动计时器
+        StopWatch sw = StopWatch.createStarted();
         //创建人脸算法
         FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm();
+        sw.stop();
+        logger.info("创建人脸算法耗时：" + sw.getTime() + "ms");
+        sw.reset();
+        sw.start();
         //使用图片路径检测
         FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+        sw.stop();
+        logger.info("人脸检测耗时：" + sw.getTime() + "ms");
         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");
@@ -66,10 +75,18 @@ public class FaceDemo {
      * 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 detectFace2() throws Exception {
+        // 创建并启动计时器
+        StopWatch sw = StopWatch.createStarted();
         //创建轻量人脸算法
         FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createLightFaceAlgorithm();
+        sw.stop();
+        logger.info("创建人脸算法耗时：" + sw.getTime() + "ms");
+        sw.reset();
+        sw.start();
         //使用图片路径检测
         FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+        sw.stop();
+        logger.info("人脸检测耗时：" + sw.getTime() + "ms");
         logger.info("轻量人脸检测结果：{}", JSONObject.toJSONString(result));
         //使用图片流检测
         //File imageFile = new File("/Users/wenjie/Downloads/djl-master/examples/src/test/resources/largest_selfie.jpg");
@@ -87,10 +104,18 @@ public class FaceDemo {
      * @throws Exception
      */
     public static void verifyIDCard() throws Exception {
+        // 创建并启动计时器
+        StopWatch sw = StopWatch.createStarted();
         //创建脸算法
         FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm();
+        sw.stop();
+        logger.info("创建人脸算法耗时：" + sw.getTime() + "ms");
+        sw.reset();
+        sw.start();
         //提取身份证人脸特征（图片仅供测试）
         float[] featureIdCard = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
+        sw.stop();
+        logger.info("人脸检测耗时：" + sw.getTime() + "ms");
         //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));

smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/FeatureExtractionAlgo.java

@@ -33,6 +33,10 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
 
     private Criteria<Image, float[]> faceFeatureCriteria;
 
+    private Predictor<Image, float[]> predictor;
+
+    private ZooModel<Image, float[]> model;
+
     public static final List<Float> mean =
             Arrays.asList(
                     127.5f / 255.0f,
@@ -62,6 +66,8 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
                         .optProgress(new ProgressBar())
                         .optEngine("PyTorch") // Use PyTorch engine
                         .build();
+        model = faceFeatureCriteria.loadModel();
+        predictor = model.newPredictor();
     }
 
 
@@ -76,10 +82,7 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
         Path imageFile = Paths.get(imagePath);
         Image img = ImageFactory.getInstance().fromFile(imageFile);
         img.getWrappedImage();
-        try (ZooModel<Image, float[]> model = faceFeatureCriteria.loadModel()) {
+        return predictor.predict(img);
-            Predictor<Image, float[]> predictor = model.newPredictor();
-            return predictor.predict(img);
-        }
     }
 
     /**
@@ -92,10 +95,7 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
     public float[] featureExtraction(InputStream inputStream) throws Exception {
         Image img = ImageFactory.getInstance().fromInputStream(inputStream);
         img.getWrappedImage();
-        try (ZooModel<Image, float[]> model = faceFeatureCriteria.loadModel()) {
+        return predictor.predict(img);
-            Predictor<Image, float[]> predictor = model.newPredictor();
-            return predictor.predict(img);
-        }
     }
 
     /**

smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/RetinaFace.java

@@ -38,6 +38,10 @@ public class RetinaFace extends AbstractFaceAlgorithm {
 
     private Criteria<Image, float[]> faceFeatureCriteria;
 
+    private Predictor<Image, DetectedObjects> predictor;
+
+    private ZooModel<Image, DetectedObjects> model;
+
     /**
      * 特征图层的基础缩放比例
      */
@@ -57,7 +61,7 @@ public class RetinaFace extends AbstractFaceAlgorithm {
      * @param config
      */
     @Override
-    public void loadModel(ModelConfig config) {
+    public void loadModel(ModelConfig config) throws ModelNotFoundException, MalformedModelException, IOException {
         FaceDetectionTranslator translator =
                 new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
         criteria =
@@ -71,6 +75,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {
                         .optProgress(new ProgressBar())
                         .optEngine("PyTorch") // Use PyTorch engine
                         .build();
+        model = criteria.loadModel();
+        predictor = model.newPredictor();
     }
 
 
@@ -85,11 +91,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {
     public FaceDetectedResult detect(String imagePath) throws Exception{
         Path facePath = Paths.get(imagePath);
         Image img = ImageFactory.getInstance().fromFile(facePath);
-        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
+        DetectedObjects detection = predictor.predict(img);
-             Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+        return convertToFaceDetectedResult(detection,img);
-            DetectedObjects detection = predictor.predict(img);
-            return convertToFaceDetectedResult(detection,img);
-        }
     }
 
     /**
@@ -101,13 +104,8 @@ public class RetinaFace extends AbstractFaceAlgorithm {
     @Override
     public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
         Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
-        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
+        DetectedObjects detection = predictor.predict(img);
-             Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+        return convertToFaceDetectedResult(detection,img);
-            DetectedObjects detection = predictor.predict(img);
-            return convertToFaceDetectedResult(detection,img);
-            /*saveBoundingBoxImage(img, detection);
-            return detection;*/
-        }
     }
 
     /**

smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/UltraLightFastGenericFace.java

@@ -1,17 +1,20 @@
 package cn.smartjavaai.face.algo;
 
+import ai.djl.MalformedModelException;
 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.ModelNotFoundException;
 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.*;
 
+import java.io.IOException;
 import java.io.InputStream;
 import java.nio.file.Path;
 import java.nio.file.Paths;
@@ -41,6 +44,10 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
      */
     private static final double[] variance = {0.1f, 0.2f};
 
+    private Predictor<Image, DetectedObjects> predictor;
+
+    private ZooModel<Image, DetectedObjects> model;
+
 
 
 
@@ -49,7 +56,7 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
      * @param config
      */
     @Override
-    public void loadModel(ModelConfig config) {
+    public void loadModel(ModelConfig config) throws ModelNotFoundException, MalformedModelException, IOException {
         FaceDetectionTranslator translator =
                 new FaceDetectionTranslator(config.getConfidenceThreshold(), config.getNmsThresh(), variance, config.getMaxFaceCount(), scales, steps);
         criteria =
@@ -60,6 +67,8 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
                         .optProgress(new ProgressBar())
                         .optEngine("PyTorch") // Use PyTorch engine
                         .build();
+        model = criteria.loadModel();
+        predictor = model.newPredictor();
     }
 
     /**
@@ -72,11 +81,8 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
     public FaceDetectedResult detect(String imagePath) throws Exception{
         Path facePath = Paths.get(imagePath);
         Image img = ImageFactory.getInstance().fromFile(facePath);
-        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
+        DetectedObjects detection = predictor.predict(img);
-             Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+        return convertToFaceDetectedResult(detection,img);
-            DetectedObjects detection = predictor.predict(img);
-            return convertToFaceDetectedResult(detection,img);
-        }
     }
 
     /**
@@ -88,13 +94,8 @@ public class UltraLightFastGenericFace extends AbstractFaceAlgorithm {
     @Override
     public FaceDetectedResult detect(InputStream imageInputStream) throws Exception {
         Image img = ImageFactory.getInstance().fromInputStream(imageInputStream);
-        try (ZooModel<Image, DetectedObjects> model = criteria.loadModel();
+        DetectedObjects detection = predictor.predict(img);
-             Predictor<Image, DetectedObjects> predictor = model.newPredictor()) {
+        return convertToFaceDetectedResult(detection,img);
-            DetectedObjects detection = predictor.predict(img);
-            return convertToFaceDetectedResult(detection,img);
-            /*saveBoundingBoxImage(img, detection);
-            return detection;*/
-        }
     }
 
     /**