- 【人脸检测】新增6个模型(MTCNN、YOLOV5、RetinaFace小尺寸版)，大幅提升性能

- 【人脸识别】新增Seetaface6轻量模型 - 【目标检测】支持视频流目标检测（rtsp、视频文件等） - 【目标检测】支持tensorflow2目标检测模型 - 【目标检测】新增行人检测模型(yolo-person) - 【通用视觉】新增4个动作识别模型 - 【通用视觉】新增语义分割模型 - 【通用视觉】新增5个实例分割模型(含yolov8-seg、yolov11-seg) - 【通用视觉】新增yolo-obb11旋转框检测(含yolov11-obb) - 【通用视觉】新增5个姿态估计模型(含yolov8-pose、yolov11-pose)
2026-09-13 13:18:58 +00:00 · 2025-09-07 17:19:19 +08:00
parent 2b044fda29
commit a8e7ce6c4e
102 changed files with 4473 additions and 1368 deletions
--- a/vision/src/main/java/cn/smartjavaai/instanceseg/config/InstanceSegModelConfig.java
+++ b/vision/src/main/java/cn/smartjavaai/instanceseg/config/InstanceSegModelConfig.java
@@ -36,7 +36,7 @@ public class InstanceSegModelConfig extends ModelConfig {
    /**
     * 置信度阈值
     */
-    private float threshold = 0.3f;
+    private float threshold = 0.25f;


    public InstanceSegModelConfig() {
--- a/vision/src/main/java/cn/smartjavaai/instanceseg/criteria/InstanceSegCriteriaFactory.java
+++ b/vision/src/main/java/cn/smartjavaai/instanceseg/criteria/InstanceSegCriteriaFactory.java
@@ -3,10 +3,12 @@ package cn.smartjavaai.instanceseg.criteria;
 import ai.djl.Device;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.output.DetectedObjects;
+import ai.djl.modality.cv.translator.InstanceSegmentationTranslatorFactory;
 import ai.djl.repository.zoo.Criteria;
 import ai.djl.training.util.ProgressBar;
 import cn.smartjavaai.common.enums.DeviceEnum;
 import cn.smartjavaai.instanceseg.config.InstanceSegModelConfig;
+import cn.smartjavaai.instanceseg.enums.InstanceSegModelEnum;
 import cn.smartjavaai.instanceseg.translator.YoloSegmentationTranslatorFactory2;
 import org.apache.commons.lang3.StringUtils;

@@ -27,21 +29,42 @@ public class InstanceSegCriteriaFactory {
            device = config.getDevice() == DeviceEnum.CPU ? Device.cpu() : Device.gpu(config.getGpuId());
        }
        Criteria<Image, DetectedObjects> criteria = null;
-        ConcurrentHashMap params = new ConcurrentHashMap<String, String>();
-        params.putAll(config.getCustomParams());
+//        ConcurrentHashMap params = new ConcurrentHashMap<String, String>();
+//        params.putAll(config.getCustomParams());
 //        YoloV5Translator.Builder builder = new YoloV5Translator.Builder()
 //                .optSynsetArtifactName("synset.txt").setPipeline()
-        criteria =
-                Criteria.builder()
-                        .setTypes(Image.class, DetectedObjects.class)
-                        .optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
-                                config.getModelEnum().getModelUri())
-                        .optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
-                        .optDevice(device)
-                        .optEngine("PyTorch")
-                        .optTranslatorFactory(new YoloSegmentationTranslatorFactory2())
-                        .optProgress(new ProgressBar())
-                        .build();
+
+        if(config.getModelEnum() == InstanceSegModelEnum.SEG_MASK_RCNN){
+            criteria =
+                    Criteria.builder()
+                            .setTypes(Image.class, DetectedObjects.class)
+                            .optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
+                                    config.getModelEnum().getModelUri())
+                            .optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
+                            .optDevice(device)
+                            .optEngine(config.getModelEnum().getEngine())
+                            .optArgument("normalize","true")
+                            .optArgument("synsetFileName","classes.txt")
+                            .optTranslatorFactory(new InstanceSegmentationTranslatorFactory())
+                            .optProgress(new ProgressBar())
+                            .build();
+        }else{
+            criteria =
+                    Criteria.builder()
+                            .setTypes(Image.class, DetectedObjects.class)
+                            .optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
+                                    config.getModelEnum().getModelUri())
+                            .optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
+                            .optDevice(device)
+                            .optArgument("width", config.getModelEnum().getInputWidth())
+                            .optArgument("height", config.getModelEnum().getInputHeight())
+                            .optArgument("resize", "true")
+                            .optArgument("threshold", config.getThreshold())
+                            .optEngine(config.getModelEnum().getEngine())
+                            .optTranslatorFactory(new YoloSegmentationTranslatorFactory2())
+                            .optProgress(new ProgressBar())
+                            .build();
+        }
        return criteria;
    }
 }
--- a/vision/src/main/java/cn/smartjavaai/instanceseg/enums/InstanceSegModelEnum.java
+++ b/vision/src/main/java/cn/smartjavaai/instanceseg/enums/InstanceSegModelEnum.java
@@ -6,15 +6,15 @@ package cn.smartjavaai.instanceseg.enums;
 */
 public enum InstanceSegModelEnum {

-    SEG_YOLO11N_PYTORCH("djl://ai.djl.pytorch/yolo11n-seg"),
+    SEG_YOLO11N_PYTORCH("PyTorch", 640, 640, "djl://ai.djl.pytorch/yolo11n-seg"),

-    SEG_YOLOV8N_PYTORCH("djl://ai.djl.pytorch/yolo11n-seg"),
+    SEG_YOLOV8N_PYTORCH("PyTorch", 640, 640,  "djl://ai.djl.pytorch/yolov8n-seg"),

-    SEG_YOLO11N_ONNX("djl://ai.djl.onnxruntime/yolo11n-seg"),
+    SEG_YOLO11N_ONNX("OnnxRuntime", 640, 640,  "djl://ai.djl.onnxruntime/yolo11n-seg"),

-    SEG_YOLOV8N_ONNX("djl://ai.djl.onnxruntime/yolov8n-seg"),
+    SEG_YOLOV8N_ONNX("OnnxRuntime", 640, 640,  "djl://ai.djl.onnxruntime/yolov8n-seg"),

-    SEG_MASK_RCNN("djl://ai.djl.mxnet/mask_rcnn");
+    SEG_MASK_RCNN("MXNet", 0,0, "djl://ai.djl.mxnet/mask_rcnn");


    /**
@@ -30,14 +30,43 @@ public enum InstanceSegModelEnum {
        throw new IllegalArgumentException("未知模型名称: " + name);
    }

+    /**
+     * 模型输入尺寸：宽
+     */
+    private final int inputWidth;
+
+    /**
+     * 模型输入尺寸：高
+     */
+    private final int inputHeight;
+
    private final String modelUri;

-    InstanceSegModelEnum(String modelUri) {
+    /**
+     * 模型引擎
+     */
+    private final String engine;
+
+    InstanceSegModelEnum(String engine, int inputWidth, int inputHeight, String modelUri) {
+        this.inputWidth = inputWidth;
+        this.inputHeight = inputHeight;
        this.modelUri = modelUri;
+        this.engine = engine;
    }

    public String getModelUri() {
        return modelUri;
    }

+    public String getEngine() {
+        return engine;
+    }
+
+    public int getInputWidth() {
+        return inputWidth;
+    }
+
+    public int getInputHeight() {
+        return inputHeight;
+    }
 }
--- a/vision/src/main/java/cn/smartjavaai/instanceseg/model/CommonInstanceSegModel.java
+++ b/vision/src/main/java/cn/smartjavaai/instanceseg/model/CommonInstanceSegModel.java
@@ -113,6 +113,9 @@ public class CommonInstanceSegModel implements InstanceSegModel {
    @Override
    public R<DetectionResponse> detectAndDraw(Image image) {
        DetectedObjects detectedObjects = detectCore(image);
+        if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
+            return R.fail(R.Status.NO_OBJECT_DETECTED);
+        }
        image.drawBoundingBoxes(detectedObjects);
        DetectionResponse detectionResponse = DetectorUtils.convertToDetectionResponse(detectedObjects, image);
        detectionResponse.setDrawnImage(image);
@@ -124,6 +127,9 @@ public class CommonInstanceSegModel implements InstanceSegModel {
        try {
            Image img = SmartImageFactory.getInstance().fromFile(Paths.get(imagePath));
            DetectedObjects detectedObjects = detectCore(img);
+            if(Objects.isNull(detectedObjects) || detectedObjects.getNumberOfObjects() == 0){
+                return R.fail(R.Status.NO_OBJECT_DETECTED);
+            }
            img.drawBoundingBoxes(detectedObjects);
            img.save(Files.newOutputStream(Paths.get(outputPath)), "png");
            DetectionResponse detectionResponse = DetectorUtils.convertToDetectionResponse(detectedObjects, img);
--- a/vision/src/main/java/cn/smartjavaai/instanceseg/model/InstanceSegModelFactory.java
+++ b/vision/src/main/java/cn/smartjavaai/instanceseg/model/InstanceSegModelFactory.java
@@ -0,0 +1,111 @@
+package cn.smartjavaai.instanceseg.model;
+
+import cn.smartjavaai.common.config.Config;
+import cn.smartjavaai.instanceseg.config.InstanceSegModelConfig;
+import cn.smartjavaai.instanceseg.enums.InstanceSegModelEnum;
+import cn.smartjavaai.objectdetection.exception.DetectionException;
+import lombok.extern.slf4j.Slf4j;
+
+import java.util.Map;
+import java.util.Objects;
+import java.util.concurrent.ConcurrentHashMap;
+
+/**
+ * 实例分割 模型工厂
+ * @author dwj
+ */
+@Slf4j
+public class InstanceSegModelFactory {
+
+    // 使用 volatile 和双重检查锁定来确保线程安全的单例模式
+    private static volatile InstanceSegModelFactory instance;
+
+    private static final ConcurrentHashMap<InstanceSegModelEnum, InstanceSegModel> modelMap = new ConcurrentHashMap<>();
+
+    /**
+     * 模型注册表
+     */
+    private static final Map<InstanceSegModelEnum, Class<? extends InstanceSegModel>> registry =
+            new ConcurrentHashMap<>();
+
+
+    // 私有构造函数，防止外部创建实例
+    private InstanceSegModelFactory() {}
+
+    // 双重检查锁定的单例方法
+    public static InstanceSegModelFactory getInstance() {
+        if (instance == null) {
+            synchronized (InstanceSegModelFactory.class) {
+                if (instance == null) {
+                    instance = new InstanceSegModelFactory();
+                }
+            }
+        }
+        return instance;
+    }
+
+    /**
+     * 获取模型（通过配置）
+     * @param config
+     * @return
+     */
+    public InstanceSegModel getModel(InstanceSegModelConfig config) {
+        if(Objects.isNull(config) || Objects.isNull(config.getModelEnum())){
+            throw new DetectionException("未配置模型");
+        }
+        return modelMap.computeIfAbsent(config.getModelEnum(), k -> {
+            return createFaceDetModel(config);
+        });
+    }
+
+    /**
+     * 使用ModelConfig创建模型
+     * @param config
+     * @return
+     */
+    private InstanceSegModel createFaceDetModel(InstanceSegModelConfig config) {
+        Class<?> clazz = registry.get(config.getModelEnum());
+        if(clazz == null){
+            throw new DetectionException("Unsupported model");
+        }
+        InstanceSegModel model = null;
+        try {
+            model = (InstanceSegModel) clazz.newInstance();
+        } catch (InstantiationException | IllegalAccessException e) {
+            throw new DetectionException(e);
+        }
+        model.loadModel(config);
+        return model;
+    }
+
+
+    /**
+     * 注册模型
+     * @param modelEnum
+     * @param clazz
+     */
+    private static void registerAlgorithm(InstanceSegModelEnum modelEnum, Class<? extends InstanceSegModel> clazz) {
+        registry.put(modelEnum, clazz);
+    }
+
+
+    /**
+     * 移除缓存的模型
+     * @param modelEnum
+     */
+    public static void removeFromCache(InstanceSegModelEnum modelEnum) {
+        modelMap.remove(modelEnum);
+    }
+
+
+    // 初始化默认算法
+    static {
+        registerAlgorithm(InstanceSegModelEnum.SEG_YOLOV8N_ONNX, CommonInstanceSegModel.class);
+        registerAlgorithm(InstanceSegModelEnum.SEG_YOLOV8N_PYTORCH, CommonInstanceSegModel.class);
+        registerAlgorithm(InstanceSegModelEnum.SEG_YOLO11N_PYTORCH, CommonInstanceSegModel.class);
+        registerAlgorithm(InstanceSegModelEnum.SEG_YOLO11N_ONNX, CommonInstanceSegModel.class);
+        registerAlgorithm(InstanceSegModelEnum.SEG_MASK_RCNN, CommonInstanceSegModel.class);
+        log.debug("缓存目录：{}", Config.getCachePath());
+    }
+}
+