refactor(ai):重构分割模型包装类继承结构- 将 Anime2ModelWrapper、Anime2VividModelWrapper 和 AnimeModelWrapper 改为继承自 VividModelWrapper 基类

- 移除重复的 ResultFiles 内部类和相关工具方法实现
- Anime2Segmenter 和 AnimeSegmenter 继承自抽象基类 Segmenter
- Anime2SegmentationResult与 AnimeSegmentationResult 继承 SegmentationResult
- 重命名 LabelPalette 为 BiSeNetLabelPalette 并调整其引用
- 更新模型路径配置以匹配新的文件命名约定
- 删除冗余的 getLabels() 和 getPalette() 方法定义
- 简化 segmentAndSave 方法中的类型转换逻辑- 移除已被继承方法替代的手动资源管理代码
- 调整 import 语句以反映包结构调整- 清理不再需要的独立主测试函数入口点- 修改字段访问权限以符合继承设计模式
- 替换具体的返回类型为更通用的 SegmentationResult 接口- 整合公共功能至基类减少子类间重复代码
- 统一分割后处理流程提高模块复用性
- 引入泛型支持增强 Wrapper 类型安全性
- 更新注释文档保持与最新架构同步
- 优化异常处理策略统一关闭资源方式
- 规范文件命名规则便于未来维护扩展
- 提取共通逻辑到父类降低耦合度
- 完善类型检查避免运行时 ClassCastException 风险

src/main/java/com/chuangzhou/vivid2D/ai/Segmenter.java

@@ -0,0 +1,154 @@
+package com.chuangzhou.vivid2D.ai;
+
+import ai.djl.MalformedModelException;
+import ai.djl.inference.Predictor;
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.ImageFactory;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.ndarray.NDManager;
+import ai.djl.ndarray.types.DataType;
+import ai.djl.repository.zoo.Criteria;
+import ai.djl.repository.zoo.ModelNotFoundException;
+import ai.djl.repository.zoo.ZooModel;
+import ai.djl.translate.Batchifier;
+import ai.djl.translate.TranslateException;
+import ai.djl.translate.Translator;
+import ai.djl.translate.TranslatorContext;
+import com.chuangzhou.vivid2D.ai.face_parsing.BiSeNetLabelPalette;
+import com.chuangzhou.vivid2D.ai.face_parsing.BiSeNetSegmentationResult;
+import com.chuangzhou.vivid2D.ai.face_parsing.BiSeNetSegmenter;
+
+import javax.imageio.ImageIO;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.IOException;
+import java.nio.file.Path;
+import java.util.*;
+
+public abstract class Segmenter implements AutoCloseable {
+    // 内部类，用于从Translator安全地传出数据
+    public static class SegmentationData {
+        public final int[] indices;
+        public final long[] shape;
+
+        public SegmentationData(int[] indices, long[] shape) {
+            this.indices = indices;
+            this.shape = shape;
+        }
+    }
+
+    private String engine = "PyTorch";
+    protected final ZooModel<Image, Segmenter.SegmentationData> modelWrapper;
+    protected final Predictor<Image, Segmenter.SegmentationData> predictor;
+    protected final List<String> labels;
+    protected final Map<String, Integer> palette;
+
+    public Segmenter(Path modelDir, List<String> labels) throws IOException, MalformedModelException, ModelNotFoundException {
+        this.labels = new ArrayList<>(labels);
+        this.palette = BiSeNetLabelPalette.defaultPalette();
+
+        Translator<Image, Segmenter.SegmentationData> translator = new Translator<Image, Segmenter.SegmentationData>() {
+            @Override
+            public NDList processInput(TranslatorContext ctx, Image input) {
+                return Segmenter.this.processInput(ctx, input);
+            }
+
+            @Override
+            public Segmenter.SegmentationData processOutput(TranslatorContext ctx, NDList list) {
+                return Segmenter.this.processOutput(ctx, list);
+            }
+
+            @Override
+            public Batchifier getBatchifier() {
+                return Segmenter.this.getBatchifier();
+            }
+        };
+
+        Criteria<Image, Segmenter.SegmentationData> criteria = Criteria.builder()
+                .setTypes(Image.class, Segmenter.SegmentationData.class)
+                .optModelPath(modelDir)
+                .optEngine(engine)
+                .optTranslator(translator)
+                .build();
+
+        this.modelWrapper = criteria.loadModel();
+        this.predictor = modelWrapper.newPredictor();
+    }
+
+    /**
+     * 处理模型输入
+     * @param ctx translator 上下文
+     * @param input  图片
+     * @return 模型输入
+     */
+    public abstract NDList processInput(TranslatorContext ctx, Image input);
+
+    /**
+     * 处理模型输出
+     * @param ctx translator 上下文
+     * @param list 模型输出
+     * @return 模型输出
+     */
+    public abstract Segmenter.SegmentationData processOutput(TranslatorContext ctx, NDList list);
+
+    /**
+     * 获取批量处理方式
+     * @return 批量处理方式
+     */
+    public Batchifier getBatchifier(){
+        return null;
+    }
+
+    public SegmentationResult segment(File imgFile) throws TranslateException, IOException {
+        Image img = ImageFactory.getInstance().fromFile(imgFile.toPath());
+
+        // predict 方法现在直接返回安全的 Java 对象
+        Segmenter.SegmentationData data = predictor.predict(img);
+
+        long[] shp = data.shape;
+        int[] indices = data.indices;
+
+        int height, width;
+        if (shp.length == 2) {
+            height = (int) shp[0];
+            width = (int) shp[1];
+        } else {
+            throw new RuntimeException("Unexpected classMap shape from SegmentationData: " + Arrays.toString(shp));
+        }
+
+        // 后续处理完全基于 Java 对象，不再有 Native resource 问题
+        BufferedImage mask = new BufferedImage(width, height, BufferedImage.TYPE_INT_ARGB);
+        Map<Integer, String> labelsMap = new HashMap<>();
+        for (int i = 0; i < labels.size(); i++) {
+            labelsMap.put(i, labels.get(i));
+        }
+
+        for (int y = 0; y < height; y++) {
+            for (int x = 0; x < width; x++) {
+                int idx = indices[y * width + x];
+                String label = labelsMap.getOrDefault(idx, "unknown");
+                int argb = palette.getOrDefault(label, 0xFF00FF00);
+                mask.setRGB(x, y, argb);
+            }
+        }
+
+        return new SegmentationResult(mask, labelsMap, palette);
+    }
+
+    public void setEngine(String engine) {
+        this.engine = engine;
+    }
+
+    @Override
+    public void close() {
+        try {
+            predictor.close();
+        } catch (Exception ignore) {
+        }
+        try {
+            modelWrapper.close();
+        } catch (Exception ignore) {
+        }
+    }
+}