体验升级：路径记忆、可视化深度扫描、文件名汉化

src/gui/panels/visualization_panel.py

@@ -503,6 +503,42 @@ class ImageCategoryTree(QTreeWidget):
         ("含量分布图", [], "📁"),
     ]
 
+    # 文件名中文翻译映射
+    NAME_MAPPING = {
+        "hsi_preview": "高光谱影像预览",
+        "hsi_original": "原始高光谱影像",
+        "hsi_deglint": "去耀斑高光谱影像",
+        "water_mask_overlay": "水域掩膜叠加图",
+        "water_mask": "水域掩膜图",
+        "glint_mask": "耀斑掩膜预览",
+        "glint_overlay": "耀斑叠加对比图",
+        "deglint_comparison": "去耀斑前后对比",
+        "training_spectra": "训练光谱特征",
+        "spectrum_by_param": "参数光谱图",
+        "model_evaluation": "模型评估散点图",
+        "model_scatter": "模型散点图",
+        "regression": "回归分析图",
+        "validation": "验证结果图",
+        "spatial_distribution": "参数空间分布图",
+        "distribution_map": "分布图",
+        "thematic_map": "水质专题图",
+        "water_quality_map": "水质分布图",
+        "prediction_map": "预测结果图",
+        "inversion_map": "反演结果图",
+        "correlation_matrix": "特征相关性矩阵",
+        "feature_correlation": "特征相关性",
+        "sampling_point_map": "采样点分布图",
+        "sampling_points": "采样点图",
+        "point_locations": "采样位置图",
+        "boxplot": "箱线图",
+        "histogram": "直方图",
+        "statistics": "统计图表",
+        "statistical_chart": "统计图",
+        "error_analysis": "误差分析图",
+        "rmse": "RMSE评估图",
+        "r2_score": "R²得分图",
+    }
+
     def __init__(self, parent=None):
         super().__init__(parent)
         self.setHeaderLabel("图像目录")
@@ -545,16 +581,18 @@ class ImageCategoryTree(QTreeWidget):
                 category_item.removeChild(category_item.child(0))
 
     def add_image(self, file_path: Path, display_name: str = None):
-        """添加图像到对应的类别"""
+        """添加图像到对应的类别（带中文名称翻译）"""
         if display_name is None:
-            display_name = file_path.stem
+            # 使用翻译映射，查询不到则用原文件名
+            file_base = file_path.stem
+            display_name = self.NAME_MAPPING.get(file_base, file_base)
 
         category = self._determine_category(file_path.name)
         category_item = self.category_items.get(category, self.category_items["含量分布图"])
 
         image_item = QTreeWidgetItem(category_item)
         image_item.setText(0, f"  └─ {display_name}")
-        image_item.setData(0, Qt.UserRole, {"type": "image", "path": str(file_path)})
+        image_item.setData(0, Qt.UserRole, {"type": "image", "path": str(file_path), "display_name": display_name})
         image_item.setToolTip(0, str(file_path))
 
         return image_item
@@ -570,7 +608,7 @@ class ImageCategoryTree(QTreeWidget):
         return "含量分布图"
 
     def scan_directory(self, work_dir: str):
-        """扫描目录中的所有图像文件"""
+        """扫描目录中的所有图像文件（深度递归扫描）"""
         self.clear_all_images()
 
         work_path = Path(work_dir)
@@ -578,13 +616,17 @@ class ImageCategoryTree(QTreeWidget):
             return
 
         image_extensions = ['*.png', '*.jpg', '*.jpeg', '*.tif', '*.tiff', '*.bmp']
-        scan_roots: List[Path] = []
-        _viz = work_path / "14_visualization"
-        if _viz.is_dir():
-            scan_roots.append(_viz)
-        _wm = work_path / "1_water_mask"
-        if _wm.is_dir():
-            scan_roots.append(_wm)
+
+        # 扫描根目录列表（按优先级）
+        scan_roots: List[Path] = [
+            work_path / "14_visualization",
+            work_path / "1_water_mask",
+            work_path / "9_water_quality_prediction",
+            work_path / "10_feature_construction",
+        ]
+
+        # 只保留存在的目录，并补充工作根目录作为兜底
+        scan_roots = [p for p in scan_roots if p.is_dir()]
         if not scan_roots:
             scan_roots.append(work_path)
 
@@ -592,7 +634,8 @@ class ImageCategoryTree(QTreeWidget):
         image_files: List[Path] = []
         for root in scan_roots:
             for ext in image_extensions:
-                for p in root.glob(f"**/{ext}"):
+                # 使用 rglob 进行深度递归扫描
+                for p in root.rglob(ext):
                     key = os.path.normcase(os.path.normpath(str(p.resolve())))
                     if key in seen_norm:
                         continue