体验升级：路径记忆、可视化深度扫描、文件名汉化
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@ -85,11 +85,10 @@ class Step5_5Panel(QWidget):
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output_group = QGroupBox("输出设置")
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output_layout = QVBoxLayout()
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output_hbox = QHBoxLayout()
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output_hbox.addWidget(QLabel("输出文件名:"))
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self.output_filename = QLineEdit("water_quality_indices.csv")
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output_hbox.addWidget(self.output_filename)
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output_layout.addLayout(output_hbox)
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self.output_file_widget = FileSelectWidget(
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"输出文件:", "CSV Files (*.csv)", mode="save"
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)
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output_layout.addWidget(self.output_file_widget)
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output_group.setLayout(output_layout)
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main_layout.addWidget(output_group)
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@ -258,11 +257,12 @@ class Step5_5Panel(QWidget):
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name for name, checkbox in self.index_checkboxes.items()
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if checkbox.isChecked()
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]
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output_path = self.output_file_widget.get_path()
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return {
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'training_spectra_path': self.training_data_widget.get_path() or None,
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'formula_csv_file': self.formula_csv_widget.get_path() or None,
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'formula_names': selected,
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'output_filename': self.output_filename.text().strip() or "water_quality_indices.csv",
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'output_file': output_path or None,
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'enabled': self.enable_checkbox.isChecked()
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}
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@ -273,7 +273,6 @@ class Step5_5Panel(QWidget):
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if 'formula_csv_file' in config:
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self.formula_csv_widget.set_path(config['formula_csv_file'])
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# 设置CSV路径后自动刷新公式信息
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self.refresh_formulas()
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if 'formula_names' in config:
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@ -281,8 +280,10 @@ class Step5_5Panel(QWidget):
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for name, checkbox in self.index_checkboxes.items():
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checkbox.setChecked(name in selected_formulas)
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if 'output_filename' in config:
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self.output_filename.setText(config['output_filename'])
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if 'output_file' in config and config['output_file']:
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self.output_file_widget.set_path(config['output_file'])
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elif 'output_filename' in config and config['output_filename']:
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self.output_file_widget.set_path(config['output_filename'])
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if 'enabled' in config:
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self.enable_checkbox.setChecked(config['enabled'])
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@ -328,9 +329,11 @@ class Step5_5Panel(QWidget):
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if training_path:
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self.training_data_widget.set_path(training_path)
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# 2. 自动填充输出文件名（通用逻辑，由 run_step 根据输入文件名动态覆盖）
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# 核心方法只接受文件名，不接受完整路径。
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# 保持默认值，run_step 会根据输入自动填入 _indices.csv 后缀
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# 2. 自动填入输出文件的绝对路径
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if self.work_dir:
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output_abs = os.path.join(self.work_dir, "6_water_quality_indices",
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"training_spectra_indices.csv").replace('\\', '/')
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self.output_file_widget.set_path(output_abs)
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def is_enabled(self) -> bool:
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return self.enable_checkbox.isChecked()
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@ -348,7 +351,7 @@ class Step5_5Panel(QWidget):
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def run_step(self):
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"""独立运行步骤5.5：计算水质指数。
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动态根据输入 CSV 文件名生成输出文件名，自动填入 output_filename 文本框。
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动态根据输入 CSV 文件名生成输出文件名，自动填入 output_file_widget。
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例如：training_spectra.csv → training_spectra_indices.csv
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sampling_spectra.csv → sampling_spectra_indices.csv
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"""
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@ -369,10 +372,18 @@ class Step5_5Panel(QWidget):
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QMessageBox.warning(self, "输入验证失败", "公式CSV文件不存在")
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return
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# 动态生成输出文件名：自动拼接 _indices 后缀
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# 动态生成输出文件：自动拼接 _indices 后缀
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input_name = Path(training_csv_path).stem
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dynamic_output = f"{input_name}_indices.csv"
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self.output_filename.setText(dynamic_output)
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# 合成完整绝对路径（优先使用 work_dir，其次从 training_csv_path 推导）
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work_dir = getattr(self, 'work_dir', None)
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if work_dir:
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dynamic_output = os.path.join(
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work_dir, "6_water_quality_indices", dynamic_output
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).replace('\\', '/')
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self.output_file_widget.set_path(dynamic_output)
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# 获取配置
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config = self.get_config()
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@ -503,6 +503,42 @@ class ImageCategoryTree(QTreeWidget):
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("含量分布图", [], "📁"),
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]
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# 文件名中文翻译映射
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NAME_MAPPING = {
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"hsi_preview": "高光谱影像预览",
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"hsi_original": "原始高光谱影像",
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"hsi_deglint": "去耀斑高光谱影像",
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"water_mask_overlay": "水域掩膜叠加图",
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"water_mask": "水域掩膜图",
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"glint_mask": "耀斑掩膜预览",
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"glint_overlay": "耀斑叠加对比图",
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"deglint_comparison": "去耀斑前后对比",
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"training_spectra": "训练光谱特征",
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"spectrum_by_param": "参数光谱图",
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"model_evaluation": "模型评估散点图",
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"model_scatter": "模型散点图",
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"regression": "回归分析图",
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"validation": "验证结果图",
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"spatial_distribution": "参数空间分布图",
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"distribution_map": "分布图",
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"thematic_map": "水质专题图",
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"water_quality_map": "水质分布图",
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"prediction_map": "预测结果图",
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"inversion_map": "反演结果图",
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"correlation_matrix": "特征相关性矩阵",
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"feature_correlation": "特征相关性",
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"sampling_point_map": "采样点分布图",
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"sampling_points": "采样点图",
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"point_locations": "采样位置图",
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"boxplot": "箱线图",
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"histogram": "直方图",
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"statistics": "统计图表",
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"statistical_chart": "统计图",
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"error_analysis": "误差分析图",
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"rmse": "RMSE评估图",
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"r2_score": "R²得分图",
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}
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def __init__(self, parent=None):
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super().__init__(parent)
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self.setHeaderLabel("图像目录")
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@ -545,16 +581,18 @@ class ImageCategoryTree(QTreeWidget):
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category_item.removeChild(category_item.child(0))
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def add_image(self, file_path: Path, display_name: str = None):
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"""添加图像到对应的类别"""
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"""添加图像到对应的类别（带中文名称翻译）"""
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if display_name is None:
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display_name = file_path.stem
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# 使用翻译映射，查询不到则用原文件名
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file_base = file_path.stem
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display_name = self.NAME_MAPPING.get(file_base, file_base)
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category = self._determine_category(file_path.name)
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category_item = self.category_items.get(category, self.category_items["含量分布图"])
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image_item = QTreeWidgetItem(category_item)
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image_item.setText(0, f" └─ {display_name}")
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image_item.setData(0, Qt.UserRole, {"type": "image", "path": str(file_path)})
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image_item.setData(0, Qt.UserRole, {"type": "image", "path": str(file_path), "display_name": display_name})
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image_item.setToolTip(0, str(file_path))
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return image_item
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@ -570,7 +608,7 @@ class ImageCategoryTree(QTreeWidget):
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return "含量分布图"
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def scan_directory(self, work_dir: str):
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"""扫描目录中的所有图像文件"""
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"""扫描目录中的所有图像文件（深度递归扫描）"""
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self.clear_all_images()
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work_path = Path(work_dir)
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@ -578,13 +616,17 @@ class ImageCategoryTree(QTreeWidget):
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return
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image_extensions = ['*.png', '*.jpg', '*.jpeg', '*.tif', '*.tiff', '*.bmp']
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scan_roots: List[Path] = []
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_viz = work_path / "14_visualization"
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if _viz.is_dir():
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scan_roots.append(_viz)
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_wm = work_path / "1_water_mask"
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if _wm.is_dir():
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scan_roots.append(_wm)
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# 扫描根目录列表（按优先级）
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scan_roots: List[Path] = [
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work_path / "14_visualization",
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work_path / "1_water_mask",
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work_path / "9_water_quality_prediction",
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work_path / "10_feature_construction",
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]
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# 只保留存在的目录，并补充工作根目录作为兜底
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scan_roots = [p for p in scan_roots if p.is_dir()]
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if not scan_roots:
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scan_roots.append(work_path)
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@ -592,7 +634,8 @@ class ImageCategoryTree(QTreeWidget):
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image_files: List[Path] = []
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for root in scan_roots:
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for ext in image_extensions:
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for p in root.glob(f"**/{ext}"):
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# 使用 rglob 进行深度递归扫描
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for p in root.rglob(ext):
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key = os.path.normcase(os.path.normpath(str(p.resolve())))
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if key in seen_norm:
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continue
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