diff --git a/src/gui/water_quality_gui.py b/src/gui/water_quality_gui.py index 9df2e2a..1faf593 100644 --- a/src/gui/water_quality_gui.py +++ b/src/gui/water_quality_gui.py @@ -118,19 +118,16 @@ from src.gui.panels.step1_panel import Step1Panel from src.gui.panels.step2_panel import Step2Panel from src.gui.panels.step3_panel import Step3Panel from src.gui.panels.step4_sampling_panel import Step4SamplingPanel # 采样点布设（原step10→新step4） -from src.gui.panels.step5_panel import Step5Panel # 数据清洗（原step4→新step5） -from src.gui.panels.step6_panel import Step6Panel # 光谱特征（原step5→新step6） -from src.gui.panels.step7_panel import Step7Panel # 水质光谱指数（原step6→新step7） -from src.gui.panels.step8_panel import Step8Panel # 水质参数指数（原step7→新step8） -from src.gui.panels.step8_waterindex_panel import Step9WaterColorPanel # 水色指数反演 -from src.gui.panels.step9_concentration_panel import Step10ConcentrationPanel # 浓度反演 -from src.gui.panels.step9_panel import Step9Panel # 机器学习建模（原step8→新step9） -from src.gui.panels.step10_ml_panel import Step10MlPanel # 机器学习预测（原step11_ml→新step10） -from src.gui.panels.step11_panel import Step11NonEmpiricalPanel # 非经验模型预测 +from src.gui.panels.step5_clean_panel import Step5CleanPanel # 数据清洗 +from src.gui.panels.step6_feature_panel import Step6FeaturePanel # 光谱特征 +from src.gui.panels.step7_index_panel import Step7IndexPanel # 水质光谱指数 +from src.gui.panels.step10_map_panel import Step10MapPanel # 水色指数反演 +from src.gui.panels.step8_ml_train_panel import Step8MlTrainPanel # 机器学习建模 +from src.gui.panels.step9_ml_predict_panel import Step9MlPredictPanel # 机器学习预测 from src.gui.panels.step14_panel import Step14Panel from src.gui.dialogs import BandConfirmDialog, AISettingsDialog -from src.gui.panels.visualization_panel import VisualizationPanel -from src.gui.panels.report_generation_panel import ReportGenerationPanel +from src.gui.panels.step11_viz_panel import Step11VizPanel # 可视化（覆盖旧 step11_viz_panel.py） +from src.gui.panels.step12_report_panel import Step12ReportPanel # 报告生成 # Pipeline 核心异常（用于预检弹窗） from src.core.pipeline.runner import PipelineHalt @@ -1379,96 +1376,67 @@ class WaterQualityGUI(QMainWindow): }, 'step3': { 'deglint_kutser': '3_deglint/deglint_kutser.bsq', - 'deglint_goodman': '3_deglint/deglint_goodman.bsq', + 'deglint_goodman': '3_deglint/deglint_goodman.bsq', 'deglint_hedley': '3_deglint/deglint_hedley.bsq', 'deglint_sugar': '3_deglint/deglint_sugar.bsq', - 'deglint_interpolated': '3_deglint/interpolated_*.bsq' # * = interpolation_method + 'deglint_interpolated': '3_deglint/interpolated_*.bsq' }, - 'step4': { + 'step5_clean': { 'processed_data': '4_processed_data/processed_data.csv' }, - 'step5': { + 'step6_feature': { 'training_spectra': '5_training_spectra/training_spectra.csv' }, - 'step6': { + 'step7_index': { 'water_indices': '6_water_quality_indices/water_quality_indices.csv' }, - 'step7': { - 'models': '7_Supervised_Model_Training/' # 目录，包含各参数子目录 + 'step8_ml_train': { + 'models': '7_Supervised_Model_Training/' }, - 'step8_non_empirical_modeling': { - 'regression_models': '8_Regression_Modeling/' # 目录，包含各参数子目录 - }, - 'step9': { - 'custom_regression_models': '9_Custom_Regression_Modeling/' # 目录 - }, - 'step10': { + 'step4_sampling': { 'sampling_points': '10_sampling/sampling_spectra.csv' }, - 'step11_ml': { - 'predictions': '11_12_13_predictions/Machine_Learning_Prediction/' # 目录，包含机器学习预测结果 + 'step9_ml_predict': { + 'predictions': '11_12_13_predictions/Machine_Learning_Prediction/' }, - 'step11': { - 'regression_predictions': '11_12_13_predictions/Non_Empirical_Prediction/' # 目录，包含非经验模型预测结果 - }, - 'step12': { - 'custom_predictions': '11_12_13_predictions/Custom_Regression_Prediction/' # 目录，包含自定义回归预测结果 - }, - 'step14': { - 'distribution_maps': '14_visualization/' # 目录，包含专题图 + 'step10_map': { + 'distribution_maps': '14_visualization/' } } # 定义步骤间的依赖关系：{当前步骤: {输入字段: (依赖步骤, 输出类型, 面板属性名)}} self.step_dependencies = { 'step2': { - 'img_path': ('step1', 'reference_img', 'img_file'), # 步骤2需要参考影像 - 'water_mask_path': ('step1', 'water_mask', 'water_mask_file') # 步骤2可选水域掩膜 + 'img_path': ('step1', 'reference_img', 'img_file'), + 'water_mask_path': ('step1', 'water_mask', 'water_mask_file') }, 'step3': { - 'img_path': ('step1', 'reference_img', 'img_file'), # 步骤3需要参考影像 - 'water_mask': ('step1', 'water_mask', 'water_mask_file'), # 步骤3需要水域掩膜 + 'img_path': ('step1', 'reference_img', 'img_file'), + 'water_mask': ('step1', 'water_mask', 'water_mask_file'), }, - 'step4': { - # 步骤4主要处理CSV文件，一般不依赖前面步骤的输出 + 'step6_feature': { + 'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'), + 'csv_path': ('step5_clean', 'processed_data', 'csv_file'), + 'boundary_mask_path': ('step1', 'water_mask', 'boundary_mask_file'), + 'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file') }, - 'step5': { - 'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'), # 步骤5需要去耀斑影像 - 'csv_path': ('step4', 'processed_data', 'csv_file'), # 步骤5需要处理后的CSV - 'boundary_mask_path': ('step1', 'water_mask', 'boundary_mask_file'), # 步骤5可选水体掩膜 - 'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file') # 步骤5可选耀斑掩膜 + 'step7_index': { + 'training_csv_path': ('step6_feature', 'training_spectra', 'output_file') }, - 'step6': { - 'training_csv_path': ('step5', 'training_spectra', 'output_file') # 步骤6需要步骤5输出的训练光谱 + 'step8_ml_train': { + 'training_csv_path': ('step7_index', 'water_indices', 'csv_file') }, - 'step7': { - 'csv_path': ('step5', 'training_spectra', 'csv_file') # 步骤7需要训练光谱数据 + 'step4_sampling': { + 'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'), + 'water_mask_path': ('step1', 'water_mask', 'water_mask_file'), + 'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file') }, - 'step8_non_empirical_modeling': { - 'csv_path': ('step5', 'training_spectra', 'csv_file') # 步骤8非经验建模需要训练光谱数据 + 'step9_ml_predict': { + 'sampling_csv_path': ('step4_sampling', 'sampling_points', 'sampling_csv_file'), + 'models_dir': ('step8_ml_train', 'models', 'models_dir_file') }, - 'step9': { - 'csv_path': ('step5', 'training_spectra', 'csv_file') # 步骤9需要训练光谱数据 - }, - 'step10': { - 'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'), # 步骤10需要去耀斑影像 - 'water_mask_path': ('step1', 'water_mask', 'water_mask_file'), # 步骤10需要水域掩膜 - 'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file') # 步骤10可选耀斑掩膜 - }, - 'step11_ml': { - 'sampling_csv_path': ('step10', 'sampling_points', 'sampling_csv_file'), # 步骤11ML需要采样点 - 'models_dir': ('step7', 'models', 'models_dir_file') # 步骤11ML需要训练好的模型 - }, - 'step11': { - 'sampling_csv_path': ('step10', 'sampling_points', 'sampling_csv_file'), # 步骤11需要采样点 - 'models_dir': ('step8_non_empirical_modeling', 'regression_models', 'models_dir') # 步骤11需要回归模型 - }, - 'step12': { - 'sampling_csv_path': ('step10', 'sampling_points', 'sampling_csv_file'), # 步骤12需要采样点 - 'models_dir': ('step9', 'custom_regression_models', 'models_dir') # 步骤12需要自定义回归模型 - }, - 'step14': { - 'prediction_csv_path': ('step11_ml', 'predictions', 'prediction_csv_file') # 步骤14需要预测结果CSV + 'step10_map': { + 'prediction_csv_path': ('step9_ml_predict', 'predictions', 'prediction_csv_file') } } @@ -1845,25 +1813,22 @@ class WaterQualityGUI(QMainWindow): "阶段一：影像预处理": [ ("step1", "1. 水域掩膜生成"), ("step2", "2. 耀斑区域识别"), - ("step3", "3. 耀斑去除与修复"), + ("step3", "3. 耀斑去除与修复") ], - "阶段二：样本数据准备 ": [ - ("step4", "4. 采样点布设"), - ("step5", "5. 数据清洗"), - ("step6", "6. 光谱特征"), - ("step7", "7. 水质光谱指数计算"), - ("step8", "8. 水质参数指数计算"), + "阶段二：样本数据准备": [ + ("step4_sampling", "4. 采样点布设"), + ("step5_clean", "5. 数据清洗"), + ("step6_feature", "6. 光谱特征提取"), + ("step7_index", "7. 水质指数计算") ], "阶段三：模型构建与训练": [ - ("step9", "9. 机器学习建模"), + ("step8_ml_train", "8. 机器学习建模") ], - "阶段四：预测与成果输出 ": [ - ("step10", "10. 机器学习预测"), - ("step8_non_empirical_modeling", "11. 水色指数反演"), - ("step9_concentration", "12. 浓度反演"), - ("step14", "14. 专题图生成"), - ("step9_viz", "15. 可视化分析"), - ("step_report", "16. 分析报告生成"), + "阶段四：预测与成果输出": [ + ("step9_ml_predict", "9. 机器学习预测"), + ("step10_map", "10. 专题图生成"), + ("step11_viz", "11. 可视化展示"), + ("step12_report", "12. 分析报告生成") ] } @@ -1883,11 +1848,7 @@ class WaterQualityGUI(QMainWindow): self.step_list.addItem(stage_item) # 添加该阶段的所有步骤 - HIDDEN_STEP_IDS = {"step8_non_empirical_modeling"} for step_id, step_display in steps: - if step_id in HIDDEN_STEP_IDS: - continue - item = QListWidgetItem(f" └─ {step_display}") item.setData(Qt.UserRole, step_id) @@ -1960,38 +1921,32 @@ class WaterQualityGUI(QMainWindow): self.step4_panel = Step4SamplingPanel() self.step_stack.addTab(self.create_scroll_area(self.step4_panel), QIcon(self.get_icon_path("4.png")), "采样点布设") - self.step5_panel = Step5Panel() - self.step_stack.addTab(self.create_scroll_area(self.step5_panel), QIcon(self.get_icon_path("5.png")), "数据清洗") + self.step5_clean_panel = Step5CleanPanel() + self.step_stack.addTab(self.create_scroll_area(self.step5_clean_panel), QIcon(self.get_icon_path("5.png")), "数据清洗") - self.step6_panel = Step6Panel() - self.step_stack.addTab(self.create_scroll_area(self.step6_panel), QIcon(self.get_icon_path("6.png")), "光谱特征") + self.step6_feature_panel = Step6FeaturePanel() + self.step_stack.addTab(self.create_scroll_area(self.step6_feature_panel), QIcon(self.get_icon_path("6.png")), "光谱特征") - self.step7_panel = Step7Panel() - self.step_stack.addTab(self.create_scroll_area(self.step7_panel), QIcon(self.get_icon_path("7.png")), "水质光谱指数计算") + self.step7_index_panel = Step7IndexPanel() + self.step_stack.addTab(self.create_scroll_area(self.step7_index_panel), QIcon(self.get_icon_path("7.png")), "水质光谱指数计算") - self.step8_panel = Step8Panel() - self.step_stack.addTab(self.create_scroll_area(self.step8_panel), QIcon(self.get_icon_path("7.png")), "水质参数指数计算") + self.step8_ml_train_panel = Step8MlTrainPanel() + self.step_stack.addTab(self.create_scroll_area(self.step8_ml_train_panel), QIcon(self.get_icon_path("8.png")), "机器学习建模") - self.step9_panel = Step9Panel() - self.step_stack.addTab(self.create_scroll_area(self.step9_panel), QIcon(self.get_icon_path("8.png")), "机器学习建模") + self.step9_ml_predict_panel = Step9MlPredictPanel() + self.step_stack.addTab(self.create_scroll_area(self.step9_ml_predict_panel), QIcon(self.get_icon_path("10.png")), "机器学习预测") - self.step8_waterindex_panel = Step9WaterColorPanel() - self.step_stack.addTab(self.create_scroll_area(self.step8_waterindex_panel), QIcon(self.get_icon_path("8.png")), "水色指数反演") - - self.step10_ml_panel = Step10MlPanel() - self.step_stack.addTab(self.create_scroll_area(self.step10_ml_panel), QIcon(self.get_icon_path("10.png")), "机器学习预测") - - self.step9_concentration_panel = Step10ConcentrationPanel() - self.step_stack.addTab(self.create_scroll_area(self.step9_concentration_panel), QIcon(self.get_icon_path("9.png")), "浓度反演") + self.step10_map_panel = Step10MapPanel() + self.step_stack.addTab(self.create_scroll_area(self.step10_map_panel), QIcon(self.get_icon_path("10.png")), "专题图生成") self.step14_panel = Step14Panel() - self.step_stack.addTab(self.create_scroll_area(self.step14_panel), QIcon(self.get_icon_path("10.png")), "专题图生成") + self.step_stack.addTab(self.create_scroll_area(self.step14_panel), QIcon(self.get_icon_path("11.png")), "专题图生成") - self.viz_panel = VisualizationPanel() - self.step_stack.addTab(self.create_scroll_area(self.viz_panel), QIcon(self.get_icon_path("9.png")), "可视化") + self.step11_viz_panel = Step11VizPanel() + self.step_stack.addTab(self.create_scroll_area(self.step11_viz_panel), QIcon(self.get_icon_path("9.png")), "可视化") - self.report_panel = ReportGenerationPanel(main_window=self) - self.step_stack.addTab(self.create_scroll_area(self.report_panel), QIcon(self.get_icon_path("10.png")), "报告生成") + self.step12_report_panel = Step12ReportPanel(main_window=self) + self.step_stack.addTab(self.create_scroll_area(self.step12_report_panel), QIcon(self.get_icon_path("10.png")), "报告生成") # 连接Tab切换信号，实现双向同步（必须在step_stack创建后） self.step_stack.currentChanged.connect(self.on_tab_changed) @@ -2133,18 +2088,15 @@ class WaterQualityGUI(QMainWindow): 'step1': 0, 'step2': 1, 'step3': 2, - 'step4': 3, - 'step5': 4, - 'step6': 5, - 'step7': 6, - 'step8': 7, - 'step9': 8, - 'step10': 9, - 'step8_non_empirical_modeling': 10, - 'step9_concentration': 11, - 'step14': 12, - 'step9_viz': 13, - 'step_report': 14, + 'step4_sampling': 3, + 'step5_clean': 4, + 'step6_feature': 5, + 'step7_index': 6, + 'step8_ml_train': 7, + 'step9_ml_predict': 8, + 'step10_map': 9, + 'step11_viz': 11, + 'step12_report': 12, } if item_data in step_id_to_tab: @@ -2163,18 +2115,16 @@ class WaterQualityGUI(QMainWindow): 0: 'step1', 1: 'step2', 2: 'step3', - 3: 'step4', - 4: 'step5', - 5: 'step6', - 6: 'step7', - 7: 'step8', - 8: 'step9', - 9: 'step10', - 10: 'step8_non_empirical_modeling', - 11: 'step9_concentration', - 12: 'step14', - 13: 'step9_viz', - 14: 'step_report', + 3: 'step4_sampling', + 4: 'step5_clean', + 5: 'step6_feature', + 6: 'step7_index', + 7: 'step8_ml_train', + 8: 'step9_ml_predict', + 9: 'step10_map', + 10: None, # 遗留 step14_panel，保留 tab 但不加入 process_stages + 11: 'step11_viz', + 12: 'step12_report', } if index not in tab_to_step_id: @@ -2207,43 +2157,35 @@ class WaterQualityGUI(QMainWindow): # Step5（数据清洗）切换时自动填充数据流转路径 elif index == 4: - self.step5_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + self.step5_clean_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) # Step6（光谱特征）切换时自动填充输出路径 elif index == 5: - self.step6_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + self.step6_feature_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) # Step7（水质光谱指数计算）切换时自动填充水质参数 CSV elif index == 6: - self.step7_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + self.step7_index_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - # Step8（水质参数指数计算）切换时自动填充水质参数 CSV + # Step8（机器学习建模）切换时自动填充训练数据和输出路径 elif index == 7: - self.step8_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + self.step8_ml_train_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - # Step9（机器学习建模）切换时自动填充训练数据和输出路径 + # Step9（机器学习预测）切换时自动填充采样光谱和模型目录 elif index == 8: - self.step9_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + self.step9_ml_predict_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - # Step10（机器学习预测）切换时自动填充采样光谱和模型目录 + # Step10（水色指数反演）切换时自动填充光谱数据和输出路径 elif index == 9: - self.step10_ml_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + self.step10_map_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - # Step11（水色指数反演）切换时自动填充光谱数据和输出路径 + # Step12（专题图生成）切换时自动填充预测结果目录 elif index == 10: - self.step8_waterindex_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - - # Step12（浓度反演）切换时自动填充 QAA 结果和输出路径 - elif index == 11: - self.step9_concentration_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - - # Step14（专题图生成）切换时自动填充预测结果目录 - elif index == 12: self.step14_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) - # Step15（可视化分析）切换时自动推断图像目录并加载目录树 - elif index == 13: - self.viz_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) + # Step13（可视化分析）切换时自动推断图像目录并加载目录树 + elif index == 11: + self.step11_viz_panel.update_from_config(work_dir=self.work_dir, pipeline=self.pipeline) def apply_stylesheet(self): """应用样式表 - 应用现代化设计风格""" @@ -2282,24 +2224,22 @@ class WaterQualityGUI(QMainWindow): self.step2_panel.set_config(config['step2']) if 'step3' in config: self.step3_panel.set_config(config['step3']) - if 'step4' in config: - self.step4_panel.set_config(config['step4']) - if 'step5' in config: - self.step5_panel.set_config(config['step5']) - if 'step6' in config: - self.step6_panel.set_config(config['step6']) - if 'step7' in config: - self.step7_panel.set_config(config['step7']) - if 'step10' in config: - self.step4_panel.set_config(config['step10']) - if 'step11_ml' in config: - self.step10_ml_panel.set_config(config['step11_ml']) + if 'step4_sampling' in config: + self.step4_sampling_panel.set_config(config['step4_sampling']) + if 'step5_clean' in config: + self.step5_clean_panel.set_config(config['step5_clean']) + if 'step6_feature' in config: + self.step6_feature_panel.set_config(config['step6_feature']) + if 'step7_index' in config: + self.step7_index_panel.set_config(config['step7_index']) + if 'step9_ml_predict' in config: + self.step9_ml_predict_panel.set_config(config['step9_ml_predict']) if 'step14' in config: self.step14_panel.set_config(config['step14']) - if 'visualization' in config: - self.viz_panel.set_config(config['visualization']) - if 'report_generation' in config: - self.report_panel.set_config(config['report_generation']) + if 'step11_viz' in config: + self.step11_viz_panel.set_config(config['step11_viz']) + if 'step12_report' in config: + self.step12_report_panel.set_config(config['step12_report']) self.config_file = file_path self.log_message(f"已加载配置: {file_path}", "info") @@ -2336,15 +2276,16 @@ class WaterQualityGUI(QMainWindow): 'step1': self.step1_panel.get_config(), 'step2': self.step2_panel.get_config(), 'step3': self.step3_panel.get_config(), - 'step4': self.step4_panel.get_config(), - 'step5': self.step5_panel.get_config(), - 'step6': self.step6_panel.get_config(), - 'step7': self.step7_panel.get_config(), - 'step10': self.step4_panel.get_config(), - 'step11_ml': self.step10_ml_panel.get_config(), + 'step4_sampling': self.step4_sampling_panel.get_config(), + 'step5_clean': self.step5_clean_panel.get_config(), + 'step6_feature': self.step6_feature_panel.get_config(), + 'step7_index': self.step7_index_panel.get_config(), + 'step8_ml_train': self.step8_ml_train_panel.get_config(), + 'step9_ml_predict': self.step9_ml_predict_panel.get_config(), + 'step10_map': self.step10_map_panel.get_config(), + 'step11_viz': self.step11_viz_panel.get_config(), + 'step12_report': self.step12_report_panel.get_config(), 'step14': self.step14_panel.get_config(), - 'visualization': self.viz_panel.get_config(), - 'report_generation': self.report_panel.get_config(), } return config @@ -2391,12 +2332,15 @@ class WaterQualityGUI(QMainWindow): 'step1': self.step1_panel, 'step2': self.step2_panel, 'step3': self.step3_panel, - 'step4': self.step4_panel, - 'step5': self.step5_panel, - 'step6': self.step6_panel, - 'step7': self.step7_panel, - 'step10': self.step4_panel, - 'step11_ml': self.step10_ml_panel, + 'step4_sampling': self.step4_sampling_panel, + 'step5_clean': self.step5_clean_panel, + 'step6_feature': self.step6_feature_panel, + 'step7_index': self.step7_index_panel, + 'step8_ml_train': self.step8_ml_train_panel, + 'step9_ml_predict': self.step9_ml_predict_panel, + 'step10_map': self.step10_map_panel, + 'step11_viz': self.step11_viz_panel, + 'step12_report': self.step12_report_panel, 'step14': self.step14_panel, } return panel_map.get(step_id) @@ -2489,17 +2433,17 @@ class WaterQualityGUI(QMainWindow): '1_water_mask': 'step1', '2_glint': 'step2', '3_deglint': 'step3', - '4_processed_data': 'step4', - '5_training_spectra': 'step5', - '6_water_quality_indices': 'step6', - '7_Supervised_Model_Training': 'step7', - '8_Regression_Modeling': 'step8_non_empirical_modeling', - '9_Custom_Regression_Modeling': 'step9', - '10_sampling': 'step10', - '11_12_13_predictions/Machine_Learning_Prediction': 'step11_ml', - '11_12_13_predictions/Non_Empirical_Prediction': 'step11', - '11_12_13_predictions/Custom_Regression_Prediction': 'step12', - '14_visualization': 'step14' + '4_processed_data': 'step4_sampling', + '5_training_spectra': 'step5_clean', + '6_water_quality_indices': 'step6_feature', + '7_Supervised_Model_Training': 'step7_index', + '8_Regression_Modeling': 'step8_ml_train', + '9_Custom_Regression_Modeling': 'step9_ml_predict', + '11_12_13_predictions/Machine_Learning_Prediction': 'step9_ml_predict', + '11_12_13_predictions/Non_Empirical_Prediction': 'step10_map', + '11_12_13_predictions/Custom_Regression_Prediction': 'step11_viz', + '14_visualization': 'step12_report', + '14_geotiff_batch_rendering': 'step14' } for subdir, step_ids in subdirs.items(): @@ -2541,15 +2485,15 @@ class WaterQualityGUI(QMainWindow): discovered_outputs[step_id]['glint_mask'] = str(file_path) elif 'deglint' in file_name and step_id == 'step3': discovered_outputs[step_id]['deglint_image'] = str(file_path) - elif 'processed_data' in file_name and step_id == 'step4': + elif 'processed_data' in file_name and step_id == 'step4_sampling': discovered_outputs[step_id]['processed_data'] = str(file_path) - elif 'training_spectra' in file_name and step_id == 'step5': + elif 'training_spectra' in file_name and step_id == 'step5_clean': discovered_outputs[step_id]['training_spectra'] = str(file_path) - elif 'water_quality_indices' in file_name and step_id == 'step6': + elif 'water_quality_indices' in file_name and step_id == 'step6_feature': discovered_outputs[step_id]['water_indices'] = str(file_path) - elif 'sampling_spectra' in file_name and step_id == 'step10': + elif 'sampling_spectra' in file_name and step_id == 'step4_sampling': discovered_outputs[step_id]['sampling_points'] = str(file_path) - elif file_name.endswith('.csv') and step_id in ['step11_ml', 'step11', 'step12']: + elif file_name.endswith('.csv') and step_id in ['step9_ml_predict', 'step10_map', 'step11_viz']: discovered_outputs[step_id]['predictions'] = str(file_path) # 更新内部记录 @@ -2572,8 +2516,8 @@ class WaterQualityGUI(QMainWindow): # 首先扫描工作目录发现已有的输出文件 self.scan_work_directory_for_files(work_path) - step_order = ['step2', 'step3', 'step4', 'step5', 'step6', 'step7', 'step8_non_empirical_modeling', 'step9', - 'step10', 'step11_ml', 'step11', 'step12', 'step14'] + step_order = ['step2', 'step3', 'step4_sampling', 'step5_clean', 'step6_feature', 'step7_index', + 'step8_ml_train', 'step9_ml_predict', 'step10_map', 'step11_viz', 'step12_report', 'step14'] filled_count = 0 for step_id in step_order: @@ -2594,11 +2538,15 @@ class WaterQualityGUI(QMainWindow): panels_with_dependencies = [ ('step2', self.step2_panel), ('step3', self.step3_panel), - ('step5', self.step5_panel), - ('step6', self.step6_panel), - ('step7', self.step7_panel), - ('step10', self.step4_panel), - ('step11_ml', self.step10_ml_panel), + ('step4_sampling', self.step4_sampling_panel), + ('step5_clean', self.step5_clean_panel), + ('step6_feature', self.step6_feature_panel), + ('step7_index', self.step7_index_panel), + ('step8_ml_train', self.step8_ml_train_panel), + ('step9_ml_predict', self.step9_ml_predict_panel), + ('step10_map', self.step10_map_panel), + ('step11_viz', self.step11_viz_panel), + ('step12_report', self.step12_report_panel), ('step14', self.step14_panel) ] @@ -2669,10 +2617,10 @@ class WaterQualityGUI(QMainWindow): self.statusBar().showMessage(f"工作目录: {dir_path}") # 同步到可视化面板 - if hasattr(self, 'viz_panel'): - self.viz_panel.set_work_dir(dir_path) - if hasattr(self, 'report_panel'): - self.report_panel.set_work_dir(dir_path) + if hasattr(self, 'step11_viz_panel'): + self.step11_viz_panel.set_work_dir(dir_path) + if hasattr(self, 'step12_report_panel'): + self.step12_report_panel.set_work_dir(dir_path) def open_work_directory(self): """打开工作目录""" @@ -2991,11 +2939,11 @@ class WaterQualityGUI(QMainWindow): # 准备实际运行配置（排除未启用的步骤） worker_config = copy.deepcopy(config) - step6_cfg = worker_config.get('step6') + step6_cfg = worker_config.get('step6_feature') if step6_cfg: enabled = step6_cfg.pop('enabled', True) if not enabled: - worker_config.pop('step6', None) + worker_config.pop('step6_feature', None) # 工作线程内创建 Pipeline，避免主线程阻塞及 Qt5Agg 子线程绘图卡死 self.worker = WorkerThread(work_dir, worker_config, mode='full', skip_list=skip_list) @@ -3225,19 +3173,18 @@ class WaterQualityGUI(QMainWindow): def update_ui_for_training_mode(self): """根据训练数据模式更新UI状态""" # 需要禁用的步骤ID（对应无训练数据模式下需要禁用的步骤） - disabled_step_ids = ['step4', 'step5', 'step6', 'step7', 'step8', 'step8_non_empirical_modeling', 'step9'] + disabled_step_ids = ['step4_sampling', 'step5_clean', 'step6_feature', 'step7_index', 'step9_ml_predict'] # 更新标签页的启用/禁用状态 - step_id_to_tab = { - 'step1': 0, 'step2': 1, 'step3': 2, 'step4': 3, - 'step5': 4, 'step6': 5, 'step7': 6, 'step8': 7, - 'step9': 8, 'step10': 9, 'step8_non_empirical_modeling': 10, - 'step9_concentration': 11, 'step14': 12, 'step9_viz': 13, 'step_report': 14 + step_id_to_tab_training = { + 'step1': 0, 'step2': 1, 'step3': 2, 'step4_sampling': 3, + 'step5_clean': 4, 'step6_feature': 5, 'step7_index': 6, 'step9_ml_predict': 7, + 'step10_map': 8, 'step14': 10, 'step11_viz': 11, 'step12_report': 12 } for step_id in disabled_step_ids: - if step_id in step_id_to_tab: - tab_index = step_id_to_tab[step_id] + if step_id in step_id_to_tab_training: + tab_index = step_id_to_tab_training[step_id] if tab_index < self.step_stack.count(): self.step_stack.setTabEnabled(tab_index, self.has_training_data)