结构修改，后端文件跟前端内容进行适配
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@ -272,10 +272,10 @@ class WorkerThread(QThread):
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ctx = PipelineContext(
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img_path=self.config.get('step1', {}).get('img_path'),
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water_mask_path=self.config.get('step1', {}).get('mask_path'),
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csv_path=self.config.get('step4', {}).get('csv_path'),
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boundary_path=self.config.get('step5', {}).get('boundary_path'),
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boundary_shp_path=self.config.get('step14', {}).get('boundary_shp_path'),
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formula_csv_path=self.config.get('step12', {}).get('formula_csv_path'),
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csv_path=self.config.get('step4_sampling', {}).get('csv_path'),
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boundary_path=self.config.get('step5_clean', {}).get('boundary_path'),
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boundary_shp_path=self.config.get('step11_map', {}).get('boundary_shp_path'),
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formula_csv_path=self.config.get('step8_non_empirical_modeling', {}).get('formula_csv_path'),
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work_dir=self.work_dir,
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user_config=self.config
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)
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@ -323,21 +323,16 @@ class WorkerThread(QThread):
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'step1': 'step1_generate_water_mask',
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'step2': 'step2_find_glint_area',
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'step3': 'step3_remove_glint',
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'step4': 'step5_process_csv',
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'step5': 'step6_extract_spectra',
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'step7': 'step7_calc_indices',
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'step4_sampling': 'step4_sampling',
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'step5_clean': 'step5_process_csv',
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'step6_feature': 'step6_extract_spectra',
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'step7_index': 'step7_calc_indices',
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'step8': 'step8_train_ml',
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'step8_ml_train': 'step8_train_ml',
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'step8_non_empirical_modeling': 'step8_non_empirical_modeling',
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'step8_qaa': 'step8_qaa_inversion',
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'step9': 'step9_watercolor_inversion',
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'step9_ml_predict': 'step9_predict_ml',
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'step10': 'step4_sampling',
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'step10_map': 'step10_map',
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'step11_ml': 'step9_predict_ml',
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'step11': 'step11_non_empirical_prediction',
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'step14': 'step10_map'
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'step10_watercolor': 'step9_watercolor_inversion',
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'step11_map': 'step10_map',
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}
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if step_name not in step_method_map:
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@ -367,17 +362,9 @@ class WorkerThread(QThread):
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step_config['skip_dependency_check'] = True
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if step_name == 'step14':
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step_config.pop('step9_batch_mode', None)
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step_config.pop('prediction_csv_dir', None)
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step_config.pop('recursive_csv_scan', None)
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if step_name in ['step2', 'step3', 'step4', 'step5', 'step7', 'step10', 'step11_ml', 'step11', 'step12']:
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if step_name in ['step2', 'step3', 'step4_sampling', 'step5_clean', 'step7_index', 'step9_ml_predict']:
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step_config.pop('output_path', None)
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if step_name == 'step11' and 'models_dir' in step_config:
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step_config['non_empirical_models_dir'] = step_config.pop('models_dir')
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method = getattr(self.pipeline, method_name)
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result = method(**step_config)
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@ -436,100 +423,76 @@ class WorkerThread(QThread):
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" → 请确认「耀斑去除」已成功运行，或重新配置路径。"
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)
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# ── 步骤4：实测水质数据 CSV ──
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step4_cfg = config.get('step4', {})
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# ── 步骤4_sampling：实测水质数据 CSV ──
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step4_cfg = config.get('step4_sampling', {})
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csv_path = step4_cfg.get('csv_path')
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if csv_path and not os.path.isfile(csv_path):
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errors.append(
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f"步骤 4：实测水质数据文件不存在：\n {csv_path}\n"
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f"步骤 4_sampling：实测水质数据文件不存在：\n {csv_path}\n"
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" → 请检查 CSV 路径是否正确，或重新上传数据文件。"
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)
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# ── 步骤5：采样点平均光谱提取 ──
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step5_cfg = config.get('step5', {})
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# ── 步骤5_clean：采样点平均光谱提取 ──
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step5_cfg = config.get('step5_clean', {})
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step5_csv = step5_cfg.get('csv_path')
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boundary_path = step5_cfg.get('boundary_path')
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if step5_csv and not os.path.isfile(step5_csv):
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errors.append(
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f"步骤 5：实测水质数据文件不存在：\n {step5_csv}\n"
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f"步骤 5_clean：实测水质数据文件不存在：\n {step5_csv}\n"
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" → 请检查「流程步骤-阶段五」中的 CSV 路径。"
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)
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if boundary_path and not os.path.isfile(boundary_path):
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errors.append(
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f"步骤 5：边界矢量文件不存在：\n {boundary_path}\n"
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f"步骤 5_clean：边界矢量文件不存在：\n {boundary_path}\n"
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" → 请确认「流程步骤-阶段五」中已填写有效的边界 shp 路径。"
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)
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# ── 步骤6（水质光谱指数）：训练光谱 CSV ──
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step6_cfg = config.get('step6', {})
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# ── 步骤6_feature（水质光谱指数）：训练光谱 CSV ──
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step6_cfg = config.get('step6_feature', {})
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training_csv = step6_cfg.get('training_csv_path')
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if training_csv and not os.path.isfile(training_csv):
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errors.append(
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f"步骤 6（水质光谱指数）：训练光谱文件不存在：\n {training_csv}\n"
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" → 请确认步骤 5 已成功运行并生成了训练光谱。"
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f"步骤 6_feature（水质光谱指数）：训练光谱文件不存在：\n {training_csv}\n"
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" → 请确认步骤 5_clean 已成功运行并生成了训练光谱。"
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)
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# ── 步骤7（ML 建模） ──
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step7_cfg = config.get('step7', {})
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# ── 步骤8_ml_train（ML 建模） ──
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step7_cfg = config.get('step8_ml_train', {})
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step7_csv = step7_cfg.get('training_csv_path')
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if step7_csv and not os.path.isfile(step7_csv):
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errors.append(
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f"步骤 7（ML 建模）：训练光谱文件不存在：\n {step7_csv}\n"
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" → 请确认步骤 5 已成功运行并生成了训练光谱。"
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f"步骤 8_ml_train（ML 建模）：训练光谱文件不存在：\n {step7_csv}\n"
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" → 请确认步骤 5_clean 已成功运行并生成了训练光谱。"
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)
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# ── 步骤11 ML 预测：密集采样点 CSV + 模型目录 ──
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step11_ml_cfg = config.get('step11_ml', {})
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ml_csv = step11_ml_cfg.get('sampling_csv_path')
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models_dir = step11_ml_cfg.get('models_dir')
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# ── 步骤9_ml_predict：密集采样点 CSV + 模型目录 ──
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step9_cfg = config.get('step9_ml_predict', {})
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ml_csv = step9_cfg.get('sampling_csv_path')
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models_dir = step9_cfg.get('models_dir')
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if ml_csv and not os.path.isfile(ml_csv):
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errors.append(
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f"步骤 11 ML 预测：采样点 CSV 不存在：\n {ml_csv}\n"
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f"步骤 9_ml_predict：采样点 CSV 不存在：\n {ml_csv}\n"
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" → 请确认「流程步骤-阶段七（采样点布设）」已成功运行。"
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)
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if models_dir and not os.path.isdir(models_dir):
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errors.append(
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f"步骤 11 ML 预测：模型目录不存在：\n {models_dir}\n"
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f"步骤 9_ml_predict：模型目录不存在：\n {models_dir}\n"
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" → 请确认「流程步骤-阶段六（机器学习建模）」已成功运行。"
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)
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# ── 步骤11 回归预测：模型目录 ──
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step11_cfg = config.get('step11', {})
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step11_csv = step11_cfg.get('sampling_csv_path')
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step11_dir = step11_cfg.get('models_dir')
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if step11_csv and not os.path.isfile(step11_csv):
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errors.append(
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f"步骤 11 回归预测：采样点 CSV 不存在：\n {step11_csv}\n"
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" → 请确认「流程步骤-阶段七（采样点布设）」已成功运行。"
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)
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if step11_dir and not os.path.isdir(step11_dir):
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errors.append(
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f"步骤 11 回归预测：模型目录不存在：\n {step11_dir}\n"
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" → 请确认「流程步骤-阶段八（非经验建模）」已成功运行。"
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)
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# ── 步骤12 自定义回归预测：公式 CSV ──
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step12_cfg = config.get('step12', {})
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formula_csv = step12_cfg.get('formula_csv_path')
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if formula_csv and not os.path.isfile(formula_csv):
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errors.append(
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f"步骤 12（自定义回归预测）：公式 CSV 文件不存在：\n {formula_csv}\n"
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" → 请确认「流程步骤-阶段十二」中已填写有效的公式文件路径。"
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)
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# ── 步骤14 专题图：预测结果 CSV + 边界 shp ──
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step14_cfg = config.get('step14', {})
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pred_csv = step14_cfg.get('prediction_csv_path')
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boundary_shp = step14_cfg.get('boundary_shp_path')
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# ── 步骤11_map 专题图：预测结果 CSV + 边界 shp ──
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step11_cfg = config.get('step11_map', {})
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pred_csv = step11_cfg.get('prediction_csv_path')
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boundary_shp = step11_cfg.get('boundary_shp_path')
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if pred_csv and not os.path.isfile(pred_csv):
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errors.append(
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f"步骤 14（专题图）：预测结果 CSV 不存在：\n {pred_csv}\n"
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f"步骤 11_map（专题图）：预测结果 CSV 不存在：\n {pred_csv}\n"
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" → 请确认机器学习或回归预测步骤已成功运行。"
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)
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if boundary_shp and not os.path.isfile(boundary_shp):
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errors.append(
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f"步骤 14（专题图）：边界 shp 文件不存在：\n {boundary_shp}\n"
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" → 请确认「流程步骤-阶段十四」中已填写有效的边界矢量文件路径。"
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f"步骤 11_map（专题图）：边界 shp 文件不存在：\n {boundary_shp}\n"
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" → 请确认「流程步骤-阶段十一」中已填写有效的边界矢量文件路径。"
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)
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# ── 汇总报错：任一缺失立即抛出 PipelineHalt ──
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