fix(runner): 14 Facade kwargs 兜底 + 4 spec parameter_map 修正 + step6_75 路由切到 indices
- 14 个 stepX_... Facade 形参表末尾加 **kwargs，杜绝 Runner 注入未声明 key 时的 TypeError（典型：step3 收到 glint_mask_path） - runner._invoke user_overrides 合并加 v is not None and v != '' 过滤，避免 GUI 面板空值覆盖 ctx 中已写入的有效路径 - PIPELINE_STEPS 加 4 个 parameter_map 修正 ctx 字段名→形参名错位：step6_5/6_75: training_csv_path→csv_path；step8_5: models_dir→non_empirical_models_dir；step8_75: models_dir→custom_regression_dir - step6_75 路由从 training_csv_path 切到 indices_path（requires + parameter_map 同步）；配合 skip_when_missing，未跑 step5_5 时自动 skip - worker_thread.py: mode='full' 切到 PipelineRunner + PipelineContext 调度
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@ -84,11 +84,13 @@ PIPELINE_STEPS: List[StepSpec] = [
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StepSpec(
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step_id="step6_5", method_name="step6_5_non_empirical_modeling",
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requires=["training_csv_path"], produces=["models_dir"],
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parameter_map={"training_csv_path": "csv_path"},
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description="非经验统计回归",
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),
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StepSpec(
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step_id="step6_75", method_name="step6_75_custom_regression",
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requires=["training_csv_path"], produces=["models_dir"],
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requires=["indices_path"], produces=["models_dir"],
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parameter_map={"indices_path": "csv_path"},
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description="自定义回归分析",
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),
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StepSpec(
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@ -104,12 +106,14 @@ PIPELINE_STEPS: List[StepSpec] = [
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StepSpec(
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step_id="step8_5", method_name="step8_5_predict_with_non_empirical_models",
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requires=["sampling_csv_path", "models_dir"], produces=["prediction_dir"],
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parameter_map={"models_dir": "non_empirical_models_dir"},
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description="非经验模型预测",
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),
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StepSpec(
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step_id="step8_75", method_name="step8_75_predict_with_custom_regression",
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requires=["sampling_csv_path", "models_dir", "formula_csv_path"],
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produces=["prediction_dir"],
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parameter_map={"models_dir": "custom_regression_dir"},
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description="自定义回归预测",
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),
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StepSpec(
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@ -185,7 +189,10 @@ class PipelineRunner:
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# 2) 允许用户在 ctx.user_config[step_id] 覆盖/补充
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user_overrides = ctx.user_config.get(spec.step_id) or {}
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if isinstance(user_overrides, dict):
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kwargs.update(user_overrides)
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for k, v in user_overrides.items():
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# ★ 关键防御：绝不用 GUI 的“空字符串”或 None 覆盖上游传来的有效路径
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if v is not None and v != "":
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kwargs[k] = v
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# 3) 状态置 start
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ctx.append_log(
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@ -267,7 +267,7 @@ class WaterQualityInversionPipeline:
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use_ndwi: bool = False,
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skip_dependency_check: bool = False,
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generate_png: bool = True,
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output_path: Optional[str] = None) -> str:
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output_path: Optional[str] = None, **kwargs) -> str:
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"""步骤1: 生成或设置水域mask（Facade）"""
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step_start_time = time.time()
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try:
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@ -392,7 +392,7 @@ class WaterQualityInversionPipeline:
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max_area: Optional[int] = None,
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buffer_size: Optional[int] = None,
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water_mask_path: Optional[str] = None,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""步骤2: 找到耀斑区域（Facade）"""
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step_start_time = time.time()
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try:
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@ -533,7 +533,7 @@ class WaterQualityInversionPipeline:
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sugar_iter: Optional[int] = 3,
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sugar_termination_thresh: float = 20.0,
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output_path: Optional[str] = None,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""步骤3: 去除耀斑（Facade）"""
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step_start_time = time.time()
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try:
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@ -588,7 +588,7 @@ class WaterQualityInversionPipeline:
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status="failed", error=str(e))
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raise
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def step4_process_csv(self, csv_path: str, skip_dependency_check: bool = False) -> str:
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def step4_process_csv(self, csv_path: str, skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤4: 对csv文件进行处理，筛选剔除异常值
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@ -615,7 +615,7 @@ class WaterQualityInversionPipeline:
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csv_path: Optional[str] = None,
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boundary_path: Optional[str] = None,
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glint_mask_path: Optional[str] = None,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤5: 根据csv文件的采样点坐标，在去除耀斑的文件中统计采样点的平均光谱
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@ -666,7 +666,7 @@ class WaterQualityInversionPipeline:
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formula_names: Optional[List[str]] = None,
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output_file: Optional[str] = None,
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enabled: bool = True,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤5.5: 根据训练光谱计算水质光谱指数
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@ -710,7 +710,7 @@ class WaterQualityInversionPipeline:
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split_methods: List[str] = None,
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cv_folds: int = 5,
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training_csv_path: Optional[str] = None,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤6: 使用采样点的平均光谱和对应的实测值建立机器学习模型，保存模型权重
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@ -753,7 +753,7 @@ class WaterQualityInversionPipeline:
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chunk_size: int = 1000,
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water_mask_path: Optional[str] = None,
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glint_mask_path: Optional[str] = None,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤7: 生成根据水域掩膜内且耀斑掩膜外的采样点，统计采样点的平均光谱
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@ -795,7 +795,7 @@ class WaterQualityInversionPipeline:
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models_dir: Optional[str] = None,
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metric: str = 'test_r2',
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prediction_column: str = 'prediction',
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skip_dependency_check: bool = False) -> Dict[str, str]:
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skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
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"""
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步骤8: 将训练好的最佳机器学习模型应用到采样点的平均光谱上，预测水质参数
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@ -835,7 +835,7 @@ class WaterQualityInversionPipeline:
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diffusion_n_neighbors: int = 15,
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cmap: Optional[str] = None,
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expand_ratio: float = 0.05,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤9: 根据采样点的坐标和反演的实测参数，以及水域掩膜，通过插值的方法，得到水质参数的可视化分布图
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@ -1764,7 +1764,7 @@ class WaterQualityInversionPipeline:
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window: int = 5,
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output_dir: Optional[str] = None,
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enabled: bool = True,
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skip_dependency_check: bool = False) -> Dict[str, str]:
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skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
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"""
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步骤6.5: 非经验统计回归模型训练
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@ -1812,7 +1812,7 @@ class WaterQualityInversionPipeline:
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methods: Union[str, List[str]] = 'all',
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output_dir: Optional[str] = None,
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enabled: bool = True,
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skip_dependency_check: bool = False) -> str:
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skip_dependency_check: bool = False, **kwargs) -> str:
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"""
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步骤6.75: 使用自定义回归方法分析指标与目标参数之间的关系
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"""
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@ -1991,7 +1991,7 @@ class WaterQualityInversionPipeline:
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metric: str = 'Average Accuracy(%)',
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prediction_column: str = 'prediction',
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enabled: bool = True,
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skip_dependency_check: bool = False) -> Dict[str, str]:
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skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
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"""
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步骤8.5: 使用非经验统计回归模型进行参数预测
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@ -2028,7 +2028,7 @@ class WaterQualityInversionPipeline:
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output_dir: Optional[str] = None,
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filename_prefix: str = "custom_regression_prediction",
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enabled: bool = True,
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skip_dependency_check: bool = False) -> Dict[str, str]:
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skip_dependency_check: bool = False, **kwargs) -> Dict[str, str]:
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"""
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步骤8.75: 使用自定义回归模型进行参数预测
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@ -247,16 +247,34 @@ class WorkerThread(QThread):
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mpl_prev = None
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try:
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from src.core.water_quality_inversion_pipeline_GUI import WaterQualityInversionPipeline
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from src.core.pipeline.runner import PipelineRunner
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from src.core.pipeline.context import PipelineContext
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self.pipeline = WaterQualityInversionPipeline(work_dir=self.work_dir)
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if self.mode == 'full':
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self.log_message.emit("开始运行完整流程...", "info")
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self.step_count = 0
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self.log_message.emit("开始运行完整流程 (Runner 调度模式)...", "info")
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if hasattr(self.pipeline, 'set_callback'):
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self.pipeline.set_callback(self.pipeline_callback)
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self.pipeline.run_full_pipeline(self.config)
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# 构造上下文 (Ctx)，将 config 整体注入 user_config
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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('step9', {}).get('boundary_shp_path'),
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formula_csv_path=self.config.get('step8_75', {}).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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# 启动新调度器
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runner = PipelineRunner(self.pipeline)
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result_ctx = runner.run(ctx)
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if result_ctx.last_error:
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raise RuntimeError(f"流水线执行失败: {result_ctx.last_error}")
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self.progress_update.emit(100, "流程执行完成")
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self.finished.emit(True, "完整流程执行成功！")
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