refactor: 提取 WorkspaceManager，将文件扫描与路径业务逻辑从主 GUI 解耦
This commit is contained in:
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src/core/workspace_manager.py
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231
src/core/workspace_manager.py
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@ -0,0 +1,231 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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工作空间管理器
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负责工作目录文件扫描、步骤输出路径发现、配置裁剪等业务逻辑，
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与 GUI 组件解耦，不直接引用任何 UI 类。
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"""
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import copy
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from pathlib import Path
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class WorkspaceManager:
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"""管理步骤默认输出路径、文件扫描与配置裁剪"""
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# 白名单：科学数据格式后缀
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SCIENTIFIC_EXTENSIONS = {'.dat', '.tif', '.tiff', '.shp'}
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# 临时文件关键词黑名单
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TMP_KEYWORDS = ('__tmp', '_tmp')
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# 掩膜类型集合
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MASK_TYPES = {'water_mask', 'glint_mask', 'boundary_mask'}
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def __init__(self):
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self.step_default_outputs = {
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'step1': "1_water_mask/water_mask_from_ndwi.dat",
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'step2': "2_Glint_Detection/severe_glint_area.dat",
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'step3': "3_deglint/deglint_goodman.bsq",
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'step4_sampling': "4_sampling/sampling_spectra.csv",
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'step5_clean': "5_Data_Cleaning/processed_data.csv",
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'step6_feature': "6_Spectral_Feature_Extraction/training_spectra.csv",
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'step7_index': "7_Water_Quality_Indices/training_spectra_indices.csv",
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'step8_ml_train': "8_Supervised_Model_Training/",
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'step9_ml_predict': "9_ML_Prediction/",
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'step10_watercolor': "10_WaterIndex_Images/",
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'step11_map': "14_visualization/"
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}
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self.step_outputs = {}
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@staticmethod
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def _is_scientific_mask(path_str):
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"""白名单判断：只有 .dat .tif .tiff .shp 才算科学数据格式"""
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p = Path(path_str)
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name_lower = str(path_str).lower()
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if any(kw in name_lower for kw in WorkspaceManager.TMP_KEYWORDS):
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return False
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return p.suffix.lower() in WorkspaceManager.SCIENTIFIC_EXTENSIONS
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def find_step_output(self, work_path, step_id, output_type, ref_img_path=None):
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"""查找指定步骤的输出文件
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Args:
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work_path: 工作目录 Path 对象
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step_id: 步骤 ID
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output_type: 输出类型（如 'water_mask', 'deglint_image' 等）
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ref_img_path: 参考影像路径（仅 output_type='reference_img' 时需要）
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Returns:
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找到的文件路径字符串，或 None
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"""
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if step_id not in self.step_default_outputs:
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return None
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raw = self.step_default_outputs[step_id]
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rel_path = None
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if isinstance(raw, str):
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rel_path = raw
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elif isinstance(raw, dict):
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rel_path = raw.get(output_type) or list(raw.values())[0]
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if not rel_path:
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return None
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# 特殊处理：从 step_outputs 记录中查找实际输出路径
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if step_id in self.step_outputs:
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actual_outputs = self.step_outputs[step_id]
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if output_type in actual_outputs:
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candidate = actual_outputs[output_type]
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if output_type in self.MASK_TYPES and not self._is_scientific_mask(candidate):
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pass
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else:
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return candidate
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if output_type == 'water_mask':
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if rel_path:
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mask_path = work_path / rel_path
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if mask_path.exists():
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return str(mask_path)
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elif output_type == 'reference_img':
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if ref_img_path and Path(ref_img_path).exists():
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return ref_img_path
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elif output_type == 'deglint_image':
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if rel_path:
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deglint_path = work_path / rel_path
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if deglint_path.exists():
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return str(deglint_path)
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deglint_dir = work_path / "3_deglint"
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if deglint_dir.exists():
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for file_path in deglint_dir.glob("deglint_*.bsq"):
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return str(file_path)
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for file_path in deglint_dir.glob("interpolated_*.bsq"):
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return str(file_path)
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elif rel_path:
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if rel_path.endswith('/'):
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output_path = work_path / rel_path.rstrip('/')
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if output_path.exists() and output_path.is_dir():
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return str(output_path)
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else:
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output_path = work_path / rel_path
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if output_path.exists():
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return str(output_path)
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return None
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def scan_work_directory_for_files(self, work_path):
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"""扫描工作目录，自动发现各步骤的输出文件
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Returns:
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discovered_outputs: dict, {step_id: {output_type: path_str}}
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"""
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discovered_outputs = {}
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subdirs = {
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'1_water_mask': 'step1',
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'2_Glint_Detection': 'step2',
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'3_deglint': 'step3',
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'5_Data_Cleaning': 'step5_clean',
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'6_Spectral_Feature_Extraction': 'step6_feature',
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'7_Water_Quality_Indices': 'step7_index',
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'8_Supervised_Model_Training': 'step8_ml_train',
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'8_Regression_Modeling': 'step8_ml_train',
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'13_Custom_Regression': 'step13',
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'9_ML_Prediction': 'step9_ml_predict',
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'11_12_13_predictions/Non_Empirical_Prediction': 'step11_map',
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'13_Custom_Regression/Custom_Regression_Prediction': 'step13',
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'14_visualization': 'step13_report',
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'10_geotiff_batch_rendering': 'step11_map'
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}
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for subdir, step_ids in subdirs.items():
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subdir_path = work_path / subdir
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if not subdir_path.exists():
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continue
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if isinstance(step_ids, str):
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step_ids = [step_ids]
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for file_path in subdir_path.rglob('*'):
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if file_path.is_file():
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file_name = file_path.name.lower()
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for step_id in step_ids:
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if step_id not in discovered_outputs:
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discovered_outputs[step_id] = {}
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if 'water_mask' in file_name and step_id == 'step1':
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if self._is_scientific_mask(file_path):
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discovered_outputs[step_id]['water_mask'] = str(file_path)
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elif 'glint' in file_name and 'mask' in file_name and step_id == 'step2':
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if self._is_scientific_mask(file_path):
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discovered_outputs[step_id]['glint_mask'] = str(file_path)
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elif 'deglint' in file_name and step_id == 'step3':
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discovered_outputs[step_id]['deglint_image'] = str(file_path)
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elif 'processed_data' in file_name and step_id == 'step4_sampling':
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discovered_outputs[step_id]['processed_data'] = str(file_path)
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elif 'training_spectra' in file_name and step_id == 'step5_clean':
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discovered_outputs[step_id]['training_spectra'] = str(file_path)
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elif 'water_quality_indices' in file_name and step_id == 'step6_feature':
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discovered_outputs[step_id]['water_indices'] = str(file_path)
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elif 'sampling_spectra' in file_name and step_id == 'step4_sampling':
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discovered_outputs[step_id]['sampling_points'] = str(file_path)
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elif file_name.endswith('.csv') and step_id in ['step9_ml_predict', 'step11_map', 'step12_viz']:
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discovered_outputs[step_id]['predictions'] = str(file_path)
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for step_id, outputs in discovered_outputs.items():
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if step_id not in self.step_outputs:
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self.step_outputs[step_id] = {}
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self.step_outputs[step_id].update(outputs)
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return discovered_outputs
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def update_step_outputs(self, step_name, work_path):
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"""更新指定步骤的输出路径记录"""
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if step_name not in self.step_default_outputs:
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return
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step_outputs = self.step_default_outputs[step_name]
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for output_type, relative_path in step_outputs.items():
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if '*' in relative_path:
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pattern_path = work_path / relative_path.replace('*', '*')
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matching_files = list(pattern_path.parent.glob(pattern_path.name))
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if matching_files:
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latest_file = max(matching_files, key=lambda p: p.stat().st_mtime)
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self.step_outputs[step_name][output_type] = str(latest_file)
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else:
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output_path = work_path / relative_path
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if output_path.exists():
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self.step_outputs[step_name][output_type] = str(output_path)
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@staticmethod
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def prune_config_for_prediction_mode(config: dict) -> dict:
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"""Prediction-only 模式：禁用训练相关步骤，保留预测和成图步骤。
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被禁用的 step dict 中统一写入 'enabled': False，
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这些配置最终传给 PipelineRunner，Runner 会跳过它们。
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同时，被跳过的步骤的 required_input_files 在 build_missing_items
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中不会被检查，从而自然规避了"CSV 缺失"等训练模式下的误报。
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Args:
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config: 完整配置字典（来自 get_current_config）
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Returns:
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裁剪后的 config（深拷贝，原 config 不被修改）
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"""
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cfg = copy.deepcopy(config)
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training_steps = [
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"step4",
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"step5",
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"step7",
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"step6",
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"step8_non_empirical_modeling",
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"step9",
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]
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for step_id in training_steps:
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step_cfg = cfg.setdefault(step_id, {})
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step_cfg["enabled"] = False
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return cfg
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@ -158,6 +158,7 @@ from src.gui.core.worker_thread import (
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from src.gui.core.preflight_dialog import PreflightDialog
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from src.gui.core.pipeline_mode_dialog import PipelineModeDialog
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from src.gui.core.viz_thread import VisualizationWorkerThread, _viz_training_spectra_csv_path
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from src.core.workspace_manager import WorkspaceManager
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class WaterQualityGUI(QMainWindow):
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@ -183,10 +184,10 @@ class WaterQualityGUI(QMainWindow):
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# 训练数据模式状态
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self.has_training_data = True # 默认有训练数据
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# 步骤输出路径记录
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self.step_outputs = {} # 记录每个步骤的输出路径
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# 工作空间管理器（文件扫描、路径发现、配置裁剪）
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self.workspace_manager = WorkspaceManager()
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# 定义步骤依赖关系和标准输出路径
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# 定义步骤依赖关系
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self._init_step_dependencies()
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self.init_ui()
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@ -198,22 +199,7 @@ class WaterQualityGUI(QMainWindow):
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QTimer.singleShot(100, self.init_workspace)
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def _init_step_dependencies(self):
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"""初始化步骤依赖关系和标准输出路径"""
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# 定义每个步骤的标准输出路径模式（相对于工作目录）
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self.step_default_outputs = {
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'step1': "1_water_mask/water_mask_from_ndwi.dat",
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'step2': "2_Glint_Detection/severe_glint_area.dat",
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'step3': "3_deglint/deglint_goodman.bsq",
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'step4_sampling': "4_sampling/sampling_spectra.csv",
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'step5_clean': "5_Data_Cleaning/processed_data.csv",
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'step6_feature': "6_Spectral_Feature_Extraction/training_spectra.csv",
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'step7_index': "7_Water_Quality_Indices/training_spectra_indices.csv",
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'step8_ml_train': "8_Supervised_Model_Training/",
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'step9_ml_predict': "9_ML_Prediction/",
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'step10_watercolor': "10_WaterIndex_Images/",
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'step11_map': "14_visualization/"
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}
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"""初始化步骤依赖关系"""
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# 依赖关系字典结构:
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# '当前步骤ID': { '依赖参数名': ('上游步骤ID', '上游输出类型/Key', '当前步骤接收该路径的组件属性名') }
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self.step_dependencies = {
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@ -1081,7 +1067,11 @@ class WaterQualityGUI(QMainWindow):
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dependencies = self.step_dependencies[step_id]
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filled_count = 0
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ref_img_path = None
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if hasattr(self, 'step1_panel'):
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ref_img_path = self.step1_panel.img_file.get_path()
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for input_field, (dep_step, output_type, panel_attr) in dependencies.items():
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# 检查面板是否有对应的属性
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if not hasattr(panel, panel_attr):
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@ -1101,7 +1091,7 @@ class WaterQualityGUI(QMainWindow):
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continue
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# 查找依赖步骤的输出文件
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output_path = self.find_step_output(work_path, dep_step, output_type)
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output_path = self.workspace_manager.find_step_output(work_path, dep_step, output_type, ref_img_path=ref_img_path)
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if output_path and Path(output_path).exists():
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# ★ 兼容 FileSelectWidget 与原生 QLineEdit
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@ -1132,173 +1122,6 @@ class WaterQualityGUI(QMainWindow):
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}
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return panel_map.get(step_id)
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def find_step_output(self, work_path, step_id, output_type):
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"""查找指定步骤的输出文件"""
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if step_id not in self.step_default_outputs:
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return None
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raw = self.step_default_outputs[step_id]
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# ★ 兼容扁平化后的纯字符串路径格式
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rel_path = None
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if isinstance(raw, str):
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rel_path = raw
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elif isinstance(raw, dict):
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rel_path = raw.get(output_type) or list(raw.values())[0]
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if not rel_path:
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return None
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# ★ 掩膜类型列表：这些类型只接受科学数据格式
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mask_types = {'water_mask', 'glint_mask', 'boundary_mask'}
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# ★ 白名单机制：只允许 .dat .tif .tiff .shp，拒绝其他一切格式
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scientific_extensions = {'.dat', '.tif', '.tiff', '.shp'}
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# ★ 临时文件关键词黑名单
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tmp_keywords = ('__tmp', '_tmp')
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def _is_scientific_mask(path_str):
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"""白名单判断：只有 .dat .tif .tiff .shp 才算科学数据格式"""
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p = Path(path_str)
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name_lower = str(path_str).lower()
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# 拒绝临时文件
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if any(kw in name_lower for kw in tmp_keywords):
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return False
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# 白名单校验
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return p.suffix.lower() in scientific_extensions
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# 特殊处理：从step_outputs记录中查找实际输出路径
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if step_id in self.step_outputs:
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actual_outputs = self.step_outputs[step_id]
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if output_type in actual_outputs:
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candidate = actual_outputs[output_type]
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# ★ 掩膜类型白名单二次校验：不在白名单内的一律拒绝
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if output_type in mask_types and not _is_scientific_mask(candidate):
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# 非科学格式被拒绝，不使用 step_outputs 中的值
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pass
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else:
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return candidate
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# 根据输出类型查找对应的文件
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if output_type == 'water_mask':
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# 水域掩膜：直接用统一路径
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if rel_path:
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mask_path = work_path / rel_path
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if mask_path.exists():
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return str(mask_path)
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elif output_type == 'reference_img':
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# 参考影像：从step1的配置中获取用户输入的影像路径
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if hasattr(self, 'step1_panel'):
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img_path = self.step1_panel.img_file.get_path()
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if img_path and Path(img_path).exists():
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return img_path
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elif output_type == 'deglint_image':
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# 去耀斑影像：直接用统一路径
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if rel_path:
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deglint_path = work_path / rel_path
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if deglint_path.exists():
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return str(deglint_path)
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# 还要检查 Kutser 算法输出与插值方法生成的文件
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deglint_dir = work_path / "3_deglint"
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if deglint_dir.exists():
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for file_path in deglint_dir.glob("deglint_*.bsq"):
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return str(file_path)
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for file_path in deglint_dir.glob("interpolated_*.bsq"):
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return str(file_path)
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elif rel_path:
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# 直接匹配的输出类型（统一使用 rel_path）
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if rel_path.endswith('/'):
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# 是目录
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output_path = work_path / rel_path.rstrip('/')
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if output_path.exists() and output_path.is_dir():
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return str(output_path)
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else:
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# 是文件
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output_path = work_path / rel_path
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if output_path.exists():
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return str(output_path)
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return None
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def scan_work_directory_for_files(self, work_path):
|
||||
"""扫描工作目录，自动发现各步骤的输出文件"""
|
||||
discovered_outputs = {}
|
||||
|
||||
# 扫描各个子目录
|
||||
subdirs = {
|
||||
'1_water_mask': 'step1',
|
||||
'2_Glint_Detection': 'step2',
|
||||
'3_deglint': 'step3',
|
||||
'5_Data_Cleaning': 'step5_clean',
|
||||
'6_Spectral_Feature_Extraction': 'step6_feature',
|
||||
'7_Water_Quality_Indices': 'step7_index',
|
||||
'8_Supervised_Model_Training': 'step8_ml_train',
|
||||
'8_Regression_Modeling': 'step8_ml_train',
|
||||
'13_Custom_Regression': 'step13',
|
||||
'9_ML_Prediction': 'step9_ml_predict',
|
||||
'11_12_13_predictions/Non_Empirical_Prediction': 'step11_map',
|
||||
'13_Custom_Regression/Custom_Regression_Prediction': 'step13',
|
||||
'14_visualization': 'step13_report',
|
||||
'10_geotiff_batch_rendering': 'step11_map'
|
||||
}
|
||||
|
||||
for subdir, step_ids in subdirs.items():
|
||||
subdir_path = work_path / subdir
|
||||
if not subdir_path.exists():
|
||||
continue
|
||||
|
||||
if isinstance(step_ids, str):
|
||||
step_ids = [step_ids]
|
||||
|
||||
# 扫描该目录下的文件
|
||||
for file_path in subdir_path.rglob('*'):
|
||||
if file_path.is_file():
|
||||
file_name = file_path.name.lower()
|
||||
|
||||
# 根据文件名模式判断输出类型
|
||||
for step_id in step_ids:
|
||||
if step_id not in discovered_outputs:
|
||||
discovered_outputs[step_id] = {}
|
||||
|
||||
# ★ 掩膜文件白名单过滤：只有 .dat .tif .tiff .shp 才通过，拒绝 .hdr .xml .png 等
|
||||
scientific_extensions = {'.dat', '.tif', '.tiff', '.shp'}
|
||||
tmp_keywords = ('__tmp', '_tmp')
|
||||
|
||||
def _is_scientific_mask(path_str):
|
||||
"""白名单判断：拒绝 .hdr .xml 临时文件等，只接受科学数据格式"""
|
||||
p = Path(path_str)
|
||||
name_lower = str(path_str).lower()
|
||||
if any(kw in name_lower for kw in tmp_keywords):
|
||||
return False
|
||||
return p.suffix.lower() in scientific_extensions
|
||||
|
||||
# 匹配不同的文件类型
|
||||
if 'water_mask' in file_name and step_id == 'step1':
|
||||
if _is_scientific_mask(file_path):
|
||||
discovered_outputs[step_id]['water_mask'] = str(file_path)
|
||||
elif 'glint' in file_name and 'mask' in file_name and step_id == 'step2':
|
||||
if _is_scientific_mask(file_path):
|
||||
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_sampling':
|
||||
discovered_outputs[step_id]['processed_data'] = str(file_path)
|
||||
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_feature':
|
||||
discovered_outputs[step_id]['water_indices'] = str(file_path)
|
||||
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 ['step9_ml_predict', 'step11_map', 'step12_viz']:
|
||||
discovered_outputs[step_id]['predictions'] = str(file_path)
|
||||
|
||||
# 更新内部记录
|
||||
for step_id, outputs in discovered_outputs.items():
|
||||
if step_id not in self.step_outputs:
|
||||
self.step_outputs[step_id] = {}
|
||||
self.step_outputs[step_id].update(outputs)
|
||||
|
||||
return discovered_outputs
|
||||
|
||||
def auto_populate_all_steps(self):
|
||||
"""自动填充所有步骤的输入路径"""
|
||||
work_dir = getattr(self, 'work_dir', './work_dir')
|
||||
@ -1309,7 +1132,7 @@ class WaterQualityGUI(QMainWindow):
|
||||
return
|
||||
|
||||
# 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)
|
||||
self.workspace_manager.scan_work_directory_for_files(work_path)
|
||||
|
||||
step_order = ['step2', 'step3', 'step4_sampling', 'step5_clean', 'step6_feature', 'step7_index',
|
||||
'step8_ml_train', 'step9_ml_predict', 'step11_map', 'step12_viz', 'step13_report']
|
||||
@ -1612,41 +1435,6 @@ class WaterQualityGUI(QMainWindow):
|
||||
|
||||
return True
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# ★ 全流程模式动态裁剪
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _prune_config_for_prediction_mode(self, config: dict) -> dict:
|
||||
"""Prediction-only 模式：禁用训练相关步骤，保留预测和成图步骤。
|
||||
|
||||
被禁用的 step dict 中统一写入 'enabled': False，
|
||||
这些配置最终传给 PipelineRunner，Runner 会跳过它们。
|
||||
同时，被跳过的步骤的 required_input_files 在 build_missing_items
|
||||
中不会被检查，从而自然规避了"CSV 缺失"等训练模式下的误报。
|
||||
|
||||
Args:
|
||||
config: 完整配置字典（来自 get_current_config）
|
||||
|
||||
Returns:
|
||||
裁剪后的 config（深拷贝，原 config 不被修改）
|
||||
"""
|
||||
cfg = copy.deepcopy(config)
|
||||
|
||||
# 在每个训练相关步骤的 dict 中写入 enabled=False
|
||||
training_steps = [
|
||||
"step4", # CSV 实测数据清洗
|
||||
"step5", # 实测点光谱提取（→ training_csv_path）
|
||||
"step7", # ML 监督建模
|
||||
"step6", # 水质指数计算（辅助训练）
|
||||
"step8_non_empirical_modeling", # 非经验回归建模
|
||||
"step9", # 自定义回归建模
|
||||
]
|
||||
for step_id in training_steps:
|
||||
step_cfg = cfg.setdefault(step_id, {})
|
||||
step_cfg["enabled"] = False
|
||||
|
||||
return cfg
|
||||
|
||||
def run_full_pipeline(self):
|
||||
"""运行完整流程"""
|
||||
if not PIPELINE_AVAILABLE:
|
||||
@ -1665,7 +1453,7 @@ class WaterQualityGUI(QMainWindow):
|
||||
# ── 1) 运行前智能预检与自动回填（硬盘已有产物自动跳过） ──
|
||||
work_path = Path(work_dir)
|
||||
self.log_message("正在进行运行前环境预检与自动扫描...", "info")
|
||||
self.scan_work_directory_for_files(work_path)
|
||||
self.workspace_manager.scan_work_directory_for_files(work_path)
|
||||
self.auto_populate_all_steps()
|
||||
self.log_message("✓ 预检完成：已扫描工作目录并自动回填已落盘的产物", "info")
|
||||
|
||||
@ -1685,7 +1473,7 @@ class WaterQualityGUI(QMainWindow):
|
||||
|
||||
# ── 2.1) ★ 根据模式动态裁剪配置 ──
|
||||
if selected_mode == "prediction_only":
|
||||
config = self._prune_config_for_prediction_mode(config)
|
||||
config = self.workspace_manager.prune_config_for_prediction_mode(config)
|
||||
self.log_message("[模式选择] 已裁剪训练相关步骤（step4/5/7/8），进入仅预测模式", "info")
|
||||
|
||||
# ── 3) ★ 一次性全预检 + 用户交互式决策 ──
|
||||
@ -1798,38 +1586,21 @@ class WaterQualityGUI(QMainWindow):
|
||||
work_path = Path(work_dir)
|
||||
|
||||
# 根据步骤名称和约定路径，记录实际输出
|
||||
if step_name not in self.step_outputs:
|
||||
self.step_outputs[step_name] = {}
|
||||
|
||||
if step_name not in self.workspace_manager.step_outputs:
|
||||
self.workspace_manager.step_outputs[step_name] = {}
|
||||
|
||||
# 扫描工作目录，更新该步骤的输出路径
|
||||
self.update_step_outputs(step_name, work_path)
|
||||
self.workspace_manager.update_step_outputs(step_name, work_path)
|
||||
|
||||
# 自动填充依赖该步骤输出的后续步骤
|
||||
self.auto_populate_dependent_steps(step_name)
|
||||
|
||||
def update_step_outputs(self, step_name, work_path):
|
||||
"""更新指定步骤的输出路径记录"""
|
||||
if step_name not in self.step_default_outputs:
|
||||
return
|
||||
|
||||
step_outputs = self.step_default_outputs[step_name]
|
||||
|
||||
for output_type, relative_path in step_outputs.items():
|
||||
if '*' in relative_path:
|
||||
# 处理通配符路径
|
||||
pattern_path = work_path / relative_path.replace('*', '*')
|
||||
matching_files = list(pattern_path.parent.glob(pattern_path.name))
|
||||
if matching_files:
|
||||
# 选择最新的文件
|
||||
latest_file = max(matching_files, key=lambda p: p.stat().st_mtime)
|
||||
self.step_outputs[step_name][output_type] = str(latest_file)
|
||||
else:
|
||||
output_path = work_path / relative_path
|
||||
if output_path.exists():
|
||||
self.step_outputs[step_name][output_type] = str(output_path)
|
||||
|
||||
def auto_populate_dependent_steps(self, completed_step):
|
||||
"""自动填充依赖于已完成步骤的后续步骤"""
|
||||
ref_img_path = None
|
||||
if hasattr(self, 'step1_panel'):
|
||||
ref_img_path = self.step1_panel.img_file.get_path()
|
||||
|
||||
for step_id, dependencies in self.step_dependencies.items():
|
||||
for input_field, (dep_step, output_type, panel_attr) in dependencies.items():
|
||||
if dep_step == completed_step:
|
||||
@ -1841,7 +1612,7 @@ class WaterQualityGUI(QMainWindow):
|
||||
if not file_widget.get_path().strip():
|
||||
work_dir = getattr(self, 'work_dir', './work_dir')
|
||||
work_path = Path(work_dir)
|
||||
output_path = self.find_step_output(work_path, dep_step, output_type)
|
||||
output_path = self.workspace_manager.find_step_output(work_path, dep_step, output_type, ref_img_path=ref_img_path)
|
||||
if output_path and Path(output_path).exists():
|
||||
file_widget.set_path(output_path)
|
||||
self.log_message(f"步骤完成后自动填充 {step_id}.{input_field}: {output_path}", "info")
|
||||
|
||||
Reference in New Issue
Block a user