232 lines
9.6 KiB
Python
232 lines
9.6 KiB
Python
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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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