298 lines
13 KiB
Python
298 lines
13 KiB
Python
#!/usr/bin/env python
|
||
# -*- coding: utf-8 -*-
|
||
"""
|
||
工作空间管理器
|
||
|
||
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，
|
||
与 GUI 组件解耦，不直接引用任何 UI 类。
|
||
"""
|
||
|
||
import copy
|
||
from pathlib import Path
|
||
|
||
from src.gui.core.event_bus import global_event_bus
|
||
|
||
|
||
class WorkspaceManager:
|
||
"""管理步骤默认输出路径、文件扫描与配置裁剪"""
|
||
|
||
# 白名单：科学数据格式后缀
|
||
SCIENTIFIC_EXTENSIONS = {'.dat', '.tif', '.tiff', '.shp'}
|
||
# 临时文件关键词黑名单
|
||
TMP_KEYWORDS = ('__tmp', '_tmp')
|
||
# 掩膜类型集合
|
||
MASK_TYPES = {'water_mask', 'glint_mask', 'boundary_mask'}
|
||
|
||
def __init__(self):
|
||
self.step_default_outputs = {
|
||
'step1': {'water_mask': [
|
||
"1_water_mask/water_mask_out.dat",
|
||
"1_water_mask/water_mask_from_ndwi.dat",
|
||
"1_water_mask/water_mask_from_shp.dat",
|
||
]},
|
||
'step2': {'glint_mask': "2_Glint_Detection/severe_glint_area.dat"},
|
||
'step3': {'deglint_image': [
|
||
"3_deglint/deglint_image.bsq",
|
||
"3_deglint/deglint_goodman.bsq",
|
||
]},
|
||
'step4_sampling': {'sampling_points': "4_sampling/sampling_spectra.csv"},
|
||
'step5_clean': {'processed_data': "5_Data_Cleaning/processed_data.csv"},
|
||
'step6_feature': {'training_spectra': "6_Spectral_Feature_Extraction/training_spectra.csv"},
|
||
'step7_index': {'training_spectra_indices': "7_Water_Quality_Indices/training_spectra_indices.csv"},
|
||
'step8_ml_train': {'Supervised_Model_Training': "8_Supervised_Model_Training/"},
|
||
'step9_ml_predict': {'9_ML_Prediction': "9_ML_Prediction/"},
|
||
'step10_watercolor': {'WaterIndex_CSV': "10_WaterIndex_CSV/"},
|
||
'step11_map': {'11_Thematic_Map': "11_Thematic_Map/"},
|
||
}
|
||
self.step_outputs = {}
|
||
|
||
# ★ 2026-07-01：切换工作目录时清空缓存，防止旧目录的 OutputUpdated 重放
|
||
def clear_all_outputs(self):
|
||
"""清空所有缓存的步骤输出路径。工作目录切换时调用。"""
|
||
self.step_outputs.clear()
|
||
|
||
def _publish_outputs(self, step_id: str, outputs: dict):
|
||
"""将发现的产出发布到 EventBus。
|
||
|
||
Args:
|
||
step_id: 面板 step_id（如 'step1', 'step5_clean'）
|
||
outputs: {output_type: path_str}
|
||
"""
|
||
for output_type, path in outputs.items():
|
||
if path:
|
||
global_event_bus.publish('OutputUpdated', {
|
||
'step_id': step_id,
|
||
'output_type': output_type,
|
||
'path': path,
|
||
})
|
||
|
||
@staticmethod
|
||
def _is_scientific_mask(path_str):
|
||
"""白名单判断：只有 .dat .tif .tiff .shp 才算科学数据格式"""
|
||
p = Path(path_str)
|
||
name_lower = str(path_str).lower()
|
||
if any(kw in name_lower for kw in WorkspaceManager.TMP_KEYWORDS):
|
||
return False
|
||
return p.suffix.lower() in WorkspaceManager.SCIENTIFIC_EXTENSIONS
|
||
|
||
def find_step_output(self, work_path, step_id, output_type, ref_img_path=None):
|
||
"""查找指定步骤的输出文件
|
||
|
||
Args:
|
||
work_path: 工作目录 Path 对象
|
||
step_id: 步骤 ID
|
||
output_type: 输出类型（如 'water_mask', 'deglint_image' 等）
|
||
ref_img_path: 参考影像路径（仅 output_type='reference_img' 时需要）
|
||
|
||
Returns:
|
||
找到的文件路径字符串，或 None
|
||
"""
|
||
if step_id not in self.step_default_outputs:
|
||
return None
|
||
|
||
raw = self.step_default_outputs[step_id]
|
||
|
||
rel_path = None
|
||
if isinstance(raw, str):
|
||
rel_path = raw
|
||
elif isinstance(raw, dict):
|
||
rel_path = raw.get(output_type) or list(raw.values())[0]
|
||
|
||
if not rel_path:
|
||
return None
|
||
|
||
# 特殊处理：从 step_outputs 记录中查找实际输出路径
|
||
if step_id in self.step_outputs:
|
||
actual_outputs = self.step_outputs[step_id]
|
||
if output_type in actual_outputs:
|
||
candidate = actual_outputs[output_type]
|
||
if output_type in self.MASK_TYPES and not self._is_scientific_mask(candidate):
|
||
pass
|
||
else:
|
||
return candidate
|
||
|
||
if output_type == 'water_mask':
|
||
if isinstance(rel_path, list):
|
||
for candidate in rel_path:
|
||
mask_path = work_path / candidate
|
||
if mask_path.exists():
|
||
return str(mask_path)
|
||
elif rel_path:
|
||
mask_path = work_path / rel_path
|
||
if mask_path.exists():
|
||
return str(mask_path)
|
||
elif output_type == 'reference_img':
|
||
if ref_img_path and Path(ref_img_path).exists():
|
||
return ref_img_path
|
||
elif output_type == 'deglint_image':
|
||
if isinstance(rel_path, list):
|
||
for candidate in rel_path:
|
||
deglint_path = work_path / candidate
|
||
if deglint_path.exists():
|
||
return str(deglint_path)
|
||
elif rel_path:
|
||
deglint_path = work_path / rel_path
|
||
if deglint_path.exists():
|
||
return str(deglint_path)
|
||
deglint_dir = work_path / "3_deglint"
|
||
if deglint_dir.exists():
|
||
for file_path in deglint_dir.glob("deglint_*.bsq"):
|
||
return str(file_path)
|
||
for file_path in deglint_dir.glob("interpolated_*.bsq"):
|
||
return str(file_path)
|
||
elif isinstance(rel_path, str):
|
||
if rel_path.endswith('/'):
|
||
output_path = work_path / rel_path.rstrip('/')
|
||
if output_path.exists() and output_path.is_dir():
|
||
return str(output_path)
|
||
else:
|
||
output_path = work_path / rel_path
|
||
if output_path.exists():
|
||
return str(output_path)
|
||
|
||
return None
|
||
|
||
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
|
||
|
||
Returns:
|
||
discovered_outputs: dict, {step_id: {output_type: path_str}}
|
||
"""
|
||
discovered_outputs = {}
|
||
|
||
subdirs = {
|
||
'1_water_mask': 'step1',
|
||
'2_Glint_Detection': 'step2',
|
||
'3_deglint': 'step3',
|
||
'4_sampling': 'step4_sampling',
|
||
'5_Data_Cleaning': 'step5_clean',
|
||
'6_Spectral_Feature_Extraction': 'step6_feature',
|
||
'7_Water_Quality_Indices': 'step7_index',
|
||
'8_Supervised_Model_Training': 'step8_ml_train',
|
||
'9_ML_Prediction': 'step9_ml_predict',
|
||
'10_WaterIndex_CSV': 'step10_watercolor',
|
||
'11_Thematic_Map': 'step11_map',
|
||
'12_visualization': ['step12_viz', 'step13_report'],
|
||
}
|
||
|
||
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]
|
||
|
||
for file_path in subdir_path.rglob('*'):
|
||
if file_path.is_file():
|
||
file_name = file_path.name.lower()
|
||
file_stem = file_path.stem.lower()
|
||
|
||
for step_id in step_ids:
|
||
if step_id not in discovered_outputs:
|
||
discovered_outputs[step_id] = {}
|
||
|
||
# 2026-06-30 加强匹配：用更精确的边界匹配替代简单的子串包含
|
||
# water_mask 匹配：必须是独立文件名的一部分（如 water_mask_out.dat, water_mask_from_ndwi.dat）
|
||
if step_id == 'step1' and self._is_scientific_mask(file_path):
|
||
if ('water_mask' in file_stem
|
||
and 'glint' not in file_stem):
|
||
discovered_outputs[step_id]['water_mask'] = str(file_path)
|
||
# glint_mask 匹配：必须同时含 glint 且文件名主体以 mask 或 area 结尾
|
||
elif step_id == 'step2' and self._is_scientific_mask(file_path):
|
||
if ('glint' in file_stem and
|
||
(file_stem.endswith('mask') or file_stem.endswith('area')
|
||
or 'severe_glint' in file_stem)):
|
||
discovered_outputs[step_id]['glint_mask'] = str(file_path)
|
||
elif 'deglint' in file_stem and step_id == 'step3':
|
||
discovered_outputs[step_id]['deglint_image'] = str(file_path)
|
||
elif file_name == 'processed_data.csv' and step_id == 'step5_clean':
|
||
discovered_outputs[step_id]['processed_data'] = str(file_path)
|
||
elif ('training_spectra' in file_stem and file_path.suffix == '.csv'
|
||
and step_id == 'step6_feature'):
|
||
discovered_outputs[step_id]['training_spectra'] = str(file_path)
|
||
elif ('water_quality_indices' in file_stem and file_path.suffix == '.csv'
|
||
and step_id == 'step7_index'):
|
||
discovered_outputs[step_id]['water_indices'] = str(file_path)
|
||
elif file_name == 'sampling_spectra.csv' 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)
|
||
|
||
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)
|
||
# ★ 发布 EventBus 事件，驱动下游面板自动填充
|
||
self._publish_outputs(step_id, outputs)
|
||
|
||
return discovered_outputs
|
||
|
||
def update_step_outputs(self, step_name, work_path):
|
||
"""更新指定步骤的输出路径记录并发布 EventBus 事件。"""
|
||
if step_name not in self.step_default_outputs:
|
||
return
|
||
|
||
step_outputs = self.step_default_outputs[step_name]
|
||
published = {}
|
||
|
||
for output_type, relative_path in step_outputs.items():
|
||
if isinstance(relative_path, list):
|
||
for candidate in relative_path:
|
||
output_path = work_path / candidate
|
||
if output_path.exists():
|
||
path_str = str(output_path)
|
||
self.step_outputs.setdefault(step_name, {})[output_type] = path_str
|
||
published[output_type] = path_str
|
||
break
|
||
elif '*' in relative_path:
|
||
pattern_path = work_path / relative_path.replace('*', '*')
|
||
matching_files = list(pattern_path.parent.glob(pattern_path.name))
|
||
if matching_files:
|
||
latest_file = max(matching_files, key=lambda p: p.stat().st_mtime)
|
||
path_str = str(latest_file)
|
||
self.step_outputs.setdefault(step_name, {})[output_type] = path_str
|
||
published[output_type] = path_str
|
||
else:
|
||
output_path = work_path / relative_path
|
||
if output_path.exists():
|
||
path_str = str(output_path)
|
||
self.step_outputs.setdefault(step_name, {})[output_type] = path_str
|
||
published[output_type] = path_str
|
||
|
||
if published:
|
||
self._publish_outputs(step_name, published)
|
||
|
||
@staticmethod
|
||
def prune_config_for_prediction_mode(config: dict) -> dict:
|
||
"""Prediction-only 模式：禁用训练相关步骤，保留预测和成图步骤。
|
||
|
||
2026-06-30 修复：步骤 ID 从旧的 PIPELINE_STEPS 体系改为 PANEL_REGISTRY 体系，
|
||
确保与 get_current_config() 返回的 key 一致，避免 enabled: False 写入无效 key。
|
||
|
||
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，key 为 PANEL_REGISTRY step_id）
|
||
|
||
Returns:
|
||
裁剪后的 config（深拷贝，原 config 不被修改）
|
||
"""
|
||
cfg = copy.deepcopy(config)
|
||
|
||
training_steps = [
|
||
"step4_sampling",
|
||
"step5_clean",
|
||
"step6_feature",
|
||
"step7_index",
|
||
"step8_ml_train",
|
||
]
|
||
for step_id in training_steps:
|
||
step_cfg = cfg.setdefault(step_id, {})
|
||
step_cfg["enabled"] = False
|
||
|
||
return cfg
|