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Mega-1.1 全链路路径归一化收尾（18 文件）

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This commit is contained in:
DXC
2026-06-15 15:20:50 +08:00
parent a9e77d2ad0
commit 82e0b92af6
18 changed files with 69 additions and 69 deletions
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4
src/gui/panels/step11_map_panel.py
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@ -524,9 +524,9 @@ class Step11MapPanel(QWidget):
# 若为相对路径，使用 work_dir 合成为绝对路径
if not os.path.isabs(step10_output):
step10_output = os.path.join(self.work_dir or '', step10_output).replace('\\', '/')
# 提取父目录后追加 Machine_Learning_Prediction（最底层真实子目录）
# 提取父目录后追加 9_ML_Prediction（最底层真实子目录）
base_pred_dir = str(Path(step10_output).parent)
ml_pred_dir = Path(base_pred_dir) / "Machine_Learning_Prediction"
ml_pred_dir = Path(base_pred_dir) / "9_ML_Prediction"
pred_dir = str(ml_pred_dir) if ml_pred_dir.exists() else base_pred_dir
# 2. 备选：从 Step11 界面读取非经验预测输出目录

20
src/gui/panels/step12_viz_panel.py
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@ -608,7 +608,7 @@ class ImageCategoryTree(QTreeWidget):
DIR_MAPPING = {
"14_visualization": "统计与分析报表",
"1_water_mask": "水域掩膜识别",
"2_glint": "耀斑区域检测",
"2_Glint_Detection": "耀斑区域检测",
"3_deglint": "去耀斑影像结果",
"5_training_spectra": "训练光谱特征",
"8_Regression_Modeling": "回归建模分析",
@ -618,7 +618,7 @@ class ImageCategoryTree(QTreeWidget):
"glint_deglint_previews": "耀斑处理预览",
"sampling_maps": "采样点空间分布",
"flight_maps": "无人机飞行轨迹",
"Machine_Learning_Prediction": "机器学习预测",
"9_ML_Prediction": "机器学习预测",
"Non_Empirical_Prediction": "非经验模型预测",
"Custom_Regression_Prediction": "自定义回归预测",
"boxplot_dir": "水质参数箱线图",
@ -822,7 +822,7 @@ class ImageCategoryTree(QTreeWidget):
self._work_path / "8_Regression_Modeling",
self._work_path / "10_feature_construction",
self._work_path / "5_training_spectra",
self._work_path / "2_glint",
self._work_path / "2_Glint_Detection",
self._work_path / "3_deglint",
self._work_path / "1_water_mask",
self._work_path / "9_water_quality_prediction",
@ -1304,7 +1304,7 @@ class Step12VizPanel(QWidget):
QMessageBox.warning(
self,
"警告",
"未找到可处理的影像文件（2_glint/3_deglint 等）。",
"未找到可处理的影像文件（2_Glint_Detection/3_deglint 等）。",
)
elif t == "sampling_map":
map_path = payload.get("map_path")
@ -1522,9 +1522,9 @@ class Step12VizPanel(QWidget):
"""从全局配置自动推断并填入图像目录，然后自动加载目录内容。
推断优先级：
1. {work_dir}/11_12_13_predictions/Machine_Learning_Prediction（机器学习预测）
1. {work_dir}/9_ML_Prediction（机器学习预测）
2. {work_dir}/11_12_13_predictions/Non_Empirical_Prediction（普通回归预测）
3. {work_dir}/11_12_13_predictions/Custom_Regression_Prediction（自定义回归预测）
3. {work_dir}/13_Custom_Regression/Custom_Regression_Prediction（自定义回归预测）
4. {work_dir}/14_visualization（可视化目录）
5. {work_dir}（工作目录根）
"""
@ -1540,9 +1540,9 @@ class Step12VizPanel(QWidget):
# 按优先级寻找存在的目录
candidates = [
pred_dir / "Machine_Learning_Prediction",
work_path / "9_ML_Prediction",
pred_dir / "Non_Empirical_Prediction",
pred_dir / "Custom_Regression_Prediction",
work_path / "13_Custom_Regression" / "Custom_Regression_Prediction",
work_path / "14_visualization",
work_path,
]
@ -1621,9 +1621,9 @@ class Step12VizPanel(QWidget):
"""设置三个预测步骤的默认输出目录"""
try:
base_prediction_dir = work_path / "11_12_13_predictions"
ml_dir = base_prediction_dir / "Machine_Learning_Prediction"
ml_dir = work_path / "9_ML_Prediction"
reg_dir = base_prediction_dir / "Regression_Model_Prediction"
custom_dir = base_prediction_dir / "Custom_Regression_Prediction"
custom_dir = work_path / "13_Custom_Regression" / "Custom_Regression_Prediction"
ml_dir.mkdir(parents=True, exist_ok=True)
reg_dir.mkdir(parents=True, exist_ok=True)
custom_dir.mkdir(parents=True, exist_ok=True)

10
src/gui/panels/step13_panel.py
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@ -31,7 +31,7 @@ class Step12Panel(QWidget):
)
layout.addWidget(self.sampling_csv_file)
# 自定义回归模型目录选择（9_Custom_Regression_Modeling）
# 自定义回归模型目录选择（13_Custom_Regression）
self.regression_models_dir = FileSelectWidget(
"回归模型目录:",
"Directories;;All Files (*.*)"
@ -133,12 +133,12 @@ class Step12Panel(QWidget):
if self.work_dir:
models_dir = self.regression_models_dir.get_path().strip()
if not models_dir:
default_models_dir = os.path.join(self.work_dir, "9_Custom_Regression_Modeling").replace('\\', '/')
default_models_dir = os.path.join(self.work_dir, "13_Custom_Regression").replace('\\', '/')
self.regression_models_dir.set_path(default_models_dir)
# 4. 自动填充输出目录（自定义回归预测目录）
if self.work_dir:
output_dir = os.path.join(self.work_dir, "11_12_13_predictions/Custom_Regression_Prediction")
output_dir = os.path.join(self.work_dir, "13_Custom_Regression/Custom_Regression_Prediction")
os.makedirs(output_dir, exist_ok=True)
existing_out = self.output_dir_widget.get_path()
if not existing_out or not existing_out.strip():
@ -161,7 +161,7 @@ class Step12Panel(QWidget):
"""浏览回归模型目录"""
default = self._get_default_work_dir()
if default:
default = os.path.join(default, "9_Custom_Regression_Modeling")
default = os.path.join(default, "13_Custom_Regression")
dir_path = QFileDialog.getExistingDirectory(self, "选择回归模型目录", default)
if dir_path:
self.regression_models_dir.set_path(dir_path)
@ -170,7 +170,7 @@ class Step12Panel(QWidget):
"""浏览输出目录"""
default = self._get_default_work_dir()
if default:
default = os.path.join(default, "11_12_13_predictions/Custom_Regression_Prediction")
default = os.path.join(default, "13_Custom_Regression/Custom_Regression_Prediction")
dir_path = QFileDialog.getExistingDirectory(self, "选择输出目录", default)
if dir_path:
self.output_dir_widget.set_path(dir_path)

4
src/gui/panels/step14_panel.py
View File

@ -524,9 +524,9 @@ class Step14Panel(QWidget):
# 若为相对路径，使用 work_dir 合成为绝对路径
if not os.path.isabs(step10_output):
step10_output = os.path.join(self.work_dir or '', step10_output).replace('\\', '/')
# 提取父目录后追加 Machine_Learning_Prediction（最底层真实子目录）
# 提取父目录后追加 9_ML_Prediction（最底层真实子目录）
base_pred_dir = str(Path(step10_output).parent)
ml_pred_dir = Path(base_pred_dir) / "Machine_Learning_Prediction"
ml_pred_dir = Path(base_pred_dir) / "9_ML_Prediction"
pred_dir = str(ml_pred_dir) if ml_pred_dir.exists() else base_pred_dir
# 2. 备选：从 Step11 界面读取非经验预测输出目录

6
src/gui/panels/step2_panel.py
View File

@ -186,10 +186,10 @@ class Step2Panel(QWidget):
# 3. 自动填充输出路径（基于工作目录）
if self.work_dir:
# 生成输出耀斑掩膜的标准路径：workspace/2_glint_mask/glint_mask_out.dat
output_dir = os.path.join(self.work_dir, "2_glint_mask")
# 生成输出耀斑掩膜的标准路径：workspace/2_Glint_Detection/severe_glint_area.dat
output_dir = os.path.join(self.work_dir, "2_Glint_Detection")
os.makedirs(output_dir, exist_ok=True)
default_output_path = os.path.join(output_dir, "glint_mask_out.dat").replace('\\', '/')
default_output_path = os.path.join(output_dir, "severe_glint_area.dat").replace('\\', '/')
self.output_file.set_path(default_output_path)
else:
# 没有工作目录时，清空输出路径

6
src/gui/panels/step9_ml_predict_panel.py
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@ -190,7 +190,7 @@ class Step9MlPredictPanel(QWidget):
"""浏览模型母文件夹，自动扫描子目录中的 .joblib 文件"""
default = self._get_default_work_dir()
if default:
default = os.path.join(default, "9_supervised_modeling")
default = os.path.join(default, "9_ML_Prediction")
dir_path = QFileDialog.getExistingDirectory(
self,
"选择模型母文件夹",
@ -352,7 +352,7 @@ class Step9MlPredictPanel(QWidget):
# 3. 自动填充输出路径（机器学习预测目录）
if self.work_dir:
output_dir = os.path.join(self.work_dir, "11_ml_prediction")
output_dir = os.path.join(self.work_dir, "9_ML_Prediction")
os.makedirs(output_dir, exist_ok=True)
existing_out = self.output_file.get_path()
if not existing_out or not existing_out.strip():
@ -375,7 +375,7 @@ class Step9MlPredictPanel(QWidget):
"""浏览模型目录"""
default = self._get_default_work_dir()
if default:
default = os.path.join(default, "9_supervised_modeling")
default = os.path.join(default, "9_ML_Prediction")
dir_path = QFileDialog.getExistingDirectory(self, "选择模型目录", default)
if dir_path:
self.models_dir_file.set_path(dir_path)

14
src/gui/water_quality_gui.py
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@ -1366,14 +1366,14 @@ class WaterQualityGUI(QMainWindow):
# 定义每个步骤的标准输出路径模式（相对于工作目录）
self.step_default_outputs = {
'step1': "1_water_mask/water_mask_from_ndwi.dat",
'step2': "2_glint/severe_glint_area.dat",
'step2': "2_Glint_Detection/severe_glint_area.dat",
'step3': "3_deglint/deglint_kutser.bsq",
'step4_sampling': "4_sampling/sampling_spectra.csv",
'step5_clean': "5_Data_Cleaning/cleaned_sampling_data.csv",
'step6_feature': "6_Spectral_Feature_Extraction/training_spectra.csv",
'step7_index': "7_Water_Quality_Indices/training_spectra_indices.csv",
'step8_ml_train': "8_Supervised_Model_Training/",
'step9_ml_predict': "11_12_13_predictions/Machine_Learning_Prediction/",
'step9_ml_predict': "9_ML_Prediction/",
'step10_watercolor': "10_WaterIndex_Images/",
'step11_map': "14_visualization/"
}
@ -1415,7 +1415,7 @@ class WaterQualityGUI(QMainWindow):
'bsq_file': ('step3', 'deglint_image', 'bsq_file') # 水色反演需要去耀斑BSQ影像
},
'step11_map': {
'prediction_csv_dir_edit': ('step9_ml_predict', 'Machine_Learning_Prediction', 'prediction_csv_dir_edit'),
'prediction_csv_dir_edit': ('step9_ml_predict', '9_ML_Prediction', 'prediction_csv_dir_edit'),
'geotiff_dir_edit': ('step10_watercolor', 'WaterIndex_Images', 'geotiff_dir_edit')
}
}
@ -2379,17 +2379,17 @@ class WaterQualityGUI(QMainWindow):
# 扫描各个子目录
subdirs = {
'1_water_mask': 'step1',
'2_glint': 'step2',
'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',
'9_Custom_Regression_Modeling': 'step9_ml_predict',
'11_12_13_predictions/Machine_Learning_Prediction': 'step9_ml_predict',
'13_Custom_Regression': 'step13',
'9_ML_Prediction': 'step9_ml_predict',
'11_12_13_predictions/Non_Empirical_Prediction': 'step11_map',
'11_12_13_predictions/Custom_Regression_Prediction': 'step12_viz',
'13_Custom_Regression/Custom_Regression_Prediction': 'step13',
'14_visualization': 'step13_report',
'10_geotiff_batch_rendering': 'step11_map'
}