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添加公式方法

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This commit is contained in:
DXC
2026-06-12 16:48:20 +08:00
parent be47b70594
commit f73a7d8999
7 changed files with 162 additions and 62 deletions
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6
src/core/algorithms/waterindex_inversion/__init__.py
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@ -348,7 +348,7 @@ class WaterIndexProcessor:
hdr_path : str, optional
ENVI HDR 文件路径（None → 自动构造）
output_dir : str, optional
输出目录（None → 与 bsq_path 同目录下的 8_WaterIndex_Images/）
输出目录（None → 与 bsq_path 同目录下的 10_WaterIndex_Images/）
formula_names : list, optional
要处理的公式名列表（None → 处理全部）
water_mask : np.ndarray, optional
@ -374,7 +374,7 @@ class WaterIndexProcessor:
# ── 自动构造输出目录 ────────────────────────────────────────────
if output_dir is None:
output_dir = os.path.join(os.path.dirname(bsq_path), '8_WaterIndex_Images')
output_dir = os.path.join(os.path.dirname(bsq_path), '10_WaterIndex_Images')
os.makedirs(output_dir, exist_ok=True)
def progress(msg: str, pct: float):
@ -610,7 +610,7 @@ class WaterIndexProcessor:
if os.path.isfile(hdr_path_alt):
hdr_path = hdr_path_alt
output_dir = os.path.join(work_dir, "8_WaterIndex_Images")
output_dir = os.path.join(work_dir, "10_WaterIndex_Images")
# ── 加载水域掩膜（可选）───────────────────────────────────────
water_mask: Optional[np.ndarray] = None

54
src/core/glint_removal/get_spectral.py
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@ -2,6 +2,7 @@ from osgeo import gdal, osr
import numpy as np
import pandas as pd
import os
import re
import spectral
from math import sin, cos, tan, sqrt, radians
@ -473,9 +474,56 @@ def get_spectral_in_coor(imgpath, coorpath, outpath, radius=0, flare_path=None,
for i in range(min(3, coor_data.shape[0])):
print(f" 行{i + 1}: {coor_data[i, :min(5, coor_data.shape[1])]}") # 只显示前5列
# 提取原始坐标
lat_array = coor_data[:, 1] # 第2列是纬度（跳过测量点ID列）
lon_array = coor_data[:, 2] # 第3列是经度
# 提取原始坐标（使用智能坐标列检测）
lon_patterns = [
r'^lon', r'^lng', r'^longitude', r'经度', r'^x$', r'^utm_x$', r'^pixel_x$'
]
lat_patterns = [
r'^lat', r'^latitude', r'纬度', r'^y$', r'^utm_y$', r'^pixel_y$'
]
x_col_name, y_col_name = None, None
if coor_df is not None and hasattr(coor_df, 'columns'):
for col in coor_df.columns:
col_str = str(col).lower().strip()
if x_col_name is None and any(re.search(p, col_str) for p in lon_patterns):
x_col_name = col
if y_col_name is None and any(re.search(p, col_str) for p in lat_patterns):
y_col_name = col
if x_col_name and y_col_name and x_col_name in coor_df.columns and y_col_name in coor_df.columns:
lon_array = coor_df[x_col_name].values
lat_array = coor_df[y_col_name].values
print(f"💡 坐标列名检测: X/经度=[{x_col_name}], Y/纬度=[{y_col_name}]")
else:
numeric_cols = coor_df.select_dtypes(include=[np.number]).columns.tolist() if coor_df is not None else []
if len(numeric_cols) >= 2:
col1, col2 = numeric_cols[0], numeric_cols[1]
mean1 = coor_df[col1].head(10).mean()
mean2 = coor_df[col2].head(10).mean()
if abs(mean1) <= 90 and abs(mean2) > 90:
y_col_name, x_col_name = col1, col2
lon_array = coor_df[x_col_name].values
lat_array = coor_df[y_col_name].values
elif abs(mean2) <= 90 and abs(mean1) > 90:
x_col_name, y_col_name = col1, col2
lon_array = coor_df[x_col_name].values
lat_array = coor_df[y_col_name].values
else:
if mean1 > mean2:
x_col_name, y_col_name = col1, col2
else:
x_col_name, y_col_name = col2, col1
lon_array = coor_df[x_col_name].values
lat_array = coor_df[y_col_name].values
print(f"💡 触发智能数值推断坐标列: X/经度=[{x_col_name}], Y/纬度=[{y_col_name}]")
else:
if coor_data is not None and coor_data.shape[1] >= 3:
lat_array = coor_data[:, 1]
lon_array = coor_data[:, 2]
else:
raise Exception("坐标文件格式错误：需要至少2列数据，且最好包含坐标列名（如lon/lat/经度/纬度）")
print(f"\n=== 原始坐标信息 ===")
print(f"原始坐标范围: 经度 {np.min(lon_array):.6f} ~ {np.max(lon_array):.6f}, 纬度 {np.min(lat_array):.6f} ~ {np.max(lat_array):.6f}")

26
src/gui/panels/step11_map_panel.py
View File

@ -253,7 +253,7 @@ class Step11MapPanel(QWidget):
self.geotiff_dir_label = QLabel("水色指数目录:")
self.geotiff_dir_label.setMinimumWidth(120)
self.geotiff_dir_edit = QLineEdit()
self.geotiff_dir_edit.setPlaceholderText("选择 8_WaterIndex_Images 文件夹（批量渲染）…")
self.geotiff_dir_edit.setPlaceholderText("选择 10_WaterIndex_Images 文件夹（批量渲染）…")
geotiff_dir_btn = QPushButton("浏览…")
geotiff_dir_btn.setMaximumWidth(80)
geotiff_dir_btn.clicked.connect(self.browse_geotiff_dir)
@ -392,7 +392,7 @@ class Step11MapPanel(QWidget):
"""浏览 GeoTIFF 文件夹（批量模式）"""
default = self._get_default_work_dir()
if default:
default = os.path.join(default, "8_WaterIndex_Images")
default = os.path.join(default, "10_WaterIndex_Images")
d = QFileDialog.getExistingDirectory(
self, "选择水色指数 GeoTIFF 文件夹", default
)
@ -514,18 +514,18 @@ class Step11MapPanel(QWidget):
pred_dir = None
if hasattr(main_window, 'step11_prediction_panel'):
step8_widget = getattr(main_window.step11_prediction_panel, 'output_file', None)
step8_output = ""
step10_output = ""
if hasattr(step8_widget, 'get_path'):
step8_output = step8_widget.get_path() or ""
step10_output = step8_widget.get_path() or ""
elif hasattr(step8_widget, 'text'):
step8_output = step8_widget.text() or ""
step10_output = step8_widget.text() or ""
if step8_output:
if step10_output:
# 若为相对路径，使用 work_dir 合成为绝对路径
if not os.path.isabs(step8_output):
step8_output = os.path.join(self.work_dir or '', step8_output).replace('\\', '/')
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）
base_pred_dir = str(Path(step8_output).parent)
base_pred_dir = str(Path(step10_output).parent)
ml_pred_dir = Path(base_pred_dir) / "Machine_Learning_Prediction"
pred_dir = str(ml_pred_dir) if ml_pred_dir.exists() else base_pred_dir
@ -594,13 +594,13 @@ class Step11MapPanel(QWidget):
print("⚠️ 提示：专题图生成模块需传入标准矢量边界文件 (.shp)，请手动选择。")
# 6. 自动探测 Step 8 输出的水色指数 GeoTIFF（GeoTIFF 渲染模式）
step8_out_dir = Path(self.work_dir) / "8_WaterIndex_Images" if self.work_dir else None
if step8_out_dir and step8_out_dir.is_dir():
step10_out_dir = Path(self.work_dir) / "10_WaterIndex_Images" if self.work_dir else None
if step10_out_dir and step10_out_dir.is_dir():
# GeoTIFF 批量模式：填充目录供批量渲染
if not (self.geotiff_dir_edit.text() or "").strip():
self.geotiff_dir_edit.setText(str(step8_out_dir))
self.geotiff_dir_edit.setText(str(step10_out_dir))
# GeoTIFF 单文件模式：默认选中第一个
tif_files = sorted(step8_out_dir.glob("*.tif"))
tif_files = sorted(step10_out_dir.glob("*.tif"))
if tif_files and not (self.geotiff_file.get_path() or "").strip():
self.geotiff_file.set_path(str(tif_files[0]))
except Exception as e:

26
src/gui/panels/step14_panel.py
View File

@ -253,7 +253,7 @@ class Step14Panel(QWidget):
self.geotiff_dir_label = QLabel("水色指数目录:")
self.geotiff_dir_label.setMinimumWidth(120)
self.geotiff_dir_edit = QLineEdit()
self.geotiff_dir_edit.setPlaceholderText("选择 8_WaterIndex_Images 文件夹（批量渲染）…")
self.geotiff_dir_edit.setPlaceholderText("选择 10_WaterIndex_Images 文件夹（批量渲染）…")
geotiff_dir_btn = QPushButton("浏览…")
geotiff_dir_btn.setMaximumWidth(80)
geotiff_dir_btn.clicked.connect(self.browse_geotiff_dir)
@ -392,7 +392,7 @@ class Step14Panel(QWidget):
"""浏览 GeoTIFF 文件夹（批量模式）"""
default = self._get_default_work_dir()
if default:
default = os.path.join(default, "8_WaterIndex_Images")
default = os.path.join(default, "10_WaterIndex_Images")
d = QFileDialog.getExistingDirectory(
self, "选择水色指数 GeoTIFF 文件夹", default
)
@ -514,18 +514,18 @@ class Step14Panel(QWidget):
pred_dir = None
if hasattr(main_window, 'step11_prediction_panel'):
step8_widget = getattr(main_window.step11_prediction_panel, 'output_file', None)
step8_output = ""
step10_output = ""
if hasattr(step8_widget, 'get_path'):
step8_output = step8_widget.get_path() or ""
step10_output = step8_widget.get_path() or ""
elif hasattr(step8_widget, 'text'):
step8_output = step8_widget.text() or ""
step10_output = step8_widget.text() or ""
if step8_output:
if step10_output:
# 若为相对路径，使用 work_dir 合成为绝对路径
if not os.path.isabs(step8_output):
step8_output = os.path.join(self.work_dir or '', step8_output).replace('\\', '/')
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）
base_pred_dir = str(Path(step8_output).parent)
base_pred_dir = str(Path(step10_output).parent)
ml_pred_dir = Path(base_pred_dir) / "Machine_Learning_Prediction"
pred_dir = str(ml_pred_dir) if ml_pred_dir.exists() else base_pred_dir
@ -594,13 +594,13 @@ class Step14Panel(QWidget):
print("⚠️ 提示：专题图生成模块需传入标准矢量边界文件 (.shp)，请手动选择。")
# 6. 自动探测 Step 8 输出的水色指数 GeoTIFF（GeoTIFF 渲染模式）
step8_out_dir = Path(self.work_dir) / "8_WaterIndex_Images" if self.work_dir else None
if step8_out_dir and step8_out_dir.is_dir():
step10_out_dir = Path(self.work_dir) / "10_WaterIndex_Images" if self.work_dir else None
if step10_out_dir and step10_out_dir.is_dir():
# GeoTIFF 批量模式：填充目录供批量渲染
if not (self.geotiff_dir_edit.text() or "").strip():
self.geotiff_dir_edit.setText(str(step8_out_dir))
self.geotiff_dir_edit.setText(str(step10_out_dir))
# GeoTIFF 单文件模式：默认选中第一个
tif_files = sorted(step8_out_dir.glob("*.tif"))
tif_files = sorted(step10_out_dir.glob("*.tif"))
if tif_files and not (self.geotiff_file.get_path() or "").strip():
self.geotiff_file.set_path(str(tif_files[0]))
except Exception as e:

32
src/gui/water_quality_gui.py
View File

@ -1378,7 +1378,8 @@ class WaterQualityGUI(QMainWindow):
'step11_map': "14_visualization/"
}
# 定义步骤间的依赖关系：{当前步骤: {输入字段: (依赖步骤, 输出类型, 面板属性名)}}
# 依赖关系字典结构:
# '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') }
self.step_dependencies = {
'step2': {
'img_path': ('step1', 'reference_img', 'img_file'),
@ -1386,31 +1387,36 @@ class WaterQualityGUI(QMainWindow):
},
'step3': {
'img_path': ('step1', 'reference_img', 'img_file'),
'water_mask': ('step1', 'water_mask', 'water_mask_file'),
'water_mask': ('step1', 'water_mask', 'water_mask_file')
},
'step4_sampling': {
'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
'water_mask_path': ('step1', 'water_mask', 'water_mask_file')
},
'step5_clean': {
'csv_path': ('step4_sampling', 'sampling_spectra', 'csv_file') # step5 寻找 step4 的采样点
},
'step6_feature': {
'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
'csv_path': ('step5_clean', 'processed_data', 'csv_file'),
'boundary_mask_path': ('step1', 'water_mask', 'boundary_mask_file'),
'boundary_mask_path': ('step1', 'water_mask', 'water_mask_file'), # step6_panel里叫water_mask_file
'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file')
},
'step7_index': {
'training_csv_path': ('step6_feature', 'training_spectra', 'output_file')
'training_csv_path': ('step6_feature', 'training_spectra', 'training_data_widget') # step7 找 step6 的光谱提取
},
'step8_ml_train': {
'training_csv_path': ('step7_index', 'water_indices', 'csv_file')
},
'step4_sampling': {
'deglint_img_path': ('step3', 'deglint_image', 'deglint_img_file'),
'water_mask_path': ('step1', 'water_mask', 'water_mask_file'),
'glint_mask_path': ('step2', 'glint_mask', 'glint_mask_file')
'training_csv_file': ('step7_index', 'training_spectra_indices', 'training_csv_file') # step8 找 step7 的指数宽表
},
'step9_ml_predict': {
'sampling_csv_path': ('step4_sampling', 'sampling_points', 'sampling_csv_file'),
'models_dir': ('step8_ml_train', 'models', 'models_dir_file')
'models_dir': ('step8_ml_train', 'Supervised_Model_Training', 'models_dir_widget')
},
'step10_watercolor': {
'bsq_file': ('step3', 'deglint_image', 'bsq_file') # 水色反演需要去耀斑BSQ影像
},
'step11_map': {
'prediction_csv_path': ('step9_ml_predict', 'predictions', 'prediction_csv_file')
'prediction_csv_dir_edit': ('step9_ml_predict', 'Machine_Learning_Prediction', 'prediction_csv_dir_edit'),
'geotiff_dir_edit': ('step10_watercolor', 'WaterIndex_Images', 'geotiff_dir_edit')
}
}

72
src/preprocessing/process_water_quality_data.py
View File

@ -4,6 +4,7 @@ if not hasattr(threading.Thread, "isAlive"):
import warnings
import os
import re
import numpy as np
import pandas as pd
from scipy import stats
@ -11,6 +12,54 @@ from scipy import stats
warnings.filterwarnings("ignore")
def auto_detect_coord_columns(df: pd.DataFrame):
"""
双重验证智能识别坐标列：
1. 严格正则匹配列名
2. 基于数值范围的地理学推断
"""
lon_patterns = [
r'^lon', r'^lng', r'^longitude', r'经度', r'^x$', r'^utm_x$', r'^pixel_x$'
]
lat_patterns = [
r'^lat', r'^latitude', r'纬度', r'^y$', r'^utm_y$', r'^pixel_y$'
]
x_col, y_col = None, None
for col in df.columns:
col_str = str(col).lower().strip()
if x_col is None and any(re.search(p, col_str) for p in lon_patterns):
x_col = col
if y_col is None and any(re.search(p, col_str) for p in lat_patterns):
y_col = col
if x_col and y_col:
return x_col, y_col
numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
if len(numeric_cols) >= 2:
col1, col2 = numeric_cols[0], numeric_cols[1]
mean1 = df[col1].head(10).mean()
mean2 = df[col2].head(10).mean()
if abs(mean1) <= 90 and abs(mean2) > 90:
y_col, x_col = col1, col2
elif abs(mean2) <= 90 and abs(mean1) > 90:
x_col, y_col = col1, col2
else:
if mean1 > mean2:
x_col, y_col = col1, col2
else:
x_col, y_col = col2, col1
print(f"💡 触发智能数值推断坐标列: X/经度->[{x_col}], Y/纬度->[{y_col}]")
return x_col, y_col
return df.columns[0], df.columns[1]
def detect_outliers_iqr(data: pd.DataFrame, column: str) -> pd.Series:
"""使用 IQR 方法检测异常值，返回与 data 同索引的布尔序列"""
s = pd.to_numeric(data[column], errors="coerce")
@ -92,11 +141,15 @@ def process_water_quality_data(input_file: str, output_file: str):
print(f"原始数据形状: {df.shape}")
print(f"列名: {list(df.columns)}")
# 0.5) 智能检测坐标列
x_col, y_col = auto_detect_coord_columns(df)
print(f"坐标列检测结果: X/经度=[{x_col}], Y/纬度=[{y_col}]")
# 1) 经纬度精度筛选（小数位 >= 7）
print("\n正在筛选经纬度精度(小数位>=7)...")
initial_count = len(df)
for col in ["经度", "纬度"]:
for col in [y_col, x_col]:
if col in df.columns:
dec_len = df[col].apply(_decimal_len)
keep_mask = dec_len >= 7
@ -109,13 +162,11 @@ def process_water_quality_data(input_file: str, output_file: str):
# 2) 异常值检测（IQR）- 只删除异常值，不删除整行
print("\n正在检测异常值(IQR)...")
# 数值列
numeric_columns = df.select_dtypes(include=[np.number]).columns.tolist()
# 排除不检测的列
exclude_columns = ["时间", "测量点", "纬度", "经度"]
exclude_columns = {"时间", "测量点", y_col, x_col}
if "原始" in df.columns:
exclude_columns.append("原始")
exclude_columns.add("原始")
columns_to_check = [c for c in numeric_columns if c not in exclude_columns]
print(f"将检测以下列的异常值: {columns_to_check}")
@ -128,7 +179,6 @@ def process_water_quality_data(input_file: str, output_file: str):
col_mask = detect_outliers_iqr(df_clean, column)
outlier_count = int(col_mask.sum())
print(f'列 "{column}" 检测到 {outlier_count} 个异常值，将其设为 NaN')
# 只将异常值设为 NaN，不删除整行
df_clean.loc[col_mask, column] = np.nan
total_outliers_removed += outlier_count
@ -140,7 +190,7 @@ def process_water_quality_data(input_file: str, output_file: str):
df_clean = df_clean.drop(columns=["原始"])
print('已去除 "原始" 列')
# 4) 字段类型处理：尽量把“时间”转为 datetime
# 4) 字段类型处理：尽量把"时间"转为 datetime
if "时间" in df_clean.columns:
try:
df_clean["时间"] = pd.to_datetime(df_clean["时间"], errors="coerce")
@ -153,22 +203,18 @@ def process_water_quality_data(input_file: str, output_file: str):
print('错误：未找到 "测量点" 列')
return
# 构建聚合字典
agg_dict = {}
if "时间" in df_clean.columns and np.issubdtype(df_clean["时间"].dtype, np.datetime64):
# 时间取平均（等价于时间戳平均）
agg_dict["时间"] = "mean"
elif "时间" in df_clean.columns:
# 如果不是时间类型，保留最常见值以避免无意义的字符串平均
agg_dict["时间"] = lambda s: s.mode().iloc[0] if not s.mode().empty else s.dropna().iloc[0] if s.dropna().size else np.nan
for col in ["纬度", "经度"]:
for col in [y_col, x_col]:
if col in df_clean.columns:
agg_dict[col] = "mean"
# 其余数值列取均值
for col in df_clean.select_dtypes(include=[np.number]).columns:
if col not in ["纬度", "经度"]:
if col not in {y_col, x_col}:
agg_dict[col] = "mean"
grouped = df_clean.groupby("测量点", as_index=False).agg(agg_dict)

6
tmp_watercolor_rescue.py
View File

@ -204,7 +204,7 @@ class Step11WaterColorPanel(QWidget):
"输出目录:",
"Directories"
)
self.output_dir.line_edit.setPlaceholderText("留空 → 工作目录/8_WaterIndex_Images")
self.output_dir.line_edit.setPlaceholderText("留空 → 工作目录/10_WaterIndex_Images")
self.output_dir.browse_btn.clicked.disconnect()
self.output_dir.browse_btn.clicked.connect(self._browse_output_dir)
output_layout.addRow("输出目录:", self.output_dir)
@ -452,7 +452,7 @@ class Step11WaterColorPanel(QWidget):
# 自动填入输出目录
if self.work_dir:
out_dir = os.path.join(self.work_dir, "8_WaterIndex_Images").replace('\\', '/')
out_dir = os.path.join(self.work_dir, "10_WaterIndex_Images").replace('\\', '/')
os.makedirs(out_dir, exist_ok=True)
if not self.output_dir.get_path():
self.output_dir.set_path(out_dir)
@ -483,7 +483,7 @@ class Step11WaterColorPanel(QWidget):
return
if not output_dir:
work_dir = self._get_default_work_dir()
output_dir = os.path.join(work_dir, "8_WaterIndex_Images").replace('\\', '/')
output_dir = os.path.join(work_dir, "10_WaterIndex_Images").replace('\\', '/')
os.makedirs(output_dir, exist_ok=True)
self.output_dir.set_path(output_dir)