添加公式方法

src/core/algorithms/waterindex_inversion/__init__.py

@@ -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

src/core/glint_removal/get_spectral.py

@@ -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}")