界面优化

src/core/water_quality_inversion_pipeline_GUI.py

@@ -989,7 +989,12 @@ class WaterQualityInversionPipeline:
         if not GDAL_AVAILABLE:
             raise ImportError("GDAL未安装，无法保存影像文件")
         
-        height, width, n_bands = image_array.shape
+        # 兼容 (H,W) 和 (H,W,C) 两种 shape 格式
+        if image_array.ndim == 2:
+            height, width = image_array.shape
+            n_bands = 1
+        else:
+            height, width, n_bands = image_array.shape
         
         # 获取驱动
         driver = gdal.GetDriverByName('ENVI')
@@ -1100,11 +1105,8 @@ class WaterQualityInversionPipeline:
         Returns:
             numpy数组或None，1表示水域，0表示非水域
         """
-        # 获取图像尺寸
-        if isinstance(image_shape, np.ndarray):
-            img_height, img_width = image_shape.shape[:2]
-        else:
-            img_height, img_width = image_shape
+        # 获取图像尺寸（统一从 shape 元组中提取前两个维度，兼容 (H,W)、(H,W,C)、(B,H,W) 等多种格式）
+        img_height, img_width = image_shape[0], image_shape[1]
         
         if water_mask is None:
             # 如果water_mask为None，使用步骤1生成的dat格式掩膜
@@ -1362,6 +1364,19 @@ class WaterQualityInversionPipeline:
                     interpolated_bands.append(band_data)
                     continue
                 
+                # 兼容中文和各种格式
+                raw_interp = str(interpolation_method).lower()
+                if 'nearest' in raw_interp or '邻近' in raw_interp or '最邻近' in raw_interp:
+                    interpolation_method = 'nearest'
+                elif 'bilinear' in raw_interp or '线性' in raw_interp or '双线性' in raw_interp:
+                    interpolation_method = 'bilinear'
+                elif 'spline' in raw_interp or '样条' in raw_interp or 'rbf' in raw_interp:
+                    interpolation_method = 'spline'
+                elif 'kriging' in raw_interp or '克里金' in raw_interp:
+                    interpolation_method = 'kriging'
+                else:
+                    interpolation_method = 'nearest'
+
                 # 对需要插值的像素进行插值
                 if interpolation_method == 'nearest':
                     # 邻近插值
@@ -1591,6 +1606,18 @@ class WaterQualityInversionPipeline:
         
         step_start_time = time.time()
         try:
+            # 兼容中文和各种格式
+            raw_method = str(method).lower()
+            if 'kutser' in raw_method:
+                method = 'kutser'
+            elif 'goodman' in raw_method:
+                method = 'goodman'
+            elif 'hedley' in raw_method:
+                method = 'hedley'
+            elif 'sugar' in raw_method:
+                method = 'sugar'
+            # 其余方法（subtract_nir, regression_slope, oxygen_absorption）保持原值
+
             # 如果未启用，直接跳过处理并把原始影像路径作为后续流程输入
             if not enabled:
                 print("已设置跳过去除耀斑（enabled=False），将直接使用原始影像。")
@@ -1807,6 +1834,16 @@ class WaterQualityInversionPipeline:
                 del corrected_bands
                 
             elif method == "sugar":
+                # 强行转换暗号，兼容中文和各种格式
+                raw_method = str(sugar_glint_mask_method).lower()
+                if 'cdf' in raw_method or '累积' in raw_method:
+                    sugar_glint_mask_method = 'cdf'
+                elif 'otsu' in raw_method or '大津' in raw_method:
+                    sugar_glint_mask_method = 'otsu'
+                else:
+                    # 默认回退到 cdf 确保不崩溃
+                    sugar_glint_mask_method = 'cdf'
+
                 print(f"使用方法: SUGAR (迭代次数={sugar_iter}, 掩膜方法={sugar_glint_mask_method})")
                 
                 # 确定输出路径
@@ -3603,6 +3640,29 @@ class WaterQualityInversionPipeline:
         Returns:
             预处理后的CSV文件路径
         """
+        # 兼容中文和各种格式
+        raw_p = str(preprocess_method).lower()
+        if raw_p == 'none' or '无' in raw_p or '跳过' in raw_p:
+            preprocess_method = 'None'
+        elif raw_p == 'mms' or 'minmax' in raw_p or '最大最小' in raw_p:
+            preprocess_method = 'MMS'
+        elif raw_p == 'ss' or '标准' in raw_p or '标准化' in raw_p:
+            preprocess_method = 'SS'
+        elif raw_p == 'snv' or '标准正态' in raw_p:
+            preprocess_method = 'SNV'
+        elif raw_p == 'ma' or '移动' in raw_p:
+            preprocess_method = 'MA'
+        elif raw_p == 'sg' or 'savitzky' in raw_p or '平滑' in raw_p:
+            preprocess_method = 'SG'
+        elif raw_p == 'msc' or '多元散射' in raw_p:
+            preprocess_method = 'MSC'
+        elif raw_p == 'd1' or 'd2' or 'dt' or '导数' in raw_p:
+            preprocess_method = {'d1': 'D1', 'd2': 'D2', 'dt': 'DT'}.get(raw_p, raw_p.upper())
+        elif raw_p == 'ct' or '去趋势' in raw_p:
+            preprocess_method = 'CT'
+        else:
+            preprocess_method = preprocess_method  # 保持原值
+
         # 如果不需要预处理，直接返回原文件
         if preprocess_method == 'None':
             return csv_path