调整缩放、多核运行、图标显示

src/core/glint_removal/Goodman.py

@@ -1,4 +1,5 @@
 import numpy as np
+import sys
 # import preprocessing
 
 try:
@@ -14,9 +15,12 @@ try:
 except ImportError:
     TQDM_AVAILABLE = False
     # 如果tqdm不可用，定义一个简单的包装器
-    def tqdm(iterable, desc=None, total=None):
+    def tqdm(iterable, desc=None, total=None, disable=None):
         return iterable
 
+# 检测是否在 PyInstaller 打包环境（无控制台）
+_is_frozen_gui = getattr(sys, "frozen", False) and (not hasattr(sys, 'stdout') or sys.stdout is None)
+
 class Goodman:
     def __init__(self, im_aligned, NIR_lower = 25, NIR_upper = 37, A = 0.000019, B = 0.1, 
                  use_gdal=True, chunk_size=None, water_mask=None, output_path=None):
@@ -170,7 +174,7 @@ class Goodman:
         water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None
         
         # 逐波段处理：每次只处理一个波段，处理完后立即添加到结果列表
-        for i in tqdm(range(self.n_bands), desc="处理波段 (numpy)", total=self.n_bands):
+        for i in tqdm(range(self.n_bands), desc="处理波段 (numpy)", total=self.n_bands, disable=_is_frozen_gui):
             # 获取当前波段（这是数组视图，不是复制）
             R = self.im_aligned[:,:,i]
             # 优化计算：减少中间数组创建
@@ -207,7 +211,7 @@ class Goodman:
         water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None
         
         # 逐波段处理：每次只读取和处理一个波段
-        for i in tqdm(range(self.n_bands), desc="处理波段 (GDAL)", total=self.n_bands):
+        for i in tqdm(range(self.n_bands), desc="处理波段 (GDAL)", total=self.n_bands, disable=_is_frozen_gui):
             # 读取当前波段（只加载一个波段到内存）
             current_band = self.dataset.GetRasterBand(i + 1)
             R = current_band.ReadAsArray().astype(np.float32)
@@ -235,7 +239,7 @@ class Goodman:
         mem_dataset = driver.Create('', self.width, self.height, self.n_bands, gdal.GDT_Float32)
         
         # 将numpy数组写入内存数据集（显示进度）
-        for i in tqdm(range(self.n_bands), desc="加载波段到内存", total=self.n_bands):
+        for i in tqdm(range(self.n_bands), desc="加载波段到内存", total=self.n_bands, disable=_is_frozen_gui):
             band = mem_dataset.GetRasterBand(i + 1)
             band.WriteArray(self.im_aligned[:,:,i])
             band.FlushCache()
@@ -316,7 +320,7 @@ class Goodman:
                 dataset.SetProjection(projection)
             
             # 直接逐波段写入（不先堆叠，节省内存）
-            for i in tqdm(range(n_bands), desc="保存波段", total=n_bands):
+            for i in tqdm(range(n_bands), desc="保存波段", total=n_bands, disable=_is_frozen_gui):
                 band = dataset.GetRasterBand(i + 1)
                 # 直接从列表中获取波段并写入，避免创建完整数组
                 band.WriteArray(corrected_bands[i])

src/core/modeling/modeling_batch.py

@@ -19,7 +19,7 @@ from sklearn.cross_decomposition import PLSRegression
 from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, ExtraTreesRegressor
 from sklearn.tree import DecisionTreeRegressor
 from sklearn.neural_network import MLPRegressor
-
+from joblib import parallel_backend
 # 第三方模型导入
 # try:
 #     import lightgbm as lgb
@@ -648,8 +648,10 @@ class WaterQualityModelingBatch:
         )
 
         # 在训练集上训练模型
+        # with parallel_backend("threading", n_jobs=-1):
+        #     grid_search.fit(X_train, y_train)
         grid_search.fit(X_train, y_train)
-
+        
         # 获取最佳模型
         best_model = grid_search.best_estimator_