Update app.py

app.py

@@ -6,11 +6,11 @@ import torch.nn.functional as F
 from torchvision.transforms import Compose
 import shutil
 import os
-import teed
+
 
 from depthAnything.depth_anything.dpt import DepthAnything
 from depthAnything.depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
-from TEED.main import parse_args
+from TEED.main import parse_args, main 
 
 # 深度处理函数
 def depth_anything_image(image, encoder='vitl', pred_only=True, grayscale=True):
@@ -54,13 +54,14 @@ def teed_process_image(image):
     temp_image_path = './teed_tmp/temp_image.png'
     cv2.imwrite(temp_image_path, np.array(image))
 
-    # 获取解析后的参数
     args, train_info = parse_args(is_testing=True, pl_opt_dir='./output/teed_imgs')
-    args.input_val_dir = './teed_tmp'  # 临时目录
-    args.output_dir = './output/teed_imgs'  # 输出目录
+    args.input_val_dir = './teed_tmp'
+    args.output_dir = './output/teed_imgs'
+
+    print(args)  # 调试信息
+    print(train_info)  # 调试信息
 
-    # 调用 TEED 主函数进行处理
-    teed.main(args, train_info)
+    main(args, train_info)  # 确保调用正确
 
     shutil.rmtree('./teed_tmp')
     return cv2.imread(os.path.join('./output/teed_imgs', 'processed_image.png'))
@@ -90,4 +91,4 @@ iface = gr.Interface(
 )
 
 # 启动 Gradio 应用
-iface.launch()
+iface.launch(share=True)