dd

app.py

@@ -4,6 +4,8 @@ import torch
 from PIL import Image
 from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
 import numpy as np
+import torchvision.transforms as T
+from PIL import Image
 
 
 def greet(url):
@@ -27,7 +29,15 @@ def greet(url):
     predicted_semantic_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
     # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
 
-    return predicted_semantic_map
+    # predicted_semantic_map을 8비트 부호 없는 정수로 변환
+    # 이미지를 부호 없는 8비트 정수로 변환 (0에서 255 사이의 값으로 스케일링)
+    predicted_semantic_map_scaled = (predicted_semantic_map - predicted_semantic_map.min()) / (predicted_semantic_map.max() - predicted_semantic_map.min()) * 255
+    predicted_semantic_map_uint8 = predicted_semantic_map_scaled.to(torch.uint8)
+
+    tensor_to_pil = T.ToPILImage()
+    image = tensor_to_pil(predicted_semantic_map_uint8)
+
+    return image
 
 url = "http://www.apparelnews.co.kr/upfiles/manage/202302/5d5f694177b26fc86e5db623bf7ae4b7.jpg"
 #greet(url)