Update app.py

app.py

@@ -13,7 +13,7 @@ import cv2
 import gradio as gr
 
 
-#Download Required Models
+# 필요한 모델 다운로드
 if not os.path.exists('realesr-general-x4v3.pth'):
     os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
 if not os.path.exists('GFPGANv1.2.pth'):
@@ -32,13 +32,11 @@ half = True if torch.cuda.is_available() else False
 upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
 
-# Save Image to the Directory
+# 이미지 저장 디렉토리에 저장하는 부분 (현재 주석 처리됨)
 # os.makedirs('output', exist_ok=True)
 
 def upscaler(img, version, scale):
-
     try:
-        
         img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
         if len(img.shape) == 3 and img.shape[2] == 4:
             img_mode = 'RGBA'
@@ -48,12 +46,10 @@ def upscaler(img, version, scale):
         else:
             img_mode = None
 
-
         h, w = img.shape[0:2]
         if h < 300:
             img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
 
-        
         face_enhancer = GFPGANer(
             model_path=f'{version}.pth', 
             upscale=2, 
@@ -62,53 +58,41 @@ def upscaler(img, version, scale):
             bg_upsampler=upsampler
         )
 
-
         try:
             _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
         except RuntimeError as error:
-            print('Error', error)
-
+            print('오류:', error)
 
         try:
-            if scale != 2:
+            # 배율 조정 계수가 2 또는 0인 경우 추가 스케일링을 수행하지 않음
+            if scale != 2 and scale != 0:
                 interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
                 h, w = img.shape[0:2]
                 output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
         except Exception as error:
-            print('wrong scale input.', error)
-
-        # Save Image to the Directory
-        # ext = os.path.splitext(os.path.basename(str(img)))[1]
-        # if img_mode == 'RGBA':
-        #     ext = 'png'
-        # else:
-        #     ext = 'jpg'
-        #
-        # save_path = f'output/out.{ext}'
-        # cv2.imwrite(save_path, output)
-        # return output, save_path
+            print('잘못된 배율 입력:', error)
 
         output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
         return output
     except Exception as error:
-        print('global exception', error)
+        print('전역 예외:', error)
         return None, None
 
 if __name__ == "__main__":
-
-    title = "Image Upscaler & Restoring [GFPGAN Algorithm]"
+    title = "이미지 업스케일러 및 복원 [GFPGAN 알고리즘]"
 
     demo = gr.Interface(
             upscaler, [
-                gr.Image(type="filepath", label="Input"),
-                gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label='version'),
-                gr.Number(label="Rescaling factor"),
+                gr.Image(type="filepath", label="입력 이미지"),
+                gr.Radio(['GFPGANv1.2', 'GFPGANv1.3', 'GFPGANv1.4', 'RestoreFormer'], type="value", label="버전", value="GFPGANv1.4", visible=False),
+                gr.Number(label="배율 조정 계수", value=0, visible=False),
             ], [
-                gr.Image(type="numpy", label="Output"),
+                gr.Image(type="numpy", label="출력 이미지"),
             ],
             title=title,
-            allow_flagging="never"
+            allow_flagging="never",
+            examples=[["예제.png", "GFPGANv1.4", 0]]
         )
 
     demo.queue()
-    demo.launch()
+    demo.launch()