Update app.py

app.py

@@ -18,36 +18,32 @@ from basicsr.utils import imwrite
 from gfpgan import GFPGANer
 
 # background upsampler
-if args.bg_upsampler == 'realesrgan':
-    if not torch.cuda.is_available():  # CPU
-        import warnings
-        warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
-                      'If you really want to use it, please modify the corresponding codes.')
-        bg_upsampler = None
-    else:
-        from basicsr.archs.rrdbnet_arch import RRDBNet
-        from realesrgan import RealESRGANer
-        model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
-        bg_upsampler = RealESRGANer(
-            scale=2,
-            model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
-            model=model,
-            tile=args.bg_tile,
-            tile_pad=10,
-            pre_pad=0,
-            half=True)  # need to set False in CPU mode
-else:
+if not torch.cuda.is_available():  # CPU
+    import warnings
+    warnings.warn('The unoptimized RealESRGAN is very slow on CPU. We do not use it. '
+                  'If you really want to use it, please modify the corresponding codes.')
     bg_upsampler = None
-    
+else:
+    from basicsr.archs.rrdbnet_arch import RRDBNet
+    from realesrgan import RealESRGANer
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
+    bg_upsampler = RealESRGANer(
+        scale=2,
+        model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth',
+        model=model,
+        tile=400,
+        tile_pad=10,
+        pre_pad=0,
+        half=True)  # need to set False in CPU mode
+
+ 
 # set up GFPGAN restorer
 restorer = GFPGANer(
-    model_path=args.model_path,
-    upscale=args.upscale,
-    arch=args.arch,
-    channel_multiplier=args.channel,
+    model_path='GFPGANCleanv1-NoCE-C2.pth',
+    upscale=2,
+    arch='clean',
+    channel_multiplier=2,
     bg_upsampler=bg_upsampler)
-img_list = sorted(glob.glob(os.path.join(args.test_path, '*')))
-
 
 
 
@@ -57,12 +53,12 @@ def inference(img):
     input_img = cv2.imread(img, cv2.IMREAD_COLOR)
     # restore faces and background if necessary
     cropped_faces, restored_faces, restored_img = restorer.enhance(
-        input_img, has_aligned=args.aligned, only_center_face=args.only_center_face, paste_back=args.paste_back)
+        input_img, has_aligned=False, only_center_face=False, paste_back=True)
     # save faces
     # save cropped face
    
     # save comparison image
-    cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
+    cmp_img = np.concatenate((cropped_faces[0], restored_faces[0]), axis=1)
    
     return Image.fromarray(cmp_img)