Update app.py

app.py

@@ -177,34 +177,24 @@ if __name__ == "__main__":
 
                 #apply seamlessClone technique here
                 #img_base
-                dict = dict['background'].convert("RGB").resize((384, 512))
-                dict = np.array(dict).astype(np.uint8)
-                dict = cv2.cvtColor(dict, cv2.COLOR_RGB2BGR)
+                dict = dict.convert("RGB").resize((384, 512))
+                dict = np.array(dict)
+                dict = cv2.cvtColor(dict, cv2.COLOR_RGB2BGR).astype(np.uint8)
 
                 #img_output
                 img_cv = rearrange(x_checked_image_torch[0], 'c h w -> h w c').cpu().numpy()
-                img_cv = (img_cv * 255).astype(np.uint8)
-                img_cv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2BGR)
+                img_cv = (img_cv * 255)
+                img_cv = cv2.cvtColor(img_cv, cv2.COLOR_RGB2BGR).astype(np.uint8)
                 
                 #mask
                 mask_cv = mask_cv.convert("L").resize((384,512))
                 mask_cv = np.array(mask_cv).astype(np.uint8)
                 mask_cv = 255-mask_cv
 
-                # Normalize mask to binary values
-                _, mask_cv = cv2.threshold(mask_cv, 128, 255, cv2.THRESH_BINARY)
-
-                # Center for seamlessClone
-                target_size = (dict.shape[1], dict.shape[0])
-                center = (target_size[0] // 2, target_size[1] // 2)
-
-                
-                img_C = cv2.seamlessClone(dict, img_cv, mask_cv, center, cv2.NORMAL_CLONE)
-                img_C = img_C.astype(np.uint8)
+                img_C = cv2.seamlessClone(dict, img_cv, mask_cv, (192, 256), cv2.NORMAL_CLONE)
                 img_C = cv2.cvtColor(img_C, cv2.COLOR_BGR2RGB)
                 img_C = Image.fromarray(img_C, mode="RGB")
     
-                
                 return img_C, mask_gray