Update app.py

app.py

@@ -171,29 +171,27 @@ def laplacian_blend(img1: np.ndarray, img2: np.ndarray, mask: np.ndarray, depth:
     if len(mask.shape) != len(img1.shape):
         mask = np.stack((mask,) * 3, axis=-1)
 
+    # Resize all images to the same size (use the size of img1)
+    h, w = img1.shape[:2]
+    img2 = cv2.resize(img2, (w, h))
+    mask = cv2.resize(mask, (w, h))
+
     mask_gaus_pyramid = _gaus_pyramid(mask, depth, sigma)
     img1_lap_pyramid, img2_lap_pyramid = _lap_pyramid(img1, depth, sigma), _lap_pyramid(img2, depth, sigma)
 
     blended = [_blend(obj, bg, mask) for obj, bg, mask in zip(img1_lap_pyramid, img2_lap_pyramid, mask_gaus_pyramid)][::-1]
     
-    h, w = blended[0].shape[:2]
-    
-    img1 = cv2.resize(img1, (w, h))
-    img2 = cv2.resize(img2, (w, h))
-    mask = cv2.resize(mask, (w, h))
-
-    blanded_img = _blend(img1, img2, mask)
-    blanded_img = cv2.resize(blanded_img, blended[0].shape[:2])
+    blended_img = _blend(img1, img2, mask)
     
-    imgs = []
-    for d in range(0, depth-1):
-        gaussian_img = _low_pass_filter(blanded_img.copy(), sigma)
+    for d in range(depth-1):
+        gaussian_img = _low_pass_filter(blended_img, sigma)
+        gaussian_img = cv2.resize(gaussian_img, blended[d].shape[:2])  # Ensure sizes match
         reconstructed_img = cv2.add(blended[d], gaussian_img)
         
-        imgs.append(reconstructed_img)
-        blanded_img = cv2.pyrUp(reconstructed_img)
-        
-    return np.clip(imgs[-1], 0, 1)
+        blended_img = cv2.pyrUp(reconstructed_img)
+        blended_img = cv2.resize(blended_img, (w, h))  # Resize to original size
+    
+    return np.clip(blended_img, 0, 1)
 
 def get_image(img_path: str, mask: bool=False, scale: bool=True) -> np.array:
     """