update mask process

app.py

@@ -158,9 +158,58 @@ def merge_white_regions_or(img1: Image.Image, img2: Image.Image) -> Image.Image:
 
 
 def preprocess_mask(mask_img: Image.Image) -> Image.Image:
+    """
+    (2번째 첨부 코드 규칙 반영)
+    - 입력(mask_img)을 전역 (W,H)로 resize (NEAREST)
+    - 이진화(threshold)
+    - padding 목표 width는 항상 1024로 고정
+      * width < 1024: 좌/우 padding (value=0)
+      * width > 1024: 중앙 crop
+    - dilation (kernel 17x17, iter=1)
+    - return: RGB mask PIL
+    """
+    global H, W
+
+    # 1) to grayscale np
     m = np.array(mask_img.convert("L"), dtype=np.uint8)
+
+    # 2) resize to global (W, H)  (2번째 코드의 "resize는 전역 (W,H)" 컨벤션)
+    if (H is not None) and (W is not None):
+        # mask는 경계 보존 위해 NEAREST 권장
+        m = cv2.resize(m, (W, H), interpolation=cv2.INTER_NEAREST)
+
+    # 3) binarize
     _, m = cv2.threshold(m, 127, 255, cv2.THRESH_BINARY)
-    return Image.fromarray(m).convert("RGB")
+
+    # 4) pad/crop to target width=1024 (항상 고정)
+    target_width = 1024
+    h, w = m.shape[:2]
+
+    if w < target_width:
+        total_padding = target_width - w
+        left_padding = total_padding // 2
+        right_padding = total_padding - left_padding
+        m = cv2.copyMakeBorder(
+            m,
+            top=0, bottom=0,
+            left=left_padding, right=right_padding,
+            borderType=cv2.BORDER_CONSTANT,
+            value=0,
+        )
+    elif w > target_width:
+        left = (w - target_width) // 2
+        m = m[:, left:left + target_width]
+
+    # 5) dilate (2번째 코드 동일)
+    kernel = np.ones((17, 17), np.uint8)
+    m = cv2.dilate(m, kernel, iterations=1)
+
+    if DEBUG_SAVE:
+        cv2.imwrite("mask_final_1024.png", m)
+
+    # 6) return as RGB PIL
+    return Image.fromarray(m, mode="L").convert("RGB")
+
 
 
 def make_depth(depth_path: str) -> Image.Image: