remove small points

app.py

@@ -180,55 +180,47 @@ def apply_parsing_white_mask_to_person_cv2(
     return result_bgr
 
 
-def clean_and_smooth_parsing_mask(
+def remove_small_white_components(
     parsing_img: Image.Image,
     *,
     white_threshold: int = 128,
-    min_white_area: int = 300,
-    close_ksize: int = 7,
+    min_white_area: int = 50,
+    use_open: bool = False,
     open_ksize: int = 3,
     morph_iters: int = 1,
-    blur_ksize: int = 0,
 ) -> Image.Image:
+    """
+    - 흰색(=foreground)으로 이진화
+    - connected components로 '작은 흰색 덩어리'만 제거
+    - (옵션) OPEN을 아주 약하게 적용해 작은 점/가시 제거 (흰색이 늘어나는 CLOSE는 사용 X)
+    """
     if not isinstance(parsing_img, Image.Image):
         raise TypeError("parsing_img must be a PIL.Image.Image")
 
     arr = np.array(parsing_img.convert("L"), dtype=np.uint8)
-    mask = np.where(arr >= white_threshold, 255, 0).astype(np.uint8)
+    mask = np.where(arr >= int(white_threshold), 255, 0).astype(np.uint8)
 
+    # 1) 작은 흰색 연결요소 제거
     num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
     keep = np.zeros_like(mask)
     for lab in range(1, num_labels):
         area = int(stats[lab, cv2.CC_STAT_AREA])
-        if area >= min_white_area:
+        if area >= int(min_white_area):
             keep[labels == lab] = 255
     mask = keep
 
-    def _odd_or_one(k: int) -> int:
-        k = int(k)
-        if k <= 1:
-            return 1
-        return k if (k % 2 == 1) else (k + 1)
-
-    close_k = _odd_or_one(close_ksize)
-    open_k = _odd_or_one(open_ksize)
-
-    if close_k > 1:
-        k_close = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (close_k, close_k))
-        mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, k_close, iterations=int(morph_iters))
-
-    if open_k > 1:
-        k_open = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (open_k, open_k))
-        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, k_open, iterations=int(morph_iters))
-
-    if blur_ksize and int(blur_ksize) > 1:
-        b = _odd_or_one(int(blur_ksize))
-        mask_blur = cv2.GaussianBlur(mask, (b, b), 0)
-        mask = np.where(mask_blur >= 128, 255, 0).astype(np.uint8)
+    # 2) (옵션) OPEN: 작은 흰 점/가시 제거 + 경계 약간 정리 (흰색 증가 방향 아님)
+    if use_open and int(open_ksize) > 1:
+        k = int(open_ksize)
+        if k % 2 == 0:
+            k += 1
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
+        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=int(morph_iters))
 
     return Image.fromarray(mask, mode="L")
 
 
+
 def compute_hw_from_person(person_path: str):
     img = _imread_or_raise(person_path)
     orig_h, orig_w = img.shape[:2]
@@ -446,16 +438,17 @@ def run_one(paths: Paths, prompt: str, steps: int = DEFAULT_STEPS, category: str
     parsing_img = res["images"][0] if res.get("images") else None
     if parsing_img is None:
         raise RuntimeError("run_simple_extractor returned no parsing images.")
+        
 
-    parsing_img = clean_and_smooth_parsing_mask(
+        
+    parsing_img = remove_small_white_components(
         parsing_img,
-        min_white_area=50,
-        close_ksize=9,
-        open_ksize=3,
-        morph_iters=1,
-        blur_ksize=7,
+        white_threshold=128,
+        min_white_area=150,   # 데이터에 맞게 30~200 사이 조절
+        use_open=False,
     )
 
+
     use_depth_path = (
         paths.depth_path is not None
         and isinstance(paths.depth_path, str)
@@ -533,8 +526,6 @@ def run_one(paths: Paths, prompt: str, steps: int = DEFAULT_STEPS, category: str
             pass
 
     style_img = Image.open(paths.style_path).convert("RGB")
-
-    prompt = extractor_category + " with " + prompt
     
     if prompt != "":
         prompt = extractor_category + " with " + prompt
@@ -659,7 +650,7 @@ with gr.Blocks(title="VISTA Demo (HF Spaces)") as demo:
         prompt_in = gr.Textbox(
             label="Prompt",
             value="",
-            placeholder="예: floral pattern, silk texture, studio lighting",
+            placeholder="ex) lace, button, …",
             lines=2,
         )
         steps_in = gr.Slider(1, 80, value=DEFAULT_STEPS, step=1, label="Steps")