fix for one-channel gray images

src/split/slicer.py

 
 
 def add_border(image: np.ndarray, border_px: int) -> np.ndarray:
-    return cv2.copyMakeBorder(
-        image,
-        top=border_px,
-        bottom=border_px,
-        left=border_px,
-        right=border_px,
-        borderType=cv2.BORDER_CONSTANT,
-        value=BORDER_COLOR,
-    )
+    if len(image.shape) == 2:
+        return cv2.copyMakeBorder(image, top=border_px, bottom=border_px,
+                                   left=border_px, right=border_px,
+                                   borderType=cv2.BORDER_CONSTANT, value=255)
+    return cv2.copyMakeBorder(image, top=border_px, bottom=border_px,
+                               left=border_px, right=border_px,
+                               borderType=cv2.BORDER_CONSTANT, value=BORDER_COLOR)
 
 
 def save_page(image: np.ndarray, path: Path) -> None:
     return cols, rows
 
 
+def _to_gray(image: np.ndarray) -> np.ndarray:
+    if len(image.shape) == 2:
+        return image
+    return cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+
 def is_empty(image: np.ndarray) -> bool:
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = _to_gray(image)
     dark_pixels = (gray < EMPTY_DARK_THRESHOLD).sum()
     threshold = int(image.shape[0] * image.shape[1] * EMPTY_DARK_RATIO)
     return dark_pixels <= threshold
     """Бинаризация: серый → Гаусс-блюр → Otsu-порог → морф.закрытие.
     При слишком низком пороге Otsu (<50) — адаптивный порог (Gaussian, окно 31).
     Морфологическое закрытие (3×3) склеивает фрагменты букв в непрерывные регионы."""
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = _to_gray(image)
     blurred = cv2.GaussianBlur(gray, (3, 3), 0)
     otsu_th, binary = cv2.threshold(
         blurred, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU,
 
 
 def unwarp_image(image: np.ndarray) -> np.ndarray:
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    gray = _to_gray(image)
     blurred = cv2.GaussianBlur(gray, (5, 5), 0)
     edges = cv2.Canny(blurred, 50, 150)
     contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
 def denoise_image(image: np.ndarray) -> np.ndarray:
     """Non-Local Means denoising. Убирает шум, сохраняя границы символов."""
+    if len(image.shape) == 2:
+        return cv2.fastNlMeansDenoising(image, None, 10, 7, 21)
     return cv2.fastNlMeansDenoisingColored(image, None, 10, 10, 7, 21)