Update FFC files

src/orisys/frame_processors.py

@@ -238,7 +238,7 @@ class FFCFrameProcessor:
     """Flat-field correction with offline weight-map calibration and fast online correction."""
 
     def __init__(self, blur_kernel=151, blur_sigma=0.0, epsilon=1e-6, masked_outside=True):
-        self.blur_kernel = ensure_odd(blur_kernel)
+        self.blur_kernel = self.ensure_odd(blur_kernel)
         self.blur_sigma = float(blur_sigma)
         self.epsilon = float(epsilon)
         self.masked_outside = bool(masked_outside)
@@ -252,8 +252,8 @@ class FFCFrameProcessor:
         self._roi_bbox = None
 
     def calibrate(self, reference_image, roi_mask=None):
-        gray = to_gray_float32(reference_image)
-        roi_mask = normalize_mask(roi_mask, gray.shape)
+        gray = self.to_gray_float32(reference_image)
+        roi_mask = self.normalize_mask(roi_mask, gray.shape)
         active = roi_mask > 0
         if not np.any(active):
             raise ValueError("ROI mask is empty; cannot calibrate FFC.")
@@ -308,7 +308,7 @@ class FFCFrameProcessor:
             cv2.convertScaleAbs(self._multiplied_buffer, dst=dst_view, alpha=1.0, beta=0.0)
             return self._output_buffer
 
-        corrected = saturate_to_dtype(self._multiplied_buffer, image.dtype)
+        corrected = self.saturate_to_dtype(self._multiplied_buffer, image.dtype)
         dst_view[...] = corrected
         return self._output_buffer
 
@@ -379,33 +379,33 @@ class FFCFrameProcessor:
                 x1, y1, x2, y2 = self._roi_bbox
                 self._multiplied_buffer = np.empty((y2 - y1, x2 - x1), dtype=np.float32)
 
+    @staticmethod
+    def ensure_odd(value, minimum=3):
+        value = max(int(value), int(minimum))
+        return value if value % 2 == 1 else value + 1
 
-def ensure_odd(value, minimum=3):
-    value = max(int(value), int(minimum))
-    return value if value % 2 == 1 else value + 1
+    @staticmethod
+    def to_gray_float32(image):
+        if image.ndim == 2:
+            gray = image
+        elif image.ndim == 3:
+            gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        else:
+            raise ValueError("image must have shape HxW or HxWxC")
+        return gray.astype(np.float32, copy=False)
 
+    @staticmethod
+    def normalize_mask(mask, shape):
+        if mask is None:
+            return np.ones(shape, dtype=np.uint8) * 255
+        mask = np.asarray(mask)
+        if mask.shape[:2] != shape:
+            mask = cv2.resize(mask, (shape[1], shape[0]), interpolation=cv2.INTER_NEAREST)
+        return np.where(mask > 0, 255, 0).astype(np.uint8)
 
-def to_gray_float32(image):
-    if image.ndim == 2:
-        gray = image
-    elif image.ndim == 3:
-        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    else:
-        raise ValueError("image must have shape HxW or HxWxC")
-    return gray.astype(np.float32, copy=False)
-
-
-def normalize_mask(mask, shape):
-    if mask is None:
-        return np.ones(shape, dtype=np.uint8) * 255
-    mask = np.asarray(mask)
-    if mask.shape[:2] != shape:
-        mask = cv2.resize(mask, (shape[1], shape[0]), interpolation=cv2.INTER_NEAREST)
-    return np.where(mask > 0, 255, 0).astype(np.uint8)
-
-
-def saturate_to_dtype(values, dtype):
-    if np.issubdtype(dtype, np.integer):
-        info = np.iinfo(dtype)
-        return np.clip(values, info.min, info.max).astype(dtype)
-    return values.astype(dtype, copy=False)
+    @staticmethod
+    def saturate_to_dtype(values, dtype):
+        if np.issubdtype(dtype, np.integer):
+            info = np.iinfo(dtype)
+            return np.clip(values, info.min, info.max).astype(dtype)
+        return values.astype(dtype, copy=False)