apply code factor suggestions

doctr/datasets/synthtext.py

@@ -21,6 +21,7 @@ class SynthText(VisionDataset):
     <https://arxiv.org/abs/1604.06646>`_.
 
     Example::
+        >>> # NOTE: This dataset has currently no train/test split
         >>> from doctr.datasets import SynthText
         >>> data_set = SynthText(download=True)
         >>> img, target = data_set[0]
@@ -36,6 +37,7 @@ class SynthText(VisionDataset):
 
     def __init__(
         self,
+        train: bool = True,
         sample_transforms: Optional[Callable[[Any], Any]] = None,
         rotated_bbox: bool = False,
         **kwargs: Any,
@@ -61,37 +63,41 @@ def __init__(
             if not os.path.exists(os.path.join(tmp_root, img_path[0])):
                 raise FileNotFoundError(f"unable to locate {os.path.join(tmp_root, img_path[0])}")
 
-            labels = self._text_to_words(txt)
+            labels = _text_to_words(txt)
             word_boxes = word_boxes.transpose(2, 1, 0) if word_boxes.ndim == 3 else np.expand_dims(word_boxes, axis=0)
 
             if rotated_bbox:
                 # x_center, y_center, w, h, alpha = 0
-                box_targets = [self._compute_rotated_box(pts) for pts in word_boxes]
+                box_targets = [_compute_rotated_box(pts) for pts in word_boxes]
             else:
                 # xmin, ymin, xmax, ymax
-                box_targets = [self._compute_straight_box(pts) for pts in word_boxes]  # type: ignore[misc]
+                box_targets = [_compute_straight_box(pts) for pts in word_boxes]  # type: ignore[misc]
 
             self.data.append((img_path[0], dict(boxes=np.asarray(box_targets, dtype=np_dtype), labels=labels)))
 
         self.root = tmp_root
 
-    def _text_to_words(self, txt: np.ndarray) -> List[str]:
-        line = '\n'.join(txt)
-        return line.split()
-
-    def _compute_straight_box(self, pts: np.ndarray) -> Tuple[float, float, float, float]:
-        # pts: Nx2
-        xmin = np.min(pts[:, 0])
-        xmax = np.max(pts[:, 0])
-        ymin = np.min(pts[:, 1])
-        ymax = np.max(pts[:, 1])
-        return xmin, ymin, xmax, ymax
-
-    def _compute_rotated_box(self, pts: np.ndarray) -> Tuple[float, float, float, float, int]:
-        # pts: Nx2
-        x = np.min(pts[:, 0])
-        y = np.min(pts[:, 1])
-        width = np.max(pts[:, 0]) - x
-        height = np.max(pts[:, 1]) - y
-        # x_center, y_center, w, h, alpha = 0
-        return x + width / 2, y + height / 2, width, height, 0
+
+def _text_to_words(txt: np.ndarray) -> List[str]:
+    """Convert np.str-Array to list of str."""
+    line = '\n'.join(txt)
+    return line.split()
+
+
+def _compute_straight_box(pts: np.ndarray) -> Tuple[float, float, float, float]:
+    # pts: Nx2
+    xmin = np.min(pts[:, 0])
+    xmax = np.max(pts[:, 0])
+    ymin = np.min(pts[:, 1])
+    ymax = np.max(pts[:, 1])
+    return xmin, ymin, xmax, ymax
+
+
+def _compute_rotated_box(pts: np.ndarray) -> Tuple[float, float, float, float, int]:
+    # pts: Nx2
+    x = np.min(pts[:, 0])
+    y = np.min(pts[:, 1])
+    width = np.max(pts[:, 0]) - x
+    height = np.max(pts[:, 1]) - y
+    # x_center, y_center, w, h, alpha = 0
+    return x + width / 2, y + height / 2, width, height, 0