[FIX] Fix mypy errors (mindee#1419)

doctr/datasets/imgur5k.py

@@ -112,7 +112,7 @@ def __init__(
                 if ann["word"] != "."
             ]
             # (x, y) coordinates of top left, top right, bottom right, bottom left corners
-            box_targets = [cv2.boxPoints(((box[0], box[1]), (box[2], box[3]), box[4])) for box in _boxes]
+            box_targets = [cv2.boxPoints(((box[0], box[1]), (box[2], box[3]), box[4])) for box in _boxes]  # type: ignore[arg-type]
 
             if not use_polygons:
                 # xmin, ymin, xmax, ymax

doctr/models/_utils.py

@@ -51,14 +51,14 @@ def estimate_orientation(img: np.ndarray, n_ct: int = 50, ratio_threshold_for_li
     if max_value <= 255 and min_value >= 0 and img.shape[-1] == 3:
         gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
         gray_img = cv2.medianBlur(gray_img, 5)
-        thresh = cv2.threshold(gray_img, thresh=0, maxval=255, type=cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
+        thresh = cv2.threshold(gray_img, thresh=0, maxval=255, type=cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]  # type: ignore[assignment]
 
     # try to merge words in lines
     (h, w) = img.shape[:2]
     k_x = max(1, (floor(w / 100)))
     k_y = max(1, (floor(h / 100)))
     kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (k_x, k_y))
-    thresh = cv2.dilate(thresh, kernel, iterations=1)
+    thresh = cv2.dilate(thresh, kernel, iterations=1)  # type: ignore[assignment]
 
     # extract contours
     contours, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

doctr/models/detection/core.py

@@ -57,7 +57,7 @@ def box_score(pred: np.ndarray, points: np.ndarray, assume_straight_pages: bool
 
         else:
             mask: np.ndarray = np.zeros((h, w), np.int32)
-            cv2.fillPoly(mask, [points.astype(np.int32)], 1.0)
+            cv2.fillPoly(mask, [points.astype(np.int32)], 1.0)  # type: ignore[call-overload]
             product = pred * mask
             return np.sum(product) / np.count_nonzero(product)
 

doctr/models/detection/differentiable_binarization/base.py

@@ -83,7 +83,7 @@ def polygon_to_box(
         if len(expanded_points) < 1:
             return None  # type: ignore[return-value]
         return (
-            cv2.boundingRect(expanded_points)
+            cv2.boundingRect(expanded_points)  # type: ignore[return-value]
             if self.assume_straight_pages
             else np.roll(cv2.boxPoints(cv2.minAreaRect(expanded_points)), -1, axis=0)
         )
@@ -233,7 +233,7 @@ def draw_thresh_map(
         padded_polygon: np.ndarray = np.array(padding.Execute(distance)[0])
 
         # Fill the mask with 1 on the new padded polygon
-        cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)
+        cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0)  # type: ignore[call-overload]
 
         # Get min/max to recover polygon after distance computation
         xmin = padded_polygon[:, 0].min()
@@ -351,7 +351,7 @@ def build_target(
                         # seg_mask[idx, box[1] : box[3] + 1, box[0] : box[2] + 1, class_idx] = False
                         seg_mask[idx, class_idx, box[1] : box[3] + 1, box[0] : box[2] + 1] = False
                         continue
-                    cv2.fillPoly(seg_target[idx, class_idx], [shrinked.astype(np.int32)], 1)
+                    cv2.fillPoly(seg_target[idx, class_idx], [shrinked.astype(np.int32)], 1.0)  # type: ignore[call-overload]
 
                     # Draw on both thresh map and thresh mask
                     poly, thresh_target[idx, class_idx], thresh_mask[idx, class_idx] = self.draw_thresh_map(

doctr/models/detection/linknet/base.py

@@ -81,7 +81,7 @@ def polygon_to_box(
         if len(expanded_points) < 1:
             return None  # type: ignore[return-value]
         return (
-            cv2.boundingRect(expanded_points)
+            cv2.boundingRect(expanded_points)  # type: ignore[return-value]
             if self.assume_straight_pages
             else np.roll(cv2.boxPoints(cv2.minAreaRect(expanded_points)), -1, axis=0)
         )
@@ -246,7 +246,7 @@ def build_target(
                     if shrunken.shape[0] <= 2 or not Polygon(shrunken).is_valid:
                         seg_mask[idx, class_idx, box[1] : box[3] + 1, box[0] : box[2] + 1] = False
                         continue
-                    cv2.fillPoly(seg_target[idx, class_idx], [shrunken.astype(np.int32)], 1)
+                    cv2.fillPoly(seg_target[idx, class_idx], [shrunken.astype(np.int32)], 1.0)  # type: ignore[call-overload]
 
         # Don't forget to switch back to channel last if Tensorflow is used
         if channels_last:

doctr/transforms/functional/base.py

@@ -200,4 +200,4 @@ def create_shadow_mask(
     mask: np.ndarray = np.zeros((*target_shape, 1), dtype=np.uint8)
     mask = cv2.fillPoly(mask, [final_contour], (255,), lineType=cv2.LINE_AA)[..., 0]
 
-    return (mask / 255).astype(np.float32).clip(0, 1) * intensity_mask.astype(np.float32)
+    return (mask / 255).astype(np.float32).clip(0, 1) * intensity_mask.astype(np.float32)  # type: ignore[operator]

doctr/utils/geometry.py

@@ -102,7 +102,7 @@ def resolve_enclosing_rbbox(rbboxes: List[np.ndarray], intermed_size: int = 1024
     # Convert to absolute for minAreaRect
     cloud *= intermed_size
     rect = cv2.minAreaRect(cloud.astype(np.int32))
-    return cv2.boxPoints(rect) / intermed_size
+    return cv2.boxPoints(rect) / intermed_size  # type: ignore[operator]
 
 
 def rotate_abs_points(points: np.ndarray, angle: float = 0.0) -> np.ndarray:

doctr/utils/metrics.py

@@ -274,7 +274,7 @@ def _rbox_to_mask(box: np.ndarray, shape: Tuple[int, int]) -> np.ndarray:
     else:
         abs_box = box
         abs_box[2:] = abs_box[2:] + 1
-    cv2.fillPoly(mask, [abs_box - 1], 1)
+    cv2.fillPoly(mask, [abs_box - 1], 1.0)  # type: ignore[call-overload]
 
     return mask.astype(bool)
 
@@ -306,7 +306,7 @@ def rbox_to_mask(boxes: np.ndarray, shape: Tuple[int, int]) -> np.ndarray:
 
         # TODO: optimize slicing to improve vectorization
         for idx, _box in enumerate(abs_boxes):
-            cv2.fillPoly(masks[idx], [_box - 1], 1)
+            cv2.fillPoly(masks[idx], [_box - 1], 1.0)  # type: ignore[call-overload]
     return masks.astype(bool)