refactor

src/microwink/common.py

@@ -1,10 +1,14 @@
+import typing
 import numpy as np
 
 from dataclasses import dataclass
 from typing import Any, Iterable, Sequence
 from PIL import Image, ImageDraw
 from PIL.Image import Image as PILImage
 
+if typing.TYPE_CHECKING:
+    from _typeshed import ConvertibleToFloat
+
 
 @dataclass
 class Box:
@@ -14,7 +18,7 @@ class Box:
     w: float
 
     @staticmethod
-    def from_xyxy(box: Iterable[Any]) -> "Box":
+    def from_xyxy(box: Iterable["ConvertibleToFloat"]) -> "Box":
         x1, y1, x2, y2 = [float(t) for t in box]
         h = y2 - y1
         w = x2 - x1
@@ -34,6 +38,7 @@ def draw_box(
     color: tuple[int, ...] | str | float = (255, 0, 0),
     width: int = 3,
 ) -> PILImage:
+    assert width >= 0
     image = image.copy()
     draw = ImageDraw.Draw(image)
     points = [(box.x, box.y), (box.x + box.w, box.y + box.h)]
@@ -51,7 +56,7 @@ def draw_mask(
     assert 0.0 <= alpha <= 1.0
     assert (image.height, image.width) == binary_mask.shape
     img = np.array(image)
-    assert len(img.shape) == len(color)
+    assert img.ndim == len(color)
     overlay = np.zeros_like(img)
     overlay[binary_mask] = color
     assert overlay.shape == img.shape

src/microwink/seg.py

@@ -94,7 +94,7 @@ def apply(
         out = []
         assert len(result.boxes) == len(result.scores) == len(result.mask_maps)
         for box, score, mask in zip(result.boxes, result.scores, result.mask_maps):
-            assert len(mask.shape) == 2
+            assert mask.ndim == 2
             assert mask.dtype == np.float64
             out.append(
                 SegResult(
@@ -166,17 +166,11 @@ def postprocess_mask(
             (ih, iw),
             (mask_height, mask_width),
         )
-        mask_maps = np.zeros(
-            (
-                len(scaled_boxes),
-                ih,
-                iw,
-            )
-        )
+        mask_maps = np.zeros((len(scaled_boxes), ih, iw))
         assert len(scaled_boxes) == len(masks)
         assert len(scaled_boxes) == len(boxes)
         for i, (box, scaled_box, mask) in enumerate(zip(boxes, scaled_boxes, masks)):
-            assert 2 == len(mask.shape)
+            assert mask.ndim == 2
 
             scale_x1 = math.floor(scaled_box[0])
             scale_y1 = math.floor(scaled_box[1])
@@ -212,7 +206,7 @@ def preprocess(self, image: PILImage) -> np.ndarray:
         if image.size != size:
             image = image.resize(size)
         img = np.array(image).astype(np.float32)
-        assert len(img.shape) == 3
+        assert img.ndim == 3
         img /= 255.0
         img = img.transpose(2, 0, 1)
         tensor = img[np.newaxis, :, :, :]
@@ -307,5 +301,5 @@ def resize(buf: np.ndarray, size: tuple[W, H]) -> np.ndarray:
     img = Image.fromarray(buf).resize(size)
     out = np.array(img)
     assert out.dtype == buf.dtype
-    assert len(out.shape) == len(buf.shape)
+    assert out.ndim == buf.ndim
     return out