🔧 mypy type check tools/ and utils/

tiatoolbox/tools/patchextraction.py

@@ -9,6 +9,7 @@
 from typing_extensions import Unpack
 
 from tiatoolbox import logger
+from tiatoolbox.annotation.storage import AnnotationStore
 from tiatoolbox.utils import misc
 from tiatoolbox.utils.exceptions import FileNotSupportedError, MethodNotSupportedError
 from tiatoolbox.utils.visualization import AnnotationRenderer
@@ -19,7 +20,6 @@
 
     from pandas import DataFrame
 
-    from tiatoolbox.annotation.storage import AnnotationStore
     from tiatoolbox.type_hints import Resolution, Units
 
 
@@ -237,7 +237,9 @@ def __init__(
 
         if input_mask is None:
             self.mask = None
-        elif isinstance(input_mask, str) and input_mask.endswith(".db"):
+        elif (isinstance(input_mask, str) and input_mask.endswith(".db")) or isinstance(
+            input_mask, AnnotationStore
+        ):
             # input_mask is an annotation store
             renderer = AnnotationRenderer(
                 max_scale=10000, edge_thickness=0, where=store_filter
@@ -670,7 +672,12 @@ def __init__(  # noqa: PLR0913
         self: SlidingWindowPatchExtractor,
         input_img: str | Path | np.ndarray | wsireader.WSIReader,
         patch_size: int | tuple[int, int],
-        input_mask: str | Path | np.ndarray | wsireader.VirtualWSIReader | None = None,
+        input_mask: str
+        | Path
+        | np.ndarray
+        | wsireader.VirtualWSIReader
+        | AnnotationStore
+        | None = None,
         resolution: Resolution = 0,
         units: Units = "level",
         stride: int | tuple[int, int] | None = None,

tiatoolbox/tools/registration/wsi_registration.py

@@ -3,7 +3,7 @@
 from __future__ import annotations
 
 import itertools
-from typing import TYPE_CHECKING, Callable
+from typing import TYPE_CHECKING, Callable, cast
 
 import cv2
 import numpy as np
@@ -338,19 +338,24 @@ def __init__(self: DFBRFeatureExtractor) -> None:
         super().__init__()
         output_layers_id: list[str] = ["16", "23", "30"]
         output_layers_key: list[str] = ["block3_pool", "block4_pool", "block5_pool"]
-        self.features: dict = dict.fromkeys(output_layers_key, None)
-        self.pretrained: torch.nn.Sequential = compile_model(
+        self.features: dict[str, torch.Tensor] = dict.fromkeys(
+            output_layers_key, torch.Tensor()
+        )
+
+        compiled_model = compile_model(
             torchvision.models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1),
             mode=rcParam["torch_compile_mode"],
-        ).features
+        )
+        self.pretrained = cast("torch.nn.Module", compiled_model.features)
+
         self.f_hooks = [
             getattr(self.pretrained, layer).register_forward_hook(
                 self.forward_hook(output_layers_key[i]),
             )
             for i, layer in enumerate(output_layers_id)
         ]
 
-    def forward_hook(self: torch.nn.Module, layer_name: str) -> Callable:
+    def forward_hook(self: DFBRFeatureExtractor, layer_name: str) -> Callable:
         """Register a hook.
 
         Args:
@@ -386,7 +391,7 @@ def hook(
 
         return hook
 
-    def forward(self: torch.nn.Module, x: torch.Tensor) -> dict[str, torch.Tensor]:
+    def forward(self: DFBRFeatureExtractor, x: torch.Tensor) -> dict[str, torch.Tensor]:
         """Forward pass for feature extraction.
 
         Args:

tiatoolbox/utils/transforms.py

@@ -189,7 +189,7 @@ def imresize(
         img_channels = [
             cv2.resize(
                 src=img[..., ch],
-                dsize=output_size_array,
+                dsize=(output_size_array[0], output_size_array[1]),
                 interpolation=cv2_interpolation,
             )[
                 ...,
@@ -199,7 +199,11 @@ def imresize(
         ]
         return np.concatenate(img_channels, axis=-1)
 
-    return cv2.resize(src=img, dsize=output_size_array, interpolation=cv2_interpolation)
+    return cv2.resize(
+        src=img,
+        dsize=(output_size_array[0], output_size_array[1]),
+        interpolation=cv2_interpolation,
+    )
 
 
 def rgb2od(img: np.ndarray) -> np.ndarray:

tiatoolbox/utils/visualization.py

@@ -475,14 +475,15 @@ def overlay_prediction_contours(
     inst_colours_array = inst_colours_array.astype(np.uint8)
 
     for idx, [_, inst_info] in enumerate(inst_dict.items()):
-        inst_contour = inst_info["contour"]
+        inst_contour: np.ndarray = inst_info["contour"]
         if "type" in inst_info and type_colours is not None:
             inst_colour = type_colours[inst_info["type"]][1]
         else:
             inst_colour = (inst_colours_array[idx]).tolist()
+        contours: list[np.ndarray] = [np.array(inst_contour)]
         cv2.drawContours(
             overlay,
-            [np.array(inst_contour)],
+            contours,
             -1,
             inst_colour,
             line_thickness,
@@ -881,9 +882,10 @@ def render_line(
             top_left,
             scale,
         )
+        pts: list[np.ndarray] = [np.array(cnt)]
         cv2.polylines(
             tile,
-            [np.array(cnt)],
+            pts,
             isClosed=False,
             color=col,
             thickness=3,