Merge branch 'develop' into sam-viz-fix

Author: shaneahmed

AUTHORS.md

@@ -2,7 +2,7 @@
 
 ## Development Lead
 
-- TIA Centre led by Nasir Rajpoot <tia@dcs.warwick.ac.uk>
+- TIA Centre led by Nasir Rajpoot <TIA@warwick.ac.uk>
 - Shan E Ahmed Raza \<@shaneahmed>
 - John Pocock \<@John-P>
 - Mark Eastwood \<@measty>

HISTORY.md

@@ -80,7 +80,7 @@ ______________________________________________________________________
 
 - Adds the bokeh visualization tool. #684
   - The tool allows a user to launch a server on their machine to visualise whole slide images, overlay the results of deep learning algorithms or to select a patch from whole slide image and run TIAToolbox deep learning engines.
-  - This tool powers the TIA demos server. For details please see https://tiademos.dcs.warwick.ac.uk/.
+  - This tool powers the TIA demos server. For details please see [https://warwick.ac.uk/tia/demos](https://warwick.ac.uk/tia/demos).
 - Extends Annotation to Support Init from WKB #639
 - Adds `IOConfig` for NuClick in `pretrained_model.yaml` #709
 - Adds functions to save the TIAToolbox Engine outputs to Zarr and AnnotationStore files. #724

README.md

@@ -49,7 +49,7 @@ TIAToolbox is a computational pathology toolbox developed by the TIA Centre. It
 
 ### All Users
 
-This package is designed for those interested in digital pathology, including graduate students, medical staff, members of the TIA Centre and PathLAKE, and anyone who may find it useful. We will continue to improve this package, taking into account developments in pathology, microscopy, computing, and related disciplines. Please send comments and feedback to **[tia@dcs.warwick.ac.uk](mailto:tialab@dcs.warwick.ac.uk)**.
+This package is designed for those interested in digital pathology, including graduate students, medical staff, members of the TIA Centre and PathLAKE, and anyone who may find it useful. We will continue to improve this package, taking into account developments in pathology, microscopy, computing, and related disciplines. Please send comments and feedback to **[TIA@warwick.ac.uk](mailto:TIA@warwick.ac.uk)**.
 
 ### Developers
 

pyproject.toml

@@ -27,7 +27,7 @@
 name = "TIA Centre"
 version = "1.6.0"
 description = "test"
-authors = ["TIA Centre <tialab@dcs.warwick.ac.uk>"]
+authors = ["TIA Centre <TIA@warwick.ac.uk>"]
 
 [tool.poetry_bumpversion.file."tiatoolbox/__init__.py"]
 search = '__version__ = "{current_version}"'

setup.py

@@ -33,7 +33,7 @@
 
 setup(
     author="TIA Centre",
-    author_email="tia@dcs.warwick.ac.uk",
+    author_email="TIA@warwick.ac.uk",
     python_requires=">=3.10, <3.14",
     classifiers=[
         "Development Status :: 2 - Pre-Alpha",

tiatoolbox/__init__.py

@@ -15,7 +15,7 @@
     from types import ModuleType
 
 __author__ = """TIA Centre"""
-__email__ = "tialab@dcs.warwick.ac.uk"
+__email__ = "TIA@warwick.ac.uk"
 __version__ = "1.6.0"
 
 # This will set the tiatoolbox external data

tiatoolbox/utils/transforms.py

@@ -95,7 +95,7 @@ def imresize(
     img: np.ndarray,
     scale_factor: float | tuple[float, float] | None = None,
     output_size: int | tuple[int, int] | None = None,
-    interpolation: str = "optimise",
+    interpolation: str | int = "optimise",
 ) -> np.ndarray:
     """Resize input image.
 
@@ -153,7 +153,7 @@ def imresize(
     # can work on out-of-the-box (anything else will cause
     # error). The `converted type` has been selected so that
     # they can maintain the numeric precision of the `original type`.
-    dtype_mapping = [
+    dtype_mapping: list[tuple[type, type]] = [
         (np.bool_, np.uint8),
         (np.int8, np.int16),
         (np.int16, np.int16),
@@ -167,7 +167,7 @@ def imresize(
         (np.float32, np.float32),
         (np.float64, np.float64),
     ]
-    source_dtypes = [v[0] for v in dtype_mapping]
+    source_dtypes = [np.dtype(v[0]) for v in dtype_mapping]
     original_dtype = img.dtype
     if original_dtype not in source_dtypes:
         msg = f"Does not support resizing for array of dtype: {original_dtype}"
@@ -415,6 +415,6 @@ def pad_bounds(
     elif np.size(padding) == ndims:  # pragma: no cover
         padding = np.tile(padding, 2)
 
-    signs = np.repeat([-1, 1], ndims)
+    signs: np.ndarray = np.repeat([-1, 1], ndims)
     result = np.add(bounds, padding * signs)
     return (result[0], result[1], result[2], result[3])