Local thickness wrapper

qim3d/processing/local_thickness.py

 """Wrapper for the local thickness function from the localthickness package including visualization functions."""
+
 import localthickness as lt
 import numpy as np
 from typing import Optional
@@ -6,36 +7,43 @@ from skimage.filters import threshold_otsu
 from qim3d.io.logger import log
 from qim3d.viz import local_thickness as viz_local_thickness
 
-def local_thickness(image: np.ndarray, scale: float = 1, mask: Optional[np.ndarray] = None, visualize = False, **viz_kwargs) -> np.ndarray:
+
+def local_thickness(
+    image: np.ndarray,
+    scale: float = 1,
+    mask: Optional[np.ndarray] = None,
+    visualize=False,
+    **viz_kwargs
+) -> np.ndarray:
     """Wrapper for the local thickness function from the localthickness package (https://github.com/vedranaa/local-thickness)
 
     Args:
-        image (np.ndarray): 2D or 3D NumPy array representing the image/volume. 
-            If binary, it will be passed directly to the local thickness function. 
+        image (np.ndarray): 2D or 3D NumPy array representing the image/volume.
+            If binary, it will be passed directly to the local thickness function.
             If grayscale, it will be binarized using Otsu's method.
-        scale (float, optional): Downscaling factor, e.g. 0.5 for halving each dim of the image. 
+        scale (float, optional): Downscaling factor, e.g. 0.5 for halving each dim of the image.
             Default is 1.
         mask (np.ndarray, optional): binary mask of the same size of the image defining parts of the
             image to be included in the computation of the local thickness. Default is None.
         visualize (bool, optional): Whether to visualize the local thickness. Default is False.
         **viz_kwargs: Additional keyword arguments for the visualization function. Only used if visualize=True.
-    
+
     Returns:
         local_thickness (np.ndarray): 2D or 3D NumPy array representing the local thickness of the input image/volume.
 
-    
+
     !!! quote "Reference"
-        Dahl, V. A., & Dahl, A. B. (2023, June). Fast Local Thickness. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 
+        Dahl, V. A., & Dahl, A. B. (2023, June). Fast Local Thickness. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
         <https://doi.org/10.1109/cvprw59228.2023.00456>
 
         ```bibtex
-        @inproceedings{Dahl_2023, title={Fast Local Thickness}, 
-        url={http://dx.doi.org/10.1109/CVPRW59228.2023.00456}, 
-        DOI={10.1109/cvprw59228.2023.00456}, 
-        booktitle={2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, 
-        publisher={IEEE}, 
-        author={Dahl, Vedrana Andersen and Dahl, Anders Bjorholm}, 
-        year={2023}, 
+        @inproceedings{Dahl_2023, title={Fast Local Thickness},
+        url={http://dx.doi.org/10.1109/CVPRW59228.2023.00456},
+        DOI={10.1109/cvprw59228.2023.00456},
+        booktitle={2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
+        publisher={IEEE},
+        author={Dahl, Vedrana Andersen and Dahl, Anders Bjorholm},
+        year={2023},
         month=jun }
 
         ```
@@ -45,7 +53,11 @@ def local_thickness(image: np.ndarray, scale: float = 1, mask: Optional[np.ndarr
     if np.unique(image).size > 2:
         # If not, binarize it using Otsu's method, log the threshold and compute the local thickness
         threshold = threshold_otsu(image=image)
-        log.warning("Input image is not binary. It will be binarized using Otsu's method with threshold: {}".format(threshold))
+        log.warning(
+            "Input image is not binary. It will be binarized using Otsu's method with threshold: {}".format(
+                threshold
+            )
+        )
         local_thickness = lt.local_thickness(image > threshold, scale=scale, mask=mask)
     else:
         # If it is binary, compute the local thickness directly
@@ -55,4 +67,4 @@ def local_thickness(image: np.ndarray, scale: float = 1, mask: Optional[np.ndarr
     if visualize:
         display(viz_local_thickness(image, local_thickness, **viz_kwargs))
 
-    return local_thickness
\ No newline at end of file
+    return local_thickness