Merge branch 'finetuning' into finetune

src/membrain_seg/segmentation/cli/ske_cli.py

@@ -7,6 +7,7 @@
     store_tomogram,
 )
 
+
 from ..skeletonize import skeletonization as _skeletonization
 from .cli import cli
 
@@ -53,19 +54,21 @@ def skeletonize(
     --batch-size <batch-size>
     """
     # Assuming _skeletonization function is already defined and can handle batch_size
+
     segmentation = load_tomogram(label_path)
     ske = _skeletonization(segmentation=segmentation.data, batch_size=batch_size)
 
     # Update the segmentation data with the skeletonized output while preserving the original header and voxel_size
     segmentation.data = ske
 
+
     if not os.path.exists(out_folder):
         os.makedirs(out_folder)
 
     out_file = os.path.join(
         out_folder,
         os.path.splitext(os.path.basename(label_path))[0] + "_skel.mrc",
     )
-
+    
     store_tomogram(filename=out_file, tomogram=segmentation)
     print("Skeleton saved to ", out_file)

src/membrain_seg/segmentation/skeletonize.py

@@ -12,6 +12,7 @@
 import scipy.ndimage as ndimage
 import torch
 
+
 from membrain_seg.segmentation.skeletonization.diff3d import (
     compute_gradients,
     compute_hessian,
@@ -23,6 +24,7 @@
 from membrain_seg.segmentation.training.surface_dice import apply_gaussian_filter
 
 
+
 def skeletonization(segmentation: np.ndarray, batch_size: int) -> np.ndarray:
     """
     Perform skeletonization on a tomogram segmentation.
@@ -36,6 +38,7 @@ def skeletonization(segmentation: np.ndarray, batch_size: int) -> np.ndarray:
     segmentation : ndarray
         Tomogram segmentation as a numpy array, where non-zero values represent
         the structures of interest.
+
     batch_size : int
         The number of elements to process in one batch during eigen decomposition.
         Useful for managing memory usage.
@@ -58,6 +61,7 @@ def skeletonization(segmentation: np.ndarray, batch_size: int) -> np.ndarray:
         --batch-size 1000000
     This command runs the skeletonization process from the command line.
     """
+
     # Convert non-zero segmentation values to 1.0
     labels = (segmentation > 0) * 1.0