Merge branch 'main' of lab.compute.dtu.dk:QIM/tools/qim3d into 3D_UNet

qim3d/viz/_metrics.py

@@ -6,9 +6,9 @@ import matplotlib.pyplot as plt
 from matplotlib.colors import LinearSegmentedColormap
 from matplotlib import colormaps
 from qim3d.utils._logger import log
-import torch
 import matplotlib
 
+
 def plot_metrics(
     *metrics: tuple[dict[str, float]],
     linestyle: str = "-",
@@ -81,12 +81,12 @@ def plot_metrics(
 
 
 def grid_overview(
-    data: list|torch.utils.data.Dataset, 
+    data: list,
     num_images: int = 7,
     cmap_im: str = "gray",
     cmap_segm: str = "viridis",
     alpha: float = 0.5,
-    show: bool = False
+    show: bool = False,
 ) -> matplotlib.figure.Figure:
     """Displays an overview grid of images, labels, and masks (if they exist).
 
@@ -121,6 +121,7 @@ def grid_overview(
             and the length of the data.
         - The grid layout and dimensions vary based on the presence of a mask.
     """
+    import torch
 
     # Check if data has a mask
     has_mask = len(data[0]) > 2 and data[0][-1] is not None
@@ -290,7 +291,9 @@ def grid_pred(
     return fig
 
 
-def vol_masked(vol: np.ndarray, vol_mask: np.ndarray, viz_delta: int=128) -> np.ndarray:
+def vol_masked(
+    vol: np.ndarray, vol_mask: np.ndarray, viz_delta: int = 128
+) -> np.ndarray:
     """
     Applies masking to a volume based on a binary volume mask.