Preparation vor release v0.4.2

docs/releases.md

@@ -9,6 +9,12 @@ As the library is still in its early development stages, **there may be breaking
 
 And remember to keep your pip installation [up to date](/qim3d/#get-the-latest-version) so that you have the latest features!
 
+### v0.4.2 (30/09/2024)
+
+- Export and import is now possible in the OME-Zarr standard, including multi-scale datasets.
+- Filters now have the option to use Dask when available ![Filters with dask](assets/screenshots/releases/filters_with_dask.png)
+
+
 ### v0.4.1 (30/07/2024)
 
 - Fixed issue with example volumes not being loaded

qim3d/__init__.py

@@ -8,7 +8,7 @@ Documentation available at https://platform.qim.dk/qim3d/
 
 """
 
-__version__ = "0.4.1"
+__version__ = "0.4.2"
 
 
 import importlib as _importlib

qim3d/processing/operations.py

@@ -8,7 +8,7 @@ def remove_background(
     median_filter_size: int = 2,
     min_object_radius: int = 3,
     background: str = "dark",
-    **median_kwargs
+    **median_kwargs,
 ) -> np.ndarray:
     """
     Remove background from a volume using a qim3d filters.
@@ -51,17 +51,22 @@ def remove_background(
     # Apply the pipeline to the volume
     return pipeline(vol)
 
-def watershed(
-        bin_vol: np.ndarray
-) -> tuple[np.ndarray, int]:
+
+def watershed(bin_vol: np.ndarray, min_distance: int = 5) -> tuple[np.ndarray, int]:
     """
     Apply watershed segmentation to a binary volume.
 
     Args:
-        bin_vol (np.ndarray): Binary volume to segment.
+        bin_vol (np.ndarray): Binary volume to segment. The input should be a 3D binary image where non-zero elements 
+                              represent the objects to be segmented.
+        min_distance (int): Minimum number of pixels separating peaks in the distance transform. Peaks that are 
+                            too close will be merged, affecting the number of segmented objects. Default is 5.
 
     Returns:
-        tuple[np.ndarray, int]: Labeled volume after segmentation, number of objects found.
+        tuple[np.ndarray, int]: 
+            - Labeled volume (np.ndarray): A 3D array of the same shape as the input `bin_vol`, where each segmented object
+              is assigned a unique integer label.
+            - num_labels (int): The total number of unique objects found in the labeled volume.
 
     Example:
         ```python
@@ -90,7 +95,9 @@ def watershed(
     distance = scipy.ndimage.distance_transform_edt(bin_vol)
 
     # Find peak coordinates in distance transform
-    coords = skimage.feature.peak_local_max(distance, labels=bin_vol)
+    coords = skimage.feature.peak_local_max(
+        distance, min_distance=min_distance, labels=bin_vol
+    )
 
     # Create a mask with peak coordinates
     mask = np.zeros(distance.shape, dtype=bool)
@@ -100,7 +107,9 @@ def watershed(
     markers, _ = scipy.ndimage.label(mask)
 
     # Apply watershed segmentation
-    labeled_volume = skimage.segmentation.watershed(-distance, markers=markers, mask=bin_vol)
+    labeled_volume = skimage.segmentation.watershed(
+        -distance, markers=markers, mask=bin_vol
+    )
 
     # Extract number of objects found
     num_labels = len(np.unique(labeled_volume)) - 1
@@ -108,6 +117,7 @@ def watershed(
 
     return labeled_volume, num_labels
 
+
 def fade_mask(
     vol: np.ndarray,
     decay_rate: float = 10,
@@ -147,7 +157,9 @@ def fade_mask(
 
     """
     if 0 > axis or axis >= vol.ndim:
-        raise ValueError("Axis must be between 0 and the number of dimensions of the volume")
+        raise ValueError(
+            "Axis must be between 0 and the number of dimensions of the volume"
+        )
 
     # Generate the coordinates of each point in the array
     shape = vol.shape
@@ -186,10 +198,9 @@ def fade_mask(
 
     return vol_faded
 
+
 def overlay_rgb_images(
-    background: np.ndarray, 
-    foreground: np.ndarray, 
-    alpha: float = 0.5
+    background: np.ndarray, foreground: np.ndarray, alpha: float = 0.5
 ) -> np.ndarray:
     """
     Overlay an RGB foreground onto an RGB background using alpha blending.