diff --git a/qim3d/__init__.py b/qim3d/__init__.py
index 07782e4e1a6ec54d7f3f605268d707ceeab557a7..ecbfe255266c3a164ec42cd6e09b3d182f9ba724 100644
--- a/qim3d/__init__.py
+++ b/qim3d/__init__.py
@@ -33,18 +33,20 @@ class _LazyLoader:
# List of submodules
_submodules = [
- "examples",
- "generate",
- "gui",
- "io",
- "models",
- "processing",
- "tests",
- "utils",
- "viz",
- "cli",
+ 'examples',
+ 'generate',
+ 'gui',
+ 'io',
+ 'models',
+ 'processing',
+ 'tests',
+ 'utils',
+ 'viz',
+ 'cli',
+ 'filters',
+ 'segmentation'
]
# Creating lazy loaders for each submodule
for submodule in _submodules:
- globals()[submodule] = _LazyLoader(f"qim3d.{submodule}")
+ globals()[submodule] = _LazyLoader(f'qim3d.{submodule}')
diff --git a/qim3d/processing/filters.py b/qim3d/filters.py
similarity index 100%
rename from qim3d/processing/filters.py
rename to qim3d/filters.py
diff --git a/qim3d/processing/__init__.py b/qim3d/processing/__init__.py
index 8f6edf04173e5781339f0ca33ff304a29851f0f9..056804d123bbd9152a362dbd4cc1fe21a1aeeb91 100644
--- a/qim3d/processing/__init__.py
+++ b/qim3d/processing/__init__.py
@@ -1,7 +1,6 @@
from .local_thickness_ import local_thickness
from .structure_tensor_ import structure_tensor
from .detection import blob_detection
-from .filters import *
from .operations import *
from .cc import get_3d_cc
from .layers2d import segment_layers, get_lines
diff --git a/qim3d/processing/operations.py b/qim3d/processing/operations.py
index e559a16d0100186e36ba96b597b6bdfa58f286fb..bf6f16f4b33c2d7b9606f0ba57bff2aa35732f9f 100644
--- a/qim3d/processing/operations.py
+++ b/qim3d/processing/operations.py
@@ -1,5 +1,5 @@
import numpy as np
-import qim3d.processing.filters as filters
+import qim3d.filters as filters
from qim3d.utils.logger import log
@@ -52,74 +52,6 @@ def remove_background(
return pipeline(vol)
-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. 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 (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
- import qim3d
-
- vol = qim3d.examples.cement_128x128x128
- binary = qim3d.processing.filters.gaussian(vol, sigma = 2)<60
-
- qim3d.viz.slices(binary, axis=1)
- ```
- 
-
- ```python
- labeled_volume, num_labels = qim3d.processing.operations.watershed(binary)
-
- cmap = qim3d.viz.colormaps.objects(num_labels)
- qim3d.viz.slices(labeled_volume, axis = 1, cmap = cmap)
- ```
- 
-
- """
- import skimage
- import scipy
-
- if len(np.unique(bin_vol)) > 2:
- raise ValueError("bin_vol has to be binary volume - it must contain max 2 unique values.")
-
- # Compute distance transform of binary volume
- distance = scipy.ndimage.distance_transform_edt(bin_vol)
-
- # Find peak coordinates in distance transform
- 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)
- mask[tuple(coords.T)] = True
-
- # Label peaks
- markers, _ = scipy.ndimage.label(mask)
-
- # Apply watershed segmentation
- labeled_volume = skimage.segmentation.watershed(
- -distance, markers=markers, mask=bin_vol
- )
-
- # Extract number of objects found
- num_labels = len(np.unique(labeled_volume)) - 1
- log.info(f"Total number of objects found: {num_labels}")
-
- return labeled_volume, num_labels
-
def fade_mask(
vol: np.ndarray,
diff --git a/qim3d/segmentation/__init__.py b/qim3d/segmentation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2b743b795bd2af1765cf7d365ce63e6c2c5c7ba
--- /dev/null
+++ b/qim3d/segmentation/__init__.py
@@ -0,0 +1 @@
+from .standard import *
\ No newline at end of file
diff --git a/qim3d/segmentation/standard.py b/qim3d/segmentation/standard.py
new file mode 100644
index 0000000000000000000000000000000000000000..333b2da0ad6009f0f6851299e49fcef9c81d9679
--- /dev/null
+++ b/qim3d/segmentation/standard.py
@@ -0,0 +1,72 @@
+import numpy as np
+from qim3d.utils.logger import log
+
+__all__ = ["watershed"]
+
+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. 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 (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
+ import qim3d
+
+ vol = qim3d.examples.cement_128x128x128
+ binary = qim3d.processing.filters.gaussian(vol, sigma = 2)<60
+
+ qim3d.viz.slices(binary, axis=1)
+ ```
+ 
+
+ ```python
+ labeled_volume, num_labels = qim3d.processing.operations.watershed(binary)
+
+ cmap = qim3d.viz.colormaps.objects(num_labels)
+ qim3d.viz.slices(labeled_volume, axis = 1, cmap = cmap)
+ ```
+ 
+
+ """
+ import skimage
+ import scipy
+
+ if len(np.unique(bin_vol)) > 2:
+ raise ValueError("bin_vol has to be binary volume - it must contain max 2 unique values.")
+
+ # Compute distance transform of binary volume
+ distance = scipy.ndimage.distance_transform_edt(bin_vol)
+
+ # Find peak coordinates in distance transform
+ 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)
+ mask[tuple(coords.T)] = True
+
+ # Label peaks
+ markers, _ = scipy.ndimage.label(mask)
+
+ # Apply watershed segmentation
+ labeled_volume = skimage.segmentation.watershed(
+ -distance, markers=markers, mask=bin_vol
+ )
+
+ # Extract number of objects found
+ num_labels = len(np.unique(labeled_volume)) - 1
+ log.info(f"Total number of objects found: {num_labels}")
+
+ return labeled_volume, num_labels
\ No newline at end of file
diff --git a/qim3d/tests/processing/test_filters.py b/qim3d/tests/processing/test_filters.py
index e63628118775b34d2c98f897d303d89e4c18b60a..5a6d0993fb4ac6cecaf9c26d06b450eeb2a9f893 100644
--- a/qim3d/tests/processing/test_filters.py
+++ b/qim3d/tests/processing/test_filters.py
@@ -1,5 +1,5 @@
import qim3d
-from qim3d.processing.filters import *
+from qim3d.filters import *
import numpy as np
import pytest
import re