Refactoring of blob detection

Refactoring of blob detection

• Implemented as a function instead of a class
• Added variable types to function arguments
• Combined the old detect and get_mask into a single function blob_detection that returns both the blobs and the binary mask
• Updated documentation example
• Updated example notebook docs/notebooks/blob_detection.ipynb

docs/notebooks/blob_detection.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 import qim3d
 ```
 
-%% Output
 
-    WARNING:root:Could not load CuPy: No module named 'cupy'
 
 %% Cell type:markdown id: tags:
 
 ### Blob detection notebook
 
 %% Cell type:markdown id: tags:
 
 This notebook shows how to do **blob detection** in a 3D volume using the `qim3d` library.
 
-Blob detection is done by initializing a `qim3d.processing.Blob` object, and then calling the `qim3d.processing.Blob.detect` method. The `qim3d.processing.Blob.detect` method detects blobs by using the Difference of Gaussian (DoG) blob detection method, and returns an array `blobs` with the blobs found in the volume stored as `(p, r, c, radius)`. Subsequently, a binary mask of the volume can be retrieved with the `qim3d.processing.get_mask` method, in which the found blobs are marked as `True`.
+Blob detection is done by using the `qim3d.processing.blob_detection` method, which detects blobs by using the Difference of Gaussian (DoG) blob detection method, and returns two arrays:
+- `blobs`: The blobs found in the volume stored as `(p, r, c, radius)`
+- `binary_volume`: A binary mask of the volume with the blobs marked as `True`
 
 %% Cell type:markdown id: tags:
 
 ### **Example 1**: Blob detection in cement volume
 
 %% Cell type:markdown id: tags:
 
 **Applying Gaussian filter to volume**
 
 %% Cell type:code id: tags:
 
 ``` python
 # Import 3D volume of cement
 cement = qim3d.examples.cement_128x128x128
 
 # Visualize slices of the original cement volume
 qim3d.viz.slices(cement, n_slices = 5, show = True)
 
 # Apply Gaussian filter to the cement volume
 cement_filtered = qim3d.processing.gaussian(cement, sigma = 2)
 
 # Visualize slices of the filtered cement volume
 qim3d.viz.slices(cement_filtered)
 ```
 
 %% Output
 
 
     <Figure size 1000x200 with 5 Axes>
 
 %% Cell type:markdown id: tags:
 
 **Detecting blobs in volume**
 
 %% Cell type:code id: tags:
 
 ``` python
-# Initialize blob detector
-blob_detector = qim3d.processing.Blob(
-    background = "bright",
-    min_sigma = 1,
-    max_sigma = 8,
-    threshold = 0.001,
-    overlap = 0.1
+# Detect blobs, and get binary mask
+blobs, mask = qim3d.processing.blob_detection(
+    cement_filtered,
+    min_sigma=1,
+    max_sigma=8,
+    threshold=0.001,
+    overlap=0.1,
+    background="bright"
     )
 
-# Detect blobs in filtered volume
-blobs = blob_detector.detect(vol = cement_filtered)
-
 # Number of blobs found
 print(f'Number of blobs found in the volume: {len(blobs)} blobs')
 ```
 
 %% Output
 
+    Bright background selected, volume will be inverted.
 
     Number of blobs found in the volume: 1813 blobs
 
 %% Cell type:code id: tags:
 
 ``` python
 # Visualize blobs on slices of cement volume
-qim3d.viz.detection.circles(blobs, cement, show = True)
+qim3d.viz.detection.circles(blobs, cement, alpha = 0.8, show = True, color = 'red')
 ```
 
 %% Output
 
     interactive(children=(IntSlider(value=64, description='Slice', max=127), Output()), layout=Layout(align_items=…
 
 %% Cell type:markdown id: tags:
 
-**Get binary mask of detected blobs**
+**Binary mask of detected blobs**
 
 %% Cell type:code id: tags:
 
 ``` python
-# Get binary mask of detected blobs
-mask = blob_detector.get_mask()
-
-# Visualize mask
+# Visualize binary mask
 qim3d.viz.slicer(mask)
 ```
 
 %% Output
 
     interactive(children=(IntSlider(value=64, description='Slice', max=127), Output()), layout=Layout(align_items=…