Implemented beta version of annoation tool + unit test

I ended up using ip adress and port in my tests as qim3d.utils.internal_tools.is_server_running needs these positional arguments

docs/notebooks/annotation_tool.ipynb

 %% Cell type:markdown id: tags:
 
-# Demo of using the annotation tool in qim3d
+# Image annotation tool
+This notebook shows how the annotation interface can be used to create masks for images
 
 %% Cell type:code id: tags:
 
 ``` python
 import qim3d
 import matplotlib.pyplot as plt
+import matplotlib as mpl
 import numpy as np
 %matplotlib inline
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # Load 2D example image
-blobs = qim3d.examples.blobs_256x256
+img = qim3d.examples.blobs_256x256
 
-plt.imshow(blobs)
+# Display image
+plt.imshow(img)
+plt.show()
 ```
 
-%% Output
 
-    <matplotlib.image.AxesImage at 0x7f1bad098af0>
 
-
 
 %% Cell type:code id: tags:
 
 ``` python
-# Start annotation tool with image
+# Start annotation tool
 interface = qim3d.gui.annotation_tool.Interface()
-interface.launch(img=blobs)
 ```
 
-%% Output
 
-    Running on local URL:  http://127.0.0.1:7860
+interface.max_masks = 4
 
+# We can directly pass the image we loaded to the interface
+interface.launch(img=img)
 ```
 
 %% Cell type:code id: tags:
 
 ``` python
 # When 'prepare mask for download' is pressed once, the mask can be retrieved with the get_result() method
 mask = interface.get_result()
 ```
 
-%% Cell type:code id: tags:
 
-``` python
-# Conform that they are of the same shape
-print(f'{blobs.shape=}, {mask.shape=}')
 ```
 
-%% Output
+%% Cell type:markdown id: tags:
 
-    blobs.shape=(256, 256), mask.shape=(256, 256)
+## Check the obtained mask
 
 %% Cell type:code id: tags:
 
 ``` python
-# Mask is saved as a one-channnel image where each non-zero value corresponds to the given mask
-print(f'{np.unique(mask)=}')
+print (f"Original image shape..: {img.shape}")
+print (f"Mask image shape......: {mask.shape}")
+print (f"\nNumber of masks: {np.max(mask)}")
 ```
 
-%% Output
+%% Cell type:markdown id: tags:
 
-    np.unique(mask)=array([0, 1, 2], dtype=uint8)
+## Show the masked regions
 
 %% Cell type:code id: tags:
 
 ``` python
-plt.imshow(mask)
 ```
+%matplotlib inline
 
-%% Output
+nmasks = np.max(mask)
+fig, axs = plt.subplots(nrows=1, ncols=nmasks+2, figsize=(12,3))
 
-    <matplotlib.image.AxesImage at 0x7f1b944cf820>
+# Show original image
+axs[0].imshow(img)
+axs[0].set_title("Original")
+axs[0].axis('off')
+
+
+# Show masks
+cmap = mpl.colormaps["rainbow"].copy()
+cmap.set_under(color='black') # Sets the background to black
+axs[1].imshow(mask, interpolation='none', cmap=cmap, vmin=1, vmax=nmasks+1)
+axs[1].set_title("Masks")
+axs[1].axis('off')
+
+# Show masked regions
+for idx in np.arange(2, nmasks+2):
+    mask_id = idx-1
+    submask = mask.copy()
+    submask[submask != mask_id] = 0
+
+    masked_img = img.copy()
+    masked_img[submask==0] = 0
+    axs[idx].imshow(masked_img)
+    axs[idx].set_title(f"Mask {mask_id}")
 
-
+    axs[idx].axis('off')
+
+plt.show()
 ```