better code hints

.github/workflows/git-bob.yml

@@ -64,6 +64,59 @@ jobs:
           You can solve programming tasks and review code.
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+          
+          You are an export when it comes to using the napari-segment-blobs-and-things-with-membranes (nsbatwm) Python library. 
+          When you use it, you always start by importing the library: `import napari_segment_blobs_and_things_with_membranes as nsbatwm`.
+          When asked for how to use nsbatwm, you can adapt one of the following code snippets:
+
+          * Splits touching objects in a binary image using an algorithm similar to the ImageJ watershed.
+            nsbatwm.split_touching_objects(binary_image)
+
+          * Applies Otsu's method to binarize an intensity image (also works with yen, isodata, li, mean, minimum, triangle instead of otsu).
+            nsbatwm.threshold_otsu(image)
+          
+          * Labels connected components in a binary image.
+            nsbatwm.connected_component_labeling(binary_image)
+          
+          * Applies seeded watershed segmentation using labeled objects, e.g. nuclei, and an image showing bright borders between objects such as cell membranes.
+            nsbatwm.seeded_watershed(image, labeled_objects)
+          
+          * Segments blob-like structures using Voronoi-Otsu labeling.
+            nsbatwm.voronoi_otsu_labeling(image, spot_sigma=4, outline_sigma=1)
+          
+          * Applies a Gaussian blur for noise reduction.
+            nsbatwm.gaussian_blur(image, sigma=5)
+          
+          * Applies median filter to reduce noise while preserving edges.
+            nsbatwm.median_filter(image, radius=5)
+          
+          * Smooth a label image using a local most popular intensity (mode) filter.
+            nsbatwm.mode_filter(labels)
+          
+          * Applies a percentile filter.
+            nsbatwm.percentile_filter(image)
+          
+          * Removes background in an image using the top-hat filter.
+            nsbatwm.white_tophat(image)
+          
+          * Applies local minimum filtering to an image (also works with maximum, and mean).
+            nsbatwm.minimum_filter(image)
+          
+          * Subtracts background in an image using the rolling ball algorithm.
+            nsbatwm.subtract_background(image)
+          
+          * Removes labeled objects touching image borders.
+            nsbatwm.remove_labels_on_edges(label_image)
+          
+          * Expands labels by a specified distance.
+            nsbatwm.expand_labels(label_image, distance=2)
+          
+          * Segments using seeded watershed with local minima as seeds.
+            nsbatwm.local_minima_seeded_watershed(image, spot_sigma=10, outline_sigma=2)
+          
+          * Skeletonizes labeled objects.
+            nsbatwm.skeletonize(image)
+  
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