Short docs - Githubissues

haesleinhuepf commented 1 week ago

We need to write a short document about all functions in init.py which have the decorator @register_function. For each function we need a bullet poinnt with a short sentence and below a single line of code demonstrating how the function works.

Examples:

Takes a binary image and draws cuts in the objects similar to the ImageJ watershed algorithm nsbatwm.split_touching_objects(binary_image)
Applies Otsu's threshold selection method to an intensity image and returns a binary image. nsbatwm.threshold_otsu(image)
Apply a median filter to an image to remove noise while preserving edges. nsbatwm.median_filter(image)

Now, do this for all the specified functions.

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To address the issue of creating a short documentation for functions with the @register_function decorator in __init__.py, I've generated a list similar to your examples:

Splits touching objects in a binary image using the ImageJ watershed algorithm.
```
nsbatwm.split_touching_objects(binary_image)
```
Applies Otsu's method to binarize an intensity image.
```
nsbatwm.threshold_otsu(image)
```
Binarizes an image using Yen's method.
```
nsbatwm.threshold_yen(image)
```
Binarizes an image using the IsoData method.
```
nsbatwm.threshold_isodata(image)
```
Binarizes using Li's method.
```
nsbatwm.threshold_li(image)
```
Binarizes using the Mean method.
```
nsbatwm.threshold_mean(image)
```
Binarizes using the Minimum method.
```
nsbatwm.threshold_minimum(image)
```
Binarizes using Triangle method.
```
nsbatwm.threshold_triangle(image)
```
Inverts a binary image.
```
nsbatwm.binary_invert(binary_image)
```

Labels connected components in a binary image.

nsbatwm.connected_component_labeling(binary_image)

Applies seeded watershed segmentation using labeled nuclei and bright membranes.
```
nsbatwm.seeded_watershed(membranes, labeled_nuclei)
```
Segments blob-like structures using Voronoi-Otsu labeling.
```
nsbatwm.voronoi_otsu_labeling(image)
```
Applies Gauss-Otsu labeling for nucleus segmentation.
```
nsbatwm.gauss_otsu_labeling(image)
```
Applies Gaussian blur for noise reduction.
```
nsbatwm.gaussian_blur(image)
```
Applies median filter to reduce noise while preserving edges.
```
nsbatwm.median_filter(image)
```
Correct labels using a mode filter.
```
nsbatwm.mode_filter(labels)
```
Applies a percentile filter.
```
nsbatwm.percentile_filter(image)
```
Removes bright regions using white top-hat filter.
```
nsbatwm.white_tophat(image)
```
Removes dark regions using black top-hat filter.
```
nsbatwm.black_tophat(image)
```
Applies local minimum filtering.
```
nsbatwm.minimum_filter(image)
```
Applies local maximum filtering.
```
nsbatwm.maximum_filter(image)
```
Enhances edges via morphological gradient.
```
nsbatwm.morphological_gradient(image)
```
Subtracts background using the rolling ball algorithm.
```
nsbatwm.subtract_background(image)
```

Removes labels on image borders.

nsbatwm.remove_labels_on_edges(label_image)

Expands labels by a specified distance.
```
nsbatwm.expand_labels(label_image)
```
Segments using seeded watershed with local minima as seeds.
```
nsbatwm.local_minima_seeded_watershed(image)
```
Watershed labeling with local minima as seeds and intensity threshold.
```
nsbatwm.thresholded_local_minima_seeded_watershed(image)
```
Sums two images with optional scaling factors.
```
nsbatwm.sum_images(image1, image2)
```
Multiplies two images.
```
nsbatwm.multiply_images(image1, image2)
```
Divides one image by another.
```
nsbatwm.divide_images(image1, image2)
```
Inverts an image.
```
nsbatwm.invert_image(image)
```
Skeletonizes labeled objects.
```
nsbatwm.skeletonize(image)
```
Rescales an image using specified scale factors.
```
nsbatwm.rescale(image)
```
Resizes an image to new dimensions.
```
nsbatwm.resize(image)
```

Manually merges selected labels.

nsbatwm.Manually_merge_labels(labels_layer, points_layer, viewer)

Manually splits selected labels.

nsbatwm.Manually_split_labels(labels_layer, points_layer, viewer)

Applies a Butterworth filter to an image.
```
nsbatwm.butterworth(image)
```
Relabels an image sequentially.
```
nsbatwm.relabel_sequential(label_image)
```

Extracts a specific slice from an image.

nsbatwm.extract_slice(image, slice_index, axis)

Sub-samples every nth pixel.
```
nsbatwm.sub_sample(image)
```
Removes axes with length 1.
```
nsbatwm.squeeze(image)
```
Applies grayscale erosion with a given radius.
```
nsbatwm.grayscale_erosion(image)
```
Applies binary erosion with a given radius.
```
nsbatwm.binary_erosion(binary_image)
```
Applies grayscale dilation with a given radius.
```
nsbatwm.grayscale_dilation(image)
```
Applies binary dilation with a given radius.
```
nsbatwm.binary_dilation(binary_image)
```
Applies grayscale opening with a given radius.
```
nsbatwm.grayscale_opening(image)
```
Applies binary opening with a given radius.
```
nsbatwm.binary_opening(binary_image)
```
Applies grayscale closing with a given radius.
```
nsbatwm.grayscale_closing(image)
```
Applies binary closing with a given radius.
```
nsbatwm.binary_closing(binary_image)
```

This listing should help in documentation by providing a concise description along with a code snippet for each function.

haesleinhuepf commented 1 week ago

included into the code in https://github.com/haesleinhuepf/napari-segment-blobs-and-things-with-membranes/commit/ebc23d5fcd37b0fd5d83a6826572a1613bbb212c

haesleinhuepf / napari-segment-blobs-and-things-with-membranes

Short docs #37