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 objects similar to the ImageJ watershed algorithm to produce a label image nsitk.touching_objects_labeling(binary_image)

Applies Otsu's threshold selection method to an intensity image and returns a binary image. nsitk.threshold_otsu(image)

Apply a median filter to an image to remove noise while preserving edges. nsitk.median_filter(image, radius=5)

Now, do this for all the specified functions.

git-bob comment

github-actions[bot] commented 1 week ago

^{This message was generated by git-bob (version: 0.6.6, model: gpt-4o-2024-08-06, Run details), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

Certainly! Here is a short document covering functions in __init__.py with the decorator @register_function.

Apply a median filter to an image to remove noise while preserving edges.
```
nsitk.median_filter(image, radius=5)
```
Apply a Gaussian blur to smooth the image.
```
nsitk.gaussian_blur(image, sigma=1.0)
```
Applies Otsu's threshold selection method to an intensity image and returns a binary image.
```
nsitk.threshold_otsu(image)
```
Computes the signed Maurer distance map of the input image.
```
nsitk.signed_maurer_distance_map(binary_image)
```
Performs morphological watershed labeling on the input image.
```
nsitk.morphological_watershed(image, markers)
```
Detects edges in the image using Canny edge detection.
```
nsitk.canny_edge_detection(image, variance=1.0)
```
Computes the gradient magnitude of an image.
```
nsitk.gradient_magnitude(image)
```
Identifies the regional maxima of an image.
```
nsitk.regional_maxima(image)
```
Performs Richardson-Lucy deconvolution to restore a blurred image.
```
nsitk.richardson_lucy_deconvolution(image, psf, iterations=10)
```
Rescales the intensity of an input image to a specified range.
```
nsitk.rescale_intensity(image, output_min=0, output_max=255)
```
Applies the Sobel operator to an image to find edges.
```
nsitk.sobel(image)
```
Extracts the black top hat operation of an image, enhances dark features.
```
nsitk.black_top_hat(image, radius=5)
```
Enhances the contrast of an image using adaptive histogram equalization.
```
nsitk.adaptive_histogram_equalization(image)
```

Denoises an image using curvature flow method.

nsitk.curvature_flow_denoise(image, timeStep=0.0625)

Relabels the components in a label image sequentially.
```
nsitk.relabel_component(label_image)
```
Computes the pixel count map of an image.
```
nsitk.pixel_count_map(label_image)
```

Please review and let me know if any further changes are needed!

haesleinhuepf commented 1 week ago

Also do this for these functions:

standard_deviation_filter
connected_component_labeling
touching_objects_labeling
bilateral_filter
laplacian_of_gaussian_filter
binominal_blur_filter
h_maxima
white_top_hat
adaptive_histogram_equalization
label_statistics

git-bob comment

github-actions[bot] commented 1 week ago

^{This message was generated by git-bob (version: 0.6.6, model: gpt-4o-2024-08-06, Run details), an experimental AI-based assistant. It can make mistakes and has limitations. Check its messages carefully.}

Certainly! Here is an updated short document covering the additional functions with the decorator @register_function:

Apply a median filter to an image to remove noise while preserving edges.
```
nsitk.median_filter(image, radius=5)
```
Apply a Gaussian blur to smooth the image.
```
nsitk.gaussian_blur(image, sigma=1.0)
```
Applies Otsu's threshold selection method to an intensity image and returns a binary image.
```
nsitk.threshold_otsu(image)
```
Computes the signed Maurer distance map of the input image.
```
nsitk.signed_maurer_distance_map(binary_image)
```
Performs morphological watershed labeling on the input image.
```
nsitk.morphological_watershed(image, markers)
```
Detects edges in the image using Canny edge detection.
```
nsitk.canny_edge_detection(image, variance=1.0)
```
Computes the gradient magnitude of an image.
```
nsitk.gradient_magnitude(image)
```
Identifies the regional maxima of an image.
```
nsitk.regional_maxima(image)
```
Performs Richardson-Lucy deconvolution to restore a blurred image.
```
nsitk.richardson_lucy_deconvolution(image, psf, iterations=10)
```
Rescales the intensity of an input image to a specified range.
```
nsitk.rescale_intensity(image, output_min=0, output_max=255)
```
Applies the Sobel operator to an image to find edges.
```
nsitk.sobel(image)
```
Extracts the black top hat operation of an image, enhances dark features.
```
nsitk.black_top_hat(image, radius=5)
```
Enhances the contrast of an image using adaptive histogram equalization.
```
nsitk.adaptive_histogram_equalization(image)
```

Denoises an image using curvature flow method.

nsitk.curvature_flow_denoise(image, timeStep=0.0625)

Relabels the components in a label image sequentially.
```
nsitk.relabel_component(label_image)
```
Computes the pixel count map of an image.
```
nsitk.pixel_count_map(label_image)
```
Applies a standard deviation filter to an image.
```
nsitk.standard_deviation_filter(image)
```
Labels the connected components in a binary image.
```
nsitk.connected_component_labeling(binary_image)
```

Labels touching objects in a binary image.

nsitk.touching_objects_labeling(binary_image)

Applies a bilateral filter to smooth the image while preserving edges.
```
nsitk.bilateral_filter(image, domainSigma=2.0, rangeSigma=50.0)
```
Applies the Laplacian of Gaussian filter to find edges.
```
nsitk.laplacian_of_gaussian_filter(image, sigma=1.0)
```
Applies a binominal blur filter.
```
nsitk.binominal_blur_filter(image)
```
Identifies h-maxima of an image, suppressing maxima smaller than h.
```
nsitk.h_maxima(image, h=10)
```
Extracts the white top hat operation of an image, enhances light features.
```
nsitk.white_top_hat(image, radius=5)
```
Computes basic statistics for labeled object regions in an image.
```
nsitk.label_statistics(image, label_image)
```

Please review and let me know if any further changes are needed!

haesleinhuepf commented 1 week ago

Added to the code in https://github.com/haesleinhuepf/napari-simpleitk-image-processing/commit/3015c823d503130c16a39f352d483317fd69a4d9

haesleinhuepf / napari-simpleitk-image-processing

Short docs #30