Short docs

[haesleinhuepf]

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

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^{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]

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

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^{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]

Added to the code in 3015c82