Module: segmentation skimage.segmentation.active_contour(image, snake) Active contour model. skimage.segmentation.clear_border(labels[, ...]) Clear objects connected to the label image border. skimage.segmentation.felzenszwalb(image[, ...]) Computes Felsenszwalb’s efficient graph based image segmentation. skimage.segmentation.find_boundaries(label_img) Return bool array where boundaries between labeled regions are True. skimage.segmentation.join_segmentations(s1, s2) Return the join of the

skimage.novice A special Python image submodule for beginners. Description skimage.novice provides a simple image manipulation interface for beginners. It allows for easy loading, manipulating, and saving of image files. This module is primarily intended for teaching and differs significantly from the normal, array-oriented image functions used by scikit-image. Note This module uses the Cartesian coordinate system, where the origin is at the lower-left corner instead of the upper-right and the

Module: restoration Image restoration module.

Module: morphology skimage.morphology.ball(radius[, dtype]) Generates a ball-shaped structuring element. skimage.morphology.binary_closing(image[, selem]) Return fast binary morphological closing of an image. skimage.morphology.binary_dilation(image[, ...]) Return fast binary morphological dilation of an image. skimage.morphology.binary_erosion(image[, selem]) Return fast binary morphological erosion of an image. skimage.morphology.binary_opening(image[, selem]) Return fast binary morpholo

Module: measure skimage.measure.approximate_polygon(coords, ...) Approximate a polygonal chain with the specified tolerance. skimage.measure.block_reduce(image, block_size) Down-sample image by applying function to local blocks. skimage.measure.compare_mse(im1, im2) Compute the mean-squared error between two images. skimage.measure.compare_nrmse(im_true, im_test) Compute the normalized root mean-squared error (NRMSE) between two images. skimage.measure.compare_psnr(im_true, im_test) Comput

Module: io Utilities to read and write images in various formats. The following plug-ins are available: Plugin Description pil Image reading via the Python Imaging Library qt Fast image display using the Qt library freeimage Load images using the FreeImage library gtk Fast image display using the GTK library matplotlib Display or save images using Matplotlib simpleitk Image reading and writing via SimpleITK imread Image reading and writing via imread imageio Image reading via the ImageIO Librar

Module: graph skimage.graph.route_through_array(array, ...) Simple example of how to use the MCP and MCP_Geometric classes. skimage.graph.shortest_path(arr[, reach, ...]) Find the shortest path through an n-d array from one side to another. skimage.graph.MCP(costs[, offsets, ...]) A class for finding the minimum cost path through a given n-d costs array. skimage.graph.MCP_Connect Connect source points using the distance-weighted minimum cost function. skimage.graph.MCP_Flexible(costs[, offs

Module: filters.rank skimage.filters.rank.autolevel(image, selem) Auto-level image using local histogram. skimage.filters.rank.autolevel_percentile(...) Return greyscale local autolevel of an image. skimage.filters.rank.bottomhat(image, selem) Local bottom-hat of an image. skimage.filters.rank.enhance_contrast(image, ...) Enhance contrast of an image. skimage.filters.rank.enhance_contrast_percentile(...) Enhance contrast of an image. skimage.filters.rank.entropy(image, selem[, ...]) Local

Module: future.graph skimage.future.graph.cut_normalized(labels, rag) Perform Normalized Graph cut on the Region Adjacency Graph. skimage.future.graph.cut_threshold(labels, ...) Combine regions separated by weight less than threshold. skimage.future.graph.draw_rag(labels, rag, img) Draw a Region Adjacency Graph on an image. skimage.future.graph.merge_hierarchical(...) Perform hierarchical merging of a RAG. skimage.future.graph.ncut(labels, rag[, ...]) Perform Normalized Graph cut on the Re

Module: filters skimage.filters.canny(*args, **kwargs) Deprecated function. Use skimage.feature.canny instead. skimage.filters.copy_func(f[, name]) Create a copy of a function. skimage.filters.gabor(image, frequency[, ...]) Return real and imaginary responses to Gabor filter. skimage.filters.gabor_filter(*args, **kwargs) Deprecated function. Use skimage.filters.gabor instead. skimage.filters.gabor_kernel(frequency[, ...]) Return complex 2D Gabor filter kernel. skimage.filters.gaussian(i