pad skimage.util.pad(array, pad_width, mode=None, **kwargs) [source] Pads an array. Parameters: array : array_like of rank N Input array pad_width : {sequence, array_like, int} Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes. mode : str or function One of the followin

Utility Functions img_as_float Convert an image to floating point format, with values in [0, 1]. img_as_uint Convert an image to unsigned integer format, with values in [0, 65535]. img_as_int Convert an image to signed integer format, with values in [-32768, 32767]. img_as_ubyte Convert an image to unsigned byte format, with values in [0, 255]. skimage.dtype_limits(image[, clip_negative]) Return intensity limits, i.e. skimage.img_as_bool(image[, force_copy]) Convert an image to boolean form

Module: exposure skimage.exposure.adjust_gamma(image[, ...]) Performs Gamma Correction on the input image. skimage.exposure.adjust_log(image[, gain, inv]) Performs Logarithmic correction on the input image. skimage.exposure.adjust_sigmoid(image[, ...]) Performs Sigmoid Correction on the input image. skimage.exposure.cumulative_distribution(image) Return cumulative distribution function (cdf) for the given image. skimage.exposure.equalize_adapthist(image, ...) Contrast Limited Adaptive Hist

corner_subpix skimage.feature.corner_subpix(image, corners, window_size=11, alpha=0.99) [source] Determine subpixel position of corners. A statistical test decides whether the corner is defined as the intersection of two edges or a single peak. Depending on the classification result, the subpixel corner location is determined based on the local covariance of the grey-values. If the significance level for either statistical test is not sufficient, the corner cannot be classified, and the outp

merge_hierarchical skimage.future.graph.merge_hierarchical(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func) [source] Perform hierarchical merging of a RAG. Greedily merges the most similar pair of nodes until no edges lower than thresh remain. Parameters: labels : ndarray The array of labels. rag : RAG The Region Adjacency Graph. thresh : float Regions connected by an edge with weight smaller than thresh are merged. rag_copy : bool If set, the RAG copied before

call_plugin skimage.io.call_plugin(kind, *args, **kwargs) [source] Find the appropriate plugin of ‘kind’ and execute it. Parameters: kind : {‘imshow’, ‘imsave’, ‘imread’, ‘imread_collection’} Function to look up. plugin : str, optional Plugin to load. Defaults to None, in which case the first matching plugin is used. *args, **kwargs : arguments and keyword arguments Passed to the plugin function.

rag_boundary skimage.future.graph.rag_boundary(labels, edge_map, connectivity=2) [source] Comouter RAG based on region boundaries Given an image’s initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the same label in labels. The weight between two adjacent regions is the average value in edge_map along their boundary. labels : ndarray The labelled image. edge_map

disk skimage.morphology.disk(radius, dtype=) [source] Generates a flat, disk-shaped structuring element. A pixel is within the neighborhood if the euclidean distance between it and the origin is no greater than radius. Parameters: radius : int The radius of the disk-shaped structuring element. Returns: selem : ndarray The structuring element where elements of the neighborhood are 1 and 0 otherwise. Other Parameters: dtype : data-type The data type of the structuring element.

plugin_order skimage.io.plugin_order() [source] Return the currently preferred plugin order. Returns: p : dict Dictionary of preferred plugin order, with function name as key and plugins (in order of preference) as value.

compare_ssim skimage.measure.compare_ssim(X, Y, win_size=None, gradient=False, dynamic_range=None, multichannel=False, gaussian_weights=False, full=False, **kwargs) [source] Compute the mean structural similarity index between two images. Parameters: X, Y : ndarray Image. Any dimensionality. win_size : int or None The side-length of the sliding window used in comparison. Must be an odd value. If gaussian_weights is True, this is ignored and the window size will depend on sigma. gradient