ransac skimage.measure.ransac(data, model_class, min_samples, residual_threshold, is_data_valid=None, is_model_valid=None, max_trials=100, stop_sample_num=inf, stop_residuals_sum=0, stop_probability=1) [source] Fit a model to data with the RANSAC (random sample consensus) algorithm. RANSAC is an iterative algorithm for the robust estimation of parameters from a subset of inliers from the complete data set. Each iteration performs the following tasks: Select min_samples random samples from th

Picture class skimage.novice.Picture(path=None, array=None, xy_array=None) [source] Bases: object A 2-D picture made up of pixels. Examples Load an image from a file: >>> from skimage import novice >>> from skimage import data >>> picture = novice.open(data.data_dir + '/chelsea.png') Load an image from a URL (the URL must start with http(s):// or ftp(s)://): >>> picture = novice.open('http://scikit-image.org/_static/img/logo.png') Create a blank 100 pixe

denoise_tv_bregman skimage.restoration.denoise_tv_bregman(image, weight, max_iter=100, eps=0.001, isotropic=True) [source] Perform total-variation denoising using split-Bregman optimization. Total-variation denoising (also know as total-variation regularization) tries to find an image with less total-variation under the constraint of being similar to the input image, which is controlled by the regularization parameter. Parameters: image : ndarray Input data to be denoised (converted using

gaussian skimage.filters.gaussian(image, sigma, output=None, mode='nearest', cval=0, multichannel=None) [source] Multi-dimensional Gaussian filter Parameters: image : array-like input image (grayscale or color) to filter. sigma : scalar or sequence of scalars standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes. output : array, optional The output param

watershed skimage.morphology.watershed(image, markers, connectivity=None, offset=None, mask=None) [source] Return a matrix labeled using the watershed segmentation algorithm Parameters: image: ndarray (2-D, 3-D, ...) of integers Data array where the lowest value points are labeled first. markers: ndarray of the same shape as `image` An array marking the basins with the values to be assigned in the label matrix. Zero means not a marker. This array should be of an integer type. connectivi

blob_log skimage.feature.blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=0.2, overlap=0.5, log_scale=False) [source] Finds blobs in the given grayscale image. Blobs are found using the Laplacian of Gaussian (LoG) method [R129]. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob. Parameters: image : ndarray Input grayscale image, blobs are assumed to be light on dark background (white on black).

canny skimage.filters.canny(*args, **kwargs) [source] Deprecated function. Use skimage.feature.canny instead.

adjust_log skimage.exposure.adjust_log(image, gain=1, inv=False) [source] Performs Logarithmic correction on the input image. This function transforms the input image pixelwise according to the equation O = gain*log(1 + I) after scaling each pixel to the range 0 to 1. For inverse logarithmic correction, the equation is O = gain*(2**I - 1). Parameters: image : ndarray Input image. gain : float The constant multiplier. Default value is 1. inv : float If True, it performs inverse logarith

Module: viewer.canvastools skimage.viewer.canvastools.LineTool(manager) Widget for line selection in a plot. skimage.viewer.canvastools.PaintTool(...[, ...]) Widget for painting on top of a plot. skimage.viewer.canvastools.RectangleTool(manager) Widget for selecting a rectangular region in a plot. skimage.viewer.canvastools.ThickLineTool(manager) Widget for line selection in a plot.

crop skimage.util.crop(ar, crop_width, copy=False, order='K') [source] Crop array ar by crop_width along each dimension. Parameters: ar : array-like of rank N Input array. crop_width : {sequence, int} Number of values to remove from the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) specifies unique crop widths at the start and end of each axis. ((before, after),) specifies a fixed start and end crop for every axis. (n,) or n for integer n is a shortcut for before = a