start_qtapp skimage.viewer.utils.start_qtapp(app=None) [source] Start Qt mainloop

ORB class skimage.feature.ORB(downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04) [source] Bases: skimage.feature.util.FeatureDetector, skimage.feature.util.DescriptorExtractor Oriented FAST and rotated BRIEF feature detector and binary descriptor extractor. Parameters: n_keypoints : int, optional Number of keypoints to be returned. The function will return the best n_keypoints according to the Harris corner response if more than n_keypoints are detect

route_through_array skimage.graph.route_through_array(array, start, end, fully_connected=True, geometric=True) [source] Simple example of how to use the MCP and MCP_Geometric classes. See the MCP and MCP_Geometric class documentation for explanation of the path-finding algorithm. Parameters: array : ndarray Array of costs. start : iterable n-d index into array defining the starting point end : iterable n-d index into array defining the end point fully_connected : bool (optional) If T

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

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

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

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.