regionprops skimage.measure.regionprops(label_image, intensity_image=None, cache=True) [source] Measure properties of labeled image regions. Parameters: label_image : (N, M) ndarray Labeled input image. Labels with value 0 are ignored. intensity_image : (N, M) ndarray, optional Intensity (i.e., input) image with same size as labeled image. Default is None. cache : bool, optional Determine whether to cache calculated properties. The computation is much faster for cached properties, wher

register_translation skimage.feature.register_translation(src_image, target_image, upsample_factor=1, space='real') [source] Efficient subpixel image translation registration by cross-correlation. This code gives the same precision as the FFT upsampled cross-correlation in a fraction of the computation time and with reduced memory requirements. It obtains an initial estimate of the cross-correlation peak by an FFT and then refines the shift estimation by upsampling the DFT only in a small ne

References [R303] François Orieux, Jean-François Giovannelli, and Thomas Rodet, “Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution”, J. Opt. Soc. Am. A 27, 1593-1607 (2010) http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-27-7-1593 [R304] Richardson, William Hadley, “Bayesian-Based Iterative Method of Image Restoration”. JOSA 62 (1): 55–59. doi:10.1364/JOSA.62.000055, 1972 [R305] B. R. Hunt “A matrix theory proof of the disc

RectangleTool class skimage.viewer.canvastools.RectangleTool(manager, on_move=None, on_release=None, on_enter=None, maxdist=10, rect_props=None) [source] Bases: skimage.viewer.canvastools.base.CanvasToolBase, matplotlib.widgets.RectangleSelector Widget for selecting a rectangular region in a plot. After making the desired selection, press “Enter” to accept the selection and call the on_enter callback function. Parameters: manager : Viewer or PlotPlugin. Skimage viewer or plot plugin object

reconstruction skimage.morphology.reconstruction(seed, mask, method='dilation', selem=None, offset=None) [source] Perform a morphological reconstruction of an image. Morphological reconstruction by dilation is similar to basic morphological dilation: high-intensity values will replace nearby low-intensity values. The basic dilation operator, however, uses a structuring element to determine how far a value in the input image can spread. In contrast, reconstruction uses two images: a “seed” im

rectangle skimage.morphology.rectangle(width, height, dtype=) [source] Generates a flat, rectangular-shaped structuring element. Every pixel in the rectangle generated for a given width and given height belongs to the neighborhood. Parameters: width : int The width of the rectangle. height : int The height of the rectangle. Returns: selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. Other Parameters: dtype : data-type The

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

rank_order skimage.filters.rank_order(image) [source] Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of image, aka the rank-order value. Parameters: image: ndarray Returns: labels: ndarray of type np.uint32, of shape image.shape New array where each pixel has the rank-order value of the corresponding pixel in image. Pixel values are between 0 and n - 1, where n is the number of distinct unique values in image

random_walker skimage.segmentation.random_walker(data, labels, beta=130, mode='bf', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None) [source] Random walker algorithm for segmentation from markers. Random walker algorithm is implemented for gray-level or multichannel images. Parameters: data : array_like Image to be segmented in phases. Gray-level data can be two- or three-dimensional; multichannel data can be three- or four- dimensional (multichannel=True) wi

random_noise skimage.util.random_noise(image, mode='gaussian', seed=None, clip=True, **kwargs) [source] Function to add random noise of various types to a floating-point image. Parameters: image : ndarray Input image data. Will be converted to float. mode : str One of the following strings, selecting the type of noise to add: ‘gaussian’ Gaussian-distributed additive noise. ‘localvar’ Gaussian-distributed additive noise, with specified local variance at each point of image ‘poisson’