resize skimage.transform.resize(image, output_shape, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Resize image to match a certain size. Performs interpolation to up-size or down-size images. For down-sampling N-dimensional images by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively. Parameters: image : ndarray Input image. output_shape : tuple or ndarray Size of the generated output i

reset_plugins skimage.io.reset_plugins() [source]

rescale skimage.transform.rescale(image, scale, order=1, mode='constant', cval=0, clip=True, preserve_range=False) [source] Scale image by a certain factor. Performs interpolation to upscale or down-scale images. For down-sampling N-dimensional images with integer factors by applying the arithmetic sum or mean, see skimage.measure.local_sum and skimage.transform.downscale_local_mean, respectively. Parameters: image : ndarray Input image. scale : {float, tuple of floats} Scale factors. Se

rescale_intensity skimage.exposure.rescale_intensity(image, in_range='image', out_range='dtype') [source] Return image after stretching or shrinking its intensity levels. The desired intensity range of the input and output, in_range and out_range respectively, are used to stretch or shrink the intensity range of the input image. See examples below. Parameters: image : array Image array. in_range, out_range : str or 2-tuple Min and max intensity values of input and output image. The possi

remove_small_objects skimage.morphology.remove_small_objects(ar, min_size=64, connectivity=1, in_place=False) [source] Remove connected components smaller than the specified size. Parameters: ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. If the array type is int, it is assumed that it contains already-labeled objects. The ints must be non-negative. min_size : int, optional (default: 64) The smallest allowable connected compone

RequiredAttr class skimage.viewer.utils.RequiredAttr(init_val=None) [source] Bases: object A class attribute that must be set before use. __init__(init_val=None) [source] instances = {(, None): 'Widget is not attached to a Plugin.', (, None): 'Plugin is not attached to ImageViewer'}

remove_small_holes skimage.morphology.remove_small_holes(ar, min_size=64, connectivity=1, in_place=False) [source] Remove continguous holes smaller than the specified size. Parameters: ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. min_size : int, optional (default: 64) The hole component size. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. in_place : bo

relabel_sequential skimage.segmentation.relabel_sequential(label_field, offset=1) [source] Relabel arbitrary labels to {offset, ... offset + number_of_labels}. This function also returns the forward map (mapping the original labels to the reduced labels) and the inverse map (mapping the reduced labels back to the original ones). Parameters: label_field : numpy array of int, arbitrary shape An array of labels. offset : int, optional The return labels will start at offset, which should be

regular_grid skimage.util.regular_grid(ar_shape, n_points) [source] Find n_points regularly spaced along ar_shape. The returned points (as slices) should be as close to cubically-spaced as possible. Essentially, the points are spaced by the Nth root of the input array size, where N is the number of dimensions. However, if an array dimension cannot fit a full step size, it is “discarded”, and the computation is done for only the remaining dimensions. Parameters: ar_shape : array-like of ints

relabel_from_one skimage.segmentation.relabel_from_one(*args, **kwargs) [source] Deprecated function. Use relabel_sequential instead. Convert labels in an arbitrary label field to {1, ... number_of_labels}. This function is deprecated, see relabel_sequential for more.