rag_mean_color skimage.future.graph.rag_mean_color(image, labels, connectivity=2, mode='distance', sigma=255.0) [source] Compute the Region Adjacency Graph using mean colors. Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within image with the same label in labels. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the mod

RAG class skimage.future.graph.RAG(label_image=None, connectivity=1, data=None, **attr) [source] Bases: networkx.classes.graph.Graph The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph Parameters: label_image : array of int An initial segmentation, with each region labeled as a different integer. Every unique value in label_image will correspond to a node in the graph. connectivity : int in {1, ..., label_image.ndim}, optional The connectivity between pixels in label_i

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

radon skimage.transform.radon(image, theta=None, circle=False) [source] Calculates the radon transform of an image given specified projection angles. Parameters: image : array_like, dtype=float Input image. The rotation axis will be located in the pixel with indices (image.shape[0] // 2, image.shape[1] // 2). theta : array_like, dtype=float, optional (default np.arange(180)) Projection angles (in degrees). circle : boolean, optional Assume image is zero outside the inscribed circle, ma

quickshift skimage.segmentation.quickshift() Segments image using quickshift clustering in Color-(x,y) space. Produces an oversegmentation of the image using the quickshift mode-seeking algorithm. Parameters: image : (width, height, channels) ndarray Input image. ratio : float, optional, between 0 and 1 (default 1). Balances color-space proximity and image-space proximity. Higher values give more weight to color-space. kernel_size : float, optional (default 5) Width of Gaussian kernel

pyramid_reduce skimage.transform.pyramid_reduce(image, downscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Smooth and then downsample image. Parameters: image : array Input image. downscale : float, optional Downscale factor. sigma : float, optional Sigma for Gaussian filter. Default is 2 * downscale / 6.0 which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines

pyramid_laplacian skimage.transform.pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Yield images of the laplacian pyramid formed by the input image. Each layer contains the difference between the downsampled and the downsampled, smoothed image: layer = resize(prev_layer) - smooth(resize(prev_layer)) Note that the first image of the pyramid will be the difference between the original, unscaled image and its smoothed version. The total

pyramid_gaussian skimage.transform.pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Yield images of the Gaussian pyramid formed by the input image. Recursively applies the pyramid_reduce function to the image, and yields the downscaled images. Note that the first image of the pyramid will be the original, unscaled image. The total number of images is max_layer + 1. In case all layers are computed, the last image is either a one-pixel im

pyramid_expand skimage.transform.pyramid_expand(image, upscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Upsample and then smooth image. Parameters: image : array Input image. upscale : float, optional Upscale factor. sigma : float, optional Sigma for Gaussian filter. Default is 2 * upscale / 6.0 which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines used in in

push skimage.io.push(img) [source] Push an image onto the shared image stack. Parameters: img : ndarray Image to push.