Module: feature skimage.feature.blob_dog(image[, min_sigma, ...]) Finds blobs in the given grayscale image. skimage.feature.blob_doh(image[, min_sigma, ...]) Finds blobs in the given grayscale image. skimage.feature.blob_log(image[, min_sigma, ...]) Finds blobs in the given grayscale image. skimage.feature.canny(image[, sigma, ...]) Edge filter an image using the Canny algorithm. skimage.feature.corner_fast(image[, n, ...]) Extract FAST corners for a given image. skimage.feature.corner_fo

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

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

local_binary_pattern skimage.feature.local_binary_pattern(image, P, R, method='default') [source] Gray scale and rotation invariant LBP (Local Binary Patterns). LBP is an invariant descriptor that can be used for texture classification. Parameters: image : (N, M) array Graylevel image. P : int Number of circularly symmetric neighbour set points (quantization of the angular space). R : float Radius of circle (spatial resolution of the operator). method : {‘default’, ‘ror’, ‘uniform’, ‘

astronaut skimage.data.astronaut() [source] Colour image of the astronaut Eileen Collins. Photograph of Eileen Collins, an American astronaut. She was selected as an astronaut in 1992 and first piloted the space shuttle STS-63 in 1995. She retired in 2006 after spending a total of 38 days, 8 hours and 10 minutes in outer space. This image was downloaded from the NASA Great Images database <http://grin.hq.nasa.gov/ABSTRACTS/GPN-2000-001177.html>`__. No known copyright restrictions, rele

enhance_contrast_percentile skimage.filters.rank.enhance_contrast_percentile(image, selem, out=None, mask=None, shift_x=False, shift_y=False, p0=0, p1=1) [source] Enhance contrast of an image. This replaces each pixel by the local maximum if the pixel greyvalue is closer to the local maximum than the local minimum. Otherwise it is replaced by the local minimum. Only greyvalues between percentiles [p0, p1] are considered in the filter. Parameters: image : 2-D array (uint8, uint16) Input ima

ncut skimage.future.graph.ncut(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0) [source] Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters: labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. A

minimum skimage.filters.rank.minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local minimum of an image. Parameters: image : 2-D array (uint8, uint16) Input image. selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : 2-D array (same dtype as input) If None, a new array is allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complete image is use

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xyz2luv skimage.color.xyz2luv(xyz, illuminant='D65', observer='2') [source] XYZ to CIE-Luv color space conversion. Parameters: xyz : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in XYZ format. Final dimension denotes channels. illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : (M, N, [P,] 3) ndarray The image in CI