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

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

pop skimage.io.pop() [source] Pop an image from the shared image stack. Returns: img : ndarray Image popped from the stack.

moon skimage.data.moon() [source] Surface of the moon. This low-contrast image of the surface of the moon is useful for illustrating histogram equalization and contrast stretching.

Module: external.tifffile skimage.external.tifffile.imread(files, **kwargs) Return image data from TIFF file(s) as numpy array. skimage.external.tifffile.imsave(filename, ...) Write image data to TIFF file. skimage.external.tifffile.imshow(data[, ...]) Plot n-dimensional images using matplotlib.pyplot. skimage.external.tifffile.TiffFile(arg[, ...]) Read image and metadata from TIFF, STK, LSM, and FluoView files. skimage.external.tifffile.TiffSequence(files) Sequence of image files. skimag

clock skimage.data.clock() [source] Motion blurred clock. This photograph of a wall clock was taken while moving the camera in an aproximately horizontal direction. It may be used to illustrate inverse filters and deconvolution. Released into the public domain by the photographer (Stefan van der Walt).

coffee skimage.data.coffee() [source] Coffee cup. This photograph is courtesy of Pikolo Espresso Bar. It contains several elliptical shapes as well as varying texture (smooth porcelain to course wood grain). Notes No copyright restrictions. CC0 by the photographer (Rachel Michetti).

text skimage.data.text() [source] Gray-level “text” image used for corner detection. Notes This image was downloaded from Wikipedia <http://en.wikipedia.org/wiki/File:Corner.png>`__. No known copyright restrictions, released into the public domain.

medial_axis skimage.morphology.medial_axis(image, mask=None, return_distance=False) [source] Compute the medial axis transform of a binary image Parameters: image : binary ndarray, shape (M, N) The image of the shape to be skeletonized. mask : binary ndarray, shape (M, N), optional If a mask is given, only those elements in image with a true value in mask are used for computing the medial axis. return_distance : bool, optional If true, the distance transform is returned as well as the

dtype_limits skimage.dtype_limits(image, clip_negative=True) [source] Return intensity limits, i.e. (min, max) tuple, of the image’s dtype. Parameters: image : ndarray Input image. clip_negative : bool If True, clip the negative range (i.e. return 0 for min intensity) even if the image dtype allows negative values.