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.

imsave skimage.external.tifffile.imsave(filename, data, **kwargs) [source] Write image data to TIFF file. Refer to the TiffWriter class and member functions for documentation. Parameters: filename : str Name of file to write. data : array_like Input image. The last dimensions are assumed to be image depth, height, width, and samples. kwargs : dict Parameters ‘byteorder’, ‘bigtiff’, and ‘software’ are passed to the TiffWriter class. Parameters ‘photometric’, ‘planarconfig’, ‘resolution’

moments skimage.measure.moments(image, order=3) [source] Calculate all raw image moments up to a certain order. The following properties can be calculated from raw image moments: Area as: m[0, 0]. Centroid as: {m[0, 1] / m[0, 0], m[1, 0] / m[0, 0]}. Note that raw moments are neither translation, scale nor rotation invariant. Parameters: image : 2D double or uint8 array Rasterized shape as image. order : int, optional Maximum order of moments. Default is 3. Returns: m : (order + 1, o

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

gradient skimage.filters.rank.gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local gradient of an image (i.e. local maximum - local minimum). 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 neighbor

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).