imread skimage.data.imread(fname, as_grey=False, plugin=None, flatten=None, **plugin_args) [source] Load an image from file. Parameters: fname : string Image file name, e.g. test.jpg or URL. as_grey : bool If True, convert color images to grey-scale (32-bit floats). Images that are already in grey-scale format are not converted. plugin : str Name of plugin to use. By default, the different plugins are tried (starting with the Python Imaging Library) until a suitable candidate is found.

ClearColormap class skimage.viewer.utils.ClearColormap(rgb, max_alpha=1, name='clear_color') [source] Bases: skimage.viewer.utils.core.LinearColormap Color map that varies linearly from alpha = 0 to 1 __init__(rgb, max_alpha=1, name='clear_color') [source]

equalize_hist skimage.exposure.equalize_hist(image, nbins=256, mask=None) [source] Return image after histogram equalization. Parameters: image : array Image array. nbins : int, optional Number of bins for image histogram. Note: this argument is ignored for integer images, for which each integer is its own bin. mask: ndarray of bools or 0s and 1s, optional Array of same shape as image. Only points at which mask == True are used for the equalization, which is applied to the whole image.

imread skimage.external.tifffile.imread(files, **kwargs) [source] Return image data from TIFF file(s) as numpy array. The first image series is returned if no arguments are provided. Parameters: files : str or list File name, glob pattern, or list of file names. key : int, slice, or sequence of page indices Defines which pages to return as array. series : int Defines which series of pages in file to return as array. multifile : bool If True (default), OME-TIFF data may include pages

richardson_lucy skimage.restoration.richardson_lucy(image, psf, iterations=50, clip=True) [source] Richardson-Lucy deconvolution. Parameters: image : ndarray Input degraded image (can be N dimensional). psf : ndarray The point spread function. iterations : int Number of iterations. This parameter plays the role of regularisation. clip : boolean, optional True by default. If true, pixel value of the result above 1 or under -1 are thresholded for skimage pipeline compatibility. Retur

gray2rgb skimage.color.gray2rgb(image, alpha=None) [source] Create an RGB representation of a gray-level image. Parameters: image : array_like Input image of shape (M, N [, P]). alpha : bool, optional Ensure that the output image has an alpha layer. If None, alpha layers are passed through but not created. Returns: rgb : ndarray RGB image of shape (M, N, [, P], 3). Raises: ValueError If the input is not a 2- or 3-dimensional image.

Module: transform skimage.transform.downscale_local_mean(...) Down-sample N-dimensional image by local averaging. skimage.transform.estimate_transform(ttype, ...) Estimate 2D geometric transformation parameters. skimage.transform.frt2(a) Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. skimage.transform.hough_circle(image, radius) Perform a circular Hough transform. skimage.transform.hough_ellipse(img[, ...]) Perform an elliptical Hough transform. skimage

windowed_histogram skimage.filters.rank.windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None) [source] Normalized sliding window histogram Parameters: image : ndarray Image array (uint8 array). selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : ndarray If None, a new array will be allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complet

moments_normalized skimage.measure.moments_normalized(mu, order=3) [source] Calculate all normalized central image moments up to a certain order. Note that normalized central moments are translation and scale invariant but not rotation invariant. Parameters: mu : (M, M) array Central image moments, where M must be > order. order : int, optional Maximum order of moments. Default is 3. Returns: nu : (order + 1, order + 1) array Normalized central image moments. References [R278] W

black_tophat skimage.morphology.black_tophat(image, selem=None, *args, **kwargs) [source] Return black top hat of an image. The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the structuring element. Note that dark spots in the original image are bright spots after the black top hat. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expre