rgb2xyz skimage.color.rgb2xyz(rgb) [source] RGB to XYZ color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Returns: out : ndarray The image in XYZ format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). Raises: ValueError If rgb is not a 3- or 4-D array of shape (.., ..,[ ..,] 3). Notes The CIE XYZ color space is derived from the CIE RGB color space. Note however that this function converts from sRGB. Ref

is_low_contrast skimage.exposure.is_low_contrast(image, fraction_threshold=0.05, lower_percentile=1, upper_percentile=99, method='linear') [source] Detemine if an image is low contrast. Parameters: image : array-like The image under test. fraction_threshold : float, optional The low contrast fraction threshold. An image is considered low- contrast when its range of brightness spans less than this fraction of its data type’s full range. [R90] lower_bound : float, optional Disregard valu

Module: color skimage.color.combine_stains(stains, conv_matrix) Stain to RGB color space conversion. skimage.color.convert_colorspace(arr, ...) Convert an image array to a new color space. skimage.color.deltaE_cie76(lab1, lab2) Euclidean distance between two points in Lab color space skimage.color.deltaE_ciede2000(lab1, lab2[, ...]) Color difference as given by the CIEDE 2000 standard. skimage.color.deltaE_ciede94(lab1, lab2[, ...]) Color difference according to CIEDE 94 standard skimage.

Module: measure skimage.measure.approximate_polygon(coords, ...) Approximate a polygonal chain with the specified tolerance. skimage.measure.block_reduce(image, block_size) Down-sample image by applying function to local blocks. skimage.measure.compare_mse(im1, im2) Compute the mean-squared error between two images. skimage.measure.compare_nrmse(im_true, im_test) Compute the normalized root mean-squared error (NRMSE) between two images. skimage.measure.compare_psnr(im_true, im_test) Comput

skimage.novice A special Python image submodule for beginners. Description skimage.novice provides a simple image manipulation interface for beginners. It allows for easy loading, manipulating, and saving of image files. This module is primarily intended for teaching and differs significantly from the normal, array-oriented image functions used by scikit-image. Note This module uses the Cartesian coordinate system, where the origin is at the lower-left corner instead of the upper-right and the

deltaE_ciede2000 skimage.color.deltaE_ciede2000(lab1, lab2, kL=1, kC=1, kH=1) [source] Color difference as given by the CIEDE 2000 standard. CIEDE 2000 is a major revision of CIDE94. The perceptual calibration is largely based on experience with automotive paint on smooth surfaces. Parameters: lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) kL : float (range), optional lightness scale factor, 1 for “acceptably close”; 2 for “imperc

Module: io Utilities to read and write images in various formats. The following plug-ins are available: Plugin Description pil Image reading via the Python Imaging Library qt Fast image display using the Qt library freeimage Load images using the FreeImage library gtk Fast image display using the GTK library matplotlib Display or save images using Matplotlib simpleitk Image reading and writing via SimpleITK imread Image reading and writing via imread imageio Image reading via the ImageIO Librar

hough_ellipse skimage.transform.hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None) [source] Perform an elliptical Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Max

blob_dog skimage.feature.blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0, overlap=0.5) [source] Finds blobs in the given grayscale image. Blobs are found using the Difference of Gaussian (DoG) method [R126]. For each blob found, the method returns its coordinates and the standard deviation of the Gaussian kernel that detected the blob. Parameters: image : ndarray Input grayscale image, blobs are assumed to be light on dark background (white on black). min_sigma :

rgb2luv skimage.color.rgb2luv(rgb) [source] RGB to CIE-Luv color space conversion. Parameters: rgb : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in RGB format. Final dimension denotes channels. Returns: out : (M, N, [P,] 3) ndarray The image in CIE Luv format. Same dimensions as input. Raises: ValueError If rgb is not a 3-D or 4-D array of shape (M, N, [P,] 3). Notes This function uses rgb2xyz and xyz2luv.