Module: viewer.viewers skimage.viewer.viewers.CollectionViewer(...) Viewer for displaying image collections. skimage.viewer.viewers.ImageViewer(image[, ...]) Viewer for displaying images.

LinearColormap class skimage.viewer.utils.LinearColormap(name, segmented_data, **kwargs) [source] Bases: matplotlib.colors.LinearSegmentedColormap LinearSegmentedColormap in which color varies smoothly. This class is a simplification of LinearSegmentedColormap, which doesn’t support jumps in color intensities. Parameters: name : str Name of colormap. segmented_data : dict Dictionary of ‘red’, ‘green’, ‘blue’, and (optionally) ‘alpha’ values. Each color key contains a list of x, y tuples.

hog skimage.feature.hog(image, orientations=9, pixels_per_cell=(8, 8), cells_per_block=(3, 3), visualise=False, transform_sqrt=False, feature_vector=True, normalise=None) [source] Extract Histogram of Oriented Gradients (HOG) for a given image. Compute a Histogram of Oriented Gradients (HOG) by (optional) global image normalisation computing the gradient image in x and y computing gradient histograms normalising across blocks flattening into a feature vector Parameters: image : (M, N) ndar

rectangle skimage.morphology.rectangle(width, height, dtype=) [source] Generates a flat, rectangular-shaped structuring element. Every pixel in the rectangle generated for a given width and given height belongs to the neighborhood. Parameters: width : int The width of the rectangle. height : int The height of the rectangle. Returns: selem : ndarray A structuring element consisting only of ones, i.e. every pixel belongs to the neighborhood. Other Parameters: dtype : data-type The

corner_orientations skimage.feature.corner_orientations() Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters: image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as

autolevel skimage.filters.rank.autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Auto-level image using local histogram. This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”. 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 :

binary_opening skimage.morphology.binary_opening(image, selem=None, *args, **kwargs) [source] Return fast binary morphological opening of an image. This function returns the same result as greyscale opening but performs faster for binary images. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. “salt”) and connect small dark cracks. This tends to “open” up (dark) gaps between (bright) features. Parameters: imag

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

ifrt2 skimage.transform.ifrt2(a) [source] Compute the 2-dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array. Parameters: a : array_like A 2-D (n+1) row x n column integer array. Returns: iFRT : 2-D n x n ndarray Inverse Finite Radon Transform array of n x n integer coefficients. See also frt2 The two-dimensional FRT Notes The FRT has a unique inverse if and only if n is prime. See [R368] for an overview. The idea for this algorithm is due to Vlad Neg

Image data types and what they mean In skimage, images are simply numpy arrays, which support a variety of data types [1], i.e. “dtypes”. To avoid distorting image intensities (see Rescaling intensity values), we assume that images use the following dtype ranges: Data type Range uint8 0 to 255 uint16 0 to 65535 uint32 0 to 232 float -1 to 1 or 0 to 1 int8 -128 to 127 int16 -32768 to 32767 int32 -231 to 231 - 1 Note that float images should be restricted to the range -1 to 1 even though the da