corner_peaks skimage.feature.corner_peaks(image, min_distance=1, threshold_abs=None, threshold_rel=0.1, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source] Find corners in corner measure response image. This differs from skimage.feature.peak_local_max in that it suppresses multiple connected peaks with the same accumulator value. Parameters: * : * See skimage.feature.peak_local_max(). Examples >>> from skimage.feature import peak_local_max >

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.

compare_psnr skimage.measure.compare_psnr(im_true, im_test, dynamic_range=None) [source] Compute the peak signal to noise ratio (PSNR) for an image. Parameters: im_true : ndarray Ground-truth image. im_test : ndarray Test image. dynamic_range : int The dynamic range of the input image (distance between minimum and maximum possible values). By default, this is estimated from the image data-type. Returns: psnr : float The PSNR metric. References [R260] https://en.wikipedia.org/wiki

xyz2lab skimage.color.xyz2lab(xyz, illuminant='D65', observer='2') [source] XYZ to CIE-LAB color space conversion. Parameters: xyz : array_like The image in XYZ format, in a 3- or 4-D array of shape (.., ..,[ ..,] 3). illuminant : {“A”, “D50”, “D55”, “D65”, “D75”, “E”}, optional The name of the illuminant (the function is NOT case sensitive). observer : {“2”, “10”}, optional The aperture angle of the observer. Returns: out : ndarray The image in CIE-LAB format, in a 3- or 4-D array

circle skimage.draw.circle(r, c, radius, shape=None) [source] Generate coordinates of pixels within circle. Parameters: r, c : double Centre coordinate of circle. radius: double Radius of circle. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for circles which exceed the image size. By default the full extent of the circle are used. Returns: rr, cc : ndarray of int Pixel coordinates of circle. May be use

rgb2hsv skimage.color.rgb2hsv(rgb) [source] RGB to HSV color space conversion. Parameters: rgb : array_like The image in RGB format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in HSV format, in a 3-D array of shape (.., .., 3). Raises: ValueError If rgb is not a 3-D array of shape (.., .., 3). Notes The conversion assumes an input data range of [0, 1] for all color components. Conversion between RGB and HSV color spaces results in some loss of precision

ncut skimage.future.graph.ncut(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0) [source] Perform Normalized Graph cut on the Region Adjacency Graph. Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output. Parameters: labels : ndarray The array of labels. rag : RAG The region adjacency graph. thresh : float The threshold. A

inverse skimage.filters.inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None) [source] Apply the filter in reverse to the given data. Parameters: data : (M,N) ndarray Input data. impulse_response : callable f(r, c, **filter_params) Impulse response of the filter. See LPIFilter2D.__init__. filter_params : dict Additional keyword parameters to the impulse_response function. max_gain : float Limit the filter gain. Often, the filter contains zeros, wh

img_as_ubyte skimage.img_as_ubyte(image, force_copy=False) [source] Convert an image to 8-bit unsigned integer format. Parameters: image : ndarray Input image. force_copy : bool Force a copy of the data, irrespective of its current dtype. Returns: out : ndarray of ubyte (uint8) Output image. Notes Negative input values will be clipped. Positive values are scaled between 0 and 255.

label skimage.morphology.label(input, neighbors=None, background=None, return_num=False, connectivity=None) [source] Label connected regions of an integer array. Two pixels are connected when they are neighbors and have the same value. In 2D, they can be neighbors either in a 1- or 2-connected sense. The value refers to the maximum number of orthogonal hops to consider a pixel/voxel a neighbor: 1-connectivity 2-connectivity diagonal connection close-up [ ] [ ] [ ]