median skimage.filters.median(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Return local median of an image. 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 neighborhood. If None, the complete image is used (defau

convert_colorspace skimage.color.convert_colorspace(arr, fromspace, tospace) [source] Convert an image array to a new color space. Parameters: arr : array_like The image to convert. fromspace : str The color space to convert from. Valid color space strings are ['RGB', 'HSV', 'RGB CIE', 'XYZ']. Value may also be specified as lower case. tospace : str The color space to convert to. Valid color space strings are ['RGB', 'HSV', 'RGB CIE', 'XYZ']. Value may also be specified as lower case.

peak_local_max skimage.feature.peak_local_max(image, min_distance=1, threshold_abs=None, threshold_rel=None, exclude_border=True, indices=True, num_peaks=inf, footprint=None, labels=None) [source] Find peaks in an image as coordinate list or boolean mask. Peaks are the local maxima in a region of 2 * min_distance + 1 (i.e. peaks are separated by at least min_distance). If peaks are flat (i.e. multiple adjacent pixels have identical intensities), the coordinates of all such pixels are returne

test skimage.test(doctest=False, verbose=False) [source] Run all unit tests.

MCP_Connect class skimage.graph.MCP_Connect Bases: skimage.graph._mcp.MCP Connect source points using the distance-weighted minimum cost function. A front is grown from each seed point simultaneously, while the origin of the front is tracked as well. When two fronts meet, create_connection() is called. This method must be overloaded to deal with the found edges in a way that is appropriate for the application. __init__() x.__init__(...) initializes x; see help(type(x)) for signature cre

polygon skimage.draw.polygon() Generate coordinates of pixels within polygon. Parameters: y : (N,) ndarray Y-coordinates of vertices of polygon. x : (N,) ndarray X-coordinates of vertices of polygon. shape : tuple, optional Image shape which is used to determine the maximum extent of output pixel coordinates. This is useful for polygons which exceed the image size. By default the full extent of the polygon are used. Returns: rr, cc : ndarray of int Pixel coordinates of polygon. May

skeletonize skimage.morphology.skeletonize(image) [source] Return the skeleton of a binary image. Thinning is used to reduce each connected component in a binary image to a single-pixel wide skeleton. Parameters: image : numpy.ndarray A binary image containing the objects to be skeletonized. ‘1’ represents foreground, and ‘0’ represents background. It also accepts arrays of boolean values where True is foreground. Returns: skeleton : ndarray A matrix containing the thinned image. Se

nl_means_denoising skimage.restoration.nl_means_denoising(*args, **kwargs) [source] Deprecated function. Use skimage.restoration.denoise_nl_means instead. Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images. Parameters: image : 2D or 3D ndarray Input image to be denoised, which can be 2D or 3D, and grayscale or RGB (for 2D images only, see multichannel parameter). patch_size : int, optional Size of patches used for denoising. patch_distance : int, option

greycomatrix skimage.feature.greycomatrix(image, distances, angles, levels=256, symmetric=False, normed=False) [source] Calculate the grey-level co-occurrence matrix. A grey level co-occurrence matrix is a histogram of co-occurring greyscale values at a given offset over an image. Parameters: image : array_like of uint8 Integer typed input image. The image will be cast to uint8, so the maximum value must be less than 256. distances : array_like List of pixel pair distance offsets. angle

ProjectiveTransform class skimage.transform.ProjectiveTransform(matrix=None) [source] Bases: skimage.transform._geometric.GeometricTransform Matrix transformation. Apply a projective transformation (homography) on coordinates. For each homogeneous coordinate , its target position is calculated by multiplying with the given matrix, , to give : [[a0 a1 a2] [b0 b1 b2] [c0 c1 1 ]]. E.g., to rotate by theta degrees clockwise, the matrix should be: [[cos(theta) -sin(theta) 0] [sin(theta) cos(