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

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

warp skimage.transform.warp(image, inverse_map=None, map_args={}, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False) [source] Warp an image according to a given coordinate transformation. Parameters: image : ndarray Input image. inverse_map : transformation object, callable cr = f(cr, **kwargs), or ndarray Inverse coordinate map, which transforms coordinates in the output images into their corresponding coordinates in the input image. There are a numb

gabor_filter skimage.filters.gabor_filter(*args, **kwargs) [source] Deprecated function. Use skimage.filters.gabor instead. Return real and imaginary responses to Gabor filter. The real and imaginary parts of the Gabor filter kernel are applied to the image and the response is returned as a pair of arrays. Gabor filter is a linear filter with a Gaussian kernel which is modulated by a sinusoidal plane wave. Frequency and orientation representations of the Gabor filter are similar to those of

wiener skimage.restoration.wiener(image, psf, balance, reg=None, is_real=True, clip=True) [source] Wiener-Hunt deconvolution Return the deconvolution with a Wiener-Hunt approach (i.e. with Fourier diagonalisation). Parameters: image : (M, N) ndarray Input degraded image psf : ndarray Point Spread Function. This is assumed to be the impulse response (input image space) if the data-type is real, or the transfer function (Fourier space) if the data-type is complex. There is no constraints o

canny skimage.filters.canny(*args, **kwargs) [source] Deprecated function. Use skimage.feature.canny instead.

ORB class skimage.feature.ORB(downscale=1.2, n_scales=8, n_keypoints=500, fast_n=9, fast_threshold=0.08, harris_k=0.04) [source] Bases: skimage.feature.util.FeatureDetector, skimage.feature.util.DescriptorExtractor Oriented FAST and rotated BRIEF feature detector and binary descriptor extractor. Parameters: n_keypoints : int, optional Number of keypoints to be returned. The function will return the best n_keypoints according to the Harris corner response if more than n_keypoints are detect

start_qtapp skimage.viewer.utils.start_qtapp(app=None) [source] Start Qt mainloop

view_as_windows skimage.util.view_as_windows(arr_in, window_shape, step=1) [source] Rolling window view of the input n-dimensional array. Windows are overlapping views of the input array, with adjacent windows shifted by a single row or column (or an index of a higher dimension). Parameters: arr_in : ndarray N-d input array. window_shape : integer or tuple of length arr_in.ndim Defines the shape of the elementary n-dimensional orthotope (better know as hyperrectangle [R383]) of the rolli

Module: restoration Image restoration module.