xyz2rgb skimage.color.xyz2rgb(xyz) [source] XYZ to RGB color space conversion. Parameters: xyz : array_like The image in XYZ format, in a 3-D array of shape (.., .., 3). Returns: out : ndarray The image in RGB format, in a 3-D array of shape (.., .., 3). Raises: ValueError If xyz is not a 3-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 to sRGB. References [R60] http://en.wikipedia.org/

xyz2luv skimage.color.xyz2luv(xyz, illuminant='D65', observer='2') [source] XYZ to CIE-Luv color space conversion. Parameters: xyz : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in XYZ format. Final dimension denotes channels. 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 : (M, N, [P,] 3) ndarray The image in CI

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

windowed_histogram skimage.filters.rank.windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None) [source] Normalized sliding window histogram Parameters: image : ndarray Image array (uint8 array). selem : 2-D array The neighborhood expressed as a 2-D array of 1’s and 0’s. out : ndarray If None, a new array will be allocated. mask : ndarray Mask array that defines (>0) area of the image included in the local neighborhood. If None, the complet

wiener skimage.filters.wiener(data, impulse_response=None, filter_params={}, K=0.25, predefined_filter=None) [source] Minimum Mean Square Error (Wiener) inverse filter. Parameters: data : (M,N) ndarray Input data. K : float or (M,N) ndarray Ratio between power spectrum of noise and undegraded image. 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 f

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

watershed skimage.morphology.watershed(image, markers, connectivity=None, offset=None, mask=None) [source] Return a matrix labeled using the watershed segmentation algorithm Parameters: image: ndarray (2-D, 3-D, ...) of integers Data array where the lowest value points are labeled first. markers: ndarray of the same shape as `image` An array marking the basins with the values to be assigned in the label matrix. Zero means not a marker. This array should be of an integer type. connectivi

white_tophat skimage.morphology.white_tophat(image, selem=None, *args, **kwargs) [source] Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters: image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1’s and 0’s. If None, use cross-shaped structuring element (connectivity

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

warp_coords skimage.transform.warp_coords(coord_map, shape, dtype=) [source] Build the source coordinates for the output of a 2-D image warp. Parameters: coord_map : callable like GeometricTransform.inverse Return input coordinates for given output coordinates. Coordinates are in the shape (P, 2), where P is the number of coordinates and each element is a (row, col) pair. shape : tuple Shape of output image (rows, cols[, bands]). dtype : np.dtype or string dtype for return value (sane