radon skimage.transform.radon(image, theta=None, circle=False) [source] Calculates the radon transform of an image given specified projection angles. Parameters: image : array_like, dtype=float Input image. The rotation axis will be located in the pixel with indices (image.shape[0] // 2, image.shape[1] // 2). theta : array_like, dtype=float, optional (default np.arange(180)) Projection angles (in degrees). circle : boolean, optional Assume image is zero outside the inscribed circle, ma

matrix_transform skimage.transform.matrix_transform(coords, matrix) [source] Apply 2D matrix transform. Parameters: coords : (N, 2) array x, y coordinates to transform matrix : (3, 3) array Homogeneous transformation matrix. Returns: coords : (N, 2) array Transformed coordinates.

lab2rgb skimage.color.lab2rgb(lab) [source] Lab to RGB color space conversion. Parameters: lab : array_like The image in Lab 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 lab is not a 3-D array of shape (.., .., 3). Notes This function uses lab2xyz and xyz2rgb.

corner_foerstner skimage.feature.corner_foerstner(image, sigma=1) [source] Compute Foerstner corner measure response image. This corner detector uses information from the auto-correlation matrix A: A = [(imx**2) (imx*imy)] = [Axx Axy] [(imx*imy) (imy**2)] [Axy Ayy] Where imx and imy are first derivatives, averaged with a gaussian filter. The corner measure is then defined as: w = det(A) / trace(A) (size of error ellipse) q = 4 * det(A) / trace(A)**2 (roundness of erro

luv2xyz skimage.color.luv2xyz(luv, illuminant='D65', observer='2') [source] CIE-Luv to XYZ color space conversion. Parameters: luv : (M, N, [P,] 3) array_like The 3 or 4 dimensional image in CIE-Luv 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 i

deltaE_cie76 skimage.color.deltaE_cie76(lab1, lab2) [source] Euclidean distance between two points in Lab color space Parameters: lab1 : array_like reference color (Lab colorspace) lab2 : array_like comparison color (Lab colorspace) Returns: dE : array_like distance between colors lab1 and lab2 References [R32] http://en.wikipedia.org/wiki/Color_difference [R33] A. R. Robertson, “The CIE 1976 color-difference formulae,” Color Res. Appl. 2, 7-11 (1977).

structural_similarity skimage.measure.structural_similarity(*args, **kwargs) [source] Deprecated function. Use compare_ssim instead.

Module: restoration Image restoration module.

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

TiffWriter class skimage.external.tifffile.TiffWriter(filename, bigtiff=False, byteorder=None, software='tifffile.py') [source] Bases: object Write image data to TIFF file. TiffWriter instances must be closed using the close method, which is automatically called when using the ‘with’ statement. Examples >>> data = numpy.random.rand(2, 5, 3, 301, 219) >>> with TiffWriter('temp.tif', bigtiff=True) as tif: ... for i in range(data.shape[0]): ... tif.save(data[i], co