autolevel skimage.filters.rank.autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False) [source] Auto-level image using local histogram. This filter locally stretches the histogram of greyvalues to cover the entire range of values from “white” to “black”. 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 :

binary_opening skimage.morphology.binary_opening(image, selem=None, *args, **kwargs) [source] Return fast binary morphological opening of an image. This function returns the same result as greyscale opening but performs faster for binary images. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. “salt”) and connect small dark cracks. This tends to “open” up (dark) gaps between (bright) features. Parameters: imag

hough_ellipse skimage.transform.hough_ellipse(img, threshold=4, accuracy=1, min_size=4, max_size=None) [source] Perform an elliptical Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. threshold: int, optional (default 4) Accumulator threshold value. accuracy : double, optional (default 1) Bin size on the minor axis used in the accumulator. min_size : int, optional (default 4) Minimal major axis length. max_size : int, optional Max

ifrt2 skimage.transform.ifrt2(a) [source] Compute the 2-dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array. Parameters: a : array_like A 2-D (n+1) row x n column integer array. Returns: iFRT : 2-D n x n ndarray Inverse Finite Radon Transform array of n x n integer coefficients. See also frt2 The two-dimensional FRT Notes The FRT has a unique inverse if and only if n is prime. See [R368] for an overview. The idea for this algorithm is due to Vlad Neg

Image data types and what they mean In skimage, images are simply numpy arrays, which support a variety of data types [1], i.e. “dtypes”. To avoid distorting image intensities (see Rescaling intensity values), we assume that images use the following dtype ranges: Data type Range uint8 0 to 255 uint16 0 to 65535 uint32 0 to 232 float -1 to 1 or 0 to 1 int8 -128 to 127 int16 -32768 to 32767 int32 -231 to 231 - 1 Note that float images should be restricted to the range -1 to 1 even though the da

PolynomialTransform class skimage.transform.PolynomialTransform(params=None) [source] Bases: skimage.transform._geometric.GeometricTransform 2D transformation of the form: ..:math: X = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j - i) * y**i )) Y = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j - i) * y**i )) Parameters: params : (2, N) array, optional Polynomial coefficients where N * 2 = (order + 1) * (order + 2). So, a_ji is defined in params[0, :] and b_ji in params[1, :]. Attributes params (

frt2 skimage.transform.frt2(a) [source] Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. Parameters: a : array_like A 2-D square n x n integer array. Returns: FRT : 2-D ndarray Finite Radon Transform array of (n+1) x n integer coefficients. See also ifrt2 The two-dimensional inverse FRT. Notes The FRT has a unique inverse if and only if n is prime. [FRT] The idea for this algorithm is due to Vlad Negnevitski. References [FRT] A. Kingston and I.

CircleModel class skimage.measure.CircleModel [source] Bases: skimage.measure.fit.BaseModel Total least squares estimator for 2D circles. The functional model of the circle is: r**2 = (x - xc)**2 + (y - yc)**2 This estimator minimizes the squared distances from all points to the circle: min{ sum((r - sqrt((x_i - xc)**2 + (y_i - yc)**2))**2) } A minimum number of 3 points is required to solve for the parameters. Attributes params (tuple) Circle model parameters in the following order xc, yc

pyramid_expand skimage.transform.pyramid_expand(image, upscale=2, sigma=None, order=1, mode='reflect', cval=0) [source] Upsample and then smooth image. Parameters: image : array Input image. upscale : float, optional Upscale factor. sigma : float, optional Sigma for Gaussian filter. Default is 2 * upscale / 6.0 which corresponds to a filter mask twice the size of the scale factor that covers more than 99% of the Gaussian distribution. order : int, optional Order of splines used in in

octagon skimage.morphology.octagon(m, n, dtype=) [source] Generates an octagon shaped structuring element. For a given size of (m) horizontal and vertical sides and a given (n) height or width of slanted sides octagon is generated. The slanted sides are 45 or 135 degrees to the horizontal axis and hence the widths and heights are equal. Parameters: m : int The size of the horizontal and vertical sides. n : int The height or width of the slanted sides. Returns: selem : ndarray The str