inverse skimage.filters.inverse(data, impulse_response=None, filter_params={}, max_gain=2, predefined_filter=None) [source] Apply the filter in reverse to the given data. Parameters: data : (M,N) ndarray Input data. 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 function. max_gain : float Limit the filter gain. Often, the filter contains zeros, wh

gabor_kernel skimage.filters.gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None, n_stds=3, offset=0) [source] Return complex 2D Gabor filter kernel. Gabor kernel is a Gaussian kernel modulated by a complex harmonic function. Harmonic function consists of an imaginary sine function and a real cosine function. Spatial frequency is inversely proportional to the wavelength of the harmonic and to the standard deviation of a Gaussian kernel. The bandwidth is also inversely pr

hough_line skimage.transform.hough_line(img, theta=None) [source] Perform a straight line Hough transform. Parameters: img : (M, N) ndarray Input image with nonzero values representing edges. theta : 1D ndarray of double Angles at which to compute the transform, in radians. Defaults to -pi/2 .. pi/2 Returns: H : 2-D ndarray of uint64 Hough transform accumulator. theta : ndarray Angles at which the transform was computed, in radians. distances : ndarray Distance values. Notes Th

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.

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

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

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

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

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 :

corner_orientations skimage.feature.corner_orientations() Compute the orientation of corners. The orientation of corners is computed using the first order central moment i.e. the center of mass approach. The corner orientation is the angle of the vector from the corner coordinate to the intensity centroid in the local neighborhood around the corner calculated using first order central moment. Parameters: image : 2D array Input grayscale image. corners : (N, 2) array Corner coordinates as