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Module: restoration Image restoration module.
skimage.restoration.ball_kernel(radius, ndim) Create a ball kernel for restoration.rolling_ball.
skimage.restoration.calibrate_denoiser(…) Calibrate a denoising function and return optimal J-invariant version.
skimage.restoration.cycle_spin(x, func, …) Cycle spinning ... | skimage.api.skimage.restoration |
skimage.restoration.ball_kernel(radius, ndim) [source]
Create a ball kernel for restoration.rolling_ball. Parameters
radiusint
Radius of the ball.
ndimint
Number of dimensions of the ball. ndim should match the dimensionality of the image the kernel will be applied to. Returns
kernelndarray
The kern... | skimage.api.skimage.restoration#skimage.restoration.ball_kernel |
skimage.restoration.calibrate_denoiser(image, denoise_function, denoise_parameters, *, stride=4, approximate_loss=True, extra_output=False) [source]
Calibrate a denoising function and return optimal J-invariant version. The returned function is partially evaluated with optimal parameter values set for denoising the i... | skimage.api.skimage.restoration#skimage.restoration.calibrate_denoiser |
skimage.restoration.cycle_spin(x, func, max_shifts, shift_steps=1, num_workers=None, multichannel=False, func_kw={}) [source]
Cycle spinning (repeatedly apply func to shifted versions of x). Parameters
xarray-like
Data for input to func.
funcfunction
A function to apply to circularly shifted versions of x. ... | skimage.api.skimage.restoration#skimage.restoration.cycle_spin |
skimage.restoration.denoise_bilateral(image, win_size=None, sigma_color=None, sigma_spatial=1, bins=10000, mode='constant', cval=0, multichannel=False) [source]
Denoise image using bilateral filter. Parameters
imagendarray, shape (M, N[, 3])
Input image, 2D grayscale or RGB.
win_sizeint
Window size for filt... | skimage.api.skimage.restoration#skimage.restoration.denoise_bilateral |
skimage.restoration.denoise_nl_means(image, patch_size=7, patch_distance=11, h=0.1, multichannel=False, fast_mode=True, sigma=0.0, *, preserve_range=None) [source]
Perform non-local means denoising on 2-D or 3-D grayscale images, and 2-D RGB images. Parameters
image2D or 3D ndarray
Input image to be denoised, w... | skimage.api.skimage.restoration#skimage.restoration.denoise_nl_means |
skimage.restoration.denoise_tv_bregman(image, weight, max_iter=100, eps=0.001, isotropic=True, *, multichannel=False) [source]
Perform total-variation denoising using split-Bregman optimization. Total-variation denoising (also know as total-variation regularization) tries to find an image with less total-variation un... | skimage.api.skimage.restoration#skimage.restoration.denoise_tv_bregman |
skimage.restoration.denoise_tv_chambolle(image, weight=0.1, eps=0.0002, n_iter_max=200, multichannel=False) [source]
Perform total-variation denoising on n-dimensional images. Parameters
imagendarray of ints, uints or floats
Input data to be denoised. image can be of any numeric type, but it is cast into an nda... | skimage.api.skimage.restoration#skimage.restoration.denoise_tv_chambolle |
skimage.restoration.denoise_wavelet(image, sigma=None, wavelet='db1', mode='soft', wavelet_levels=None, multichannel=False, convert2ycbcr=False, method='BayesShrink', rescale_sigma=True) [source]
Perform wavelet denoising on an image. Parameters
imagendarray ([M[, N[, …P]][, C]) of ints, uints or floats
Input d... | skimage.api.skimage.restoration#skimage.restoration.denoise_wavelet |
skimage.restoration.ellipsoid_kernel(shape, intensity) [source]
Create an ellipoid kernel for restoration.rolling_ball. Parameters
shapearraylike
Length of the principal axis of the ellipsoid (excluding the intensity axis). The kernel needs to have the same dimensionality as the image it will be applied to.
i... | skimage.api.skimage.restoration#skimage.restoration.ellipsoid_kernel |
skimage.restoration.estimate_sigma(image, average_sigmas=False, multichannel=False) [source]
Robust wavelet-based estimator of the (Gaussian) noise standard deviation. Parameters
imagendarray
Image for which to estimate the noise standard deviation.
average_sigmasbool, optional
If true, average the channel ... | skimage.api.skimage.restoration#skimage.restoration.estimate_sigma |
skimage.restoration.inpaint_biharmonic(image, mask, multichannel=False) [source]
Inpaint masked points in image with biharmonic equations. Parameters
image(M[, N[, …, P]][, C]) ndarray
Input image.
mask(M[, N[, …, P]]) ndarray
Array of pixels to be inpainted. Have to be the same shape as one of the ‘image’ ... | skimage.api.skimage.restoration#skimage.restoration.inpaint_biharmonic |
skimage.restoration.richardson_lucy(image, psf, iterations=50, clip=True, filter_epsilon=None) [source]
Richardson-Lucy deconvolution. Parameters
imagendarray
Input degraded image (can be N dimensional).
psfndarray
The point spread function.
iterationsint, optional
Number of iterations. This parameter p... | skimage.api.skimage.restoration#skimage.restoration.richardson_lucy |
skimage.restoration.rolling_ball(image, *, radius=100, kernel=None, nansafe=False, num_threads=None) [source]
Estimate background intensity by rolling/translating a kernel. This rolling ball algorithm estimates background intensity for a ndimage in case of uneven exposure. It is a generalization of the frequently use... | skimage.api.skimage.restoration#skimage.restoration.rolling_ball |
skimage.restoration.unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True, clip=True) [source]
Unsupervised Wiener-Hunt deconvolution. Return the deconvolution with a Wiener-Hunt approach, where the hyperparameters are automatically estimated. The algorithm is a stochastic iterative process (Gibbs ... | skimage.api.skimage.restoration#skimage.restoration.unsupervised_wiener |
skimage.restoration.unwrap_phase(image, wrap_around=False, seed=None) [source]
Recover the original from a wrapped phase image. From an image wrapped to lie in the interval [-pi, pi), recover the original, unwrapped image. Parameters
image1D, 2D or 3D ndarray of floats, optionally a masked array
The values shou... | skimage.api.skimage.restoration#skimage.restoration.unwrap_phase |
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
psfndarray
Point Spread Function. This is assume... | skimage.api.skimage.restoration#skimage.restoration.wiener |
Module: segmentation
skimage.segmentation.active_contour(image, snake) Active contour model.
skimage.segmentation.chan_vese(image[, mu, …]) Chan-Vese segmentation algorithm.
skimage.segmentation.checkerboard_level_set(…) Create a checkerboard level set with binary values.
skimage.segmentation.circle_level_set(…... | skimage.api.skimage.segmentation |
skimage.segmentation.active_contour(image, snake, alpha=0.01, beta=0.1, w_line=0, w_edge=1, gamma=0.01, max_px_move=1.0, max_iterations=2500, convergence=0.1, *, boundary_condition='periodic', coordinates='rc') [source]
Active contour model. Active contours by fitting snakes to features of images. Supports single and... | skimage.api.skimage.segmentation#skimage.segmentation.active_contour |
skimage.segmentation.chan_vese(image, mu=0.25, lambda1=1.0, lambda2=1.0, tol=0.001, max_iter=500, dt=0.5, init_level_set='checkerboard', extended_output=False) [source]
Chan-Vese segmentation algorithm. Active contour model by evolving a level set. Can be used to segment objects without clearly defined boundaries. P... | skimage.api.skimage.segmentation#skimage.segmentation.chan_vese |
skimage.segmentation.checkerboard_level_set(image_shape, square_size=5) [source]
Create a checkerboard level set with binary values. Parameters
image_shapetuple of positive integers
Shape of the image.
square_sizeint, optional
Size of the squares of the checkerboard. It defaults to 5. Returns
outarray... | skimage.api.skimage.segmentation#skimage.segmentation.checkerboard_level_set |
skimage.segmentation.circle_level_set(image_shape, center=None, radius=None) [source]
Create a circle level set with binary values. Parameters
image_shapetuple of positive integers
Shape of the image
centertuple of positive integers, optional
Coordinates of the center of the circle given in (row, column). I... | skimage.api.skimage.segmentation#skimage.segmentation.circle_level_set |
skimage.segmentation.clear_border(labels, buffer_size=0, bgval=0, in_place=False, mask=None) [source]
Clear objects connected to the label image border. Parameters
labels(M[, N[, …, P]]) array of int or bool
Imaging data labels.
buffer_sizeint, optional
The width of the border examined. By default, only obj... | skimage.api.skimage.segmentation#skimage.segmentation.clear_border |
skimage.segmentation.disk_level_set(image_shape, *, center=None, radius=None) [source]
Create a disk level set with binary values. Parameters
image_shapetuple of positive integers
Shape of the image
centertuple of positive integers, optional
Coordinates of the center of the disk given in (row, column). If n... | skimage.api.skimage.segmentation#skimage.segmentation.disk_level_set |
skimage.segmentation.expand_labels(label_image, distance=1) [source]
Expand labels in label image by distance pixels without overlapping. Given a label image, expand_labels grows label regions (connected components) outwards by up to distance pixels without overflowing into neighboring regions. More specifically, eac... | skimage.api.skimage.segmentation#skimage.segmentation.expand_labels |
skimage.segmentation.felzenszwalb(image, scale=1, sigma=0.8, min_size=20, multichannel=True) [source]
Computes Felsenszwalb’s efficient graph based image segmentation. Produces an oversegmentation of a multichannel (i.e. RGB) image using a fast, minimum spanning tree based clustering on the image grid. The parameter ... | skimage.api.skimage.segmentation#skimage.segmentation.felzenszwalb |
skimage.segmentation.find_boundaries(label_img, connectivity=1, mode='thick', background=0) [source]
Return bool array where boundaries between labeled regions are True. Parameters
label_imgarray of int or bool
An array in which different regions are labeled with either different integers or boolean values.
c... | skimage.api.skimage.segmentation#skimage.segmentation.find_boundaries |
skimage.segmentation.flood(image, seed_point, *, selem=None, connectivity=None, tolerance=None) [source]
Mask corresponding to a flood fill. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found. Parameters
imagendarray
An n-dimensional array.
seed_pointtupl... | skimage.api.skimage.segmentation#skimage.segmentation.flood |
skimage.segmentation.flood_fill(image, seed_point, new_value, *, selem=None, connectivity=None, tolerance=None, in_place=False, inplace=None) [source]
Perform flood filling on an image. Starting at a specific seed_point, connected points equal or within tolerance of the seed value are found, then set to new_value. P... | skimage.api.skimage.segmentation#skimage.segmentation.flood_fill |
skimage.segmentation.inverse_gaussian_gradient(image, alpha=100.0, sigma=5.0) [source]
Inverse of gradient magnitude. Compute the magnitude of the gradients in the image and then inverts the result in the range [0, 1]. Flat areas are assigned values close to 1, while areas close to borders are assigned values close t... | skimage.api.skimage.segmentation#skimage.segmentation.inverse_gaussian_gradient |
skimage.segmentation.join_segmentations(s1, s2) [source]
Return the join of the two input segmentations. The join J of S1 and S2 is defined as the segmentation in which two voxels are in the same segment if and only if they are in the same segment in both S1 and S2. Parameters
s1, s2numpy arrays
s1 and s2 are l... | skimage.api.skimage.segmentation#skimage.segmentation.join_segmentations |
skimage.segmentation.mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0) [source]
Return image with boundaries between labeled regions highlighted. Parameters
image(M, N[, 3]) array
Grayscale or RGB image.
label_img(M, N) array of int
Label array where re... | skimage.api.skimage.segmentation#skimage.segmentation.mark_boundaries |
skimage.segmentation.morphological_chan_vese(image, iterations, init_level_set='checkerboard', smoothing=1, lambda1=1, lambda2=1, iter_callback=<function <lambda>>) [source]
Morphological Active Contours without Edges (MorphACWE) Active contours without edges implemented with morphological operators. It can be used t... | skimage.api.skimage.segmentation#skimage.segmentation.morphological_chan_vese |
skimage.segmentation.morphological_geodesic_active_contour(gimage, iterations, init_level_set='circle', smoothing=1, threshold='auto', balloon=0, iter_callback=<function <lambda>>) [source]
Morphological Geodesic Active Contours (MorphGAC). Geodesic active contours implemented with morphological operators. It can be ... | skimage.api.skimage.segmentation#skimage.segmentation.morphological_geodesic_active_contour |
skimage.segmentation.quickshift(image, ratio=1.0, kernel_size=5, max_dist=10, return_tree=False, sigma=0, convert2lab=True, random_seed=42) [source]
Segments image using quickshift clustering in Color-(x,y) space. Produces an oversegmentation of the image using the quickshift mode-seeking algorithm. Parameters
im... | skimage.api.skimage.segmentation#skimage.segmentation.quickshift |
skimage.segmentation.random_walker(data, labels, beta=130, mode='cg_j', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None, *, prob_tol=0.001) [source]
Random walker algorithm for segmentation from markers. Random walker algorithm is implemented for gray-level or multichannel images. Para... | skimage.api.skimage.segmentation#skimage.segmentation.random_walker |
skimage.segmentation.relabel_sequential(label_field, offset=1) [source]
Relabel arbitrary labels to {offset, … offset + number_of_labels}. This function also returns the forward map (mapping the original labels to the reduced labels) and the inverse map (mapping the reduced labels back to the original ones). Paramet... | skimage.api.skimage.segmentation#skimage.segmentation.relabel_sequential |
skimage.segmentation.slic(image, n_segments=100, compactness=10.0, max_iter=10, sigma=0, spacing=None, multichannel=True, convert2lab=None, enforce_connectivity=True, min_size_factor=0.5, max_size_factor=3, slic_zero=False, start_label=None, mask=None) [source]
Segments image using k-means clustering in Color-(x,y,z)... | skimage.api.skimage.segmentation#skimage.segmentation.slic |
skimage.segmentation.watershed(image, markers=None, connectivity=1, offset=None, mask=None, compactness=0, watershed_line=False) [source]
Find watershed basins in image flooded from given markers. Parameters
imagendarray (2-D, 3-D, …) of integers
Data array where the lowest value points are labeled first.
mar... | skimage.api.skimage.segmentation#skimage.segmentation.watershed |
skimage Image Processing for Python scikit-image (a.k.a. skimage) is a collection of algorithms for image processing and computer vision. The main package of skimage only provides a few utilities for converting between image data types; for most features, you need to import one of the following subpackages: Subpackages... | skimage.api.skimage |
Module: transform
skimage.transform.downscale_local_mean(…) Down-sample N-dimensional image by local averaging.
skimage.transform.estimate_transform(ttype, …) Estimate 2D geometric transformation parameters.
skimage.transform.frt2(a) Compute the 2-dimensional finite radon transform (FRT) for an n x n integer arra... | skimage.api.skimage.transform |
class skimage.transform.AffineTransform(matrix=None, scale=None, rotation=None, shear=None, translation=None) [source]
Bases: skimage.transform._geometric.ProjectiveTransform 2D affine transformation. Has the following form: X = a0*x + a1*y + a2 =
= sx*x*cos(rotation) - sy*y*sin(rotation + shear) + a2
Y = b0*x + b... | skimage.api.skimage.transform#skimage.transform.AffineTransform |
property rotation | skimage.api.skimage.transform#skimage.transform.AffineTransform.rotation |
property scale | skimage.api.skimage.transform#skimage.transform.AffineTransform.scale |
property shear | skimage.api.skimage.transform#skimage.transform.AffineTransform.shear |
property translation | skimage.api.skimage.transform#skimage.transform.AffineTransform.translation |
__init__(matrix=None, scale=None, rotation=None, shear=None, translation=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.AffineTransform.__init__ |
skimage.transform.downscale_local_mean(image, factors, cval=0, clip=True) [source]
Down-sample N-dimensional image by local averaging. The image is padded with cval if it is not perfectly divisible by the integer factors. In contrast to interpolation in skimage.transform.resize and skimage.transform.rescale this func... | skimage.api.skimage.transform#skimage.transform.downscale_local_mean |
class skimage.transform.EssentialMatrixTransform(rotation=None, translation=None, matrix=None) [source]
Bases: skimage.transform._geometric.FundamentalMatrixTransform Essential matrix transformation. The essential matrix relates corresponding points between a pair of calibrated images. The matrix transforms normalize... | skimage.api.skimage.transform#skimage.transform.EssentialMatrixTransform |
estimate(src, dst) [source]
Estimate essential matrix using 8-point algorithm. The 8-point algorithm requires at least 8 corresponding point pairs for a well-conditioned solution, otherwise the over-determined solution is estimated. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) array
Destination ... | skimage.api.skimage.transform#skimage.transform.EssentialMatrixTransform.estimate |
__init__(rotation=None, translation=None, matrix=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.EssentialMatrixTransform.__init__ |
skimage.transform.estimate_transform(ttype, src, dst, **kwargs) [source]
Estimate 2D geometric transformation parameters. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters
ttype{‘euclidean’, s... | skimage.api.skimage.transform#skimage.transform.estimate_transform |
class skimage.transform.EuclideanTransform(matrix=None, rotation=None, translation=None) [source]
Bases: skimage.transform._geometric.ProjectiveTransform 2D Euclidean transformation. Has the following form: X = a0 * x - b0 * y + a1 =
= x * cos(rotation) - y * sin(rotation) + a1
Y = b0 * x + a0 * y + b1 =
= x * s... | skimage.api.skimage.transform#skimage.transform.EuclideanTransform |
estimate(src, dst) [source]
Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) ... | skimage.api.skimage.transform#skimage.transform.EuclideanTransform.estimate |
property rotation | skimage.api.skimage.transform#skimage.transform.EuclideanTransform.rotation |
property translation | skimage.api.skimage.transform#skimage.transform.EuclideanTransform.translation |
__init__(matrix=None, rotation=None, translation=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.EuclideanTransform.__init__ |
skimage.transform.frt2(a) [source]
Compute the 2-dimensional finite radon transform (FRT) for an n x n integer array. Parameters
aarray_like
A 2-D square n x n integer array. Returns
FRT2-D ndarray
Finite Radon Transform array of (n+1) x n integer coefficients. See also
ifrt2
The two-dimensiona... | skimage.api.skimage.transform#skimage.transform.frt2 |
class skimage.transform.FundamentalMatrixTransform(matrix=None) [source]
Bases: skimage.transform._geometric.GeometricTransform Fundamental matrix transformation. The fundamental matrix relates corresponding points between a pair of uncalibrated images. The matrix transforms homogeneous image points in one image to e... | skimage.api.skimage.transform#skimage.transform.FundamentalMatrixTransform |
estimate(src, dst) [source]
Estimate fundamental matrix using 8-point algorithm. The 8-point algorithm requires at least 8 corresponding point pairs for a well-conditioned solution, otherwise the over-determined solution is estimated. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) array
Destinatio... | skimage.api.skimage.transform#skimage.transform.FundamentalMatrixTransform.estimate |
inverse(coords) [source]
Apply inverse transformation. Parameters
coords(N, 2) array
Destination coordinates. Returns
coords(N, 3) array
Epipolar lines in the source image. | skimage.api.skimage.transform#skimage.transform.FundamentalMatrixTransform.inverse |
residuals(src, dst) [source]
Compute the Sampson distance. The Sampson distance is the first approximation to the geometric error. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) array
Destination coordinates. Returns
residuals(N, ) array
Sampson distance. | skimage.api.skimage.transform#skimage.transform.FundamentalMatrixTransform.residuals |
__init__(matrix=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.FundamentalMatrixTransform.__init__ |
skimage.transform.hough_circle(image, radius, normalize=True, full_output=False) [source]
Perform a circular Hough transform. Parameters
image(M, N) ndarray
Input image with nonzero values representing edges.
radiusscalar or sequence of scalars
Radii at which to compute the Hough transform. Floats are conve... | skimage.api.skimage.transform#skimage.transform.hough_circle |
skimage.transform.hough_circle_peaks(hspaces, radii, min_xdistance=1, min_ydistance=1, threshold=None, num_peaks=inf, total_num_peaks=inf, normalize=False) [source]
Return peaks in a circle Hough transform. Identifies most prominent circles separated by certain distances in given Hough spaces. Non-maximum suppression... | skimage.api.skimage.transform#skimage.transform.hough_circle_peaks |
skimage.transform.hough_ellipse(image, threshold=4, accuracy=1, min_size=4, max_size=None) [source]
Perform an elliptical Hough transform. Parameters
image(M, N) ndarray
Input image with nonzero values representing edges.
thresholdint, optional
Accumulator threshold value.
accuracydouble, optional
Bin s... | skimage.api.skimage.transform#skimage.transform.hough_ellipse |
skimage.transform.hough_line(image, theta=None) [source]
Perform a straight line Hough transform. Parameters
image(M, N) ndarray
Input image with nonzero values representing edges.
theta1D ndarray of double, optional
Angles at which to compute the transform, in radians. Defaults to a vector of 180 angles ev... | skimage.api.skimage.transform#skimage.transform.hough_line |
skimage.transform.hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10, threshold=None, num_peaks=inf) [source]
Return peaks in a straight line Hough transform. Identifies most prominent lines separated by a certain angle and distance in a Hough transform. Non-maximum suppression with different sizes ... | skimage.api.skimage.transform#skimage.transform.hough_line_peaks |
skimage.transform.ifrt2(a) [source]
Compute the 2-dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array. Parameters
aarray_like
A 2-D (n+1) row x n column integer array. Returns
iFRT2-D n x n ndarray
Inverse Finite Radon Transform array of n x n integer coefficients. See ... | skimage.api.skimage.transform#skimage.transform.ifrt2 |
skimage.transform.integral_image(image) [source]
Integral image / summed area table. The integral image contains the sum of all elements above and to the left of it, i.e.: \[S[m, n] = \sum_{i \leq m} \sum_{j \leq n} X[i, j]\] Parameters
imagendarray
Input image. Returns
Sndarray
Integral image/summed a... | skimage.api.skimage.transform#skimage.transform.integral_image |
skimage.transform.integrate(ii, start, end) [source]
Use an integral image to integrate over a given window. Parameters
iindarray
Integral image.
startList of tuples, each tuple of length equal to dimension of ii
Coordinates of top left corner of window(s). Each tuple in the list contains the starting row, ... | skimage.api.skimage.transform#skimage.transform.integrate |
skimage.transform.iradon(radon_image, theta=None, output_size=None, filter_name='ramp', interpolation='linear', circle=True, preserve_range=True) [source]
Inverse radon transform. Reconstruct an image from the radon transform, using the filtered back projection algorithm. Parameters
radon_imagearray
Image conta... | skimage.api.skimage.transform#skimage.transform.iradon |
skimage.transform.iradon_sart(radon_image, theta=None, image=None, projection_shifts=None, clip=None, relaxation=0.15, dtype=None) [source]
Inverse radon transform. Reconstruct an image from the radon transform, using a single iteration of the Simultaneous Algebraic Reconstruction Technique (SART) algorithm. Paramet... | skimage.api.skimage.transform#skimage.transform.iradon_sart |
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. | skimage.api.skimage.transform#skimage.transform.matrix_transform |
skimage.transform.order_angles_golden_ratio(theta) [source]
Order angles to reduce the amount of correlated information in subsequent projections. Parameters
theta1D array of floats
Projection angles in degrees. Duplicate angles are not allowed. Returns
indices_generatorgenerator yielding unsigned integer... | skimage.api.skimage.transform#skimage.transform.order_angles_golden_ratio |
class skimage.transform.PiecewiseAffineTransform [source]
Bases: skimage.transform._geometric.GeometricTransform 2D piecewise affine transformation. Control points are used to define the mapping. The transform is based on a Delaunay triangulation of the points to form a mesh. Each triangle is used to find a local aff... | skimage.api.skimage.transform#skimage.transform.PiecewiseAffineTransform |
estimate(src, dst) [source]
Estimate the transformation from a set of corresponding points. Number of source and destination coordinates must match. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) array
Destination coordinates. Returns
successbool
True, if model estimation succeeds. | skimage.api.skimage.transform#skimage.transform.PiecewiseAffineTransform.estimate |
inverse(coords) [source]
Apply inverse transformation. Coordinates outside of the mesh will be set to - 1. Parameters
coords(N, 2) array
Source coordinates. Returns
coords(N, 2) array
Transformed coordinates. | skimage.api.skimage.transform#skimage.transform.PiecewiseAffineTransform.inverse |
__init__() [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.PiecewiseAffineTransform.__init__ |
class skimage.transform.PolynomialTransform(params=None) [source]
Bases: skimage.transform._geometric.GeometricTransform 2D polynomial transformation. Has the following form: 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(... | skimage.api.skimage.transform#skimage.transform.PolynomialTransform |
estimate(src, dst, order=2) [source]
Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. The transformation is defined as: X = sum[j=0:order]( su... | skimage.api.skimage.transform#skimage.transform.PolynomialTransform.estimate |
inverse(coords) [source]
Apply inverse transformation. Parameters
coords(N, 2) array
Destination coordinates. Returns
coords(N, 2) array
Source coordinates. | skimage.api.skimage.transform#skimage.transform.PolynomialTransform.inverse |
__init__(params=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.PolynomialTransform.__init__ |
skimage.transform.probabilistic_hough_line(image, threshold=10, line_length=50, line_gap=10, theta=None, seed=None) [source]
Return lines from a progressive probabilistic line Hough transform. Parameters
image(M, N) ndarray
Input image with nonzero values representing edges.
thresholdint, optional
Threshold... | skimage.api.skimage.transform#skimage.transform.probabilistic_hough_line |
class skimage.transform.ProjectiveTransform(matrix=None) [source]
Bases: skimage.transform._geometric.GeometricTransform Projective transformation. Apply a projective transformation (homography) on coordinates. For each homogeneous coordinate \(\mathbf{x} = [x, y, 1]^T\), its target position is calculated by multiply... | skimage.api.skimage.transform#skimage.transform.ProjectiveTransform |
estimate(src, dst) [source]
Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. The transformation is defined as: X = (a0*x + a1*y + a2) / (c0*x ... | skimage.api.skimage.transform#skimage.transform.ProjectiveTransform.estimate |
inverse(coords) [source]
Apply inverse transformation. Parameters
coords(N, 2) array
Destination coordinates. Returns
coords(N, 2) array
Source coordinates. | skimage.api.skimage.transform#skimage.transform.ProjectiveTransform.inverse |
__init__(matrix=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.ProjectiveTransform.__init__ |
skimage.transform.pyramid_expand(image, upscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=False, preserve_range=False) [source]
Upsample and then smooth image. Parameters
imagendarray
Input image.
upscalefloat, optional
Upscale factor.
sigmafloat, optional
Sigma for Gaussian filter. D... | skimage.api.skimage.transform#skimage.transform.pyramid_expand |
skimage.transform.pyramid_gaussian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=False, preserve_range=False) [source]
Yield images of the Gaussian pyramid formed by the input image. Recursively applies the pyramid_reduce function to the image, and yields the downscaled i... | skimage.api.skimage.transform#skimage.transform.pyramid_gaussian |
skimage.transform.pyramid_laplacian(image, max_layer=-1, downscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=False, preserve_range=False) [source]
Yield images of the laplacian pyramid formed by the input image. Each layer contains the difference between the downsampled and the downsampled, smoothe... | skimage.api.skimage.transform#skimage.transform.pyramid_laplacian |
skimage.transform.pyramid_reduce(image, downscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=False, preserve_range=False) [source]
Smooth and then downsample image. Parameters
imagendarray
Input image.
downscalefloat, optional
Downscale factor.
sigmafloat, optional
Sigma for Gaussian f... | skimage.api.skimage.transform#skimage.transform.pyramid_reduce |
skimage.transform.radon(image, theta=None, circle=True, *, preserve_range=False) [source]
Calculates the radon transform of an image given specified projection angles. Parameters
imagearray_like
Input image. The rotation axis will be located in the pixel with indices (image.shape[0] // 2, image.shape[1] // 2). ... | skimage.api.skimage.transform#skimage.transform.radon |
skimage.transform.rescale(image, scale, order=None, mode='reflect', cval=0, clip=True, preserve_range=False, multichannel=False, anti_aliasing=None, anti_aliasing_sigma=None) [source]
Scale image by a certain factor. Performs interpolation to up-scale or down-scale N-dimensional images. Note that anti-aliasing should... | skimage.api.skimage.transform#skimage.transform.rescale |
skimage.transform.resize(image, output_shape, order=None, mode='reflect', cval=0, clip=True, preserve_range=False, anti_aliasing=None, anti_aliasing_sigma=None) [source]
Resize image to match a certain size. Performs interpolation to up-size or down-size N-dimensional images. Note that anti-aliasing should be enabled... | skimage.api.skimage.transform#skimage.transform.resize |
skimage.transform.rotate(image, angle, resize=False, center=None, order=None, mode='constant', cval=0, clip=True, preserve_range=False) [source]
Rotate image by a certain angle around its center. Parameters
imagendarray
Input image.
anglefloat
Rotation angle in degrees in counter-clockwise direction.
resi... | skimage.api.skimage.transform#skimage.transform.rotate |
class skimage.transform.SimilarityTransform(matrix=None, scale=None, rotation=None, translation=None) [source]
Bases: skimage.transform._geometric.EuclideanTransform 2D similarity transformation. Has the following form: X = a0 * x - b0 * y + a1 =
= s * x * cos(rotation) - s * y * sin(rotation) + a1
Y = b0 * x + a0... | skimage.api.skimage.transform#skimage.transform.SimilarityTransform |
estimate(src, dst) [source]
Estimate the transformation from a set of corresponding points. You can determine the over-, well- and under-determined parameters with the total least-squares method. Number of source and destination coordinates must match. Parameters
src(N, 2) array
Source coordinates.
dst(N, 2) ... | skimage.api.skimage.transform#skimage.transform.SimilarityTransform.estimate |
property scale | skimage.api.skimage.transform#skimage.transform.SimilarityTransform.scale |
__init__(matrix=None, scale=None, rotation=None, translation=None) [source]
Initialize self. See help(type(self)) for accurate signature. | skimage.api.skimage.transform#skimage.transform.SimilarityTransform.__init__ |
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