transform
¶skimage.transform.match_histograms (image, …) 
Adjust an image so that its cumulative histogram matches that of another. 
skimage.transform.hough_circle (image, radius) 
Perform a circular Hough transform. 
skimage.transform.hough_ellipse (image[, …]) 
Perform an elliptical Hough transform. 
skimage.transform.hough_line (image[, theta]) 
Perform a straight line Hough transform. 
skimage.transform.probabilistic_hough_line (image) 
Return lines from a progressive probabilistic line Hough transform. 
skimage.transform.hough_circle_peaks (…[, …]) 
Return peaks in a circle Hough transform. 
skimage.transform.hough_line_peaks (hspace, …) 
Return peaks in a straight line Hough transform. 
skimage.transform.radon (image[, theta, circle]) 
Calculates the radon transform of an image given specified projection angles. 
skimage.transform.iradon (radon_image[, …]) 
Inverse radon transform. 
skimage.transform.iradon_sart (radon_image[, …]) 
Inverse radon transform 
skimage.transform.order_angles_golden_ratio (theta) 
Order angles to reduce the amount of correlated information in subsequent projections. 
skimage.transform.frt2 (a) 
Compute the 2dimensional finite radon transform (FRT) for an n x n integer array. 
skimage.transform.ifrt2 (a) 
Compute the 2dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array. 
skimage.transform.integral_image (image) 
Integral image / summed area table. 
skimage.transform.integrate (ii, start, end) 
Use an integral image to integrate over a given window. 
skimage.transform.warp (image, inverse_map[, …]) 
Warp an image according to a given coordinate transformation. 
skimage.transform.warp_coords (coord_map, shape) 
Build the source coordinates for the output of a 2D image warp. 
skimage.transform.estimate_transform (ttype, …) 
Estimate 2D geometric transformation parameters. 
skimage.transform.matrix_transform (coords, …) 
Apply 2D matrix transform. 
skimage.transform.swirl (image[, center, …]) 
Perform a swirl transformation. 
skimage.transform.resize (image, output_shape) 
Resize image to match a certain size. 
skimage.transform.rotate (image, angle[, …]) 
Rotate image by a certain angle around its center. 
skimage.transform.rescale (image, scale[, …]) 
Scale image by a certain factor. 
skimage.transform.downscale_local_mean (…) 
Downsample Ndimensional image by local averaging. 
skimage.transform.pyramid_reduce (image[, …]) 
Smooth and then downsample image. 
skimage.transform.pyramid_expand (image[, …]) 
Upsample and then smooth image. 
skimage.transform.pyramid_gaussian (image[, …]) 
Yield images of the Gaussian pyramid formed by the input image. 
skimage.transform.pyramid_laplacian (image[, …]) 
Yield images of the laplacian pyramid formed by the input image. 
skimage.transform.seam_carve (*args, **kwargs) 
Seam carving has been removed because it is a patented algorithm. 
skimage.transform.EuclideanTransform ([…]) 
2D Euclidean transformation. 
skimage.transform.SimilarityTransform ([…]) 
2D similarity transformation. 
skimage.transform.AffineTransform ([matrix, …]) 
2D affine transformation. 
skimage.transform.ProjectiveTransform ([matrix]) 
Projective transformation. 
skimage.transform.EssentialMatrixTransform ([…]) 
Essential matrix transformation. 
skimage.transform.FundamentalMatrixTransform ([…]) 
Fundamental matrix transformation. 
skimage.transform.PolynomialTransform ([params]) 
2D polynomial transformation. 
skimage.transform.PiecewiseAffineTransform () 
2D piecewise affine transformation. 
skimage.transform.
match_histograms
(image, reference, multichannel=False)[source]¶Adjust an image so that its cumulative histogram matches that of another.
The adjustment is applied separately for each channel.
Parameters: 


Returns: 

Raises: 

References
[1]  http://paulbourke.net/miscellaneous/equalisation/ 
skimage.transform.match_histograms
¶skimage.transform.
hough_circle
(image, radius, normalize=True, full_output=False)[source]¶Perform a circular Hough transform.
Parameters: 


Returns: 

Examples
>>> from skimage.transform import hough_circle
>>> from skimage.draw import circle_perimeter
>>> img = np.zeros((100, 100), dtype=np.bool_)
>>> rr, cc = circle_perimeter(25, 35, 23)
>>> img[rr, cc] = 1
>>> try_radii = np.arange(5, 50)
>>> res = hough_circle(img, try_radii)
>>> ridx, r, c = np.unravel_index(np.argmax(res), res.shape)
>>> r, c, try_radii[ridx]
(25, 35, 23)
skimage.transform.hough_circle
¶skimage.transform.
hough_ellipse
(image, threshold=4, accuracy=1, min_size=4, max_size=None)[source]¶Perform an elliptical Hough transform.
Parameters: 


Returns: 

Notes
The accuracy must be chosen to produce a peak in the accumulator distribution. In other words, a flat accumulator distribution with low values may be caused by a too low bin size.
References
[1]  Xie, Yonghong, and Qiang Ji. “A new efficient ellipse detection method.” Pattern Recognition, 2002. Proceedings. 16th International Conference on. Vol. 2. IEEE, 2002 
Examples
>>> from skimage.transform import hough_ellipse
>>> from skimage.draw import ellipse_perimeter
>>> img = np.zeros((25, 25), dtype=np.uint8)
>>> rr, cc = ellipse_perimeter(10, 10, 6, 8)
>>> img[cc, rr] = 1
>>> result = hough_ellipse(img, threshold=8)
>>> result.tolist()
[(10, 10.0, 10.0, 8.0, 6.0, 0.0)]
skimage.transform.hough_ellipse
¶skimage.transform.
hough_line
(image, theta=None)[source]¶Perform a straight line Hough transform.
Parameters: 


Returns: 

Notes
The origin is the top left corner of the original image. X and Y axis are horizontal and vertical edges respectively. The distance is the minimal algebraic distance from the origin to the detected line. The angle accuracy can be improved by decreasing the step size in the theta array.
Examples
Generate a test image:
>>> img = np.zeros((100, 150), dtype=bool)
>>> img[30, :] = 1
>>> img[:, 65] = 1
>>> img[35:45, 35:50] = 1
>>> for i in range(90):
... img[i, i] = 1
>>> img += np.random.random(img.shape) > 0.95
Apply the Hough transform:
>>> out, angles, d = hough_line(img)
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import hough_line
from skimage.draw import line
img = np.zeros((100, 150), dtype=bool)
img[30, :] = 1
img[:, 65] = 1
img[35:45, 35:50] = 1
rr, cc = line(60, 130, 80, 10)
img[rr, cc] = 1
img += np.random.random(img.shape) > 0.95
out, angles, d = hough_line(img)
fix, axes = plt.subplots(1, 2, figsize=(7, 4))
axes[0].imshow(img, cmap=plt.cm.gray)
axes[0].set_title('Input image')
axes[1].imshow(
out, cmap=plt.cm.bone,
extent=(np.rad2deg(angles[1]), np.rad2deg(angles[0]), d[1], d[0]))
axes[1].set_title('Hough transform')
axes[1].set_xlabel('Angle (degree)')
axes[1].set_ylabel('Distance (pixel)')
plt.tight_layout()
plt.show()
skimage.transform.hough_line
¶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: 


Returns: 

References
[1]  C. Galamhos, J. Matas and J. Kittler, “Progressive probabilistic Hough transform for line detection”, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999. 
skimage.transform.probabilistic_hough_line
¶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 a Hough space. Nonmaximum suppression with different sizes is applied separately in the first and second dimension of the Hough space to identify peaks.
Parameters: 


Returns: 

Examples
>>> from skimage import transform, draw
>>> img = np.zeros((120, 100), dtype=int)
>>> radius, x_0, y_0 = (20, 99, 50)
>>> y, x = draw.circle_perimeter(y_0, x_0, radius)
>>> img[x, y] = 1
>>> hspaces = transform.hough_circle(img, radius)
>>> accum, cx, cy, rad = hough_circle_peaks(hspaces, [radius,])
skimage.transform.hough_circle_peaks
¶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. Nonmaximum suppression with different sizes is applied separately in the first (distances) and second (angles) dimension of the Hough space to identify peaks.
Parameters: 


Returns: 

Examples
>>> from skimage.transform import hough_line, hough_line_peaks
>>> from skimage.draw import line
>>> img = np.zeros((15, 15), dtype=np.bool_)
>>> rr, cc = line(0, 0, 14, 14)
>>> img[rr, cc] = 1
>>> rr, cc = line(0, 14, 14, 0)
>>> img[cc, rr] = 1
>>> hspace, angles, dists = hough_line(img)
>>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists)
>>> len(angles)
2
skimage.transform.hough_line_peaks
¶skimage.transform.
radon
(image, theta=None, circle=True)[source]¶Calculates the radon transform of an image given specified projection angles.
Parameters: 


Returns: 

Notes
Based on code of Justin K. Romberg (https://www.clear.rice.edu/elec431/projects96/DSP/bpanalysis.html)
References
[1]  AC Kak, M Slaney, “Principles of Computerized Tomographic Imaging”, IEEE Press 1988. 
[2]  B.R. Ramesh, N. Srinivasa, K. Rajgopal, “An Algorithm for Computing the Discrete Radon Transform With Some Applications”, Proceedings of the Fourth IEEE Region 10 International Conference, TENCON ‘89, 1989 
skimage.transform.radon
¶skimage.transform.
iradon
(radon_image, theta=None, output_size=None, filter='ramp', interpolation='linear', circle=True)[source]¶Inverse radon transform.
Reconstruct an image from the radon transform, using the filtered back projection algorithm.
Parameters: 


Returns: 

Notes
It applies the Fourier slice theorem to reconstruct an image by multiplying the frequency domain of the filter with the FFT of the projection data. This algorithm is called filtered back projection.
References
[1]  AC Kak, M Slaney, “Principles of Computerized Tomographic Imaging”, IEEE Press 1988. 
[2]  B.R. Ramesh, N. Srinivasa, K. Rajgopal, “An Algorithm for Computing the Discrete Radon Transform With Some Applications”, Proceedings of the Fourth IEEE Region 10 International Conference, TENCON ‘89, 1989 
skimage.transform.iradon
¶skimage.transform.
iradon_sart
(radon_image, theta=None, image=None, projection_shifts=None, clip=None, relaxation=0.15)[source]¶Inverse radon transform
Reconstruct an image from the radon transform, using a single iteration of the Simultaneous Algebraic Reconstruction Technique (SART) algorithm.
Parameters: 


Returns: 

Notes
Algebraic Reconstruction Techniques are based on formulating the tomography reconstruction problem as a set of linear equations. Along each ray, the projected value is the sum of all the values of the cross section along the ray. A typical feature of SART (and a few other variants of algebraic techniques) is that it samples the cross section at equidistant points along the ray, using linear interpolation between the pixel values of the cross section. The resulting set of linear equations are then solved using a slightly modified Kaczmarz method.
When using SART, a single iteration is usually sufficient to obtain a good reconstruction. Further iterations will tend to enhance highfrequency information, but will also often increase the noise.
References
[1]  AC Kak, M Slaney, “Principles of Computerized Tomographic Imaging”, IEEE Press 1988. 
[2]  AH Andersen, AC Kak, “Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm”, Ultrasonic Imaging 6 pp 81–94 (1984) 
[3]  S Kaczmarz, “Angenäherte auflösung von systemen linearer gleichungen”, Bulletin International de l’Academie Polonaise des Sciences et des Lettres 35 pp 355–357 (1937) 
[4]  Kohler, T. “A projection access scheme for iterative reconstruction based on the golden section.” Nuclear Science Symposium Conference Record, 2004 IEEE. Vol. 6. IEEE, 2004. 
[5]  Kaczmarz’ method, Wikipedia, https://en.wikipedia.org/wiki/Kaczmarz_method 
skimage.transform.iradon_sart
¶skimage.transform.
order_angles_golden_ratio
(theta)[source]¶Order angles to reduce the amount of correlated information in subsequent projections.
Parameters: 


Returns: 

Notes
The method used here is that of the golden ratio introduced by T. Kohler.
References
[1]  Kohler, T. “A projection access scheme for iterative reconstruction based on the golden section.” Nuclear Science Symposium Conference Record, 2004 IEEE. Vol. 6. IEEE, 2004. 
[2]  Winkelmann, Stefanie, et al. “An optimal radial profile order based on the Golden Ratio for timeresolved MRI.” Medical Imaging, IEEE Transactions on 26.1 (2007): 6876. 
skimage.transform.
frt2
(a)[source]¶Compute the 2dimensional finite radon transform (FRT) for an n x n integer array.
Parameters: 


Returns: 

See also
ifrt2
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. Svalbe, “Projective transforms on periodic discrete image arrays,” in P. Hawkes (Ed), Advances in Imaging and Electron Physics, 139 (2006) 
Examples
Generate a test image: Use a prime number for the array dimensions
>>> SIZE = 59
>>> img = np.tri(SIZE, dtype=np.int32)
Apply the Finite Radon Transform:
>>> f = frt2(img)
skimage.transform.
ifrt2
(a)[source]¶Compute the 2dimensional inverse finite radon transform (iFRT) for an (n+1) x n integer array.
Parameters: 


Returns: 

See also
frt2
Notes
The FRT has a unique inverse if and only if n is prime. See [1] for an overview. The idea for this algorithm is due to Vlad Negnevitski.
References
[1]  (1, 2) A. Kingston and I. Svalbe, “Projective transforms on periodic discrete image arrays,” in P. Hawkes (Ed), Advances in Imaging and Electron Physics, 139 (2006) 
Examples
>>> SIZE = 59
>>> img = np.tri(SIZE, dtype=np.int32)
Apply the Finite Radon Transform:
>>> f = frt2(img)
Apply the Inverse Finite Radon Transform to recover the input
>>> fi = ifrt2(f)
Check that it’s identical to the original
>>> assert len(np.nonzero(imgfi)[0]) == 0
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.:
Parameters: 


Returns: 

References
[1]  F.C. Crow, “Summedarea tables for texture mapping,” ACM SIGGRAPH Computer Graphics, vol. 18, 1984, pp. 207212. 
skimage.transform.
integrate
(ii, start, end)[source]¶Use an integral image to integrate over a given window.
Parameters: 


Returns: 

Examples
>>> arr = np.ones((5, 6), dtype=np.float)
>>> ii = integral_image(arr)
>>> integrate(ii, (1, 0), (1, 2)) # sum from (1, 0) to (1, 2)
array([ 3.])
>>> integrate(ii, [(3, 3)], [(4, 5)]) # sum from (3, 3) to (4, 5)
array([ 6.])
>>> # sum from (1, 0) to (1, 2) and from (3, 3) to (4, 5)
>>> integrate(ii, [(1, 0), (3, 3)], [(1, 2), (4, 5)])
array([ 3., 6.])
skimage.transform.
warp
(image, inverse_map, 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: 


Returns: 

Notes
Examples
>>> from skimage.transform import warp
>>> from skimage import data
>>> image = data.camera()
The following image warps are all equal but differ substantially in execution time. The image is shifted to the bottom.
Use a geometric transform to warp an image (fast):
>>> from skimage.transform import SimilarityTransform
>>> tform = SimilarityTransform(translation=(0, 10))
>>> warped = warp(image, tform)
Use a callable (slow):
>>> def shift_down(xy):
... xy[:, 1] = 10
... return xy
>>> warped = warp(image, shift_down)
Use a transformation matrix to warp an image (fast):
>>> matrix = np.array([[1, 0, 0], [0, 1, 10], [0, 0, 1]])
>>> warped = warp(image, matrix)
>>> from skimage.transform import ProjectiveTransform
>>> warped = warp(image, ProjectiveTransform(matrix=matrix))
You can also use the inverse of a geometric transformation (fast):
>>> warped = warp(image, tform.inverse)
For ND images you can pass a coordinate array, that specifies the coordinates in the input image for every element in the output image. E.g. if you want to rescale a 3D cube, you can do:
>>> cube_shape = np.array([30, 30, 30])
>>> cube = np.random.rand(*cube_shape)
Setup the coordinate array, that defines the scaling:
>>> scale = 0.1
>>> output_shape = (scale * cube_shape).astype(int)
>>> coords0, coords1, coords2 = np.mgrid[:output_shape[0],
... :output_shape[1], :output_shape[2]]
>>> coords = np.array([coords0, coords1, coords2])
Assume that the cube contains spatial data, where the first array element center is at coordinate (0.5, 0.5, 0.5) in real space, i.e. we have to account for this extra offset when scaling the image:
>>> coords = (coords + 0.5) / scale  0.5
>>> warped = warp(cube, coords)
skimage.transform.
warp_coords
(coord_map, shape, dtype=<class 'numpy.float64'>)[source]¶Build the source coordinates for the output of a 2D image warp.
Parameters: 


Returns: 

Notes
This is a lowerlevel routine that produces the source coordinates for 2D images used by warp().
It is provided separately from warp to give additional flexibility to users who would like, for example, to reuse a particular coordinate mapping, to use specific dtypes at various points along the the imagewarping process, or to implement different postprocessing logic than warp performs after the call to ndi.map_coordinates.
Examples
Produce a coordinate map that shifts an image up and to the right:
>>> from skimage import data
>>> from scipy.ndimage import map_coordinates
>>>
>>> def shift_up10_left20(xy):
... return xy  np.array([20, 10])[None, :]
>>>
>>> image = data.astronaut().astype(np.float32)
>>> coords = warp_coords(shift_up10_left20, image.shape)
>>> warped_image = map_coordinates(image, coords)
skimage.transform.
estimate_transform
(ttype, src, dst, **kwargs)[source]¶Estimate 2D geometric transformation parameters.
You can determine the over, well and underdetermined parameters with the total leastsquares method.
Number of source and destination coordinates must match.
Parameters: 


Returns: 

Examples
>>> import numpy as np
>>> from skimage import transform as tf
>>> # estimate transformation parameters
>>> src = np.array([0, 0, 10, 10]).reshape((2, 2))
>>> dst = np.array([12, 14, 1, 20]).reshape((2, 2))
>>> tform = tf.estimate_transform('similarity', src, dst)
>>> np.allclose(tform.inverse(tform(src)), src)
True
>>> # warp image using the estimated transformation
>>> from skimage import data
>>> image = data.camera()
>>> warp(image, inverse_map=tform.inverse) # doctest: +SKIP
>>> # create transformation with explicit parameters
>>> tform2 = tf.SimilarityTransform(scale=1.1, rotation=1,
... translation=(10, 20))
>>> # unite transformations, applied in order from left to right
>>> tform3 = tform + tform2
>>> np.allclose(tform3(src), tform2(tform(src)))
True
skimage.transform.
swirl
(image, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='reflect', cval=0, clip=True, preserve_range=False)[source]¶Perform a swirl transformation.
Parameters: 


Returns: 

Other Parameters:  

skimage.transform.swirl
¶skimage.transform.
resize
(image, output_shape, order=1, mode='reflect', cval=0, clip=True, preserve_range=False, anti_aliasing=True, anti_aliasing_sigma=None)[source]¶Resize image to match a certain size.
Performs interpolation to upsize or downsize images. Note that anti aliasing should be enabled when downsizing images to avoid aliasing artifacts. For downsampling Ndimensional images with an integer factor also see skimage.transform.downscale_local_mean.
Parameters: 


Returns: 

Other Parameters:  

Notes
Modes ‘reflect’ and ‘symmetric’ are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2].
Examples
>>> from skimage import data
>>> from skimage.transform import resize
>>> image = data.camera()
>>> resize(image, (100, 100)).shape
(100, 100)
skimage.transform.resize
¶skimage.transform.
rotate
(image, angle, resize=False, center=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False)[source]¶Rotate image by a certain angle around its center.
Parameters: 


Returns: 

Other Parameters:  

Notes
Modes ‘reflect’ and ‘symmetric’ are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2].
Examples
>>> from skimage import data
>>> from skimage.transform import rotate
>>> image = data.camera()
>>> rotate(image, 2).shape
(512, 512)
>>> rotate(image, 2, resize=True).shape
(530, 530)
>>> rotate(image, 90, resize=True).shape
(512, 512)
skimage.transform.
rescale
(image, scale, order=1, mode='reflect', cval=0, clip=True, preserve_range=False, multichannel=None, anti_aliasing=True, anti_aliasing_sigma=None)[source]¶Scale image by a certain factor.
Performs interpolation to upscale or downscale images. Note that anti aliasing should be enabled when downsizing images to avoid aliasing artifacts. For downsampling Ndimensional images with an integer factor also see skimage.transform.downscale_local_mean.
Parameters: 


Returns: 

Other Parameters:  

Notes
Modes ‘reflect’ and ‘symmetric’ are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2].
Examples
>>> from skimage import data
>>> from skimage.transform import rescale
>>> image = data.camera()
>>> rescale(image, 0.1).shape
(51, 51)
>>> rescale(image, 0.5).shape
(256, 256)
skimage.transform.
downscale_local_mean
(image, factors, cval=0, clip=True)[source]¶Downsample Ndimensional image by local averaging.
The image is padded with cval if it is not perfectly divisible by the integer factors.
In contrast to the 2D interpolation in skimage.transform.resize and skimage.transform.rescale this function may be applied to Ndimensional images and calculates the local mean of elements in each block of size factors in the input image.
Parameters: 


Returns: 

Examples
>>> a = np.arange(15).reshape(3, 5)
>>> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]])
>>> downscale_local_mean(a, (2, 3))
array([[ 3.5, 4. ],
[ 5.5, 4.5]])
skimage.transform.downscale_local_mean
¶skimage.transform.
pyramid_reduce
(image, downscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=None)[source]¶Smooth and then downsample image.
Parameters: 


Returns: 

References
[1]  http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf 
skimage.transform.
pyramid_expand
(image, upscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=None)[source]¶Upsample and then smooth image.
Parameters: 


Returns: 

References
[1]  http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf 
skimage.transform.
pyramid_gaussian
(image, max_layer=1, downscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=None)[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 images.
Note that the first image of the pyramid will be the original, unscaled image. The total number of images is max_layer + 1. In case all layers are computed, the last image is either a onepixel image or the image where the reduction does not change its shape.
Parameters: 


Returns: 

References
[1]  http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf 
skimage.transform.pyramid_gaussian
¶skimage.transform.
pyramid_laplacian
(image, max_layer=1, downscale=2, sigma=None, order=1, mode='reflect', cval=0, multichannel=None)[source]¶Yield images of the laplacian pyramid formed by the input image.
Each layer contains the difference between the downsampled and the downsampled, smoothed image:
layer = resize(prev_layer)  smooth(resize(prev_layer))
Note that the first image of the pyramid will be the difference between the original, unscaled image and its smoothed version. The total number of images is max_layer + 1. In case all layers are computed, the last image is either a onepixel image or the image where the reduction does not change its shape.
Parameters: 


Returns: 

References
[1]  http://web.mit.edu/persci/people/adelson/pub_pdfs/pyramid83.pdf 
[2]  http://sepwww.stanford.edu/data/media/public/sep/morgan/texturematch/paper_html/node3.html 
skimage.transform.
seam_carve
(*args, **kwargs)[source]¶Seam carving has been removed because it is a patented algorithm.
See https://github.com/scikitimage/scikitimage/issues/3646
EuclideanTransform
¶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 * sin(rotation) + y * cos(rotation) + b1
where the homogeneous transformation matrix is:
[[a0 b0 a1]
[b0 a0 b1]
[0 0 1]]
The Euclidean transformation is a rigid transformation with rotation and translation parameters. The similarity transformation extends the Euclidean transformation with a single scaling factor.
Parameters: 


Attributes: 

__init__
(matrix=None, rotation=None, translation=None)[source]¶Initialize self. See help(type(self)) for accurate signature.
estimate
(src, dst)[source]¶Estimate the transformation from a set of corresponding points.
You can determine the over, well and underdetermined parameters with the total leastsquares method.
Number of source and destination coordinates must match.
Parameters: 


Returns: 

rotation
¶translation
¶SimilarityTransform
¶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 * y + b1 =
= s * x * sin(rotation) + s * y * cos(rotation) + b1
where s
is a scale factor and the homogeneous transformation matrix is:
[[a0 b0 a1]
[b0 a0 b1]
[0 0 1]]
The similarity transformation extends the Euclidean transformation with a single scaling factor in addition to the rotation and translation parameters.
Parameters: 


Attributes: 

__init__
(matrix=None, scale=None, rotation=None, translation=None)[source]¶Initialize self. See help(type(self)) for accurate signature.
estimate
(src, dst)[source]¶Estimate the transformation from a set of corresponding points.
You can determine the over, well and underdetermined parameters with the total leastsquares method.
Number of source and destination coordinates must match.
Parameters: 


Returns: 

scale
¶AffineTransform
¶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 + b1*y + b2 =
= sx*x*sin(rotation) + sy*y*cos(rotation + shear) + b2
where sx
and sy
are scale factors in the x and y directions,
and the homogeneous transformation matrix is:
[[a0 a1 a2]
[b0 b1 b2]
[0 0 1]]
Parameters: 


Attributes: 

__init__
(matrix=None, scale=None, rotation=None, shear=None, translation=None)[source]¶Initialize self. See help(type(self)) for accurate signature.
rotation
¶scale
¶shear
¶translation
¶ProjectiveTransform
¶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 multiplying with the given matrix, \(H\), to give \(H \mathbf{x}\):
[[a0 a1 a2]
[b0 b1 b2]
[c0 c1 1 ]].
E.g., to rotate by theta degrees clockwise, the matrix should be:
[[cos(theta) sin(theta) 0]
[sin(theta) cos(theta) 0]
[0 0 1]]
or, to translate x by 10 and y by 20:
[[1 0 10]
[0 1 20]
[0 0 1 ]].
Parameters: 


Attributes: 

estimate
(src, dst)[source]¶Estimate the transformation from a set of corresponding points.
You can determine the over, well and underdetermined parameters with the total leastsquares method.
Number of source and destination coordinates must match.
The transformation is defined as:
X = (a0*x + a1*y + a2) / (c0*x + c1*y + 1)
Y = (b0*x + b1*y + b2) / (c0*x + c1*y + 1)
These equations can be transformed to the following form:
0 = a0*x + a1*y + a2  c0*x*X  c1*y*X  X
0 = b0*x + b1*y + b2  c0*x*Y  c1*y*Y  Y
which exist for each set of corresponding points, so we have a set of N * 2 equations. The coefficients appear linearly so we can write A x = 0, where:
A = [[x y 1 0 0 0 x*X y*X X]
[0 0 0 x y 1 x*Y y*Y Y]
...
...
]
x.T = [a0 a1 a2 b0 b1 b2 c0 c1 c3]
In case of total leastsquares the solution of this homogeneous system of equations is the right singular vector of A which corresponds to the smallest singular value normed by the coefficient c3.
In case of the affine transformation the coefficients c0 and c1 are 0. Thus the system of equations is:
A = [[x y 1 0 0 0 X]
[0 0 0 x y 1 Y]
...
...
]
x.T = [a0 a1 a2 b0 b1 b2 c3]
Parameters: 


Returns: 

EssentialMatrixTransform
¶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 normalized, homogeneous image points in one image to epipolar lines in the other image.
The essential matrix is only defined for a pair of moving images capturing a nonplanar scene. In the case of pure rotation or planar scenes, the homography describes the geometric relation between two images (ProjectiveTransform). If the intrinsic calibration of the images is unknown, the fundamental matrix describes the projective relation between the two images (FundamentalMatrixTransform).
Parameters: 


References
[1]  Hartley, Richard, and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003. 
Attributes: 


__init__
(rotation=None, translation=None, matrix=None)[source]¶Initialize self. See help(type(self)) for accurate signature.
estimate
(src, dst)[source]¶Estimate essential matrix using 8point algorithm.
The 8point algorithm requires at least 8 corresponding point pairs for a wellconditioned solution, otherwise the overdetermined solution is estimated.
Parameters: 


Returns: 

FundamentalMatrixTransform
¶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 epipolar lines in the other image.
The fundamental matrix is only defined for a pair of moving images. In the case of pure rotation or planar scenes, the homography describes the geometric relation between two images (ProjectiveTransform). If the intrinsic calibration of the images is known, the essential matrix describes the metric relation between the two images (EssentialMatrixTransform).
Parameters: 


References
[1]  Hartley, Richard, and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge university press, 2003. 
Attributes: 


estimate
(src, dst)[source]¶Estimate fundamental matrix using 8point algorithm.
The 8point algorithm requires at least 8 corresponding point pairs for a wellconditioned solution, otherwise the overdetermined solution is estimated.
Parameters: 


Returns: 

PolynomialTransform
¶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: 


Attributes: 

estimate
(src, dst, order=2)[source]¶Estimate the transformation from a set of corresponding points.
You can determine the over, well and underdetermined parameters with the total leastsquares method.
Number of source and destination coordinates must match.
The transformation is defined as:
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 ))
These equations can be transformed to the following form:
0 = sum[j=0:order]( sum[i=0:j]( a_ji * x**(j  i) * y**i ))  X
0 = sum[j=0:order]( sum[i=0:j]( b_ji * x**(j  i) * y**i ))  Y
which exist for each set of corresponding points, so we have a set of N * 2 equations. The coefficients appear linearly so we can write A x = 0, where:
A = [[1 x y x**2 x*y y**2 ... 0 ... 0 X]
[0 ... 0 1 x y x**2 x*y y**2 Y]
...
...
]
x.T = [a00 a10 a11 a20 a21 a22 ... ann
b00 b10 b11 b20 b21 b22 ... bnn c3]
In case of total leastsquares the solution of this homogeneous system of equations is the right singular vector of A which corresponds to the smallest singular value normed by the coefficient c3.
Parameters: 


Returns: 

PiecewiseAffineTransform
¶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 affine transform.
Attributes: 


estimate
(src, dst)[source]¶Estimate the transformation from a set of corresponding points.
Number of source and destination coordinates must match.
Parameters: 


Returns: 
