estimate_transform
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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 : {‘similarity’, ‘affine’, ‘piecewise-affine’, ‘projective’, ‘polynomial’}
Type of transform.
kwargs : array or int
Function parameters (src, dst, n, angle):
NAME / TTYPE FUNCTION PARAMETERS 'similarity' `src, `dst` 'affine' `src, `dst` 'piecewise-affine' `src, `dst` 'projective' `src, `dst` 'polynomial' `src, `dst`, `order` (polynomial order, default order is 2)
Also see examples below.
Returns: tform :
GeometricTransform
Transform object containing the transformation parameters and providing access to forward and inverse transformation functions.
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)
>>> # 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
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