estimate-transform

estimate_transform

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): 1 2 3 4 5 6 7 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

1 2	>>> import numpy as np >>> from skimage import transform as tf

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>>> # estimate transformation parameters >>> src = np.array([0, 0, 10, 10]).reshape((2, 2)) >>> dst = np.array([12, 14, 1, -20]).reshape((2, 2))

1	>>> tform = tf.estimate_transform('similarity', src, dst)

1 2	>>> np.allclose(tform.inverse(tform(src)), src) True

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>>> # warp image using the estimated transformation >>> from skimage import data >>> image = data.camera()

1	>>> warp(image, inverse_map=tform.inverse)

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>>> # create transformation with explicit parameters >>> tform2 = tf.SimilarityTransform(scale=1.1, rotation=1, ...     translation=(10, 20))

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>>> # unite transformations, applied in order from left to right >>> tform3 = tform + tform2 >>> np.allclose(tform3(src), tform2(tform(src))) True