mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-08 01:38:52 +08:00
Stefan van der Walt
92 lines
2.6 KiB
Python
92 lines
2.6 KiB
Python
__all__ = ['warp']
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import numpy as np
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from scipy import ndimage
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from skimage.util import img_as_float
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eps = np.finfo(float).eps
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def _stackcopy(a, b):
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"""a[:,:,0] = a[:,:,1] = ... = b"""
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if a.ndim == 3:
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a.transpose().swapaxes(1, 2)[:] = b
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else:
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a[:] = b
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def warp(image, coord_tf, tf_args={},
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output_shape=None, order=1, mode='constant', cval=0.):
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"""Warp an image according to a given coordinate transformation.
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Parameters
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----------
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image : 2-D array
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Input image.
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coord_tf : callable xy = f(xy, **kwargs)
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Function that transforms an Nx2 array of ``(x, y)`` coordinates
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in the *output image* into their corresponding coordinates in the
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*source image*. Note that this is a reverse mapping (also
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see examples below).
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tf_args : dict, optional
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Keyword arguments passed to `coord_tf`.
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output_shape : tuple (rows, cols)
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Shape of the output image generated.
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order : int
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Order of splines used in interpolation.
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mode : string
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How to handle values outside the image borders. Passed as-is
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to ndimage.
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cval : string
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Used in conjunction with mode 'constant', the value outside
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the image boundaries.
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Examples
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--------
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Shift an image to the right:
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>>> from skimage import data
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>>> image = data.camera()
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>>>
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>>> def shift_right(xy):
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... xy[:, 0] -= 10
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... return xy
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>>>
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>>> warp(image, shift_right)
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"""
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if image.ndim < 2:
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raise ValueError("Input must have more than 1 dimension.")
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image = np.atleast_3d(img_as_float(image))
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ishape = np.array(image.shape)
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bands = ishape[2]
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if output_shape is None:
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output_shape = ishape
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coords = np.empty(np.r_[3, output_shape], dtype=float)
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# Construct transformed coordinates
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rows, cols = output_shape[:2]
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tf_coords = np.indices((cols, rows), dtype=float).reshape(2, -1).T
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tf_coords = coord_tf(tf_coords, **tf_args)
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tf_coords = tf_coords.T.reshape((-1, cols, rows)).swapaxes(1, 2)
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# y-coordinate mapping
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_stackcopy(coords[1, ...], tf_coords[0, ...])
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# x-coordinate mapping
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_stackcopy(coords[0, ...], tf_coords[1, ...])
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# colour-coordinate mapping
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coords[2, ...] = range(bands)
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# Prefilter not necessary for order 1 interpolation
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prefilter = order > 1
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mapped = ndimage.map_coordinates(image, coords, prefilter=prefilter,
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mode=mode, order=order, cval=cval)
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# The spline filters sometimes return results outside [0, 1],
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# so clip to ensure valid data
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return np.clip(mapped.squeeze(), 0, 1)
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