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88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
import numpy as np
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import warnings
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from ._unwrap_2d import unwrap_2d
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from ._unwrap_3d import unwrap_3d
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from .._shared.six import string_types
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def unwrap_phase(image, wrap_around=False):
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'''From ``image``, wrapped to lie in the interval [-pi, pi), recover the
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original, unwrapped image.
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Parameters
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----------
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image : 2D or 3D ndarray of floats, optionally a masked array
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The values should be in the range ``[-pi, pi)``. If a masked array is
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provided, the masked entries will not be changed, and their values
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will not be used to guide the unwrapping of neighboring, unmasked
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values.
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wrap_around : bool or sequence of bool
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When an element of the sequence is ``True``, the unwrapping process
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will regard the edges along the corresponding axis of the image to be
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connected and use this connectivity to guide the phase unwrapping
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process. If only a single boolean is given, it will apply to all axes.
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Returns
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-------
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image_unwrapped : array_like, float32
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Unwrapped image of the same shape as the input. If the input ``image``
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was a masked array, the mask will be preserved.
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Examples
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--------
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>>> c0, c1 = np.ogrid[-1:1:128j, -1:1:128j]
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>>> image = 12 * np.pi * np.exp(-(c0**2 + c1**2))
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>>> image_wrapped = np.angle(np.exp(1j * image))
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>>> image_unwrapped = unwrap_phase(image_wrapped)
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>>> np.std(image_unwrapped - image) < 1e-6 # A constant offset is normal
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True
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References
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----------
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.. [1] Miguel Arevallilo Herraez, David R. Burton, Michael J. Lalor,
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and Munther A. Gdeisat, "Fast two-dimensional phase-unwrapping
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algorithm based on sorting by reliability following a noncontinuous
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path", Journal Applied Optics, Vol. 41, No. 35 (2002) 7437,
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.. [2] Abdul-Rahman, H., Gdeisat, M., Burton, D., & Lalor, M., "Fast
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three-dimensional phase-unwrapping algorithm based on sorting by
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reliability following a non-continuous path. In W. Osten,
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C. Gorecki, & E. L. Novak (Eds.), Optical Metrology (2005) 32--40,
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International Society for Optics and Photonics.
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'''
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if image.ndim not in (2, 3):
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raise ValueError('image must be 2 or 3 dimensional')
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if isinstance(wrap_around, bool):
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wrap_around = [wrap_around] * image.ndim
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elif (hasattr(wrap_around, '__getitem__')
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and not isinstance(wrap_around, string_types)):
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if len(wrap_around) != image.ndim:
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raise ValueError('Length of wrap_around must equal the '
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'dimensionality of image')
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wrap_around = [bool(wa) for wa in wrap_around]
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else:
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raise ValueError('wrap_around must be a bool or a sequence with '
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'length equal to the dimensionality of image')
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if image.ndim == 3 and 1 in image.shape:
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warnings.warn('image is 3D and has a length 1 dimension; consider '
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'using a 2D array to use the 2D unwrapping algorithm')
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if np.ma.isMaskedArray(image):
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mask = np.require(image.mask, np.uint8, ['C'])
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else:
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mask = np.zeros_like(image, dtype=np.uint8, order='C')
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image_not_masked = np.asarray(image, dtype=np.float32, order='C')
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image_unwrapped = np.empty_like(image, dtype=np.float32, order='C')
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if image.ndim == 2:
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unwrap_2d(image_not_masked, mask, image_unwrapped,
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wrap_around)
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elif image.ndim == 3:
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unwrap_3d(image_not_masked, mask, image_unwrapped,
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wrap_around)
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if np.ma.isMaskedArray(image):
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return np.ma.array(image_unwrapped, mask=mask)
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else:
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return image_unwrapped
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