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| # emacs: -*- mode: python-mode; py-indent-offset: 4; indent-tabs-mode: nil -*- | ||
| # vi: set ft=python sts=4 ts=4 sw=4 et: | ||
| ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## | ||
| # | ||
| # See COPYING file distributed along with the NiBabel package for the | ||
| # copyright and license terms. | ||
| # | ||
| ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## | ||
| """Resampling utilities.""" | ||
| from pathlib import Path | ||
| import numpy as np | ||
| import h5py | ||
| import warnings | ||
| from nibabel.loadsave import load as _nbload | ||
| from nibabel import funcs as _nbfuncs | ||
| from nibabel.nifti1 import intent_codes as INTENT_CODES | ||
| from nibabel.cifti2 import Cifti2Image | ||
| from scipy import ndimage as ndi | ||
|
|
||
|
|
||
| def apply( | ||
| transform, | ||
| spatialimage, | ||
| reference=None, | ||
| order=3, | ||
| mode="constant", | ||
| cval=0.0, | ||
| prefilter=True, | ||
| output_dtype=None, | ||
| ): | ||
| """ | ||
| Apply a transformation to an image, resampling on the reference spatial object. | ||
| Parameters | ||
| ---------- | ||
| spatialimage : `spatialimage` | ||
| The image object containing the data to be resampled in reference | ||
| space | ||
| reference : spatial object, optional | ||
| The image, surface, or combination thereof containing the coordinates | ||
| of samples that will be sampled. | ||
| order : int, optional | ||
| The order of the spline interpolation, default is 3. | ||
| The order has to be in the range 0-5. | ||
| mode : {'constant', 'reflect', 'nearest', 'mirror', 'wrap'}, optional | ||
| Determines how the input image is extended when the resamplings overflows | ||
| a border. Default is 'constant'. | ||
| cval : float, optional | ||
| Constant value for ``mode='constant'``. Default is 0.0. | ||
| prefilter: bool, optional | ||
| Determines if the image's data array is prefiltered with | ||
| a spline filter before interpolation. The default is ``True``, | ||
| which will create a temporary *float64* array of filtered values | ||
| if *order > 1*. If setting this to ``False``, the output will be | ||
| slightly blurred if *order > 1*, unless the input is prefiltered, | ||
| i.e. it is the result of calling the spline filter on the original | ||
| input. | ||
| output_dtype: dtype specifier, optional | ||
| The dtype of the returned array or image, if specified. | ||
| If ``None``, the default behavior is to use the effective dtype of | ||
| the input image. If slope and/or intercept are defined, the effective | ||
| dtype is float64, otherwise it is equivalent to the input image's | ||
| ``get_data_dtype()`` (on-disk type). | ||
| If ``reference`` is defined, then the return value is an image, with | ||
| a data array of the effective dtype but with the on-disk dtype set to | ||
| the input image's on-disk dtype. | ||
| Returns | ||
| ------- | ||
| resampled : `spatialimage` or ndarray | ||
| The data imaged after resampling to reference space. | ||
| """ | ||
| if reference is not None and isinstance(reference, (str, Path)): | ||
| reference = _nbload(str(reference)) | ||
|
|
||
| _ref = ( | ||
| transform.reference if reference is None else SpatialReference.factory(reference) | ||
| ) | ||
|
|
||
| if _ref is None: | ||
| raise TransformError("Cannot apply transform without reference") | ||
|
|
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| if isinstance(spatialimage, (str, Path)): | ||
| spatialimage = _nbload(str(spatialimage)) | ||
|
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| data = np.asanyarray(spatialimage.dataobj) | ||
| targets = ImageGrid(spatialimage).index( # data should be an image | ||
| _as_homogeneous(transform.map(_ref.ndcoords.T), dim=_ref.ndim) | ||
| ) | ||
|
|
||
| resampled = ndi.map_coordinates( | ||
| data, | ||
| targets.T, | ||
| output=output_dtype, | ||
| order=order, | ||
| mode=mode, | ||
| cval=cval, | ||
| prefilter=prefilter, | ||
| ) | ||
|
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||
| if isinstance(_ref, ImageGrid): # If reference is grid, reshape | ||
| hdr = None | ||
| if _ref.header is not None: | ||
| hdr = _ref.header.copy() | ||
| hdr.set_data_dtype(output_dtype or spatialimage.get_data_dtype()) | ||
| moved = spatialimage.__class__( | ||
| resampled.reshape(_ref.shape), | ||
| _ref.affine, | ||
| hdr, | ||
| ) | ||
| return moved | ||
|
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||
| return resampled |