Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.

nilearn.image.resample_to_img¶

nilearn.image.resample_to_img(source_img, target_img, interpolation='continuous', copy=True, order='F', clip=False, fill_value=0, force_resample=True, copy_header=True)[source]¶

Resample a Niimg-like source image on a target Niimg-like image.

No registration is performed: the image should already be aligned.

Added in Nilearn 0.2.4.

Parameters:

source_imgNiimg-like object: See Input and output: neuroimaging data representation. Image(s) to resample.
target_imgNiimg-like object: See Input and output: neuroimaging data representation. Reference image taken for resampling.
interpolationstr, default=’continuous’: Can be ‘continuous’, ‘linear’, or ‘nearest’. Indicates the resample method.
copybool, default=True: If True, guarantees that output array has no memory in common with input array. In all cases, input images are never modified by this function.
order“F” or “C”, default=”F”: Data ordering in output array. This function is slightly faster with Fortran ordering.
clipbool, default=False: If False, no clip is performed. If True, all resampled image values above max(img) and under min(img) are cllipped to min(img) and max(img).
fill_valuefloat, default=0: Use a fill value for points outside of input volume.
force_resamplebool, default=True: False is intended for testing, this prevents the use of a padding optimization.
copy_headerbool, default=True: Indicated if the header of the reference image should be used to create the new image. Ignored for SurfaceImage.

Added in Nilearn 0.11.0.

Returns:

resamplednibabel.Nifti1Image: input image, resampled to have respectively target image shape and affine as shape and affine.

See also

nilearn.image.resample_img

Examples

Resample a source image to match the shape and affine of a target image:

>>> import numpy as np
>>> import nibabel as nib
>>> from nilearn import image
>>> source_data = np.zeros((4, 4, 4))
>>> source_img = nib.Nifti1Image(source_data, affine=np.eye(4))
>>> target_data = np.zeros((2, 2, 2))
>>> target_img = nib.Nifti1Image(
...     target_data,
...     affine=np.eye(4) * 2,  # 2 mm voxels
... )
>>> resampled = image.resample_to_img(source_img, target_img)
>>> resampled.shape
(2, 2, 2)
>>> resampled.affine[:3, :3]
array([[2., 0., 0.],
      [0., 2., 0.],
      [0., 0., 2.]])

The resampled image inherits the shape and affine of the target image:

>>> np.array_equal(resampled.affine, target_img.affine)
True
>>> resampled.shape == target_img.shape
True

Examples using `nilearn.image.resample_to_img`¶

Understanding parameters of the first-level model

Voxel-Based Morphometry on OASIS dataset

Resample an image to a template

Working with long time series fMRI images

nilearn.image.resample_to_img¶

Examples using nilearn.image.resample_to_img¶

Examples using `nilearn.image.resample_to_img`¶