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# nipype.interfaces.image module

## Reorient

Link to code

> Bases: SimpleInterface

> Conform an image to a given orientation

> Flips and reorder the image data array so that the axes match the directions indicated in `orientation`. The default `RAS` orientation corresponds to the first axis being ordered from left to right, the second axis from posterior to anterior, and the third axis from inferior to superior.

> For oblique images, the original orientation is considered to be the closest plumb orientation.

> No resampling is performed, and thus the output image is not de-obliqued or registered to any other image or template.

> The effective transform is calculated from the original affine matrix to the reoriented affine matrix.

> Examples

> If an image is not reoriented, the original file is not modified

>     >>> import numpy as np
>     >>> from nipype.interfaces.image import Reorient
>     >>> reorient = Reorient(orientation='LPS')
>     >>> reorient.inputs.in_file = 'segmentation0.nii.gz'
>     >>> res = reorient.run()
>     >>> res.outputs.out_file
>     'segmentation0.nii.gz'

>     >>> print_affine(np.loadtxt(res.outputs.transform))
>     1.  0.  0.  0.
>     0.  1.  0.  0.
>     0.  0.  1.  0.
>     0.  0.  0.  1.

>     >>> reorient.inputs.orientation = 'RAS'
>     >>> res = reorient.run()
>     >>> res.outputs.out_file
>     '.../segmentation0_ras.nii.gz'

>     >>> print_affine(np.loadtxt(res.outputs.transform))
>     -1.   0.   0.  60.
>      0.  -1.   0.  72.
>      0.   0.   1.   0.
>      0.   0.   0.   1.

> in_filea pathlike object or string representing an existing file

>   Input image.

> orientation‘RAS’ or ‘RAI’ or ‘RPS’ or ‘RPI’ or ‘LAS’ or ‘LAI’ or ‘LPS’ or ‘LPI’ or ‘RSA’ or ‘RSP’ or ‘RIA’ or ‘RIP’ or ‘LSA’ or ‘LSP’ or ‘LIA’ or ‘LIP’ or ‘ARS’ or ‘ARI’ or ‘ALS’ or ‘ALI’ or ‘PRS’ or ‘PRI’ or ‘PLS’ or ‘PLI’ or ‘ASR’ or ‘ASL’ or ‘AIR’ or ‘AIL’ or ‘PSR’ or ‘PSL’ or ‘PIR’ or ‘PIL’ or ‘SRA’ or ‘SRP’ or ‘SLA’ or ‘SLP’ or ‘IRA’ or ‘IRP’ or ‘ILA’ or ‘ILP’ or ‘SAR’ or ‘SAL’ or ‘SPR’ or ‘SPL’ or ‘IAR’ or ‘IAL’ or ‘IPR’ or ‘IPL’

>   Target axis orientation. (Nipype default value: `RAS`)

> out_filea pathlike object or string representing an existing file

>   Reoriented image.

> transforma pathlike object or string representing an existing file

>   Affine transform from input orientation to output.

## Rescale

Link to code

> Bases: SimpleInterface

> Rescale an image

> Rescales the non-zero portion of `in_file` to match the bounds of the non-zero portion of `ref_file`. Reference values in the input and reference images are defined by the `percentile` parameter, and the reference values in each image are identified and the remaining values are scaled accordingly. In the case of `percentile == 0`, the reference values are the maxima and minima of each image. If the `invert` parameter is set, the input file is inverted prior to rescaling.

> Examples

> To use a high-resolution T1w image as a registration target for a T2* image, it may be useful to invert the T1w image and rescale to the T2* range. Using the 1st and 99th percentiles may reduce the impact of outlier voxels.

>     >>> from nipype.interfaces.image import Rescale
>     >>> invert_t1w = Rescale(invert=True)
>     >>> invert_t1w.inputs.in_file = 'structural.nii'
>     >>> invert_t1w.inputs.ref_file = 'functional.nii'
>     >>> invert_t1w.inputs.percentile = 1.
>     >>> res = invert_t1w.run()

> in_filea pathlike object or string representing an existing file

>   Skull-stripped image to rescale.

> ref_filea pathlike object or string representing an existing file

>   Skull-stripped reference image.

> inverta boolean

>   Invert contrast of rescaled image.

> percentile0.0 <= a floating point number <= 50.0

>   Percentile to use for reference to allow for outliers - 1 indicates the 1st and 99th percentiles in the input file will be mapped to the 99th and 1st percentiles in the reference; 0 indicates minima and maxima will be mapped. (Nipype default value: `0.0`)

> out_filea pathlike object or string representing an existing file

>   Rescaled image.