From 9924ee86aace336452543ca6faddd92ebc3ff110 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauldmccarthy@gmail.com>
Date: Tue, 9 Apr 2019 12:33:41 +0100
Subject: [PATCH] RF: Changed shape.affine generation methods to be
staticmethods, as they may be useful outside of the Nifti class (for testing,
at least)
---
fsl/data/image.py | 36 +++++++++++++++++++++++++-----------
1 file changed, 25 insertions(+), 11 deletions(-)
diff --git a/fsl/data/image.py b/fsl/data/image.py
index 6832e560f..5570be15f 100644
--- a/fsl/data/image.py
+++ b/fsl/data/image.py
@@ -244,7 +244,7 @@ class Nifti(notifier.Notifier, meta.Meta):
raise ValueError('Unrecognised header: {}'.format(header))
header = header
- origShape, shape, pixdim = self.__determineShape(header)
+ origShape, shape, pixdim = Nifti.determineShape(header)
self.header = header
self.__shape = shape
@@ -253,14 +253,15 @@ class Nifti(notifier.Notifier, meta.Meta):
self.__intent = header.get('intent_code',
constants.NIFTI_INTENT_NONE)
- voxToWorldMat = self.__determineAffine(header)
- affines, isneuro = self.__generateAffines(voxToWorldMat)
+ voxToWorldMat = Nifti.determineAffine(header)
+ affines, isneuro = Nifti.generateAffines(voxToWorldMat, shape, pixdim)
self.__affines = affines
self.__isNeurological = isneuro
- def __determineShape(self, header):
+ @staticmethod
+ def determineShape(header):
"""This method is called by :meth:`__init__`. It figures out the actual
shape of the image data, and the zooms/pixdims for each data axis. Any
empty trailing dimensions are squeezed, but the returned shape is
@@ -287,10 +288,17 @@ class Nifti(notifier.Notifier, meta.Meta):
pixdims = pixdims[:len(shape)]
+ # should never happen, but if we only
+ # have zoom values for the original
+ # (< 3D) shape, pad them with 1s.
+ if len(pixdims) < len(shape):
+ pixdims = pixdims + [1] * (len(shape) - len(pixdims))
+
return origShape, shape, pixdims
- def __determineAffine(self, header):
+ @staticmethod
+ def determineAffine(header):
"""Called by :meth:`__init__`. Figures out the voxel-to-world
coordinate transformation matrix that is associated with this
``Nifti`` instance.
@@ -333,7 +341,8 @@ class Nifti(notifier.Notifier, meta.Meta):
return voxToWorldMat
- def __generateAffines(self, voxToWorldMat):
+ @staticmethod
+ def generateAffines(voxToWorldMat, shape, pixdim):
"""Called by :meth:`__init__`, and the :meth:`voxToWorldMat` setter.
Generates and returns a dictionary containing affine transformations
between the ``voxel``, ``fsl``, and ``world`` coordinate
@@ -341,6 +350,9 @@ class Nifti(notifier.Notifier, meta.Meta):
method.
:arg voxToWorldMat: The voxel-to-world affine transformation
+ :arg shape: Image shape (number of voxels along each dimension
+ :arg pixdim: Image pixdims (size of one voxel along each
+ dimension)
:returns: A tuple containing:
- a dictionary of affine transformations between
@@ -353,8 +365,8 @@ class Nifti(notifier.Notifier, meta.Meta):
import numpy.linalg as npla
affines = {}
- shape = list(self.shape[ :3])
- pixdim = list(self.pixdim[:3])
+ shape = list(shape[ :3])
+ pixdim = list(pixdim[:3])
voxToScaledVoxMat = np.diag(pixdim + [1.0])
isneuro = npla.det(voxToWorldMat) > 0
@@ -370,8 +382,8 @@ class Nifti(notifier.Notifier, meta.Meta):
affines['fsl', 'voxel'] = transform.invert(voxToScaledVoxMat)
affines['fsl', 'world'] = transform.concat(affines['voxel', 'world'],
affines['fsl', 'voxel'])
- affines['world', 'fsl'] = transform.concat(affines['fsl', 'voxel'],
- affines['voxel', 'world'])
+ affines['world', 'fsl'] = transform.concat(affines['voxel', 'fsl'],
+ affines['world', 'voxel'])
return affines, isneuro
@@ -546,7 +558,9 @@ class Nifti(notifier.Notifier, meta.Meta):
header.set_sform(xform, code=sformCode)
- affines, isneuro = self.__generateAffines(xform)
+ affines, isneuro = Nifti.generateAffines(xform,
+ self.shape,
+ self.pixdim)
self.__affines = affines
self.__isNeurological = isneuro
--
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