Add base implementation of transforms and change base implementation …

…of linear and nonlinear transforms
nipy · oesteban · Sep 20, 2019 · Sep 20, 2019 · Sep 22, 2019 · Sep 23, 2019
commit 4478c062a336fd58b35e7d517bcfc8eef8d65291
diff --git a/nibabel/transform/base.py b/nibabel/transform/base.py
@@ -14,7 +14,8 @@
 
 class ImageSpace(object):
     '''Class to represent spaces of gridded data (images)'''
-    __slots__ = ['_affine', '_shape', '_ndim', '_ndindex', '_coords', '_nvox']
+    __slots__ = ['_affine', '_shape', '_ndim', '_ndindex', '_coords', '_nvox',
+                 '_inverse']
 
     def __init__(self, image):
         self._affine = image.affine
@@ -23,13 +24,18 @@ def __init__(self, image):
         self._nvox = np.prod(image.shape)  # Do not access data array
         self._ndindex = None
         self._coords = None
+        self._inverse = np.linalg.inv(image.affine)
         if self._ndim not in [2, 3]:
             raise ValueError('Invalid image space (%d-D)' % self._ndim)
 
     @property
     def affine(self):
         return self._affine
 
+    @property
+    def inverse(self):
+        return self._inverse
+
     @property
     def shape(self):
         return self._shape
@@ -166,133 +172,7 @@ def map_voxel(self, index, moving=None):
         input reference voxel'''
         raise NotImplementedError
 
-
-class Affine(TransformBase):
-    '''Represents linear transforms on image data'''
-    __slots__ = ['_matrix']
-
-    def __init__(self, matrix):
-        '''Initialize a transform
-
-        Parameters
-        ----------
-
-        matrix : ndarray
-            The inverse coordinate transformation matrix **in physical
-            coordinates**, mapping coordinates from *reference* space
-            into *moving* space.
-            This matrix should be provided in homogeneous coordinates.
-
-        Examples
-        --------
-
-        >>> xfm = Affine([[1, 0, 0, 4], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
-        >>> xfm.matrix  # doctest: +NORMALIZE_WHITESPACE
-        array([[1, 0, 0, 4],
-               [0, 1, 0, 0],
-               [0, 0, 1, 0],
-               [0, 0, 0, 1]])
-
+    def to_filename(self, filename):
+        '''Store the transform in BIDS-Transforms HDF5 file format (.x5).
         '''
-        self._matrix = np.array(matrix)
-        assert self._matrix.ndim == 2, 'affine matrix should be 2D'
-        assert self._matrix.shape[0] == self._matrix.shape[1], 'affine matrix is not square'
-        super(Affine, self).__init__()
-
-    @property
-    def matrix(self):
-        return self._matrix
-
-    def resample(self, moving, order=3, mode='constant', cval=0.0, prefilter=True,
-                 output_
8000
dtype=None):
-        '''Resample the moving image in reference space
-
-        Parameters
-        ----------
-
-        moving : `spatialimage`
-            The image object containing the data to be resampled in reference
-            space
-        order : int, optional
-            The order of the spline interpolation, default is 3.
-            The order has to be in the range 0-5.
-        mode : {'reflect', 'constant', '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 moving 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.
-
-        Returns
-        -------
-
-        moved_image : `spatialimage`
-            The moving imaged after resampling to reference space.
-
-
-        Examples
-        --------
-
-        >>> import nibabel as nib
-        >>> xfm = Affine([[1, 0, 0, 4], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
-        >>> ref = nib.load('image.nii.gz')
-        >>> xfm.reference = ref
-        >>> xfm.resample(ref, order=0)
-
-        '''
-        if output_dtype is None:
-            output_dtype = moving.header.get_data_dtype()
-
-        try:
-            reference = self.reference
-        except ValueError:
-            print('Warning: no reference space defined, using moving as reference')
-            reference = moving
-
-        # Compose an index to index affine matrix
-        matrix = reference.affine.dot(self._matrix.dot(np.linalg.inv(moving.affine)))
-        mdata = moving.get_data()
-        moved = ndi.affine_transform(
-            mdata, matrix=matrix[:mdata.ndim, :mdata.ndim],
-            offset=matrix[:mdata.ndim, mdata.ndim],
-            output_shape=reference.shape, order=order, mode=mode,
-            cval=cval, prefilter=prefilter)
-
-        moved_image = moving.__class__(moved, reference.affine, moving.header)
-        moved_image.header.set_data_dtype(output_dtype)
-
-        return moved_image
-
-    def map_point(self, coords, forward=True):
-        coords = np.array(coords)
-        if coords.shape[0] == self._matrix.shape[0] - 1:
-            coords = np.append(coords, [1])
-        affine = self._matrix if forward else np.linalg.inv(self._matrix)
-        return affine.dot(coords)[:-1]
-
-    def map_voxel(self, index, moving=None):
-        try:
-            reference = self.reference
-        except ValueError:
-            print('Warning: no reference space defined, using moving as reference')
-            reference = moving
-        else:
-            if moving is None:
-                moving = reference
-        finally:
-            if reference is None:
-                raise ValueError('Reference and moving spaces are both undefined')
-
-        index = np.array(index)
-        if index.shape[0] == self._matrix.shape[0] - 1:
-            index = np.append(index, [1])
-
-        matrix = reference.affine.dot(self._matrix.dot(np.linalg.inv(moving.affine)))
-        return tuple(matrix.dot(index)[:-1])
+        raise NotImplementedError
diff --git a/nibabel/transform/linear.py b/nibabel/transform/linear.py
@@ -0,0 +1,151 @@
+# 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.
+#
+### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
+''' Linear transforms '''
+from __future__ import division, print_function, absolute_import
+import numpy as np
+from scipy import ndimage as ndi
+
+from .base import ImageSpace, TransformBase
+from ..funcs import four_to_three
+
+
+class Affine(TransformBase):
+    '''Represents linear transforms on image data'''
+    __slots__ = ['_matrix']
+
+    def __init__(self, matrix):
+        '''Initialize a transform
+
+        Parameters
+        ----------
+
+        matrix : ndarray
+            The inverse coordinate transformation matrix **in physical
+            coordinates**, mapping coordinates from *reference* space
+            into *moving* space.
+            This matrix should be provided in homogeneous coordinates.
+
+        Examples
+        --------
+
+        >>> xfm = Affine([[1, 0, 0, 4], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
+        >>> xfm.matrix  # doctest: +NORMALIZE_WHITESPACE
+        array([[1, 0, 0, 4],
+               [0, 1, 0, 0],
+               [0, 0, 1, 0],
+               [0, 0, 0, 1]])
+
+        '''
+        self._matrix = np.array(matrix)
+        assert self._matrix.ndim == 2, 'affine matrix should be 2D'
+        assert self._matrix.shape[0] == self._matrix.shape[1], 'affine matrix is not square'
+        super(Affine, self).__init__()
+
+    @property
+    def matrix(self):
+        return self._matrix
+
+    def resample(self, moving, order=3, mode='constant', cval=0.0, prefilter=True,
+                 output_dtype=None):
+        '''Resample the moving image in reference space
+
+        Parameters
+        ----------
+
+        moving : `spatialimage`
+            The image object containing the data to be resampled in reference
+            space
+        order : int, optional
+            The order of the spline interpolation, default is 3.
+            The order has to be in the range 0-5.
+        mode : {'reflect', 'constant', '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 moving 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.
+
+        Returns
+        -------
+
+        moved_image : `spatialimage`
+            The moving imaged after resampling to reference space.
+
+
+        Examples
+        --------
+
+        >>> import nibabel as nib
+        >>> xfm = Affine([[1, 0, 0, 4], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
+        >>> ref = nib.load('image.nii.gz')
+        >>> xfm.reference = ref
+        >>> xfm.resample(ref, order=0)
+
+        '''
+        if output_dtype is None:
+            output_dtype = moving.header.get_data_dtype()
+
+        try:
+            reference = self.reference
+        except ValueError:
+            print('Warning: no reference space defined, using moving as reference')
+            reference = moving
+
+        # Compose an index to index affine matrix
+        matrix = reference.affine.dot(self._matrix.dot(np.linalg.inv(moving.affine)))
+        mdata = moving.get_data()
+        moved = ndi.affine_transform(
+            mdata, matrix=matrix[:mdata.ndim, :mdata.ndim],
+            offset=matrix[:mdata.ndim, mdata.ndim],
+            output_sha
8000
pe=reference.shape, order=order, mode=mode,
+            cval=cval, prefilter=prefilter)
+
+        moved_image = moving.__class__(moved, reference.affine, moving.header)
+        moved_image.header.set_data_dtype(output_dtype)
+
+        return moved_image
+
+    def map_point(self, coords, forward=True):
+        coords = np.array(coords)
+        if coords.shape[0] == self._matrix.shape[0] - 1:
+            coords = np.append(coords, [1])
+        affine = self._matrix if forward else np.linalg.inv(self._matrix)
+        return affine.dot(coords)[:-1]
+
+    def map_voxel(self, index, moving=None):
+        try:
+            reference = self.reference
+        except ValueError:
+            print('Warning: no reference space defined, using moving as reference')
+            reference = moving
+        else:
+            if moving is None:
+                moving = reference
+        finally:
+            if reference is None:
+                raise ValueError('Reference and moving spaces are both undefined')
+
+        index = np.array(index)
+        if index.shape[0] == self._matrix.shape[0] - 1:
+            index = np.append(index, [1])
+
+        matrix = reference.affine.dot(self._matrix.dot(np.linalg.inv(moving.affine)))
+        return tuple(matrix.dot(index)[:-1])
+
+    def to_filename(self, filename):
+        '''Store the transform in BIDS-Transforms HDF5 file format (.x5).
+        '''
+        raise NotImplementedError