Object-oriented interface, with fit and apply methods.

Author: arokem

dmriprep/utils/register.py

@@ -1,6 +1,8 @@
 """
 Linear affine registration tools for motion correction.
 """
+import attr
+
 import numpy as np
 import nibabel as nb
 from dipy.align.metrics import CCMetric, EMMetric, SSDMetric
@@ -72,7 +74,7 @@ def c_of_mass(
 ):
     transform = transform_centers_of_mass(static, static_affine, moving, moving_affine)
     transformed = transform.transform(moving)
-    return transformed, transform.affine
+    return transform
 
 
 def translation(
@@ -89,7 +91,7 @@ def translation(
         starting_affine=starting_affine,
     )
 
-    return translation.transform(moving), translation.affine
+    return translation
 
 
 def rigid(
@@ -105,12 +107,13 @@ def rigid(
         moving_affine,
         starting_affine=starting_affine,
     )
-    return rigid.transform(moving), rigid.affine
+    return rigid
 
 
-def affine(
-    moving, static, static_affine, moving_affine, reg, starting_affine, params0=None
-):
+def affine(moving, static, static_affine, moving_affine, reg, starting_affine,
+           params0=None):
+    """
+    """
     transform = AffineTransform3D()
     affine = reg.optimize(
         static,
@@ -122,49 +125,62 @@ def affine(
         starting_affine=starting_affine,
     )
 
-    return affine.transform(moving), affine.affine
-
-
-def affine_registration(
-    moving,
-    static,
-    nbins,
-    sampling_prop,
-    metric,
-    pipeline,
-    level_iters,
-    sigmas,
-    factors,
-    params0,
-):
-
-    """
-    Find the affine transformation between two 3D images.
-
-    Parameters
-    ----------
-
-    """
-    # Define the Affine registration object we'll use with the chosen metric:
-    use_metric = affine_metric_dict[metric](nbins, sampling_prop)
-    affreg = AffineRegistration(
-        metric=use_metric, level_iters=level_iters, sigmas=sigmas, factors=factors
-    )
-
-    if not params0:
-        starting_affine = np.eye(4)
-    else:
-        starting_affine = params0
-
-    # Go through the selected transformation:
-    for func in pipeline:
-        transformed, starting_affine = func(
-            np.asarray(moving.dataobj),
-            np.asarray(static.dataobj),
-            static.affine,
-            moving.affine,
-            affreg,
-            starting_affine,
-            params0,
-        )
-    return nb.Nifti1Image(np.array(transformed), static.affine), starting_affine
+    return affine
+
+
+@attr.s(slots=True, frozen=True)
+class AffineRegistration():
+    def __init__(self):
+        nbins = attr.ib(default=32)
+        sampling_prop = attr.ib(default=1.0)
+        metric = attr.ib(default="MI")
+        level_iters = attr.ib(default=[10000, 1000, 100])
+        sigmas = attr.ib(defaults=[3, 1, 0.0])
+        factors = attr.ib(defaults=[4, 2, 1])
+        pipeline = attr.ib(defaults=[c_of_mass, translation, rigid, affine])
+
+    def fit(self, static, moving, params0=None):
+        """
+        static, moving : nib.Nifti1Image class images
+        """
+        if params0 is None:
+            starting_affine = np.eye(4)
+        else:
+            starting_affine = params0
+
+        use_metric = affine_metric_dict[self.metric](self.nbins,
+                                                     self.sampling_prop)
+        affreg = AffineRegistration(
+            metric=use_metric,
+            level_iters=self.level_iters,
+            sigmas=self.sigmas,
+            factors=self.factors)
+
+        # Go through the selected transformation:
+        for func in self.pipeline:
+            transform = func(
+                np.asarray(moving.dataobj),
+                np.asarray(static.dataobj),
+                static.affine,
+                moving.affine,
+                affreg,
+                starting_affine,
+                params0,
+            )
+            starting_affine = transform.affine
+
+        self.static_affine_ = static.affine
+        self.moving_affine_ = moving.affine
+        self.affine_ = starting_affine
+        self.reg_ = AffineMap(starting_affine,
+                              static.shape, static.affine,
+                              moving.shape, moving.affine)
+
+    def apply(self, moving):
+        """
+
+        """
+        data = moving.get_fdata()
+        assert np.all(moving.affine, self.moving_affine_)
+        return nb.Nifti1Image(np.array(self.reg_.transform(data)),
+                              self.static_affine_)