SurfaceTransform class

.gitignore

@@ -82,3 +82,5 @@ local_settings.py
 
 *.swp
 .vscode/
+
+.DS_Store

Dockerfile

@@ -57,7 +57,9 @@ RUN mkdir -p /opt/afni-latest \
         -name "3dTshift" -or \
         -name "3dUnifize" -or \
         -name "3dAutomask" -or \
-        -name "3dvolreg" \) -delete
+        -name "3dvolreg" -or \
+        -name "3dNwarpApply" \
+    \) -delete
 
 # Micromamba
 FROM downloader as micromamba

nitransforms/__init__.py

@@ -16,7 +16,7 @@
 
    transform
 """
-from . import linear, manip, nonlinear
+from . import linear, manip, nonlinear, surface
 from .linear import Affine, LinearTransformsMapping
 from .nonlinear import DenseFieldTransform
 from .manip import TransformChain
@@ -37,6 +37,7 @@
 __copyright__ = "Copyright (c) 2021 The NiPy developers"
 
 __all__ = [
+    "surface",
     "linear",
     "manip",
     "nonlinear",

nitransforms/base.py

@@ -15,6 +15,7 @@
 from nibabel import funcs as _nbfuncs
 from nibabel.nifti1 import intent_codes as INTENT_CODES
 from nibabel.cifti2 import Cifti2Image
+import nibabel as nb
 
 EQUALITY_TOL = 1e-5
 
@@ -88,6 +89,75 @@
         return self._shape
 
 
+class SurfaceMesh(SampledSpatialData):
+    """Class to represent surface meshes."""
+
+    __slots__ = ["_triangles"]
+
+    def __init__(self, dataset):
+        """Create a sampling reference."""
+        self._shape = None
+
+        if isinstance(dataset, SurfaceMesh):
+            self._coords = dataset._coords
+            self._triangles = dataset._triangles
+            self._ndim = dataset._ndim
+            self._npoints = dataset._npoints
+            self._shape = dataset._shape
+            return
+
+        if isinstance(dataset, (str, Path)):
+            dataset = _nbload(str(dataset))
+
+        if hasattr(dataset, "numDA"):  # Looks like a Gifti file
+            _das = dataset.get_arrays_from_intent(INTENT_CODES["pointset"])
+            if not _das:
+                raise TypeError(
+                    "Input Gifti file does not contain reference coordinates."
+                )
+            self._coords = np.vstack([da.data for da in _das])
+            _tris = dataset.get_arrays_from_intent(INTENT_CODES["triangle"])
+            self._triangles = np.vstack([da.data for da in _tris])
+            self._npoints, self._ndim = self._coords.shape
+            self._shape = self._coords.shape
+            return
+
+        if isinstance(dataset, Cifti2Image):
+            raise NotImplementedError
+
+        raise ValueError("Dataset could not be interpreted as an irregular sample.")
+
+    def check_sphere(self, tolerance=1.001):
+        """Check sphericity of surface.
+        Based on https://github.com/Washington-University/workbench/blob/7ba3345d161d567a4b628ceb02ab4471fc96cb20/src/Files/SurfaceResamplingHelper.cxx#L503
+        """
+        dists = np.linalg.norm(self._coords, axis=1)
+        return (dists.min() * tolerance) > dists.max()
+
+    def set_radius(self, radius=100):
+        if not self.check_sphere():
+            raise ValueError("You should only set the radius on spherical surfaces.")
+        dists = np.linalg.norm(self._coords, axis=1)
+        self._coords = self._coords * (radius / dists).reshape((-1, 1))
+
+    @classmethod
+    def from_arrays(cls, coordinates, triangles):
+        darrays = [
+            nb.gifti.GiftiDataArray(
+                coordinates.astype(np.float32),
+                intent=nb.nifti1.intent_codes['NIFTI_INTENT_POINTSET'],
+                datatype=nb.nifti1.data_type_codes['NIFTI_TYPE_FLOAT32'],
+            ),
+            nb.gifti.GiftiDataArray(
+                triangles.astype(np.int32),
+                intent=nb.nifti1.intent_codes['NIFTI_INTENT_TRIANGLE'],
+                datatype=nb.nifti1.data_type_codes['NIFTI_TYPE_INT32'],
+            ),
+        ]
+        gii = nb.gifti.GiftiImage(darrays=darrays)
+        return cls(gii)
+
+
 class ImageGrid(SampledSpatialData):
     """Class to represent spaces of gridded data (images)."""