Added support for loading native object for openvdb

CHANGELOG

@@ -13,6 +13,17 @@ The rules for this file:
   * accompany each entry with github issue/PR number (Issue #xyz)
 
 -------------------------------------------------------------------------------
+??/??/???? orbeckst, spyke7
+
+  * 1.2.1
+
+  Enhancements
+
+  * Implemented loading of a Grid from a native object for OpenVDB (Issue #162, PR #170)
+
+  Fixes
+
+
 05/22/2026 orbeckst, spyke7
 
   * 1.2.0

gridData/OpenVDB.py

@@ -109,20 +109,21 @@
 @dataclass
 class DownCastTo:
     """:func:`~dataclasses.dataclass` decorator serving as a marker for a downcast.
-    
+
     This function is used to create a proxy for an OpenVDB grid type.
     The field :attr:`gridType` contains the OpenVDB grid type that it represents.
     :meth:`OpenVDBField._get_best_grid_type` selects a OpenVDB grid that best matches
     the numpy dtype of the data but in some cases, only target OpenVDB grid types are
     available that loose precision. In this case, this class wraps the orginal OpenVDB
     class to indicate that the downcast. For example, ::
-    
+
        np.dtype("int32"): ["Int32Grid", DownCastTo("FloatGrid")]
-       
+
     indicates that NumPy int32 data should be represented by a :class:`openvdb.Int32Grid`
     but if this is not available, a :class:`openvdb.FloatGrid` is used instead,
     which, however, is only able to represent a subset of all 32-bit integers.
     """
+
     gridType: str
 
 
@@ -142,7 +143,7 @@ class OpenVDBField(object):
 
       import gridData.OpenVDB as OpenVDB
 
-      vdb_field = OpenVDB.OpenVDBField(grid=np.ones((3, 4, 5)), 
+      vdb_field = OpenVDB.OpenVDBField(grid=np.ones((3, 4, 5)),
                                        origin=np.array([1.5, 0, 0]),
                                        delta=np.array([0.5, 0.5, 0.25]),
                                        name='density')
@@ -154,6 +155,22 @@ class OpenVDBField(object):
       g.export('output.vdb', format='vdb')
     """
 
+    # dtype maps
+    _DATATYPES = {
+        np.dtype("bool"): ["BoolGrid"],
+        np.dtype("int8"): ["Int32Grid", "FloatGrid"],
+        np.dtype("uint8"): ["Int32Grid", "FloatGrid"],
+        np.dtype("int16"): ["Int32Grid", "FloatGrid"],
+        np.dtype("uint16"): ["Int32Grid", "FloatGrid"],
+        np.dtype("int32"): ["Int32Grid", DownCastTo("FloatGrid")],
+        np.dtype("uint32"): [DownCastTo("Int32Grid"), DownCastTo("FloatGrid")],
+        np.dtype("int64"): ["Int64Grid", DownCastTo("FloatGrid")],
+        np.dtype("uint64"): ["Int64Grid", DownCastTo("FloatGrid")],
+        np.dtype("float16"): ["HalfGrid", "FloatGrid"],
+        np.dtype("float32"): ["FloatGrid"],
+        np.dtype("float64"): ["DoubleGrid", DownCastTo("FloatGrid")],
+    }
+
     def __init__(
         self,
         grid=None,
@@ -205,8 +222,13 @@ def __init__(
             self.metadata = {}
 
         if grid is not None:
-            self._populate(grid, origin, delta)
-            self.vdb_grid = self._create_openvdb_grid()
+            if isinstance(grid, vdb.GridBase):
+                self.vdb_grid = grid
+                self._extract_from_vdb_grid()
+            else:
+                self._populate(grid, origin, delta)
+                self.vdb_grid = self._create_openvdb_grid()
+
         else:
             self.grid = None
             self.origin = None
@@ -272,6 +294,57 @@ def native(self):
         """
         return self.vdb_grid
 
+    def _extract_from_vdb_grid(self):
+        """Extract numpy array, origin, delta from stored VDB grid.
+
+        This method converts the sparse VDB grid to a dense numpy array
+        and extracts the transform information.
+        """
+        for key in self.vdb_grid.metadata:
+            try:
+                self.metadata[key] = self.vdb_grid[key]
+            except (TypeError, ValueError):
+                pass
+
+        transformation = self.vdb_grid.transform
+
+        v_origin = np.array(transformation.indexToWorld([0, 0, 0]))
+        v_delta = np.array(transformation.indexToWorld([1, 1, 1])) - v_origin
+
+        self.origin = v_origin
+        self.delta = v_delta
+
+        dtype = np.dtype("float32")
+        vdb_class_name = type(self.vdb_grid).__name__
+        for numpy_dtype, vdb_names in self._DATATYPES.items():
+            name_dtype = vdb_names[0]
+            canonical_name = (
+                name_dtype.gridType
+                if isinstance(name_dtype, DownCastTo)
+                else name_dtype
+            )
+
+            if vdb_class_name == canonical_name:
+                dtype = numpy_dtype
+                break
+
+        bbox = self.vdb_grid.evalActiveVoxelBoundingBox()
+
+        if bbox is None or bbox[0] == bbox[1]:
+            self.grid = np.zeros((0, 0, 0), dtype=dtype)
+            return
+
+        shape = tuple(np.array(bbox[1]) - np.array(bbox[0]) + 1)
+
+        self.grid = np.zeros(shape, dtype=dtype)
+        print(dtype)
+        self.vdb_grid.copyToArray(self.grid, ijk=bbox[0])
+
+        if not np.all(np.array(bbox[0]) == 0):
+            self.origin = np.array(
+                transformation.indexToWorld(np.array(bbox[0]).tolist())
+            )
+
     def _populate(self, grid, origin, delta):
         """Populate the field with grid data.
 
@@ -331,23 +404,8 @@ def _get_best_grid_type(self):
         TypeError
             If dtype is not supported or no suitable grid type is available
         """
-        datatypes = {
-            np.dtype("bool"): ["BoolGrid"],
-            np.dtype("int8"): ["Int32Grid", "FloatGrid"],
-            np.dtype("uint8"): ["Int32Grid", "FloatGrid"],
-            np.dtype("int16"): ["Int32Grid", "FloatGrid"],
-            np.dtype("uint16"): ["Int32Grid", "FloatGrid"],
-            np.dtype("int32"): ["Int32Grid", DownCastTo("FloatGrid")],
-            np.dtype("uint32"): [DownCastTo("Int32Grid"), DownCastTo("FloatGrid")],
-            np.dtype("int64"): ["Int64Grid", DownCastTo("FloatGrid")],
-            np.dtype("uint64"): ["Int64Grid", DownCastTo("FloatGrid")],
-            np.dtype("float16"): ["HalfGrid", "FloatGrid"],
-            np.dtype("float32"): ["FloatGrid"],
-            np.dtype("float64"): ["DoubleGrid", DownCastTo("FloatGrid")],
-        }
-
         try:
-            vdb_gridtypes = datatypes[self.grid.dtype]
+            vdb_gridtypes = self._DATATYPES[self.grid.dtype]
         except KeyError:
             raise TypeError(f"Data type {self.grid.dtype} not supported for VDB")
 

gridData/core.py

@@ -249,6 +249,21 @@ def __init__(self, grid=None, edges=None, origin=None, delta=None,
         self.interpolation_cval = None  # default to using min(grid)
 
         if grid is not None:
+            try:
+                # if a openvdb native grid is passed
+                import openvdb as vdb
+                if isinstance(grid, vdb.GridBase):
+                    vdb_field = OpenVDB.OpenVDBField(grid=grid)
+                    self.metadata = vdb_field.metadata
+                    self._load(
+                        grid=vdb_field.grid,
+                        origin=vdb_field.origin,
+                        delta=vdb_field.delta,
+                        metadata=vdb_field.metadata,
+                    )
+                    return
+            except ImportError:
+                pass
             if isinstance(grid, str):
                 # can probably safely try to load() it...
                 filename = grid

gridData/tests/test_vdb.py

@@ -392,6 +392,20 @@ def test_from_native_grid_shape_values_and_dimension(self, grid345):
         world = native_grid.transform.indexToWorld((0, 0, 0))
         assert_allclose([world[0], world[1], world[2]], g.origin, rtol=1e-5)
 
+    def test_extract_from_vdb_grid(self, grid345):
+        data, g = grid345
+        g.metadata["name"] = "new_density"
+
+        native = g.convert_to("vdb")
+        new_vdb_grid = Grid(grid=native)
+
+        assert_allclose(new_vdb_grid.grid, g.grid, rtol=1e-5)
+        assert_allclose(new_vdb_grid.origin, g.origin, rtol=1e-5)
+        assert_allclose(new_vdb_grid.delta, g.delta, rtol=1e-5)
+        assert new_vdb_grid.metadata["name"] == "new_density"
+        assert new_vdb_grid.grid.dtype == np.dtype("float32")
+        assert_allclose(new_vdb_grid.grid, data, rtol=1e-5)
+
 
 @pytest.mark.skipif(
     not HAS_OPENVDB, reason="Need openvdb to test import error handling"