Fix nifti fetch voi

e2e/core/test_space.py

@@ -1,4 +1,5 @@
 import pytest
+from typing import Tuple
 import siibra
 
 
@@ -14,3 +15,28 @@ def test_space(space: siibra.core.space.Space):
 @pytest.mark.parametrize("space", should_have_desc)
 def test_has_desc(space: siibra.core.space.Space):
     assert space.description
+
+
+voi_params = [
+    ("mni152", "nii", (57, 56, 57), {}),
+    ("mni152", "neuroglancer/precomputed", (27, 27, 27), {}),
+    ("mni152", "neuroglancer/precomputed", (55, 55, 55), {"resolution_mm": 1.0}),
+    ("bigbrain", "neuroglancer/precomputed", (40, 43, 40), {}),
+]
+
+
+@pytest.mark.parametrize("space, format, shape, kwargs", voi_params)
+def test_voi_fetching(
+    space_spec: str, format: str, shape: Tuple[int, int, int], kwargs
+):
+    space = siibra.spaces.get(space_spec)
+    voi = siibra.locations.BoundingBox(
+        [-12.033761221226529, -45.77021322122653, -0.28205922122652827],
+        [43.50666522122653, 9.770213221226529, 55.25836722122653],
+        space,
+    )
+    template = space.get_template()
+    img = template.fetch(format=format, voi=voi, **kwargs)
+    assert (
+        img.shape == shape
+    ), f"Expected fetched voi to have the shape {shape} but has {img.shape}"

siibra/volumes/providers/nifti.py

@@ -188,19 +188,24 @@ def fetch(
             result = loader()
 
         if voi is not None:
-            bb_vox = voi.transform(np.linalg.inv(self.affine))
-            (x0, y0, z0), (x1, y1, z1) = bb_vox.minpoint, bb_vox.maxpoint
+            vox_offset = result.header.get_data_offset()
+            zoom_xyz = np.array(result.header.get_zooms())  # voxel dimensions in mm
+            bb_vox = voi.transform(np.linalg.inv(result.affine))
+            x0, y0, z0 = np.floor((np.array(bb_vox.minpoint.coordinate) - vox_offset) / zoom_xyz).astype(int)
+            x1, y1, z1 = np.ceil((np.array(bb_vox.maxpoint.coordinate) - vox_offset) / zoom_xyz).astype(int)
             shift = np.identity(4)
             shift[:3, -1] = bb_vox.minpoint
             result = nib.Nifti1Image(
                 dataobj=result.dataobj[x0:x1, y0:y1, z0:z1],
                 affine=np.dot(result.affine, shift),
+                dtype=result.get_data_dtype()
             )
 
         if label is not None:
             result = nib.Nifti1Image(
                 (result.get_fdata() == label).astype('uint8'),
-                result.affine
+                result.affine,
+                dtype='uint8'
             )
 
         return result