Support for biomedical image formats NIFTI

setup.py

@@ -3,9 +3,10 @@
 zarr = ["ome-zarr>=0.9.0"]
 openslide = ["openslide-python"]
 tiff = ["tifffile", "imagecodecs"]
+nifti = ["nibabel"]
 cloud = ["tiledb-cloud"]
 
-full = sorted({*zarr, *openslide, *tiff})
+full = sorted({*zarr, *openslide, *tiff, *nifti})
 setuptools.setup(
     setup_requires=["setuptools_scm"],
     use_scm_version={
@@ -25,6 +26,7 @@
         "zarr": zarr,
         "openslide": openslide,
         "tiff": tiff,
+        "nifti": nifti,
         "cloud": cloud,
         "full": full,
     },
@@ -34,17 +36,20 @@
             "zarr_reader = tiledb.bioimg.converters.ome_zarr:OMEZarrReader",
             "osd_reader = tiledb.bioimg.converters.openslide:OpenSlideReader",
             "png_reader = tiledb.bioimg.converters.png.PNGReader",
+            "nifti_reader = tiledb.bioimg.converters.nifti:NiftiReader",
         ],
         "bioimg.writers": [
             "tiff_writer = tiledb.bioimg.converters.ome_tiff:OMETiffWriter",
             "zarr_writer = tiledb.bioimg.converters.ome_tiff:OMEZarrWriter",
             "png_writer = tiledb.bioimg.converters.png.PNGWriter",
+            "nifti_writer = tiledb.bioimg.converters.nifti:NiftiWriter",
         ],
         "bioimg.converters": [
             "tiff_converter = tiledb.bioimg.converters.ome_tiff:OMETiffConverter",
             "zarr_converter = tiledb.bioimg.converters.ome_zarr:OMEZarrConverter",
             "osd_converter = tiledb.bioimg.converters.openslide:OpenSlideConverter",
             "png_converter = tiledb.bioimg.converters.png:PNGConverter",
+            "nifti_converter = tiledb.bioimg.converters.nifti:NiftiConverter",
         ],
     },
 )

tests/integration/converters/test_nifti.py

@@ -0,0 +1,114 @@
+import json
+
+import nibabel as nib
+import numpy as np
+import pytest
+
+import tiledb
+from tests import get_path
+from tiledb.bioimg.converters import DATASET_TYPE, FMT_VERSION
+from tiledb.bioimg.converters.nifti import NiftiConverter
+from tiledb.bioimg.openslide import TileDBOpenSlide
+
+
+def compare_nifti_images(file1, file2, scaled_test):
+    img1 = nib.load(file1)
+    img2 = nib.load(file2)
+
+    # Compare the affine matrices (spatial information)
+    assert np.array_equal(img1.affine, img2.affine)
+
+    # Compare the image data (voxel data)
+    data1 = np.array(img1.dataobj, dtype=img1.get_data_dtype())
+    data2 = np.array(img2.dataobj, dtype=img2.get_data_dtype())
+
+    assert np.array_equal(data1, data2)
+
+    # Compare the image data scaled (voxel data)
+    if scaled_test:
+        data_sc = img1.get_fdata()
+        data_sc_2 = img2.get_fdata()
+
+        assert np.array_equal(data_sc, data_sc_2)
+
+
+@pytest.mark.parametrize(
+    "filename",
+    [
+        "nifti/example4d.nii",
+        "nifti/functional.nii",
+        "nifti/standard.nii",
+        "nifti/visiblehuman.nii",
+        "nifti/anatomical.nii",
+    ],
+)
+@pytest.mark.parametrize("preserve_axes", [False, True])
+@pytest.mark.parametrize("chunked", [False])
+@pytest.mark.parametrize(
+    "compressor, lossless",
+    [
+        (tiledb.ZstdFilter(level=0), True),
+        # WEBP is not supported for these images
+    ],
+)
+def test_nifti_converter_roundtrip(
+    tmp_path, preserve_axes, chunked, compressor, lossless, filename
+):
+    # For lossy WEBP we cannot use random generated images as they have so much noise
+    input_path = str(get_path(filename))
+    tiledb_path = str(tmp_path / "to_tiledb")
+    output_path = str(tmp_path / "from_tiledb.nii")
+
+    NiftiConverter.to_tiledb(
+        input_path,
+        tiledb_path,
+        preserve_axes=preserve_axes,
+        chunked=chunked,
+        compressor=compressor,
+        log=False,
+    )
+    # Store it back to PNG
+    NiftiConverter.from_tiledb(tiledb_path, output_path)
+    # The dtype of this image is complex and nibabel breaks originally
+    compare_nifti_images(
+        input_path,
+        output_path,
+        scaled_test=False if filename == "nifti/visiblehuman.nii" else True,
+    )
+
+
+@pytest.mark.parametrize(
+    "filename, axes, canonical",
+    [
+        ("nifti/example4d.nii", "XYZT", "TZYX"),
+        ("nifti/functional.nii", "XYZT", "TZYX"),
+        ("nifti/standard.nii", "XYZ", "ZYX"),
+        ("nifti/visiblehuman.nii", "XYZTC", "CZYX"),
+        ("nifti/anatomical.nii", "XYZ", "ZYX"),
+    ],
+)
+def test_nifti_converter_group_metadata(tmp_path, filename, axes, canonical):
+    input_path = get_path(filename)
+    tiledb_path = str(tmp_path / "to_tiledb")
+    NiftiConverter.to_tiledb(input_path, tiledb_path, preserve_axes=False)
+
+    with TileDBOpenSlide(tiledb_path) as t:
+        group_properties = t.properties
+        assert group_properties["dataset_type"] == DATASET_TYPE
+        assert group_properties["fmt_version"] == FMT_VERSION
+        assert isinstance(group_properties["pkg_version"], str)
+        assert group_properties["axes"] == axes
+
+        levels_group_meta = json.loads(group_properties["levels"])
+        assert t.level_count == len(levels_group_meta)
+        for level, level_meta in enumerate(levels_group_meta):
+            assert level_meta["level"] == level
+            assert level_meta["name"] == f"l_{level}.tdb"
+
+            level_axes = level_meta["axes"]
+            shape = level_meta["shape"]
+            level_width, level_height = t.level_dimensions[level]
+            assert level_axes == canonical
+            assert len(shape) == len(level_axes)
+            assert shape[level_axes.index("X")] == level_width
+            assert shape[level_axes.index("Y")] == level_height