Initial commit

darwin/dataset/remote_dataset_v2.py

@@ -11,7 +11,7 @@
     Tuple,
     Union,
 )
-
+import numpy as np
 from pydantic import ValidationError
 from requests.models import Response
 
@@ -873,10 +873,15 @@ def register_multi_slotted(
         print(f"Reistration complete. Check your items in the dataset: {self.slug}")
         return results
 
-    def _get_remote_files_that_require_legacy_scaling(self) -> List[Path]:
+    def _get_remote_files_that_require_legacy_scaling(
+        self,
+    ) -> Dict[str, Dict[str, Any]]:
         """
         Get all remote files that have been scaled upon upload. These files require that
-        NifTI annotations are similarly scaled during import
+        NifTI annotations are similarly scaled during import.
+
+        The in-platform affines are returned for each legacy file, as this is required
+        to properly re-orient the annotations during import.
 
         Parameters
         ----------
@@ -885,21 +890,31 @@ def _get_remote_files_that_require_legacy_scaling(self) -> List[Path]:
 
         Returns
         -------
-        List[Path]
-            A list of full remote paths of dataset items that require NifTI annotations to be scaled
+        Dict[str, Dict[str, Any]]
+            A dictionary of remote file full paths to their slot affine maps
         """
-        remote_files_that_require_legacy_scaling = []
+        remote_files_that_require_legacy_scaling = {}
         remote_files = self.fetch_remote_files(
             filters={"statuses": ["new", "annotate", "review", "complete", "archived"]}
         )
         for remote_file in remote_files:
+            if not remote_file.slots[0].get("metadata", {}).get("medical", {}):
+                continue
             if not (
                 remote_file.slots[0]
                 .get("metadata", {})
                 .get("medical", {})
                 .get("handler")
             ):
-                remote_files_that_require_legacy_scaling.append(remote_file.full_path)
+                slot_affine_map = {}
+                for slot in remote_file.slots:
+                    slot_affine_map[slot["slot_name"]] = np.array(
+                        slot["metadata"]["medical"]["affine"],
+                        dtype=np.float64,
+                    )
+                remote_files_that_require_legacy_scaling[
+                    Path(remote_file.full_path)
+                ] = slot_affine_map
 
         return remote_files_that_require_legacy_scaling
 

darwin/exporter/formats/nifti.py

@@ -25,7 +25,6 @@ def _console_theme() -> Theme:
 console = Console(theme=_console_theme())
 try:
     import nibabel as nib
-    from nibabel.orientations import io_orientation, ornt_transform
 except ImportError:
     import_fail_string = r"""
     You must install darwin-py with pip install darwin-py\[medical]
@@ -128,7 +127,11 @@ def export(
                 polygon_annotations, slot_map, output_volumes, legacy=legacy
             )
         write_output_volume_to_disk(
-            output_volumes, image_id=image_id, output_dir=output_dir, legacy=legacy
+            output_volumes,
+            image_id=image_id,
+            output_dir=output_dir,
+            legacy=legacy,
+            filename=video_annotation.filename,
         )
         # Need to map raster layers to SeriesInstanceUIDs
         if mask_present:
@@ -456,6 +459,7 @@ def write_output_volume_to_disk(
     image_id: str,
     output_dir: Union[str, Path],
     legacy: bool = False,
+    filename: str = None,
 ) -> None:
     """Writes the given output volumes to disk.
 
@@ -470,6 +474,8 @@ def write_output_volume_to_disk(
     legacy : bool, default=False
         If ``True``, the exporter will use the legacy calculation.
         If ``False``, the exporter will use the new calculation by dividing with pixdims.
+    filename: str
+        The filename of the dataset item
 
     Returns
     -------
@@ -489,18 +495,10 @@ def unnest_dict_to_list(d: Dict) -> List:
     volumes = unnest_dict_to_list(output_volumes)
     for volume in volumes:
         img = nib.Nifti1Image(
-            dataobj=np.flip(volume.pixel_array, (0, 1, 2)).astype(np.int16),
+            dataobj=volume.pixel_array.astype(np.int16),
             affine=volume.affine,
         )
-        if legacy and volume.original_affine is not None:
-            orig_ornt = io_orientation(
-                volume.original_affine
-            )  # Get orientation of current affine
-            img_ornt = io_orientation(volume.affine)  # Get orientation of RAS affine
-            from_canonical = ornt_transform(
-                img_ornt, orig_ornt
-            )  # Get transform from RAS to current affine
-            img = img.as_reoriented(from_canonical)
+        img = get_reoriented_nifti_image(img, volume)
         if volume.from_raster_layer:
             output_path = Path(output_dir) / f"{image_id}_{volume.class_name}_m.nii.gz"
         else:
@@ -510,6 +508,27 @@ def unnest_dict_to_list(d: Dict) -> List:
         nib.save(img=img, filename=output_path)
 
 
+def get_reoriented_nifti_image(img: nib.Nifti1Image, volume: Dict) -> nib.Nifti1Image:
+    """
+    Reorients the given NIfTI image based on the original affine.
+
+    Parameters
+    ----------
+    img: nib.Nifti1Image
+        The NIfTI image to be reoriented
+    volume: Dict
+        The volume containing the affine and original affine
+    """
+    if volume.original_affine is not None:
+        img_ax_codes = nib.orientations.aff2axcodes(volume.affine)
+        orig_ax_codes = nib.orientations.aff2axcodes(volume.original_affine)
+        img_ornt = nib.orientations.axcodes2ornt(img_ax_codes)
+        orig_ornt = nib.orientations.axcodes2ornt(orig_ax_codes)
+        transform = nib.orientations.ornt_transform(img_ornt, orig_ornt)
+        img = img.as_reoriented(transform)
+    return img
+
+
 def shift_polygon_coords(
     polygon: List[Dict], pixdim: List[Number], legacy: bool = False
 ) -> List:

darwin/importer/formats/nifti.py

@@ -3,7 +3,7 @@
 import warnings
 from collections import OrderedDict, defaultdict
 from pathlib import Path
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple, Any
 
 from rich.console import Console
 
@@ -31,8 +31,7 @@
 
 def parse_path(
     path: Path,
-    legacy: bool = False,
-    remote_files_that_require_legacy_scaling: Optional[List] = [],
+    remote_files_that_require_legacy_scaling: Dict[Path, Dict[str, Any]] = {},
 ) -> Optional[List[dt.AnnotationFile]]:
     """
     Parses the given ``nifti`` file and returns a ``List[dt.AnnotationFile]`` with the parsed
@@ -42,9 +41,8 @@ def parse_path(
     ----------
     path : Path
         The ``Path`` to the ``nifti`` file.
-    legacy : bool, default: False
-        If ``True``, the function will not attempt to resize the annotations to isotropic pixel dimensions.
-        If ``False``, the function will resize the annotations to isotropic pixel dimensions.
+    remote_files_that_require_legacy_scaling : Optional[Dict[Path, Dict[str, Any]]]
+        A dictionary of remote file full paths to their slot affine maps
 
     Returns
     -------
@@ -78,16 +76,20 @@ def parse_path(
         return None
     annotation_files = []
     for nifti_annotation in nifti_annotations:
-        legacy = nifti_annotation["image"] in remote_files_that_require_legacy_scaling
+        remote_file_path = Path(nifti_annotation["image"])
+        if not str(remote_file_path).startswith("/"):
+            remote_file_path = Path("/" + str(remote_file_path))
+
         annotation_file = _parse_nifti(
             Path(nifti_annotation["label"]),
-            nifti_annotation["image"],
+            Path(nifti_annotation["image"]),
             path,
             class_map=nifti_annotation.get("class_map"),
             mode=nifti_annotation.get("mode", "image"),
             slot_names=nifti_annotation.get("slot_names", []),
             is_mpr=nifti_annotation.get("is_mpr", False),
-            legacy=legacy,
+            remote_file_path=remote_file_path,
+            remote_files_that_require_legacy_scaling=remote_files_that_require_legacy_scaling,
         )
         annotation_files.append(annotation_file)
     return annotation_files
@@ -101,10 +103,16 @@ def _parse_nifti(
     mode: str,
     slot_names: List[str],
     is_mpr: bool,
-    legacy: bool = False,
+    remote_file_path: Path,
+    remote_files_that_require_legacy_scaling: Dict[Path, Dict[str, Any]] = {},
 ) -> dt.AnnotationFile:
-    img, pixdims = process_nifti(nib.load(nifti_path))
+    img, pixdims = process_nifti(
+        nib.load(nifti_path),
+        remote_file_path=remote_file_path,
+        remote_files_that_require_legacy_scaling=remote_files_that_require_legacy_scaling,
+    )
 
+    legacy = remote_file_path in remote_files_that_require_legacy_scaling
     processed_class_map = process_class_map(class_map)
     video_annotations = []
     if mode == "instances":  # For each instance produce a video annotation
@@ -159,11 +167,12 @@ def _parse_nifti(
             dt.AnnotationClass(class_name, "mask", "mask")
             for class_name in class_map.values()
         }
-
+    remote_path = "/" if filename.parent == "." else filename.parent
+    filename = Path(filename.name)
     return dt.AnnotationFile(
         path=json_path,
         filename=str(filename),
-        remote_path="/",
+        remote_path=str(remote_path),
         annotation_classes=annotation_classes,
         annotations=video_annotations,
         slots=[
@@ -353,7 +362,7 @@ def nifti_to_video_polygon_annotation(
     if len(all_frame_ids) == 1:
         segments = [[all_frame_ids[0], all_frame_ids[0] + 1]]
     elif len(all_frame_ids) > 1:
-        segments = [[min(all_frame_ids), max(all_frame_ids)]]
+        segments = [[min(all_frame_ids), max(all_frame_ids) + 1]]
     video_annotation = dt.make_video_annotation(
         frame_annotations,
         keyframes={f_id: True for f_id in all_frame_ids},
@@ -513,16 +522,22 @@ def correct_nifti_header_if_necessary(img_nii):
 def process_nifti(
     input_data: nib.nifti1.Nifti1Image,
     ornt: Optional[List[List[float]]] = [[0.0, -1.0], [1.0, -1.0], [2.0, -1.0]],
+    remote_file_path: Path = Path("/"),
+    remote_files_that_require_legacy_scaling: Dict[Path, Dict[str, Any]] = {},
 ) -> Tuple[np.ndarray, Tuple[float]]:
     """
     Converts a nifti object of any orientation to the passed ornt orientation.
     The default ornt is LPI.
 
     Args:
         input_data: nibabel nifti object.
-        ornt: (n,2) orientation array. It defines a transformation from RAS.
+        ornt: (n,2) orientation array. It defines a transformation to LPI
             ornt[N,1] is a flip of axis N of the array, where 1 means no flip and -1 means flip.
             ornt[:,0] is the transpose that needs to be done to the implied array, as in arr.transpose(ornt[:,0]).
+        remote_file_path: Path
+            The full path of the remote file
+        remote_files_that_require_legacy_scaling: Dict[Path, Dict[str, Any]]
+            A dictionary of remote file full paths to their slot affine maps
 
     Returns:
         data_array: pixel array with orientation ornt.
@@ -531,9 +546,13 @@ def process_nifti(
     img = correct_nifti_header_if_necessary(input_data)
     orig_ax_codes = nib.orientations.aff2axcodes(img.affine)
     orig_ornt = nib.orientations.axcodes2ornt(orig_ax_codes)
+    if remote_file_path in remote_files_that_require_legacy_scaling:
+        slot_affine_map = remote_files_that_require_legacy_scaling[remote_file_path]
+        affine = slot_affine_map[next(iter(slot_affine_map))]  # Take the 1st slot
+        ax_codes = nib.orientations.aff2axcodes(affine)
+        ornt = nib.orientations.axcodes2ornt(ax_codes)
     transform = nib.orientations.ornt_transform(orig_ornt, ornt)
     reoriented_img = img.as_reoriented(transform)
-
     data_array = reoriented_img.get_fdata()
     pixdims = reoriented_img.header.get_zooms()
 

tests/darwin/dataset/remote_dataset_test.py

@@ -6,6 +6,7 @@
 from pathlib import Path
 from typing import Any, Dict
 from unittest.mock import MagicMock, patch
+import numpy as np
 
 import orjson as json
 import pytest
@@ -1906,7 +1907,14 @@ def mock_remote_files(self):
                 seq=1,
                 current_workflow_id=None,
                 path="/path/to/file",
-                slots=[{"metadata": {"medical": {"handler": "MONAI"}}}],
+                slots=[
+                    {
+                        "slot_name": "0",
+                        "metadata": {
+                            "medical": {"handler": "MONAI", "affine": [1, 0, 0, 0]}
+                        },
+                    }
+                ],
                 layout={},
                 current_workflow=None,
             ),
@@ -1921,7 +1929,21 @@ def mock_remote_files(self):
                 seq=2,
                 current_workflow_id=None,
                 path="/path/to/file",
-                slots=[{"metadata": {"medical": {}}}],
+                slots=[
+                    {
+                        "slot_name": "0",
+                        "metadata": {
+                            "medical": {
+                                "affine": [
+                                    [-1, 0, 0, 0],
+                                    [0, -1, 0, 0],
+                                    [0, 0, -1, 0],
+                                    [0, 0, 0, 1],
+                                ]
+                            }
+                        },
+                    }
+                ],
                 layout={},
                 current_workflow=None,
             ),
@@ -1941,6 +1963,7 @@ def test_get_remote_files_that_require_legacy_scaling(
         )
 
         result = remote_dataset._get_remote_files_that_require_legacy_scaling()
-
-        assert len(result) == 1
-        assert result[0] == "/path/to/file/filename"
+        assert Path("/path/to/file/filename") in result
+        np.testing.assert_array_equal(
+            result[Path("/path/to/file/filename")]["0"], np.array([[-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])  # type: ignore
+        )

tests/darwin/importer/formats/import_nifti_test.py

@@ -190,7 +190,13 @@ def test_image_annotation_nifti_import_single_slot_to_mask_legacy(
             with patch("darwin.importer.formats.nifti.zoom") as mock_zoom:
                 mock_zoom.side_effect = ndimage.zoom
 
-                remote_files_that_require_legacy_scaling = ["vol0 (1).nii"]
+                remote_files_that_require_legacy_scaling = {
+                    Path("/vol0 (1).nii"): {
+                        "0": np.array(
+                            [[-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]
+                        )
+                    }
+                }
                 annotation_files = parse_path(
                     path=upload_json,
                     remote_files_that_require_legacy_scaling=remote_files_that_require_legacy_scaling,
@@ -516,7 +522,11 @@ def test_parse_path_nifti_with_legacy_scaling():
     )
     adjust_nifti_label_filepath(nifti_annotation_filepath, nifti_filepath)
     expected_annotations = parse_darwin_json(expected_annotations_filepath)
-    remote_files_that_require_legacy_scaling = ["BRAINIX_NIFTI_ROI.nii.gz"]
+    remote_files_that_require_legacy_scaling = {
+        Path("/BRAINIX_NIFTI_ROI.nii.gz"): {
+            "0": np.array([[-1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]])
+        }
+    }
     parsed_annotations = parse_path(
         nifti_annotation_filepath,
         remote_files_that_require_legacy_scaling=remote_files_that_require_legacy_scaling,
@@ -551,9 +561,13 @@ def test_parse_path_nifti_without_legacy_scaling():
         / "no-legacy"
         / "BRAINIX_NIFTI_ROI.nii.json"
     )
+    remote_files_that_require_legacy_scaling = {}
     adjust_nifti_label_filepath(nifti_annotation_filepath, nifti_filepath)
     expected_annotations = parse_darwin_json(expected_annotations_filepath)
-    parsed_annotations = parse_path(nifti_annotation_filepath, legacy=False)
+    parsed_annotations = parse_path(
+        nifti_annotation_filepath,
+        remote_files_that_require_legacy_scaling=remote_files_that_require_legacy_scaling,
+    )
     for frame_idx in expected_annotations.annotations[0].frames:
         expected_annotation = (
             expected_annotations.annotations[0].frames[frame_idx].data["paths"]