[ANN-760][external] nifti import to multi slot item

darwin/cli.py

@@ -50,7 +50,7 @@ def main() -> None:
         fd.write("Darwin CLI error log")
         fd.write(f"Version: {__version__}")
         fd.write(f"OS: {platform.platform()}")
-        fd.write(f"Command: {dumps(args, check_circular=True)}")
+        fd.write(f"Command: {dumps(vars(args), check_circular=True)}")
         fd.write(f"Error: {dumps(e, check_circular=True)}")
         fd.close()
 

darwin/exporter/formats/nifti.py

@@ -1,3 +1,4 @@
+import ast
 import json as native_json
 from asyncore import loop
 from pathlib import Path
@@ -161,9 +162,6 @@ def get_view_idx(frame_idx, groups):
 
 
 def get_view_idx_from_slot_name(slot_name):
-    # if mpr:
-    #     #do this correct treatment volumetrically.
-    #     pass
     slot_names = {"0.1": 0, "0.2": 1, "0.3": 2}
     slot_names.get(slot_name, 0)
     return slot_names.get(slot_name, 0)
@@ -176,7 +174,7 @@ def process_metadata(metadata):
     original_affine = process_affine(metadata.get("original_affine"))
     # If the original affine is in the medical payload of metadata then use it
     if isinstance(pixdim, str):
-        pixdim = eval(pixdim)
+        pixdim = ast.literal_eval(pixdim)
         if isinstance(pixdim, tuple) or isinstance(pixdim, list):
             if len(pixdim) == 4:
                 pixdim = pixdim[1:]
@@ -195,7 +193,7 @@ def process_metadata(metadata):
 
 def process_affine(affine):
     if isinstance(affine, str):
-        affine = np.squeeze(np.array([eval(l) for l in affine.split("\n")]))
+        affine = np.squeeze(np.array([ast.literal_eval(l) for l in affine.split("\n")]))
     elif isinstance(affine, list):
         affine = np.array(affine).astype(np.float)
     else:

darwin/importer/formats/nifti.py

@@ -1,3 +1,4 @@
+import sys
 import warnings
 import zipfile
 from collections import OrderedDict, defaultdict
@@ -17,7 +18,7 @@
     in order to import with using nifti format
     """
     console.print(import_fail_string)
-    exit()
+    sys.exit(1)
 import numpy as np
 from jsonschema import validate
 from upolygon import find_contours
@@ -69,12 +70,14 @@ def parse_path(path: Path) -> Optional[List[dt.AnnotationFile]]:
             path,
             class_map=nifti_annotation.get("class_map"),
             mode=nifti_annotation.get("mode", "image"),
+            slot_name=nifti_annotation.get("slot_name"),
         )
         annotation_files.append(annotation_file)
     return annotation_files
 
 
-def _parse_nifti(nifti_path: Path, filename: Path, json_path: Path, class_map: Dict, mode: str) -> dt.AnnotationFile:
+def _parse_nifti(nifti_path: Path, filename: Path, json_path: Path, 
+                 class_map: Dict, mode: str, slot_name: Optional[str] = None) -> dt.AnnotationFile:
 
     img: np.ndarray = process_nifti(nib.load(nifti_path))
 
@@ -88,7 +91,7 @@ def _parse_nifti(nifti_path: Path, filename: Path, json_path: Path, class_map: D
             class_img = np.isin(img, class_idxs).astype(np.uint8)
             cc_img, num_labels = cc3d.connected_components(class_img, return_N=True)
             for instance_id in range(1, num_labels):
-                video_annotation = get_video_annotation(cc_img, class_idxs=[instance_id], class_name=class_name)
+                video_annotation = get_video_annotation(cc_img, class_idxs=[instance_id], class_name=class_name, slot_name=slot_name)
                 if video_annotation:
                     video_annotations.append(video_annotation)
     elif mode == "image":  # For each frame and each class produce a single frame video annotation
@@ -108,14 +111,14 @@ def _parse_nifti(nifti_path: Path, filename: Path, json_path: Path, class_map: D
                     keyframes={i: True, i + 1: True},
                     segments=[[i, i + 1]],
                     interpolated=False,
-                    slot_names=[],
+                    slot_names=[] if slot_name is None else [slot_name],
                 )
                 video_annotations.append(video_annotation)
     elif mode == "video":  # For each class produce a single video annotation
         for class_name, class_idxs in processed_class_map.items():
             if class_name == "background":
                 continue
-            video_annotation = get_video_annotation(img, class_idxs=class_idxs, class_name=class_name)
+            video_annotation = get_video_annotation(img, class_idxs=class_idxs, class_name=class_name, slot_name=slot_name)
             if video_annotation is None:
                 continue
             video_annotations.append(video_annotation)
@@ -128,11 +131,11 @@ def _parse_nifti(nifti_path: Path, filename: Path, json_path: Path, class_map: D
         remote_path="/",
         annotation_classes=annotation_classes,
         annotations=video_annotations,
-        slots=[dt.Slot(name=None, type="dicom", source_files=[{"url": None, "file_name": str(filename)}])],
+        slots=[dt.Slot(name=slot_name, type="dicom", source_files=[{"url": None, "file_name": str(filename)}])],
     )
 
 
-def get_video_annotation(volume: np.ndarray, class_name: str, class_idxs: List[int]) -> Optional[dt.VideoAnnotation]:
+def get_video_annotation(volume: np.ndarray, class_name: str, class_idxs: List[int], slot_name: Optional[str]) -> Optional[dt.VideoAnnotation]:
     frame_annotations = OrderedDict()
     for i in range(volume.shape[-1]):
         slice_mask = volume[:, :, i].astype(np.uint8)
@@ -156,7 +159,7 @@ def get_video_annotation(volume: np.ndarray, class_name: str, class_idxs: List[i
         keyframes={f_id: True for f_id in all_frame_ids},
         segments=segments,
         interpolated=False,
-        slot_names=[],
+        slot_names=[] if slot_name is None else [slot_name],
     )
     return video_annotation
 

darwin/importer/formats/nifti_schemas.py

@@ -7,6 +7,7 @@
         "label": {"type": "string"},
         "class_map": class_map,
         "mode": {"type": "string", "enum": ["image", "video", "instances"]},
+        "slot_name": {"type": "string"},
     },
     "required": ["image", "label", "class_map"],
     "additionalProperties": False,

darwin/importer/importer.py

@@ -145,8 +145,12 @@ def maybe_console(*args: Union[str, int, float]) -> None:
         parsed_files = list(map(importer, tqdm(files) if is_console else files))
 
     maybe_console("Finished.")
-
-    if not isinstance(parsed_files, list):
+    # Sometimes we have a list of lists of AnnotationFile, sometimes we have a list of AnnotationFile
+    # We flatten the list of lists
+    if isinstance(parsed_files, list):
+        if isinstance(parsed_files[0], list):
+            parsed_files = [item for sublist in parsed_files for item in sublist]
+    else:
         parsed_files = [parsed_files]
 
     return parsed_files
@@ -467,7 +471,8 @@ def import_annotations(
             _warn_unsupported_annotations(files_to_track)
             for parsed_file in track(files_to_track):
                 image_id, default_slot_name = remote_files[parsed_file.full_path]
-
+                if parsed_file.slots:
+                    default_slot_name = parsed_file.slots[0].name
                 _import_annotations(
                     dataset.client,
                     image_id,
@@ -577,7 +582,6 @@ def _import_annotations(
         # Insert the default slot name if not available in the import source
         if not annotation.slot_names and dataset.version > 1:
             annotation.slot_names.extend([default_slot_name])
-
         serialized_annotations.append(
             {
                 "annotation_class_id": annotation_class_id,