upload_annotations can be executed as script

Improve README with the commands for download and upload

README.md

@@ -2,6 +2,36 @@
 
 Utility functions for MD.ai
 
+## Download data
+
+- Download all data, dicom and annotations. Dicoms are only needed once,
+or when data is added to the mdai dataset)
+
+```bash
+python -m mdai_utils.download_annotations \
+--parameters myparameters.json \
+-o ./data \
+--download_dicoms
+```
+
+Once dicoms are downloaded, just download annotations, a json file will be generated in `./data`:
+
+```bash
+python -m mdai_utils.download_annotations \
+ --parameters myparameters.json \
+ -o ./data
+```
+
+## Upload annotations / segmentations
+
+```bash
+python -m mdai_utils.upload_annotations \
+ --parameters ./tests/test_local_parameters.json \
+ --sop_instance_uid "1.2.826.0.1.3680043.2.1125.1.75064541463040.2005072610414630768" \
+ --label_name mylabel \
+ -i ./tests/fixtures/humanct_0002_1000_seg.nii.gz
+```
+
 ## Development
 
 For information about building, running, and contributing to this code base,

mdai_utils/download_annotations.py

@@ -163,9 +163,11 @@ def main(args):
 
     out_folder = args.out_folder or parameters.get("out_folder", DEFAULT_DATA_PATH)
     no_download = args.no_download or parameters.get("no_download", False)
-    mdai_annotations_only = not args.download_dicoms or parameters.get(
+
+    download_dicoms = args.download_dicoms or not parameters.get(
         "mdai_annotations_only", True
     )
+    mdai_annotations_only = not download_dicoms
     no_fixing_metadata = args.no_fixing_metadata or parameters.get(
         "mdai_no_fixing_metadata", False
     )

mdai_utils/upload_annotations.py

@@ -80,3 +80,93 @@ def upload_image_annotation_slice(
         mdai_label_id=mdai_label_id,
         sop_instance_uid=sop_instance_uid,
     )
+
+
+def _get_parser():
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "-i",
+        "--input_annotation",
+        type=str,
+        required=True,
+        help="Path to the segmentation image to upload.",
+    )
+    parser.add_argument(
+        "-l",
+        "--label_name",
+        type=str,
+        required=True,
+        help="label name corresponding to the annotation.",
+    )
+    parser.add_argument(
+        "--sop_instance_uid",
+        type=str,
+        default=None,
+        help="sop_instance_uid of the annotation file. Needed to match the annotation with the DICOM image in mdai.",
+    )
+    parser.add_argument(
+        "--sop_instance_metadata",
+        type=str,
+        default=None,
+        help="json file generated by storing the metadata resulting from dicom_utils.read_dicoms_into_volume",
+    )
+    parser.add_argument(
+        "-p",
+        "--parameters",
+        type=str,
+        default=None,
+        help="""
+Path to a json file containing the parameters for md.ai variables: mdai_project_id, mdai_dataset_id, mdai_label_ids, etc.
+See example in tests/test_parameters.json.
+""",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    import json
+
+    from mdai_utils.common import get_mdai_access_token
+
+    parser = _get_parser()
+    args = parser.parse_args()
+    print(args)
+
+    with open(args.parameters, "r") as f:
+        parameters = json.load(f)
+
+    mdai_project_id = parameters["mdai_project_id"]
+    mdai_dataset_id = parameters["mdai_dataset_id"]
+    mdai_label_group_id = parameters["mdai_label_group_id"]
+    mdai_label_ids = parameters["mdai_label_ids"]
+    mdai_domain = parameters["mdai_domain"]
+
+    input_annotation = args.input_annotation
+    label_name = args.label_name
+    mdai_label_id = mdai_label_ids[label_name]
+    sop_instance_uid = args.sop_instance_uid
+    sop_instance_metadata = args.sop_instance_metadata
+    if sop_instance_uid is None:
+        raise ValueError(
+            "Either sop_instance_uid or sop_instance_metadata must be set."
+        )
+    token = get_mdai_access_token()
+    mdai_client = mdai.Client(domain=mdai_domain, access_token=token)
+
+    failed_annotations = upload_image_annotation_slice(
+        segmentation_image_path=input_annotation,
+        sop_instance_uid=sop_instance_uid,
+        mdai_client=mdai_client,
+        mdai_project_id=mdai_project_id,
+        mdai_dataset_id=mdai_dataset_id,
+        mdai_label_id=mdai_label_id,
+    )
+    if len(failed_annotations) == 0:
+        print("All annotations uploaded successfully.")
+        exit(0)
+    else:
+        print(f"Failed annotations: {failed_annotations}")
+        exit(1)