test: add test for extract_tile_shape_features cli

src/luna/common/utils.py

@@ -90,7 +90,7 @@ def wrapper(*args, **kwargs):
 
             tmp_dir_dest = []
             for key, write_mode in dir_key_write_mode.items():
-                if not args_dict[key]:
+                if key not in args_dict or not args_dict[key]:
                     continue
                 storage_options_key = "storage_options"
                 if "w" in write_mode:
@@ -107,7 +107,7 @@ def wrapper(*args, **kwargs):
             result = None
             with ExitStack() as stack:
                 for key, write_mode in file_key_write_mode.items():
-                    if not args_dict[key]:
+                    if key not in args_dict or not args_dict[key]:
                         continue
                     storage_options_key = "storage_options"
                     if "w" in write_mode:

src/luna/pathology/cli/extract_tile_shape_features.py

@@ -76,6 +76,8 @@ def cli(
     statistical_descriptors: str = StatisticalDescriptors.ALL,
     cellular_features: str = CellularFeatures.ALL,
     property_type: str = PropertyType.ALL,
+    include_smaller_regions: bool = False,
+    label_cols: List[str] = None,
     storage_options: dict = {},
     output_storage_options: dict = {},
     local_config: str = "",
@@ -92,6 +94,8 @@ def cli(
         statistical_descriptors (str): statistical descriptors to calculate. One of All, Quantiles, Stats, or Density
         cellular_features (str): cellular features to include. One of All, Nucleus, Cell, Cytoplasm, and Membrane
         property_type (str): properties to include. One of All, Geometric, or Stain
+        include_smaller_regions (bool): include smaller regions in output
+        label_cols (List[str]): list of score columns to use for the classification. Tile is classified as the column with the max score
         storage_options (dict): storage options to pass to reading functions
         output_storage_options (dict): storage options to pass to writing functions
         local_config (str): local config yaml file
@@ -141,6 +145,8 @@ def cli(
         statistical_descriptors,
         cellular_features,
         property_type,
+        config["include_smaller_regions"],
+        config["label_cols"],
     )
 
     with fs.open(output_fpath, "wb") as of:
@@ -167,6 +173,7 @@ def extract_tile_shape_features(
     cellular_features: CellularFeatures = CellularFeatures.ALL,
     property_type: PropertyType = PropertyType.ALL,
     include_smaller_regions: bool = False,
+    label_cols: List[str] = None,
     properties: List[str] = [
         "area",
         "convex_area",
@@ -195,15 +202,14 @@ def extract_tile_shape_features(
         statistical_descriptors (StatisticalDescriptors): statistical descriptors to calculate
         cellular_features (CellularFeatures): cellular features to include
         property_type (PropertyType): properties to include
+        label_cols (List[str]): list of score columns to use for the classification. Tile is classified as the column with the max score
         properties (List[str]): list of whole slide image properties to
             extract. Needs to be parquet compatible (numeric).
-
     Returns:
         dict: output paths and the number of features generated
     """
-    import pdb
-
-    pdb.set_trace()
+    if label_cols:
+        tiles_df["Classification"] = tiles_df[label_cols].idxmax(axis=1)
     LabeledTileSchema.validate(tiles_df.reset_index())
 
     tile_area = tiles_df.iloc[0].tile_size ** 2
@@ -260,6 +266,9 @@ def extract_tile_shape_features(
 
     logger.info("Spatially joining tiles and objects")
     gdf = object_gdf.sjoin(tiles_gdf, how="inner", predicate="within")
+    if len(gdf) == 0:
+        logger.info("No objects found within tiles")
+        return None
     try:
         measurement_keys = list(gdf.measurements.iloc[0].keys())
         gdf = gdf.join(gdf.measurements.apply(lambda x: pd.Series(x)))

src/luna/pathology/cli/generate_tile_mask.py

@@ -77,8 +77,8 @@ def convert_tiles_to_mask(
     tiles_df: pd.DataFrame,
     slide: tiffslide.TiffSlide,
     label_cols: Union[str, List[str]],
-    output_urlpath: str,
-    output_storage_options: dict,
+    output_urlpath: str = "",
+    output_storage_options: dict = {},
 ):
     """Converts categorical tile labels to a slide image mask. This mask can be used for feature extraction and spatial analysis.
 
@@ -113,8 +113,8 @@ def convert_tiles_to_mask(
     slide_width: int,
     slide_height: int,
     label_cols: Union[str, List[str]],
-    output_urlpath: str,
-    output_storage_options: dict,
+    output_urlpath: str = "",
+    output_storage_options: dict = {},
 ):
     """Converts categorical tile labels to a slide image mask. This mask can be used for feature extraction and spatial analysis.
 
@@ -154,10 +154,11 @@ def convert_tiles_to_mask(
 
         logger.info(f"{address}, {row['mask']}, {value}")
 
-    slide_mask = Path(output_urlpath) / "tile_mask.tif"
-    logger.info(f"Saving output mask to {slide_mask}")
-    with open(slide_mask, "wb") as of:
-        tifffile.imwrite(of, mask_arr)
+    if output_urlpath:
+        slide_mask = Path(output_urlpath) / "tile_mask.tif"
+        logger.info(f"Saving output mask to {slide_mask}")
+        with open(slide_mask, "wb") as of:
+            tifffile.imwrite(of, mask_arr)
 
     return mask_arr, mask_values
 

tests/luna/pathology/cli/test_extract_tile_shape_features.py

@@ -0,0 +1,64 @@
+import os
+
+import fire
+import pandas as pd
+
+from luna.pathology.cli.extract_tile_shape_features import cli
+
+
+def test_cli_extract_tile_shape_features(tmp_path):
+    fire.Fire(
+        cli,
+        [
+            "--slide_urlpath",
+            "tests/testdata/pathology/123.svs",
+            "--object_urlpath",
+            "tests/testdata/pathology/test_cell_detections.geojson",
+            "--tiles_urlpath",
+            "tests/testdata/pathology/infer_tumor_background/123/tile_scores_and_labels_pytorch_inference.parquet",
+            "--output_urlpath",
+            str(tmp_path),
+            "--label_cols",
+            "Background,Tumor",
+        ],
+    )
+
+    assert os.path.exists(f"{tmp_path}/shape_features.parquet")
+    assert os.path.exists(f"{tmp_path}/metadata.yml")
+    df = pd.read_parquet(f"{tmp_path}/shape_features.parquet")
+
+    assert len(df) == 866
+
+
+def test_cli_extract_tile_shape_features_s3(s3fs_client):
+    s3fs_client.mkdirs("testtile", exist_ok=True)
+    s3fs_client.put("tests/testdata/pathology/123.svs", "testtile/test/")
+    s3fs_client.put(
+        "tests/testdata/pathology/test_cell_detections.geojson", "testtile/test/"
+    )
+    s3fs_client.put(
+        "tests/testdata/pathology/infer_tumor_background/123/tile_scores_and_labels_pytorch_inference.parquet",
+        "testtile/test/",
+    )
+    fire.Fire(
+        cli,
+        [
+            "--slide_urlpath",
+            "s3://testtile/test/123.svs",
+            "--object_urlpath",
+            "s3://testtile/test/test_cell_detections.geojson",
+            "--tiles_urlpath",
+            "s3://testtile/test/tile_scores_and_labels_pytorch_inference.parquet",
+            "--output_urlpath",
+            "s3://testtile/out/",
+            "--label_cols",
+            "Background,Tumor",
+            "--storage_options",
+            "{'key': '', 'secret': '', 'client_kwargs': {'endpoint_url': '"
+            + s3fs_client.client_kwargs["endpoint_url"]
+            + "'}}",
+        ],
+    )
+
+    assert s3fs_client.exists("s3://testtile/out/shape_features.parquet")
+    assert s3fs_client.exists("s3://testtile/out/metadata.yml")