fix: minor fixes

Author: okunator

cellseg_models_pytorch/utils/__init__.py

@@ -27,7 +27,6 @@
     soft_type_flatten,
     type_map_flatten,
 )
-from .multiproc import run_pool
 from .tensor_utils import to_device, to_tensor
 
 __all__ = [

cellseg_models_pytorch/utils/file_manager.py

@@ -8,7 +8,6 @@
 import scipy.io as sio
 
 from .mask_utils import bounding_box, get_inst_centroid, get_inst_types
-from .vectorize import inst2gdf, sem2gdf
 
 try:
     import tables as tb
@@ -328,132 +327,6 @@ def gdf_to_file(
         elif suffix == ".geojson":
             gdf.to_file(path.with_suffix(".geojson"), driver="GeoJSON")
 
-    @staticmethod
-    def to_gson(
-        masks: Dict[str, np.ndarray],
-        path: Union[str, Path],
-        coords: Tuple[int, int, int, int] = None,
-        compute_centroids: bool = False,
-        compute_bboxes: bool = False,
-        class_dict_inst: Dict[int, str] = None,
-        class_dict_sem: Dict[int, str] = None,
-        class_dict_cyto: Dict[int, str] = None,
-        use_subfolders: bool = True,
-        silence_warnings: bool = True,
-    ) -> None:
-        """Write a geojson/feather/parquet files from a dict of model output masks.
-
-        Note:
-            Additonal computed arrays are saved if the corresponding flags are set:
-            - centroid: The centroids of the instance masks. Shape (N, 2).
-            - bbox: The bounding boxes of the instance masks. Shape (N, 4).
-
-        Note:
-            Each specific mask type is either embedded to the end of the filename or
-            saved to it's corresponding subfolder if `use_subfolders` is set to True:
-            - inst: instance masks
-            - sem: semantic masks
-            - cyto: cytoplasm masks
-
-        Parameters:
-            masks (Dict[str, np.ndarray]):
-                The masks to be saved. E.g. {"inst": np.ndarray, "type": np.ndarray}.
-            path (str or Path):
-                The output filename. One of .geojson, .feather, .parquet.
-            coords (Tuple[int, int, int, int], default=None):
-                The XYWH-coordinates of the image patch. (x0, y0, width, height).
-            compute_centroids (bool, default=False):
-                Compute the centroids of the instance masks.
-            compute_bboxes (bool, default=False):
-                Compute the bounding boxes of the instance masks.
-            class_dict_inst (Dict[int, str], default=None):
-                A dictionary mapping class indices to class names.
-                E.g. {1: 'neoplastic', 2: 'immune'}.
-            class_dict_sem (Dict[int, str], default=None):
-                A dictionary mapping class indices to class names.
-                E.g. {1: 'tumor', 2: 'stroma'}.
-            class_dict_cyto (Dict[int, str], default=None):
-                A dictionary mapping class indices to class names.
-                E.g. {1: 'neoplastic_cyto', 2: 'connective_cyto'}.
-            use_subfolders (bool, default=True):
-                If True, saves the masks to their respective subfolders. I.e. subfolders
-                called 'inst', 'sem', 'cyto'. If False, the mask type is embedded in the
-                end of the filename.
-            silence_warnings (bool, default=True):
-                If True, warnings are silenced.
-        """
-        path = Path(path)
-
-        xoff = coords[0] if coords is not None else None
-        yoff = coords[1] if coords is not None else None
-
-        if masks.get("inst", None) is not None:
-            if use_subfolders:
-                inst_subdir = path.parent / "inst"
-                inst_subdir.mkdir(parents=True, exist_ok=True)
-                inst_path = inst_subdir / path.name
-            else:
-                inst_path = path.parent / f"{path.stem}_inst{path.suffix}"
-
-            if masks.get("type", None) is not None:
-                inst_gdf = inst2gdf(
-                    masks["inst"],
-                    masks["type"],
-                    xoff=xoff,
-                    yoff=yoff,
-                    class_dict=class_dict_inst,
-                )
-            else:
-                inst_gdf = inst2gdf(masks["inst"], xoff=xoff, yoff=yoff)
-
-            if compute_centroids:
-                inst_gdf["centroid"] = inst_gdf["geometry"].centroid
-            if compute_bboxes:
-                inst_gdf["bbox"] = inst_gdf["geometry"].apply(lambda x: x.bounds)
-
-            FileHandler.gdf_to_file(inst_gdf, inst_path, silence_warnings)
-
-        if masks.get("sem", None) is not None:
-            if use_subfolders:
-                sem_subdir = path.parent / "sem"
-                sem_subdir.mkdir(parents=True, exist_ok=True)
-                sem_path = sem_subdir / path.name
-            else:
-                sem_path = path.parent / f"{path.stem}_sem{path.suffix}"
-
-            sem_gdf = sem2gdf(
-                masks["sem"],
-                xoff=xoff,
-                yoff=yoff,
-                class_dict=class_dict_sem,
-            )
-            FileHandler.gdf_to_file(sem_gdf, sem_path, silence_warnings)
-
-        if masks.get("cyto", None) is not None:
-            if use_subfolders:
-                cyto_subdir = path.parent / "cyto"
-                cyto_subdir.mkdir(parents=True, exist_ok=True)
-                cyto_path = cyto_subdir / path.name
-            else:
-                cyto_path = path.parent / f"{path.stem}_cyto{path.suffix}"
-
-            if masks.get("cyto_type", None) is not None:
-                cyto_gdf = inst2gdf(
-                    masks["cyto"],
-                    masks["cyto_type"],
-                    xoff=xoff,
-                    yoff=yoff,
-                    class_dict=class_dict_cyto,
-                )
-            else:
-                cyto_gdf = inst2gdf(
-                    masks["cyto"],
-                    xoff=xoff,
-                    yoff=yoff,
-                    class_dict=class_dict_cyto,
-                )
-            FileHandler.gdf_to_file(cyto_gdf, cyto_path, silence_warnings)
-
     @staticmethod
     def to_mat(
         masks: Dict[str, np.ndarray],

cellseg_models_pytorch/utils/mask_utils.py

@@ -173,8 +173,7 @@ def binarize(inst_map: np.ndarray) -> np.ndarray:
         np.ndarray:
             Binary mask. Shape (H, W). Type: uint8.
     """
-    binary = np.copy(inst_map > 0)
-    return binary.astype("uint8")
+    return (inst_map > 0).astype("uint8")
 
 
 # ported from https://github.com/vqdang/hover_net/blob/master/src/loader/augs.py