Fix/bug in masking building

CHANGELOG.md

@@ -1,6 +1,12 @@
 # Changelog
 
-## [Unreleased]
+## [0.5.2]
+
+### Fix
+- Fix critical bug in masking roi image handling causing incorrect results when image and mask have different pixel sizes.
+- Fix bug in loading masking roi images when paths other than default are used.
+
+## [0.5.1]
 
 ### Fix
 - Fix bug causing incorrect channel metadata when creating an image.

pyproject.toml

@@ -37,6 +37,8 @@ dependencies = [
     "numpy",
     "filelock",
     "zarr>3",
+    "scipy",
+    "fsspec",
     "anndata",
     "pydantic",
     "pandas>=1.2.0,<3.0.0",

src/ngio/images/_ome_zarr_container.py

@@ -565,7 +565,7 @@ def get_masked_image(
         masking_label, masking_table = self._find_matching_masking_label(
             masking_label_name=masking_label_name,
             masking_table_name=masking_table_name,
-            pixel_size=pixel_size,
+            pixel_size=image.pixel_size,
         )
         return MaskedImage(
             group_handler=image._group_handler,

src/ngio/io_pipes/_io_pipes_masked.py

@@ -34,7 +34,6 @@ def _numpy_label_to_bool_mask(
     data_shape: tuple[int, ...],
     label_axes: tuple[str, ...],
     data_axes: tuple[str, ...],
-    allow_rescaling: bool = True,
 ) -> np.ndarray:
     """Convert label data to a boolean mask."""
     if label is not None:
@@ -47,7 +46,6 @@ def _numpy_label_to_bool_mask(
         reference_shape=data_shape,
         array_axes=label_axes,
         reference_axes=data_axes,
-        allow_rescaling=allow_rescaling,
     )
     return bool_mask
 
@@ -84,15 +82,14 @@ def _setup_numpy_getters(
 
     if allow_rescaling:
         _zoom_transform = BaseZoomTransform(
-            input_dimensions=dimensions,
-            target_dimensions=label_dimensions,
+            input_dimensions=label_dimensions,
+            target_dimensions=dimensions,
             order="nearest",
         )
         if label_transforms is None or len(label_transforms) == 0:
             label_transforms = [_zoom_transform]
         else:
             label_transforms = [_zoom_transform, *label_transforms]
-
     label_slicing_dict = roi_to_slicing_dict(
         roi=roi,
         pixel_size=label_dimensions.pixel_size,
@@ -147,7 +144,6 @@ def __init__(
 
         self._label_id = roi.label
         self._fill_value = fill_value
-        self._allow_rescaling = allow_rescaling
         super().__init__(
             zarr_array=zarr_array,
             slicing_ops=self._data_getter.slicing_ops,
@@ -164,14 +160,12 @@ def get(self) -> np.ndarray:
         """Get the masked data as a numpy array."""
         data = self._data_getter()
         label_data = self._label_data_getter()
-
         bool_mask = _numpy_label_to_bool_mask(
             label_data=label_data,
             label=self.label_id,
             data_shape=data.shape,
             label_axes=self._label_data_getter.axes_ops.output_axes,
             data_axes=self._data_getter.axes_ops.output_axes,
-            allow_rescaling=self._allow_rescaling,
         )
         if bool_mask.shape != data.shape:
             bool_mask = np.broadcast_to(bool_mask, data.shape)
@@ -214,7 +208,6 @@ def __init__(
         self._data_getter = _data_getter
         self._label_data_getter = _label_data_getter
         self._label_id = roi.label
-        self._allow_rescaling = allow_rescaling
 
         self._data_setter = NumpySetter(
             zarr_array=zarr_array,
@@ -246,7 +239,6 @@ def set(self, patch: np.ndarray) -> None:
             data_shape=data.shape,
             label_axes=self._label_data_getter.axes_ops.output_axes,
             data_axes=self._data_getter.axes_ops.output_axes,
-            allow_rescaling=self._allow_rescaling,
         )
         if bool_mask.shape != data.shape:
             bool_mask = np.broadcast_to(bool_mask, data.shape)
@@ -267,7 +259,6 @@ def _dask_label_to_bool_mask(
     data_shape: tuple[int, ...],
     label_axes: tuple[str, ...],
     data_axes: tuple[str, ...],
-    allow_rescaling: bool = True,
 ) -> DaskArray:
     """Convert label data to a boolean mask."""
     if label is not None:
@@ -280,7 +271,6 @@ def _dask_label_to_bool_mask(
         reference_shape=data_shape,
         array_axes=label_axes,
         reference_axes=data_axes,
-        allow_rescaling=allow_rescaling,
     )
     return bool_mask
 
@@ -317,15 +307,14 @@ def _setup_dask_getters(
 
     if allow_rescaling:
         _zoom_transform = BaseZoomTransform(
-            input_dimensions=dimensions,
-            target_dimensions=label_dimensions,
+            input_dimensions=label_dimensions,
+            target_dimensions=dimensions,
             order="nearest",
         )
         if label_transforms is None or len(label_transforms) == 0:
             label_transforms = [_zoom_transform]
         else:
             label_transforms = [_zoom_transform, *label_transforms]
-
     label_slicing_dict = roi_to_slicing_dict(
         roi=roi,
         pixel_size=label_dimensions.pixel_size,
@@ -379,7 +368,6 @@ def __init__(
         self._label_data_getter = _label_data_getter
         self._label_id = roi.label
         self._fill_value = fill_value
-        self._allow_rescaling = allow_rescaling
         super().__init__(
             zarr_array=zarr_array,
             slicing_ops=self._data_getter.slicing_ops,
@@ -402,7 +390,6 @@ def get(self) -> DaskArray:
             data_shape=data_shape,
             label_axes=self._label_data_getter.axes_ops.output_axes,
             data_axes=self._data_getter.axes_ops.output_axes,
-            allow_rescaling=self._allow_rescaling,
         )
         if bool_mask.shape != data.shape:
             bool_mask = da.broadcast_to(bool_mask, data.shape)
@@ -446,7 +433,6 @@ def __init__(
         self._label_data_getter = _label_data_getter
 
         self._label_id = roi.label
-        self._allow_rescaling = allow_rescaling
 
         self._data_setter = DaskSetter(
             zarr_array=zarr_array,
@@ -480,7 +466,6 @@ def set(self, patch: DaskArray) -> None:
             data_shape=data_shape,
             label_axes=self._label_data_getter.axes_ops.output_axes,
             data_axes=self._data_getter.axes_ops.output_axes,
-            allow_rescaling=self._allow_rescaling,
         )
         if bool_mask.shape != data.shape:
             bool_mask = da.broadcast_to(bool_mask, data.shape)

src/ngio/io_pipes/_match_shape.py

@@ -1,6 +1,6 @@
 import logging
 from collections.abc import Sequence
-from enum import Enum
+from enum import StrEnum
 
 import dask.array as da
 import numpy as np
@@ -10,11 +10,10 @@
 logger = logging.getLogger(f"ngio:{__name__}")
 
 
-class Action(str, Enum):
+class Action(StrEnum):
     NONE = "none"
     PAD = "pad"
     TRIM = "trim"
-    RESCALING = "rescaling"
 
 
 def _compute_pad_widths(
@@ -103,51 +102,6 @@ def _dask_trim(
     return array[tuple(slices)]
 
 
-def _compute_rescaling_shape(
-    array_shape: tuple[int, ...],
-    actions: list[Action],
-    target_shape: tuple[int, ...],
-) -> tuple[int, ...]:
-    rescaling_shape = []
-    factor = []
-    for act, s, ts in zip(actions, array_shape, target_shape, strict=True):
-        if act == Action.RESCALING:
-            rescaling_shape.append(ts)
-            factor.append(ts / s)
-        else:
-            rescaling_shape.append(s)
-            factor.append(1.0)
-
-    logger.warning(
-        f"Images have a different shape ({array_shape} vs {target_shape}). "
-        f"Resolving by scaling with factors {factor}."
-    )
-    return tuple(rescaling_shape)
-
-
-def _numpy_rescaling(
-    array: np.ndarray, actions: list[Action], target_shape: tuple[int, ...]
-) -> np.ndarray:
-    if all(act != Action.RESCALING for act in actions):
-        return array
-    from ngio.common._zoom import numpy_zoom
-
-    rescaling_shape = _compute_rescaling_shape(array.shape, actions, target_shape)
-    return numpy_zoom(source_array=array, target_shape=rescaling_shape, order="nearest")
-
-
-def _dask_rescaling(
-    array: da.Array, actions: list[Action], target_shape: tuple[int, ...]
-) -> da.Array:
-    if all(act != Action.RESCALING for act in actions):
-        return array
-    from ngio.common._zoom import dask_zoom
-
-    shape = tuple(int(s) for s in array.shape)
-    rescaling_shape = _compute_rescaling_shape(shape, actions, target_shape)
-    return dask_zoom(source_array=array, target_shape=rescaling_shape, order="nearest")
-
-
 def _check_axes(array_shape, reference_shape, array_axes, reference_axes):
     if len(array_shape) != len(array_axes):
         raise NgioValueError(
@@ -183,7 +137,6 @@ def _compute_reshape_and_actions(
     array_axes: list[str],
     reference_axes: list[str],
     tolerance: int = 1,
-    allow_rescaling: bool = True,
 ) -> tuple[tuple[int, ...], list[Action]]:
     # Reshape array to match reference shape
     # And determine actions to be taken
@@ -206,24 +159,20 @@ def _compute_reshape_and_actions(
             elif s2 < ref_shape:
                 if (ref_shape - s2) <= tolerance:
                     actions.append(Action.PAD)
-                elif allow_rescaling:
-                    actions.append(Action.RESCALING)
                 else:
                     errors.append(
-                        f"Cannot pad axis={ref_ax}:{s2}->{ref_shape} "
+                        f"Cannot pad axis={ref_ax} from {s2} to {ref_shape} "
                         "because shape difference is outside tolerance "
-                        f"{tolerance}."
+                        f"of {tolerance} pixels."
                     )
             elif s2 > ref_shape:
                 if (s2 - ref_shape) <= tolerance:
                     actions.append(Action.TRIM)
-                elif allow_rescaling:
-                    actions.append(Action.RESCALING)
                 else:
                     errors.append(
-                        f"Cannot trim axis={ref_ax}:{s2}->{ref_shape} "
+                        f"Cannot trim axis={ref_ax} from {s2} to {ref_shape} "
                         "because shape difference is outside tolerance "
-                        f"{tolerance}."
+                        f"of {tolerance} pixels."
                     )
             else:
                 raise RuntimeError("Unreachable code reached.")
@@ -233,8 +182,9 @@ def _compute_reshape_and_actions(
                 "Cannot match shapes if the order is different."
             )
     if errors:
+        error_msg = "\n - ".join(errors)
         raise NgioValueError(
-            "Array shape cannot be matched to reference shape:\n\n".join(errors)
+            f"Array shape cannot be matched to reference shape:\n - {error_msg}"
         )
     return tuple(reshape_tuple), actions
 
@@ -247,7 +197,6 @@ def numpy_match_shape(
     tolerance: int = 1,
     pad_mode: str = "constant",
     pad_values: int | float = 0,
-    allow_rescaling: bool = True,
 ):
     """Match the shape of a numpy array to a reference shape.
 
@@ -268,9 +217,6 @@ def numpy_match_shape(
         pad_mode (str): The mode to use for padding. See numpy.pad for options.
         pad_values (int | float): The constant value to use for padding if
             pad_mode is 'constant'.
-        allow_rescaling (bool): If True, when the array differs more than the
-            tolerance, it will be rescaled to the reference shape. If False,
-            an error will be raised.
     """
     _check_axes(
         array_shape=array.shape,
@@ -291,10 +237,8 @@ def numpy_match_shape(
         array_axes=array_axes,
         reference_axes=reference_axes,
         tolerance=tolerance,
-        allow_rescaling=allow_rescaling,
     )
     array = array.reshape(reshape_tuple)
-    array = _numpy_rescaling(array=array, actions=actions, target_shape=reference_shape)
     array = _numpy_pad(
         array=array,
         actions=actions,
@@ -314,7 +258,6 @@ def dask_match_shape(
     tolerance: int = 1,
     pad_mode: str = "constant",
     pad_values: int | float = 0,
-    allow_rescaling: bool = True,
 ) -> da.Array:
     """Match the shape of a dask array to a reference shape.
 
@@ -335,9 +278,6 @@ def dask_match_shape(
         pad_mode (str): The mode to use for padding. See numpy.pad for options.
         pad_values (int | float): The constant value to use for padding if
             pad_mode is 'constant'.
-        allow_rescaling (bool): If True, when the array differs more than the
-            tolerance, it will be rescalingd to the reference shape. If False,
-            an error will be raised.
     """
     array_shape = tuple(int(s) for s in array.shape)
     _check_axes(
@@ -358,10 +298,8 @@ def dask_match_shape(
         array_axes=array_axes,
         reference_axes=reference_axes,
         tolerance=tolerance,
-        allow_rescaling=allow_rescaling,
     )
     array = da.reshape(array, reshape_tuple)
-    array = _dask_rescaling(array=array, actions=actions, target_shape=reference_shape)
     array = _dask_pad(
         array=array,
         actions=actions,

src/ngio/io_pipes/_zoom_transform.py

@@ -28,7 +28,7 @@ def __init__(
         self._order: InterpolationOrder = order
 
     def _normalize_shape(
-        self, slice_: slice | int | tuple, scale: float, max_dim: int
+        self, slice_: slice | int | list[int], scale: float, max_dim: int
     ) -> int:
         if isinstance(slice_, slice):
             _start = slice_.start or 0
@@ -43,7 +43,7 @@ def _normalize_shape(
 
         elif isinstance(slice_, int):
             target_shape = 1
-        elif isinstance(slice_, tuple):
+        elif isinstance(slice_, list):
             target_shape = len(slice_) * scale
         else:
             raise ValueError(f"Unsupported slice type: {type(slice_)}")