Fix the use of allow_smaller in CropForeground

monai/transforms/croppad/array.py

@@ -824,7 +824,7 @@ def __init__(
         select_fn: Callable = is_positive,
         channel_indices: IndexSelection | None = None,
         margin: Sequence[int] | int = 0,
-        allow_smaller: bool = True,
+        allow_smaller: bool = False,
         return_coords: bool = False,
         k_divisible: Sequence[int] | int = 1,
         mode: str = PytorchPadMode.CONSTANT,
@@ -837,9 +837,9 @@ def __init__(
             channel_indices: if defined, select foreground only on the specified channels
                 of image. if None, select foreground on the whole image.
             margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
-            allow_smaller: when computing box size with `margin`, whether allow the image size to be smaller
-                than box size, default to `True`. if the margined size is larger than image size, will pad with
-                specified `mode`.
+            allow_smaller: when computing box size with `margin`, whether to allow the final box edges to be outside of
+                the image edges (the image is smaller than the box). If `True`, part of a padded output box might be outside
+                of the original image, if `False`, the image edges will be used as the box edges.
             return_coords: whether return the coordinates of spatial bounding box for foreground.
             k_divisible: make each spatial dimension to be divisible by k, default to 1.
                 if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions.

monai/transforms/croppad/dictionary.py

@@ -729,7 +729,7 @@ def __init__(
         select_fn: Callable = is_positive,
         channel_indices: IndexSelection | None = None,
         margin: Sequence[int] | int = 0,
-        allow_smaller: bool = True,
+        allow_smaller: bool = False,
         k_divisible: Sequence[int] | int = 1,
         mode: SequenceStr = PytorchPadMode.CONSTANT,
         start_coord_key: str = "foreground_start_coord",
@@ -747,9 +747,9 @@ def __init__(
             channel_indices: if defined, select foreground only on the specified channels
                 of image. if None, select foreground on the whole image.
             margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
-            allow_smaller: when computing box size with `margin`, whether allow the image size to be smaller
-                than box size, default to `True`. if the margined size is larger than image size, will pad with
-                specified `mode`.
+            allow_smaller: when computing box size with `margin`, whether to allow the final box edges to be outside of
+                the image edges (the image is smaller than the box). If `True`, part of a padded output box might be outside
+                of the original image, if `False`, the image edges will be used as the box edges.
             k_divisible: make each spatial dimension to be divisible by k, default to 1.
                 if `k_divisible` is an int, the same `k` be applied to all the input spatial dimensions.
             mode: available modes for numpy array:{``"constant"``, ``"edge"``, ``"linear_ramp"``, ``"maximum"``,

monai/transforms/utils.py

@@ -950,7 +950,7 @@ def generate_spatial_bounding_box(
     select_fn: Callable = is_positive,
     channel_indices: IndexSelection | None = None,
     margin: Sequence[int] | int = 0,
-    allow_smaller: bool = True,
+    allow_smaller: bool = False,
 ) -> tuple[list[int], list[int]]:
     """
     Generate the spatial bounding box of foreground in the image with start-end positions (inclusive).
@@ -961,7 +961,6 @@ def generate_spatial_bounding_box(
         [1st_spatial_dim_start, 2nd_spatial_dim_start, ..., Nth_spatial_dim_start],
         [1st_spatial_dim_end, 2nd_spatial_dim_end, ..., Nth_spatial_dim_end]
 
-    If `allow_smaller`, the bounding boxes edges are aligned with the input image edges.
     This function returns [0, 0, ...], [0, 0, ...] if there's no positive intensity.
 
     Args:
@@ -970,8 +969,10 @@ def generate_spatial_bounding_box(
         channel_indices: if defined, select foreground only on the specified channels
             of image. if None, select foreground on the whole image.
         margin: add margin value to spatial dims of the bounding box, if only 1 value provided, use it for all dims.
-        allow_smaller: when computing box size with `margin`, whether allow the image size to be smaller
-            than box size, default to `True`.
+        allow_smaller: when computing box size with `margin`, whether to allow the final box edges to be outside of
+                the image edges (the image is smaller than the box). If `False`, the bounding boxes edges are aligned
+                with the input image edges, default to `False`.
+
     """
     check_non_lazy_pending_ops(img, name="generate_spatial_bounding_box")
     spatial_size = img.shape[1:]
@@ -998,7 +999,7 @@ def generate_spatial_bounding_box(
         arg_max = where(dt == dt.max())[0]
         min_d = arg_max[0] - margin[di]
         max_d = arg_max[-1] + margin[di] + 1
-        if allow_smaller:
+        if not allow_smaller:
             min_d = max(min_d, 0)
             max_d = min(max_d, spatial_size[di])
 

tests/test_crop_foreground.py

@@ -63,7 +63,7 @@
 
     TESTS.append(
         [
-            {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": [2, 1], "allow_smaller": True},
+            {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": [2, 1], "allow_smaller": False},
             p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),
             p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),
             True,
@@ -72,7 +72,7 @@
 
     TESTS.append(
         [
-            {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": [2, 1], "allow_smaller": False},
+            {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": [2, 1], "allow_smaller": True},
             p([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),
             p([[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),
             True,

tests/test_crop_foregroundd.py

@@ -88,7 +88,7 @@
                 "select_fn": lambda x: x > 0,
                 "channel_indices": None,
                 "margin": [2, 1],
-                "allow_smaller": True,
+                "allow_smaller": False,
             },
             {
                 "img": p(
@@ -107,7 +107,7 @@
                 "select_fn": lambda x: x > 0,
                 "channel_indices": None,
                 "margin": [2, 1],
-                "allow_smaller": False,
+                "allow_smaller": True,
             },
             {
                 "img": p(

tests/test_generate_spatial_bounding_box.py

@@ -82,7 +82,7 @@
                 "select_fn": lambda x: x > 0,
                 "channel_indices": None,
                 "margin": [2, 1],
-                "allow_smaller": False,
+                "allow_smaller": True,
             },
             ([-1, 0], [6, 5]),
         ]
@@ -96,7 +96,7 @@
                 "select_fn": lambda x: x > 0,
                 "channel_indices": None,
                 "margin": [2, 1],
-                "allow_smaller": True,
+                "allow_smaller": False,
             },
             ([0, 0], [5, 5]),
         ]