Repository: Project-MONAI/MONAI (version 0.8)

Issue:
CropForeground pads asymmetric 
**Describe the bug**
In `CropForeground`/`CropForegroundd` the user can request a margin around the foreground voxels. However, currently the margin is only correctly padded on the upper side of a dimension.

See:
https://github.com/Project-MONAI/MONAI/blob/c3db337644d9525610d9fab3cfd30f963438dc6a/monai/transforms/utils.py#L924-L926

**To Reproduce**
```python
from monai.transforms import CropForeground
import numpy as np

transform = CropForeground(margin=2)
arr = np.eye(3)[np.newaxis, ...]
for i in range(3):
    inp = arr[:, i]
    res = transform(inp)
    print(inp, res.shape)
# [[1. 0. 0.]] (1, 3)
# [[0. 1. 0.]] (1, 4)
# [[0. 0. 1.]] (1, 5)
```

**Expected behavior**
The specified margin should be enforced by cropping or padding on both ends of a dimension. 

**Environment**
Monai 0.8.0, but see Permalink above


Discussion:

Patch:
diff --git a/monai/apps/deepgrow/transforms.py b/monai/apps/deepgrow/transforms.py
--- a/monai/apps/deepgrow/transforms.py
+++ b/monai/apps/deepgrow/transforms.py
@@ -369,6 +369,9 @@ class SpatialCropForegroundd(MapTransform):
         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 bigger than image size, will pad with
+            specified `mode`.
         meta_keys: explicitly indicate the key of the corresponding meta data dictionary.
             for example, for data with key `image`, the metadata by default is in `image_meta_dict`.
             the meta data is a dictionary object which contains: filename, original_shape, etc.
@@ -393,6 +396,7 @@ def __init__(
         select_fn: Callable = is_positive,
         channel_indices: Optional[IndexSelection] = None,
         margin: int = 0,
+        allow_smaller: bool = True,
         meta_keys: Optional[KeysCollection] = None,
         meta_key_postfix=DEFAULT_POST_FIX,
         start_coord_key: str = "foreground_start_coord",
@@ -408,6 +412,7 @@ def __init__(
         self.select_fn = select_fn
         self.channel_indices = channel_indices
         self.margin = margin
+        self.allow_smaller = allow_smaller
         self.meta_keys = ensure_tuple_rep(None, len(self.keys)) if meta_keys is None else ensure_tuple(meta_keys)
         if len(self.keys) != len(self.meta_keys):
             raise ValueError("meta_keys should have the same length as keys.")
@@ -420,7 +425,7 @@ def __init__(
     def __call__(self, data):
         d = dict(data)
         box_start, box_end = generate_spatial_bounding_box(
-            d[self.source_key], self.select_fn, self.channel_indices, self.margin
+            d[self.source_key], self.select_fn, self.channel_indices, self.margin, self.allow_smaller
         )
 
         center = list(np.mean([box_start, box_end], axis=0).astype(int, copy=False))
diff --git a/monai/transforms/croppad/array.py b/monai/transforms/croppad/array.py
--- a/monai/transforms/croppad/array.py
+++ b/monai/transforms/croppad/array.py
@@ -691,6 +691,7 @@ def __init__(
         select_fn: Callable = is_positive,
         channel_indices: Optional[IndexSelection] = None,
         margin: Union[Sequence[int], int] = 0,
+        allow_smaller: bool = True,
         return_coords: bool = False,
         k_divisible: Union[Sequence[int], int] = 1,
         mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = NumpyPadMode.CONSTANT,
@@ -702,6 +703,9 @@ 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 bigger than image size, will pad with
+                specified `mode`.
             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.
@@ -718,6 +722,7 @@ def __init__(
         self.select_fn = select_fn
         self.channel_indices = ensure_tuple(channel_indices) if channel_indices is not None else None
         self.margin = margin
+        self.allow_smaller = allow_smaller
         self.return_coords = return_coords
         self.k_divisible = k_divisible
         self.mode: NumpyPadMode = look_up_option(mode, NumpyPadMode)
@@ -729,7 +734,9 @@ def compute_bounding_box(self, img: NdarrayOrTensor):
         And adjust bounding box coords to be divisible by `k`.
 
         """
-        box_start, box_end = generate_spatial_bounding_box(img, self.select_fn, self.channel_indices, self.margin)
+        box_start, box_end = generate_spatial_bounding_box(
+            img, self.select_fn, self.channel_indices, self.margin, self.allow_smaller
+        )
         box_start_, *_ = convert_data_type(box_start, output_type=np.ndarray, dtype=np.int16, wrap_sequence=True)
         box_end_, *_ = convert_data_type(box_end, output_type=np.ndarray, dtype=np.int16, wrap_sequence=True)
         orig_spatial_size = box_end_ - box_start_
@@ -1183,7 +1190,7 @@ class BoundingRect(Transform):
          Nth_spatial_dim_start, Nth_spatial_dim_end]]
 
     The bounding boxes edges are aligned with the input image edges.
-    This function returns [-1, -1, ...] if there's no positive intensity.
+    This function returns [0, 0, ...] if there's no positive intensity.
 
     Args:
         select_fn: function to select expected foreground, default is to select values > 0.
diff --git a/monai/transforms/croppad/dictionary.py b/monai/transforms/croppad/dictionary.py
--- a/monai/transforms/croppad/dictionary.py
+++ b/monai/transforms/croppad/dictionary.py
@@ -834,6 +834,7 @@ def __init__(
         select_fn: Callable = is_positive,
         channel_indices: Optional[IndexSelection] = None,
         margin: Union[Sequence[int], int] = 0,
+        allow_smaller: bool = True,
         k_divisible: Union[Sequence[int], int] = 1,
         mode: Optional[Union[NumpyPadMode, PytorchPadMode, str]] = NumpyPadMode.CONSTANT,
         start_coord_key: str = "foreground_start_coord",
@@ -850,6 +851,9 @@ 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 bigger than image size, will pad with
+                specified `mode`.
             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"``,
@@ -871,7 +875,12 @@ def __init__(
         self.start_coord_key = start_coord_key
         self.end_coord_key = end_coord_key
         self.cropper = CropForeground(
-            select_fn=select_fn, channel_indices=channel_indices, margin=margin, k_divisible=k_divisible, **np_kwargs
+            select_fn=select_fn,
+            channel_indices=channel_indices,
+            margin=margin,
+            allow_smaller=allow_smaller,
+            k_divisible=k_divisible,
+            **np_kwargs,
         )
         self.mode = ensure_tuple_rep(mode, len(self.keys))
 
diff --git a/monai/transforms/utils.py b/monai/transforms/utils.py
--- a/monai/transforms/utils.py
+++ b/monai/transforms/utils.py
@@ -881,6 +881,7 @@ def generate_spatial_bounding_box(
     select_fn: Callable = is_positive,
     channel_indices: Optional[IndexSelection] = None,
     margin: Union[Sequence[int], int] = 0,
+    allow_smaller: bool = True,
 ) -> Tuple[List[int], List[int]]:
     """
     Generate the spatial bounding box of foreground in the image with start-end positions (inclusive).
@@ -891,8 +892,8 @@ 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]
 
-    The bounding boxes edges are aligned with the input image edges.
-    This function returns [-1, -1, ...], [-1, -1, ...] if there's no positive intensity.
+    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:
         img: a "channel-first" image of shape (C, spatial_dim1[, spatial_dim2, ...]) to generate bounding box from.
@@ -900,7 +901,10 @@ 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`.
     """
+    spatial_size = img.shape[1:]
     data = img[list(ensure_tuple(channel_indices))] if channel_indices is not None else img
     data = select_fn(data).any(0)
     ndim = len(data.shape)
@@ -922,8 +926,11 @@ def generate_spatial_bounding_box(
             return [0] * ndim, [0] * ndim
 
         arg_max = where(dt == dt.max())[0]
-        min_d = max(arg_max[0] - margin[di], 0)
+        min_d = arg_max[0] - margin[di]
         max_d = arg_max[-1] + margin[di] + 1
+        if allow_smaller:
+            min_d = max(min_d, 0)
+            max_d = min(max_d, spatial_size[di])
 
         box_start[di] = min_d.detach().cpu().item() if isinstance(min_d, torch.Tensor) else min_d  # type: ignore
         box_end[di] = max_d.detach().cpu().item() if isinstance(max_d, torch.Tensor) else max_d  # type: ignore


Test patch:
diff --git a/tests/test_crop_foreground.py b/tests/test_crop_foreground.py
--- a/tests/test_crop_foreground.py
+++ b/tests/test_crop_foreground.py
@@ -55,12 +55,20 @@
 
     TESTS.append(
         [
-            {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": [2, 1]},
+            {"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, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),
         ]
     )
 
+    TESTS.append(
+        [
+            {"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, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]]),
+        ]
+    )
+
     TESTS.append(
         [
             {"select_fn": lambda x: x > 0, "channel_indices": None, "margin": 0, "k_divisible": 4},
diff --git a/tests/test_crop_foregroundd.py b/tests/test_crop_foregroundd.py
--- a/tests/test_crop_foregroundd.py
+++ b/tests/test_crop_foregroundd.py
@@ -82,13 +82,46 @@
                 "select_fn": lambda x: x > 0,
                 "channel_indices": None,
                 "margin": [2, 1],
+                "allow_smaller": True,
             },
             {
                 "img": p(
-                    np.array([[[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]]])
+                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])
                 )
             },
-            p(np.array([[[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(np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])),
+        ]
+    )
+    TESTS.append(
+        [
+            {
+                "keys": ["img"],
+                "source_key": "img",
+                "select_fn": lambda x: x > 0,
+                "channel_indices": None,
+                "margin": [2, 1],
+                "allow_smaller": False,
+            },
+            {
+                "img": p(
+                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])
+                )
+            },
+            p(
+                np.array(
+                    [
+                        [
+                            [0, 0, 0, 0, 0],
+                            [0, 0, 0, 0, 0],
+                            [0, 1, 2, 1, 0],
+                            [0, 2, 3, 2, 0],
+                            [0, 1, 2, 1, 0],
+                            [0, 0, 0, 0, 0],
+                            [0, 0, 0, 0, 0],
+                        ]
+                    ]
+                )
+            ),
         ]
     )
     TESTS.append(
diff --git a/tests/test_generate_spatial_bounding_box.py b/tests/test_generate_spatial_bounding_box.py
--- a/tests/test_generate_spatial_bounding_box.py
+++ b/tests/test_generate_spatial_bounding_box.py
@@ -75,11 +75,26 @@
         [
             {
                 "img": p(
-                    np.array([[[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]]])
+                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])
+                ),
+                "select_fn": lambda x: x > 0,
+                "channel_indices": None,
+                "margin": [2, 1],
+                "allow_smaller": False,
+            },
+            ([-1, 0], [6, 5]),
+        ]
+    )
+    TESTS.append(
+        [
+            {
+                "img": p(
+                    np.array([[[0, 0, 0, 0, 0], [0, 1, 2, 1, 0], [0, 2, 3, 2, 0], [0, 1, 2, 1, 0], [0, 0, 0, 0, 0]]])
                 ),
                 "select_fn": lambda x: x > 0,
                 "channel_indices": None,
                 "margin": [2, 1],
+                "allow_smaller": True,
             },
             ([0, 0], [5, 5]),
         ]