[proto] Added functional `crop_bounding_box` op (#5781)

* [proto] Added crop_bounding_box op

* Removed "pass"

* Updated comment

* Removed unused args from signature

test/test_prototype_transforms_functional.py

@@ -321,6 +321,17 @@ def rotate_segmentation_mask():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
+def crop_bounding_box():
+    for bounding_box, top, left in itertools.product(make_bounding_boxes(), [-8, 0, 9], [-8, 0, 9]):
+        yield SampleInput(
+            bounding_box,
+            format=bounding_box.format,
+            top=top,
+            left=left,
+        )
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -808,3 +819,44 @@ def test_correctness_rotate_segmentation_mask_on_fixed_input(device):
     expected_mask = torch.rot90(mask, k=1, dims=(-2, -1))
     out_mask = F.rotate_segmentation_mask(mask, 90, expand=False)
     torch.testing.assert_close(out_mask, expected_mask)
+
+
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize(
+    "top, left, height, width, expected_bboxes",
+    [
+        [8, 12, 30, 40, [(-2.0, 7.0, 13.0, 27.0), (38.0, -3.0, 58.0, 14.0), (33.0, 38.0, 44.0, 54.0)]],
+        [-8, 12, 70, 40, [(-2.0, 23.0, 13.0, 43.0), (38.0, 13.0, 58.0, 30.0), (33.0, 54.0, 44.0, 70.0)]],
+    ],
+)
+def test_correctness_crop_bounding_box(device, top, left, height, width, expected_bboxes):
+
+    # Expected bboxes computed using Albumentations:
+    # import numpy as np
+    # from albumentations.augmentations.crops.functional import crop_bbox_by_coords, normalize_bbox, denormalize_bbox
+    # expected_bboxes = []
+    # for in_box in in_boxes:
+    #     n_in_box = normalize_bbox(in_box, *size)
+    #     n_out_box = crop_bbox_by_coords(
+    #         n_in_box, (left, top, left + width, top + height), height, width, *size
+    #     )
+    #     out_box = denormalize_bbox(n_out_box, height, width)
+    #     expected_bboxes.append(out_box)
+
+    size = (64, 76)
+    # xyxy format
+    in_boxes = [
+        [10.0, 15.0, 25.0, 35.0],
+        [50.0, 5.0, 70.0, 22.0],
+        [45.0, 46.0, 56.0, 62.0],
+    ]
+    in_boxes = features.BoundingBox(in_boxes, format=features.BoundingBoxFormat.XYXY, image_size=size, device=device)
+
+    output_boxes = F.crop_bounding_box(
+        in_boxes,
+        in_boxes.format,
+        top,
+        left,
+    )
+
+    torch.testing.assert_close(output_boxes.tolist(), expected_bboxes)

torchvision/prototype/transforms/functional/__init__.py

@@ -57,9 +57,10 @@ from ._geometry import (
     rotate_image_tensor,
     rotate_image_pil,
     rotate_segmentation_mask,
+    pad_bounding_box,
     pad_image_tensor,
     pad_image_pil,
-    pad_bounding_box,
+    crop_bounding_box,
     crop_image_tensor,
     crop_image_pil,
     perspective_image_tensor,

torchvision/prototype/transforms/functional/_geometry.py

@@ -419,6 +419,27 @@ crop_image_tensor = _FT.crop
 crop_image_pil = _FP.crop
 
 
+def crop_bounding_box(
+    bounding_box: torch.Tensor,
+    format: features.BoundingBoxFormat,
+    top: int,
+    left: int,
+) -> torch.Tensor:
+    shape = bounding_box.shape
+
+    bounding_box = convert_bounding_box_format(
+        bounding_box, old_format=format, new_format=features.BoundingBoxFormat.XYXY
+    ).view(-1, 4)
+
+    # Crop or implicit pad if left and/or top have negative values:
+    bounding_box[:, 0::2] -= left
+    bounding_box[:, 1::2] -= top
+
+    return convert_bounding_box_format(
+        bounding_box, old_format=features.BoundingBoxFormat.XYXY, new_format=format, copy=False
+    ).view(shape)
+
+
 def perspective_image_tensor(
     img: torch.Tensor,
     perspective_coeffs: List[float],