mmcv.ops.bbox_overlaps¶

mmcv.ops.bbox_overlaps(bboxes1: torch.Tensor, bboxes2: torch.Tensor, mode: str = 'iou', aligned: bool = False, offset: int = 0) → torch.Tensor [source]¶

Calculate overlap between two set of bboxes.

If aligned is False, then calculate the ious between each bbox of bboxes1 and bboxes2, otherwise the ious between each aligned pair of bboxes1 and bboxes2.

Parameters

bboxes1 (torch.Tensor) – shape (m, 4) in <x1, y1, x2, y2> format or empty.
bboxes2 (torch.Tensor) – shape (n, 4) in <x1, y1, x2, y2> format or empty. If aligned is True, then m and n must be equal.
mode (str) – “iou” (intersection over union) or iof (intersection over foreground).

Returns

Return the ious betweens boxes. If aligned is False, the shape of ious is (m, n) else (m, 1).

Return type

torch.Tensor

Example

>>> bboxes1 = torch.FloatTensor([
>>>     [0, 0, 10, 10],
>>>     [10, 10, 20, 20],
>>>     [32, 32, 38, 42],
>>> ])
>>> bboxes2 = torch.FloatTensor([
>>>     [0, 0, 10, 20],
>>>     [0, 10, 10, 19],
>>>     [10, 10, 20, 20],
>>> ])
>>> bbox_overlaps(bboxes1, bboxes2)
tensor([[0.5000, 0.0000, 0.0000],
        [0.0000, 0.0000, 1.0000],
        [0.0000, 0.0000, 0.0000]])

Example

>>> empty = torch.FloatTensor([])
>>> nonempty = torch.FloatTensor([
>>>     [0, 0, 10, 9],
>>> ])
>>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
>>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
>>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)