import torch
from .builder import IOU_CALCULATORS
[docs]@IOU_CALCULATORS.register_module()
class BboxOverlaps2D(object):
"""2D IoU Calculator."""
def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
"""Calculate IoU between 2D bboxes.
Args:
bboxes1 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
format, or shape (m, 5) in <x1, y1, x2, y2, score> format.
bboxes2 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
format, shape (m, 5) in <x1, y1, x2, y2, score> format, or be
empty. If is_aligned is ``True``, then m and n must be equal.
mode (str): "iou" (intersection over union) or iof (intersection
over foreground).
Returns:
ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)
"""
assert bboxes1.size(-1) in [0, 4, 5]
assert bboxes2.size(-1) in [0, 4, 5]
if bboxes2.size(-1) == 5:
bboxes2 = bboxes2[..., :4]
if bboxes1.size(-1) == 5:
bboxes1 = bboxes1[..., :4]
return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
def __repr__(self):
"""str: a string describing the module"""
repr_str = self.__class__.__name__ + '()'
return repr_str
[docs]def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6):
"""Calculate overlap between two set of bboxes.
If ``is_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.
Args:
bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
B indicates the batch dim, in shape (B1, B2, ..., Bn).
If is_aligned is ``True``, then m and n must be equal.
mode (str): "iou" (intersection over union) or iof (intersection over
foreground).
Returns:
ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)
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)
"""
assert mode in ['iou', 'iof']
# Either the boxes are empty or the length of boxes's last dimenstion is 4
assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
# Batch dim must be the same
# Batch dim: (B1, B2, ... Bn)
assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
batch_shape = bboxes1.shape[:-2]
rows = bboxes1.size(-2)
cols = bboxes2.size(-2)
if is_aligned:
assert rows == cols
if rows * cols == 0:
if is_aligned:
return bboxes1.new(batch_shape + (rows, 1))
else:
return bboxes1.new(batch_shape + (rows, cols))
if is_aligned:
lt = torch.max(bboxes1[..., :2], bboxes2[..., :2]) # [B, rows, 2]
rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:]) # [B, rows, 2]
wh = (rb - lt).clamp(min=0) # [B, rows, 2]
overlap = wh[..., 0] * wh[..., 1]
area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (
bboxes1[..., 3] - bboxes1[..., 1])
if mode == 'iou':
area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (
bboxes2[..., 3] - bboxes2[..., 1])
union = area1 + area2 - overlap
else:
union = area1
else:
lt = torch.max(bboxes1[..., :, None, :2],
bboxes2[..., None, :, :2]) # [B, rows, cols, 2]
rb = torch.min(bboxes1[..., :, None, 2:],
bboxes2[..., None, :, 2:]) # [B, rows, cols, 2]
wh = (rb - lt).clamp(min=0) # [B, rows, cols, 2]
overlap = wh[..., 0] * wh[..., 1]
area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (
bboxes1[..., 3] - bboxes1[..., 1])
if mode == 'iou':
area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (
bboxes2[..., 3] - bboxes2[..., 1])
union = area1[..., None] + area2[..., None, :] - overlap
else:
union = area1[..., None]
eps = union.new_tensor([eps])
union = torch.max(union, eps)
ious = overlap / union
return ious