Bounding Box(BBOX) IOU Calculation and Transformation in PyTorch

Object Detection: Calculating IOU

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 (m, 4)

bboxes2 (Tensor): shape (n, 4), 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)

def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False):
assert mode in ['iou', 'iof']

rows = bboxes1.size(0)
cols = bboxes2.size(0)
if is_aligned:
assert rows == cols

if rows * cols == 0:
return bboxes1.new(rows, 1) if is_aligned else bboxes1.new(rows, cols)

if is_aligned:
lt = torch.max(bboxes1[:, :2], bboxes2[:, :2])  # [rows, 2]
rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:])  # [rows, 2]

wh = (rb - lt + 1).clamp(min=0)  # [rows, 2]
overlap = wh[:, 0] * wh[:, 1]
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
bboxes1[:, 3] - bboxes1[:, 1] + 1)

if mode == 'iou':
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
bboxes2[:, 3] - bboxes2[:, 1] + 1)
ious = overlap / (area1 + area2 - overlap)
else:
ious = overlap / area1
else:
lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2])  # [rows, cols, 2]
rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:])  # [rows, cols, 2]

wh = (rb - lt + 1).clamp(min=0)  # [rows, cols, 2]
overlap = wh[:, :, 0] * wh[:, :, 1]
area1 = (bboxes1[:, 2] - bboxes1[:, 0] + 1) * (
bboxes1[:, 3] - bboxes1[:, 1] + 1)

if mode == 'iou':
area2 = (bboxes2[:, 2] - bboxes2[:, 0] + 1) * (
bboxes2[:, 3] - bboxes2[:, 1] + 1)
ious = overlap / (area1[:, None] + area2 - overlap)
else:
ious = overlap / (area1[:, None])

return ious


Bounding Box(BBOX) Transformation in Fast R-CNN


def bbox2delta(proposals, gt, means=[0, 0, 0, 0], stds=[1, 1, 1, 1]):
assert proposals.size() == gt.size()

proposals = proposals.float()
gt = gt.float()
px = (proposals[..., 0] + proposals[..., 2]) * 0.5
py = (proposals[..., 1] + proposals[..., 3]) * 0.5
pw = proposals[..., 2] - proposals[..., 0] + 1.0
ph = proposals[..., 3] - proposals[..., 1] + 1.0

gx = (gt[..., 0] + gt[..., 2]) * 0.5
gy = (gt[..., 1] + gt[..., 3]) * 0.5
gw = gt[..., 2] - gt[..., 0] + 1.0
gh = gt[..., 3] - gt[..., 1] + 1.0

dx = (gx - px) / pw
dy = (gy - py) / ph
dw = torch.log(gw / pw)
dh = torch.log(gh / ph)
deltas = torch.stack([dx, dy, dw, dh], dim=-1)

means = deltas.new_tensor(means).unsqueeze(0)
stds = deltas.new_tensor(stds).unsqueeze(0)
deltas = deltas.sub_(means).div_(stds)

return deltas

def delta2bbox(rois,
deltas,
means=[0, 0, 0, 0],
stds=[1, 1, 1, 1],
max_shape=None,
wh_ratio_clip=16 / 1000):
means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4)
stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4)
denorm_deltas = deltas * stds + means
dx = denorm_deltas[:, 0::4]
dy = denorm_deltas[:, 1::4]
dw = denorm_deltas[:, 2::4]
dh = denorm_deltas[:, 3::4]
max_ratio = np.abs(np.log(wh_ratio_clip))
dw = dw.clamp(min=-max_ratio, max=max_ratio)
dh = dh.clamp(min=-max_ratio, max=max_ratio)
px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx)
py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy)
pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw)
ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh)
gw = pw * dw.exp()
gh = ph * dh.exp()
gx = torch.addcmul(px, 1, pw, dx)  # gx = px + pw * dx
gy = torch.addcmul(py, 1, ph, dy)  # gy = py + ph * dy
x1 = gx - gw * 0.5 + 0.5
y1 = gy - gh * 0.5 + 0.5
x2 = gx + gw * 0.5 - 0.5
y2 = gy + gh * 0.5 - 0.5
if max_shape is not None:
x1 = x1.clamp(min=0, max=max_shape[1] - 1)
y1 = y1.clamp(min=0, max=max_shape[0] - 1)
x2 = x2.clamp(min=0, max=max_shape[1] - 1)
y2 = y2.clamp(min=0, max=max_shape[0] - 1)
bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas)
return bboxes


Code from mmdetection