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"""
Utilities for bounding box manipulation and GIoU.
"""
from typing import Tuple
import torch
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
return torch.stack(b, dim=-1)
def box_cxcywh_to_xywh(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (w), (h)]
return torch.stack(b, dim=-1)
def box_xywh_to_xyxy(x):
x, y, w, h = x.unbind(-1)
b = [(x), (y), (x + w), (y + h)]
return torch.stack(b, dim=-1)
def box_xywh_to_cxcywh(x):
x, y, w, h = x.unbind(-1)
b = [(x + 0.5 * w), (y + 0.5 * h), (w), (h)]
return torch.stack(b, dim=-1)
def box_xyxy_to_xywh(x):
x, y, X, Y = x.unbind(-1)
b = [(x), (y), (X - x), (Y - y)]
return torch.stack(b, dim=-1)
def box_xyxy_to_cxcywh(x):
x0, y0, x1, y1 = x.unbind(-1)
b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
return torch.stack(b, dim=-1)
def box_area(boxes):
"""
Batched version of box area. Boxes should be in [x0, y0, x1, y1] format.
Inputs:
- boxes: Tensor of shape (..., 4)
Returns:
- areas: Tensor of shape (...,)
"""
x0, y0, x1, y1 = boxes.unbind(-1)
return (x1 - x0) * (y1 - y0)
def masks_to_boxes(masks):
"""Compute the bounding boxes around the provided masks
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
Returns a [N, 4] tensors, with the boxes in xyxy format
"""
if masks.numel() == 0:
return torch.zeros((0, 4), device=masks.device)
h, w = masks.shape[-2:]
y = torch.arange(0, h, dtype=torch.float, device=masks.device)
x = torch.arange(0, w, dtype=torch.float, device=masks.device)
y, x = torch.meshgrid(y, x)
x_mask = masks * x.unsqueeze(0)
x_max = x_mask.flatten(1).max(-1)[0] + 1
x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
y_mask = masks * y.unsqueeze(0)
y_max = y_mask.flatten(1).max(-1)[0] + 1
y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
boxes = torch.stack([x_min, y_min, x_max, y_max], 1)
# Invalidate boxes corresponding to empty masks.
boxes = boxes * masks.flatten(-2).any(-1)
return boxes
def box_iou(boxes1, boxes2):
"""
Batched version of box_iou. Boxes should be in [x0, y0, x1, y1] format.
Inputs:
- boxes1: Tensor of shape (..., N, 4)
- boxes2: Tensor of shape (..., M, 4)
Returns:
- iou, union: Tensors of shape (..., N, M)
"""
area1 = box_area(boxes1)
area2 = box_area(boxes2)
# boxes1: (..., N, 4) -> (..., N, 1, 2)
# boxes2: (..., M, 4) -> (..., 1, M, 2)
lt = torch.max(boxes1[..., :, None, :2], boxes2[..., None, :, :2])
rb = torch.min(boxes1[..., :, None, 2:], boxes2[..., None, :, 2:])
wh = (rb - lt).clamp(min=0) # (..., N, M, 2)
inter = wh[..., 0] * wh[..., 1] # (..., N, M)
union = area1[..., None] + area2[..., None, :] - inter
iou = inter / union
return iou, union
def generalized_box_iou(boxes1, boxes2):
"""
Batched version of Generalized IoU from https://giou.stanford.edu/
Boxes should be in [x0, y0, x1, y1] format
Inputs:
- boxes1: Tensor of shape (..., N, 4)
- boxes2: Tensor of shape (..., M, 4)
Returns:
- giou: Tensor of shape (..., N, M)
"""
iou, union = box_iou(boxes1, boxes2)
# boxes1: (..., N, 4) -> (..., N, 1, 2)
# boxes2: (..., M, 4) -> (..., 1, M, 2)
lt = torch.min(boxes1[..., :, None, :2], boxes2[..., None, :, :2])
rb = torch.max(boxes1[..., :, None, 2:], boxes2[..., None, :, 2:])
wh = (rb - lt).clamp(min=0) # (..., N, M, 2)
area = wh[..., 0] * wh[..., 1] # (..., N, M)
return iou - (area - union) / area
@torch.jit.script
def fast_diag_generalized_box_iou(boxes1, boxes2):
assert len(boxes1) == len(boxes2)
box1_xy = boxes1[:, 2:]
box1_XY = boxes1[:, :2]
box2_xy = boxes2[:, 2:]
box2_XY = boxes2[:, :2]
# assert (box1_xy >= box1_XY).all()
# assert (box2_xy >= box2_XY).all()
area1 = (box1_xy - box1_XY).prod(-1)
area2 = (box2_xy - box2_XY).prod(-1)
lt = torch.max(box1_XY, box2_XY) # [N,2]
lt2 = torch.min(box1_XY, box2_XY)
rb = torch.min(box1_xy, box2_xy) # [N,2]
rb2 = torch.max(box1_xy, box2_xy)
inter = (rb - lt).clamp(min=0).prod(-1)
tot_area = (rb2 - lt2).clamp(min=0).prod(-1)
union = area1 + area2 - inter
iou = inter / union
return iou - (tot_area - union) / tot_area
@torch.jit.script
def fast_diag_box_iou(boxes1, boxes2):
assert len(boxes1) == len(boxes2)
box1_xy = boxes1[:, 2:]
box1_XY = boxes1[:, :2]
box2_xy = boxes2[:, 2:]
box2_XY = boxes2[:, :2]
# assert (box1_xy >= box1_XY).all()
# assert (box2_xy >= box2_XY).all()
area1 = (box1_xy - box1_XY).prod(-1)
area2 = (box2_xy - box2_XY).prod(-1)
lt = torch.max(box1_XY, box2_XY) # [N,2]
rb = torch.min(box1_xy, box2_xy) # [N,2]
inter = (rb - lt).clamp(min=0).prod(-1)
union = area1 + area2 - inter
iou = inter / union
return iou
def box_xywh_inter_union(
boxes1: torch.Tensor, boxes2: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# Asuumes boxes in xywh format
assert boxes1.size(-1) == 4 and boxes2.size(-1) == 4
boxes1 = box_xywh_to_xyxy(boxes1)
boxes2 = box_xywh_to_xyxy(boxes2)
box1_tl_xy = boxes1[..., :2]
box1_br_xy = boxes1[..., 2:]
box2_tl_xy = boxes2[..., :2]
box2_br_xy = boxes2[..., 2:]
area1 = (box1_br_xy - box1_tl_xy).prod(-1)
area2 = (box2_br_xy - box2_tl_xy).prod(-1)
assert (area1 >= 0).all() and (area2 >= 0).all()
tl = torch.max(box1_tl_xy, box2_tl_xy)
br = torch.min(box1_br_xy, box2_br_xy)
inter = (br - tl).clamp(min=0).prod(-1)
union = area1 + area2 - inter
return inter, union
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