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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .bounding_box import BoxList
from maskrcnn_benchmark.layers import nms as _box_nms
from maskrcnn_benchmark.layers import ml_nms as _box_ml_nms
def boxlist_nms(boxlist, nms_thresh, max_proposals=-1, score_field="score"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the top max_proposals are kept
after non-maxium suppression
score_field (str)
"""
if nms_thresh <= 0:
return boxlist
mode = boxlist.mode
boxlist = boxlist.convert("xyxy")
boxes = boxlist.bbox
score = boxlist.get_field(score_field)
keep = _box_nms(boxes, score, nms_thresh)
if max_proposals > 0:
keep = keep[:max_proposals]
boxlist = boxlist[keep]
return boxlist.convert(mode)
def boxlist_ml_nms(boxlist, nms_thresh, max_proposals=-1, score_field="scores", label_field="labels"):
"""
Performs non-maximum suppression on a boxlist, with scores specified
in a boxlist field via score_field.
Arguments:
boxlist(BoxList)
nms_thresh (float)
max_proposals (int): if > 0, then only the top max_proposals are kept
after non-maximum suppression
score_field (str)
"""
if nms_thresh <= 0:
return boxlist
mode = boxlist.mode
boxlist = boxlist.convert("xyxy")
boxes = boxlist.bbox
scores = boxlist.get_field(score_field)
labels = boxlist.get_field(label_field)
if boxes.device == torch.device("cpu"):
keep = []
unique_labels = torch.unique(labels)
print(unique_labels)
for j in unique_labels:
inds = (labels == j).nonzero().view(-1)
scores_j = scores[inds]
boxes_j = boxes[inds, :].view(-1, 4)
keep_j = _box_nms(boxes_j, scores_j, nms_thresh)
keep += keep_j
else:
keep = _box_ml_nms(boxes, scores, labels.float(), nms_thresh)
if max_proposals > 0:
keep = keep[:max_proposals]
boxlist = boxlist[keep]
return boxlist.convert(mode)
def remove_small_boxes(boxlist, min_size):
"""
Only keep boxes with both sides >= min_size
Arguments:
boxlist (Boxlist)
min_size (int)
"""
# WORK AROUND: work around unbind using split + squeeze.
xywh_boxes = boxlist.convert("xywh").bbox
_, _, ws, hs = xywh_boxes.split(1, dim=1)
ws = ws.squeeze(1)
hs = hs.squeeze(1)
keep = ((ws >= min_size) & (hs >= min_size)).nonzero().squeeze(1)
return boxlist[keep]
# implementation from https://github.com/kuangliu/torchcv/blob/master/torchcv/utils/box.py
# with slight modifications
def boxlist_iou(boxlist1, boxlist2):
"""Compute the intersection over union of two set of boxes.
The box order must be (xmin, ymin, xmax, ymax).
Arguments:
box1: (BoxList) bounding boxes, sized [N,4].
box2: (BoxList) bounding boxes, sized [M,4].
Returns:
(tensor) iou, sized [N,M].
Reference:
https://github.com/chainer/chainercv/blob/master/chainercv/utils/bbox/bbox_iou.py
"""
if boxlist1.size != boxlist2.size:
raise RuntimeError("boxlists should have same image size, got {}, {}".format(boxlist1, boxlist2))
N = len(boxlist1)
M = len(boxlist2)
area1 = boxlist1.area()
area2 = boxlist2.area()
box1, box2 = boxlist1.bbox, boxlist2.bbox
lt = torch.max(box1[:, None, :2], box2[:, :2]) # [N,M,2]
rb = torch.min(box1[:, None, 2:], box2[:, 2:]) # [N,M,2]
TO_REMOVE = 1
wh = (rb - lt + TO_REMOVE).clamp(min=0) # [N,M,2]
inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
iou = inter / (area1[:, None] + area2 - inter)
return iou
# TODO redundant, remove
def _cat(tensors, dim=0):
"""
Efficient version of torch.cat that avoids a copy if there is only a single element in a list
"""
assert isinstance(tensors, (list, tuple))
if len(tensors) == 1:
return tensors[0]
if isinstance(tensors[0], torch.Tensor):
return torch.cat(tensors, dim)
else:
return cat_boxlist(tensors)
def cat_boxlist(bboxes):
"""
Concatenates a list of BoxList (having the same image size) into a
single BoxList
Arguments:
bboxes (list[BoxList])
"""
assert isinstance(bboxes, (list, tuple))
assert all(isinstance(bbox, BoxList) for bbox in bboxes)
size = bboxes[0].size
assert all(bbox.size == size for bbox in bboxes)
mode = bboxes[0].mode
assert all(bbox.mode == mode for bbox in bboxes)
fields = set(bboxes[0].fields())
assert all(set(bbox.fields()) == fields for bbox in bboxes)
cat_boxes = BoxList(_cat([bbox.bbox for bbox in bboxes], dim=0), size, mode)
for field in fields:
data = _cat([bbox.get_field(field) for bbox in bboxes], dim=0)
cat_boxes.add_field(field, data)
return cat_boxes
def getUnionBBox(aBB, bBB, margin=10):
assert aBB.size == bBB.size
assert aBB.mode == bBB.mode
ih, iw = aBB.size
union_boxes = torch.cat(
[
(torch.min(aBB.bbox[:, [0, 1]], bBB.bbox[:, [0, 1]]) - margin).clamp(min=0),
(torch.max(aBB.bbox[:, [2]], bBB.bbox[:, [2]]) + margin).clamp(max=iw),
(torch.max(aBB.bbox[:, [3]], bBB.bbox[:, [3]]) + margin).clamp(max=ih),
],
dim=1,
)
return BoxList(union_boxes, aBB.size, mode=aBB.mode)
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