def boxlist_box_voting(top_boxlist,
all_boxlist,
thresh,
scoring_method='ID',
beta=1.0,
score_field="scores"):
if thresh <= 0:
return top_boxlist
mode = top_boxlist.mode
top_boxes = top_boxlist.convert("xyxy").bbox.cpu()
all_boxes = all_boxlist.convert("xyxy").bbox.cpu()
top_score = top_boxlist.get_field(score_field).cpu()
all_score = all_boxlist.get_field(score_field).cpu()
top_dets = np.hstack((top_boxes, top_score[:,
np.newaxis])).astype(np.float32,
copy=False)
all_dets = np.hstack((all_boxes, all_score[:,
np.newaxis])).astype(np.float32,
copy=False)
dets = box_utils.box_voting(top_dets, all_dets, thresh, scoring_method,
beta)
boxlist = BoxList(torch.from_numpy(dets[:, :4]).cuda(),
all_boxlist.size,
mode="xyxy")
boxlist.add_field("scores", torch.from_numpy(dets[:, -1]).cuda())
return boxlist.convert(mode)
def boxlist_soft_nms(boxlist,
sigma=0.5,
overlap_thresh=0.3,
score_thresh=0.001,
method='linear',
score_field="scores"):
"""
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 overlap_thresh <= 0:
return boxlist
mode = boxlist.mode
boxlist = boxlist.convert("xyxy")
boxes = boxlist.bbox.cpu()
score = boxlist.get_field(score_field).cpu()
dets = np.hstack((boxes, score[:, np.newaxis])).astype(np.float32,
copy=False)
dets, _ = box_utils.soft_nms(dets, sigma, overlap_thresh, score_thresh,
method)
boxlist = BoxList(torch.from_numpy(dets[:, :4]).cuda(),
boxlist.size,
mode="xyxy")
boxlist.add_field("scores", torch.from_numpy(dets[:, -1]).cuda())
return boxlist.convert(mode)