def decode_detection(self, output, h, w):
ct_hm = output['ct_hm']
wh = output['wh']
ct, detection = snake_decode.decode_ct_hm(torch.sigmoid(ct_hm), wh)
detection[..., :4] = data_utils.clip_to_image(detection[..., :4], h, w)
output.update({'ct': ct, 'detection': detection})
return ct, detection
def decode_ext_hm(ext_hm, bbox, vote, ct):
h, w = ext_hm.size(2), ext_hm.size(3)
ext_hm = nms(ext_hm)
bbox = data_utils.clip_to_image(bbox, h, w)
radius = gaussian_radius(bbox[..., 2] - bbox[..., 0], bbox[..., 3] - bbox[..., 1])
bbox = torch.round(bbox).long()
extreme_point = extreme_utils.collect_extreme_point(ext_hm, bbox, radius, vote.permute(0, 2, 3, 1), ct)
return extreme_point
def decode_detection(self, output, h, w):
ct_hm = output['ct_hm']
wh = output['wh']
if cfg.train.ct_reg:
ct_reg = output['reg']
nms_ct_hm, ct, detection = snake_decode.decode_ct_hm(
torch.sigmoid(ct_hm), wh, ct_reg)
else:
nms_ct_hm, ct, detection = snake_decode.decode_ct_hm(
torch.sigmoid(ct_hm), wh)
detection[..., :4] = data_utils.clip_to_image(detection[..., :4], h, w)
output.update({
'ct': ct,
'detection': detection,
'nms_ct_hm': nms_ct_hm
})
return ct, detection