>|h &ddlZddlZdZdZdZy)Nc(dt|k(rgS|dddf|dddf|dddf|dddf|dddff\}}}}}||z dz||z dzz}|jddd}g} |jdkDr|d} | j| t j || ||ddt j || ||dd} } t j || ||ddt j || ||dd}} t j d| | z dzt j d|| z dz}}||z|| ||ddz||zz z }t j||kd}||dz}|jdkDr| S)Nrg)lenargsortsizeappendnpmaximumminimumwhere)detsthreshx1y1x2y2scoresareasorderkeepixx1yy1xx2yy2whovrindss `/var/www/html/test/engine/venv/lib/python3.12/site-packages/face_alignment/detection/sfd/bbox.pynmsr%sCI~ !!Q$ZadT!Q$ZadTRSUVRVZWBBF "Wq[R"Wq[ )E NN TrT "E D **q. !H A::beRab ]3RZZ1r%PQPR)}5US::beRab ]3RZZ1r%PQPR)}5USzz#sSy1}-rzz#sSy1}/M1!euQx%ab "22QU:;xxv &q)dQh **q. Kc"|ddddf|ddddfzdz |ddddfz }||d|ddddfzz}|ddddf|ddddfz |ddddfz }tj||dz }tj||gdS)aEncode the variances from the priorbox layers into the ground truth boxes we have matched (based on jaccard overlap) with the prior boxes. Args: matched: (tensor) Coords of ground truth for each prior in point-form Shape: [num_priors, 4]. priors: (tensor) Prior boxes in center-offset form Shape: [num_priors,4]. variances: (list[float]) Variances of priorboxes Return: encoded boxes (tensor), Shape: [num_priors, 4] Nrrr)r log concatenate)matchedpriors variancesg_cxcyg_whs r$encoder/sa!enwq!"u~- 2VArrE] BF y|fQUm+,F AqrENWQU^ +vae} >64.! ,,r&c Vtj|ddddf|ddddf|dz|ddddfzz|ddddftj|ddddf|dzzfd}|ddddfxx|ddddfdz zcc<|ddddfxx|ddddfz cc<|S)aDecode locations from predictions using priors to undo the encoding we did for offset regression at train time. Args: loc (tensor): location predictions for loc layers, Shape: [num_priors,4] priors (tensor): Prior boxes in center-offset form. Shape: [num_priors,4]. variances: (list[float]) Variances of priorboxes Return: decoded bounding box predictions Nrrr)r r)exp)locr+r,boxess r$decoder45s NNq"1"u ArrE Yq\1F1ab5MAAq!"u s1ab5zIaL899;<= ?E !RaR%LE!QR%L1$$L !QR%LE!RaR%L L Lr&)mathnumpyr r%r/r4r&r$r8s .-2r&