o ‹ÄŽi Yã@sddlmZddlZddlmZddlmZddlmZddl m Z m Z m Z ddl mZddlmZd)d*dd„Z d)d+dd„Z d)d,dd„Z d)d-dd„Z d)d-dd„Z d)d-dd„ZGdd „d ƒZGd!d"„d"eƒZGd#d$„d$eƒZGd%d&„d&eƒZGd'd(„d(eƒZdS).é)Ú annotationsN)Úbox)ÚSTRtree)Úlogger)ÚObjectPredictionListÚ has_matchÚmerge_object_prediction_pair)ÚObjectPrediction)Úcheck_requirementsÚIOUçà?Ú predictionsú torch.tensorÚ match_metricÚstrÚmatch_thresholdÚfloatc Cs¤|dd…df ¡}|dd…df ¡}tj|tjd}t |¡D]}t ||k¡d}t||||ƒ}d|||<q!t |¡d} | || jddd ¡} | S) a°Apply non-maximum suppression to avoid detecting too many overlapping bounding boxes for a given object. Args: predictions: (tensor) The location preds for the image along with the class predscores, Shape: [num_boxes,5]. match_metric: (str) IOU or IOS match_threshold: (float) The overlap thresh for match metric. Returns: A list of filtered indexes, Shape: [ ,] Néé)ÚdtyperT©Ú descendingé) ÚsqueezeÚtorchÚ zeros_likeÚboolÚuniqueÚwhereÚnmsÚsortÚtolist) r rrÚscoresÚ category_idsÚ keep_maskÚ category_idÚ curr_indicesÚcurr_keep_indicesÚ keep_indices©r)úT/home/jeff/fluffinator/venv/lib/python3.10/site-packages/sahi/postprocess/combine.pyÚ batched_nms s r+ú torch.Tensorc CsD|dd…df}|dd…df}|dd…df}|dd…df}|dd…df}||||}g} tt|ƒƒD]} |  t||  ¡||  ¡||  ¡||  ¡ƒ¡q8tj|dd ¡} t| ƒ} g} t ƒ}| D]¶}||vrpqi|  |¡| |}|| ¡}|   |¡}|D]˜}||ks||vr‘q†||||kršq†||||krÓ|| ¡|| ¡|| ¡|| ¡f}|| ¡|| ¡|| ¡|| ¡f}||krÓq†| |}|  |¡j }|d krö||| ¡|}|dkró||nd}n|d krt ||| ¡ƒ}|dkr ||nd}ntd ƒ‚||kr| |¡q†qi| S) a’ Optimized non-maximum suppression for axis-aligned bounding boxes using STRTree. Args: predictions: (tensor) The location preds for the image along with the class predscores, Shape: [num_boxes,5]. match_metric: (str) IOU or IOS match_threshold: (float) The overlap thresh for match metric. Returns: A list of filtered indexes, Shape: [ ,] NrréérTrr ÚIOSúInvalid match_metric)ÚrangeÚlenÚappendrÚitemrÚargsortr!rÚsetÚqueryÚ intersectionÚareaÚminÚ ValueErrorÚadd)r rrÚx1Úy1Úx2Úy2r"ÚareasÚboxesÚiÚ sorted_idxsÚtreeÚkeepÚ suppressedÚ current_idxÚ current_boxÚ current_areaÚcandidate_idxsÚ candidate_idxÚcurrent_coordsÚcandidate_coordsÚ candidate_boxr8ÚunionÚmetricÚsmallerr)r)r*r'sv    üÿ        ü    ü    €Ô.rÚobject_predictions_as_tensorc óŠ|dd…df ¡}i}t |¡D]1}t ||k¡d}t||||ƒ}| ¡‰| ¡D]\}} ˆ|} ‡fdd„| Dƒ} | || <q,q|S)a@Apply greedy version of non-maximum merging per category to avoid detecting too many overlapping bounding boxes for a given object. Args: object_predictions_as_tensor: (tensor) The location preds for the image along with the class predscores, Shape: [num_boxes,5]. match_metric: (str) IOU or IOS match_threshold: (float) The overlap thresh for match metric. Returns: keep_to_merge_list: (Dict[int:List[int]]) mapping from prediction indices to keep to a list of prediction indices to be merged. Nrrcóg|]}ˆ|‘qSr)r)©Ú.0Úcurr_merge_ind©Úcurr_indices_listr)r*Ú ®óz&batched_greedy_nmm..)rrrrÚ greedy_nmmr!Úitems© rSrrr#Úkeep_to_merge_listr%r&Úcurr_keep_to_merge_listÚ curr_keepÚcurr_merge_listrFÚ merge_listr)rYr*Úbatched_greedy_nmm”s ýrec Cs^|dd…df}|dd…df}|dd…df}|dd…df}|dd…df}||||}g} tt|ƒƒD]} |  t||  ¡||  ¡||  ¡||  ¡ƒ¡q8tj|dd ¡} t| ƒ} i} t ƒ}| D]Ã}||vrpqi| |}|| ¡}|   |¡}g}|D]}||ks||vrŽqƒ||||kr—qƒ||||krÐ|| ¡|| ¡|| ¡|| ¡f}|| ¡|| ¡|| ¡|| ¡f}||krÐqƒ| |}|  |¡j }|d kró||| ¡|}|dkrð||nd}n|d kr t ||| ¡ƒ}|dkr ||nd}ntd ƒ‚||kr | |¡| |¡qƒd d „|Dƒ| t|ƒ<qi| S)a Optimized greedy non-maximum merging for axis-aligned bounding boxes using STRTree. Args: object_predictions_as_tensor: (tensor) The location preds for the image along with the class predscores, Shape: [num_boxes,5]. match_metric: (str) IOU or IOS match_threshold: (float) The overlap thresh for match metric. Returns: keep_to_merge_list: (dict[int, list[int]]) mapping from prediction indices to keep to a list of prediction indices to be merged. Nrrr-r.rTrr r/r0cSsg|]}t|ƒ‘qSr))Úint)rWÚidxr)r)r*r[r\zgreedy_nmm..)r1r2r3rr4rr5r!rr6r7r8r9r:r;r<rf)rSrrr=r>r?r@r"rArBrCrDrEr`rGrHrIrJrKrdrLrMrNrOr8rPrQrRr)r)r*r]³sx    üÿ       ü    ü     €r]c rT)a*Apply non-maximum merging per category to avoid detecting too many overlapping bounding boxes for a given object. Args: object_predictions_as_tensor: (tensor) The location preds for the image along with the class predscores, Shape: [num_boxes,5]. match_metric: (str) IOU or IOS match_threshold: (float) The overlap thresh for match metric. Returns: keep_to_merge_list: (Dict[int:List[int]]) mapping from prediction indices to keep to a list of prediction indices to be merged. NrrcrUr)r)rVrYr)r*r[;r\zbatched_nmm..)rrrrÚnmmr!r^r_r)rYr*Ú batched_nmm"s ýric CsÜ|dd…df}|dd…df}|dd…df}|dd…df}|dd…df}||||}g} tt|ƒƒD]} |  t||  ¡||  ¡||  ¡||  ¡ƒ¡q8tj|dd ¡} t| ƒ} i} i}| D]}| |}|| ¡}|   |¡}g}|D]”}||kr…q~||||krŽq~||||krÇ|| ¡|| ¡|| ¡|| ¡f}|| ¡|| ¡|| ¡|| ¡f}||krÇq~| |}|  |¡j }|d krê||| ¡|}|dkrç||nd}n|d krt ||| ¡ƒ}|dkr||nd}nt d ƒ‚||kr| |¡q~t|ƒ}||vr;g| |<|D]}t|ƒ}||vr8| | |¡|||<q"qh||}|D])}t|ƒ}||  |g¡vri||vri|| vr^g| |<| | |¡|||<qAqh| S) aApply non-maximum merging to avoid detecting too many overlapping bounding boxes for a given object. Args: object_predictions_as_tensor: (tensor) The location preds for the image along with the class predscores, Shape: [num_boxes,5]. match_metric: (str) IOU or IOS match_threshold: (float) The overlap thresh for match metric. Returns: keep_to_merge_list: (Dict[int:List[int]]) mapping from prediction indices to keep to a list of prediction indices to be merged. Nrrr-r.rTrr r/r0)r1r2r3rr4rr5r!rr7r8r9r:r;rfÚget) rSrrr=r>r?r@r"rArBrCrDrEr`Ú merge_to_keeprHrIrJrKÚmatched_box_indicesrLrMrNrOr8rPrQrRÚcurrent_idx_nativeÚmatched_box_idxÚmatched_box_idx_nativeÚkeep_idxr)r)r*rh@sš    üÿ        ü    ü    €  €ü  €÷ rhc@s,eZdZdZ   ddd d „Zddd„ZdS)ÚPostprocessPredictionszJUtilities for calculating IOU/IOS based match for given ObjectPredictions.r r TrrrrÚclass_agnosticrcCs ||_||_||_tdgƒdS)Nr)rrrrr )Úselfrrrrr)r)r*Ú__init__ÄszPostprocessPredictions.__init__r úlist[ObjectPrediction]cCstƒ‚)N)ÚNotImplementedError)rsr r)r)r*Ú__call__ÐszPostprocessPredictions.__call__N)r r T)rrrrrrr)r ru)Ú__name__Ú __module__Ú __qualname__Ú__doc__rtrwr)r)r)r*rqÁs ü rqc@óeZdZddd„ZdS)ÚNMSPostprocessÚobject_predictionsrucCs\t|ƒ}| ¡}|jrt||j|jd}n t||j|jd}|| ¡}t|t ƒs,|g}|S©N)rr) rÚtotensorrrrrrr+r!Ú isinstanceÚlist)rsr~Úobject_prediction_listÚobject_predictions_as_torchrFÚselected_object_predictionsr)r)r*rwÕs ÿ ÿ  zNMSPostprocess.__call__N©r~ru©rxryrzrwr)r)r)r*r}Ôór}c@r|)ÚNMMPostprocessr~ruc Có²t|ƒ}| ¡}|jrt||j|jd}n t||j|jd}g}| ¡D]2\}}|D]"}t||  ¡||  ¡|j|jƒrLt ||  ¡||  ¡ƒ||<q*|  ||  ¡¡q$|Sr) rr€rrrhrrrir^rr!rr3© rsr~rƒr„r`r…Úkeep_indÚmerge_ind_listÚ merge_indr)r)r*rwìó8ýý  üÿ€zNMMPostprocess.__call__Nr†r‡r)r)r)r*r‰ërˆr‰c@r|)ÚGreedyNMMPostprocessr~ruc CrŠr) rr€rrr]rrrer^rr!rr3r‹r)r)r*rwrzGreedyNMMPostprocess.__call__Nr†r‡r)r)r)r*rrˆrc@r|)ÚLSNMSPostprocessr~ruc CsÔzddlm}Wn tytdƒ‚w|jdkr"td|j›dƒt d¡t|ƒ}| ¡}|dd…dd…f}|dd…df}|dd…d f  d ¡}||||j |j rVdn|d }||  ¡} t | tƒsh| g} | S) Nr)rzPPlease run "pip install lsnms>0.3.1" to install lsnms first for lsnms utilities.r/z match_metric=z& is not supported for LSNMSPostprocesszs<        ýo ý! ýq ý  ý%%