|hPddlZddlmZddlmZmZmZmZmZm Z ddl Z ddl Z ddl mZmZmZddlmZddlmZmZddlmZddlmZmZmZdd lmZGd d eZy) N)Path)AnyDictListOptionalTupleUnion)build_dataloaderbuild_yolo_dataset converter) BaseValidator)LOGGERops)check_requirements)ConfusionMatrix DetMetricsbox_iou) plot_imagesc,eZdZdZd.d/fd ZdeeefdeeeffdZde jjddfdZ defd Z d e jdeeee jffd Zd edeeefdeeeffd Zdeee jfdeeefdeee jffdZd eeee jfdeeefddfdZd/dZdeeeffdZd/dZd eee jfdeeefdeeej2ffdZd0dededeede j8j:j<fdZdedede j8j:j@fdZ!deeefdeddfdZ" d1deeefd eeee jfdedeeddf dZ#d eee jfd!e$d"e%eefd#e&ddf d$Z'd eee jfd%eddfd&Z(d'eeefdeeeffd(Z) d2d'eeefd)ed*ed+e*eeefd,e*eeefdeeeff d-Z+xZ,S)3DetectionValidatora~ A class extending the BaseValidator class for validation based on a detection model. This class implements validation functionality specific to object detection tasks, including metrics calculation, prediction processing, and visualization of results. Attributes: is_coco (bool): Whether the dataset is COCO. is_lvis (bool): Whether the dataset is LVIS. class_map (List[int]): Mapping from model class indices to dataset class indices. metrics (DetMetrics): Object detection metrics calculator. iouv (torch.Tensor): IoU thresholds for mAP calculation. niou (int): Number of IoU thresholds. lb (List[Any]): List for storing ground truth labels for hybrid saving. jdict (List[Dict[str, Any]]): List for storing JSON detection results. stats (Dict[str, List[torch.Tensor]]): Dictionary for storing statistics during validation. Examples: >>> from ultralytics.models.yolo.detect import DetectionValidator >>> args = dict(model="yolo11n.pt", data="coco8.yaml") >>> validator = DetectionValidator(args=args) >>> validator() Nreturnc t|||||d|_d|_d|_d|j _tjddd|_ |jj|_ t|_ y)a Initialize detection validator with necessary variables and settings. Args: dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation. save_dir (Path, optional): Directory to save results. args (Dict[str, Any], optional): Arguments for the validator. _callbacks (List[Any], optional): List of callback functions. FNdetectg?gffffff? )super__init__is_cocois_lvis class_mapargstasktorchlinspaceiouvnumelniourmetrics)self dataloadersave_dirr _callbacks __class__s a/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/yolo/detect/val.pyrzDetectionValidator.__init__+sg XtZ@  ! NN3b1 IIOO% !| batchc |dj|jd|d<|jjr|djn|dj dz |d<dD]#}||j|j||<%|S)z Preprocess batch of images for YOLO validation. Args: batch (Dict[str, Any]): Batch containing images and annotations. Returns: (Dict[str, Any]): Preprocessed batch. imgT) non_blocking>clsbboxes batch_idx)todevicer halffloat)r(r/ks r- preprocesszDetectionValidator.preprocess>sU|t{{Fe /3yy~~e ))+5yy!!!!II'';'MMH,$uY7G7K2L2Q2Q1RRV/WW 7 r.cD|jjr9dD]4}|jj|j||j 6|j |j_|j|j_|j|j_y)z8Set final values for metrics speed and confusion matrix.)TF)r* normalizeon_plotN)r rrYplotr*rspeedr')r(rs r-finalize_metricsz#DetectionValidator.finalize_metricssv 99??( n %%**DMMY`d`l`l*m n!ZZ (,(=(= % $  r.c|jj|j|jj|j |jj |jjS)z Calculate and return metrics statistics. Returns: (Dict[str, Any]): Dictionary containing metrics results. )r*rr)r'processr*r rr clear_stats results_dictrbs r- get_statszDetectionValidator.get_statssP dmm$))//SWS_S_`   "||(((r.cddt|jjzz}tj|d|j |jj jg|jjz|jj jdk(r-tjd|jjd|jjr|js|jdkDrt|jjrt!|jj"D]w\}}tj||j$||jj&||jj |g|jj)|zyyyyyy) z0Print training/validation set metrics per class.z %22s%11i%11iz%11.3gallrzno labels found in z, set, can not compute metrics without labelsrBN)rPr'keysrinforV nt_per_classsum mean_resultswarningr r!verboserSrTstatsrap_class_indexrD nt_per_image class_result)r(pfics r- print_resultsz DetectionValidator.print_resultss] 8c$,,2C2C.D#D D B%DLL,E,E,I,I,KjdllNgNgNijjk << $ $ ( ( *a / NN00@@lm n 99  T]]tww{s4< 1  1  11!4 11!42215  H_{] r.cBt|ddk(st|ddk(r5dtjt|d|jftiSt |d|d}d|j |d|d|jjiS)a Return correct prediction matrix. Args: preds (Dict[str, torch.Tensor]): Dictionary containing prediction data with 'bboxes' and 'cls' keys. batch (Dict[str, Any]): Batch dictionary containing ground truth data with 'bboxes' and 'cls' keys. Returns: (Dict[str, np.ndarray]): Dictionary containing 'tp' key with correct prediction matrix of shape (N, 10) for 10 IoU levels. r4rtpdtyper5) rPrrr&boolrmatch_predictionsrr)r(rdr/rqs r-rz!DetectionValidator._process_batchs uU|  !Su%6!%;"((Ce $5tyy#ANO OeHouX7d,,U5\5<MQQSYY[\\r.img_pathmodec`t|j|||j||jS)al Build YOLO Dataset. Args: img_path (str): Path to the folder containing images. mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode. batch (int, optional): Size of batches, this is for `rect`. Returns: (Dataset): YOLO dataset. )rstride)r r rEr)r(rrr/s r- build_datasetz DetectionValidator.build_datasets("$))Xudiid[_[f[fggr. dataset_path batch_sizecr|j||d}t|||jjddS)a Construct and return dataloader. Args: dataset_path (str): Path to the dataset. batch_size (int): Size of each batch. Returns: (torch.utils.data.DataLoader): Dataloader for validation. rR)r/rFry)shufflerank)rr r workers)r(rrdatasets r-get_dataloaderz!DetectionValidator.get_dataloaders:$$\%$PTYY5F5FPU\^__r.nictt||d|jd|dz |j|jy)z Plot validation image samples. Args: batch (Dict[str, Any]): Batch containing images and annotations. ni (int): Batch index. r val_batchz _labels.jpg)rpathsfnamerDrN)rr*rDr)r(r/rs r-plot_val_samplesz#DetectionValidator.plot_val_samples+s:  "--IbT"==**LL  r.ric t|D]#\}}tj|d|z|d<%|dj}|xs|jj }|D cic].}|tj |D cgc] } | |d| c} d0} }} tj| dddddf| dddddf<t|d| |d |jd |d z |j|j ycc} wcc} }w) au Plot predicted bounding boxes on input images and save the result. Args: batch (Dict[str, Any]): Batch containing images and annotations. preds (List[Dict[str, torch.Tensor]]): List of predictions from the model. ni (int): Batch index. max_det (Optional[int]): Maximum number of detections to plot. rnr6rNdimr5rkr1rrz _pred.jpg)imagesrrrrDr) rr" ones_likerr ricatr xyxy2xywhrr*rDr) r(r/rdrrirrrr;ru batched_predss r-plot_predictionsz#DetectionValidator.plot_predictions;s!' BGAt %V = AD  BQx}}.TYY..W[\RSEIIu&E!qtHW~&E1MM\ \),}X7NqRTSTRTu7U)V h2A2&<  "--IbT";;**LL  'F\s$D =D D D rrrfilec 2ddlm}|tj|d|dftjd|j t j|d|djd|d jdgd  j|| y) a Save YOLO detections to a txt file in normalized coordinates in a specific format. Args: predn (Dict[str, torch.Tensor]): Dictionary containing predictions with keys 'bboxes', 'conf', and 'cls'. save_conf (bool): Whether to save confidence scores. shape (Tuple[int, int]): Shape of the original image (height, width). file (Path): File path to save the detections. r)ResultsrBrNr5rnryr4r)pathrDboxes)r) ultralytics.engine.resultsrrruint8rDr"r unsqueezer)r(rrrrrs r-rzDetectionValidator.save_one_txtXs 7 HHeAha) :**))U8_eFm.E.Eb.I5QV>#aΛ4QU1a[4"1a[    5sD rc|jdz }|jddz |jrdnd|jjdz }|j |||S)a Evaluate YOLO output in JSON format and return performance statistics. Args: stats (Dict[str, Any]): Current statistics dictionary. Returns: (Dict[str, Any]): Updated statistics dictionary with COCO/LVIS evaluation results. zpredictions.jsonr annotationszinstances_val2017.jsonlvis_v1_z.json)r*rErr rG coco_evaluate)r(r pred_json anno_jsons r- eval_jsonzDetectionValidator.eval_jsonshMM$66 IIf  +/<<'x GXX]=^ `  !!%I>>r.rr iou_typessuffixc  |jjr7|js |jrt |j rt jd|d|d ||fD]}|jrJ|dt|tr|gn|}t|tr|gn|}tdddl m }m}||} | j|} t!|D]M\} } || | | |jt j} |j"j$j&Dcgc] }t)t+|j,"c}| j._| j3| j5| j7| j8d |d || dd <| j8d |d || dd <|js| j8d|d|| dd <| j8d|d|| dd <| j8d|d|| dd <P|jr|d|d<|S|Scc}w#t:$r#}t j<d|Yd}~|Sd}~wwxYw)az Evaluate COCO/LVIS metrics using faster-coco-eval library. Performs evaluation using the faster-coco-eval library to compute mAP metrics for object detection. Updates the provided stats dictionary with computed metrics including mAP50, mAP50-95, and LVIS-specific metrics if applicable. Args: stats (Dict[str, Any]): Dictionary to store computed metrics and statistics. pred_json (str | Path]): Path to JSON file containing predictions in COCO format. anno_json (str | Path]): Path to JSON file containing ground truth annotations in COCO format. iou_types (str | List[str]]): IoU type(s) for evaluation. Can be single string or list of strings. Common values include "bbox", "segm", "keypoints". Defaults to "bbox". suffix (str | List[str]]): Suffix to append to metric names in stats dictionary. Should correspond to iou_types if multiple types provided. Defaults to "Box". Returns: (Dict[str, Any]): Updated stats dictionary containing the computed COCO/LVIS evaluation metrics. z' Evaluating faster-coco-eval mAP using z and z...z file not foundzfaster-coco-eval>=1.6.7r)COCOCOCOeval_faster)iouType lvis_styleprint_functionAP_allzmetrics/mAP50()AP_50zmetrics/mAP50-95(APrz metrics/APr(APcz metrics/APc(APfz metrics/APf(zmetrics/mAP50-95(B)fitnessz faster-coco-eval unable to run: N)r rQrrrPrWrris_filerHrIrfaster_coco_evalrrloadResrr)rim_filesrrrparamsimgIdsevaluate accumulate summarize stats_as_dict Exceptionr)r(rrrrrrurrannorriou_typerRes r-rz DetectionValidator.coco_evaluatesw6 99  DLLDLLc$**o KKB9+US\R]]`a b G"I->A99;=1#_(==;>+5i+EYK9 %/%<&&"#<=BI||I.#,Y#7YKAx)dH^d^i^iCEIOOD[D[DdDd(eqT!W\\):(eCJJ%LLNNN$MMO?B>O>OPX>YEN6!9Q<.:;ADARARSZA[E-fQil^1=>||@C@Q@QRW@X VAYq\N!<=@C@Q@QRW@X VAYq\N!<=@C@Q@QRW@X VAYq\N!<=!Y$<<',-B'CE)$ u %)f  G!A!EFF  Gs9$I"R%((//RdR0l#lkk$tCDVZ[^`e`l`l[lVmD((Dc5<<.?)@$A($sTWx.(]a(T. )4S> )(]Dell):$;]DcN]W[\_acakak\kWl] hc h hXc] h^c^i^i^n^n^v^v h `3 `C `EKKDTDTD_D_ ` d38n # $ "nr #s(^ ,0c5<<6G1H,I OR ]efi]j  :.$sELL'8"9.d.SXY\^aYaSb.jn.sw.&$sELL'8"9ST.?tCH~?$sCx.?.,2(- =CH~== = d3i( = c49n% = c3h=r.r)rKpathlibrtypingrrrrrr rrr"ultralytics.datar r r ultralytics.engine.validatorr ultralytics.utilsrrultralytics.utils.checksrultralytics.utils.metricsrrrultralytics.utils.plottingrrrar.r-r2s? :: LL6)7JJ2r.