.h1,ddlmZddlmZddlmZddlmZddlZ ddl Z ddl m cm ZddlmZddlmZmZmZddlmZdd lmZmZGd d eZy) ) annotations) ThreadPool)Path)AnyN)DetectionValidator)LOGGER NUM_THREADSops)check_requirements)SegmentMetricsmask_iouceZdZdZddfd Zdfd Zdfd ZddZdfd Zdfd Z dfd Z dfd Z dd Z dfd Z dfd Zdfd ZxZS)SegmentationValidatora A class extending the DetectionValidator class for validation based on a segmentation model. This validator handles the evaluation of segmentation models, processing both bounding box and mask predictions to compute metrics such as mAP for both detection and segmentation tasks. Attributes: plot_masks (list): List to store masks for plotting. process (callable): Function to process masks based on save_json and save_txt flags. args (namespace): Arguments for the validator. metrics (SegmentMetrics): Metrics calculator for segmentation tasks. stats (dict): Dictionary to store statistics during validation. Examples: >>> from ultralytics.models.yolo.segment import SegmentationValidator >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml") >>> validator = SegmentationValidator(args=args) >>> validator() cxt|||||d|_d|j_t |_y)a Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics. Args: dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation. save_dir (Path, optional): Directory to save results. args (namespace, optional): Arguments for the validator. _callbacks (list, optional): List of callback functions. Nsegment)super__init__processargstaskr metrics)self dataloadersave_dirr _callbacks __class__s a/var/www/html/ai-service/venv/lib/python3.12/site-packages/ultralytics/models/yolo/segment/val.pyrzSegmentationValidator.__init__(s4 XtZ@ " %' cRt||}|dj|d<|S)z Preprocess batch of images for YOLO segmentation validation. Args: batch (dict[str, Any]): Batch containing images and annotations. Returns: (dict[str, Any]): Preprocessed batch. masks)r preprocessfloat)rbatchrs rr!z SegmentationValidator.preprocess7s/"5)w--/g rct|||jjr t d|jjs|jj rt j|_ yt j|_ y)z Initialize metrics and select mask processing function based on save_json flag. Args: model (torch.nn.Module): Model to validate. zfaster-coco-eval>=1.6.7N) r init_metricsr save_jsonr save_txtr process_mask_native process_maskr)rmodelrs rr%z"SegmentationValidator.init_metricsEsZ U# 99   8 926))2E2EI[I[s.. adaqaq rc ddzS)z5Return a formatted description of evaluation metrics.z,%22s%11s%11s%11s%11s%11s%11s%11s%11s%11s%11s) ClassImages InstanceszBox(PRmAP50 mAP50-95)zMask(Pr/r0r1)rs rget_desczSegmentationValidator.get_descRs$ )  rct|ddk(r|ddn|d}t| |d}|jddDcgc]}d|z }}t |D]\}}|j d}|jdr|j ||||d | natjdg|j tjur|n|jddtj|d j |d <|Scc}w) a Post-process YOLO predictions and return output detections with proto. Args: preds (list[torch.Tensor]): Raw predictions from the model. Returns: list[dict[str, torch.Tensor]]: Processed detection predictions with masks. rNextrabboxes)shape)dtypedevicer ) lenr postprocessr< enumeratepoprtorchzerosr r(uint8r>) rpredsprotoximgszipred coefficientrs rr@z!SegmentationValidator.postprocessbs!$E!H  2a a#E!H- % AB01Q00 ' GAt((7+K$$Q' U1X{DN% P[[a4<<33J3J#J%PUP[P[\]\^P_a++>00 M  1s Dc:t |||}|djd}|jjrR|d|}t j d|dz|jj|dd}||k(j}n|d|d|k(}|ru|dDcgc]%}|jtjur|n|dz'}}|jdd |k7r0tj|d |d d d}|jd }||d<|Scc}w)a< Prepare a batch for training or inference by processing images and targets. Args: si (int): Batch index. batch (dict[str, Any]): Batch data containing images and annotations. Returns: (dict[str, Any]): Prepared batch with processed annotations. clsrr r5)r> batch_idxrIr9NbilinearF)mode align_cornersg?)r_prepare_batchr<r overlap_maskrCaranger>viewr"rr r(F interpolategt_) rsir#prepared_batchnlr indexs mask_sizers rrSz$SegmentationValidator._prepare_batch|s%/E: E " ( ( + 99 ! !'N2&ELLBF5<<@EEb!QOEe^**,E'N5#5#;>> preds = {"cls": torch.tensor([1, 0]), "masks": torch.rand(2, 640, 640), "bboxes": torch.rand(2, 4)} >>> batch = {"cls": torch.tensor([1, 0]), "masks": torch.rand(2, 640, 640), "bboxes": torch.rand(2, 4)} >>> correct_preds = validator._process_batch(preds, batch) rNrr=r r5tp_m)r_process_batchr<nprDniouboolr flattenr"match_predictionscpunumpyupdate)rrFr#tpgt_clsrbiours rrcz$SegmentationValidator._process_batchs(W #E5 1u <<?a 5<#5#5a#8A#=88U5\//2DII>dKD5>11!4eGn6L6LQ6O6U6U6WXC))%,DHHJPPRD 64.! rc|D]}|d}|jd|jjkDr-tjd|jjdt j |d|jjt jj|d<t|)||||jjy)a Plot batch predictions with masks and bounding boxes. 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. r rz&Limiting validation plots to 'max_det=z' items.Nra)max_det) r<rrprwarningrC as_tensorrErirplot_predictions)rr#rFnipr rs rrsz&SegmentationValidator.plot_predictionss ^AgJE{{1~ 1 11!G HYHYGZZbcd/B1B1B)C5;;W[[]AgJ  ^  r499;L;L Mrc ddlm}|tj|d|dftjd|j t j|d|djd|d jdgd t j|d t j j|| y)a Save YOLO detections to a txt file in normalized coordinates in a specific format. Args: predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class). save_conf (bool): Whether to save confidence scores. shape (tuple[int, int]): Shape of the original image. file (Path): File path to save the detections. r)Resultsr5raNr;confr7rN)dimr )pathnamesboxesr ) save_conf) ultralytics.engine.resultsrwrdrDrEr{rCcat unsqueezerrr')rprednr}r<filerws r save_one_txtz"SegmentationValidator.save_one_txts 7 HHeAha) :**))U8_eFm.E.Eb.I5QV.single_encodesFAaDjMGLMaPCM009CMJrr )r8rr5N segmentation) faster_coco_eval.core.maskrrd transposerr mapr pred_to_jsonrAjdictr?) rrpbatchr pred_maskspoolrlesrJrrrs @rrz"SegmentationValidator.pred_to_jsons 6  \\%.)<  $ 788M:6D 7 UF+dO ;DAq9:DJJD zA~ &~ 6 ; 7 7s BBc *it|||dtjt j |dtj jdddjjj|d|diS) z.Scales predictions to the original image size.r rar5r8r ori_shape ratio_pad)r) r scale_predsr scale_imagerCrrrEpermute contiguousrirj)rrrrs rrz!SegmentationValidator.scale_predss g!%0 S__gekkBJJ1aQRS^^`ddflln{# -  rc|jdz }|jddz |jrdnd|jjdz }t ||||ddgd d g S) z;Return COCO-style instance segmentation evaluation metrics.zpredictions.jsonrzrzinstances_val2017.jsonlvis_v1_z.jsonbboxsegmBoxMask)suffix)rdatais_cocorsplitr coco_evaluate)rstats pred_json anno_jsonrs r eval_jsonzSegmentationValidator.eval_jsonsyMM$66 IIf  +/<<'x GXX]=^ `  w$UIy66BR\aci[j$kkr)NNNN)returnNone)r#dict[str, Any]rr)r*ztorch.nn.Modulerr)rstr)rFzlist[torch.Tensor]rlist[dict[str, torch.Tensor]])rZintr#rrr)rFdict[str, torch.Tensor]r#rrzdict[str, np.ndarray])r#rrFrrtrrr) rz torch.Tensorr}rfr<ztuple[int, int]rrrr)rrrrrr)rrrrrr)rrrr)__name__ __module__ __qualname____doc__rr!r%r3r@rSrcrsrrrr __classcell__)rs@rrrsM( (  r 46<N .(;.  llrr) __future__rmultiprocessing.poolrpathlibrtypingrrjrdrCtorch.nn.functionalnn functionalrWultralytics.models.yolo.detectrultralytics.utilsrr r ultralytics.utils.checksr ultralytics.utils.metricsr r rr2rrrs?#+ =667>pl.plr