|hdZddlZddlZddlmZddlmZmZmZm Z m Z ddl Z ddl Z ddlmZmZmZmZmZmZe j*gddz ZdMd e j.d e j.d ed ed e j.f dZdNd e j6d e j6d ed e j6fdZ dOd e j6d e j6dedededed ed e j6fdZdNde j6de j6d ed e j6fdZ dNde j6de j6de j6deed ed e j6f dZde j6d e e j6e j6e j6ffdZ dMde j6d e j6ded ed e j6f d!Z! dNde e j6e j.fd e e j6e j.fd ed e j6fd"Z"dPd ed e eeffd#Z#Gd$d%eZ$dQd&e j.d'ed e j.fd(Z%eed)idfd*e j.d+e j.d,e j.d-ed.ee&e'ff d/Z(eed0id1d2dfd*e j.d+e j.d-ed.ee&e'fd3e'd4e'f d5Z)d6eed7eed e ee j.e j.ffd8Z*ddeid9d:fd;e j.de j.d?ed-ed.ee&e'fd ed@e'd e fdAZ+GdBd2eZ,GdCdDeeZ-GdEdFe-Z.GdGdHe-Z/GdIdJeeZ0GdKdLe-Z1y)RzModel validation metrics.N)Path)AnyDictListTupleUnion)LOGGERDataExportMixin SimpleClass TryExceptchecks plt_settings)gp= ף??rffffff?rHzG?r ףp= ?rףp= ?rQ?rףp= ?r{Gz?rg$@Fbox1box2iouepsreturnc|j\}}}}|j\}} } } tj|dddf| tj|dddf|z j dtj|dddf| tj|dddf| z j dz} | |z | | z z} |r||z ||z z}| |dddfz| z } | | |zz S)aD Calculate the intersection over box2 area given box1 and box2. Args: box1 (np.ndarray): A numpy array of shape (N, 4) representing N bounding boxes in x1y1x2y2 format. box2 (np.ndarray): A numpy array of shape (M, 4) representing M bounding boxes in x1y1x2y2 format. iou (bool, optional): Calculate the standard IoU if True else return inter_area/box2_area. eps (float, optional): A small value to avoid division by zero. Returns: (np.ndarray): A numpy array of shape (N, M) representing the intersection over box2 area. Nr)Tnpminimummaximumclip)rrrrb1_x1b1_y1b1_x2b1_y2b2_x1b2_y1b2_x2b2_y2 inter_areaarea box1_areas X/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/utils/metrics.pybbox_ioar.s"&E5%!%E5%**U1d7^U3bjjq$wQV6WW]]^_` 5D>5)BJJuQW~u,MM d1gJ EMeem ,D U]uu}5 i4((:5  $$c|jjdjdd|jjdjddc\}}\}}tj||tj ||z j djd}|||z jd||z jdz|z |zz S)aG Calculate intersection-over-union (IoU) of boxes. Args: box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes in (x1, y1, x2, y2) format. box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes in (x1, y1, x2, y2) format. eps (float, optional): A small value to avoid division by zero. Returns: (torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2. References: https://github.com/pytorch/vision/blob/main/torchvision/ops/boxes.py r)float unsqueezechunktorchminmaxclamp_prod)rrra1a2b1b2inters r-box_iour@4s"//288A> @V@VWX@Y@_@_`acd@eHRhr2 YYr2 2r!2 2 : :1 = B B1 EE R"WNN1%bq(99EACG HHr/xywhGIoUDIoUCIoUc6|ru|jdd|jddc\}}} } \} } } }| dz | dz | dz |dz f\}}}}||z ||z||z ||zf\}}}}| |z | |z| |z | |zf\}}}}nH|jdd\}}}}|jdd\}}}}||z ||z |z} } ||z ||z |z}} |j||j|z jd|j||j|z jdz}| | z| |zz|z |z}||z }|s|s|rK|j||j|z }|j||j|z }|s|r|j d|j dz|z} ||z|z |z j d||z|z |z j dzdz }!|rdt j dzz | |z j| | z jz j dz}"tj5|"|"|z d|zzz }#ddd||!| z |"#zzz S||!| z z S||z|z}$||$|z |$z z S|S#1swY4xYw)a Calculate the Intersection over Union (IoU) between bounding boxes. This function supports various shapes for `box1` and `box2` as long as the last dimension is 4. For instance, you may pass tensors shaped like (4,), (N, 4), (B, N, 4), or (B, N, 1, 4). Internally, the code will split the last dimension into (x, y, w, h) if `xywh=True`, or (x1, y1, x2, y2) if `xywh=False`. Args: box1 (torch.Tensor): A tensor representing one or more bounding boxes, with the last dimension being 4. box2 (torch.Tensor): A tensor representing one or more bounding boxes, with the last dimension being 4. xywh (bool, optional): If True, input boxes are in (x, y, w, h) format. If False, input boxes are in (x1, y1, x2, y2) format. GIoU (bool, optional): If True, calculate Generalized IoU. DIoU (bool, optional): If True, calculate Distance IoU. CIoU (bool, optional): If True, calculate Complete IoU. eps (float, optional): A small value to avoid division by zero. Returns: (torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags. r2rr1N) r5rr r9powmathpiatanr6no_grad)%rrrArBrCrDrx1y1w1h1x2y2w2h2w1_h1_w2_h2_r"r$r#r%r&r(r'r)r?unionrcwchc2rho2valphac_areas% r-bbox_iouraLs> -1ZZ2-> 1b@Q*RR*2r2r!VR!VR!VR!V;S#s%'#XrCxc28%K"ueU%'#XrCxc28%K"ueU%)ZZ2%6"ueU%)ZZ2%6"ueU  3B  3B]]5 !EMM%$8 8 @ @ C eu}}U33 fQi E Gb2g  % +E %-C tt ]]5 !EMM%$8 8 ]]5 !EMM%$8 8 4RVVAY&,B&.33A6%%-%:ORW:W9\9\]^9__D!^b(8BG>>;K(K'P'PQR'SS]]_6SAG!45E6dRi!e)344? "b3fun... J 66s JJmask1mask2ctj||jjd}|j ddddf|j ddz|z }|||zz S)a Calculate masks IoU. Args: mask1 (torch.Tensor): A tensor of shape (N, n) where N is the number of ground truth objects and n is the product of image width and height. mask2 (torch.Tensor): A tensor of shape (M, n) where M is the number of predicted objects and n is the product of image width and height. eps (float, optional): A small value to avoid division by zero. Returns: (torch.Tensor): A tensor of shape (N, M) representing masks IoU. rr1N)r6matmulrr9sum)rbrcr intersectionrYs r-mask_iourhs_<<uww/66q9L YYq\!T' "UYYq\$%7 7< GE 53; ''r/kpt1kpt2r+sigmac|ddddddf|dz jd|ddddddf|dz jdz}tj||j|j}|ddk7}|d|zjd|ddddf|zzdzz }| j |dddfzj d |j d dddf|zz S) aP Calculate Object Keypoint Similarity (OKS). Args: kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints. kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints. area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth. sigma (list): A list containing 17 values representing keypoint scales. eps (float, optional): A small value to avoid division by zero. Returns: (torch.Tensor): A tensor of shape (N, M) representing keypoint similarities. Nr).rr2r1).r1)devicedtype).r2rG)rHr6tensorrmrnexprf)rirjr+rkrdkpt_maskes r-kpt_iourts aq!m tF| +003tAtQM7JTRX\7Y6^6^_`6aaA LLt{{$** EEF|q H a%i__Q 44 #6#< = ABARHHJ!T'* * / / 3x||B7G47PSV7V WWr/boxescntj|ddddfjddz |ddddffd}|jdd\}}}|j }|j }|jd}|jd}||z||zz||z||zz||z |z|zfS)a( Generate covariance matrix from oriented bounding boxes. Args: boxes (torch.Tensor): A tensor of shape (N, 5) representing rotated bounding boxes, with xywhr format. Returns: (torch.Tensor): Covariance matrices corresponding to original rotated bounding boxes. Nr2rF rGdimr1)r6catrHsplitcossin) rugbbsabcr|r}cos2sin2s r-_get_covariance_matrixrs 99eAqsFm''*R/q!"u>B GDjjj#GAq! %%'C %%'C 771:D 771:D t8a$h D1t8 3a!es]S5H HHr/obb1obb2c|dddfjdd\}}|dddfjdd\}}t|\}} } t|\} } } || z||z jdz| | z||z jdzz|| z| | zz| | zjdz |zz dz}| | z||z z||z z|| z| | zz| | zjdz |zz dz}|| z| | zz| | zjdz d || z| jdz jd | | z| jdz jd zj z|zz |zj dz}||z|zj |d }d | jz |zj }d|z }|r|ddd fjdd\}}|ddd fjdd\}}d tjdzz ||z j||z jz jdz}tj5|||z d|zzz }ddd||zz S|S#1swYxYw) a= Calculate probabilistic IoU between oriented bounding boxes. Args: obb1 (torch.Tensor): Ground truth OBBs, shape (N, 5), format xywhr. obb2 (torch.Tensor): Predicted OBBs, shape (N, 5), format xywhr. CIoU (bool, optional): If True, calculate CIoU. eps (float, optional): Small value to avoid division by zero. Returns: (torch.Tensor): OBB similarities, shape (N,). Notes: OBB format: [center_x, center_y, width, height, rotation_angle]. References: https://arxiv.org/pdf/2106.06072v1.pdf .Nr2r1rGrxr?rFrY@?) r{rrHr9sqrtlogclamprprIrJrKr6rL)rrrDrrMrNrQrRr;r=c1r<r>r\t1t2t3bdhdrrOrPrSrTr^r_s r-probiours&#rr']  +FB #rr']  +FB'-JBB'-JBB r'b2g]]1% %bR"WMM!4D(D D"r'VX[]V]I^bdgibiananopaqIqtwIwx  B GR BG ,"r'b2g1F"r'WXIY1Y\_1_ `dg gB r'b2g "r'q!1 1 b266!9$,,Q/27RVVAY3F2N2Nq2QQWWY Y\_ _ a   cec  B r'B,  c5 )B " c ! ' ' )B b&C c1Q3h%%aR%0Bc1Q3h%%aR%0B !^b 0BG>>3C CHHK K ]]_ .SAG,-E .QY J . .s "JJ cjt|tjrtj|n|}t|tjrtj|n|}|dddfj dd\}}d|dddfj ddD\}}t |\}}} dt |D\} } } || z||z jdz|| z||z jdzz|| z|| zz| | zjdz |zz d z} | | z||z z||z z|| z|| zz| | zjdz |zz d z}|| z|| zz| | zjdz d ||z| jdz jd | | z| jdz jd zjz|zz |zjd z}| |z|zj|d }d| jz |zj}d|z S)a$ Calculate the probabilistic IoU between oriented bounding boxes. Args: obb1 (torch.Tensor | np.ndarray): A tensor of shape (N, 5) representing ground truth obbs, with xywhr format. obb2 (torch.Tensor | np.ndarray): A tensor of shape (M, 5) representing predicted obbs, with xywhr format. eps (float, optional): A small value to avoid division by zero. Returns: (torch.Tensor): A tensor of shape (N, M) representing obb similarities. References: https://arxiv.org/pdf/2106.06072v1.pdf .Nr2r1rGrxc3DK|]}|jddywrGNsqueeze.0xs r- z batch_probiou..s JaaiimD! J c3DK|]}|jddywrrrs r-rz batch_probiou..sL!!))B-%LrrrrFrrr) isinstancerndarrayr6 from_numpyr{rrHr9rrrrp)rrrrMrNrQrRr;r=rr<r>r\rrrrrs r- batch_probioursS"&0bjj%A5  D !tD%/bjj%A5  D !tD #rr']  +FB J4RaR=+>+>qb+>+I JFB'-JBBL/Ed/KLJBB r'b2g]]1% %bR"WMM!4D(D D"r'VX[]V]I^bdgibiananopaqIqtwIwx  B GR BG ,"r'b2g1F"r'WXIY1Y\_1_ `dg gB r'b2g "r'q!1 1 b266!9$,,Q/27RVVAY3F2N2Nq2QQWWY Y\_ _ a   cec  B r'B,  c5 )B " c ! ' ' )B r6Mr/cdd|zz d|zfS)a Compute smoothed positive and negative Binary Cross-Entropy targets. Args: eps (float, optional): The epsilon value for label smoothing. Returns: pos (float): Positive label smoothing BCE target. neg (float): Negative label smoothing BCE target. References: https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 rr)rs r- smooth_bcer's s?C#I %%r/c (eZdZdZgdfdeedefdZdZ ddeee jfdeee fd e d e d d f d Z dZd eej"ej"ffdZededdedefdZdZddeded eeee ffdZy )ConfusionMatrixa A class for calculating and updating a confusion matrix for object detection and classification tasks. Attributes: task (str): The type of task, either 'detect' or 'classify'. matrix (np.ndarray): The confusion matrix, with dimensions depending on the task. nc (int): The number of category. names (List[str]): The names of the classes, used as labels on the plot. detectnamestaskc ||_t||_|jdk(r1tj|jdz|jdzfn*tj|j|jf|_||_y)z Initialize a ConfusionMatrix instance. Args: names (List[str], optional): Names of classes, used as labels on the plot. task (str, optional): Type of task, either 'detect' or 'classify'. rr1N)rlenncrzerosmatrixr)selfrrs r-__init__zConfusionMatrix.__init__Csn e*>Bii8>Sbhh! TWWq[9:Y[YaYacgcjcjlplslsbtYu  r/c2tj|dddftj|}}t|jj |jj D]\}}|j ||xxdz cc<!y)z Update confusion matrix for classification task. Args: preds (Array[N, min(nc,5)]): Predicted class labels. targets (Array[N, 1]): Ground truth class labels. Nrr1)r6rzzipcpunumpyr)rpredstargetspts r-process_cls_predsz!ConfusionMatrix.process_cls_predsPsx5)!Q$/71Cw ))+W[[]-@-@-BC #DAq KKN1  "  #r/ detectionsbatchconf iou_thresrNc`|dvrdn|}|d|d}}t|ddk(}|jddk(rj|sgdDcic]}||||d|kD}}|djj} | D]%} |j| |j fxxdz cc<'y |rI|jj} | D]%} |j|j | fxxdz cc<'y dDcic]}||||d|kD}}|jj} |djj} |d} | jdd k(}|r t || n t|| }tj||kD}|djdrtjtj|d||d|dfd d d ffdjj}|djddkDr||d d d fjd d d }|tj |d d dfd d}||d d d fjd d d }|tj |d d dfd d}ntj"d}|jddkD}|j%j't\}}}t)| D]k\}} ||k(}|rNMbP?rclsbboxesr>rrrr1Nr2rGT) return_index)r)rshapeinttolistrrrr@r6whererzstackrrargsortruniquer transposeastype enumeraterfitemany)rrrrrgt_cls gt_bboxesno_predkdetection_classesdc gt_classesgcris_obbrrmatchesnm0m1_ijs r- process_batchzConfusionMatrix.process_batch\s},t$!%L%/ j'(A- <<?a SdeaaAz&/AD/H!IIe e$.u$5$9$9$;$B$B$D!+2BKKDGG ,1,2  ,,.J  . DGGRK(A-( . K\]aaAz&'9D'@AA] ]ZZ\((* &u-113::<H%aA%28mIv.giQW>X KKi ( Q4::a=iiQ!2C!ad OAtG4L MqQUUW]]_Gtzz!}q !'!Q$-"7"7"9$B$"?@!"))GAqDM"Ma"PQ!'!Q$-"7"7"9$B$"?@!"))GAqDM"Ma"PQhhv&G MM! q %%'..s3 Bz* .EAraASVq[ -bejjl;R?@AE@ DGGRK(A-(  .01 .EArrQw< BK(A-( .Mf^s N&/N+c|jS)zReturn the confusion matrix.)rrs r-rzConfusionMatrix.matrixs {{r/c|jj}|jjd|z }|jdk(r |dd|ddfS||fS)z Return true positives and false positives. Returns: tp (np.ndarray): True positives. fp (np.ndarray): False positives. r1rNrG)rdiagonalrfr)rtpfps r-tp_fpzConfusionMatrix.tp_fpsX[[ ! ! # [[__Q " $%)YY(%:3BCR!HRHr/zConfusionMatrix plot failure)msg normalizesave_dirc ddlm}|j|r.|jjdj dddzndz }t j ||dk<|jddd\}}|jd k\rxtd |jd z}tdd|} |j| |_ || ddfdd| f}|j|zdz |z} |jd k(r| n| dzx} } n,|jd k(r |jn|jdzx} } d| cxkrd krnn| | k(r|jdgznd} t jt| }tddd| zz }tddd| zz }tddd| zz }tddd| zz }tj 5tj"d|j%|ddd}|j&j)d| dkrd|rdnt j*|z}t-|d| D]g\}}t-|d| D]Q\}}|||f}t j.|r#|j1|||r|dn t3|ddd ||kDrd!nd"#Si|j5||d$d%&}dddd'd(|zz}|j7d)|d *|j9d+|d *|j;||d,-|j=||j?||jAd.d/d0d/d01|jAd2d/d0d/d03| dk7r(|jC| |d4d5|jE| |6d7D]M}|d8k7r|jF|jId0jJjF|jId0O|jMdd9d:|;tO||jQjSdz }|jU|d?@|jW||r ||yy#1swYxYw)Aav Plot the confusion matrix using matplotlib and save it to a file. Args: normalize (bool, optional): Whether to normalize the confusion matrix. save_dir (str, optional): Directory where the plot will be saved. on_plot (callable, optional): An optional callback to pass plots path and data when they are rendered. rNr1rG& .>g{Gzt?)rw )figsizedr2<classifyc backgroundauto皙?rwrrignoreBluesnone)cmapvmin interpolationbottom?.2fcenter whiteblack)havafontsizecolorgZd;O?皙?)axfractionpadzConfusion Matrixz NormalizedTrue)r labelpad Predicted)r rrTF)axisrtop labelbottomlabeltopy)rleftright labelleft labelrightZ)r rotationr )r >rrrroutliner!gzG?gGz?)rrrr rz.pngdpi),matplotlib.pyplotpyplotrrfreshapernansubplotsrr8slicerrarangerwarningscatch_warnings simplefilterimshowxaxisset_label_positionnanmaxrisnantextrcolorbar set_xlabel set_ylabel set_title set_xticks set_yticks tick_paramsset_xticklabelsset_yticklabelsspines set_visiblersubplots_adjustrlowerreplacesavefigclose)rrron_plotpltarrayfigrrkeep_idxrrnn ticklabelsxy_ticks tick_fontsizelabel_fontsizetitle_fontsizebtmimcolor_thresholdrrowrvalcbartitles plot_fnames r-plotzConfusionMatrix.plots+ ( Y  2 : :1b AD H\]^!vveem,,q!W,5R 77c>Atww"}%AT4+HH-DJ(A+&q({3E1qQ&A99 2aA =B!%j!8dggdggk IB782{{rdjjL>1X^ 99S_-ArC"H}- QS2X .QS2X .#tebj()  $ $ & E  ! !( +5wSOB HH ' ' 1Bw"&y!bii>N"O'cr 3 FAs"+CH"5 3#AqDk88C=$,5s3ic#hZ''%'-0?-B'   <<rEt<DD) E*#]Y%>> f~ C kNR H U^ < h h C%TTYZ Cd%4TYZ     zMBS[  \   zM  B> 1AI~ ! ((/ GGNN1  ) )% 0 1 $DE(^)>)>sC)H(I&NN  JC ( #  J  Q E Es C4QQc t|jjdD]A}tjdj t t|j|Cy)z*Print the confusion matrix to the console.rr"N)rangerrr infojoinmapstrrrs r-printzConfusionMatrix.printsIt{{((+, c} } Scc} wcc} } w) a Generate a summarized representation of the confusion matrix as a list of dictionaries, with optional normalization. This is useful for exporting the matrix to various formats such as CSV, XML, HTML, JSON, or SQL. Args: normalize (bool): Whether to normalize the confusion matrix values. decimals (int): Number of decimal places to round the output values to. Returns: (List[Dict[str, float]]): A list of dictionaries, each representing one predicted class with corresponding values for all actual classes. Examples: >>> results = model.val(data="coco8.yaml", plots=True) >>> cm_dict = results.confusion_matrix.summary(normalize=True, decimals=5) >>> print(cm_dict) rNrrz [^a-zA-Z0-9_]rr1rGrr)rerrsetsubrBaddappendrrfr(roundr\rdict)rrrcrer clean_namesseenname clean_nameoriginal_cleancounterrHrrs r-summaryzConfusionMatrix.summarysn" "ii:5 4::;VT  +D 0#t5A;3N2p q  2p s (E*E%E*%E*)rr)TN)Fr)__name__ __module__ __qualname____doc__rr`rrrr6Tensorrr3rrrrrrr rboolrZrbrrrrr/r-rr8s+-( d3i C  #rv<.sELL01<.:>sCx.<.PU<.in<. <.| IuRZZ34 I12^G dG SG 3G R< " " " T$sTYzJZE[" r/rrfctt||zdzdzdz}tj|dz}tj||dz|||dzfd}tj |tj||z dS)zBox filter of fraction f.r2r1rrGvalid)mode)rjrrones concatenateconvolve)rrznfryps r-smoothr s{ s1vzA~ ! #a 'B aA QqT1a!B%i0! 4B ;;r2772;+' ::r/z pr_curve.pngpxpyaprrc ddlm}|jdddd\}}tj|d}dt |cxkrdkrDnnAt |jD](\} } |j|| d|| d || dfd  *n|j||dd |j||jdddd|dddfjd d|jd|jd|jdd|jdd|jdd|jd|j!|d|j#||r ||yy)a Plot precision-recall curve. Args: px (np.ndarray): X values for the PR curve. py (np.ndarray): Y values for the PR curve. ap (np.ndarray): Average precision values. save_dir (Path, optional): Path to save the plot. names (Dict[int, str], optional): Dictionary mapping class indices to class names. on_plot (callable, optional): Function to call after plot is saved. rNr1rrTr tight_layout)rr".3f linewidthlabelgreyrr rblue all classes z mAP@0.5rr rRecall Precisiongp= ף?r1 upper leftbbox_to_anchorloczPrecision-Recall Curver#r$)r&r'r*rrrrrrZmeanr7r8set_xlimset_ylimlegendr9rDrE) rrrrrrFrGrIrrrs r- plot_pr_curver(sY($ll1adlCGC "1 B3u:bddO LDAq GGBQq !Bq!tHS>.JG K L B!62GGB av|BqRStHMMO\_K``h=iGjMM(MM+KK1KK1IIYLI9LL)*KKcK"IIcNr/z mc_curve.png ConfidenceMetricxlabelylabelc ddlm}|jdddd\}} dt|cxkrdkr0nn-t |D]\} } | j || d||  n| j ||j dd t|jdd } | j || d d d| jdd|| jd| j|| j|| jdd| jdd| jdd| j!|d|j#|d|j%||r ||yy)a Plot metric-confidence curve. Args: px (np.ndarray): X values for the metric-confidence curve. py (np.ndarray): Y values for the metric-confidence curve. save_dir (Path, optional): Path to save the plot. names (Dict[int, str], optional): Dictionary mapping class indices to class names. xlabel (str, optional): X-axis label. ylabel (str, optional): Y-axis label. on_plot (callable, optional): Function to call after plot is saved. rNr1rTrrrrrrrrrrz at rrrrrz-Confidence Curver#r$)r&r'r*rrrZrrrr8argmaxr7r8rrrr9rDrE) rrrrrrrFrGrIrrrs r- plot_mc_curverTsS,$ll1adlCGC3u:bM =DAq GGBQq G < = BDDAV4rwwqz3AGGBQfl15573-tTVWXW_W_WaTbcfSg4hGiMM&MM&KK1KK1IIYLI9LLF8,-.KKcK"IIcNr/recall precisionctjdg|dgf}tjdg|dgf}tjtjj tj|}d}|dk(rztj ddd}t jtjdrtjntj}|tj||||}nKtj|dd|dd k7d}tj||dz||z ||dzz}|||fS) a Compute the average precision (AP) given the recall and precision curves. Args: recall (list): The recall curve. precision (list): The precision curve. Returns: ap (float): Average precision. mpre (np.ndarray): Precision envelope curve. mrec (np.ndarray): Modified recall curve with sentinel values added at the beginning and end. rrinterprr1ez>=2.0NrG)rrflipr accumulatelinspacer check_version __version__ trapezoidtrapzrrrf) rrmrecmpremethodrfuncrrs r- compute_aprs >>C5&3%0 1D >>C5)cU3 4D 772::((7 8DF  KK1c "%33BNNGLr||RTRZRZ "))AtT*A . HHT!"Xcr* +A . VVT!a%[47*d1q5k9 : tT>r/gؗҜ*=A Mfeeg !8sax 2a5y  #ell1oc "iiT!WHfQTlC 39% iiT!WHi1oAF rxx{# =A$.vad|Yq!t_$M !Br1uItTAv""299Qd#;< =)=2,7"((;'BHHY MTQ!u*Qa[ ME M ab(x|5L*Le]dea8 .E#EuUYcjka(x{-C"CUS^hopa(x{-C"CUS[elmx}}Q%,,.Aq!t}gadmXad^"qA b& B C.2  $ $ &B r1aR!6!6s!;WgxYZ\g gg Ns 6 L=L=ceZdZdZddZedeeje ffdZ edeeje ffdZ ede fdZ ede fdZede fd Zede fd Zede fd Zde e fd Zd edee e e e ffdZedejfdZde fdZdefdZede fdZede e fdZy)ra Class for computing evaluation metrics for Ultralytics YOLO models. Attributes: p (list): Precision for each class. Shape: (nc,). r (list): Recall for each class. Shape: (nc,). f1 (list): F1 score for each class. Shape: (nc,). all_ap (list): AP scores for all classes and all IoU thresholds. Shape: (nc, 10). ap_class_index (list): Index of class for each AP score. Shape: (nc,). nc (int): Number of classes. Methods: ap50(): AP at IoU threshold of 0.5 for all classes. Returns: List of AP scores. Shape: (nc,) or []. ap(): AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: List of AP scores. Shape: (nc,) or []. mp(): Mean precision of all classes. Returns: Float. mr(): Mean recall of all classes. Returns: Float. map50(): Mean AP at IoU threshold of 0.5 for all classes. Returns: Float. map75(): Mean AP at IoU threshold of 0.75 for all classes. Returns: Float. map(): Mean AP at IoU thresholds from 0.5 to 0.95 for all classes. Returns: Float. mean_results(): Mean of results, returns mp, mr, map50, map. class_result(i): Class-aware result, returns p[i], r[i], ap50[i], ap[i]. maps(): mAP of each class. Returns: Array of mAP scores, shape: (nc,). fitness(): Model fitness as a weighted combination of metrics. Returns: Float. update(results): Update metric attributes with new evaluation results. rNcXg|_g|_g|_g|_g|_d|_y)zSInitialize a Metric instance for computing evaluation metrics for the YOLOv8 model.rN)rrrall_apap_class_indexrrs r-rzMetric.__init__s-  r/cVt|jr|jdddfSgS)z Return the Average Precision (AP) at an IoU threshold of 0.5 for all classes. Returns: (np.ndarray | list): Array of shape (nc,) with AP50 values per class, or an empty list if not available. Nr)rrrs r-ap50z Metric.ap50(s'%( $4t{{1a4 <"B>r/cdt|jr|jjSdS)z Return the Mean Precision of all classes. Returns: (float): The mean precision of all classes. r)rrrrs r-mpz Metric.mp<#!$DFF tvv{{}44r/cdt|jr|jjSdS)z Return the Mean Recall of all classes. Returns: (float): The mean recall of all classes. r)rrrrs r-mrz Metric.mrFrr/crt|jr!|jdddfjSdS)z Return the mean Average Precision (mAP) at an IoU threshold of 0.5. Returns: (float): The mAP at an IoU threshold of 0.5. Nrrrrs r-map50z Metric.map50P0,/t{{+;t{{1a4 %%'DDr/crt|jr!|jdddfjSdS)z Return the mean Average Precision (mAP) at an IoU threshold of 0.75. Returns: (float): The mAP at an IoU threshold of 0.75. Nrrrrs r-map75z Metric.map75Zrr/cdt|jr|jjSdS)z Return the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05. Returns: (float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05. rrrs r-r_z Metric.mapds'&)%5t{{!>3>r/c^|j|j|j|jgS)z+Return mean of results, mp, mr, map50, map.)rrrr_rs r- mean_resultszMetric.mean_resultsns!$**dhh77r/rcv|j||j||j||j|fS)z6Return class-aware result, p[i], r[i], ap50[i], ap[i].)rrrrras r- class_resultzMetric.class_resultrs1vvay$&&)TYYq\4771:==r/ctj|j|jz}t |j D]\}}|j |||<|S)zReturn mAP of each class.)rrrr_rrr)rmapsrrs r-rz Metric.mapsvsSxx 488+d112 !DAqggajDG ! r/cgd}tjtj|j|zj S)z:Return model fitness as a weighted combination of metrics.)rrrg?)r nan_to_numrHrrf)rws r-fitnesszMetric.fitness~s6  bhht'8'8':;Return a list of curves for accessing specific metrics curves.rrs r-curvesz Metric.curves  r/c|j|jddg|j|jddg|j|jddg|j|jddggS)rrrrr)rrrrrrs r-curves_resultszMetric.curves_resultss`WWd&&+ > WWdmm\4 8 WWdllL+ > WWdllL( ;   r/rN)rtrurvrwrpropertyrrrrrrr3rrrrr_rrrrrrtuplerrrrr/r-rrs4=eBJJ,-==?E"**d*+??5E555E55EuEEEuEE?U??8d5k8>c>eE5%,F&G>bjjHH e< T   r/c eZdZdZifdeeefddfdZdeeefddfdZ e dd dfd e d e deee jffd Zd ZedeefdZdeefdZdedeeeeeffdZede jfdZedefdZedefdZedeeeffdZedeefdZedeefdZdde dedeeeeffdZy) DetMetricsa Utility class for computing detection metrics such as precision, recall, and mean average precision (mAP). Attributes: names (Dict[int, str]): A dictionary of class names. box (Metric): An instance of the Metric class for storing detection results. speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process. task (str): The task type, set to 'detect'. stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images. nt_per_class: Number of targets per class. nt_per_image: Number of targets per image. rrNc||_t|_ddddd|_d|_t ggggg|_d|_d|_y)z Initialize a DetMetrics instance with a save directory, plot flag, and class names. Args: names (Dict[int, str], optional): Dictionary of class names. r preprocess inferenceloss postprocessr)rrrr target_imgN) rrboxspeedrrkstats nt_per_class nt_per_imagerrs r-rzDetMetrics.__init__sQ 8$'c3WZ[  Rb2"QST   r/statc|jjD]#}|j|j||%y)a  Update statistics by appending new values to existing stat collections. Args: stat (Dict[str, any]): Dictionary containing new statistical values to append. Keys should match existing keys in self.stats. N)rkeysri)rr rs r- update_statszDetMetrics.update_statss8" *A JJqM a ) *r/.FrrZc |jjDcic]\}}|tj|d}}}t |dk(r|St |d|d|d|d|||j |d dd }t |j |j_|jj|tj|djtt |j  |_ tj|d jtt |j  |_|Scc}}w) a Process predicted results for object detection and update metrics. Args: save_dir (Path): Directory to save plots. Defaults to Path("."). plot (bool): Whether to plot precision-recall curves. Defaults to False. on_plot (callable, optional): Function to call after plots are generated. Defaults to None. Returns: (Dict[str, np.ndarray]): Dictionary containing concatenated statistics arrays. rrrrrBox)rZrrrFrr2N) minlengthr)ritemsrrrrrrrrbincountrrrr)rrrZrFrr^rrs r-processzDetMetrics.processs6:ZZ5E5E5GHTQBNN1a((HH u:?L $K &M *  , **   " $**o   KKl(;(B(B3(GSVW[WaWaSbcKKl(;(B(B3(GSVW[WaWaSbc %Is!E cb|jjD]}|jy)zClear the stored statistics.N)rvaluesclear)rr^s r- clear_statszDetMetrics.clear_statss'""$ A GGI r/c gdS)z5Return a list of keys for accessing specific metrics.)zmetrics/precision(B)zmetrics/recall(B)zmetrics/mAP50(B)zmetrics/mAP50-95(B)rrs r-r zDetMetrics.keyss hgr/c6|jjS)zSCalculate mean of detected objects & return precision, recall, mAP50, and mAP50-95.)rrrs r-rzDetMetrics.mean_resultssxx$$&&r/rc8|jj|S)zaReturn the result of evaluating the performance of an object detection model on a specific class.)rrras r-rzDetMetrics.class_resultsxx$$Q''r/c.|jjS)z5Return mean Average Precision (mAP) scores per class.)rrrs r-rzDetMetrics.maps sxx}}r/c6|jjS)z!Return the fitness of box object.)rrrs r-rzDetMetrics.fitnesssxx!!r/c.|jjS)z-Return the average precision index per class.)rrrs r-rzDetMetrics.ap_class_indexxx&&&r/ctt|jdgz|j|jgzS)AReturn dictionary of computed performance metrics and statistics.r)rkrr rrrs r- results_dictzDetMetrics.results_dicts5C YK/1B1B1D ~1UVWWr/c gdS)r)Precision-Recall(B)F1-Confidence(B)Precision-Confidence(B)Recall-Confidence(B)rrs r-rzDetMetrics.curvess nmr/c.|jjSr )rrrs r-rzDetMetrics.curves_results$rr/rrccr|jj|jj|jjd}t t |dDcgc]}|j |j||j|j||j|j|d|jDcic]\}}|t|||c}}t|j|d|t|j|d|dc}}}Scc}}wcc}}}w)a Generate a summarized representation of per-class detection metrics as a list of dictionaries. Includes shared scalar metrics (mAP, mAP50, mAP75) alongside precision, recall, and F1-score for each class. Args: normalize (bool): For Detect metrics, everything is normalized by default [0-1]. decimals (int): Number of decimal places to round the metrics values to. Returns: (List[Dict[str, Any]]): A list of dictionaries, each representing one class with corresponding metric values. Examples: >>> results = model.val(data="coco8.yaml") >>> detection_summary = results.summary() >>> print(detection_summary) )Box-PzBox-RzBox-F1r*)ClassImages Instancesr2r)mAP50zmAP50-95) rrrrr\rrrrrrrjr)rrrc per_classrrr^s r-rrzDetMetrics.summary)s!$XXZZXXZZhhkk 3y123   D$7$7$:;++D,?,?,BC!..t/B/B1/EF 9B8IJ11eAaD(++J   t003A6A!$"3"3A"6q"98D    K  sA+D2 D,#AD2,D2Tr)rtrurvrwrrr`rrr rryrrrrrrr r3rrrrrrr!rrrrrr/r-rrs 02 !d38n !d ! *c3h *D *(,CyudW[\_acakak\kWl@ hd3ihh'd5k'(c(eE5%,F&G(bjj"""'''Xd3:.XXnS nn'T ''      DcSVhDX  r/rc VeZdZdZifdeeefddfdZedddfded e deee jffd Z e deefd Zdeefd Zd edeefdZe de jfdZe defdZe deefdZe deefdZdde dedeeeeffdZy)SegmentMetricsa Calculate and aggregate detection and segmentation metrics over a given set of classes. Attributes: names (Dict[int, str]): Dictionary of class names. box (Metric): An instance of the Metric class for storing detection results. seg (Metric): An instance of the Metric class to calculate mask segmentation metrics. speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process. task (str): The task type, set to 'segment'. stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images. nt_per_class: Number of targets per class. nt_per_image: Number of targets per image. rrNcztj||t|_d|_g|j d<y)z Initialize a SegmentMetrics instance with a save directory, plot flag, and class names. Args: names (Dict[int, str], optional): Dictionary of class names. segmenttp_mN)rrrsegrrrs r-rzSegmentMetrics.__init__[s2 D%(8  6r/r FrrZc tj||||}t|d|d|d|d||||jd dd }t |j|j _|j j||S) a Process the detection and segmentation metrics over the given set of predictions. Args: save_dir (Path): Directory to save plots. Defaults to Path("."). plot (bool): Whether to plot precision-recall curves. Defaults to False. on_plot (callable, optional): Function to call after plots are generated. Defaults to None. Returns: (Dict[str, np.ndarray]): Dictionary containing concatenated statistics arrays. rr5rrrMaskrZrFrrrr2N)rrrrrr6rr)rrrZrFr results_masks r-rzSegmentMetrics.processgs""44"I# &M &M *  , **   "  $**o   % r/cJtjj|gdzS)z,Return a list of keys for accessing metrics.)zmetrics/precision(M)zmetrics/recall(M)zmetrics/mAP50(M)zmetrics/mAP50-95(M)rr fgetrs r-r zSegmentMetrics.keys%##D)-   r/cbtj||jjzS)zBReturn the mean metrics for bounding box and segmentation results.)rrr6rs r-rzSegmentMetrics.mean_resultss%&&t,txx/D/D/FFFr/rcftj|||jj|zS)z:Return classification results for a specified class index.)rrr6ras r-rzSegmentMetrics.class_results)&&tQ/$((2G2G2JJJr/cntjj||jjzS)zHReturn mAP scores for object detection and semantic segmentation models.)rrr=r6rs r-rzSegmentMetrics.mapss&##D)DHHMM99r/cv|jjtjj|zS)zGReturn the fitness score for both segmentation and bounding box models.)r6rrr=rs r-rzSegmentMetrics.fitnesss-xx!J$6$6$;$;D$AAAr/cJtjj|gdzS)r)zPrecision-Recall(M)zF1-Confidence(M)zPrecision-Confidence(M)zRecall-Confidence(M)rrr=rs r-rzSegmentMetrics.curvess'  %%d+/   r/cntjj||jjzSr()rrr=r6rs r-rzSegmentMetrics.curves_resultss*((--d3dhh6M6MMMr/rrcc n|jj|jj|jjd}tj |||}t |D]G\}}|ji|jDcic]\}}|t|||c}}I|Scc}}w)a Generate a summarized representation of per-class segmentation metrics as a list of dictionaries. Includes both box and mask scalar metrics (mAP, mAP50, mAP75) alongside precision, recall, and F1-score for each class. Args: normalize (bool): For Segment metrics, everything is normalized by default [0-1]. decimals (int): Number of decimal places to round the metrics values to. Returns: (List[Dict[str, Any]]): A list of dictionaries, each representing one class with corresponding metric values. Examples: >>> results = model.val(data="coco8-seg.yaml") >>> seg_summary = results.summary(decimals=4) >>> print(seg_summary) )zMask-PzMask-RzMask-F1) r6rrrrrrrrrrj rrrcr/rrrrXrr^s r-rrzSegmentMetrics.summarys$hhjjhhjjxx{{ $$T9h?g& SDAq HHQioo>OPdaE!A$11PQ R SQ B1r0)rtrurvrwrrr`rrryrrrrrr r3rrrrrrrrrrr/r-r2r2LsR 02  d38n  d  (,CyudW[\_acakak\kWl8 d3i  Gd5kGKcKd5kK:bjj::BBB S   NT NNDcSVhDXr/r2c beZdZdZifdeeefddffd Zedddfded e deee jffd Z e deefd Zdeefd Zd edeefdZe de jfdZe defdZe deefdZe deefdZdde dedeeeeffdZxZS) PoseMetricsa Calculate and aggregate detection and pose metrics over a given set of classes. Attributes: names (Dict[int, str]): Dictionary of class names. pose (Metric): An instance of the Metric class to calculate pose metrics. box (Metric): An instance of the Metric class for storing detection results. speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process. task (str): The task type, set to 'pose'. stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images. nt_per_class: Number of targets per class. nt_per_image: Number of targets per image. Methods: process(tp_m, tp_b, conf, pred_cls, target_cls): Process metrics over the given set of predictions. mean_results(): Return the mean of the detection and segmentation metrics over all the classes. class_result(i): Return the detection and segmentation metrics of class `i`. maps: Return the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. fitness: Return the fitness scores, which are a single weighted combination of metrics. ap_class_index: Return the list of indices of classes used to compute Average Precision (AP). results_dict: Return the dictionary containing all the detection and segmentation metrics and fitness score. rrNcnt||t|_d|_g|j d<y)z Initialize the PoseMetrics class with directory path, class names, and plotting options. Args: names (Dict[int, str], optional): Dictionary of class names. posetp_pN)superrrrLrr)rr __class__s r-rzPoseMetrics.__init__s0 H   6r/r FrrZc tj||||}t|d|d|d|d||||jd dd }t |j|j _|j j||S) a Process the detection and pose metrics over the given set of predictions. Args: save_dir (Path): Directory to save plots. Defaults to Path("."). plot (bool): Whether to plot precision-recall curves. Defaults to False. on_plot (callable, optional): Function to call after plots are generated. Returns: (Dict[str, np.ndarray]): Dictionary containing concatenated statistics arrays. rrMrrrPoser9r2N)rrrrrrLrr)rrrZrFr results_poses r-rzPoseMetrics.processs""44"I# &M &M *  , **   "  4::  & r/cJtjj|gdzS)z&Return list of evaluation metric keys.)zmetrics/precision(P)zmetrics/recall(P)zmetrics/mAP50(P)zmetrics/mAP50-95(P)r<rs r-r zPoseMetrics.keys r>r/cbtj||jjzS)z(Return the mean results of box and pose.)rrrLrs r-rzPoseMetrics.mean_resultss%&&t,tyy/E/E/GGGr/rcftj|||jj|zS)z?Return the class-wise detection results for a specific class i.)rrrLras r-rzPoseMetrics.class_results)&&tQ/$))2H2H2KKKr/cntjj||jjzS)zSReturn the mean average precision (mAP) per class for both box and pose detections.)rrr=rLrs r-rzPoseMetrics.mapss&##D)DIINN::r/cv|jjtjj|zS)z9Return combined fitness score for pose and box detection.)rLrrr=rs r-rzPoseMetrics.fitness!s-yy  "Z%7%7%<%>> results = model.val(data="coco8-pose.yaml") >>> pose_summary = results.summary(decimals=4) >>> print(pose_summary) )zPose-PzPose-RzPose-F1) rLrrrrrrrrrrjrGs r-rrzPoseMetrics.summary9s$iikkiikkyy|| $$T9h?g& SDAq HHQioo>OPdaE!A$11PQ R SQrHr0)rtrurvrwrrr`rrryrrrrrr r3rrrrrrrrr __classcell__)rOs@r-rJrJsS.02  d38n  d  (,CyudW[\_acakak\kWl8 d3i  Hd5kHLcLd5kL;bjj;;CCC  S    OT OODcSVhDXr/rJc eZdZdZddZdej dej fdZede fdZ ede e e ffd Z edee fd Zedefd Zedefd Zdd ededee e e ffdZy)ClassifyMetricsaO Class for computing classification metrics including top-1 and top-5 accuracy. Attributes: top1 (float): The top-1 accuracy. top5 (float): The top-5 accuracy. speed (dict): A dictionary containing the time taken for each step in the pipeline. task (str): The task type, set to 'classify'. rNcFd|_d|_ddddd|_d|_y)z&Initialize a ClassifyMetrics instance.rrrrN)top1top5rrrs r-rzClassifyMetrics.__init__`s(  $'c3WZ[  r/rpredcVtj|tj|}}|dddf|k(j}tj|dddf|j dj fd}|j dj\|_|_ y)z Process target classes and predicted classes to compute metrics. Args: targets (torch.Tensor): Target classes. pred (torch.Tensor): Predicted classes. Nrr1rx) r6rzr3rr8rrrr_r`)rrracorrectaccs r-rzClassifyMetrics.processgs $7);g1d7#t+224kk71a4='++a.*?*?@aH"xx{113 49r/c:|j|jzdz S)z;Return mean of top-1 and top-5 accuracies as fitness score.r2)r_r`rs r-rzClassifyMetrics.fitnessts DII%**r/ctt|jdgz|j|j|j gS)zGReturn a dictionary with model's performance metrics and fitness score.r)rkrr r_r`rrs r-r!zClassifyMetrics.results_dictys4C YK/$))TYY 1UVWWr/c ddgS)z4Return a list of keys for the results_dict property.zmetrics/accuracy_top1zmetrics/accuracy_top5rrs r-r zClassifyMetrics.keys~s()@AAr/cgSrrrs r-rzClassifyMetrics.curvesrr/cgSrrrs r-rzClassifyMetrics.curves_resultsrr/rrcc^t|j|t|j|dgS)aa Generate a single-row summary of classification metrics (Top-1 and Top-5 accuracy). Args: normalize (bool): For Classify metrics, everything is normalized by default [0-1]. decimals (int): Number of decimal places to round the metrics values to. Returns: (List[Dict[str, float]]): A list with one dictionary containing Top-1 and Top-5 classification accuracy. Examples: >>> results = model.val(data="imagenet10") >>> classify_summary = results.summary(decimals=4) >>> print(classify_summary) )top1_acctop5_acc)rjr_r`)rrrcs r-rrzClassifyMetrics.summarys) #499h7U499V^E_`aar/rr0)rtrurvrwrr6rxrrr3rrr`r!rr rrryrrrrr/r-r]r]Us 4u|| 45<< 4+++Xd3:.XXBd3iBBbbbDcSXjIYDZbr/r]c.eZdZdZifdeeefddfdZy) OBBMetricsa Metrics for evaluating oriented bounding box (OBB) detection. Attributes: names (Dict[int, str]): Dictionary of class names. box (Metric): An instance of the Metric class for storing detection results. speed (Dict[str, float]): A dictionary for storing execution times of different parts of the detection process. task (str): The task type, set to 'obb'. stats (Dict[str, List]): A dictionary containing lists for true positives, confidence scores, predicted classes, target classes, and target images. nt_per_class: Number of targets per class. nt_per_image: Number of targets per image. References: https://arxiv.org/pdf/2106.06072.pdf rrNc>tj||d|_y)z Initialize an OBBMetrics instance with directory, plotting, and class names. Args: names (Dict[int, str], optional): Dictionary of class names. obbN)rrrrs r-rzOBBMetrics.__init__s D%( r/)rtrurvrwrrr`rrr/r-rnrns' 02 d38n d r/rn)FHz>)rq)TFFFrq)r)r)2rwrIr-pathlibrtypingrrrrrrrr6ultralytics.utilsr r r r r rrH OKS_SIGMArryr3r.rxr@rarhrtrrrrrrrr`rrrrrrr2rJr]rnrr/r-rvs 00 cc BHH st   %2::%RZZ%d%%Z\ZdZd%@I%,,IellII%,,I6D ,,D ,,D D  D  D  D D \\DN(ELL((E(U\\((bfX ,,X#llX27,,XGKE{XY^X \\X0I%,,I5u||UZUaUa9a3bI(+%,,+ell+$+U+^c^j^j+^`d$  bjj( )$16u||RZZ7O1P$W\$ \\$N&E&E%,$7&"e oe P;bjj;U;bjj; .)  ( ( ( ( ( S> ((V.) * * ** S> *  *  **ZtE{tE{uUBJJXZXbXbEb?cL V^h ^h **^hjj^h ^h  ^h^h S>^h ^h ^h ^hBj [j ZX oX v{Z{|H*HVHbk?HbVr/