a yµÿf§Òã@s’dZddlZddlZddlmZddlmZddlZ ddl Z ddlmZm Z mZmZe gd¢¡dZd;d d „Zddd„Zd?dd„Zdd„Zd@dd„ZdAdd„ZdBdd„ZGdd„dƒZdCd d!„Zeƒed"ƒidfd#d$„ƒZeƒed%ƒid&d'dfd(d)„ƒZd*d+„Zddeƒid,d-fd.d/„Z Gd0d'„d'e ƒZ!Gd1d2„d2e ƒZ"Gd3d4„d4e ƒZ#Gd5d6„d6e#ƒZ$Gd7d8„d8e ƒZ%Gd9d:„d:e ƒZ&dS)DzModel validation metrics.éN)ÚPath)ÚLOGGERÚSimpleClassÚ TryExceptÚplt_settings)g¤p= ×£Ð?çÐ?rçffffffÖ?rçHáz®Gé?r ç ×£p= ç?r ç×£p= ×ã?rç…ëQ¸ñ?rç×£p= ×ë?r ç{®Gázì?rg$@FçH¯¼šò×z>cCsÐ|j\}}}}|j\}} } }t |dd…df| ¡t |dd…df|¡ d¡t |dd…df|¡t |dd…df| ¡ d¡}| ||| } |rÄ||||}| |dd…df|} || |S)aD Calculate the intersection over box2 area given box1 and box2. Boxes are in x1y1x2y2 format. Args: box1 (np.ndarray): A numpy array of shape (n, 4) representing n bounding boxes. box2 (np.ndarray): A numpy array of shape (m, 4) representing m bounding boxes. iou (bool): Calculate the standard IoU if True else return inter_area/box2_area. eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. Returns: (np.ndarray): A numpy array of shape (n, m) representing the intersection over box2 area. Nr)ÚTÚnpÚminimumÚmaximumZclip)Úbox1Úbox2ÚiouÚepsÚb1_x1Úb1_y1Úb1_x2Úb1_y2Úb2_x1Úb2_y1Úb2_x2Úb2_y2Z inter_areaÚareaZ box1_area©r!úU/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/utils/metrics.pyÚbbox_ioas4.ÿþr#cCs‚| ¡ d¡ dd¡| ¡ d¡ dd¡\}}\}}t ||¡t ||¡ d¡ d¡}||| d¡|| d¡||S)a\ Calculate intersection-over-union (IoU) of boxes. Both sets of boxes are expected to be in (x1, y1, x2, y2) format. Based on https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py. Args: box1 (torch.Tensor): A tensor of shape (N, 4) representing N bounding boxes. box2 (torch.Tensor): A tensor of shape (M, 4) representing M bounding boxes. eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. Returns: (torch.Tensor): An NxM tensor containing the pairwise IoU values for every element in box1 and box2. éér)ÚfloatZ unsqueezeÚchunkÚtorchÚminÚmaxÚclamp_Úprod)rrrÚa1Úa2Úb1Úb2Úinterr!r!r"Úbox_iou4s6$r2Tc%Cs‚|rœ| dd¡| dd¡\}}} } \}}} }| d| d| d|df\}}}}||||||||f\}}}}||||||||f\}}}}nT| dd¡\}}}}| dd¡\}}}}|||||} } |||||} }| |¡| |¡ d¡| |¡| |¡ d¡}| | | |||}||}|sV|sV|r~| |¡| |¡}| |¡| |¡}|sŠ|rb| d¡| d¡|} |||| d¡|||| d¡d}!|rVdtjd| | ¡| | ¡ d¡}"t ¡$|"|"|d|}#Wdƒn1s80Y||!| |"|#S||!| S|||}$||$||$S|S)a Calculate Intersection over Union (IoU) of box1(1, 4) to box2(n, 4). Args: box1 (torch.Tensor): A tensor representing a single bounding box with shape (1, 4). box2 (torch.Tensor): A tensor representing n bounding boxes with shape (n, 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. Defaults to True. GIoU (bool, optional): If True, calculate Generalized IoU. Defaults to False. DIoU (bool, optional): If True, calculate Distance IoU. Defaults to False. CIoU (bool, optional): If True, calculate Complete IoU. Defaults to False. eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. Returns: (torch.Tensor): IoU, GIoU, DIoU, or CIoU values depending on the specified flags. ééÿÿÿÿr%rr$N) r'rrr+ÚpowÚmathÚpiÚatanr(Úno_grad)%rrZxywhZGIoUZDIoUÚCIoUrÚx1Úy1Úw1Úh1Úx2Úy2Úw2Úh2Zw1_Zh1_Zw2_Zh2_rrrrrrrrr1ÚunionrZcwÚchÚc2Zrho2ÚvÚalphaZc_arear!r!r"Úbbox_iouJsB*$$&ÿþ*þ, 4rHcCsHt ||j¡ d¡}| d¡dd…df| d¡d|}|||S)a1 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. Defaults to 1e-7. Returns: (torch.Tensor): A tensor of shape (N, M) representing masks IoU. rr$N)r(Úmatmulrr+Úsum)Zmask1Zmask2rÚintersectionrCr!r!r"Úmask_iou„s(rLcCsÒ|dd…ddd…df|d d¡|dd…ddd…df|d d¡}tj||j|jd}|ddk}|d| d¡|dd…ddf|d}| ¡|dd…df d ¡| d ¡dd…df|S) ab Calculate Object Keypoint Similarity (OKS). Args: kpt1 (torch.Tensor): A tensor of shape (N, 17, 3) representing ground truth keypoints. kpt2 (torch.Tensor): A tensor of shape (M, 17, 3) representing predicted keypoints. area (torch.Tensor): A tensor of shape (N,) representing areas from ground truth. sigma (list): A list containing 17 values representing keypoint scales. eps (float, optional): A small value to avoid division by zero. Defaults to 1e-7. Returns: (torch.Tensor): A tensor of shape (N, M) representing keypoint similarities. Nr).rr%r$).r$)ÚdeviceÚdtype).r%r4)r5r(ZtensorrMrNÚexprJ)Zkpt1Zkpt2r ÚsigmarÚdZkpt_maskÚer!r!r"Úkpt_iou—s L,rSc Cs¢tj|dd…dd…f d¡d|dd…dd…ffdd}|jddd\}}}| ¡}| ¡}| d¡}| d¡}||||||||||||fS)a Generating covariance matrix from obbs. Args: boxes (torch.Tensor): A tensor of shape (N, 5) representing rotated bounding boxes, with xywhr format. Returns: (torch.Tensor): Covariance matrices corresponding to original rotated bounding boxes. Nr%r3ér4©Údimr$)r(Úcatr5ÚsplitÚcosÚsin) ZboxesZgbbsÚaÚbÚcrYrZZcos2Zsin2r!r!r"Ú_get_covariance_matrixs< r^cCs<|ddd…fjddd\}}|ddd…fjddd\}}t|ƒ\}} } t|ƒ\}}} |||| d¡| ||| d¡||| || | d¡|d}| | ||||||| || | d¡|d}||| || | d¡d || | d¡ d ¡||| d¡ d ¡ ¡|| ¡d}||| |d¡}d| ¡| ¡}d|}|r8|ddd …fjddd\}}|ddd …fjddd\}}d tj d|| ¡|| ¡ d¡}t ¡$|||d|}Wdƒn1s"0Y|||S|S) a¢ Calculate probabilistic IoU between oriented bounding boxes. Implements the algorithm from https://arxiv.org/pdf/2106.06072v1.pdf. Args: obb1 (torch.Tensor): Ground truth OBBs, shape (N, 5), format xywhr. obb2 (torch.Tensor): Predicted OBBs, shape (N, 5), format xywhr. CIoU (bool, optional): If True, calculate CIoU. Defaults to False. eps (float, optional): Small value to avoid division by zero. Defaults to 1e-7. Returns: (torch.Tensor): OBB similarities, shape (N,). Note: OBB format: [center_x, center_y, width, height, rotation_angle]. If CIoU is True, returns CIoU instead of IoU. .Nr%r$r4rUrçà?r3rçY@çð?) rXr^r5r+ÚsqrtÚlogÚclamprOr6r7r8r(r9)Úobb1Úobb2r:rr;r<r?r@r-r/Úc1r.r0rEÚt1Út2Út3ÚbdÚhdrr=r>rArBrFrGr!r!r"ÚprobiouÁs6Lþ>:ÿþÿü, 4rmcCsÈt|tjƒrt |¡n|}t|tjƒr0t |¡n|}|ddd…fjddd\}}dd„|ddd…fjdddDƒ\}}t|ƒ\}}} d d„t|ƒDƒ\} }}|| || d¡|||| d¡|| ||| | d¡|d } | ||||||| ||| | d¡|d}|| ||| | d¡d||| d¡ d ¡| || d¡ d ¡ ¡|| ¡d}| || |d¡}d| ¡| ¡}d|S)a Calculate the prob IoU between oriented bounding boxes, https://arxiv.org/pdf/2106.06072v1.pdf. 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. Defaults to 1e-7. Returns: (torch.Tensor): A tensor of shape (N, M) representing obb similarities. .Nr%r$r4rUcss|]}| d¡dVqdS©r4N©Zsqueeze©Ú.0Úxr!r!r"Ú ÿóz batch_probiou..css|]}| d¡dVqdSrnrorpr!r!r"rsrtrr_r3rr`ra) Ú isinstancerZndarrayr(Z from_numpyrXr^r5r+rbrcrdrO)rerfrr;r<r?r@r-r/rgr.r0rErhrirjrkrlr!r!r"Ú batch_probiouïs*(Lþ>:ÿþÿürvçš™™™™™¹?cCsdd|d|fS)aü Computes smoothed positive and negative Binary Cross-Entropy targets. This function calculates positive and negative label smoothing BCE targets based on a given epsilon value. For implementation details, refer to https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441. Args: eps (float, optional): The epsilon value for label smoothing. Defaults to 0.1. Returns: (tuple): A tuple containing the positive and negative label smoothing BCE targets. rar_r!)rr!r!r"Ú smooth_BCEs rxc@sZeZdZdZddd„Zdd„Zd d „Zdd„Zd d„Ze dƒe ƒddd„ƒƒZdd„ZdS)ÚConfusionMatrixaÊ A class for calculating and updating a confusion matrix for object detection and classification tasks. Attributes: task (str): The type of task, either 'detect' or 'classify'. matrix (np.ndarray): The confusion matrix, with dimensions depending on the task. nc (int): The number of classes. conf (float): The confidence threshold for detections. iou_thres (float): The Intersection over Union threshold. rçÍÌÌÌÌÌÜ?ÚdetectcCsX||_|jdkr&t |d|df¡nt ||f¡|_||_|dvrHdn||_||_dS)z)Initialize attributes for the YOLO model.r{r$>Ngü©ñÒMbP?rN)ÚtaskrÚzerosÚmatrixÚncÚconfÚ iou_thres)Úselfrr€rr|r!r!r"Ú__init__-s 0zConfusionMatrix.__init__cCs`t |¡dd…dft |¡}}t| ¡ ¡| ¡ ¡ƒD]\}}|j||d7<qg{®Gázt?)rTé T©ZfigsizeZtight_layouté2ragš™™™™™é?)Z font_scaleécÚ backgroundÚautoÚignoreéÚsizeéZBluesú.2fz.0fç) ÚaxÚannotZ annot_kwsZcmapÚfmtZsquareZvminZxticklabelsZyticklabels)r$r$r$zConfusion Matrixz NormalizedÚTrueZ Predictedú rœz.pngéú©Zdpi)Úseabornr~rJZreshaperÚnanÚpltÚsubplotsrÚlenZ set_themeÚlistÚwarningsÚcatch_warningsÚsimplefilterZheatmapZ set_facecolorÚ set_xlabelÚ set_ylabelÚ set_titlerÚlowerÚreplaceÚsavefigÚclose)r‚Ú normalizeÚsave_dirÚnamesÚon_plotr¸ÚarrayÚfigr±rÚnnÚlabelsZ ticklabelsÚtitleZ plot_fnamer!r!r"Úplot‡sB& öõ$ zConfusionMatrix.plotc Cs4t|jdƒD] }t d tt|j|ƒ¡¡qdS)z*Print the confusion matrix to the console.r$rµN)ÚrangerrÚinfoÚjoinÚmapÚstrr~©r‚rr!r!r"ÚprintµszConfusionMatrix.print)rrzr{)Tr¤r!N) Ú__name__Ú __module__Ú__qualname__Ú__doc__rƒr‹rŸr~r£rrrÑrØr!r!r!r"ry!s ;,ryçš™™™™™©?cCsftt|ƒ|dƒdd}t |d¡}t ||d|||dfd¡}tj|t |¡|ddS)zBox filter of fraction f.r%r$rr4Zvalid)Úmode)Úroundr¼rZonesÚconcatenateZconvolve)ÚyÚfÚnfr‰Zypr!r!r"Úsmooth»s"räzpr_curve.pngc Cs4tjddddd\}}tj|dd}dt|ƒkr`rtz ap_per_class..zPR_curve.png)rËzF1_curve.pngÚF1)rrËzP_curve.pngrózR_curve.pngròrw)rr“r”rr r}r–rJZcumsumrrÒrÚappendrÌÚitemsÚdictrÿrrärørrßr•r)"r¡r€Úpred_clsÚ target_clsrÑrËrÉrÊrÚprefixrÚntrrrÚprec_valuesrþÚp_curveÚr_curveÚcir]Zn_lZn_pZfpcZtpcr rržr rÚf1_curver‰ÚrÚf1r¢r!rr"Úap_per_classsH" 4**0 4r#c@s¶eZdZdZddœdd„Zedd„ƒZedd „ƒZed d„ƒZedd „ƒZ edd„ƒZ edd„ƒZedd„ƒZdd„Z dd„Zedd„ƒZdd„Zdd„Zedd„ƒZed d!„ƒZdS)"ra½ Class for computing evaluation metrics for YOLOv8 model. 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. N©ÚreturncCs(g|_g|_g|_g|_g|_d|_dS)zTInitializes a Metric instance for computing evaluation metrics for the YOLOv8 model.rN)r‰r!r"Úall_apÚap_class_indexrr r!r!r"rƒŠszMetric.__init__cCs t|jƒr|jdd…dfSgS)zæ Returns 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)r¼r&r r!r!r"Úap50“szMetric.ap50cCst|jƒr|j d¡SgS)zî Returns the Average Precision (AP) at an IoU threshold of 0.5-0.95 for all classes. Returns: (np.ndarray, list): Array of shape (nc,) with AP50-95 values per class, or an empty list if not available. r$©r¼r&rør r!r!r"rþsz Metric.apcCst|jƒr|j ¡SdS)z† Returns the Mean Precision of all classes. Returns: (float): The mean precision of all classes. r°)r¼r‰rør r!r!r"Úmp§sz Metric.mpcCst|jƒr|j ¡SdS)z€ Returns the Mean Recall of all classes. Returns: (float): The mean recall of all classes. r°)r¼r!rør r!r!r"Úmr±sz Metric.mrcCs$t|jƒr |jdd…df ¡SdS)z¡ Returns the mean Average Precision (mAP) at an IoU threshold of 0.5. Returns: (float): The mAP at an IoU threshold of 0.5. Nrr°r)r r!r!r"Úmap50»szMetric.map50cCs$t|jƒr |jdd…df ¡SdS)z£ Returns the mean Average Precision (mAP) at an IoU threshold of 0.75. Returns: (float): The mAP at an IoU threshold of 0.75. NrŒr°r)r r!r!r"Úmap75ÅszMetric.map75cCst|jƒr|j ¡SdS)zÑ Returns the mean Average Precision (mAP) over IoU thresholds of 0.5 - 0.95 in steps of 0.05. Returns: (float): The mAP over IoU thresholds of 0.5 - 0.95 in steps of 0.05. r°r)r r!r!r"rÕÏsz Metric.mapcCs|j|j|j|jgS)z+Mean of results, return mp, mr, map50, map.)r*r+r,rÕr r!r!r"Úmean_resultsÙszMetric.mean_resultscCs$|j||j||j||j|fS)z6Class-aware result, return p[i], r[i], ap50[i], ap[i].)r‰r!r(rþr×r!r!r"Úclass_resultÝszMetric.class_resultcCs8t |j¡|j}t|jƒD]\}}|j|||<q|S)zMAP of each class.)rr}rrÕr–r'rþ)r‚Úmapsrr]r!r!r"r0ászMetric.mapscCsgd¢}t | ¡¡| ¡S)z3Model fitness as a weighted combination of metrics.)r°r°rwgÍÌÌÌÌÌì?)rrÌr.rJ)r‚Úwr!r!r"ÚfitnessészMetric.fitnesscCs0|\ |_|_|_|_|_|_|_|_|_|_ dS)a Updates the evaluation metrics of the model with a new set of results. Args: results (tuple): A tuple containing the following evaluation metrics: - 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,). Side Effects: Updates the class attributes `self.p`, `self.r`, `self.f1`, `self.all_ap`, and `self.ap_class_index` based on the values provided in the `results` tuple. N) r‰r!r"r&r'rrr rür)r‚Úresultsr!r!r"Úupdateîsõöz Metric.updatecCsgS©ú?Returns a list of curves for accessing specific metrics curves.r!r r!r!r"Úcurvessz Metric.curvescCs<|j|jddg|j|jddg|j|jddg|j|jddggS)r6ròrórr)rürr rrr r!r!r"Úcurves_resultss üzMetric.curves_results)rÙrÚrÛrÜrƒÚpropertyr(rþr*r+r,r-rÕr.r/r0r2r4r7r8r!r!r!r"ros4 c@s˜eZdZdZedƒddifddœdd„Zdd „Zed d„ƒZdd „Z dd„Z edd„ƒZedd„ƒZedd„ƒZ edd„ƒZedd„ƒZedd„ƒZdS)Ú DetMetricsa% Utility class for computing detection metrics such as precision, recall, and mean average precision (mAP) of an object detection model. Args: save_dir (Path): A path to the directory where the output plots will be saved. Defaults to current directory. plot (bool): A flag that indicates whether to plot precision-recall curves for each class. Defaults to False. on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. names (dict of str): A dict of strings that represents the names of the classes. Defaults to an empty tuple. Attributes: save_dir (Path): A path to the directory where the output plots will be saved. plot (bool): A flag that indicates whether to plot the precision-recall curves for each class. on_plot (func): An optional callback to pass plots path and data when they are rendered. names (dict of str): A dict of strings that represents the names of the classes. box (Metric): An instance of the Metric class for storing the results of the detection metrics. speed (dict): A dictionary for storing the execution time of different parts of the detection process. Methods: process(tp, conf, pred_cls, target_cls): Updates the metric results with the latest batch of predictions. keys: Returns a list of keys for accessing the computed detection metrics. mean_results: Returns a list of mean values for the computed detection metrics. class_result(i): Returns a list of values for the computed detection metrics for a specific class. maps: Returns a dictionary of mean average precision (mAP) values for different IoU thresholds. fitness: Computes the fitness score based on the computed detection metrics. ap_class_index: Returns a list of class indices sorted by their average precision (AP) values. results_dict: Returns a dictionary that maps detection metric keys to their computed values. curves: TODO curves_results: TODO Ú.FNr$cCs:||_||_||_||_tƒ|_dddddœ|_d|_dS)zfInitialize a DetMetrics instance with a save directory, plot flag, callback function, and class names.r°©Ú preprocessZ inferenceZlossÚpostprocessr{N)rÉrÑrËrÊrÚboxÚspeedr|©r‚rÉrÑrËrÊr!r!r"rƒ;szDetMetrics.__init__c CsFt|||||j|j|j|jddd…}t|jƒ|j_|j |¡dS©zBProcess predicted results for object detection and update metrics.)rÑrÉrÊrËr%N© r#rÑrÉrÊrËr¼r?rr4©r‚r¡r€rrr3r!r!r"ÚprocessEsø ÷ zDetMetrics.processcCsgd¢S©z6Returns a list of keys for accessing specific metrics.)úmetrics/precision(B)úmetrics/recall(B)úmetrics/mAP50(B)úmetrics/mAP50-95(B)r!r r!r!r"ÚkeysTszDetMetrics.keyscCs |j ¡S©zSCalculate mean of detected objects & return precision, recall, mAP50, and mAP50-95.©r?r.r r!r!r"r.YszDetMetrics.mean_resultscCs|j |¡S©zaReturn the result of evaluating the performance of an object detection model on a specific class.©r?r/r×r!r!r"r/]szDetMetrics.class_resultcCs|jjS©z6Returns mean Average Precision (mAP) scores per class.©r?r0r r!r!r"r0aszDetMetrics.mapscCs |j ¡S©z"Returns the fitness of box object.©r?r2r r!r!r"r2fszDetMetrics.fitnesscCs|jjS©z.Returns the average precision index per class.©r?r'r r!r!r"r'kszDetMetrics.ap_class_indexcCs"tt|jdg| ¡|jgƒƒS©úBReturns dictionary of computed performance metrics and statistics.r2©rr„rKr.r2r r!r!r"Úresults_dictpszDetMetrics.results_dictcCsgd¢S)r6)úPrecision-Recall(B)úF1-Confidence(B)úPrecision-Confidence(B)úRecall-Confidence(B)r!r r!r!r"r7uszDetMetrics.curvescCs|jjS©rW)r?r8r r!r!r"r8zszDetMetrics.curves_results©rÙrÚrÛrÜrrƒrEr9rKr.r/r0r2r'rYr7r8r!r!r!r"r:s& r:c@s˜eZdZdZedƒdddfddœdd„Zd d „Zedd„ƒZd d„Z dd„Z edd„ƒZedd„ƒZedd„ƒZ edd„ƒZedd„ƒZedd„ƒZdS)ÚSegmentMetricsa3 Calculates and aggregates detection and segmentation metrics over a given set of classes. Args: save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory. plot (bool): Whether to save the detection and segmentation plots. Default is False. on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. names (list): List of class names. Default is an empty list. Attributes: save_dir (Path): Path to the directory where the output plots should be saved. plot (bool): Whether to save the detection and segmentation plots. on_plot (func): An optional callback to pass plots path and data when they are rendered. names (list): List of class names. box (Metric): An instance of the Metric class to calculate box detection metrics. seg (Metric): An instance of the Metric class to calculate mask segmentation metrics. speed (dict): Dictionary to store the time taken in different phases of inference. Methods: process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions. mean_results(): Returns the mean of the detection and segmentation metrics over all the classes. class_result(i): Returns the detection and segmentation metrics of class `i`. maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. fitness: Returns the fitness scores, which are a single weighted combination of metrics. ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP). results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score. r;FNr!r$cCsB||_||_||_||_tƒ|_tƒ|_dddddœ|_d|_dS)zjInitialize a SegmentMetrics instance with a save directory, plot flag, callback function, and class names.r°r<ÚsegmentN) rÉrÑrËrÊrr?Úsegr@r|rAr!r!r"rƒszSegmentMetrics.__init__cCsŒt|||||j|j|j|jdd dd…}t|jƒ|j_|j |¡t|||||j|j|j|jdd dd…}t|jƒ|j _|j |¡dS)a Processes the detection and segmentation metrics over the given set of predictions. Args: tp (list): List of True Positive boxes. tp_m (list): List of True Positive masks. conf (list): List of confidence scores. pred_cls (list): List of predicted classes. target_cls (list): List of target classes. ÚMask©rÑrËrÉrÊrr%NÚBox) r#rÑrËrÉrÊr¼rbrr4r?)r‚r¡Ztp_mr€rrZresults_maskÚresults_boxr!r!r"rE¨s<÷ ö÷ özSegmentMetrics.processcCsgd¢S)z-Returns a list of keys for accessing metrics.)rGrHrIrJzmetrics/precision(M)zmetrics/recall(M)zmetrics/mAP50(M)zmetrics/mAP50-95(M)r!r r!r!r"rKÎszSegmentMetrics.keyscCs|j ¡|j ¡S)zBReturn the mean metrics for bounding box and segmentation results.)r?r.rbr r!r!r"r.ÜszSegmentMetrics.mean_resultscCs|j |¡|j |¡S)z;Returns classification results for a specified class index.)r?r/rbr×r!r!r"r/àszSegmentMetrics.class_resultcCs|jj|jjS)zIReturns mAP scores for object detection and semantic segmentation models.)r?r0rbr r!r!r"r0äszSegmentMetrics.mapscCs|j ¡|j ¡S)zDGet the fitness score for both segmentation and bounding box models.)rbr2r?r r!r!r"r2észSegmentMetrics.fitnesscCs|jjS)z-Boxes and masks have the same ap_class_index.rUr r!r!r"r'îszSegmentMetrics.ap_class_indexcCs"tt|jdg| ¡|jgƒƒS)z9Returns results of object detection model for evaluation.r2rXr r!r!r"rYószSegmentMetrics.results_dictcCsgd¢S)r6)rZr[r\r]zPrecision-Recall(M)zF1-Confidence(M)zPrecision-Confidence(M)zRecall-Confidence(M)r!r r!r!r"r7øszSegmentMetrics.curvescCs|jj|jjSr^)r?r8rbr r!r!r"r8szSegmentMetrics.curves_resultsr_r!r!r!r"r`€s&& r`csˆeZdZdZedƒdddfddœ‡fdd„ Zd d „Zedd„ƒZd d„Z dd„Z edd„ƒZedd„ƒZedd„ƒZ edd„ƒZ‡ZS)ÚPoseMetricsa, Calculates and aggregates detection and pose metrics over a given set of classes. Args: save_dir (Path): Path to the directory where the output plots should be saved. Default is the current directory. plot (bool): Whether to save the detection and segmentation plots. Default is False. on_plot (func): An optional callback to pass plots path and data when they are rendered. Defaults to None. names (list): List of class names. Default is an empty list. Attributes: save_dir (Path): Path to the directory where the output plots should be saved. plot (bool): Whether to save the detection and segmentation plots. on_plot (func): An optional callback to pass plots path and data when they are rendered. names (list): List of class names. box (Metric): An instance of the Metric class to calculate box detection metrics. pose (Metric): An instance of the Metric class to calculate mask segmentation metrics. speed (dict): Dictionary to store the time taken in different phases of inference. Methods: process(tp_m, tp_b, conf, pred_cls, target_cls): Processes metrics over the given set of predictions. mean_results(): Returns the mean of the detection and segmentation metrics over all the classes. class_result(i): Returns the detection and segmentation metrics of class `i`. maps: Returns the mean Average Precision (mAP) scores for IoU thresholds ranging from 0.50 to 0.95. fitness: Returns the fitness scores, which are a single weighted combination of metrics. ap_class_index: Returns the list of indices of classes used to compute Average Precision (AP). results_dict: Returns the dictionary containing all the detection and segmentation metrics and fitness score. r;FNr!r$csRtƒ |||¡||_||_||_||_tƒ|_tƒ|_dddddœ|_ d|_ dS)zXInitialize the PoseMetrics class with directory path, class names, and plotting options.r°r<ÚposeN)ÚsuperrƒrÉrÑrËrÊrr?rhr@r|rA©Ú __class__r!r"rƒ)szPoseMetrics.__init__cCsŒt|||||j|j|j|jdd dd…}t|jƒ|j_|j |¡t|||||j|j|j|jdd dd…}t|jƒ|j _|j |¡dS)a} Processes the detection and pose metrics over the given set of predictions. Args: tp (list): List of True Positive boxes. tp_p (list): List of True Positive keypoints. conf (list): List of confidence scores. pred_cls (list): List of predicted classes. target_cls (list): List of target classes. ZPoserdr%Nre) r#rÑrËrÉrÊr¼rhrr4r?)r‚r¡Ztp_pr€rrZresults_poserfr!r!r"rE5s<÷ ö÷ özPoseMetrics.processcCsgd¢S)z'Returns list of evaluation metric keys.)rGrHrIrJzmetrics/precision(P)zmetrics/recall(P)zmetrics/mAP50(P)zmetrics/mAP50-95(P)r!r r!r!r"rK[szPoseMetrics.keyscCs|j ¡|j ¡S)z(Return the mean results of box and pose.)r?r.rhr r!r!r"r.iszPoseMetrics.mean_resultscCs|j |¡|j |¡S)z?Return the class-wise detection results for a specific class i.)r?r/rhr×r!r!r"r/mszPoseMetrics.class_resultcCs|jj|jjS)zTReturns the mean average precision (mAP) per class for both box and pose detections.)r?r0rhr r!r!r"r0qszPoseMetrics.mapscCs|j ¡|j ¡S)zPComputes classification metrics and speed using the `targets` and `pred` inputs.)rhr2r?r r!r!r"r2vszPoseMetrics.fitnesscCsgd¢S)r6)rZr[r\r]zPrecision-Recall(P)zF1-Confidence(P)zPrecision-Confidence(P)zRecall-Confidence(P)r!r r!r!r"r7{szPoseMetrics.curvescCs|jj|jjSr^)r?r8rhr r!r!r"r8‰szPoseMetrics.curves_results)rÙrÚrÛrÜrrƒrEr9rKr.r/r0r2r7r8Ú __classcell__r!r!rjr"rgs & rgc@sbeZdZdZddœdd„Zdd„Zedd „ƒZed d„ƒZedd „ƒZ edd„ƒZ edd„ƒZdS)ÚClassifyMetricsa¥ 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[str, float]): A dictionary containing the time taken for each step in the pipeline. fitness (float): The fitness of the model, which is equal to top-5 accuracy. results_dict (Dict[str, Union[float, str]]): A dictionary containing the classification metrics and fitness. keys (List[str]): A list of keys for the results_dict. Methods: process(targets, pred): Processes the targets and predictions to compute classification metrics. Nr$cCs&d|_d|_dddddœ|_d|_dS)z&Initialize a ClassifyMetrics instance.rr°r<ZclassifyN)Útop1Útop5r@r|r r!r!r"rƒŸszClassifyMetrics.__init__cCsnt |¡t |¡}}|dd…df|k ¡}tj|dd…df| d¡jfdd}| d¡ ¡\|_|_ dS)z%Target classes and predicted classes.Nrr$rU) r(rWr&r’r*ÚvaluesrøÚtolistrnro)r‚rˆÚpredZcorrectÚaccr!r!r"rE¦s&zClassifyMetrics.processcCs|j|jdS)zsNÿÿ : . " !ÿ Z-e 8