Ng8PddlZddlZddlmZmZmZddlmZGddZ dS)N)compute_precision_recallcompute_average_precisioncompute_cor_loc)PerImageEvaluationc ReZdZdZddddddddef dZd Zd Z dd Z dd Z dZ d S)ObjectDetectionEvaluationz;Internal implementation of Pascal object detection metrics.g?g?i'gFrc |dkrtd| ||||| |_||_||_| |_||_||_||_i|_i|_ i|_ i|_ i|_ tj|jt|_tj|jt"|_|dS)aConstructor. Args: num_gt_classes: Number of ground-truth classes. matching_iou_threshold: IOU threshold used for matching detected boxes to ground-truth boxes. nms_iou_threshold: IOU threshold used for non-maximum suppression. nms_max_output_boxes: Maximum number of boxes returned by non-maximum suppression. recall_lower_bound: lower bound of recall operating area recall_upper_bound: upper bound of recall operating area use_weighted_mean_ap: (optional) boolean which determines if the mean average precision is computed directly from the scores and tp_fp_labels of all classes. label_id_offset: The label id offset. group_of_weight: Weight of group-of boxes.If set to 0, detections of the correct class within a group-of box are ignored. If weight is > 0, then if at least one detection falls within a group-of box with matching_iou_threshold, weight group_of_weight is added to true positives. Consequently, if no detection falls within a group-of box, weight group_of_weight is added to false negatives. per_image_eval_class: The class that contains functions for computing per image metrics. Raises: ValueError: if num_gt_classes is smaller than 1. z1Need at least 1 groundtruth class for evaluation.)num_gt_classesmatching_iou_thresholdnms_iou_thresholdnms_max_output_boxesgroup_of_weightdtypeN) ValueErrorper_image_evalrecall_lower_boundrecall_upper_boundr num_classuse_weighted_mean_aplabel_id_offsetgt_boxesgt_class_labelsgt_masksgt_is_difficult_listgt_is_group_of_listnpzerosfloatnum_gt_instances_per_classintnum_gt_imgs_per_class_initialize_detections) selfr r r rrrrrrper_image_eval_classs i/var/www/html/ai-engine/env/lib/python3.11/site-packages/effdet/evaluation/object_detection_evaluation.py__init__z"ObjectDetectionEvaluation.__init__ s@ A  PQQ Q22)#9/!5+ --- #5"4.'$8!. ! $&!#% *,(4>*O*O*O'%'XdnC%H%H%H" ##%%%%%clt|_dt|jD|_dt|jD|_t j|j|_t j |jt|_ |j t j t j g|jz|_t j g|jz|_t j g|jz|_t j|jt|_dS)z%Initializes internal data structures.cg|]}gSr,.0_s r' zDObjectDetectionEvaluation._initialize_detections..Is C C C C C Cr)cg|]}gSr,r,r-s r'r0zDObjectDetectionEvaluation._initialize_detections..Js&I&I&Iar&I&I&Ir)rN)setdetection_keysrangerscores_per_classtp_fp_labels_per_classrr'num_images_correctly_detected_per_classemptyr average_precision_per_classfillnanprecisions_per_classrecalls_per_class sum_tp_classonescorloc_per_classr%s r'r$z0ObjectDetectionEvaluation._initialize_detectionsFs!ee C CU4>-B-B C C C&I&I53H3H&I&I&I#79x7O7O4+-8DN%+P+P+P( (--bf555%'VHt~$=!"$&DN!:VHt~5 "e D D Dr)c.|dSN)r$rAs r'clear_detectionsz*ObjectDetectionEvaluation.clear_detectionsTs ##%%%%%r)Nc||jvrtjd|dS||j|<||j|<||j|<|(|jd}t j|t}| t}||j |<|(|jd}t j|t}|)|jd}t j|t}n3t j |ddk t}| t}||j |<||z} t|jD]} t j || |z| k} |jt j ||| z| kz} |j| xx| | zz cc<t j|| kr|j| xxdz cc<dS)awAdds groundtruth for a single image to be used for evaluation. Args: image_key: A unique string/integer identifier for the image. gt_boxes: float32 numpy array of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates. gt_class_labels: integer numpy array of shape [num_boxes] containing 0-indexed groundtruth classes for the boxes. gt_is_difficult_list: A length M numpy boolean array denoting whether a ground truth box is a difficult instance or not. To support the case that no boxes are difficult, it is by default set as None. gt_is_group_of_list: A length M numpy boolean array denoting whether a ground truth box is a group-of box or not. To support the case that no boxes are groups-of, it is by default set as None. gt_masks: uint8 numpy array of shape [num_boxes, height, width] containing `num_boxes` groundtruth masks. The mask values range from 0 to 1. z=image %s has already been added to the ground truth database.Nrr)r )axisr )rloggingwarningrrshaperrboolastypersumrr4rrr!anyr#) r% image_keyrrrrr num_boxesmask_presence_indicatormasked_gt_is_difficult_list class_indexnum_gt_instancesnum_groupof_gt_instancess r'"add_single_ground_truth_image_infozt jj|}t jj|}jr*t j||}t j||}t%||j|\}}fdt'|D}||} ||} | j|<| j|<|j|<t1| | } | j|<tjd| st7jj_jrit jj} t%||| \}}fdt'|D}||} ||} t1| | } nt jj} t jj}tAj| jjj|S)a#Compute evaluation result. Returns: A dict with the following fields - average_precision: float numpy array of average precision for each class. mean_ap: mean average precision of all classes, float scalar precisions: List of precisions, each precision is a float numpy array recalls: List of recalls, each recall is a float numpy array corloc: numpy float array mean_corloc: Mean CorLoc score for each class, float scalar rz7The following classes have no ground truth examples: %srcFg|]\}}|jk|jk|Sr,rrr.indexvaluer%s r'r0z6ObjectDetectionEvaluation.evaluate..B+++&%000Ud>U5U5U5U5U5Ur)zaverage_precision: %fcFg|]\}}|jk|jk|Sr,rjrks r'r0z6ObjectDetectionEvaluation.evaluate..rnr)) per_class_apmean_apper_class_precisionper_class_recallper_class_corlocs mean_corloc)!r!rNrHrIrsqueezeargwhererrr`r rKr4rr5 concatenater6rbr enumerater<r=rMr>rr9debugrr#r7r@nanmeandict)r% all_scoresall_tp_fp_labelsrSrcrd precisionrecallrecall_within_bound_indicesrecall_within_boundprecision_within_boundaverage_precisionrTrqrus` r'evaluatez"ObjectDetectionEvaluation.evaluatesN  +q 0 5 5 7 7 f OI 2;t'F!'KLLMMPTPdd f f f  $ 8"E222J!x$777  00 F FK.{;q@@(5 X"E222!x%888 (=k(JKK!~d.I+.VWW ( MYz6:: #%9-=|#L#L 8 d&Ek&R!T!T Iv++++*3F*;*;+++ '#))D"E %./J%K "5KD %k 22ED "; /-9-=-=-?-?D k * 9:PRe f f ]>]>]>]@DNDNDNDNDNr)r) rHnumpyreffdet.evaluation.metricsrrr&effdet.evaluation.per_image_evaluationrrr,r)r'rsjjjjjjjjjjEEEEEEHNHNHNHNHNHNHNHNHNHNr)