kh8HddlZddlZddlmZmZmZddlmZGddZ y)N)compute_precision_recallcompute_average_precisioncompute_cor_loc)PerImageEvaluationc PeZdZdZddddddddef dZd Zd Z dd Z dd Z dZ y )ObjectDetectionEvaluationz;Internal implementation of Pascal object detection metrics.g?g?i'gFrc |dkr td| ||||| |_||_||_| |_||_||_||_i|_i|_ i|_ i|_ i|_ tj|j t|_tj|j t"|_|j'y)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/teggl/fontify/venv/lib/python3.12/site-packages/effdet/evaluation/object_detection_evaluation.py__init__z"ObjectDetectionEvaluation.__init__ s@ A PQ Q2)#9/!5+ - #5"4.'$8!. ! $&!#% *,((4>>*O'%'XXdnnC%H" ##%ct|_t|jDcgc]}gc}|_t|jDcgc]}gc}|_t j|j|_t j|jt|_ |jjt jt jg|jz|_t jg|jz|_t jg|jz|_t j"|jt|_ycc}wcc}w)z%Initializes internal data structures.rN)setdetection_keysrangerscores_per_classtp_fp_labels_per_classrr'num_images_correctly_detected_per_classemptyr average_precision_per_classfillnanprecisions_per_classrecalls_per_class sum_tp_classonescorloc_per_class)r%_s r'r$z0ObjectDetectionEvaluation._initialize_detectionsFs!e-24>>-B C C383H&Iar&I#79xx7O4+-88DNN%+P( ((--bff5%'VVHt~~$=!"$&&DNN!:VVHt~~5 "e D!D&Is E+ E0c$|jyN)r$)r%s r'clear_detectionsz*ObjectDetectionEvaluation.clear_detectionsTs ##%r)Nc ||jvrtjd|y||j|<||j|<||j|<|*|j d}t j|t}|jt}||j|<|*|j d}t j|t}|+|j d}t j|t}n.t j|ddk(jt}|jt}||j|<||z} t|jD]} t j|| |z| k(} |jt j||| z| k(z} |j | xx| | zz cc<t j"|| k(s|j$| xxdz cc<y)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 )rloggingwarningrrshaperrboolastypersumrr-rrr!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_infoz|_ |jrt j0|j}t%|\}}t'|D cgc]%\}} | |j(k\r| |j*kr|'} }} || } || } t5| | }nt jB|j6}t jB|j@}tE|j6||j,|j.|j@|Scc} }wcc} }w)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: %srzaverage_precision: %f) per_class_apmean_apper_class_precisionper_class_recallper_class_corlocs mean_corloc)#r!rGrArBrsqueezeargwhererrrYr rDr-rr. concatenater/r[r enumeraterrr5r6rFr7rr2debugrr#r0r9nanmeandict)r% all_scoresall_tp_fp_labelsrLr\r] precisionrecallindexvaluerecall_within_bound_indicesrecall_within_boundprecision_within_boundaverage_precisionrMrcrgs r'evaluatez"ObjectDetectionEvaluation.evaluatesO  + +q 0 5 5 7 OOI 2;;t'F'F!'KLMPTPdPdd f  $ $"E2J!xx$7  0 FK..{;q@((5"E2!xx%8 (=(=k(JK!~~d.I.I+.VW ((YYz6: #%99-=|#L 8 d&E&Ek&R!T Iv+4F*;+&%000Ud>U>U5U+ '+#))D"E %./J%K "5KD % %k 22ED " "; /-9-=-=-?D  k * 9:PRe f U>U5U+ '+#))D"E %./J%K "/0FH[\Gjj!A!ABGjj!6!67 997 $ 9 9!33"33 N N?+(+s *N#*N$)NNNr<) __name__ __module__ __qualname____doc__rr(r$r=rOr`ryr)r'rr sUE),#&&+$'$'&+!"!$&88&t E& KO4=nei>]@DNr)r) rAnumpyreffdet.evaluation.metricsrrr&effdet.evaluation.per_image_evaluationrrr~r)r'rs"jjEHNHNr)