Ng,ddlZdZdZdZdZdZdS)Nct|tjrt|jdkrt d|jtjkr$|jtjkrt dt|tjrt|jdkrt d|tj |krt dt|t|krt d|dkrdStj |}|d d d }||}|dk t}tj |}tj |}| t||zz }| t|z } || fS) apCompute precision and recall. Args: scores: A float numpy array representing detection score labels: A float numpy array representing weighted true/false positive labels num_gt: Number of ground truth instances Raises: ValueError: if the input is not of the correct format Returns: precision: Fraction of positive instances over detected ones. This value is None if no ground truth labels are present. recall: Fraction of detected positive instance over all positive instances. This value is None if no ground truth labels are present. z+labels must be single dimension numpy arrayz(labels type must be either bool or floatz+scores must be single dimension numpy arrayz5Number of true positives must be smaller than num_gt.z+scores and labels must be of the same size.r)NNN) isinstancenpndarraylenshape ValueErrordtypefloat_bool_sumargsortastypefloatcumsum) scoreslabelsnum_gtsorted_indicestrue_positive_labelsfalse_positive_labelscum_true_positivescum_false_positives precisionrecalls U/var/www/html/ai-engine/env/lib/python3.11/site-packages/effdet/evaluation/metrics.pycompute_precision_recallrs fbj ) )HS->->!-C-CFGGG |ry  V\RX%=%=CDDD fbj ) )HS->->!-C-CFGGG vPQQQ 6{{c&kk!!FGGG {{zZ''N#DDbD)N!.11Q6>>uEE#788)$9::"))%004FI\4\]I  & &u - - 6F f c|tdtjSt|tjrttjstd|jtjksjtjkrtdt|tkrtd|jsdStj |dkstj |dkrtd tj dkstj dkrtd tfd ttdz Dstd tj dgdggtj dg|dgg}tt|d z ddD])}tj||||dz||<*tjddddkddz}tj||dz z ||z}|S)aCompute Average Precision according to the definition in VOCdevkit. Precision is modified to ensure that it does not decrease as recall decrease. Args: precision: A float [N, 1] numpy array of precisions recall: A float [N, 1] numpy array of recalls Raises: ValueError: if the input is not of the correct format Returns: average_precison: The area under the precision recall curve. NaN if precision and recall are None. Nz.If precision is None, recall must also be Nonez(precision and recall must be numpy arrayz input must be float numpy array.z.precision and recall must be of the same size.grrz)Precision must be in the range of [0, 1].z&recall must be in the range of [0, 1].c3BK|]}||dzkVdS)rN).0irs r z,compute_average_precision..Ms4JJavayF1q5M)JJJJJJr z%recall must be a non-decreasing arrayr)r rNANrrr r r sizeaminamaxallrange concatenatemaximumwherer)rrr%indicesaverage_precisions ` rcompute_average_precisionr3/sV  MNN Nv i , ,EJvrz4R4RECDDD")##v|ry'@'@;<<< 9~~V$$IJJJ >s wyA!3!3a!7!7DEEE wvbgfoo11ABBB JJJJ5Vq3I3IJJJ J JB@AAA ^aS&1#. / /FY455I3y>>A%r2 . .BBz)A, !a%0@AA ! hvabbzVCRC[011!4q8Gw&12E ESZI[[\\ r cPtj|dktj||z S)aCompute CorLoc according to the definition in the following paper. https://www.robots.ox.ac.uk/~vgg/rg/papers/deselaers-eccv10.pdf Returns nans if there are no ground truth images for a class. Args: num_gt_imgs_per_class: 1D array, representing number of images containing at least one object instance of a particular class num_images_correctly_detected_per_class: 1D array, representing number of images that are correctly detected at least one object instance of a particular class Returns: corloc_per_class: A float numpy array represents the corloc score of each class r)rr0nan)num_gt_imgs_per_class'num_images_correctly_detected_per_classs rcompute_cor_locr8\s0 8"BF/2GG I IIr c Pg}tt|D]`}|tj||dt |||jddkdatj|}tj|S)agComputes MedianRank@k, where k is the top-scoring labels. Args: tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all detection on a single image, where the detections are sorted by score in descending order. Further, each numpy array element can have boolean or float values. True positive elements have either value >0.0 or True; any other value is considered false positive. k: number of top-scoring proposals to take. Returns: median_rank: median rank of all true positive proposals among top k by score. r) r-r appendrr0minr r.median) tp_fp_listkranksr%concatenated_rankss rcompute_median_rank_at_krAms E 3z?? # #WW RXjmAc!Z]5H5K.L.L,LMPQQRRSTUVVVV.. 9' ( ((r c g}tt|D]D}|||dt|||jdEt j|}t j||z S)aComputes Recall@k, MedianRank@k, where k is the top-scoring labels. Args: tp_fp_list: a list of numpy arrays; each numpy array corresponds to the all detection on a single image, where the detections are sorted by score in descending order. Further, each numpy array element can have boolean or float values. True positive elements have either value >0.0 or True; any other value is considered false positive. num_gt: number of groundtruth anotations. k: number of top-scoring proposals to take. Returns: recall: recall evaluated on the top k by score detections. r)r-r r:r;r rr.r)r=rr> tp_fp_evalr%s rcompute_recall_at_krDsJ 3z?? # #KK*Q-#aA1DQ1G*H*H(HIJJJJ ++J 6*   &&r )numpyrrr3r8rArDr#r rrFsk(((V***ZIII")))&'''''r