AOC指的是PR(精度-召回)曲线的曲线下面积，用于评估目标检测网络的性能。
AOC的计算过程如下：
1、获得网络推断的pred_bbox、pred_class、pred_score和真值gt_bbox、gt_class
2、对于每个类别，根据iou条件，增加一个正例(TP)或负例(FP)和对应的score
3、按照score升序，计算精度和召回的前缀和向量
4、计算不同召回下的精度，累加得到AOC

参考代码：
https://github.com/garg-abhinav/FasterRCNN

import numpy as np
import itertools
from collections import defaultdictdef bbox_iou(bbox_a, bbox_b):'''This function calculates IoU (intersection of union) between bounding boxes. The IoU ratios are used to eliminate overlapping bounding boxes and for training we only take into account with IoU < 0.3 and IoU > 0.7 for labeling as foreground and background.'''    if bbox_a.shape[1] != 4 or bbox_b.shape[1] != 4:raise IndexError# top lefttl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2])  # bottom rightbr = np.minimum(bbox_a[:, None, 2:], bbox_b[:, 2:])area_i = np.prod(br - tl, axis=2) * (tl < br).all(axis=2)area_a = np.prod(bbox_a[:, 2:] - bbox_a[:, :2], axis=1)area_b = np.prod(bbox_b[:, 2:] - bbox_b[:, :2], axis=1)return area_i / (area_a[:, None] + area_b - area_i)'''
These functions are used to calculate the mAP values for testing phase
'''def eval_detection_voc(pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels,iou_thresh=0.5, use_07_metric=False):prec, rec = calc_detection_voc_prec_rec(pred_bboxes, pred_labels, pred_scores,gt_bboxes, gt_labels,iou_thresh=iou_thresh) ##  计算累加的precision 和 recall 矩阵ap = calc_detection_voc_ap(prec, rec) ## 所有类别的ap## use 07 metric 就是11点形式return {'ap': ap, 'map': np.nanmean(ap)}## 预处理 precision和 recall 
def calc_detection_voc_prec_rec(pred_bboxes, pred_labels, pred_scores, gt_bboxes, gt_labels,iou_thresh=0.5):pred_bboxes = iter(pred_bboxes)pred_labels = iter(pred_labels)pred_scores = iter(pred_scores)gt_bboxes = iter(gt_bboxes)gt_labels = iter(gt_labels)n_pos = defaultdict(int)score = defaultdict(list)match = defaultdict(list) ## 默认value为listfor pred_bbox, pred_label, pred_score, gt_bbox, gt_label in \zip(pred_bboxes, pred_labels, pred_scores,gt_bboxes, gt_labels):for l in np.unique(np.concatenate((pred_label, gt_label)).astype(int)):pred_mask_l = pred_label == lpred_bbox_l = pred_bbox[pred_mask_l]pred_score_l = pred_score[pred_mask_l]gt_mask_l = gt_label == lgt_bbox_l = gt_bbox[gt_mask_l]n_pos[l] += gt_mask_l.sum() ## 用于计算recall， tp+fn score[l].extend(pred_score_l)if len(pred_bbox_l) == 0:continueif len(gt_bbox_l) == 0:match[l].extend((0,) * pred_bbox_l.shape[0])continue# VOC evaluation follows integer typed bounding boxes.pred_bbox_l = pred_bbox_l.copy() ## pred_bbox_l[:, 2:] += 1gt_bbox_l = gt_bbox_l.copy()gt_bbox_l[:, 2:] += 1iou = bbox_iou(pred_bbox_l, gt_bbox_l) ## 删掉iou小于0.5的结果gt_index = iou.argmax(axis=1)# set -1 if there is no matching ground truthgt_index[iou.max(axis=1) < iou_thresh] = -1del iouselec = np.zeros(gt_bbox_l.shape[0], dtype=bool)for gt_idx in gt_index:if gt_idx >= 0:if not selec[gt_idx]:match[l].append(1)else:match[l].append(0)selec[gt_idx] = Trueelse:match[l].append(0)for iter_ in (pred_bboxes, pred_labels, pred_scores,gt_bboxes, gt_labels):if next(iter_, None) is not None:raise ValueError('Length of input iterables need to be same.')n_fg_class = max(n_pos.keys()) + 1prec = [None] * n_fg_classrec = [None] * n_fg_classfor l in n_pos.keys():score_l = np.array(score[l])match_l = np.array(match[l], dtype=np.int8)order = score_l.argsort()[::-1] ## 排序后保留前缀和，便于计算AOCmatch_l = match_l[order]tp = np.cumsum(match_l == 1)fp = np.cumsum(match_l == 0)# If an element of fp + tp is 0,# the corresponding element of prec[l] is nan.prec[l] = tp / (fp + tp)# If n_pos[l] is 0, rec[l] is None.if n_pos[l] > 0:rec[l] = tp / n_pos[l]return prec, rec## 计算AOC 
def calc_detection_voc_ap(prec, rec):##  prec 精度 ， rec 召回n_fg_class = len(prec) ## 类别数ap = np.empty(n_fg_class)for l in range(n_fg_class):if prec[l] is None or rec[l] is None:ap[l] = np.nancontinue# 11 point metricap[l] = 0for t in np.arange(0., 1.1, 0.1):if np.sum(rec[l] >= t) == 0:p = 0else:p = np.max(np.nan_to_num(prec[l])[rec[l] >= t])ap[l] += p / 11return ap