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 defaultdict
def 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 left
tl = np.maximum(bbox_a[:, None, :2], bbox_b[:, :2])
# bottom right
br = 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list
for 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 == l
pred_bbox_l = pred_bbox[pred_mask_l]
pred_score_l = pred_score[pred_mask_l]
gt_mask_l = gt_label == l
gt_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:
continue
if 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:] += 1
gt_bbox_l = gt_bbox_l.copy()
gt_bbox_l[:, 2:] += 1
iou = 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 truth
gt_index[iou.max(axis=1) < iou_thresh] = -1
del iou
selec = 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] = True
else:
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()) + 1
prec = [None] * n_fg_class
rec = [None] * n_fg_class
for 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AOC
match_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.nan
continue
# 11 point metric
ap[l] = 0
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec[l] >= t) == 0:
p = 0
else:
p = np.max(np.nan_to_num(prec[l])[rec[l] >= t])
ap[l] += p / 11
return ap
