某场景下，输出目标漏检率和虚警次数
本项目包含两类红外目标UAV_S与UAV_L，分别对两类目标求漏检率和虚警次数并显示，最后求平均值后显示（实际上两类目标为对数据集进行分析后进行判断得到，实际只有一类目标UAV。以10×10像素为分界分类，有助于提升网络对红外大目标与小目标特征的学习）
可以看到在这张图像中有两个无人机目标，但二者特征差距巨大。通过数据分析，10×10像素以下的无人机目标没有轮廓信息，10×10以上的无人机目标可以看出旋翼等轮廓信息。
相关原理
基础概念
(1)P=Positive:
目标检测中的类别m，设其为正样本；
(2)N=negative:
目标检测中的类别background，设其为负样本；
(3)TP=True Positive:
把m正确检测为m框的数量(正确的m框)；
(4)FP=False Positive:
把background错误检测为m框的数量(错误的m框)；
(5)TN=True Negative:
把background正确检测为background框的数量(正确的background框)，识别为背景的框(非目标)一般在算法结束时，统一清除不显示；
(6)FN=False Negative:
把m错误检测为background框的数量(错误的background框)。
四个常用指标
(1)精确率(Precision):TP/(TP+FP)
所有判断为正例的例子中，真正为正例的所占的比例
(2)召回率(Recall):TP/(TP+FN)
所有正例中，被判断为正例的比例
(3)漏检率:FN/(TP+FN)=1-Recall
(4)虚警率:FP/(TP+FP)=1-Precision
(5)虚警次数:FP
代码：
yolov5_eval.py
# -*- coding: utf-8 -*-
# --------------------------------------------------------
# Fast/er R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Bharath Hariharan
# --------------------------------------------------------
import xml.etree.ElementTree as ET
import os
import pickle
import numpy as np
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = (obj.find('name').text).replace(" ", "")
obj_struct['pose'] = obj.find('pose').text
obj_struct['truncated'] = int(obj.find('truncated').text)
obj_struct['difficult'] = int(obj.find('difficult').text)
bbox = obj.find('bndbox')
obj_struct['bbox'] = [int(bbox.find('xmin').text),
int(bbox.find('ymin').text),
int(bbox.find('xmax').text),
int(bbox.find('ymax').text)]
objects.append(obj_struct)
return objects
def voc_ap(rec, prec, use_07_metric=False): # voc2007的计算方式和voc2012的计算方式不同，目前一般采用第二种
""" ap = voc_ap(rec, prec, [use_07_metric])
Compute VOC AP given precision and recall.
If use_07_metric is true, uses the
VOC 07 11 point method (default:False).
"""
if use_07_metric:
# 11 point metric
ap = 0.
for t in np.arange(0., 1.1, 0.1):
if np.sum(rec >= t) == 0:
p = 0
else:
p = np.max(prec[rec >= t])
ap = ap + p / 11.
else:
# correct AP calculation
# first append sentinel values at the end
mrec = np.concatenate(([0.], rec, [1.]))
mpre = np.concatenate(([0.], prec, [0.]))
# compute the precision envelope
for i in range(mpre.size - 1, 0, -1):
mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
# to calculate area under PR curve, look for points
# where X axis (recall) changes value
i = np.where(mrec[1:] != mrec[:-1])[0]
# and sum (Delta recall) * prec
ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
return ap
## 程序入口
def yolov5_eval(detpath, # 保存检测到的目标框的文件路径，每一类的目标框单独保存在一个文件
annopath, # Annotations的路径
imagesetfile, # 测试图片名字列表
classname, # 类别名称
cachedir, # 缓存文件夹
ovthresh=0.01, # IoU阈值
use_07_metric=False): # mAP计算方法
"""rec, prec, ap = voc_eval(eval_classtxt_path,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
eval_classtxt_path: Path to detections
eval_classtxt_path.format(classname) should produce the detection results file.
annopath: Path to annotations
annopath.format(imagename) should be the xml annotations file.
imagesetfile: Text file containing the list of images, one image per line.
classname: Category name (duh)
cachedir: Directory for caching the annotations
[ovthresh]: Overlap threshold (default = 0.5)
[use_07_metric]: Whether to use VOC07's 11 point AP computation
(default False)
"""
# assumes detections are in eval_classtxt_path.format(classname)
# assumes annotations are in annopath.format(imagename)
# assumes imagesetfile is a text file with each line an image name
# cachedir caches the annotations in a pickle file
# first load gt 获取真实目标框
# 当程序第一次运行时，会读取Annotations下的xml文件获取每张图片中真实的目标框
# 然后把获取的结果保存在annotations_cache文件夹中
# 以后再次运行时直接从缓存文件夹中读取真实目标
if not os.path.isdir(cachedir):
os.mkdir(cachedir)
cachefile = os.path.join(cachedir, 'annots.pkl')
# read list of images
with open(imagesetfile, 'r') as f:
lines = f.readlines()
imagenames = [x.strip() for x in lines]
if not os.path.isfile(cachefile):
# load annots
recs = {}
for i, imagename in enumerate(imagenames):
recs[imagename] = parse_rec(annopath.format(imagename))
if i % 100 == 0:
print('Reading annotation for {:d}/{:d}'.format(i + 1, len(imagenames)))
# save
print('Saving cached annotations to {:s}'.format(cachefile))
# with open(cachefile, 'w') as cls:
# pickle.dump(recs, cls)
with open(cachefile, 'wb') as f:
pickle.dump(recs, f)
else:
# load
with open(cachefile, 'rb') as f:
recs = pickle.load(f)
# extract gt objects for this class 提取该类的真实目标
class_recs = {}
npos = 0 # 保存该类一共有多少真实目标
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname] # 保存名字为imagename的图片中，类别为classname的目标框的信息
bbox = np.array([x['bbox'] for x in R]) # 目标框的坐标
difficult = np.array([x['difficult'] for x in R]).astype(np.bool) # 是否是难以识别的目标
det = [False] * len(R) # 每一个目标框对应一个det[i]，用来判断该目标框是否已经处理过
npos = npos + sum(~difficult) # 计算总的目标个数
class_recs[imagename] = {'bbox': bbox, # 把每一张图像中的目标框信息放到class_recs中
'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname) # 打开classname类别检测到的目标框文件
with open(detfile, 'r') as f:
lines = f.readlines()
splitlines = [x.strip().split(' ') for x in lines]
image_ids = [x[0] for x in splitlines] # 图像名字
confidence = np.array([float(x[1]) for x in splitlines]) # 置信度
BB = np.array([[float(z) for z in x[2:]] for x in splitlines]) # 目标框坐标
# sort by confidence 按照置信度排序
sorted_ind = np.argsort(-confidence)
sorted_scores = np.sort(-confidence)
BB = BB[sorted_ind, :]
image_ids = [image_ids[x] for x in sorted_ind]
# go down dets and mark TPs and FPs
nd = len(image_ids) # 统计检测到的目标框个数
tp = np.zeros(nd) # 创建tp列表，列表长度为目标框个数
fp = np.zeros(nd) # 创建fp列表，列表长度为目标框个数
# FN = 0
for d in range(nd):
R = class_recs[image_ids[d]] # 得到图像名字为image_ids[d]真实的目标框信息
bb = BB[d, :].astype(float) # 得到图像名字为image_ids[d]检测的目标框坐标
ovmax = -np.inf
BBGT = R['bbox'].astype(float) # 得到图像名字为image_ids[d]真实的目标框坐标
if BBGT.size > 0:
# compute overlaps 计算IoU
# intersection
ixmin = np.maximum(BBGT[:, 0], bb[0])
iymin = np.maximum(BBGT[:, 1], bb[1])
ixmax = np.minimum(BBGT[:, 2], bb[2])
iymax = np.minimum(BBGT[:, 3], bb[3])
iw = np.maximum(ixmax - ixmin + 1., 0.)
ih = np.maximum(iymax - iymin + 1., 0.)
inters = iw * ih
# union
uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
(BBGT[:, 2] - BBGT[:, 0] + 1.) *
(BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
overlaps = inters / uni
ovmax = np.max(overlaps) # 检测到的目标框可能预若干个真实目标框都有交集，选择其中交集最大的
jmax = np.argmax(overlaps)
if ovmax > ovthresh: # IoU是否大于阈值
if not R['difficult'][jmax]: # 真实目标框是否难以识别
if not R['det'][jmax]: # 该真实目标框是否已经统计过
tp[d] = 1. # 将tp对应第d个位置变成1
R['det'][jmax] = 1 # 将该真实目标框做标记
else:
fp[d] = 1. # 否则将fp对应的位置变为1
else:
fp[d] = 1. # 否则将fp对应的位置变为1
# compute precision recall
fp = np.cumsum(fp) # 按列累加，最大值即为fp数量
tp = np.cumsum(tp) # 按列累加，最大值即为tp数量
rec = tp / float(npos) # 计算recall
FN = fp[d]
# avoid divide by zero in case the first detection matches a difficult
# ground truth
prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps) # 计算精度
# print('Number of FP for {} = {:d}'.format(classname, int(fp[d])))
# print('All number of FP = {:d}'.format(int(FN)))
# print('Average Number of FP = {:d}'.format(int(np.mean(fp[d]))))
ap = voc_ap(rec, prec, use_07_metric) # 计算ap
return rec, prec, ap, tp, fp, FN
compute_mAP.py
# -*- coding: utf-8 -*-
import os
import numpy as np
from yolov5_eval import yolov5_eval # 注意将yolov4_eval.py和compute_mAP.py放在同一级目录下
from cfg_mAP import Cfg
import pickle
import shutil
cfg = Cfg
eval_classtxt_path = cfg.eval_classtxt_path # 各类txt文件路径
eval_classtxt_files = os.listdir(eval_classtxt_path)
classes = cfg.names # ['combustion_lining', 'fan', 'fan_stator_casing_and_support', 'hp_core_casing', 'hpc_spool', 'hpc_stage_5','mixer', 'nozzle', 'nozzle_cone', 'stand']
aps = [] # 保存各类ap
cls_rec = {} # 保存recall
cls_prec = {} # 保存精度
cls_ap = {}
fns = []
FNS = 0
annopath = cfg.eval_Annotations_path + '/{:s}.xml' # annotations的路径，{:s}.xml方便后面根据图像名字读取对应的xml文件
imagesetfile = cfg.eval_imgs_name_txt # 读取图像名字列表文件
cachedir = cfg.cachedir
if os.path.exists(cachedir):
shutil.rmtree(cachedir) # delete output folder
os.makedirs(cachedir) # make new output folder
for cls in eval_classtxt_files: # 读取cls类对应的txt文件
filename = eval_classtxt_path + cls
rec, prec, ap, tp, fp, FN = yolov5_eval( # yolov4_eval.py计算cls类的recall precision ap
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.01,
use_07_metric=False)
aps += [ap]
cls_ap[cls] = ap
cls_rec[cls] = rec[-1]
cls_prec[cls] = prec[-1]
fn = 1 - rec[-1]
fns += [fn]
FNS += FN
# print('AP for {} = {:.4f}'.format(cls, ap))
# print('recall for {} = {:.4f}'.format(cls, rec[-1]))
# print('precision for {} = {:.4f}'.format(cls, prec[-1]))
# print('FN for {} = {:.4f}'.format(cls, fn))
with open(os.path.join(cfg.cachedir, 'cls_ap.pkl'), 'wb') as in_data:
pickle.dump(cls_ap, in_data, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(cfg.cachedir, 'cls_rec.pkl'), 'wb') as in_data:
pickle.dump(cls_rec, in_data, pickle.HIGHEST_PROTOCOL)
with open(os.path.join(cfg.cachedir, 'cls_prec.pkl'), 'wb') as in_data:
pickle.dump(cls_prec, in_data, pickle.HIGHEST_PROTOCOL)
# print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('Mean FN = {:.4f}'.format(np.mean(fns)))
print('All number of FP = {:d}'.format(int(FNS)))
# print('~~~~~~~~')
# print('Results:')
# for ap in aps:
# print('{:.3f}'.format(ap))
# print('~~~~~~~~')
# print('{:.3f}'.format(np.mean(aps)))
# print('~~~~~~~~')
输出结果：
参考：
https://blog.csdn.net/qq_29007291/article/details/86080456
https://blog.csdn.net/tpz789/article/details/110675268
