搜了半天，都是要下载voc_eval.py文件，但该文件只能在python2下运行。以下是python3版本的：
# coding:utf-8
import xml.etree.ElementTree as ET
import os
#import cPickle
import _pickle as cPickle
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
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):
""" 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
def voc_eval(detpath,
annopath,
imagesetfile,
classname,
cachedir,
ovthresh=0.5,
use_07_metric=False):
"""rec, prec, ap = voc_eval(detpath,
annopath,
imagesetfile,
classname,
[ovthresh],
[use_07_metric])
Top level function that does the PASCAL VOC evaluation.
detpath: Path to detections
detpath.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 detpath.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
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] #文件名
if not os.path.isfile(cachefile):
#print("zaybnzazazazazazazaza")
# 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, 'wb') as f:
cPickle.dump(recs, f)
else:
# load
with open(cachefile, 'rb') as f:
try:
recs = cPickle.load(f)
except EOFError:
return
#recs = cPickle.load(f)
# extract gt objects for this class
class_recs = {}
npos = 1
for imagename in imagenames:
R = [obj for obj in recs[imagename] if obj['name'] == classname]
bbox = np.array([x['bbox'] for x in R])
difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
det = [False] * len(R)
npos = npos + sum(~difficult)
class_recs[imagename] = {'bbox': bbox,
'difficult': difficult,
'det': det}
# read dets
detfile = detpath.format(classname)
with open(detfile, 'rb+') as f:
lines = f.readlines()
#print("type(lines[0]):",type(lines[0]))
#print("type(x):",type(str(lines[0]).strip().split(" ")))
splitlines = [str(x).strip().split(' ') for x in lines]
#splitlines = splitlines.encode()
#print(type(splitlines))
image_ids = [x[0] for x in splitlines]
confidence = np.array([float(x[1]) for x in splitlines])
a = "\\n'"
# for x in splitlines:
# for z in x[2:]:
# if a in z:
# print(z[:len(z)-3])
# else:
# print(z)
#remove \n
BB = np.array([[float(z) if a not in z else float(z[:len(z)-3]) for z in x[2:]] for x in splitlines])
#print(BB)
# sort by confidence
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)
tp = np.zeros(nd)
fp = np.zeros(nd)
for d in range(nd):
#print(image_ids[d][2:])
#print(class_recs)
R = class_recs[image_ids[d][2:]]
bb = BB[d, :].astype(float)
ovmax = -np.inf
BBGT = R['bbox'].astype(float)
if BBGT.size > 0:
# compute overlaps
# 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)
jmax = np.argmax(overlaps)
if ovmax > ovthresh:
if not R['difficult'][jmax]:
if not R['det'][jmax]:
tp[d] = 1.
R['det'][jmax] = 1
else:
fp[d] = 1.
fp[d] = 1.
# compute precision recall
fp = np.cumsum(fp)
tp = np.cumsum(tp)
rec = tp / float(npos)
# 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)
ap = voc_ap(rec, prec, use_07_metric)
return rec, prec, ap
文件放在yolov3根目录下，然后编写计算文件，computer_single_all_map.py
from voc_eval import voc_eval
import os
current_path = os.getcwd()
results_path = current_path+"/results"
sub_files = os.listdir(results_path)
mAP = []
for i in range(len(sub_files)):
class_name = sub_files[i].split(".txt")[0]
rec, prec, ap = voc_eval('./results/{}.txt', './xml/{}.xml', './data/pig_val_map.txt', class_name, '.')
print("{} :\t {} ".format(class_name, ap))
mAP.append(ap)
mAP = tuple(mAP)
print("***************************")
print("mAP :\t {}".format( float( sum(mAP)/len(mAP)) ))
需要改3个位置：
./results/{}.txt改为存放验证文件的路径，如图；
./xml/{}.xml改为存放数据集xml文件的路径，如图；
./data/pig_val_map.txt改为验证文件的路径（该文件只填写数据集文件的名称，不要加路径和后缀），如图；
生成验证文件
./darknet detector valid ./cfg/pig.data ./cfg/yolov3-pig.cfg backup/yolov3-pig_last.weights -out "" -gpu 0 -thresh .5
会在result目录下生成各分类的验证文件，我只有一个分类：pig，所以生成了pig.txt。
计算mAP
rm -f annots.pkl
python computer_Single_ALL_mAP.py.py
每次重新计算，都要删掉annots.pkl文件。
参考：YOLOv3 训练自己的数据附优化与问题总结 (mamicode.com)
[yolov3]./darknet detector valid “eval: Using default ‘voc‘ 4 段错误“_yuchen的博客-CSDN博客