首先需要對測試集做批量測試，即需要將每個測試圖像輸入到模型中，得到測試結果。然後統計測試結果；
本文用的事darknet中valid接口函數，這裏valid可以作爲訓練時候，使用驗證集檢測模型訓練情況，這裏使用valid對訓練好的模型做測試；（即用來批量統計輸入測試圖像經過模型得到的結果）
看下源碼detector.c中run_detector函數中valid接口用法
具體用法：
./darknet detector valid cfg/voc.data cfg/yolov3-voc-6.cfg yolov3-voc-6_final.weights -out ""
注意voc.data 中vailde改成測試集路徑
數據集路徑格式
測試結果默認保存在當前路徑下的./results文件夾下，如果沒有，新建；
輸出測試圖像數
vim bicycle.txt
008153 0.005640 231.401428 410.399536 375.000000 490.319397
按列，分別爲：圖像名稱 | 置信度 | xmin,ymin,xmax,ymax
計算各類的MAP
python reval_voc_py3.py --year 2007 --classes data/coco-6.names --image_set test --voc_dir /home/nxt/xxx/darknet/VOCdevkit --output_dir results
部分輸出結果:
resultsEvaluating detections
VOC07 metric? Yes
devkit_path= /home/nxt/xx/darknet/VOCdevkit , year = 2007
!!! cachefile = /home/nxt/xxx/darknet/VOCdevkit/annotations_cache/annots.pkl
AP for bicycle = 0.8458
!!! cachefile = /home/nxt/xxx/darknet/VOCdevkit/annotations_cache/annots.pkl
AP for bus = 0.8877
!!! cachefile = /home/nxt/xxx/darknet/VOCdevkit/annotations_cache/annots.pkl
AP for car = 0.8566
.....
reval_voc_py3.py 爲非官方計算方法，從google瞭解，官方使用的MATALB的工具箱計算法，需自行了解，此處代碼從github找到的，時間就有點忘，後期想起來，補從地址
# reval_voc_py3.py
# !/usr/bin/env python
# Adapt from ->
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
# <- Written by Yaping Sun
"""Reval = re-eval. Re-evaluate saved detections."""
import os, sys, argparse
import numpy as np
import _pickle as cPickle
#import cPickle
from voc_eval_py3 import voc_eval
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Re-evaluate results')
parser.add_argument('output_dir', nargs=1, help='results directory',
type=str)
parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
parser.add_argument('--year', dest='year', default='2017', type=str)
parser.add_argument('--image_set', dest='image_set', default='test', type=str)
parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
def get_voc_results_file_template(image_set, out_dir = 'results'):
#filename = 'comp4_det_' + image_set + '_{:s}.txt'
filename = '{:s}.txt'
path = os.path.join(out_dir, filename)
return path
def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
annopath = os.path.join(
devkit_path,
'VOC' + year+'_test', # voc2007_test
'Annotations',
'{}.xml')
imagesetfile = os.path.join(
devkit_path,
'VOC' + year+'_test',
'ImageSets',
'Main',
image_set + '.txt')
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(year) < 2010 else False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
print('devkit_path=',devkit_path,', year = ',year)
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(classes):
if cls == '__background__':
continue
filename = get_voc_results_file_template(image_set).format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
print('rec:', rec.shape)
#np.savetxt('%s.txt',i, rec)
print('prec:', prec.shape)
#print(prec)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
cls_prec = cls+'_prec'
np.savetxt(cls,rec)
np.savetxt(cls_prec,prec)
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
if __name__ == '__main__':
args = parse_args() # input parameter
output_dir = os.path.abspath(args.output_dir[0]) # output dir
with open(args.class_file, 'r') as f:
lines = f.readlines()
classes = [t.strip('\n') for t in lines] # class names
print('Evaluating detections')
do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
voc_eval.py代碼原味python2這裏我更爲python3版本
#!/usr/bin/env python
# Adapt from ->
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
# <- Written by Yaping Sun
"""Reval = re-eval. Re-evaluate saved detections."""
import os, sys, argparse
import numpy as np
import _pickle as cPickle
#import cPickle
from voc_eval_py3 import voc_eval
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Re-evaluate results')
parser.add_argument('output_dir', nargs=1, help='results directory',
type=str)
parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
parser.add_argument('--year', dest='year', default='2017', type=str)
parser.add_argument('--image_set', dest='image_set', default='test', type=str)
parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args
def get_voc_results_file_template(image_set, out_dir = 'results'):
#filename = 'comp4_det_' + image_set + '_{:s}.txt'
filename = '{:s}.txt'
path = os.path.join(out_dir, filename)
return path
def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
annopath = os.path.join(
devkit_path,
'VOC' + year+'_test', # voc2007_test
'Annotations',
'{}.xml')
imagesetfile = os.path.join(
devkit_path,
'VOC' + year+'_test',
'ImageSets',
'Main',
image_set + '.txt')
cachedir = os.path.join(devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(year) < 2010 else False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
print('devkit_path=',devkit_path,', year = ',year)
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(classes):
if cls == '__background__':
continue
filename = get_voc_results_file_template(image_set).format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric)
print('rec:', rec.shape)
#np.savetxt('%s.txt',i, rec)
print('prec:', prec.shape)
#print(prec)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
cls_prec = cls+'_prec'
np.savetxt(cls,rec)
np.savetxt(cls_prec,prec)
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
print('Mean AP = {:.4f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('Results:')
for ap in aps:
print('{:.3f}'.format(ap))
print('{:.3f}'.format(np.mean(aps)))
print('~~~~~~~~')
print('')
print('--------------------------------------------------------------')
print('Results computed with the **unofficial** Python eval code.')
print('Results should be very close to the official MATLAB eval code.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
if __name__ == '__main__':
args = parse_args() # input parameter
output_dir = os.path.abspath(args.output_dir[0]) # output dir
with open(args.class_file, 'r') as f:
lines = f.readlines()
classes = [t.strip('\n') for t in lines] # class names
print('Evaluating detections')
do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
本人略菜，有問題請指出；