def do_python_eval(devkit_path,
year,
image_set,
classes,
output_dir='results',
model='yolov1',
eval_f=None):
annopath = os.path.join(devkit_path, 'VOC' + year, 'Annotations', '{}.xml')
imagesetfile = os.path.join(devkit_path, 'VOC' + year, '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)
# if not os.path.exists(output_dir+'/yolov1.eval.txt'):
# eval_f = open(output_dir+'/yolov1.eval.txt','w')
# eval_f.write('{},'.format('weights'))
# for cls in classes:
# eval_f.write('{},'.format(cls))
# eval_f.write('{}\n'.format('mAP'))
# else:
# eval_f = open(output_dir+'/yolov1.eval.txt','a')
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)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
with open(os.path.join(output_dir, model + '_' + 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:')
eval_f.write('{},'.format(model))
for ap in aps:
#print('{:.3f}'.format(ap))
eval_f.write('{:.3f},'.format(ap))
#print('{:.3f}'.format(np.mean(aps)))
eval_f.write('{:.3f}\n'.format(np.mean(aps)))
def do_python_eval(devkit_path,
year,
image_set,
classes,
output_dir='results'):
annopath = os.path.join(devkit_path, 'VOC' + year, 'Annotations', '{}.xml')
imagesetfile = os.path.join(devkit_path, 'VOC' + year, '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):
print(cls)
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)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
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('--------------------------------------------------------------')
def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
annopath = os.path.join(
devkit_path,
'VOC' + year,
'Annotations',
'{}.xml')
imagesetfile = os.path.join(
devkit_path,
'VOC' + year,
'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)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
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('--------------------------------------------------------------')
from voc_eval_py3 import voc_eval
#/Users/liqinghua/projects/medical-rib/data/voc2007/ImageSets/test.txt
rec,prec,ap = voc_eval('/Users/liqinghua/projects/medical-rib/models/darknet/results/{}.txt', '/Users/liqinghua/voc2007.xoy/Annotations/{}.xml',
'/Users/liqinghua/voc2007.xoy/ImageSets/2007_test.txt', 'hurt', '.')
print('rec',rec)
print('prec',prec)
print('ap',ap)
from voc_eval_py3 import voc_eval
print('car:')
print(
voc_eval(
'/home/magic/Desktop/CNN models/darknet/results/{}.txt',
'/home/magic/Desktop/CNN models/darknet/VOCdevkit/VOC2007/Annotations/{}.xml',
'/home/magic/Desktop/CNN models/darknet/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
'car', '.'))
print(
'\n###############################################################################'
)
print('person:')
print(
voc_eval(
'/home/magic/Desktop/CNN models/darknet/results/{}.txt',
'/home/magic/Desktop/CNN models/darknet/VOCdevkit/VOC2007/Annotations/{}.xml',
'/home/magic/Desktop/CNN models/darknet/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
'person', '.'))
from voc_eval_py3 import voc_eval
rec, prec, ap = voc_eval(
'/userhome/darknet/results/comp4_det_test_p.txt',
'/userhome/darknet/data/VOC2018/Annotations/{}.xml',
'/userhome/darknet/data/VOC2018/ImageSets/Segmentation/val_num.txt', 'p',
'.')
print('rec', rec)
print('prec', prec)
print('ap', ap)
def do_python_eval(devkit_path,
year,
image_set,
classes,
output_dir='results'):
print(
f"devkit_path:{devkit_path}, year:{year}, image_set:{image_set}, classes:{classes}, output_dir:{output_dir}"
)
#devkit_path:., year:2017, image_set:test, classes:['car', 'truck', 'bus', 'motorcycle', 'bicycle', 'trains'],
# output_dir:/home/yuanpu/vehicle/data/MIO-TCD-Localization/evaluator/results
annopath = os.path.join(
devkit_path,
# 'VOC' + year,
'Annotations',
'{}.xml')
imagesetfile = os.path.join(
devkit_path,
# 'VOC' + year,
# 'JPEGImages',
# 'Main',
image_set + '.txt')
print(f'=========={annopath}-------------{imagesetfile}')
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)
print(f'=========={filename}-------------')
rec, prec, ap = voc_eval(filename,
annopath,
imagesetfile,
cls,
cachedir,
ovthresh=0.45,
use_07_metric=True)
aps += [ap]
print('AP for {} = {:.4f}'.format(cls, ap))
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('--------------------------------------------------------------')
