def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(
self._devkit_path,
'VOC' + self._year,
'Annotations',
'{:s}.xml')
imagesetfile = os.path.join(
self._devkit_path,
'VOC' + self._year,
'ImageSets',
'Main',
self._image_set + '.txt')
cachedir = os.path.join(self._devkit_path, 'annotations_cache')
aps = []
# The PASCAL VOC metric changed in 2010
use_07_metric = True if int(self._year) < 2010 else False
print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
# 求每个类型的ap值，不包含背景。
if cls == '__background__':
continue
filename = self._get_voc_results_file_template().format(cls)
rec, prec, ap = voc_eval(
filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
use_07_metric=use_07_metric, use_diff=self.config['use_diff'])
aps += [ap]
print(('AP for {} = {:.4f}'.format(cls, ap)))
with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
pickle.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('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')
def _do_python_eval(json_dataset, salt, output_dir='output'):
info = voc_info(json_dataset)
year = info['year']
anno_path = info['anno_path']
image_set_path = info['image_set_path']
devkit_path = info['devkit_path']
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
logger.info('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for _, cls in enumerate(json_dataset.classes):
if cls == '__background__':
continue
filename = _get_voc_results_file_template(json_dataset,
salt).format(cls)
rec, prec, ap = voc_eval(filename,
anno_path,
image_set_path,
cls,
cachedir,
ovthresh=0.5,
use_07_metric=use_07_metric)
aps += [ap]
logger.info('AP for {} = {:.4f}'.format(cls, ap))
res_file = os.path.join(output_dir, cls + '_pr.pkl')
save_object({'rec': rec, 'prec': prec, 'ap': ap}, res_file)
logger.info('Mean AP = {:.4f}'.format(np.mean(aps)))
logger.info('~~~~~~~~')
logger.info('Results:')
for ap in aps:
logger.info('{:.3f}'.format(ap))
logger.info('{:.3f}'.format(np.mean(aps)))
logger.info('~~~~~~~~')
logger.info('')
logger.info('----------------------------------------------------------')
logger.info('Results computed with the **unofficial** Python eval code.')
logger.info('Results should be very close to the official MATLAB code.')
logger.info('Use `./tools/reval.py --matlab ...` for your paper.')
logger.info('-- Thanks, The Management')
logger.info('----------------------------------------------------------')
def _do_python_eval(self, output_dir='output'):
annopath = os.path.join(self._data_path, 'annotations', '{:s}.txt')
imagesetfile = os.path.join(self._data_path, 'datasets', 'main',
self._image_set + '.txt')
cachedir = os.path.join(self._data_path, 'annotations_cache')
aps = []
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
for i, cls in enumerate(self._classes):
if cls == '__background__':
continue
filename = self._get_zlrm_results_file_template().format(cls)
rec, prec, ap = voc_eval(filename,
annopath,
imagesetfile,
cls,
cachedir,
ovthresh=0.1)
print('rec', rec)
print('prec', prec)
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('Recompute with `./tools/reval.py --matlab ...` for your paper.')
print('-- Thanks, The Management')
print('--------------------------------------------------------------')