本文整理匯總了Python中model.test.apply_nms方法的典型用法代碼示例。如果您正苦於以下問題：Python test.apply_nms方法的具體用法？Python test.apply_nms怎麽用？Python test.apply_nms使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類model.test的用法示例。
在下文中一共展示了test.apply_nms方法的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: parse_args
# 需要導入模塊: from model import test [as 別名]
# 或者: from model.test import apply_nms [as 別名]
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('--imdb', dest='imdb_name',
help='dataset to re-evaluate',
default='voc_2007_test', type=str)
parser.add_argument('--matlab', dest='matlab_eval',
help='use matlab for evaluation',
action='store_true')
parser.add_argument('--comp', dest='comp_mode', help='competition mode',
action='store_true')
parser.add_argument('--nms', dest='apply_nms', help='apply nms',
action='store_true')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:26，代碼來源:reval.py
示例2: parse_args
# 需要導入模塊: from model import test [as 別名]
# 或者: from model.test import apply_nms [as 別名]
def parse_args():
"""
Parse input arguments
"""
parser = argparse.ArgumentParser(description='Re-evaluate results')
parser.add_argument('detection_file', type=str)
parser.add_argument('--output_dir', help='results directory', type=str)
parser.add_argument('--imdb', dest='imdb_name',
help='dataset to re-evaluate',
default='voc_2007_test', type=str)
parser.add_argument('--matlab', dest='matlab_eval',
help='use matlab for evaluation',
action='store_true')
parser.add_argument('--comp', dest='comp_mode', help='competition mode',
action='store_true')
parser.add_argument('--nms', dest='apply_nms', help='apply nms',
action='store_true')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
return args 示例3: from_dets
# 需要導入模塊: from model import test [as 別名]
# 或者: from model.test import apply_nms [as 別名]
def from_dets(imdb_name, detection_file, args):
imdb = pascal_voc('test', '2007')
imdb.competition_mode(args.comp_mode)
imdb.config['matlab_eval'] = args.matlab_eval
with open(os.path.join(detection_file), 'rb') as f:
if 'json' in detection_file:
dets = json.load(f)
else:
dets = pickle.load(f, encoding='latin1')
# import pdb; pdb.set_trace()
if args.apply_nms:
print('Applying NMS to all detections')
test_nms = 0.3
nms_dets = apply_nms(dets, test_nms)
else:
nms_dets = dets
print('Evaluating detections')
imdb.evaluate_detections(nms_dets) 示例4: from_dets
# 需要導入模塊: from model import test [as 別名]
# 或者: from model.test import apply_nms [as 別名]
def from_dets(imdb_name, output_dir, args):
imdb = get_imdb(imdb_name)
imdb.competition_mode(args.comp_mode)
imdb.config['matlab_eval'] = args.matlab_eval
with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
dets = pickle.load(f)
if args.apply_nms:
print('Applying NMS to all detections')
nms_dets = apply_nms(dets, cfg.TEST.NMS)
else:
nms_dets = dets
print('Evaluating detections')
imdb.evaluate_detections(nms_dets, output_dir) 開發者ID:Sunarker，項目名稱:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代碼行數:17，代碼來源:reval.py