def __init__(self, path, is_train=True, min_size=600, max_size=1000):
self.path = path
self.is_train = is_train
if self.is_train:
self.db = VOCBboxDataset(path)
self.tsf = Transform(min_size, max_size)
else:
self.db = VOCBboxDataset(path, split='val', use_difficult=True)
def __init__(self, opt):
self.opt = opt
if opt.data == 'voc':
self.db = VOCBboxDataset(opt.voc_data_dir)
else:
self.db = CITYBboxDataset(opt.city_data_dir, train=True)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt):
self.opt = opt
if opt.data == 'voc':
self.db = VOCBboxDataset(opt.voc_data_dir)
elif opt.data == 's3d':
self.db = S3DBboxDataset(opt.s3d_data_dir)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, split='test', use_difficult=True):
self.opt = opt
if opt.data == 'voc':
self.db = VOCBboxDataset(opt.voc_data_dir,
split=split,
use_difficult=use_difficult)
else:
self.db = CITYBboxDataset(opt.city_data_dir, train=False)
def __init__(self,
opt,
split='test',
use_difficult=True): #add use_difficult
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir,
split=split,
use_difficult=use_difficult)
def __init__(self, opt):
self.opt = opt
if opt.data == 'cityperson':
self.db = CityPersonTrainset(
img_path=opt.cityperson_data_dir + '/leftImg8bit/train',
annotation_path=opt.cityperson_data_dir +
'/gtBboxCityPersons/train')
else:
self.db = VOCBboxDataset(opt.voc_data_dir)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, split='test', use_difficult=True):
self.opt = opt
if self.opt.data == 'markers':
self.db = MarkersDataset(opt.data_dir)
elif self.opt.data == 'voc':
self.db = VOCBboxDataset(opt.data_dir)
else:
raise Exception('database type not recognised: {}'.format(
self.opt.data))
self.label_names = self.db.get_label_names()
def __init__(self, opt, split='test', use_difficult=True):
self.opt = opt
if opt.data == 'cityperson':
self.db = CityPersonTestset(
img_path=opt.cityperson_data_dir + '/leftImg8bit/val',
annotation_path=opt.cityperson_data_dir +
'/gtBboxCityPersons/val')
else:
self.db = VOCBboxDataset(opt.voc_data_dir,
split=split,
use_difficult=use_difficult)
def __init__(self, opt):
self.opt = opt
if self.opt.data == 'markers':
self.db = MarkersDataset(opt.data_dir)
elif self.opt.data == 'voc':
self.db = VOCBboxDataset(opt.data_dir)
else:
raise Exception('database type not recognised: {}'.format(
self.opt.data))
self.tsf = Transform(opt.min_size, opt.max_size)
self.label_names = self.db.get_label_names()
def __init__(self, configurations):
self.configurations = configurations
self.db = VOCBboxDataset(configurations.voc_data_dir)
self.tsf = Transform(configurations.min_size, configurations.max_size)
def __init__(self, configurations, split='val', use_difficult=True):
self.configurations = configurations
self.db = VOCBboxDataset(configurations.voc_data_dir,
split=split,
use_difficult=use_difficult)
def __init__(self, opt, split='test', use_difficult=True):
self.opt = opt
self.db = VOCBboxDataset('/data2/dechunwang/dataset/',
split=split,
use_difficult=use_difficult)
def __init__(self, opt, id_file=None, img_dir=None, anno_dir=None, use_diff=True):
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir, id_file=id_file, img_dir=img_dir, anno_dir=anno_dir, use_difficult=use_diff)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, use_difficult=True, id_file=None, img_dir=None, anno_dir=None):
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir, id_file=id_file, img_dir=img_dir, anno_dir=anno_dir, use_difficult=use_difficult)
def __init__(self, opt, split='test_small_32', use_difficult=True):
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult, small_obj=True)
def __init__(self, opt):
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir, split='trainval_small_32', small_obj=True)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt, split='test', use_difficult=True):
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir, split=split, use_difficult=use_difficult)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self):
self.db = VOCBboxDataset(voc_data_dir)
self.tsf = Transform(min_size=600, max_size=1000)
def __init__(self, opt):
self.opt = opt
self.db = VOCBboxDataset(self.opt.test_data_dir)
def __init__(self, opt):
self.opt = opt
self.db = VOCBboxDataset(opt.train_data_dir)
self.tsf = Transform()
def __init__(self):
self.db = VOCBboxDataset(
'D:/pyworks/FasterRCNN/data/testset/VOCdevkit/VOC2007', 'test',
True)
def __init__(self):
self.db = VOCBboxDataset(
'D:/pyworks/FasterRCNN/data/VOCdevkit/VOC2007', 'trainval')
self.transform = Transform()
def __init__(self, opt):
self.opt = opt
self.db = VOCBboxDataset(opt.voc_data_dir)
self.tsf = Transform(opt.min_size, opt.max_size)
def __init__(self, opt):
self.opt = opt #预设参数
self.db = VOCBboxDataset(opt.voc_data_dir) #用预设参数创建数据集类
self.tsf = Transform(opt.min_size, opt.max_size) #实例化预处理类