def __init__(self, data_dir='auto', split='train', year='2012', use_difficult=False, return_difficult=False): if data_dir == 'auto' and year in ['2007', '2012']: data_dir = voc_utils.get_voc(year, split) if split not in ['train', 'trainval', 'val']: if not (split == 'test' and year == '2007'): warnings.warn( 'please pick split from \'train\', \'trainval\', \'val\'' 'for 2012 dataset. For 2007 dataset, you can pick \'test\'' ' in addition to the above mentioned splits.') id_list_file = os.path.join(data_dir, 'ImageSets/Main/{0}.txt'.format(split)) # self.ids = [id_.strip() for id_ in open(id_list_file)] import re self.ids = list() for id_ in open(id_list_file): id_ = id_.strip() id_ = re.split(" +", id_) if id_[1] == "1": self.ids.append(id_[0]) self.data_dir = data_dir self.use_difficult = use_difficult self.return_difficult = return_difficult
def __init__(self, data_dir='auto', split='train', year='2012', use_difficult=False, return_difficult=False): super(VOCBboxDataset, self).__init__() if data_dir == 'auto' and year in ['2007', '2012']: data_dir = voc_utils.get_voc(year, split) if split not in ['train', 'trainval', 'val']: if not (split == 'test' and year == '2007'): warnings.warn( 'please pick split from \'train\', \'trainval\', \'val\'' 'for 2012 dataset. For 2007 dataset, you can pick \'test\'' ' in addition to the above mentioned splits.' ) id_list_file = os.path.join( data_dir, 'ImageSets/Main/{0}.txt'.format(split)) self.ids = [id_.strip() for id_ in open(id_list_file)] self.data_dir = data_dir self.use_difficult = use_difficult self.add_getter('img', self._get_image) self.add_getter(('bbox', 'label', 'difficult'), self._get_annotations) if not return_difficult: self.keys = ('img', 'bbox', 'label')
def __init__(self, data_dir='auto', split='train'): if split not in ['train', 'trainval', 'val']: raise ValueError( 'please pick split from \'train\', \'trainval\', \'val\'') if data_dir == 'auto': data_dir = voc_utils.get_voc('2012', split) id_list_file = os.path.join( data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split)) self.ids = [id_.strip() for id_ in open(id_list_file)] self.data_dir = data_dir
def __init__(self, data_dir='auto', split='train'): super(VOCInstanceSegmentationDataset, self).__init__() if split not in ['train', 'trainval', 'val']: raise ValueError( 'please pick split from \'train\', \'trainval\', \'val\'') if data_dir == 'auto': data_dir = voc_utils.get_voc('2012', split) id_list_file = os.path.join( data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split)) self.ids = [id_.strip() for id_ in open(id_list_file)] self.data_dir = data_dir self.add_getter('img', self._get_image) self.add_getter(('mask', 'label'), self._get_annotations)
def __init__(self, data_dir='auto', split='train', year='2012', use_difficult=False, return_difficult=False): if data_dir == 'auto' and year in ['2007', '2012']: data_dir = voc_utils.get_voc(year, split) if split not in ['train', 'trainval', 'val']: if not (split == 'test' and year == '2007'): warnings.warn( 'please pick split from \'train\', \'trainval\', \'val\'' 'for 2012 dataset. For 2007 dataset, you can pick \'test\'' ' in addition to the above mentioned splits.' ) id_list_file = os.path.join( data_dir, 'ImageSets/Main/{0}.txt'.format(split)) self.ids = [id_.strip() for id_ in open(id_list_file)] self.data_dir = data_dir self.use_difficult = use_difficult self.return_difficult = return_difficult
def __init__(self, data_dir='auto', split='aug'): super(VOCSemanticSegmentationWithBboxDataset, self).__init__() if data_dir == 'auto': data_dir = voc_utils.get_voc('2012', split) if split == 'aug': file_dir = os.path.dirname(os.path.realpath(__file__)) f = open(os.path.join(file_dir, 'splits/voc_trainaug.txt')) self.ids = [] for l in f.readlines(): path = l.split()[0] self.ids.append(os.path.split(os.path.splitext(path)[0])[1]) elif split == 'val': id_list_file = os.path.join( data_dir, 'ImageSets/Segmentation/{0}.txt'.format(split)) self.ids = [id_.strip() for id_ in open(id_list_file)] self.data_dir = data_dir self.add_getter('img', self._get_image) self.add_getter('label_map', self._get_label) self.add_getter(('bbox', 'label'), self._get_annotations)
def __init__(self, data_dir='auto', split='train', year='2012', use_difficult=False, return_difficult=False): super(VOCBboxDataset, self).__init__() if data_dir == 'auto' and year in ['2007', '2012']: data_dir = voc_utils.get_voc(year, split) id_list_file = os.path.join(data_dir, 'ImageSets/Main/{0}.txt'.format(split)) self.ids = [id_.strip() for id_ in open(id_list_file)] self.data_dir = data_dir self.use_difficult = use_difficult self.add_getter('img', self._get_image) self.add_getter(('bbox', 'label', 'difficult'), self._get_annotations) if not return_difficult: self.keys = ('img', 'bbox', 'label')