Secure your code as it's written. Use Snyk Code to scan source code in minutes - no build needed - and fix issues immediately.
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'top_k' : 2000,
'use_salt' : True,
'cleanup' : True,
'crowd_thresh' : 0.7,
'rpn_file': None,
'min_size' : 2}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = os.environ['HOME'] + '/data/Object_Detection/coco'
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
def __init__(self, version, image_set, ):
imdb.__init__(self, 'vg_' + version + '_' + image_set)
self._version = version
self._image_set = image_set
self._data_path = os.path.join(cfg.DATA_DIR, 'genome')
self._img_path = os.path.join(cfg.DATA_DIR, 'vg')
# VG specific config options
self.config = {'cleanup': False}
# Load classes
self._classes = ['__background__']
self._class_to_ind = {}
self._class_to_ind[self._classes[0]] = 0
with open(os.path.join(self._data_path, self._version,
'objects_vocab.txt')) as f:
count = 1
for object in f.readlines():
names = [n.lower().strip() for n in object.split(',')]
def __init__(self, image_set, year, use_diff=False):
name = 'voc_' + year + '_' + image_set
if use_diff:
name += '_diff'
imdb.__init__(self, name)
self._year = year
self._image_set = image_set
self._devkit_path = self._get_default_path()
self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
self._classes = ('__background__', # always index 0
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._image_ext = '.jpg'
self._image_index = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.gt_roidb
self._salt = str(uuid.uuid4())
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'use_salt': True,
'cleanup': True}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))
self._class_to_coco_cat_id = dict(list(zip([c['name'] for c in cats],
self._COCO.getCatIds())))
self._image_index = self._load_image_set_index()
# Default to roidb handler
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'top_k' : 2000,
'use_salt' : True,
'cleanup' : True,
'crowd_thresh' : 0.7,
'min_size' : 2}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],
def __init__(self, roidb_file, dict_file, imdb_file, rpndb_file, split, num_im):
imdb.__init__(self, roidb_file[:-3])
# read in dataset from a h5 file and a dict (json) file
self.im_h5 = h5py.File(os.path.join(cfg.VG_DIR, imdb_file), 'r')
self.roi_h5 = h5py.File(os.path.join(cfg.VG_DIR, roidb_file), 'r')
# roidb metadata
self.info = json.load(open(os.path.join(cfg.VG_DIR,
dict_file), 'r'))
self.im_refs = self.im_h5['images'] # image data reference
im_scale = self.im_refs.shape[2]
print('split==%i' % split)
data_split = self.roi_h5['split'][:]
self.split = split
if split >= 0:
split_mask = data_split == split # current split
def __init__(self, image_set, split, devkit_path):
imdb.__init__(self, 'wider')
self._image_set = image_set # {'train', 'test'}
self._split = split # {1, 2, ..., 10}
self._devkit_path = devkit_path # /data2/hzjiang/Data/CS2
# self._data_path = os.path.join(self._devkit_path, 'data')
self._data_path = self._devkit_path;
self._classes = ('__background__', # always index 0
'face')
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._image_ext = ['.png']
self._image_index, self._gt_roidb = self._load_image_set_index()
# Default to roidb handler
self._roidb_handler = self.selective_search_roidb
# Specific config options
self.config = {'cleanup' : True,
'use_salt' : True,
def __init__(self, image_set, year):
imdb.__init__(self, 'coco-mask_' + year + '_' + image_set) #TODO: figure out why super().__init__(...) will fail
# COCO specific config options
self.config = {'use_salt': True,
'cleanup': True}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats]) # 1 + 80
self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes))))) # 0 ~ 80
self._class_to_coco_cat_id = dict(list(zip([c['name'] for c in cats],
self._COCO.getCatIds())))
self._image_index = self._load_image_set_index()
# Default to roidb handler
def __init__(self, image_set, year):
imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {'top_k' : 2000,
'use_salt' : True,
'cleanup' : True,
'crowd_thresh' : 0.7,
'min_size' : 2}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
self._classes = tuple(['__background__'] + [c['name'] for c in cats])
self._class_to_ind = dict(zip(self.classes, xrange(self.num_classes)))
self._class_to_coco_cat_id = dict(zip([c['name'] for c in cats],