coco.py 文件源码

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
    self.set_proposal_method('gt')
    self.competition_mode(False)

    # Some image sets are "views" (i.e. subsets) into others.
    # For example, minival2014 is a random 5000 image subset of val2014.
    # This mapping tells us where the view's images and proposals come from.
    self._view_map = {
      'minival2014': 'val2014',  # 5k val2014 subset
      'valminusminival2014': 'val2014',  # val2014 \setminus minival2014
      'test-dev2015': 'test2015',
    }
    coco_name = image_set + year  # e.g., "val2014"
    self._data_name = (self._view_map[coco_name]
                       if coco_name in self._view_map
                       else coco_name)
    # Dataset splits that have ground-truth annotations (test splits
    # do not have gt annotations)
    self._gt_splits = ('train', 'val', 'minival')