def register_lvis_instances(name, metadata, json_file, image_root):
"""
Register a dataset in LVIS's json annotation format for instance detection.
Args:
name (str): a name that identifies the dataset, e.g. "lvis_v0.5_train".
metadata (dict): extra metadata associated with this dataset. It can be an empty dict.
json_file (str): path to the json instance annotation file.
image_root (str): directory which contains all the images.
"""
DatasetCatalog.register(
name, lambda: load_lvis_json(json_file, image_root, name))
MetadataCatalog.get(name).set(json_file=json_file,
image_root=image_root,
evaluator_type="lvis",
**metadata)
def register_meta_pascal_voc(
name, metadata, dirname, split, year, keepclasses, sid):
if keepclasses.startswith('base_novel'):
thing_classes = metadata["thing_classes"][sid]
elif keepclasses.startswith('base'):
thing_classes = metadata["base_classes"][sid]
elif keepclasses.startswith('novel'):
thing_classes = metadata["novel_classes"][sid]
DatasetCatalog.register(
name, lambda: load_filtered_voc_instances(
name, dirname, split, thing_classes)
)
MetadataCatalog.get(name).set(
thing_classes=thing_classes, dirname=dirname, year=year, split=split,
base_classes=metadata["base_classes"][sid],
novel_classes=metadata["novel_classes"][sid]
)
def register_coco_instances(name, metadata, json_file, image_root):
"""
Register a dataset in COCO's json annotation format for instance detection.
This is an example of how to register a new dataset.
You can do something similar to this function, to register new datasets.
Args:
name (str): the name that identifies a dataset, e.g. "coco_2014_train".
metadata (dict): extra metadata associated with this dataset. You can
leave it as an empty dict.
json_file (str): path to the json instance annotation file.
image_root (str): directory which contains all the images.
"""
# 1. register a function which returns dicts
DatasetCatalog.register(
name, lambda: load_coco_json(json_file, image_root, name))
# 2. Optionally, add metadata about this dataset,
# since they might be useful in evaluation, visualization or logging
MetadataCatalog.get(name).set(json_file=json_file,
image_root=image_root,
evaluator_type="coco",
**metadata)
def register_pascal_voc(name, dirname, split, year):
DatasetCatalog.register(name, lambda: load_voc_instances(dirname, split))
MetadataCatalog.get(name).set(thing_classes=CLASS_NAMES,
dirname=dirname,
year=year,
split=split)