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import logging
import os
from fvcore.common.timer import Timer
from detectron2.structures import BoxMode
from fvcore.common.file_io import PathManager
from detectron2.data import DatasetCatalog, MetadataCatalog
from lvis import LVIS
logger = logging.getLogger(__name__)
__all__ = ["load_o365_json", "register_o365_instances"]
def register_o365_instances(name, metadata, json_file, image_root):
DatasetCatalog.register(name, lambda: load_o365_json(
json_file, image_root, name))
MetadataCatalog.get(name).set(
json_file=json_file, image_root=image_root,
evaluator_type="lvis", **metadata
)
def get_o365_meta():
categories = [{'supercategory': 'object', 'id': 1, 'name': 'object'}]
o365_categories = sorted(categories, key=lambda x: x["id"])
thing_classes = [k["name"] for k in o365_categories]
meta = {"thing_classes": thing_classes}
return meta
def load_o365_json(json_file, image_root, dataset_name=None):
'''
Load Object365 class name text for object description for GRiT
'''
json_file = PathManager.get_local_path(json_file)
timer = Timer()
lvis_api = LVIS(json_file)
if timer.seconds() > 1:
logger.info("Loading {} takes {:.2f} seconds.".format(
json_file, timer.seconds()))
class_names = {}
sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id'])
for x in sort_cat:
if '/' in x['name']:
text = ''
for xx in x['name'].split('/'):
text += xx
text += ' '
text = text[:-1]
else:
text = x['name']
class_names[x['id']] = text
img_ids = sorted(lvis_api.imgs.keys())
imgs = lvis_api.load_imgs(img_ids)
anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]
ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
assert len(set(ann_ids)) == len(ann_ids), \
"Annotation ids in '{}' are not unique".format(json_file)
imgs_anns = list(zip(imgs, anns))
logger.info("Loaded {} images in the LVIS v1 format from {}".format(
len(imgs_anns), json_file))
dataset_dicts = []
for (img_dict, anno_dict_list) in imgs_anns:
record = {}
if "file_name" in img_dict:
file_name = img_dict["file_name"]
record["file_name"] = os.path.join(image_root, file_name)
record["height"] = int(img_dict["height"])
record["width"] = int(img_dict["width"])
image_id = record["image_id"] = img_dict["id"]
objs = []
for anno in anno_dict_list:
assert anno["image_id"] == image_id
if anno.get('iscrowd', 0) > 0:
continue
obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
obj["category_id"] = 0
obj["object_description"] = class_names[anno['category_id']]
objs.append(obj)
record["annotations"] = objs
if len(record["annotations"]) == 0:
continue
record["task"] = "ObjectDet"
dataset_dicts.append(record)
return dataset_dicts
_CUSTOM_SPLITS_LVIS = {
"object365_train": ("object365/images/train/", "object365/annotations/train_v1.json"),
}
for key, (image_root, json_file) in _CUSTOM_SPLITS_LVIS.items():
register_o365_instances(
key,
get_o365_meta(),
os.path.join("datasets", json_file) if "://" not in json_file else json_file,
os.path.join("datasets", image_root),
) |