in detic/data/datasets/lvis_v1.py [0:0]
def custom_load_lvis_json(json_file, image_root, dataset_name=None):
'''
Modifications:
use `file_name`
convert neg_category_ids
add pos_category_ids
'''
from lvis import LVIS
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()))
catid2contid = {x['id']: i for i, x in enumerate(
sorted(lvis_api.dataset['categories'], key=lambda x: x['id']))}
if len(lvis_api.dataset['categories']) == 1203:
for x in lvis_api.dataset['categories']:
assert catid2contid[x['id']] == x['id'] - 1
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"]
if img_dict["file_name"].startswith("COCO"):
file_name = file_name[-16:]
record["file_name"] = os.path.join(image_root, file_name)
elif 'coco_url' in img_dict:
# e.g., http://images.cocodataset.org/train2017/000000391895.jpg
file_name = img_dict["coco_url"][30:]
record["file_name"] = os.path.join(image_root, file_name)
elif 'tar_index' in img_dict:
record['tar_index'] = img_dict['tar_index']
record["height"] = img_dict["height"]
record["width"] = img_dict["width"]
record["not_exhaustive_category_ids"] = img_dict.get(
"not_exhaustive_category_ids", [])
record["neg_category_ids"] = img_dict.get("neg_category_ids", [])
# NOTE: modified by Xingyi: convert to 0-based
record["neg_category_ids"] = [
catid2contid[x] for x in record["neg_category_ids"]]
if 'pos_category_ids' in img_dict:
record['pos_category_ids'] = [
catid2contid[x] for x in img_dict.get("pos_category_ids", [])]
if 'captions' in img_dict:
record['captions'] = img_dict['captions']
if 'caption_features' in img_dict:
record['caption_features'] = img_dict['caption_features']
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"] = catid2contid[anno['category_id']]
if 'segmentation' in anno:
segm = anno["segmentation"]
valid_segm = [poly for poly in segm \
if len(poly) % 2 == 0 and len(poly) >= 6]
# assert len(segm) == len(
# valid_segm
# ), "Annotation contains an invalid polygon with < 3 points"
if not len(segm) == len(valid_segm):
print('Annotation contains an invalid polygon with < 3 points')
assert len(segm) > 0
obj["segmentation"] = segm
objs.append(obj)
record["annotations"] = objs
dataset_dicts.append(record)
return dataset_dicts