Datasets:
Tasks:
Image Segmentation
Languages:
English
Size:
10K<n<100K
Tags:
referring-expression-segmentation
video-understanding
spatio-temporal
dynamic-scenes
instance-segmentation
License:
File size: 2,075 Bytes
edae372 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | """
Resolve COCO JSON paths under ``data/scenes/<scene>/`` after renames:
- ``_annotations_fixed.coco.json`` -> ``_annotations_original.coco.json``
- ``_annotations_supplementary.coco.json`` -> ``_annotations_extended.coco.json``
``extended`` adds annotations on the same images as ``original``; both may be present.
"""
from __future__ import annotations
from pathlib import Path
from typing import List, Optional
# Single canonical file for frame list / image dimensions (first match wins).
RESOLVE_ORDER: tuple[str, ...] = (
"_annotations_original.coco.json",
"_annotations_extended.coco.json",
"_annotations_fixed.coco.json",
"_annotations.coco.json",
)
# All COCO files to rasterize for per-instance masks (original then extended; skip legacy dup).
MASK_GENERATION_ORDER: tuple[str, ...] = (
"_annotations_original.coco.json",
"_annotations_extended.coco.json",
"_annotations_fixed.coco.json",
)
def resolve_scene_coco_annotation(scene_dir: Path) -> Optional[Path]:
"""One annotation file for frame_id / H,W lookup."""
for name in RESOLVE_ORDER:
p = scene_dir / name
if p.is_file():
return p
return None
def iter_scene_coco_for_masks(scene_dir: Path) -> List[Path]:
"""
COCO files to generate instance_masks from. Uses ``_annotations.coco.json`` only if
no renamed file exists (older scenes).
"""
out: List[Path] = []
for name in MASK_GENERATION_ORDER:
p = scene_dir / name
if p.is_file():
out.append(p)
if not out:
p = scene_dir / "_annotations.coco.json"
if p.is_file():
out.append(p)
return out
def open_coco_json(path: Path):
import json
for enc in ("utf-8-sig", "utf-8"):
try:
with path.open("r", encoding=enc) as f:
return json.load(f)
except UnicodeDecodeError:
continue
with path.open("r", encoding="utf-8", errors="replace") as f:
return json.load(f)
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