Chengfang Lu
7c43b44c57
Huaxu Sentinel Active Safety Platform with embedded algorithm code, Docker Compose setup, and vendored dataset scaffolds for clone-and-run. Co-authored-by: Cursor <cursoragent@cursor.com>
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
"""DMS COCO-format adapter."""
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from __future__ import annotations
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import json
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from pathlib import Path
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from typing import Any
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from as_platform.data.ingest.base import IngestAdapter, IngestContext, NormalizedDataset
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COCO_NAMES = ("instances_train.json", "instances_val.json", "instances_test.json", "annotations.json")
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def _read_json(path: Path) -> dict[str, Any] | None:
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try:
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return json.loads(path.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError):
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return None
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class DmsCocoAdapter(IngestAdapter):
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format_id = "dms_coco"
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projects = ("dms",)
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def _find_coco_files(self, root: Path) -> list[Path]:
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files: list[Path] = []
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for name in COCO_NAMES:
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p = root / "annotations" / name
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if p.is_file():
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files.append(p)
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for name in COCO_NAMES:
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p = root / name
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if p.is_file():
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files.append(p)
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return files
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def can_handle(self, ctx: IngestContext) -> bool:
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root = ctx.source_path
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return len(self._find_coco_files(root)) > 0
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def inspect(self, ctx: IngestContext) -> NormalizedDataset:
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root = ctx.source_path
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files = self._find_coco_files(root)
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split_counts = {"train": 0, "val": 0, "test": 0}
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ann_count = 0
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categories: set[str] = set()
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warnings: list[str] = []
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for f in files:
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data = _read_json(f)
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if not data:
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warnings.append(f"failed to parse {f.name}")
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continue
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images = data.get("images") or []
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anns = data.get("annotations") or []
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cats = data.get("categories") or []
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ann_count += len(anns)
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for c in cats:
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name = c.get("name")
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if isinstance(name, str):
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categories.add(name)
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lower = f.name.lower()
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if "train" in lower:
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split_counts["train"] += len(images)
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elif "val" in lower:
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split_counts["val"] += len(images)
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elif "test" in lower:
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split_counts["test"] += len(images)
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else:
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split_counts["train"] += len(images)
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return NormalizedDataset(
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format_id=self.format_id,
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project=ctx.project,
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task=ctx.task,
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source_path=str(root),
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split_counts=split_counts,
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sample_count=sum(split_counts.values()),
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annotation_count=ann_count,
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artifacts=[self._artifact_name(root, f) for f in files],
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warnings=warnings,
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extra={"categories": sorted(categories)},
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)
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@staticmethod
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def _artifact_name(root: Path, path: Path) -> str:
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try:
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return str(path.relative_to(root))
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except ValueError:
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return path.name
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