Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
144 lines
4.0 KiB
Python
144 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""export_ls_to_yolo 单元测试（无 pytest 依赖）。"""
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from __future__ import annotations
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import hashlib
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import json
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import sys
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import tempfile
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from pathlib import Path
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SCRIPT_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(SCRIPT_DIR))
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from export_ls_to_yolo import ( # noqa: E402
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convert_regions_to_yolo_lines,
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export_batch,
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)
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from ingest_incremental import validate_detect_label, validate_pose_label # noqa: E402
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def _task_id(rel: str) -> str:
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return hashlib.sha256(rel.encode()).hexdigest()[:16]
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def test_detect_conversion() -> None:
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regions = [
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{
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"type": "rectanglelabels",
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"value": {
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"x": 10.0,
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"y": 20.0,
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"width": 30.0,
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"height": 40.0,
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"rectanglelabels": ["face"],
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},
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}
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]
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lines = convert_regions_to_yolo_lines(
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regions,
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mode="detect",
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class_map={"face": 0, "eye_open": 1},
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)
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assert len(lines) == 1
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parts = lines[0].split()
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assert len(parts) == 5
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assert parts[0] == "0"
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assert abs(float(parts[1]) - 0.25) < 1e-5 # cx = (10+15)/100
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assert abs(float(parts[2]) - 0.40) < 1e-5 # cy = (20+20)/100
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err = validate_detect_label("\n".join(lines), 4)
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assert err is None, err
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def test_pose_conversion() -> None:
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regions = [
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{
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"type": "rectanglelabels",
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"value": {
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"x": 10.0,
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"y": 20.0,
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"width": 30.0,
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"height": 40.0,
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"rectanglelabels": ["face"],
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},
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},
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{
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"type": "keypointlabels",
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"value": {"x": 35.6, "y": 52.9, "width": 0.5, "keypointlabels": ["kp_01"]},
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},
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{
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"type": "keypointlabels",
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"value": {"x": 50.0, "y": 50.0, "width": 0.5, "keypointlabels": ["kp_10"]},
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},
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]
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kpt_map = {f"kp_{i:02d}": i for i in range(37)}
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lines = convert_regions_to_yolo_lines(
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regions,
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mode="pose",
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class_map={"face": 0},
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kpt_map=kpt_map,
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kpt_shape=[37, 3],
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)
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assert len(lines) == 1
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parts = lines[0].split()
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assert len(parts) == 116
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assert parts[0] == "0"
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# kp_01 at index 1 -> fields 5+3..5+5
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assert abs(float(parts[8]) - 0.356) < 1e-3
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assert abs(float(parts[9]) - 0.529) < 1e-3
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assert parts[10] == "2.000000"
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err = validate_pose_label("\n".join(lines), [37, 3])
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assert err is None, err
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def test_export_batch_end_to_end() -> None:
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with tempfile.TemporaryDirectory() as tmp:
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batch = Path(tmp)
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img_rel = "images/train/sample.jpg"
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img_path = batch / img_rel
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img_path.parent.mkdir(parents=True)
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img_path.write_bytes(b"\xff\xd8\xff")
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tid = _task_id(img_rel)
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ann = {
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"task_id": tid,
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"result": [
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{
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"type": "rectanglelabels",
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"value": {
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"x": 10.0,
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"y": 20.0,
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"width": 30.0,
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"height": 40.0,
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"rectanglelabels": ["face"],
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},
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},
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{
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"type": "keypointlabels",
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"value": {"x": 25.0, "y": 40.0, "width": 0.5, "keypointlabels": ["kp_00"]},
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},
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],
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}
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ann_dir = batch / "labels" / "ls_annotations"
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ann_dir.mkdir(parents=True)
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(ann_dir / f"{tid}.json").write_text(json.dumps(ann), encoding="utf-8")
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result = export_batch(batch, "addw_face", mode="pose")
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assert result["written"] == 1
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out = batch / "labels" / "train" / "sample.txt"
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assert out.is_file()
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parts = out.read_text().strip().split()
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assert len(parts) == 116
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def main() -> int:
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test_detect_conversion()
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test_pose_conversion()
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test_export_batch_end_to_end()
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print("OK export_ls_to_yolo tests")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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