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feat!: reorganize detection and tracking pipeline

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Refactor the package into common, schema, detection, and tracking namespaces and move dataset-specific ActualTest utilities into tests/support.

Add a pluggable detection stack with typed protocols, pydantic-settings config, loguru-based runner logging, cvmmap and headless video sources, NATS and parquet sinks, and a structured coco-wholebody133 payload path.

Teach tracking replay loading to consume parquet detection directories directly, preserve empty frames, and keep the video-to-parquet-to-tracking workflow usable for offline E2E runs.

Vendor the local mmcv and xtcocotools wheels under Git LFS, update uv sources/lock state, and refresh the mmcv build so mmcv.ops loads successfully with the current torch+cu130 environment.
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crosstyan
2026-03-26 16:24:27 +08:00
parent f1a2372b3c
commit 2c0d51ab31
56 changed files with 5179 additions and 889 deletions
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tests/test_tracker_smoke.py
+3 -3
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@@ -6,9 +6,9 @@ import pytest
pytest.importorskip("rpt")
from pose_tracking_exp.models import CameraFrame, FrameBundle, PoseDetection, TrackerConfig
from pose_tracking_exp.replay import load_scene_file
from pose_tracking_exp.tracker import PoseTracker
from pose_tracking_exp.schema import CameraFrame, FrameBundle, PoseDetection, TrackerConfig
from pose_tracking_exp.tracking import PoseTracker
from pose_tracking_exp.tracking.replay_io import load_scene_file
RPT_ROOT = Path("/home/crosstyan/Code/RapidPoseTriangulation")