feat!: reorganize detection and tracking pipeline
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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@@ -5,9 +5,9 @@ import pytest
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pytest.importorskip("rpt")
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from pose_tracking_exp.joints import BODY20_INDEX_BY_NAME
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from pose_tracking_exp.models import CameraCalibration, CameraFrame, FrameBundle, ProposalCluster, SceneConfig, TrackerConfig
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from pose_tracking_exp.tracker import PoseTracker
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from pose_tracking_exp.common.joints import BODY20_INDEX_BY_NAME
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from pose_tracking_exp.schema import CameraCalibration, CameraFrame, FrameBundle, ProposalCluster, SceneConfig, TrackerConfig
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from pose_tracking_exp.tracking import PoseTracker
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def _make_scene() -> SceneConfig:
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@@ -96,7 +96,7 @@ def test_single_person_mode_caps_active_tracks(monkeypatch) -> None:
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tracker = PoseTracker(
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_make_scene(),
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TrackerConfig(
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mode="single_person",
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max_active_tracks=1,
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tentative_min_age=1,
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tentative_hits_required=1,
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tentative_promote_score=0.0,
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@@ -127,7 +127,7 @@ def test_single_person_mode_reuses_lost_track_id(monkeypatch) -> None:
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tracker = PoseTracker(
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_make_scene(),
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TrackerConfig(
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mode="single_person",
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max_active_tracks=1,
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tentative_min_age=1,
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tentative_hits_required=1,
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tentative_promote_score=0.0,
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