crosstyan
f1a2372b3c
Set up pose_tracking_exp as a uv-managed Python package for offline multiview body tracking experiments. This initial commit includes: - the typed package scaffold, CLI entrypoints, and repo-local uv configuration - scene and replay loaders for generic JSON replays and ActualTest parquet inputs - ParaJumping payload conversion and RTMPose-to-body20 normalization - a custom articulated tracker with tentative, active, and lost lifecycle handling - RPT-backed proposal generation, camera convention handling, and multiview reprojection updates - regression tests for normalization, camera conventions, ActualTest ingestion, seeding, and tracker smoke flows - project documentation covering extrinsic formats and the ActualTest calibration caveat
149 lines
4.7 KiB
Python
149 lines
4.7 KiB
Python
import json
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from pathlib import Path
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from typing import NamedTuple, cast
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import cv2
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import numpy as np
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import pytest
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pytest.importorskip("rpt")
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from pose_tracking_exp.models import CameraCalibration, SceneConfig
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from pose_tracking_exp.replay import load_scene_file
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from pose_tracking_exp.rpt_adapter import build_rpt_config
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class _CameraArgs(NamedTuple):
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name: str
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width: int
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height: int
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K: np.ndarray
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DC: np.ndarray
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model: str
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def _camera_args() -> _CameraArgs:
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return _CameraArgs(
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name="cam0",
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width=640,
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height=480,
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K=np.asarray([[500.0, 0.0, 320.0], [0.0, 500.0, 240.0], [0.0, 0.0, 1.0]], dtype=np.float64),
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DC=np.zeros(5, dtype=np.float64),
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model="pinhole",
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)
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def test_from_opencv_extrinsics_derives_rpt_pose() -> None:
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args = _camera_args()
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rotation_vec = np.asarray([0.0, 0.2, 0.0], dtype=np.float64).reshape(3, 1)
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rotation, _ = cv2.Rodrigues(rotation_vec)
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translation = np.asarray([0.5, -0.1, 2.0], dtype=np.float64)
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camera = CameraCalibration.from_opencv_extrinsics(
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name=args.name,
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width=args.width,
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height=args.height,
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K=args.K,
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DC=args.DC,
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model=args.model,
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R=rotation,
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T=translation,
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rvec=rotation_vec.reshape(3),
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)
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np.testing.assert_allclose(camera.pose_R, rotation.T)
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np.testing.assert_allclose(camera.pose_T, -(rotation.T @ translation))
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def test_from_rpt_pose_derives_opencv_extrinsics() -> None:
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args = _camera_args()
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pose_rotation_vec = np.asarray([0.0, -0.3, 0.0], dtype=np.float64).reshape(3, 1)
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pose_rotation, _ = cv2.Rodrigues(pose_rotation_vec)
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pose_translation = np.asarray([1.5, 0.2, -0.4], dtype=np.float64)
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camera = CameraCalibration.from_rpt_pose(
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name=args.name,
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width=args.width,
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height=args.height,
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K=args.K,
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DC=args.DC,
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model=args.model,
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R=pose_rotation,
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T=pose_translation,
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)
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np.testing.assert_allclose(camera.pose_R, pose_rotation)
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np.testing.assert_allclose(camera.pose_T, pose_translation)
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np.testing.assert_allclose(camera.R, pose_rotation.T)
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np.testing.assert_allclose(camera.T, -(pose_rotation.T @ pose_translation))
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def test_load_scene_file_supports_explicit_rpt_pose(tmp_path: Path) -> None:
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scene_path = tmp_path / "scene.json"
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payload = {
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"extrinsic_format": "rpt_camera_pose",
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"room_size": [6.0, 4.0, 3.0],
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"room_center": [0.0, 0.0, 1.0],
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"cameras": [
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{
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"name": "cam0",
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"width": 640,
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"height": 480,
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"K": [[500.0, 0.0, 320.0], [0.0, 500.0, 240.0], [0.0, 0.0, 1.0]],
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"DC": [0.0, 0.0, 0.0, 0.0, 0.0],
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"R": [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]],
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"T": [[1.0], [2.0], [3.0]],
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}
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],
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}
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scene_path.write_text(json.dumps(payload), encoding="utf-8")
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scene = load_scene_file(scene_path)
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np.testing.assert_allclose(scene.cameras[0].pose_T, [1.0, 2.0, 3.0])
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np.testing.assert_allclose(scene.cameras[0].T, [-1.0, -2.0, -3.0])
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def test_build_rpt_config_uses_pose_convention(monkeypatch: pytest.MonkeyPatch) -> None:
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args = _camera_args()
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camera = CameraCalibration.from_opencv_extrinsics(
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name=args.name,
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width=args.width,
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height=args.height,
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K=args.K,
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DC=args.DC,
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model=args.model,
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R=np.eye(3, dtype=np.float64),
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T=np.asarray([1.0, 2.0, 3.0], dtype=np.float64),
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rvec=np.zeros(3, dtype=np.float64),
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)
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scene = SceneConfig(
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room_size=np.asarray([6.0, 4.0, 3.0], dtype=np.float64),
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room_center=np.asarray([0.0, 0.0, 1.0], dtype=np.float64),
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cameras=(camera,),
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)
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captured: dict[str, object] = {}
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def fake_make_triangulation_config(
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cameras: list[dict[str, object]],
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roomparams: np.ndarray,
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joint_names: list[str],
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*,
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min_match_score: float,
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min_group_size: int,
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) -> dict[str, object]:
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captured["cameras"] = cameras
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captured["roomparams"] = roomparams
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captured["joint_names"] = joint_names
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captured["min_match_score"] = min_match_score
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captured["min_group_size"] = min_group_size
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return captured
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monkeypatch.setattr("pose_tracking_exp.rpt_adapter.rpt.make_triangulation_config", fake_make_triangulation_config)
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build_rpt_config(scene, min_match_score=0.5, min_group_size=2)
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camera_payload = cast(list[dict[str, object]], captured["cameras"])[0]
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assert camera_payload["R"] == camera.pose_R.tolist()
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assert camera_payload["T"] == camera.pose_T.reshape(3, 1).tolist()
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