Simplify triangulation API with config struct

src/rpt/__init__.py

@@ -6,6 +6,7 @@ from typing import TYPE_CHECKING
 from ._core import (
     Camera,
     CameraModel,
+    TriangulationConfig,
     TriangulationOptions,
     CoreProposalDebug,
     FullProposalDebug,
@@ -43,9 +44,29 @@ def pack_poses_2d(
     return _pack_poses_2d(views, joint_count=joint_count)
 
 
+def make_triangulation_config(
+    cameras: "Sequence[CameraLike]",
+    roomparams: "npt.NDArray[np.generic] | Sequence[Sequence[float]]",
+    joint_names: "Sequence[str]",
+    *,
+    min_match_score: float = 0.95,
+    min_group_size: int = 1,
+) -> TriangulationConfig:
+    from ._helpers import make_triangulation_config as _make_triangulation_config
+
+    return _make_triangulation_config(
+        cameras,
+        roomparams,
+        joint_names,
+        min_match_score=min_match_score,
+        min_group_size=min_group_size,
+    )
+
+
 __all__ = [
     "Camera",
     "CameraModel",
+    "TriangulationConfig",
     "TriangulationOptions",
     "CoreProposalDebug",
     "FullProposalDebug",
@@ -59,6 +80,7 @@ __all__ = [
     "build_pair_candidates",
     "convert_cameras",
     "filter_pairs_with_previous_poses",
+    "make_triangulation_config",
     "pack_poses_2d",
     "triangulate_debug",
     "triangulate_poses",

src/rpt/_helpers.py

@@ -6,10 +6,11 @@ from typing import Literal, TypeAlias, TypedDict
 import numpy as np
 import numpy.typing as npt
 
-from ._core import Camera, CameraModel
+from ._core import Camera, CameraModel, TriangulationConfig, TriangulationOptions
 
 Matrix3x3Like: TypeAlias = Sequence[Sequence[float]]
 VectorLike: TypeAlias = Sequence[float]
+RoomParamsLike: TypeAlias = npt.NDArray[np.generic] | Sequence[Sequence[float]]
 PoseViewLike: TypeAlias = npt.NDArray[np.generic] | Sequence[Sequence[Sequence[float]]] | Sequence[Sequence[float]]
 
 
@@ -129,3 +130,28 @@ def pack_poses_2d(
             packed[view_idx, :person_count, :, :] = array
 
     return packed, counts
+
+
+def make_triangulation_config(
+    cameras: Sequence[CameraLike],
+    roomparams: RoomParamsLike,
+    joint_names: Sequence[str],
+    *,
+    min_match_score: float = 0.95,
+    min_group_size: int = 1,
+) -> TriangulationConfig:
+    config = TriangulationConfig()
+    config.cameras = convert_cameras(cameras)
+
+    roomparams_array = np.asarray(roomparams, dtype=np.float32)
+    if roomparams_array.shape != (2, 3):
+        raise ValueError("roomparams must have shape [2, 3].")
+    config.roomparams = roomparams_array.tolist()
+
+    config.joint_names = [str(joint_name) for joint_name in joint_names]
+
+    options = TriangulationOptions()
+    options.min_match_score = float(min_match_score)
+    options.min_group_size = int(min_group_size)
+    config.options = options
+    return config