Document Python API and ship package stubs

bindings/CMakeLists.txt

@@ -17,6 +17,7 @@ find_package(nanobind CONFIG REQUIRED)
 set(RPT_PYTHON_PACKAGE_DIR "${CMAKE_CURRENT_BINARY_DIR}/rpt")
 file(MAKE_DIRECTORY "${RPT_PYTHON_PACKAGE_DIR}")
 configure_file("${PROJECT_SOURCE_DIR}/src/rpt/__init__.py" "${RPT_PYTHON_PACKAGE_DIR}/__init__.py" COPYONLY)
+configure_file("${PROJECT_SOURCE_DIR}/src/rpt/__init__.pyi" "${RPT_PYTHON_PACKAGE_DIR}/__init__.pyi" COPYONLY)
 configure_file("${PROJECT_SOURCE_DIR}/src/rpt/_helpers.py" "${RPT_PYTHON_PACKAGE_DIR}/_helpers.py" COPYONLY)
 configure_file("${PROJECT_SOURCE_DIR}/src/rpt/py.typed" "${RPT_PYTHON_PACKAGE_DIR}/py.typed" COPYONLY)
 
@@ -40,4 +41,5 @@ nanobind_add_stub(rpt_core_stub
 )
 
 install(TARGETS rpt_core_ext LIBRARY DESTINATION rpt)
+install(FILES "${RPT_PYTHON_PACKAGE_DIR}/__init__.pyi" DESTINATION rpt)
 install(FILES "${RPT_PYTHON_PACKAGE_DIR}/_core.pyi" DESTINATION rpt)

src/rpt/__init__.py

@@ -21,30 +21,83 @@ from ._core import (
     PreviousPoseMatch,
     ProposalGroupDebug,
     TriangulationTrace,
-    build_pair_candidates,
+    build_pair_candidates as _build_pair_candidates,
-    filter_pairs_with_previous_poses,
+    filter_pairs_with_previous_poses as _filter_pairs_with_previous_poses,
-    make_camera,
+    make_camera as _make_camera,
-    triangulate_debug,
+    triangulate_debug as _triangulate_debug,
-    triangulate_poses,
+    triangulate_poses as _triangulate_poses,
-    triangulate_with_report,
+    triangulate_with_report as _triangulate_with_report,
 )
 
 if TYPE_CHECKING:
     import numpy as np
     import numpy.typing as npt
 
-    from ._helpers import CameraLike, PoseViewLike
+    from ._helpers import CameraLike, CameraModelLike, Matrix3x3Like, PoseViewLike, VectorLike
+
+    PoseArray2D = npt.NDArray[np.float32]
+    PoseArray3D = npt.NDArray[np.float32]
+    PersonCountArray = npt.NDArray[np.uint32]
+    TrackIdArray = npt.NDArray[np.int64]
+
+
+Camera.__doc__ = """Immutable camera calibration with precomputed projection cache fields."""
+TriangulationConfig.__doc__ = """Stable scene configuration used for triangulation."""
+TriangulationOptions.__doc__ = """Score and grouping thresholds used by triangulation."""
+TriangulationResult.__doc__ = """Tracked triangulation output containing poses and association metadata."""
+AssociationReport.__doc__ = """Track-association summary for a tracked triangulation call."""
+TriangulationTrace.__doc__ = """Full debug trace for triangulation, including pair, grouping, and association stages."""
 
 
 def convert_cameras(cameras: "Sequence[CameraLike]") -> list[Camera]:
+    """Normalize mapping-like camera inputs into immutable bound `Camera` instances."""
+
     from ._helpers import convert_cameras as _convert_cameras
 
     return _convert_cameras(cameras)
 
 
+def make_camera(
+    name: str,
+    K: "Matrix3x3Like",
+    DC: "VectorLike",
+    R: "Matrix3x3Like",
+    T: "Sequence[Sequence[float]]",
+    width: int,
+    height: int,
+    model: "CameraModel | CameraModelLike",
+) -> Camera:
+    """Create an immutable camera and precompute its cached projection fields."""
+
+    from ._helpers import _coerce_camera_model, _coerce_distortion
+
+    camera_model = _coerce_camera_model(model)
+    return _make_camera(
+        name,
+        K,
+        _coerce_distortion(DC, camera_model),
+        R,
+        T,
+        width,
+        height,
+        camera_model,
+    )
+
+
+def build_pair_candidates(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+) -> list[PairCandidate]:
+    """Enumerate all cross-view person pairs implied by the padded 2D pose batch."""
+
+    return _build_pair_candidates(poses_2d, person_counts)
+
+
 def pack_poses_2d(
     views: "Sequence[PoseViewLike]", *, joint_count: int | None = None
 ) -> "tuple[npt.NDArray[np.float32], npt.NDArray[np.uint32]]":
+    """Pack ragged per-view pose detections into the padded tensor expected by the core API."""
+
     from ._helpers import pack_poses_2d as _pack_poses_2d
 
     return _pack_poses_2d(views, joint_count=joint_count)
@@ -58,6 +111,8 @@ def make_triangulation_config(
     min_match_score: float = 0.95,
     min_group_size: int = 1,
 ) -> TriangulationConfig:
+    """Build a triangulation config from cameras, room parameters, and joint names."""
+
     from ._helpers import make_triangulation_config as _make_triangulation_config
 
     return _make_triangulation_config(
@@ -69,6 +124,77 @@ def make_triangulation_config(
     )
 
 
+def filter_pairs_with_previous_poses(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+    previous_poses_3d: "PoseArray3D",
+    previous_track_ids: "TrackIdArray",
+) -> PreviousPoseFilterDebug:
+    """Filter raw cross-view pairs against caller-owned previous 3D tracks."""
+
+    return _filter_pairs_with_previous_poses(
+        poses_2d,
+        person_counts,
+        config,
+        previous_poses_3d,
+        previous_track_ids,
+    )
+
+
+def triangulate_debug(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+    previous_poses_3d: "PoseArray3D | None" = None,
+    previous_track_ids: "TrackIdArray | None" = None,
+) -> TriangulationTrace:
+    """Run triangulation and return the full debug trace.
+
+    If previous-frame 3D tracks are supplied, `previous_track_ids` must be supplied as an
+    aligned `int64` array of the same length.
+    """
+
+    if previous_poses_3d is None:
+        return _triangulate_debug(poses_2d, person_counts, config)
+    if previous_track_ids is None:
+        raise ValueError("previous_track_ids is required when previous_poses_3d is provided.")
+    return _triangulate_debug(poses_2d, person_counts, config, previous_poses_3d, previous_track_ids)
+
+
+def triangulate_poses(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+) -> "PoseArray3D":
+    """Triangulate a frame into anonymous 3D poses without track association."""
+
+    return _triangulate_poses(poses_2d, person_counts, config)
+
+
+def triangulate_with_report(
+    poses_2d: "PoseArray2D",
+    person_counts: "PersonCountArray",
+    config: TriangulationConfig,
+    previous_poses_3d: "PoseArray3D",
+    previous_track_ids: "TrackIdArray",
+) -> TriangulationResult:
+    """Triangulate a frame and return caller-owned track association results.
+
+    The previous-frame poses and IDs are treated as immutable caller state. The returned
+    association report classifies each final pose as matched, new, or ambiguous, and lists
+    unmatched previous tracks as deletion candidates.
+    """
+
+    return _triangulate_with_report(
+        poses_2d,
+        person_counts,
+        config,
+        previous_poses_3d,
+        previous_track_ids,
+    )
+
+
 __all__ = [
     "Camera",
     "CameraModel",

src/rpt/__init__.pyi

@@ -0,0 +1,115 @@
+from collections.abc import Sequence
+from typing import TypeAlias, overload
+
+import numpy as np
+import numpy.typing as npt
+
+from ._core import (
+    AssociationReport,
+    AssociationStatus,
+    Camera,
+    CameraModel,
+    CoreProposalDebug,
+    FinalPoseAssociationDebug,
+    FullProposalDebug,
+    GroupingDebug,
+    MergeDebug,
+    PairCandidate,
+    PreviousPoseFilterDebug,
+    PreviousPoseMatch,
+    ProposalGroupDebug,
+    TriangulationConfig,
+    TriangulationOptions,
+    TriangulationResult,
+    TriangulationTrace,
+)
+from ._helpers import CameraLike, CameraModelLike, Matrix3x3Like, PoseViewLike, RoomParamsLike, VectorLike
+
+PoseArray2D: TypeAlias = npt.NDArray[np.float32]
+PoseArray3D: TypeAlias = npt.NDArray[np.float32]
+PersonCountArray: TypeAlias = npt.NDArray[np.uint32]
+TrackIdArray: TypeAlias = npt.NDArray[np.int64]
+
+
+def convert_cameras(cameras: Sequence[CameraLike]) -> list[Camera]: ...
+
+
+def make_camera(
+    name: str,
+    K: Matrix3x3Like,
+    DC: VectorLike,
+    R: Matrix3x3Like,
+    T: Sequence[Sequence[float]],
+    width: int,
+    height: int,
+    model: CameraModel | CameraModelLike,
+) -> Camera: ...
+
+
+def build_pair_candidates(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+) -> list[PairCandidate]: ...
+
+
+def pack_poses_2d(
+    views: Sequence[PoseViewLike],
+    *,
+    joint_count: int | None = None,
+) -> tuple[npt.NDArray[np.float32], npt.NDArray[np.uint32]]: ...
+
+
+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: ...
+
+
+def filter_pairs_with_previous_poses(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+    previous_poses_3d: PoseArray3D,
+    previous_track_ids: TrackIdArray,
+) -> PreviousPoseFilterDebug: ...
+
+
+@overload
+def triangulate_debug(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+) -> TriangulationTrace: ...
+
+
+@overload
+def triangulate_debug(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+    previous_poses_3d: PoseArray3D,
+    previous_track_ids: TrackIdArray,
+) -> TriangulationTrace: ...
+
+
+def triangulate_poses(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+) -> PoseArray3D: ...
+
+
+def triangulate_with_report(
+    poses_2d: PoseArray2D,
+    person_counts: PersonCountArray,
+    config: TriangulationConfig,
+    previous_poses_3d: PoseArray3D,
+    previous_track_ids: TrackIdArray,
+) -> TriangulationResult: ...
+
+
+__all__: list[str]