Refactor triangulation stages and camera model

src/rpt/__init__.py

@@ -3,7 +3,23 @@ from __future__ import annotations
 from collections.abc import Sequence
 from typing import TYPE_CHECKING
 
-from ._core import Camera, triangulate_poses
+from ._core import (
+    Camera,
+    CameraModel,
+    CoreProposalDebug,
+    FullProposalDebug,
+    GroupingDebug,
+    MergeDebug,
+    PairCandidate,
+    PreviousPoseFilterDebug,
+    PreviousPoseMatch,
+    ProposalGroupDebug,
+    TriangulationTrace,
+    build_pair_candidates,
+    filter_pairs_with_previous_poses,
+    triangulate_debug,
+    triangulate_poses,
+)
 
 if TYPE_CHECKING:
     import numpy as np
@@ -28,7 +44,20 @@ def pack_poses_2d(
 
 __all__ = [
     "Camera",
+    "CameraModel",
+    "CoreProposalDebug",
+    "FullProposalDebug",
+    "GroupingDebug",
+    "MergeDebug",
+    "PairCandidate",
+    "PreviousPoseFilterDebug",
+    "PreviousPoseMatch",
+    "ProposalGroupDebug",
+    "TriangulationTrace",
+    "build_pair_candidates",
     "convert_cameras",
+    "filter_pairs_with_previous_poses",
     "pack_poses_2d",
+    "triangulate_debug",
     "triangulate_poses",
 ]

src/rpt/_helpers.py

@@ -1,12 +1,12 @@
 from __future__ import annotations
 
 from collections.abc import Sequence
-from typing import TypeAlias, TypedDict
+from typing import Literal, TypeAlias, TypedDict
 
 import numpy as np
 import numpy.typing as npt
 
-from ._core import Camera
+from ._core import Camera, CameraModel
 
 Matrix3x3Like: TypeAlias = Sequence[Sequence[float]]
 VectorLike: TypeAlias = Sequence[float]
@@ -21,12 +21,39 @@ class CameraDict(TypedDict, total=False):
     T: Sequence[Sequence[float]]
     width: int
     height: int
-    type: str
+    type: Literal["pinhole", "fisheye"]
+    model: Literal["pinhole", "fisheye"] | CameraModel
 
 
+CameraModelLike: TypeAlias = CameraModel | Literal["pinhole", "fisheye"]
 CameraLike = Camera | CameraDict
 
 
+def _coerce_camera_model(model: CameraModelLike) -> CameraModel:
+    if isinstance(model, CameraModel):
+        return model
+    if model == "pinhole":
+        return CameraModel.PINHOLE
+    if model == "fisheye":
+        return CameraModel.FISHEYE
+    raise ValueError(f"Unsupported camera model: {model}")
+
+
+def _coerce_distortion(distortion: VectorLike, camera_model: CameraModel) -> tuple[float, float, float, float, float]:
+    values = tuple(float(value) for value in distortion)
+    expected = 4 if camera_model is CameraModel.FISHEYE else 5
+    if len(values) not in {expected, 5}:
+        raise ValueError(
+            f"{camera_model.name.lower()} cameras require {expected} distortion coefficients"
+            + (" (or 5 with a trailing zero)." if camera_model is CameraModel.FISHEYE else ".")
+        )
+    if camera_model is CameraModel.FISHEYE and len(values) == 4:
+        values = values + (0.0,)
+    if len(values) != 5:
+        raise ValueError("Distortion coefficients must normalize to exactly 5 values.")
+    return values
+
+
 def convert_cameras(cameras: Sequence[CameraLike]) -> list[Camera]:
     """Normalize mappings or existing Camera objects into bound Camera instances."""
 
@@ -39,12 +66,13 @@ def convert_cameras(cameras: Sequence[CameraLike]) -> list[Camera]:
         camera = Camera()
         camera.name = str(cam["name"])
         camera.K = cam["K"]
-        camera.DC = cam["DC"]
+        camera_model = _coerce_camera_model(cam.get("model", cam.get("type", "pinhole")))
+        camera.DC = _coerce_distortion(cam["DC"], camera_model)
         camera.R = cam["R"]
         camera.T = cam["T"]
         camera.width = int(cam["width"])
         camera.height = int(cam["height"])
-        camera.type = str(cam.get("type", "pinhole"))
+        camera.model = camera_model
         converted.append(camera)
     return converted