Move the panorama keypoint detector onto the engine, and probe with a detector

XFeat's two exports are a Keypoints role now; the crate no longer names
tract, and the app compiles TensorRT engines for both ahead of the
first merge. The probe picks the smallest *detector* rather than the
smallest file: the tablet's first run chose the 112 KB eye classifier,
which has no int8 form, and reported the Hexagon as failed for want of
one.

core/dr-pano/Cargo.toml

@@ -12,11 +12,12 @@ thiserror.workspace = true
 log.workspace = true
 
 # Inference for the learned keypoint detector, on the same footing as
-# `dr-segment`: `ort` is the API, tract is the engine, and both are optional
-# so that the geometry — matching, the rotation solve, the projections — is a
-# dependency-free crate that tests without a model.
+# `dr-segment`: `ort` is the API, `dr-inference-engine` decides what runs
+# it (docs/inference.md), and both are optional so that the geometry —
+# matching, the rotation solve, the projections — is a dependency-free crate
+# that tests without a model.
 ort = { workspace = true, optional = true }
-ort-tract = { workspace = true, optional = true }
+dr-inference-engine = { workspace = true, optional = true }
 ndarray = { workspace = true, optional = true }
 
 [dev-dependencies]
@@ -30,7 +31,7 @@ default = ["xfeat", "embedded-model"]
 
 # The XFeat detector (FR-MRG-8). Off, the crate has no model and no runtime,
 # and `Detector` has no implementation — a build that only wants the geometry.
-xfeat = ["dep:ort", "dep:ort-tract", "dep:ndarray"]
+xfeat = ["dep:ort", "dep:dr-inference-engine", "dep:ndarray"]
 
 # Compile the weights into the binary, for the same reason `dr-segment` does:
 # Android hands the app no path to read a model from (ARCH §6.9).