dr-pano: a second XFeat shape for portrait frames, and a matcher that takes seconds

Twelve real frames from the fixture set now align in 4.5 s — 4.4 s of
matching, 118 ms of bundle adjustment — where the first run took 51 s and
left the first two frames out.

The matcher computes each pair's similarity matrix once, across the
cores, with a dot product written to vectorise; both nearest-neighbour
directions read it. The frames that failed were portrait: fitted into the
landscape input they used 512 of 1024 px, and their thin overlap did not
survive at half resolution. The same weights are now exported at 768×1024
as well and the detector picks the shape by aspect. The example aligns
from embedded previews and draws the set on a cylinder; on the fixture the
sweep is 152° at a fitted 47.9 mm against the EXIF's 50, RMS 1.5 px, and
the overlaps show no ghosting.

tools/export-xfeat.sh

@@ -6,8 +6,12 @@
 # someone once produced and nobody can regenerate. Run it when bumping the
 # model or changing its input size.
 #
-#   ./tools/export-xfeat.sh              # 768×1024 -> models/keypoints/xfeat-1024.onnx
-#   ./tools/export-xfeat.sh 576 768      # another fixed size
+#   ./tools/export-xfeat.sh    # both shapes -> models/keypoints/xfeat-{1024,768}.onnx
+#
+# Two files from one set of weights: 1024 wide × 768 tall for landscape
+# frames and 768 × 1024 for portrait, chosen by the frame's aspect at run
+# time. One landscape file would fit a portrait frame at half the width and
+# waste half the detector on padding, which is what the 6D fixture did.
 #
 # Requires `uv`. Everything else is fetched into a throwaway venv, including
 # a CPU-only torch — the export needs no GPU and the CUDA wheels are 2 GB.
@@ -37,23 +41,25 @@ set -euo pipefail
 HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
 REPO="$(cd "${HERE}/.." && pwd)"
 
-H="${1:-768}"
-W="${2:-1024}"
 OUT="${REPO}/models/keypoints"
-NAME="xfeat-${W}"
 
 # Not `mktemp -d` under /tmp: a tmpfs, and torch is a gigabyte.
 WORK="$(mktemp -d -p "${TMPDIR:-/var/tmp}")"
 trap 'rm -rf "${WORK}"' EXIT
 
-echo "==> exporting XFeat at ${H}×${W} in ${WORK}"
+echo "==> exporting XFeat in ${WORK}"
 cd "${WORK}"
 git clone -q --depth 1 https://github.com/verlab/accelerated_features.git xfeat
 uv venv -q --python 3.12 venv
 VIRTUAL_ENV="${WORK}/venv" uv pip install -q --index-url https://download.pytorch.org/whl/cpu torch
 VIRTUAL_ENV="${WORK}/venv" uv pip install -q onnx onnxslim
 
-VIRTUAL_ENV="${WORK}/venv" "${WORK}/venv/bin/python" - "${WORK}/xfeat" "${WORK}/${NAME}.onnx" "${H}" "${W}" <<'PY'
+mkdir -p "${OUT}"
+for shape in "768 1024" "1024 768"; do
+    set -- $shape
+    H="$1"; W="$2"
+    NAME="xfeat-${W}"
+    VIRTUAL_ENV="${WORK}/venv" "${WORK}/venv/bin/python" - "${WORK}/xfeat" "${WORK}/${NAME}.onnx" "${H}" "${W}" <<'PY'
 import sys, torch, onnx, onnxslim
 sys.path.insert(0, sys.argv[1])
 from modules.model import XFeatModel
@@ -72,7 +78,6 @@ print("ops:", sorted({n.op_type for n in m.graph.node}))
 for o in m.graph.output:
     print("out", o.name, [d.dim_value for d in o.type.tensor_type.shape.dim])
 PY
-
-mkdir -p "${OUT}"
-cp "${WORK}/${NAME}.onnx" "${OUT}/${NAME}.onnx"
-echo "==> ${OUT}/${NAME}.onnx"
+    cp "${WORK}/${NAME}.onnx" "${OUT}/${NAME}.onnx"
+    echo "==> ${OUT}/${NAME}.onnx"
+done