patch

opendbc_repo/opendbc/dbc/SConscript

@@ -10,6 +10,7 @@ source_files = [
   for f in Path("generator").rglob("*")
   if f.is_file() and f.suffix in {".py", ".dbc"}
 ]
+source_files.append(File("hyundai_kia_generic.dbc"))
 
 output_files = [
   f.name.replace(".dbc", "_generated.dbc")

opendbc_repo/opendbc/dbc/generator/hyundai/hyundai_can_refresh.py

@@ -0,0 +1,22 @@
+#!/usr/bin/env python3
+from pathlib import Path
+
+
+GENERIC_LFAHDA = "BO_ 1157 LFAHDA_MFC: 4 XXX"
+REFRESH_LFAHDA = "BO_ 1157 LFAHDA_MFC: 8 XXX"
+
+
+def main() -> None:
+  dbc_dir = Path(__file__).resolve().parents[2]
+  source = dbc_dir / "hyundai_kia_generic.dbc"
+  target = dbc_dir / "hyundai_can_refresh_generated.dbc"
+
+  dbc = source.read_text(encoding="utf-8")
+  if dbc.count(GENERIC_LFAHDA) != 1:
+    raise RuntimeError(f"expected exactly one {GENERIC_LFAHDA!r} definition")
+
+  target.write_text(dbc.replace(GENERIC_LFAHDA, REFRESH_LFAHDA, 1), encoding="utf-8")
+
+
+if __name__ == "__main__":
+  main()

scripts/speed_limit_vision/mine_connect_route_bookmarks.py

@@ -45,6 +45,7 @@ def parse_args() -> argparse.Namespace:
   parser.add_argument("--search-after", type=float, default=1.0, help="Seconds after each bookmark to scan for the most sign-like frame.")
   parser.add_argument("--localize-sample-every", type=float, default=0.25, help="Seconds between frames while searching for the best sign candidate.")
   parser.add_argument("--top-k", type=int, default=1, help="Number of localized candidates to keep per bookmark.")
+  parser.add_argument("--model-only", action="store_true", help="Match the production detector/classifier path without crop OCR.")
   parser.add_argument("--overwrite", action="store_true", help="Overwrite any existing outputs.")
   return parser.parse_args()
 
@@ -227,7 +228,7 @@ def main() -> int:
         args.search_after,
         args.localize_sample_every,
       ):
-        scored = score_frame(daemon, frame_bgr)
+        scored = score_frame(daemon, frame_bgr, use_ocr=not args.model_only)
         if scored is None:
           continue
         ranked.append((scored["score"], relative_time_s, source_video_path, frame_bgr, scored))