Initial commit: Wood knot detection model and GUI

export_onnx.py

@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Export trained RF-DETR to ONNX for deployment.")
+    parser.add_argument("--weights", type=Path, required=True, help="Path to trained checkpoint")
+    parser.add_argument("--output-onnx", type=Path, default=Path("model.onnx"), help="Output ONNX file")
+    args = parser.parse_args()
+
+    if not args.weights.exists():
+        raise SystemExit(f"Weights not found: {args.weights}")
+
+    from rfdetr import RFDETRBase
+
+    model = RFDETRBase(pretrain_weights=str(args.weights))
+
+    # Export to ONNX. This saves to the current directory by default, but we can specify.
+    # RF-DETR's export() method saves to 'output/model.onnx' I think, but let's check docs.
+    # From earlier fetch: model.export() saves to 'output' dir.
+    # But to make it flexible, perhaps run it and then move.
+
+    model.export()  # This should create output/model.onnx
+
+    # Move to desired location
+    onnx_path = Path("output/model.onnx")
+    if onnx_path.exists():
+        onnx_path.rename(args.output_onnx)
+        print(f"Exported ONNX: {args.output_onnx}")
+    else:
+        raise SystemExit("ONNX export failed - check output/ dir")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())