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name: cpp-onnx-port description: "Operate the optional 3DDFA C++ OpenCV DNN port, checkpoint-to-ONNX export, weight placement, build/run expectations, and C++ troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

cpp-onnx-port

Use this sub-skill when the task is about the optional 3DDFA C++ demo, OpenCV DNN execution, MobileNet checkpoint export to ONNX, C++ weight placement, CMake/OpenCV build behavior, or C++ output failures.

Do not use this sub-skill as the primary guide for Python inference, rendering internals, training, or benchmark evaluation. Route those tasks to sibling skills when available:

• Python image/video inference and checkpoint choice: ../python-inference/SKILL.md
• Geometry, vertices, OBJ/PLY, PNCC, depth, and rendering internals: ../geometry-rendering/SKILL.md
• Training recipes, losses, checkpoints, and benchmarks: ../training-evaluation/SKILL.md

Operating Map

1. For C++ build and demo behavior, read references/cpp-build-and-runtime.md.
2. For checkpoint export, read references/onnx-export.md and use scripts/export_mobilenet_to_onnx.py instead of relying on the original conversion helper.
3. For missing faces, missing weights, OpenCV DNN, ONNX, CMake, or output-image failures, read references/troubleshooting.md.

Key Constraints

• The C++ port is optional and unoptimized; it relies on OpenCV DNN and was documented for OpenCV 4.2.0 or newer.
• The C++ demo uses CPU OpenCV DNN backends for both the YOLO face detector and MobileNet landmark predictor.
• The source distribution includes C++ matrix/config text files, but the MobileNet ONNX file and YOLO binary weights are external artifacts that must be supplied by the operator.
• The C++ demo is hard-coded around a single sample image and writes a landmark-overlay image under its result directory unless the C++ source is adapted.
• Keep num_classes=62 for C++ landmark compatibility: 12 pose/projection values, 40 shape coefficients, and 10 expression coefficients.

Safe Actions

• Build only after confirming OpenCV development headers/libraries are available and the required external model files are present.
• Export ONNX from a known 3DDFA MobileNet checkpoint with the bundled helper and inspect remapped checkpoint keys before trusting the file.
• Treat CMake, OpenCV, detector, predictor, and output-path problems as separate failure classes; do not debug them as one generic C++ failure.