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Reproduce the results

Install the AI block

git clone https://github.com/SCAI-Engineering/dronevision-ai.git
cd dronevision-ai
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[pi,dev]"
pytest

The core package requires Python 3.10 or later. The pi extra installs ZeroMQ and ONNX Runtime; the 191-test suite requires no simulator or hardware.

Run the shipped corpus

python -m bench.accuracy

python -m bench.accuracy \
  --detector yolo --runtime onnx --threads 4

python -m bench.accuracy \
  --occlude cam_ne,cam_sw

python -m bench.accuracy --mode encoded

The corpus contains 337 synchronized four-camera frame sets, 30 seconds of JPEG quality-90 imagery and time-aligned ground truth. It is approximately 13 MB and remains inspectable as meta.json, manifest.jsonl and frames.zip.

Benchmark a Raspberry Pi

The board does not need GitHub credentials. From the development machine:

./bench/pi.sh info
./bench/pi.sh setup
./bench/pi.sh bench
./bench/pi.sh run -m bench.accuracy \
  --detector yolo --runtime onnx --threads 4

The helper transfers the committed tree, prepares the environment, runs the sweep and pulls machine-readable JSON back into bench/out/.

Connect to the live simulator

# In the companion simulator repository
./services.sh up
./drone.sh takeoff

# In dronevision-ai, on a laptop or Arm board
python -m dronevision.service \
  --frames tcp://<simulator-host>:5555 \
  --state <simulator-host>:5601

To record another deterministic corpus:

python -m bench.record --seconds 30

Preview this documentation

python3 -m venv .venv-docs
source .venv-docs/bin/activate
pip install -r requirements-docs.txt
mkdocs serve

Open http://127.0.0.1:8000. The server rebuilds when Markdown, CSS or configuration files change. Nothing is published by this command.