Intended as the spiritual sucessor to initialed85/cameranator, built on initialed85/djangolang
- Lots of nice caching etc thanks to initialed85/djangolang
- Frontend is a PWA for a good mobile / offline experience
- Uses YOLO for object detection
- See initialed85/home-ops/tree/master/applications/camry for some inspiration on how to deploy
- [WIP] Rust object detector
- [TODO] Transcoding on the GPU
- [TODO] Work out how to make cache more effective (the regular camera + video claim stuff means very few cache hits); there's a TODO about smarter cache invalidation in Djangolang for this
# shell 1 ./run-env.sh # shell 2 ./build.sh # shell 3 REDIS_URL=redis://localhost:6379 DJANGOLANG_API_ROOT=/api POSTGRES_DB=camry POSTGRES_PASSWORD=NoNVR\!11 go run ./cmd/ serve # shell 4 websocat -B 1048576 ws://localhost:7070/api/__stream | jq # shell 5 devserver --address 0.0.0.0:6060 # shell 6 curl -X POST http://localhost:7070/api/cameras -d '[{"name": "Driveway", "stream_url": "rtsp://192.168.137.31:554/Streaming/Channels/101"}, {"name": "Front door", "stream_url": "rtsp://192.168.137.32:554/Streaming/Channels/101"}, {"name": "Side gate", "stream_url": "rtsp://192.168.137.33:554/Streaming/Channels/101"}]' | jq # shell 7 DJANGOLANG_API_ROOT=/api DESTINATION_PATH=media ENABLE_PASSTHROUGH=1 POSTGRES_DB=camry POSTGRES_PASSWORD=NoNVR\!11 go run ./cmd segment_producer # shell 8 DEBUG=1 API_URL=http://localhost:7070 SOURCE_PATH=media ~/.venv/camry/bin/python3 -m object_detector
docker run --rm -it --gpus=all --ipc=host pytorch/pytorch:2.4.0-cuda11.8-cudnn9-runtime python3 -c 'import torch; print([torch.cuda.is_available(), torch.cuda.device_count(), torch.cuda.current_device()])'