mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-07-15 00:11:15 +03:00
52ba582e54
Adds a community-supported hardware detector for the Qualcomm Hexagon NPU on QCS6490 SoCs (e.g. Radxa Dragon Q6A) via QAIRT 2.37.1 / qai_appbuilder. Mirrors the existing community-board pattern (Rockchip / Synaptics): - frigate/detectors/plugins/qnn.py: detector plugin using yolo-generic model type, lazy SDK import, runs pre-compiled QNN context binaries from Qualcomm AI Hub. - docker/qcs6490/: Dockerfile (two-stage; rebuilds qai_appbuilder wheel inside Frigate's image to match libstdc++ ABI), bake target (qcs6490.hcl), make targets (qcs6490.mk), and a host-side user_installation.sh that installs fastrpc, the QCS6490 firmware (cDSP image + skel libs), and configures cdsprpcd. - .github/workflows/ci.yml: qcs6490_build job mirroring synaptics_build. - CODEOWNERS: /docker/qcs6490/ + qnn.py. - docs: new "Qualcomm Hexagon NPU" sections under Community Supported Detectors in object_detectors.md, plus matching entries in installation.md and hardware.md. Performance on Radxa Dragon Q6A (Hexagon v68, ~12 TOPS), YOLOv8n 640x640: ~10ms per inference under light load, ~24ms with 5 RTSP cameras live. Closes #18602.
28 lines
519 B
HCL
28 lines
519 B
HCL
target wheels {
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dockerfile = "docker/main/Dockerfile"
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platforms = ["linux/arm64"]
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target = "wheels"
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}
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target deps {
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dockerfile = "docker/main/Dockerfile"
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platforms = ["linux/arm64"]
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target = "deps"
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}
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target rootfs {
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dockerfile = "docker/main/Dockerfile"
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platforms = ["linux/arm64"]
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target = "rootfs"
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}
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target qcs6490 {
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dockerfile = "docker/qcs6490/Dockerfile"
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contexts = {
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wheels = "target:wheels",
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deps = "target:deps",
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rootfs = "target:rootfs"
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}
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platforms = ["linux/arm64"]
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}
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