* Run ONNX models on a Mac's Neural Engine through lighter's plugin provider
lighter's lighter.sh/ane device places an ONNX Runtime plugin execution
provider in the container. When it is present, the ONNX session setup
registers it once and opens sessions on its Neural Engine device, the same
place CUDA, ROCm and OpenVINO are chosen, so the onnx detector (and any
model that is not pinned to the CPU) runs there with no configuration. The
hardware probe reports it as an onnx unit.
* docs: hardware decode on an Apple Silicon Mac under lighter
A community section on the video decoding page: lighter's lighter.sh/video
device, hwaccel_args -c:v h264_v4l2m2m, and why the Raspberry Pi presets
decode a single-stream camera in software. The detector docs link to it.
* docs: set the lighter decoder per camera when codecs are mixed
* docs: the ONNX detector on a Mac's Neural Engine under lighter
* Format the Neural Engine provider setup
* Fall back to the default providers when the Neural Engine cannot load a model
* Apple Silicon ffmpeg presets for lighter's media engine, recommended when it is present
* add sub stream recording with adaptive quality playback
Optionally record a second, lower bitrate stream alongside the main
recording stream via a `record_sub` input role and `record.sub` config block, with its own retention windows.
Recordings rows now carry the stream type plus the media details needed to serve both streams from one manifest: video codec, audio presence, audio codec and rate, and a record-time keyframe index.
Playback resolves coverage across both streams and merges them into a single VOD sequence, falling back to a discontinuity manifest with per-clip init segments when the media signatures differ. The player exposes a quality selector, and an auto governor picks the stream from stall time, bandwidth, codec support, and the save-data hint.
* fix tests and i18n
* sort preview cameras in history by ui order
* sort cameras by UI order in various components for consistent display
* add no recordings faq
* fix link
* recording cache faq
* add link
* improve anchor naming in object detector docs
rather than #configuration-1, #configuration-2, etc
* use yaml instead of json for object detector docs
* fix anchor
* fix stale active object indicators on the live dashboard
The camera_activity/<camera> snapshot cache is only written when a client sends onConnect, and object "end" events only update the local state of mounted useCameraActivity hooks, never the cache. As a result, a hook that seeded from a stale cache or missed an "end" event while disconnected showed objects that had already left, with no path to correct itself short of a full page reload.
This change will re-request the snapshot on hook mount (collapsed to one onConnect per task across camera cards), and always re-notify camera_activity topics so hooks reconcile against their own local state instead of relying on snapshot-vs-snapshot comparison, and clear the payload dedup cache on reconnect and resync so byte-identical snapshots still apply.
* docs tweaks
* fix mqtt log message
* use consistent values for lpr debug frame filenames
with millisecond resolution
* apply object events through a functional updater to prevent lost updates
The events effect derived a new objects list from the value captured at render time and wrote the whole list back. When events arrived close
together, a run derived from a stale list erased a concurrent run's removal; the resurrected object then had no remaining "end" event to clear it, and the add branch could mint a duplicate entry that no splice could ever remove, leaving the live dashboard showing active objects the backend had already cleared, until a page reload.
The fix is to apply each event inside setObjects so it operates on the true current list exactly once. Unchanged results return the same reference so React bails out of re-rendering, and the label rewrite is hoisted so added objects get the sub_label/verified label directly instead of relying on the effect re-running against its own state update.
* add generation script
a script to read yaml code blocks from docs markdown files and generate corresponding "Frigate UI" tab instructions based on the json schema, i18n, section configs (hidden fields), and nav mappings
* first pass
* components
* add to gitignore
* second pass
* fix broken anchors
* fixes
* clean up tabs
* version bump
* tweaks
* remove role mapping config from ui
* Non-Jetson changes
Required for later commits:
- Allow base image to be overridden (and don't assume its WORKDIR)
- Ensure python3.9
- Map hwaccel decode presets as strings instead of lists
Not required:
- Fix existing documentation
- Simplify hwaccel scale logic
* Prepare for multi-arch tensorrt build
* Add tensorrt images for Jetson boards
* Add Jetson ffmpeg hwaccel
* Update docs
* Add CODEOWNERS
* CI
* Change default model from yolov7-tiny-416 to yolov7-320
In my experience the tiny models perform markedly worse without being
much faster
* fixup! Update docs
* Add tables for ffmpeg presets and how to use them
* Make it clear that ffmepg processes may not show when nvidia-smi is run inside the container
* Add specific example of mixed input arg presets
* Update docs/docs/configuration/ffmpeg_presets.md
Co-authored-by: Nate Meyer <Nate.Devel@gmail.com>
* typos
Co-authored-by: Nate Meyer <Nate.Devel@gmail.com>
Co-authored-by: Blake Blackshear <blakeb@blakeshome.com>
