* Revise GPU and AI accelerator recommendations
Updated hardware recommendations for AI acceleration.
* Revise PCIe Coral driver installation instructions
Updated instructions for PCIe Coral driver installation.
* Revise Coral driver installation instructions
Updated driver installation instructions for PCIe and M.2 versions of Google Coral.
* Change PCIe Coral driver link in getting_started.md
Updated the link for PCIe Coral driver instructions.
* Change PCIe Coral driver link in installation guide
Updated the link for PCIe Coral driver instructions.
* Update Coral TPU recommendation in hardware documentation
Added a warning about the Coral TPU's recommendation status for new Frigate installations and suggested alternatives.
* Implement extraction of images for classification state models
* Add object classification dataset preparation
* Add first step wizard
* Update i18n
* Add state classification image selection step
* Improve box handling
* Add object selector
* Improve object cropping implementation
* Fix state classification selection
* Finalize training and image selection step
* Cleanup
* Design optimizations
* Cleanup mobile styling
* Update no models screen
* Cleanups and fixes
* Fix bugs
* Improve model training and creation process
* Cleanup
* Dynamically add metrics for new model
* Add loading when hitting continue
* Improve image selection mechanism
* Remove unused translation keys
* Adjust wording
* Add retry button for image generation
* Make no models view more specific
* Adjust plus icon
* Adjust form label
* Start with correct type selected
* Cleanup sizing and more font colors
* Small tweaks
* Add tips and more info
* Cleanup dialog sizing
* Add cursor rule for frontend
* Cleanup
* remove underline
* Lazy loading
* Add cutoff for object classification
* Add selector for classifiction model type
* Improve model selection view
* Clean up design of classification card
* Tweaks
* Adjust button colors
* Improvements to gradients and making face library consistent
* Add basic classification model wizard
* Use relative coordinates
* Properly get resolution
* Clean up exports
* Cleanup
* Cleanup
* Update to use pre-defined component for image shadow
* Refactor image grouping
* Clean up mobile
* Clean up decision logic
* Remove max check on classification objects
* Increase default number of faces shown
* Cleanup
* Improve mobile layout
* Clenaup
* Update vocabulary
* Fix layout
* Fix page
* Cleanup
* Choose last item for unknown objects
* Move explore button
* Cleanup grid
* Cleanup classification
* Cleanup grid
* Cleanup
* Set transparency
* Set unknown
* Don't filter all configs
* Check length
* Add optional idle heartbeat for Birdseye (periodic frame emission when idle)
birdseye: add optional idle heartbeat and FFmpeg tuning envs (default off)
This adds an optional configuration field `birdseye.idle_heartbeat_fps` to
enable a lightweight idle heartbeat mechanism in Birdseye. When set to a value
greater than 0, Birdseye periodically re-sends the last composed frame during
idle periods (no motion or active updates).
This helps downstream consumers such as go2rtc, Alexa, or Scrypted to attach
faster and maintain a low-latency RTSP stream when the system is idle.
Key details:
- Config-based (`birdseye.idle_heartbeat_fps`), default `0` (disabled).
- Uses existing Birdseye rendering pipeline; minimal performance impact.
- Does not alter behavior when unset.
Documentation: added tip section in docs/configuration/restream.md.
* Update docs/docs/configuration/restream.md
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Update docs/docs/configuration/reference.md
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Refactors Birdseye idle frame broadcasting
Simplifies the idle frame broadcasting logic by removing the dedicated thread.
The idle frame is now resent directly within the main loop,
improving efficiency and reducing complexity. Also, limits the idle
heartbeat FPS to a maximum of 10 since the framebuffer is limited to 10 anyway
* ruff fix
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
Co-authored-by: Francesco Durighetto <francesco.durighetto@subbyx.com>
Co-authored-by: duri <duri@homelabubuntu.durihome.unifi>
* save clean webp instead of png
* send clean webp to plus with fallback for old events
* manual event webp
* event cleanup
* api def
* convert png to webp if exists
* update reference config
* change quality
* camera level config
* set up model runner on thread start to avoid unpickling error
* ensure feature is enabled globally
* suppress info logs from faster_whisper
* fix incorrect event_type for api and audio timeline entries
* docs
* fix
* clean up
* Update classification API docs
* Add information to events api
* Fix tag
* Add exports
* Add generic response to model for classification apis
* Add preview API information
* Cleanup
* Cleanup
* Update ROCm to 7.0.1
* Update ONNXRuntime
* Add back in
* Get basic detection working
* Use env vars
* Handle complex migraphx models
* Enable model caching
* Remove unused
* Add tip to docs
* [Init] Initial commit for Synaptics SL1680 NPU
* add a rough detector which is testing with yolov8 tflite model.
* [Feat] Add dependencies installation in docker build
- Add runtime library and wheels installation in main/Dockerfile
- Add model.synap(default model, transfer from mobilenet_224full80) in docker/synap1680
* [Update] Remove dependencies installation from main Dockerfile
- remove deps installation from Dockerfile
- add dependencies installation and split wheels, deps stage in synap1680 Dockerfile
* Refactor synap detector to more closely match other implementations
* [Update] Add model path configuration check
* [Update] update ModelType to ssd
* [Update] Remove unuse script
- install_deps.sh has already been executing in deps download stage
- Dockerfile.toolchain is for testing to extract runtime libraries from Synaptics toolchain
* [Update] update Synaptics SL1680 setup description
* [Update] remove install_synap1680
- The deps download and installation is existed in synap1680
* [Fix] update document content
* [Update] Update detector from synap1680 to synaptics
This update is in order to make the synaptics SL-series NPU detector more general.
- Fix detector `os` module not import bug
- Update detector type `synap1680` to `synaptics`
- Update document description `SL1680` to `Synaptics` only
- Update docker build content `synap1680` to `synaptics`
* [Fix] Update configuration document
* Update docs/docs/configuration/object_detectors.md
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* [Update] Update document content and detector default layout
- Update object_detectors document
- Update detector's default layout
- Update default model name
* [Update] Update object detector document content
* [Fix] Fix InputTensorEnum not defined error
- import InputTensorEnum from detector_config
* [Update] Update detector script coding format
* [Update] Update synaptics detector coding format
* [Update] Add synaptics ci workflow
* [Update] update synaptics runtime libs download path
- Fork Synaptics astra sdk repo and put the runtime lib package on it
- Frigate team can update this download path later
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Implement stationary car classifier to base stationary state on visual changes and not just bounding box stability
* Cleanup
* Fix mypy
* Move to new file and add config to disable if needed
* Cleanup
* Undo
This supports systemd credentials, see https://systemd.io/CREDENTIALS/.
Default to `/run/secrets` (the Docker Secrets dir) for backwards
compatibility.
