This change migrates the frigate container build to use Debian trixie as the base image.
This permits us to use newer upstream packages (and, for example, stop needing to use a custom ffmpeg
build). The main hitch was the need for Python 3.9 for Pycoral from the Google apt repository, for
Coral Edge TPU support. Fortunately, the open source community has stepped up, and there are now
TFLite and Pycoral wheels available for Python 3.10-3.12 as well.
* Initial re-implementation of semantic search
* put docker-compose back and make reindex match docs
* remove debug code and fix import
* fix docs
* manually build pysqlite3 as binaries are only available for x86-64
* update comment in build_pysqlite3.sh
* only embed objects
* better error handling when genai fails
* ask ollama to pull requested model at startup
* update ollama docs
* address some PR review comments
* fix lint
* use IPC to write description, update docs for reindex
* remove gemini-pro-vision from docs as it will be unavailable soon
* fix OpenAI doc available models
* fix api error in gemini and metadata for embeddings
* reload the window on 401
* backend apis for auth
* add login page
* re-enable web linter
* fix login page routing
* bypass csrf for internal auth endpoint
* disable healthcheck in devcontainer target
* include login page in vite build
* redirect to login page on 401
* implement config for users and settings
* implement JWT actual secret
* add brute force protection on login
* add support for redirecting from auth failures on api calls
* return location for redirect
* default cookie name should pass regex test
* set hash iterations to current OWASP recommendation
* move users to database instead of config
* config option to reset admin password on startup
* user management UI
* check for deleted user on refresh
* validate username and fixes
* remove password constraint
* cleanup
* fix user check on refresh
* web fixes
* implement auth via new external port
* use x-forwarded-for to rate limit login attempts by ip
* implement logout and profile
* fixes
* lint fixes
* add support for user passthru from upstream proxies
* add support for specifying a logout url
* add documentation
* Update docs/docs/configuration/authentication.md
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Update docs/docs/configuration/authentication.md
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* ROCm AMD/GPU based build and detector, WIP
* detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters
* AMD/ROCm: add couple of more ultralytics models; comments
* docker/rocm: make imported model files readable by all
* docker/rocm: readme about running on AMD GPUs
* docker/rocm: updated README
* docker/rocm: updated README
* docker/rocm: updated README
* detectors/rocm: separated preprocessing functions into yolo_utils.py
* detector/plugins: added onnx cpu plugin
* docker/rocm: updated container with limite label sets
* example detectors view
* docker/rocm: updated README.md
* docker/rocm: update README.md
* docker/rocm: do not set HSA_OVERRIDE_GFX_VERSION at all for the general version as the empty value broke rocm
* detectors: simplified/optimized yolov8_postprocess
* detector/yolo_utils: indentation, remove unused variable
* detectors/rocm: default option to conserve cpu usage at the expense of latency
* detectors/yolo_utils: use nms to prefilter overlapping boxes if too many detected
* detectors/edgetpu_tfl: add support for yolov8
* util/download_models: script to download yolov8 model files
* docker/main: add download-models overlay into s6 startup
* detectors/rocm: assume models are in /config/model_cache/yolov8/
* docker/rocm: compile onnx files into mxr files at startup
* switch model download into bash script
* detectors/rocm: automatically override HSA_OVERRIDE_GFX_VERSION for couple of known chipsets
* docs: rocm detector first notes
* typos
* describe builds (harakas temporary)
* docker/rocm: also build a version for gfx1100
* docker/rocm: use cp instead of tar
* docker.rocm: remove README as it is now in detector config
* frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type
* docker/main: use newer openvino (2023.3.0)
* detectors: implement class aggregation
* update yolov8 model
* add openvino/yolov8 support for label aggregation
* docker: remove pointless s6/timeout-up files
* Revert "detectors: implement class aggregation"
This reverts commit dcfe6bbf6f.
* detectors/openvino: remove class aggregation
* detectors: increase yolov8 postprocessing score trershold to 0.5
* docker/rocm: separate rocm distributed files into its own build stage
* Update object_detectors.md
* updated CODEOWNERS file for rocm
* updated build names for documentation
* Revert "docker/main: use newer openvino (2023.3.0)"
This reverts commit dee95de908.
* reverrted openvino detector
* reverted edgetpu detector
* scratched rocm docs from any mention of edgetpu or openvino
* Update docs/docs/configuration/object_detectors.md
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* renamed frigate.detectors.yolo_utils.py -> frigate.detectors.util.py
* clarified rocm example performance
* Improved wording and clarified text
* Mentioned rocm detector for AMD GPUs
* applied ruff formating
* applied ruff suggested fixes
* docker/rocm: fix missing argument resulting in larger docker image sizes
* docs/configuration/object_detectors: fix links to yolov8 release files
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* 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
* Make main frigate build non rpi specific and build rpi using base image
* Add boards to sidebar
* Fix docker build
* Fix docs build
* Update pr branch for testing
* remove target from rpi build
* Remove manual build
* Add push build for rpi
* fix typos, improve wording
* Add arm build for rpi
* Cleanup and add default github ref name
* Cleanup docker build file system
* Setup to use docker bake
* Add ci/cd for bake
* Fix path
* Fix devcontainer
* Set targets
* Fix build
* Fix syntax
* Add wheels target
* Move dev container to trt
* Update key and fix rpi local
* Move requirements files and set intermediate targets
* Add back --load
* Update docs for community board development
* Update installation docs to reflect different builds available
* Update docs with official and community supported headers
* Update codeowners docs
* Update docs
* Assemble main and standard builds
* Change order of pushes
* Remove community board after successful build
* Fix rpi bake file names
