* make go2rtc always rebuild config at startup
/dev/shm can be left mounted (in fact im pretty sure it's always left mounted) on the docker host after shutting down the frigate container.
If we only check that the file doesn't exist, stale data gets re-read every startup
This will make troubleshooting a nightmare for the average user.
I had given up troubleshooting go2rtc several times because of this.
* generate config after supervisor data is loaded
* Fix fi
* fix fi
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* support for other yolov models and config checks
* apply code formatting
* Information about core mask and inference speed
* update rknn postprocess and remove params
* update model selection
* Apply suggestions from code review
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* support rknn on all socs
* apply changes from review and fix post process bug
* apply code formatting
* update tip in object_detectors docs
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* support for other yolov models and config checks
* apply code formatting
* Information about core mask and inference speed
* update rknn postprocess and remove params
* update model selection
* Apply suggestions from code review
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* implement nginx caching
* bypass cache from frigate frontend, reduce to 5s
* set cache time to 2s
* cache 200s for 5s
* exclude vod endpoints from cache
* Revert numpy upgrade
* Upgrade arm64 onnx version to match amd64
* Increase CODEOWNERS granularity
Not sure if it has an effect since I don't have repository write access
* 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
* Update to latest tensorrt (8.6.1) release
* Build trt libyolo_layer.so in container
* Update tensorrt_models script to convert models from the frigate container
* Fix typo in model script
* Fix paths to yolo lib and models folder
* Add S6 scripts to test and convert specified TensortRT models at startup.
Rearrange tensorrt files into a docker support folder.
* Update TensorRT documentation to reflect the new model conversion process and minimum HW support.
* Fix model_cache path to live in config directory
* Move tensorrt s6 files to the correct directory
* Fix issues in model generation script
* Disable global timeout for s6 services
* Add version folder to tensorrt model_cache path
* Include TensorRT version 8.5.3
* Add numpy requirement prior to removal of np.bool
* This TRT version uses a mixture of cuda dependencies
* Redirect stdout from noisy model conversion
* Check ffmpeg version instead of checking for presence of BTBN_PATH
* Query ffmpeg version in s6 run script instead of subprocessing in every import
* Define LIBAVFORMAT_VERSION_MAJOR in devcontainer too
* Formatting
* Default ffmpeg version to current btbn version so unit tests pass
* Add ccache to libusb and nginx build scripts
* Add ccache support to Dockerfile for faster builds
* Add ccache to PATH and use it for compiling nginx with Makefile in build_nginx.sh script
* Add ability to export frigate clips
* Add http endpoint
* Add dir to nginx
* Add webUI
* Formatting
* Cleanup unused
* Optimize timelapse
* Fix pts
* Use JSON body for params
* Use hwaccel to encode when available
* Print ffmpeg command when fail
* Print ffmpeg command when fail
* Add separate ffmpeg preset for timelapse
* Add docs outlining the export directory
* Add export docs
* Use ''
* Fix playlist max time
* Lower max playlist time
* Add api docs for export
* isort fixes
* Add isort and ruff linter
Both linters are pretty common among modern python code bases.
The isort tool provides stable sorting and grouping, as well as pruning
of unused imports.
Ruff is a modern linter, that is very fast due to being written in rust.
It can detect many common issues in a python codebase.
Removes the pylint dev requirement, since ruff replaces it.
* treewide: fix issues detected by ruff
* treewide: fix bare except clauses
* .devcontainer: Set up isort
* treewide: optimize imports
* treewide: apply black
* treewide: make regex patterns raw strings
This is necessary for escape sequences to be properly recognized.
