* bump backend and frontend dependencies
Bumps every outdated dependency that passed testing, with the code changes each one needs.
- peewee 4.5 and peewee_migrate 2.3: use SqliteDatabase in place of the removed SqliteExtDatabase, and fix types for peewee's bundled stubs
- mypy 2.4.0: remove unused type ignores and add the annotations mypy 2 needs
- pandas 3.0: convert the motion activity index with as_unit("s"), because pandas 3 infers second resolution for whole-second timestamps and the old // 10**9 returned 1
- transformers 5.19: load Jina tokenizers from the saved directory with trust_remote_code off and use CLIPImageProcessor, since v5 removed CLIPFeatureExtractor and remote code pulled in torch
- tensorflow-cpu 2.21: pin protobuf to 7.36 in the TensorRT amd64 image, because the old 3.20.3 pin was installed over TensorFlow and broke its import
- pydantic 2.13 and google-genai 2.29: regenerate the API spec
- ruff 0.16: set an explicit lint select, since 0.16 widened the default rules
- react-router-dom 7: set useTransitions={false} on BrowserRouter to keep v6 update timing, which useSearchEffect relies on
- typescript 6: drop baseUrl and add node types in tsconfig
- radix: update to the latest release and remove the slot patch, which upstream now includes
- apexcharts 7.8: guard the tooltip formatter against null values
- cryptography 50, joserfc 1.7, pywebpush 2.5, openai 3.26, aiohttp, uvicorn, onvif-zeep-async, scipy, framer-motion 14, react-day-picker 10 and other minor and patch bumps
* silence httpx2 request logging
* redownload an incomplete jina tokenizer
An interrupted tokenizer download left the Hugging Face cache directory without the saved tokenizer files, and since the download is skipped whenever that directory exists, every restart failed to load the tokenizer. The Jina constructors now remove a tokenizer directory that has no tokenizer_config.json so the download runs again.
* don't display audio transcription provider message as health notice
* show remote provider for audio transcription in health pane
* adjust trigger and notifications messages to be consistent with the rest of the settings UI
* disable save buttons when there are no changes in config editor
* fix audio manager crash when a camera is added at runtime
The audio processor and the camera maintainer both poll the same `add` config update on their own one second timers, and the maintainer is what creates `camera_metrics[name]`. When the audio processor got there first it looked the new camera up before that entry existed, and the `KeyError` took down the whole `frigate.audio_manager` process. Whether it happens depends purely on which poll fires first, so cloning a camera from the UI fails or succeeds at random. `spawn_if_needed` now skips a camera whose metrics aren't there yet and picks it up on the next poll, the same way it already waits on a late ffmpeg update.
`AudioEventMaintainer` holds the `CameraMetrics` object now instead of indexing the manager dict on every audio chunk, which drops the IPC round trips and means a removed camera can't `KeyError` out of `detect_audio` after the maintainer pops the entry. The audio process is also registered with the watchdog, since a crash there previously left audio detection dead for every camera until a full restart, and it now receives the shared `DataProcessorMetrics` so `AudioTranscriptionRealTimeProcessor` gets the same type as the other real time processors.
* fix stationary max_frames dropping other tracked objects
When `max_frames` was set for a label, deregistering one object rebuilt norfair's list with a filter that kept an object only if it was both not the target and already on its way out, so every other healthy object of that label was dropped along with it. Any car leaving the frame took the rest of the cars with it and they came back as new tracked objects a few frames later. The filter now removes only the target, and objects that are expiring are still reaped by norfair on the next update.
* fix test
* fix skip_motion_threshold permanently disabling motion detection
The skip check returned before the two `accumulateWeighted` calls at the end of `detect`, so a skipped frame never made it into the background and setting `calibrating` there only picked a faster alpha for calls that never ran. `avg_frame` starts as an all zero image and a normally lit scene differs from black across nearly the whole frame, so the cameras I tested measure 0.84 to 0.98 against it. Any `skip_motion_threshold` below that number skips the first frame, leaves the background black, and skips every frame after it. Motion detection is dead for that camera until the setting is removed or Frigate restarts, with no motion boxes, no motion recordings, and no regions for the tracker since the detector stays calibrating.
Startup isn't the only way in. `update_mask` zeroes the background on any motion config change, and once a camera has calibrated the first IR switch or PTZ move freezes the background on the old scene, so it can't transition to the new one, which is the case the option exists for. The frame is now blended in before the early return at the same 0.2 alpha the calibrating path uses elsewhere, so a large scene change is still suppressed while the background catches up, about a second on a 5 fps camera, and then motion comes back.
* dump ffmpeg logs on every restart
The record watchdog restarted ffmpeg without flushing its `LogPipe`, so a camera whose recording segments went stale never showed a single line of ffmpeg output. The dump now happens in `start_or_restart_ffmpeg` right after the stop, which covers the stale record path, the record crash path, and the audio restart. `reset_capture_thread` and the audio `log_and_restart` fallback keep their own dumps since both pass `ffmpeg_process=None`.
* dump ffmpeg logs once per restart
The audio restart path dumped the log pipe itself before calling the helper, so the restart dump printed a second "last 100 lines" heading over an already drained deque and split the tail that `stop_ffmpeg` flushed into its own section. The heading is now only printed when there's something under it, and the audio path leaves the dump to the restart so each failure produces one section.
* keep all logpipe dumps consistent
* Rename face model to face recognizer
* Refactor face detection into own module
* Return face and face landmarks
* Align faces with 5 points instead of just the eyes
* Add landmark validation to throw out images which do not fit a landmark
* Fix circular import and lock face detector for concurrent runs across threads
* Fix mypy
* Pin ruff
* Add python upgrade fixes
This enables python upgrade checks in ruff to look for deprecated types and patterns. This namely fixes:
- usage of deprecated `Typing` which is now built in
- some specific exceptions which are caught and have new aliases
Some specific UP checks were also ignored as they are stylistic / unimportant and likely to cause bugs
* Remove async blocking calls
Use asyncio.to_thread on two remaining blocking calls to fix hanging event thread loop. Enable this specific rule to block it in the future.
* Use proper logging mechanism
* Correctly format logs
* Raise with context
When raising an exception include the from context to improve debugging
* Cleanup
* Fix jetson stats reading
* Return result
* Avoid unknown class for cover image
* fix double encoding of passwords in camera wizard
* formatting
* empty homekit config fixes
* add locks to jina v1 embeddings
protect tokenizer and feature extractor in jina_v1_embedding with per-instance thread lock to avoid the "Already borrowed" RuntimeError during concurrent tokenization
* Capitalize correctly
* replace deprecated google-generativeai with google-genai
update gemini genai provider with new calls from SDK
provider_options specifies any http options
suppress unneeded info logging
* fix attribute area on detail stream hover
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* Fix genai callbacks in MQTT
* Cleanup cursor pointer for classification cards
* Cleanup
* Handle unknown SOCs for RKNN converter by only using known SOCs
* don't allow "none" as a classification class name
* change internal port user to admin and default unspecified username to viewer
* keep 5000 as anonymous user
* suppress tensorflow logging during classification training
* Always apply base log level suppressions for noisy third-party libraries even if no specific logConfig is provided
* remove decorator and specifically suppress TFLite delegate creation messages
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* Ignore numpy get limits warning
* Add function wrapper to redirect stdout and stderr to logpipe
* Save stderr too
* Add more to catch
* run logpipe
* Use other logging redirect class
* Use other logging redirect class
* add decorator for redirecting c/c++ level output to logger
* fix typing
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* Move log level initialization to log
* Use logger config
* Formatting
* Fix config order
* Set process names
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Set runtime
* Use count correctly
* Don't assume camera sizes
* Use separate zmq proxy for object detection
* Correct order
* Use forkserver
* Only store PID instead of entire process reference
* Cleanup
* Catch correct errors
* Fix typing
* Remove before_run from process util
The before_run never actually ran because:
You're right to suspect an issue with before_run not being called and a potential deadlock. The way you've implemented the run_wrapper using __getattribute__ for the run method of BaseProcess is a common pitfall in Python's multiprocessing, especially when combined with how multiprocessing.Process works internally.
Here's a breakdown of why before_run isn't being called and why you might be experiencing a deadlock:
The Problem: __getattribute__ and Process Serialization
When you create a multiprocessing.Process object and call start(), the multiprocessing module needs to serialize the process object (or at least enough of it to re-create the process in the new interpreter). It then pickles this serialized object and sends it to the newly spawned process.
The issue with your __getattribute__ implementation for run is that:
run is retrieved during serialization: When multiprocessing tries to pickle your Process object to send to the new process, it will likely access the run attribute. This triggers your __getattribute__ wrapper, which then tries to bind run_wrapper to self.
run_wrapper is bound to the parent process's self: The run_wrapper closure, when created in the parent process, captures the self (the Process instance) from the parent's memory space.
Deserialization creates a new object: In the child process, a new Process object is created by deserializing the pickled data. However, the run_wrapper method that was pickled still holds a reference to the self from the parent process. This is a subtle but critical distinction.
The child's run is not your wrapped run: When the child process starts, it internally calls its own run method. Because of the serialization and deserialization process, the run method that's ultimately executed in the child process is the original multiprocessing.Process.run or the Process.run if you had directly overridden it. Your __getattribute__ magic, which wraps run, isn't correctly applied to the Process object within the child's context.
* Cleanup
* Logging bugfix (#18465)
* use mp Manager to handle logging queues
A Python bug (https://github.com/python/cpython/issues/91555) was preventing logs from the embeddings maintainer process from printing. The bug is fixed in Python 3.14, but a viable workaround is to use the multiprocessing Manager, which better manages mp queues and causes the logging to work correctly.
* consolidate
* fix typing
* Fix typing
* Use global log queue
* Move to using process for logging
* Convert camera tracking to process
* Add more processes
* Finalize process
* Cleanup
* Cleanup typing
* Formatting
* Remove daemon
---------
Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
* use mp Manager to handle logging queues
A Python bug (https://github.com/python/cpython/issues/91555) was preventing logs from the embeddings maintainer process from printing. The bug is fixed in Python 3.14, but a viable workaround is to use the multiprocessing Manager, which better manages mp queues and causes the logging to work correctly.
* consolidate
* fix typing
* Subclass Process for audio_process
* Introduce custom mp.Process subclass
In preparation to switch the multiprocessing startup method away from
"fork", we cannot rely on os.fork cloning the log state at fork time.
Instead, we have to set up logging before we run the business logic of
each process.
* Make camera_metrics into a class
* Make ptz_metrics into a class
* Fixed PtzMotionEstimator.ptz_metrics type annotation
* Removed pointless variables
* Do not start audio processor when no audio cameras are configured
* Moved FrigateApp.init_config() into FrigateConfig.load()
* Move frigate config loading into main
* Store PlusApi in FrigateConfig
* Register SIGTERM handler in main
* Ensure logging is setup during config parsing
* Removed pointless try
* Moved config initialization out of FrigateApp
* Made FrigateApp.shm_frame_count into a function
* Removed log calls from signal handlers
python's logging calls are not re-entrant, which caused at least one of
these to deadlock randomly.
* Reopen stdout/err on process fork
This helps avoid deadlocks (https://github.com/python/cpython/issues/91776).
* Make mypy happy
* Whoops. I might have forgotten to save.
Truly an amateur mistake.
* Always call FrigateApp.stop()
* Make logging code self-contained.
Rewrite logging code to use python's builting QueueListener, effectively
moving the logging process into a thread of the Frigate app.
Also, wrap this behaviour in a easy-to-use context manager to encourage
some consistency.
* Fixed typing errors
* Remove todo note from log filter
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Do not access log record's msg directly
* Clear all root handlers before starting app
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* intentionally handle queues during shutdown and carefully manage shutdown order
* more carefully manage shutdown to avoid threadlocks
* use debug for signal logging
* ensure disabled cameras dont break shutdown
* typo
* 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.
* Add support for ptz commands via websocket
* Fix startup issues
* Fix bugs
* Set config manually
* Add more commands
* Add presets
* Add zooming
* Fixes
* Set name
* Cleanup
* Add ability to set presets from UI
* Add ability to set preset from UI
* Cleanup for errors
* Ui tweaks
* Add visual design for pan / tilt
* Add pan/tilt support
* Support zooming
* Try to set wsdl
* Fix duplicate logs
* Catch auth errors
* Don't init onvif for disabled cameras
* Fix layout sizing
* Don't comment out
* Fix formatting
* Add ability to control camera with keyboard shortcuts
* Disallow user selection
* Fix mobile pressing
* Remove logs
* Substitute onvif password
* Add ptz controls ot birdseye
* Put wsdl back
* Add padding
* Formatting
* Catch onvif error
* Optimize layout for mobile and web
* Place ptz controls next to birdseye view in large layout
* Fix pt support
* Center text titles
* Update tests
* Update docs
* Write camera docs for PTZ
* Add MQTT docs for PTZ
* Add ptz info docs for http
* Fix test
* Make half width when full screen
* Fix preset panel logic
* Fix parsing
* Update mqtt.md
* Catch preset error
* Add onvif example to docs
* Remove template example from main camera docs
* dont wait so long for queues
* implement stop methods for comms
* set the detection events on exit and return early from processing
* handle the stop event in the broadcast threads
* short circuit the detection process exit code if it already exited
* some logging for stats thread
* just keep the log process alive 1 second after the last log message
* ensure the multiprocessing queues are emptied and closed
* Update frigate/log.py
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Update frigate/log.py
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* mypy fixes
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* Consts for regex
* Add regex for camera username and password
* Redact user:pass from ffmpeg logs
* Redact ffmpeg commands
* Move common function to util
* Add tests
* Formatting
* Remove unused imports
* Fix test
* Add port to test
* Support special characters in passwords
* Add tests for special character handling
* Remove docs about not supporting special characters
