* semantic trigger test
* database and model
* config
* embeddings maintainer and trigger post-processor
* api to create, edit, delete triggers
* frontend and i18n keys
* use thumbnail and description for trigger types
* image picker tweaks
* initial sync
* thumbnail file management
* clean up logs and use saved thumbnail on frontend
* publish mqtt messages
* webpush changes to enable trigger notifications
* add enabled switch
* add triggers from explore
* renaming and deletion fixes
* fix typing
* UI updates and add last triggering event time and link
* log exception instead of return in endpoint
* highlight entry in UI when triggered
* save and delete thumbnails directly
* remove alert action for now and add descriptions
* tweaks
* clean up
* fix types
* docs
* docs tweaks
* docs
* reuse enum
* 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>
* Combine base and arm trt detectors
* Remove unused deps for amd64 build
* Add missing packages and cleanup ldconfig
* Expand packages for tensorflow model training
* Cleanup
* Refactor training to not reserve memory
* Implement model training via ZMQ and add model states to represent training
* Get model updates working
* Improve toasts and model state
* Clean up logging
* Add back in
* install new packages for transcription support
* add config options
* audio maintainer modifications to support transcription
* pass main config to audio process
* embeddings support
* api and transcription post processor
* embeddings maintainer support for post processor
* live audio transcription with sherpa and faster-whisper
* update dispatcher with live transcription topic
* frontend websocket
* frontend live transcription
* frontend changes for speech events
* i18n changes
* docs
* mqtt docs
* fix linter
* use float16 and small model on gpu for real-time
* fix return value and use requestor to embed description instead of passing embeddings
* run real-time transcription in its own thread
* tweaks
* publish live transcriptions on their own topic instead of tracked_object_update
* config validator and docs
* clarify docs
* Fix the `Any` typing hint treewide
There has been confusion between the Any type[1] and the any function[2]
in typing hints.
[1] https://docs.python.org/3/library/typing.html#typing.Any
[2] https://docs.python.org/3/library/functions.html#any
* Fix typing for various frame_shape members
Frame shapes are most likely defined by height and width, so a single int
cannot express that.
* Wrap gpu stats functions in Optional[]
These can return `None`, so they need to be `Type | None`, which is what
`Optional` expresses very nicely.
* Fix return type in get_latest_segment_datetime
Returns a datetime object, not an integer.
* Make the return type of FrameManager.write optional
This is necessary since the SharedMemoryFrameManager.write function can
return None.
* Fix total_seconds() return type in get_tz_modifiers
The function returns a float, not an int.
https://docs.python.org/3/library/datetime.html#datetime.timedelta.total_seconds
* Account for floating point results in to_relative_box
Because the function uses division the return types may either be int or
float.
* Resolve ruff deprecation warning
The config has been split into formatter and linter, and the global
options are deprecated.
* face library i18n fixes
* face library i18n fixes
* add ability to use ctrl/cmd S to save in the config editor
* Use datetime as ID
* Update metrics inference speed to start with 0 ms
* fix android formatted thumbnail
* ensure role is comma separated and stripped correctly
* improve face library deletion
- add a confirmation dialog
- add ability to select all / delete faces in collections
* Implement lazy loading for video previews
* Force GPU for large embedding model
* GPU is required
* settings i18n fixes
* Don't delete train tab
* webpush debugging logs
* Fix incorrectly copying zones
* copy path data
* Ensure that cache dir exists for Frigate+
* face docs update
* Add description to upload image step to clarify the image
* Clean up
---------
Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
* add zoom time to movement predictions
* config migrator
* add space to face rename regex
* more debug
* only calculate zoom time of relative move
* fix test
* make migrated movement weight a zero
* check for str and bool for movestatus support
* Add api to run face recognition on image
* Rework save attempts option
* Cleanup mobile object pane buttons
* Adjust api signature
* Remove param
* Cleanup
* recordings data pub/sub
* function to process recording stream frames
* model runner
* lpr model runner
* refactor to mixin class and use model runner
* separate out realtime and post processors
* move model and mixin folders
* basic postprocessor
* clean up
* docs
* postprocessing logic
* clean up
* return none if recordings are disabled
* run postprocessor handle_requests too
* tweak expansion
* add put endpoint
* postprocessor tweaks with endpoint
* Actually send result to face registration
* Define postprocessing api and move face processing to fit
* Standardize request handling
* Standardize handling of processors
* Rename processing metrics
* Cleanup
* Standardize object end
* Update to newer formatting
* One more
* One more
* Get stats for embeddings inferences
* cleanup embeddings inferences
* Enable UI for feature metrics
* Change threshold
* Fix check
* Update python for actions
* Set python version
* Ignore type for now
* Validate faces using cosine distance and SVC
* Formatting
* Use opencv instead of face embedding
* Update docs for training data
* Adjust to score system
* Set bounds
* remove face embeddings
* Update writing images
* Add face library page
* Add ability to select file
* Install opencv deps
* Cleanup
* Use different deps
* Move deps
* Cleanup
* Only show face library for desktop
* Implement deleting
* Add ability to upload image
* Add support for uploading images
* Add basic config and face recognition table
* Reconfigure updates processing to handle face
* Crop frame to face box
* Implement face embedding calculation
* Get matching face embeddings
* Add support face recognition based on existing faces
* Use arcface face embeddings instead of generic embeddings model
* Add apis for managing faces
* Implement face uploading API
* Build out more APIs
* Add min area config
* Handle larger images
* Add more debug logs
* fix calculation
* Reduce timeout
* Small tweaks
* Use webp images
* Use facenet model
* add generic onnx model class and use jina ai clip models for all embeddings
* fix merge confligt
* add generic onnx model class and use jina ai clip models for all embeddings
* fix merge confligt
* preferred providers
* fix paths
* disable download progress bar
* remove logging of path
* drop and recreate tables on reindex
* use cache paths
* fix model name
* use trust remote code per transformers docs
* ensure tokenizer and feature extractor are correctly loaded
* revert
* manually download and cache feature extractor config
* remove unneeded
* remove old clip and minilm code
* docs update
* swap sqlite_vec for chroma in requirements
* load sqlite_vec in embeddings manager
* remove chroma and revamp Embeddings class for sqlite_vec
* manual minilm onnx inference
* remove chroma in clip model
* migrate api from chroma to sqlite_vec
* migrate event cleanup from chroma to sqlite_vec
* migrate embedding maintainer from chroma to sqlite_vec
* genai description for sqlite_vec
* load sqlite_vec in main thread db
* extend the SqliteQueueDatabase class and use peewee db.execute_sql
* search with Event type for similarity
* fix similarity search
* install and add comment about transformers
* fix normalization
* add id filter
* clean up
* clean up
* fully remove chroma and add transformers env var
* readd uvicorn for fastapi
* readd tokenizer parallelism env var
* remove chroma from docs
* remove chroma from UI
* try removing custom pysqlite3 build
* hard code limit
* optimize queries
* revert explore query
* fix query
* keep building pysqlite3
* single pass fetch and process
* remove unnecessary re-embed
* update deps
* move SqliteVecQueueDatabase to db directory
* make search thumbnail take up full size of results box
* improve typing
* improve model downloading and add status screen
* daemon downloading thread
* catch case when semantic search is disabled
* fix typing
* build sqlite_vec from source
* resolve conflict
* file permissions
* try build deps
* remove sources
* sources
* fix thread start
* include git in build
* reorder embeddings after detectors are started
* build with sqlite amalgamation
* non-platform specific
* use wget instead of curl
* remove unzip -d
* remove sqlite_vec from requirements and load the compiled version
* fix build
* avoid race in db connection
* add scale_factor and bias to description zscore normalization
* 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
