* Convert face crops to RGB before embedding
Face crops flow through the cv2 pipeline as BGR arrays, but
_process_image passes ndarrays to PIL without any channel conversion,
so the FaceNet and ArcFace embedders receive BGR input while both
models expect RGB. The error is symmetric between enrollment and
recognition so it partially cancels, but it still costs accuracy.
* Move BGR to RGB conversion into a shared helper
Deduplicate the channel swap from both _preprocess_inputs methods
into a BaseEmbedding._bgr_to_rgb static helper, as suggested in
review.
The fallback to tensorflow was established back in 2023, because we could
not provide tflite-runtime downstream in nixpkgs.
By now we have ai-edge-litert available, which is the successor to the
tflite-runtime. It still provides the same entrypoints as tflite-runtime
and functionality has been verified in multiple deployments for the last
two weeks.
* 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>
* Cleanup onnx detector
* Fix
* Fix classification cropping
* Deprioritize openvino
* Send model type
* Use model type to decide if model can use full optimization
* Clenanup
* Cleanup
* Use re-usable inference request to reduce CPU usage
* Share tensor
* Don't count performance
* Create openvino runner class
* Break apart onnx runner
* Add specific note about inability to use CUDA graphs for some models
* Adjust rknn to use RKNNRunner
* Use optimized runner
* Add support for non-complex models for CudaExecutionProvider
* Use core mask for rknn
* Correctly handle cuda input
* Cleanup
* Sort imports
* 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>
* 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>
