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* [Init] Initial commit for Synaptics SL1680 NPU * add a rough detector which is testing with yolov8 tflite model. * [Feat] Add dependencies installation in docker build - Add runtime library and wheels installation in main/Dockerfile - Add model.synap(default model, transfer from mobilenet_224full80) in docker/synap1680 * [Update] Remove dependencies installation from main Dockerfile - remove deps installation from Dockerfile - add dependencies installation and split wheels, deps stage in synap1680 Dockerfile * Refactor synap detector to more closely match other implementations * [Update] Add model path configuration check * [Update] update ModelType to ssd * [Update] Remove unuse script - install_deps.sh has already been executing in deps download stage - Dockerfile.toolchain is for testing to extract runtime libraries from Synaptics toolchain * [Update] update Synaptics SL1680 setup description * [Update] remove install_synap1680 - The deps download and installation is existed in synap1680 * [Fix] update document content * [Update] Update detector from synap1680 to synaptics This update is in order to make the synaptics SL-series NPU detector more general. - Fix detector `os` module not import bug - Update detector type `synap1680` to `synaptics` - Update document description `SL1680` to `Synaptics` only - Update docker build content `synap1680` to `synaptics` * [Fix] Update configuration document * Update docs/docs/configuration/object_detectors.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * [Update] Update document content and detector default layout - Update object_detectors document - Update detector's default layout - Update default model name * [Update] Update object detector document content * [Fix] Fix InputTensorEnum not defined error - import InputTensorEnum from detector_config * [Update] Update detector script coding format * [Update] Update synaptics detector coding format * [Update] Add synaptics ci workflow * [Update] update synaptics runtime libs download path - Fork Synaptics astra sdk repo and put the runtime lib package on it - Frigate team can update this download path later --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
Frigate - NVR With Realtime Object Detection for IP Cameras
[English] | 简体中文
A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a GPU or AI accelerator such as a Google Coral or Hailo is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
- Tight integration with Home Assistant via a custom component
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTSP to reduce the number of connections to your camera
- WebRTC & MSE support for low-latency live view
Documentation
View the documentation at https://docs.frigate.video
Donations
If you would like to make a donation to support development, please use Github Sponsors.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
