mirror of
https://github.com/blakeblackshear/frigate.git
synced 2025-09-26 11:31:28 +08:00
4a35573210
* Initial support for Hailo-8L Added file for Hailo-8L detector including dockerfile, h8l.mk, h8l.hcl, hailo8l.py, ci.yml and ssd_mobilenat_v1.hef as the inference network. Added files to help with the installation of Hailo-8L dependences like generate_wheel_conf.py, requirements-wheel-h8l.txt and modified setup.py to try and work with any hardware. Updated docs to reflect Initial Hailo-8L support including oject_detectors.md, hardware.md and installation.md. * Update .github/workflows/ci.yml typo h8l not arm64 Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/object_detectors.md Clarity for the end user and correct uses of words Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/frigate/installation.md typo Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * update Installation.md to clarify Hailo-8L installation process. * Update docs/docs/frigate/hardware.md Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Update hardware.md add Inference time. * Oops no new line at the end of the file. * Update docs/docs/frigate/hardware.md typo Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com> * Update dockerfile to download the ssd_modilenet_v1 model instead of having it in the repo. * Updated dockerfile so it dose not download the model file. add function to download it at runtime. update model path. * fix formatting according to ruff and removed unnecessary functions. --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
Frigate - NVR With Realtime Object Detection for IP Cameras
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 Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS 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.