mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-10-23 23:14:46 +03:00
61fd753aed
* Let the zmq detector skip the input transform when the detector applies it _transform_input converts the frame to the model's layout and dtype before the detector sees it. For a remote detector that can apply the transform itself, often on a GPU, that work is done twice over and the tensor is sent in its most expensive form. The detector now reports input_transform_offload in the model handshake. When it does, Frigate sends the frame as captured (uint8 NHWC) and adds the model's expected input_tensor and input_dtype to the request header, so the detector can apply them. The model config is unchanged either way: the expected format is relayed, not overridden. The choice is read per frame rather than cached, because the detector redoes its handshake on a timeout or socket error, so restarting or upgrading it while Frigate runs already works and keeps working here. Negotiated rather than configured, for two reasons. The detector is what knows whether it can apply the transform, and a user who merely believed it could would send unconverted frames to a model that cannot take them. It also needs no config surface, which the device string format has no room for. On a 640x640 region, measured on a Ryzen 9 5900XT, the host-side cost of handing the detector a frame drops from about 4 ms to under 0.1 ms per detection, and the payload from 4.9 MB to 1.2 MB. Nearly all of it is tobytes(): np.transpose returns a view and astype keeps its strides by default, so the transpose is deferred to serialization, where it becomes a strided gather over the whole float32 tensor. The YUV to RGB conversion is unaffected and stays on the host. * Test that the handshake decides whether the input transform is applied Parameterized over the three handshake values against the header the detector ends up sending: reported, the frame goes over as captured uint8 NHWC with the model's input_tensor and input_dtype declared; not reported or absent, the existing transform runs and those fields are omitted. A second case drives a real reconnect -- a timed out request, then a handshake from an upgraded detector -- and checks the next frame follows the new answer rather than the one from startup. --------- Co-authored-by: Eric <eric@localhost>
Frigate NVR™ - 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 is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- 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.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
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.
Copyright © 2026 Frigate, Inc.