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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
3f05f74ecb
* Initial WIP dockerfile and scripts to add tensorrt support * Add tensorRT detector * WIP attempt to install TensorRT 8.5 * Updates to detector for cuda python library * TensorRT Cuda library rework WIP Does not run * Fixes from rebase to detector factory * Fix parsing output memory pointer * Handle TensorRT logs with the python logger * Use non-async interface and convert input data to float32. Detection runs without error. * Make TensorRT a separate build from the base Frigate image. * Add script and documentation for generating TRT Models * Add support for TensorRT devcontainer * Add labelmap to trt model script and docs. Cleanup of old scripts. * Update detect to normalize input tensor using model input type * Add config for selecting GPU. Fix Async inference. Update documentation. * Update some CUDA libraries to clean up version warning * Add CI stage to build TensorRT tag * Add note in docs for image tag and model support |
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| docs | ||
| frigate | ||
| migrations | ||
| web | ||
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| benchmark.py | ||
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| Dockerfile | ||
| labelmap.txt | ||
| LICENSE | ||
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| process_clip.py | ||
| README.md | ||
| requirements-dev.txt | ||
| requirements-ov.txt | ||
| requirements-tensorrt.txt | ||
| requirements-wheels.txt | ||
| requirements.txt | ||
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 RTMP to reduce the number of connections to your camera
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
Integration into Home Assistant
Also comes with a builtin UI:
