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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
44d8cdbba1
* ROCm AMD/GPU based build and detector, WIP * detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters * AMD/ROCm: add couple of more ultralytics models; comments * docker/rocm: make imported model files readable by all * docker/rocm: readme about running on AMD GPUs * docker/rocm: updated README * docker/rocm: updated README * docker/rocm: updated README * detectors/rocm: separated preprocessing functions into yolo_utils.py * detector/plugins: added onnx cpu plugin * docker/rocm: updated container with limite label sets * example detectors view * docker/rocm: updated README.md * docker/rocm: update README.md * docker/rocm: do not set HSA_OVERRIDE_GFX_VERSION at all for the general version as the empty value broke rocm * detectors: simplified/optimized yolov8_postprocess * detector/yolo_utils: indentation, remove unused variable * detectors/rocm: default option to conserve cpu usage at the expense of latency * detectors/yolo_utils: use nms to prefilter overlapping boxes if too many detected * detectors/edgetpu_tfl: add support for yolov8 * util/download_models: script to download yolov8 model files * docker/main: add download-models overlay into s6 startup * detectors/rocm: assume models are in /config/model_cache/yolov8/ * docker/rocm: compile onnx files into mxr files at startup * switch model download into bash script * detectors/rocm: automatically override HSA_OVERRIDE_GFX_VERSION for couple of known chipsets * docs: rocm detector first notes * typos * describe builds (harakas temporary) * docker/rocm: also build a version for gfx1100 * docker/rocm: use cp instead of tar * docker.rocm: remove README as it is now in detector config * frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type * docker/main: use newer openvino (2023.3.0) * detectors: implement class aggregation * update yolov8 model * add openvino/yolov8 support for label aggregation * docker: remove pointless s6/timeout-up files * Revert "detectors: implement class aggregation" This reverts commit |
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| frigate | ||
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| audio-labelmap.txt | ||
| benchmark_motion.py | ||
| benchmark.py | ||
| CODEOWNERS | ||
| docker-compose.yml | ||
| labelmap.txt | ||
| LICENSE | ||
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| README.md | ||
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.
Screenshots
Integration into Home Assistant
Also comes with a builtin UI:
