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Nicolas Mowen 1377d33e25 Redesign Recordings View (#10690 )

* Use full width top bar

* Make each item in review filter group optional

* Remove export creation from export page

* Consolidate packages and fix opening recording from event

* Use common type for time range

* Move timeline to separate component

* Add events list view to recordings view

* Fix loading of images

* Fix incorrect labels

* use overlay state for selected timeline type

* Fix up for mobile view for now

* replace overlay state

* fix comparison

* remove unused

2024-03-26 15:03:58 -06:00

.devcontainer

Remove deprecated RTMP port 1935 (#9137 )

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Bump dependabot/fetch-metadata from 1 to 2 (#10607 )

2024-03-24 19:55:15 -06:00

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Set User Agent for FFmpeg calls (#4555 )

2022-11-30 16:53:45 -06:00

config

Improve the devcontainer experience (#3492 )

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docker

Bump pytz from 2023.3.post1 to 2024.1 in /docker/main (#9592 )

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docs

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frigate

Redesign Recordings View (#10690 )

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migrations

Add region count to database and use for motion activity (#10480 )

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web

Redesign Recordings View (#10690 )

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.dockerignore

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.gitignore

Small autotracking changes (#9571 )

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.pylintrc

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audio-labelmap.txt

Audio events (#6848 )

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benchmark_motion.py

use a different method for blur and contrast to reduce CPU (#6940 )

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benchmark.py

Add isort and ruff linter (#6575 )

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CODEOWNERS

AMD GPU support with the rocm detector and YOLOv8 pretrained model download (#9762 )

2024-02-10 06:41:46 -06:00

docker-compose.yml

Docs improvements (#8641 )

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labelmap.txt

Cleanup Detector labelmap (#4932 )

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LICENSE

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Makefile

increment version

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netlify.toml

Docs improvements (#8641 )

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process_clip.py

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pyproject.toml

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README.md

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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