mirror of
https://github.com/Squidly271/AppFeed.git
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107 lines
7.3 KiB
XML
107 lines
7.3 KiB
XML
<?xml version="1.0" encoding="UTF-8"?>
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<Container version="2">
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<Name>frigate</Name>
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<Repository>blakeblackshear/frigate:stable-amd64</Repository>
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<Registry>https://hub.docker.com/r/blakeblackshear/frigate/</Registry>
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<Network>bridge</Network>
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<Shell>sh</Shell>
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<Privileged>false</Privileged>
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<Support>https://forums.unraid.net/topic/98064-support-blakeblackshear-frigate/</Support>
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<Project>https://github.com/blakeblackshear/frigate</Project>
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<Overview>[b]NVR With Realtime Object Detection for IP Cameras[/b]
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Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
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Use of a Google Coral Accelerator is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
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*Leverages multiprocessing heavily with an emphasis on realtime over processing every frame&amp;#xD;
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*Uses a very low overhead motion detection to determine where to run object detection&amp;#xD;
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*Object detection with Tensorflow runs in a separate process&amp;#xD;
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*Object info is published over MQTT for integration into HomeAssistant as a binary sensor&amp;#xD;
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*An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously&amp;#xD;
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[span style='color: red;'][b]Note:[/b][/span] A config.yml file [b]must exist[/b] in the config directory. See example [a href="https://github.com/blakeblackshear/frigate/blob/master/config/config.example.yml"]here[/a] and device specific info can be found [a href="https://github.com/blakeblackshear/frigate/blob/master/docs/DEVICES.md"]here[/a]. See the documentation for more details.
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[span style='color: red;'][b]Note:[/b][/span] You can map an Intel integrated GPU or an Nvidia GPU to use hardware accel. on ffmpeg image encode/decode. In the first place you only need to keep the /dev/dri/ device. If you want to use a nvidia GPU on ffmpeg you need to follow [https://forums.unraid.net/topic/98978-plugin-nvidia-driver/?tab=comments#comment-913250&amp;searchlight=1"]this steps[/a]
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[span style='color: red;'][b]Note:[/b][/span] If you are using a PCI Coral instead of a USB one, upgrade your Unraid system to stable 6.9.0 and then install first the needed drivers going to the CA APP and searching for [b]Coral-Driver[/b] (thanks to ich777)</Overview>
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<WebUI>http://[IP]:[PORT:5000]</WebUI>
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<TemplateURL>https://raw.githubusercontent.com/yayitazale/unraid-templates/main/yayitazale/frigate-amd64.xml</TemplateURL>
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<Icon>https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate_unraid.PNG</Icon>
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<ExtraParams>--shm-size=5G</ExtraParams>
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<PostArgs></PostArgs>
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<CPUset></CPUset>
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<Networking>
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<Mode>bridge</Mode>
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<Publish>
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<Port>
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<HostPort>5000</HostPort>
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<ContainerPort>5000</ContainerPort>
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<Protocol>tcp</Protocol>
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</Port>
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<Port>
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<HostPort>1935</HostPort>
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<ContainerPort>1935</ContainerPort>
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<Protocol>tcp</Protocol>
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</Port>
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</Publish>
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</Networking>
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<Data>
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<Volume>
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<HostDir>/mnt/user/appdata/frigate</HostDir>
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<ContainerDir>/config</ContainerDir>
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<Mode>rw</Mode>
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</Volume>
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<Volume>
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<HostDir>/mnt/user/Media/frigate/clips</HostDir>
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<ContainerDir>/media/frigate/clips</ContainerDir>
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<Mode>rw</Mode>
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</Volume>
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<Volume>
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<HostDir>/mnt/user/Media/frigate/recordings</HostDir>
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<ContainerDir>/media/frigate/recordings</ContainerDir>
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<Mode>rw</Mode>
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</Volume>
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<Volume>
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<HostDir>/tmp/frigate</HostDir>
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<ContainerDir>/tmp/cache</ContainerDir>
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<Mode>rw</Mode>
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</Volume>
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<Volume>
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<HostDir>/etc/localtime</HostDir>
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<ContainerDir>/etc/localtime</ContainerDir>
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<Mode>rw</Mode>
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</Volume>
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</Data>
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<Environment>
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<Variable>
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<Value>enterpassword</Value>
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<Name>FRIGATE_RTSP_PASSWORD</Name>
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<Mode></Mode>
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</Variable>
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</Environment>
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<Config Name="Config Path" Target="/config" Default="/mnt/user/appdata/frigate" Mode="rw" Description="/mnt/user/appdata/frigate" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/appdata/frigate</Config>
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<Config Name="Clips path" Target="/media/frigate/clips" Default="/mnt/user/Media/frigate/clips" Mode="rw" Description="Container Path: /media/frigate/clips" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/Media/frigate/clips</Config>
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<Config Name="Recordings path" Target="/media/frigate/recordings" Default="/mnt/user/Media/frigate/recordings" Mode="rw" Description="Container Path: /media/frigate/recordings" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/Media/frigate/recordings</Config>
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<Config Name="HTTP port" Target="5000" Default="" Mode="tcp" Description="Container Port: 5000" Type="Port" Display="always" Required="true" Mask="false">5000</Config>
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<Config Name="RTMP port" Target="1935" Default="" Mode="tcp" Description="Container Port: 1935" Type="Port" Display="always" Required="true" Mask="false">1935</Config>
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<Config Name="Frigate RTSP Password" Target="FRIGATE_RTSP_PASSWORD" Default="" Mode="" Description="Container Variable: FRIGATE_RTSP_PASSWORD" Type="Variable" Display="always" Required="true" Mask="false">enterpassword</Config>
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<Config Name="TPU Mapping" Target="/dev/bus/usb" Default="" Mode="" Description="Container Path: /dev/bus/usb" Type="Device" Display="always" Required="false" Mask="false">/dev/bus/usb</Config>
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<Config Name="Intel/AMD GPU mapping" Target="" Default="" Mode="" Description="Container Device: /dev/dri/renderD128" Type="Device" Display="always" Required="false" Mask="false">/dev/dri/renderD128</Config>
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<Config Name="Cache to RAM" Target="/tmp/cache" Default="" Mode="rw" Description="Container Path: /tmp/cache" Type="Path" Display="advanced-hide" Required="true" Mask="false">/tmp/frigate</Config>
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<Config Name="Localtime" Target="/etc/localtime" Default="" Mode="rw" Description="/etc/localtime" Type="Path" Display="advanced-hide" Required="true" Mask="false">/etc/localtime</Config>
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<Date>1619032310</Date>
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<Description>NVR With Realtime Object Detection for IP Cameras
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Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.
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Use of a Google Coral Accelerator is optional, but highly recommended. On my Intel i7 processor, I can process 2-3 FPS with the CPU. The Coral can process 100+ FPS with very low CPU load.
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*Leverages multiprocessing heavily with an emphasis on realtime over processing every frame&amp;#xD;
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*Uses a very low overhead motion detection to determine where to run object detection&amp;#xD;
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*Object detection with Tensorflow runs in a separate process&amp;#xD;
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*Object info is published over MQTT for integration into HomeAssistant as a binary sensor&amp;#xD;
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*An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously&amp;#xD;</Description>
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<templatePath>/tmp/GitHub/AppFeed/templates/yayitazalesRepository/yayitazale/frigate-amd64.xml</templatePath>
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</Container>
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