mirror of
https://github.com/Squidly271/AppFeed.git
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Update
This commit is contained in:
Squidly271
parent
d88a4380ea
commit
bf3d0522a7
@ -1,3 +1,3 @@
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"EndTag: '</' not found",
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"Network": "bridge",
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"Project": "https://github.com/blakeblackshear/frigate",
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"Overview": "NVR With Realtime Object Detection for IP Cameras\r\n\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.\r\n\r\nUse 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.\r\n\r\n *Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n *Uses a very low overhead motion detection to determine where to run object detection\r\n *Object detection with Tensorflow runs in a separate process\r\n *Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n *An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously",
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"ExtraParams": "--shm-size=512m",
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"HostPort": "5000",
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"ContainerPort": "5000",
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"Protocol": "tcp"
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"Data": {
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"HostDir": "/mnt/user/appdata/frigate",
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"ContainerDir": "/config",
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"Mode": "rw"
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{
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"HostDir": "/etc/localtime",
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"ContainerDir": "/etc/localtime",
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"HostDir": "/mnt/user/Media/frigate/clips",
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"ContainerDir": "/clips",
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{
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"Variable": {
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"Value": "enterpassword",
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"Mode": ""
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"Target": "/config",
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"Mode": "rw",
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"Description": "/mnt/user/appdata/frigate",
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"Type": "Path",
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"Display": "always",
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"Required": "true",
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"Mask": "false"
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"value": "/mnt/user/appdata/frigate"
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"@attributes": {
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"Type": "Port",
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"Mask": "false"
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"value": "5000"
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{
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"@attributes": {
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"Name": "Frigate RTSP Password",
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"Target": "FRIGATE_RTSP_PASSWORD",
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"Default": "",
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"Mode": "",
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"Type": "Variable",
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"Display": "always",
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"Mask": "false"
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"value": "enterpassword"
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{
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"@attributes": {
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"Name": "Recordings if enabled in config",
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"Target": "/clips",
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"Default": "/mnt/user/Media/frigate/clips",
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"Mode": "rw",
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"Description": "Container Path: /clips",
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"Type": "Path",
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"value": "/mnt/user/Media/frigate/clips"
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{
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"@attributes": {
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"Name": "Cache TempFS is best",
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"Target": "/tmpfs",
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"Description": "Container Path: /tmpfs",
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"Type": "Path",
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"value": "/cache"
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{
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"Mode": "",
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"Description": "Container Device: ",
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"Type": "Device",
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"Display": "advanced-hide",
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"Mask": "false"
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"value": "/dev/dri/"
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}
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],
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"LastUpdateScan": 1607519296,
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"FirstSeen": 1538604219
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"Repo": "yayitazale's Repository",
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"Date": "1607672518",
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"Category": "HomeAutomation: Security:",
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"OriginalOverview": "[b]NVR With Realtime Object Detection for IP Cameras[/b]\r\n\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.\r\n\r\nUse 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.\r\n\r\n *Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n *Uses a very low overhead motion detection to determine where to run object detection\r\n *Object detection with Tensorflow runs in a separate process\r\n *Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n *An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously\r\n\r\n[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.\r\n\r\n",
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"Description": "NVR With Realtime Object Detection for IP Cameras\r\n\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT.\r\n\r\nUse 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.\r\n\r\n *Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n *Uses a very low overhead motion detection to determine where to run object detection\r\n *Object detection with Tensorflow runs in a separate process\r\n *Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n *An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously",
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"templatePath": "/tmp/GitHub/AppFeed/templates/yayitazalesRepository/yayitazale/frigate-amd64.xml",
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"CategoryList": [
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@ -38805,13 +38805,20 @@
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