Squidly271/AppFeed
1
0
Fork 0
mirror of https://github.com/Squidly271/AppFeed.git synced 2024-09-21 10:27:48 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update

Browse Source
This commit is contained in:
Andrew Z 2021-09-30 06:03:40 -04:00
parent e475b8275f
commit d5bd0e769f
4 changed files with 11 additions and 12 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
applicationFeed-lastUpdated.json
View File

@ -1,3 +1,3 @@
{
"last_updated_timestamp": 1632989050
"last_updated_timestamp": 1632996218
}

4
applicationFeed-raw.json
View File

@ -171659,7 +171659,7 @@
"Overview": "[b]NVR With Realtime Object Detection for IP Cameras[/b]\r\n[br][br]\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT. 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.\r\n[br][br]\r\n- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n[br]\r\n- Uses a very low overhead motion detection to determine where to run object detection\r\n[br]\r\n- Object detection with Tensorflow runs in a separate process\r\n[br]\r\n- Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n[br]\r\n- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously\r\n[br][br]\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[br]\r\n[span style='color: red;'][b]Note:[/b][/span] If you want add a Nvidia GPU to use hardware accel. on ffmpeg image encode/decode use the correspondig app.\r\n[br]\r\n[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)",
"WebUI": "http://[IP]:[PORT:5000]",
"TemplateURL": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/yayitazale/frigate-amd64.xml",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate_unraid.PNG",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate.png",
"ExtraParams": "--shm-size=5G",
"PostArgs": "",
"CPUset": "",
@ -171835,7 +171835,7 @@
"Overview": "[b]NVR With Realtime Object Detection for IP Cameras[/b]\r\n[br][br]\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT. 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. This is the an Nvidia GPU to use hardware accel. on ffmpeg image encode/decode.\r\n[br][br]\r\n- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n[br]\r\n- Uses a very low overhead motion detection to determine where to run object detection\r\n[br]\r\n- Object detection with Tensorflow runs in a separate process\r\n[br]\r\n- Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n[br]\r\n- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously\r\n[br][br]\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[br]\r\n[span style='color: red;'][b]Note:[/b][/span]You need to install the nvidia drivers plugin before installing this app following [https://forums.unraid.net/topic/98978-plugin-nvidia-driver/?tab=comments#comment-913250&searchlight=1\"]this steps[/a]\r\n[br]\r\n[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)",
"WebUI": "http://[IP]:[PORT:5000]",
"TemplateURL": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/yayitazale/frigate-amd64-nvidia.xml",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate_unraid.PNG",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate.png",
"ExtraParams": "--shm-size=5G --runtime=nvidia",
"PostArgs": "",
"CPUset": "",

8
applicationFeed.json
View File

@ -1,7 +1,7 @@
{
"apps": 1375,
"last_updated_timestamp": 1632989050,
"last_updated": "2021-09-30 04:04",
"last_updated_timestamp": 1632996218,
"last_updated": "2021-09-30 06:03",
"categories": [
{
"Cat": "Backup:",
@ -171821,7 +171821,7 @@
"Overview": "[b]NVR With Realtime Object Detection for IP Cameras[/b]\r\n[br][br]\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT. 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.\r\n[br][br]\r\n- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n[br]\r\n- Uses a very low overhead motion detection to determine where to run object detection\r\n[br]\r\n- Object detection with Tensorflow runs in a separate process\r\n[br]\r\n- Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n[br]\r\n- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously\r\n[br][br]\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[br]\r\n[span style='color: red;'][b]Note:[/b][/span] If you want add a Nvidia GPU to use hardware accel. on ffmpeg image encode/decode use the correspondig app.\r\n[br]\r\n[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)",
"WebUI": "http://[IP]:[PORT:5000]",
"TemplateURL": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/yayitazale/frigate-amd64.xml",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate_unraid.PNG",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate.png",
"ExtraParams": "--shm-size=5G",
"PostArgs": "",
"CPUset": "",
@ -171997,7 +171997,7 @@
"Overview": "[b]NVR With Realtime Object Detection for IP Cameras[/b]\r\n[br][br]\r\nUses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Designed for integration with HomeAssistant or others via MQTT. 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. This is the an Nvidia GPU to use hardware accel. on ffmpeg image encode/decode.\r\n[br][br]\r\n- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame\r\n[br]\r\n- Uses a very low overhead motion detection to determine where to run object detection\r\n[br]\r\n- Object detection with Tensorflow runs in a separate process\r\n[br]\r\n- Object info is published over MQTT for integration into HomeAssistant as a binary sensor\r\n[br]\r\n- An endpoint is available to view an MJPEG stream for debugging, but should not be used continuously\r\n[br][br]\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[br]\r\n[span style='color: red;'][b]Note:[/b][/span]You need to install the nvidia drivers plugin before installing this app following [https://forums.unraid.net/topic/98978-plugin-nvidia-driver/?tab=comments#comment-913250&searchlight=1\"]this steps[/a]\r\n[br]\r\n[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)",
"WebUI": "http://[IP]:[PORT:5000]",
"TemplateURL": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/yayitazale/frigate-amd64-nvidia.xml",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate_unraid.PNG",
"Icon": "https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate.png",
"ExtraParams": "--shm-size=5G --runtime=nvidia",
"PostArgs": "",
"CPUset": "",

9
old-applicationFeed-raw.json
View File

@ -14108,7 +14108,6 @@
"Security",
"Plugins"
],
"LastUpdateScan": 1632981816,
"FirstSeen": 1581012664
},
{
@ -93097,10 +93096,10 @@
"TemplateURL": "https://raw.githubusercontent.com/ich777/docker-templates/master/ich777/Intel-GVT-g.xml",
"Repository": "https://raw.githubusercontent.com/ich777/intel-gvt-g/master/intel-gvt-g.plg",
"Plugin": true,
"pluginVersion": "2021.09.17",
"pluginVersion": "2021.09.30",
"MinVer": "6.9.2",
"Overview": "Intel-GVT-g is a technology that provides mediated device passthrough for Intel iGPUs (Broadwell and newer). It can be used to virtualize the iGPU for multiple guest virtual machines and also in Docker containers, effectively providing near-native graphics performance in the virtual machine and still letting your host use the virtualized iGPU normally. This is useful if you want accelerated graphics in Windows virtual machines running without dedicated GPUs for full device passthrough.\n\nThis means less power consumption, less heat output and better performance for your VMs.",
"Date": 1631851200,
"Date": 1632974400,
"Requires": "Intel CPU (Broadwell or newer)",
"templatePath": "/tmp/GitHub/AppFeed/templates/ich777sRepository/ich777/Intel-GVT-g.xml",
"CategoryList": [
@ -185702,9 +185701,9 @@
"CategoryList": [
"Tools-Utilities"
],
"downloads": 4281,
"downloads": 4910,
"stars": 4,
"LastUpdateScan": 1630389805,
"LastUpdateScan": 1632989049,
"FirstSeen": 1625191390
},
{