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This commit is contained in:
Andrew Z 2021-03-04 16:02:45 -05:00
parent b7782f07da
commit 30f2c51696
5 changed files with 535 additions and 550 deletions
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2
applicationFeed-lastUpdated.json
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119
old-applicationFeed-raw.json
View File

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"Repo": "yayitazale's Repository",
"Date": "1611751168",
"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[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)\r\n",
"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",
"Date": "1614883235",
"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[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&searchlight=1\"]this steps[/a]\r\n\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)\r\n\r\n\r\n\r\n\r\n[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[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)\r\n",
"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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