yayitazale

Members
  • Content Count

    56
  • Joined

  • Last visited

Community Reputation

11 Good

About yayitazale

  • Rank
    Newbie

Recent Profile Visitors

The recent visitors block is disabled and is not being shown to other users.

  1. I don't think so but ask to the creator of the app on github.
  2. Support for Antennas docker container. HDHomeRun emulator for Plex/Jellyfin/Emby DVR to connect to Tvheadend. Application Name: Antennas Application Site: https://github.com/TheJF/antennas Docker Hub: https://hub.docker.com/r/thejf/antennas Github: https://github.com/TheJF/antennas To be able to stream from Tvheadend, you need to set up an anonymous user in Tvheadend that has streaming rights. You can do this in the users section, by creating a user *: To run this container, first create a config.yaml on the config folder with the follo
  3. Yes, you can use it as standalone service but keep in mind that there is no notification channel implemented aside from mqtt.
  4. ¿Did you install the Unraid Nvidia plugin? I dind't have time to create a nvidia frigate version but I can try to create it this weekend. Not sure about how complex the hard acc env variable setup will be...
  5. Ok, so if you want to avoid using SSD cache you just can add a path to mount the media folder onto a non cache share of unraid. Create a new share on unraid and set the use of cache to "no", like this, including the disks you want to use to store NVR: Be aware that this is going to increase a lot the write/reads on your disk, so I recomend you to use a proper disk to do this job (a WD purple series or so https://amzn.to/3e6UIjC (refered link)). You should config all your shares except the NVR one to not to use this disk and only use it to store NVR)
  6. Do yo know on whitch folder of hass is storing all the media?
  7. I'm not the developer of the app, I'm just mantaining the Template of the container for Unraid. The modified addon is mainteined by another guy so ask him if he is going to maintain it. https://github.com/pdecat
  8. I only own a USB coral so I don't need drivers to make it work and I can use it on mobile proyects with a raspi. 🤑🤑🤑
  9. As far as I know, the TPU chip inside the all Coral boards is the same, so the driver should be valid for all of the coral PCI-M2 devices. Correct me please... @ich777
  10. Your CPU is not supported, the only way to make it work is using the modified version of the hass addon: https://github.com/blakeblackshear/frigate/issues/695 https://github.com/pdecat/frigate-hass-addons
  11. The first try is the way but you are trying to match /labelmap.txt with a folder labelmap. The correct way it should be to match a file with a file: container path : /labelmap.txt host path: /mnt/user/appdata/frigate/labelmap/labelmap.txt You can see it on the docs: https://blakeblackshear.github.io/frigate/configuration/objects#customizing-the-labelmap Cheers
  12. As far as I'm not the developer of the APP, I'm not sure about any of the questions you have, but I'll try to help. Anyway, you can ask these questions on the Github page. 1. I think currently it doesn't ignore stationary objects because in the end you are passing static frames to TFlite and this can't really know witch of the 2 cars is the one tha't it's moving. To avoid false positives from wind motion you have to adjust the values of the detection as you can read here: https://blakeblackshear.github.io/frigate/configuration/false_positives On next releases maybe this
  13. 1) To use the M.2 coral you need to install the "Coral Accelerator Module Drivers" plugin and then edit this: to this: 2) I already changed this on the template this week, look at the hidden settings and you should see this: If you don't see it, delete your container and add it again from the app store.
  14. Yes you are right, this is an error. I deleted the duplicated line, sorry about that. The Intel/AMD GPU is only used for optimizing the performance of the FFMPEG image decoder using integrated GPU for hardware acceleration, reducing your CPU load. The CORAL is used to offloading TensorFlow of the CPU. Is an order of magnitude faster and will reduce your CPU load dramatically. https://blakeblackshear.github.io/frigate/configuration/optimizing If you can't start the container maybe you don't have a hard. acc. compatible CPU, so you just can delete