[HW Accel Support]: FFMPEG CPU usage

[Reapuh-UwU]

Describe the problem you are having

I am having high ffmpeg cpu usage(21%) for one of my cameras(ReoLink RLC-811A). I have preset-nvidia in ffmpeg but i am not sure if its working or not. also is it possible to use the gpu to record? because in the system metrics gpu usage its currently just doing decode and no encoding

edit: i also completely forgot to add but this camera is also having recording issue where its hitch, like it would smooth motion/fps then slows down then smooth again.

Version

0.15.0-6cb5cfb

Frigate config file

mqtt:
  enabled: false
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detectors:
  tensorrt:
    type: tensorrt
    device: 0 #This is the default, select the first GPU
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ffmpeg:
  hwaccel_args: preset-nvidia
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model:
  path: /config/model_cache/tensorrt/yolov7-320.trt
  input_tensor: nchw
  input_pixel_format: rgb
  width: 320
  height: 320
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go2rtc:
  streams:
    porch:
      - rtsp://RTSP:[redacted]@192.168.2.193:554/h265Preview_01_main#audio=aac#video=h265#hardware
    living_room:
      - rtsp://RTSP:[redacted]@192.168.2.194:554/h265Preview_01_main#audio=aac#video=h265#hardware
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cameras:
  front_porch: # <--- this will be changed to your actual camera later
    enabled: true
    ffmpeg:
      output_args:
        record: preset-record-generic-audio-aac
      inputs:
        - path: rtsp://127.0.0.1:8554/porch
          roles:
            - detect
            - record
    objects:
      track:
        - person
      filters:
        person:
          # Optional: minimum width/height of the bounding box for the detected object (default: 0)
          min_ratio: 0.5
          # Optional: maximum width/height of the bounding box for the detected object (default: 24000000)
          max_ratio: 2.0
          # # Optional: minimum score for the object to initiate tracking (default: shown below)
          # min_score: 0.6
          # # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
          # threshold: 0.65
          mask:
            - 0.148,0.178,0.156,0.175,0.166,0.18,0.186,0.214,0.194,0.218,0.219,0.213,0.228,0.203,0.227,0.177,0.221,0.156,0.213,0.15,0.21,0.131,0.214,0.104,0.211,0.071,0.189,0.07,0.16,0.07,0.148,0.091,0.149,0.146
            - 0.564,0.124,0.541,0.339,0.647,0.378,0.668,0.14
    record:
      enabled: true
      retain:
        days: 0
        mode: all
      alerts:
        retain:
          days: 13
      detections:
        retain:
          days: 13
    detect:
      enabled: true
    motion:
      mask:
        - 2965,1171,2287,620,2390,193,2405,161,2429,145,2430,112,2554,123,2723,149,2944,203,3216,269,3563,367,3840,487,3840,1430,3840,1739
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  living_room: # <--- this will be changed to your actual camera later
    enabled: true
    ffmpeg:
      output_args:
        record: preset-record-generic-audio-aac
      inputs:
        - path: rtsp://127.0.0.1:8554/living_room
          roles:
            - detect
            - record
    objects:
      track:
        - person
      filters:
        person:
          # Optional: minimum width/height of the bounding box for the detected object (default: 0)
          min_ratio: 0.5
          # Optional: maximum width/height of the bounding box for the detected object (default: 24000000)
          max_ratio: 2.0
          # Optional: minimum score for the object to initiate tracking (default: shown below)
          # min_score: 0.6
          # # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
          # threshold: 0.65
          mask:
            - 917,834,1385,1078,1409,492,921,422
            - 554,490,832,465,792,153,544,193
    record:
      enabled: true
      retain:
        days: 0
        mode: all
      alerts:
        retain:
          days: 13
      detections:
        retain:
          days: 13
    detect:
      enabled: true
    motion:
      mask:
        - 515,0,530,409,548,414,585,410,655,402,704,471,844,456,812,0
version: 0.15-1

docker-compose file or Docker CLI command

docker run
  -d
  --name='frigate'
  --net='bridge'
  --pids-limit 2048
  -e TZ="America/New_York"
  -e HOST_OS="Unraid"
  -e HOST_HOSTNAME="Fuana"
  -e HOST_CONTAINERNAME="frigate"
  -e 'FRIGATE_RTSP_PASSWORD'='[redacted]'
  -e 'PLUS_API_KEY'=''
  -e 'NVIDIA_VISIBLE_DEVICES'='0'
  -e 'NVIDIA_DRIVER_CAPABILITIES'='compute,utility,video'
  -e 'YOLO_MODELS'='yolov4-416,yolov4-tiny-416,yolov7-320,yolov7x-320'
  -e 'USE_FP16'='false'
  -e 'TRT_MODEL_PREP_DEVICE'='0'
  -l net.unraid.docker.managed=dockerman
  -l net.unraid.docker.webui='http://[IP]:[PORT:8971]'
  -l net.unraid.docker.icon='https://raw.githubusercontent.com/yayitazale/unraid-templates/main/frigate.png'
  -p '5000:5000/tcp'
  -p '8554:8554/tcp'
  -p '8555:8555/tcp'
  -p '8555:8555/udp'
  -p '1984:1984/tcp'
  -p '8971:8971/tcp'
  -v '/mnt/user/appdata/frigate_config/config':'/config':'rw'
  -v '/mnt/user/Disk3/frigate_storage/storage':'/media/frigate':'rw'
  -v '/etc/localtime':'/etc/localtime':'rw'
  --runtime=nvidia
  --gpus=all
  --shm-size=5126mb
  --mount type=tmpfs,target=/tmp/cache,tmpfs-size=1000000000
  --restart unless-stopped 'ghcr.io/blakeblackshear/frigate:stable-tensorrt'

Relevant Frigate log output

N/A

Relevant go2rtc log output

N/A

FFprobe output from your camera

Input #0, rtsp, from 'rtsp://RTSP:[redacted]@192.168.2.194:554/h265Preview_01_main':
  Metadata:
    title           : Session streamed by "preview"
  Duration: N/A, start: 0.000063, bitrate: N/A
  Stream #0:0: Video: hevc (Main), yuv420p(tv), 2560x1920, 30 fps, 30 tbr, 90k tbn
  Stream #0:1: Audio: aac (LC), 16000 Hz, mono, fltp

Input #0, rtsp, from 'rtsp://127.0.0.1:8554/porch':
  Metadata:
    title           : go2rtc/1.9.2
  Duration: N/A, start: 0.000000, bitrate: N/A
  Stream #0:0: Video: hevc (Main), yuv420p(tv), 3840x2160, 25 fps, 25 tbr, 90k tbn
  Stream #0:1: Audio: aac (LC), 16000 Hz, mono, fltp

Install method

Docker CLI

Object Detector

TensorRT

Network connection

Wired

Camera make and model

ReoLink RLC-811A

Screenshots of the Frigate UI's System metrics pages

Any other information that may be helpful

CPU: ryzen 7 2700
ram: 16gb ddr4
gpu: rtx 2070
operating system: unraid 7.0.0

[NickM-27]

you're running recording on the full res stream, so this is expected. You will either want to use a sub stream, or at least use the detect config to reduce the stream down to 1280x720 or similar.

[Reapuh-UwU]

Ah I see I see thank you, but what about using the gpu to encode the recordings? I was looking at the ffmpeg presets py file and I say encoding preset and it has one for Nvidia

[hawkeye217]

Frigate only copies and does not decode the record stream, so there's no need for hardware acceleration in that case.

[Reapuh-UwU]

ooh i see i see alright thank you so much.