Speed zone measurements fixed in meter. #20085

deweycash · 2025-09-15T23:15:22Z

deweycash
Sep 15, 2025

Checklist

I have updated to the latest available Frigate version.
I have cleared the cache of my browser.
I have tried a different browser to see if it is related to my browser.
I have tried reproducing the issue in incognito mode to rule out problems with any third party extensions or plugins I have installed.
I have asked the AI at https://docs.frigate.video about my issue.

Describe the problem you are having

I configured the UI setting for imperial and then tried to set up a speed zone. The speeds were way off until I entered metric measurements even thought the UI is configured for imperial.

Steps to reproduce

Configure UI for imperial
Enter imperial (feet) measurements for the Speed Estimation in a zone
The reported speeds will be way off
Leave the UI configured for imperial
Enter the measurements in meters for the Speed Estimation (even though UI asks for feet)
The reported speeds will be much closer to actual
...

Version

0.16.1-e664cb2

In which browser(s) are you experiencing the issue with?

No response

Frigate config file

mqtt:
  enabled: true
  user: '{FRIGATE_MQTT_USER}'
  password: '{FRIGATE_MQTT_PASSWORD}'
  host: 192.168.1.120
  port: 1883

detectors:
  ov_detector:
    type: openvino
    device: GPU
#  coral:
#    type: edgetpu
#    device: usb
#    detect:
#      enabled: true # <---- disable detection until you have a working camera feed
#      width: 1280
#      height: 720
#      fps: 24

ui:
  # can be "metric" or "imperial", default is metric
  unit_system: imperial

model:
  width: 720
  height: 480
  path: plus://ba01e988083d8a3359f77f085cc0c218
  attributes_map:
    person:
      - amazon
      - face

genai:
  enabled: true
  provider: ollama
  base_url: XXXXXXXXXXXXXXXXXXXXXXXXX
  model: llava:7b
  prompt: Analyze the {label} in these images from the {camera} security camera.
    Focus on the actions, and behavior rather than just describing its 
    appearance.
  object_prompts:
    person: Examine the main person in these images. What are they doing and 
      what might their actions suggest about their intent (e.g., approaching a 
      door, leaving an area, standing still, delivery company)? Provide a 
      summary of the images without describing each one. Do not describe the 
      surroundings or static details.
    car: Observe the primary vehicle in these images. Focus on its movement, 
      direction, or purpose (e.g., parking, approaching, circling). If it's a 
      delivery vehicle, mention the company.
#  provider: gemini
#  api_key: XXXXXXXXXXXXXXXXXXXXXXXXXX
#  model: gemini-2.5-flash

ffmpeg:
#  hwaccel_args: preset-vaapi
  hwaccel_args: preset-intel-qsv-h264

birdseye:
  enabled: true
  mode: motion
  restream: true

go2rtc:
  streams:
    Living-Room:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.212:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac
    driveway:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.213:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac
    garage:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.214:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac
    frontdoor:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.211:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac#backchannel=0
    Shop-Inside:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.215:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac
    backyard:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.216:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac
    frontyard:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.217:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
      - ffmpeg:back#video=h264#audio=aac
    Shop_Outside_LQ:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.218:554/1
      - ffmpeg:back#video=h264#audio=aac
    Shop_Outside_HQ:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.218:554/0
      - ffmpeg:back#video=h264#audio=aac
    Backyard_Shop_LQ:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.219:554/1
      - ffmpeg:back#video=h264#audio=aac
    Backyard_Shop_HQ:
      - rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.219:554/0
      - ffmpeg:back#video=h264#audio=aac

    webrtc:
      candidates:
        - listen: :8555
        - 192.168.1.200:8555

cameras:
  backyard:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    webui_url: http://192.168.1.216
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.216:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.216:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
  frontdoor:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.211:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.211:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
  Living-Room:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    webui_url: http://192.168.1.212
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.212:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.212:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
    zones:
      zone_0:
        coordinates: 1280,0,0,0,0,720,1280,720
    onvif:
      host: 192.168.1.212
      port: 80
      user: '{FRIGATE_RTSP_USER}'
      password: '{FRIGATE_RTSP_PASSWORD}'
      autotracking:
        enabled: true
        calibrate_on_startup: false
        zooming: disabled
        track:
          - person
          - dog
        #Required: Begin automatically tracking an object when it enters any of the listed zones.
        required_zones:
          - zone_0
        #Required: Name of ONVIF preset in camera's firmware to return to when tracking is over. (default: shown below)
        return_preset: Preset1
        #Optional: Seconds to delay before returning to preset. (default: shown below)
        timeout: 10
        movement_weights: 0.0, 1.0, 0.17342472076416016, 0.4009148141389252, 
          0.35347215129483145, 0
  Shop-Inside:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    webui_url: http://192.168.1.215
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.215:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.215:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
    zones:
      zone_0:
        coordinates: 0,720,1280,720,1280,0,0,0
    onvif:
      host: 192.168.1.215
      port: 80
      user: '{FRIGATE_RTSP_USER}'
      password: '{FRIGATE_RTSP_PASSWORD}'

  driveway:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    webui_url: http://192.168.1.213
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.213:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.213:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
    motion: {}
    zones:
      Waste_Bin:
        coordinates: 0.133,0.115,0.313,0.101,0.337,0.375,0.145,0.399
        loitering_time: 2
        objects: waste_bin
        inertia: 3
      Driveway:
        coordinates: 
          0.753,1,0.766,0.968,0.762,1,0.799,1,0.843,1,0.877,1,0.988,0.735,0.988,0.494,0.637,0.182,0,0.335,0,1,0.123,1
        inertia: 3
        loitering_time: 0
      Speed:
        coordinates: 0.185,0.268,0.606,0.17,0.511,0.103,0.172,0.181
        loitering_time: 0
        speed_threshold: 10
        distances: 9.7,5.4,9.7,5.4
        objects: car
        inertia: 3
    objects:
      track:
        - car
        - person
        - dog
        - waste_bin
        - deer
      filters:
        car:
          mask: 
            0.398,0.105,0.596,0.09,0.86,0.141,0.994,0.205,0.988,0,0.385,0.003
        person:
          mask: 0.002,0.309,0.606,0.166,0.941,0.204,0.999,0.259,0.995,0,0,0
        waste_bin:
          mask: 0.44,0.052,0.47,0.205,0.989,0.218,0.948,0.016
    review:
      alerts:
        required_zones:
          - Waste_Bin
          - Driveway
      detections:
        required_zones: Driveway
  garage:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    webui_url: http://192.168.1.214
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.214:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.214:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
    objects:
      track:
        - person
        - car
  frontyard:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    webui_url: http://192.168.1.217
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.217:554/cam/realmonitor?channel=1&subtype=0&authbasic=64
          roles:
            - record
        #Low Resolution Stream
        - path: 
            rtsp://{FRIGATE_RTSP_USER}:{FRIGATE_RTSP_PASSWORD}@192.168.1.217:554/cam/realmonitor?channel=1&subtype=1&authbasic=64
          roles:
            - detect
    objects:
      track:
        - person
        - dog
        - deer
        - amazon
  Shop_Outside:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: rtsp://127.0.0.1:8554/Shop_Outside_HQ
          input_args: preset-rtsp-restream
          roles:
            - record
        - path: rtsp://127.0.0.1:8554/Shop_Outside_LQ
          input_args: preset-rtsp-restream
          roles:
            - detect
    live:
      streams:
        Shop_Outside_HQ: Shop_Outside_HQ
    onvif:
      host: 192.168.1.218
      port: 80
      user: '{FRIGATE_RTSP_USER}'
      password: '{FRIGATE_RTSP_PASSWORD}'
      autotracking:
        enabled: false
        calibrate_on_startup: true
        zooming: disabled
        track:
          - person
          - dog
          - car
        #Required: Begin automatically tracking an object when it enters any of the listed zones.
        required_zones:
          - zone_0
        #Required: Name of ONVIF preset in camera's firmware to return to when tracking is over. (default: shown below)
        return_preset: Preset01
        #Optional: Seconds to delay before returning to preset. (default: shown below)
        timeout: 10
    zones:
      zone_0:
        coordinates: 0.002,0.002,0.994,0,0.998,0.999,0.002,0.998
        loitering_time: 0


  Backyard_Shop:  # <------ Name the camera
    genai:
      enabled: true # <- enable GenAI for your front camera
      use_snapshot: true
    enabled: true
    ffmpeg:
      inputs:
        #High Resolution Stream
        - path: rtsp://127.0.0.1:8554/Backyard_Shop_HQ
          input_args: preset-rtsp-restream
          roles:
            - record
        - path: rtsp://127.0.0.1:8554/Backyard_Shop_LQ
          input_args: preset-rtsp-restream
          roles:
            - detect
    live:
      streams:
        Shop_Outside_HQ: Backyard_Shop_HQ
    onvif:
      host: 192.168.1.219
      port: 80
      user: '{FRIGATE_RTSP_USER}'
      password: '{FRIGATE_RTSP_PASSWORD}'

motion:
  threshold: 25
  contour_area: 100

#Global Object Settings
objects:
  track:
    - person
    - dog
    - deer
    - amazon
  filters:
    person:
      min_area: 5000
      max_area: 100000
    dog:
      min_area: 5000
      max_area: 100000
snapshots:
  # Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
  enabled: true
  # Optional: save a clean PNG copy of the snapshot image (default: shown below)
  clean_copy: true
  # Optional: print a timestamp on the snapshots (default: shown below)
  timestamp: true
  # Optional: draw bounding box on the snapshots (default: shown below)
  bounding_box: true
  # Optional: crop the snapshot (default: shown below)
  crop: false
  # Optional: height to resize the snapshot to (default: original size)
  height: 720
  # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
  required_zones: []
  # Optional: Camera override for retention settings (default: global values)
  retain:
    # Required: Default retention days (default: shown below)
    default: 10
    # Optional: Per object retention days
    objects:
      person: 15
record:
  enabled: true
  retain:
    # Optional: Number of days to retain recordings regardless of events (default: shown below)
    # NOTE: This should be set to 0 and retention should be defined in events section below
    #       if you only want to retain recordings of events.
    days: 10
    # Optional: Mode for retention. Available options are: all, motion, and active_objects
    #   all - save all recording segments regardless of activity
    #   motion - save all recordings segments with any detected motion
    #   active_objects - save all recording segments with active/moving objects
    # NOTE: this mode only applies when the days setting above is greater than 0
    mode: motion
  # Optional: Event recording settings
  alerts:
    retain:
      days: 10
    pre_capture: 5
    post_capture: 5
  detections:
    retain:
      days: 10
    pre_capture: 5
    post_capture: 5
version: 0.16-0
camera_groups:
  Birdseye:
    order: 1
    icon: LuBird
    cameras: birdseye
semantic_search:
  enabled: true
  reindex: false
  model_size: large

detect:
  # Optional: enables detection for the camera (default: shown below)
  enabled: true
  # Optional: width of the frame for the input with the detect role (default: use native stream resolution)
  width: 1280
  # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
  height: 720
  # Optional: desired fps for your camera for the input with the detect role (default: shown below)
  # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
  fps: 5
  # Optional: Number of consecutive detection hits required for an object to be initialized in the tracker. (default: 1/2 the frame rate)
  min_initialized: 2
  # Optional: Number of frames without a detection before Frigate considers an object to be gone. (default: 5x the frame rate)
  max_disappeared: 25
  # Optional: Configuration for stationary object tracking
  stationary:
    # Optional: Frequency for confirming stationary objects (default: same as threshold)
    # When set to 1, object detection will run to confirm the object still exists on every frame.
    # If set to 10, object detection will run to confirm the object still exists on every 10th frame.
    interval: 50
    # Optional: Number of frames without a position change for an object to be considered stationary (default: 10x the frame rate or 10s)
    threshold: 50
    # Optional: Define a maximum number of frames for tracking a stationary object (default: not set, track forever)
    # This can help with false positives for objects that should only be stationary for a limited amount of time.
    # It can also be used to disable stationary object tracking. For example, you may want to set a value for person, but leave
    # car at the default.
    # WARNING: Setting these values overrides default behavior and disables stationary object tracking.
    #          There are very few situations where you would want it disabled. It is NOT recommended to
    #          copy these values from the example config into your config unless you know they are needed.
    max_frames:
      # Optional: Default for all object types (default: not set, track forever)
      default: 3000
      # Optional: Object specific values
      objects:
        waste_bin: 1000
  # Optional: Milliseconds to offset detect annotations by (default: shown below).
  # There can often be latency between a recording and the detect process,
  # especially when using separate streams for detect and record.
  # Use this setting to make the timeline bounding boxes more closely align
  # with the recording. The value can be positive or negative.
  # TIP: Imagine there is an tracked object clip with a person walking from left to right.
  #      If the tracked object lifecycle bounding box is consistently to the left of the person
  #      then the value should be decreased. Similarly, if a person is walking from
  #      left to right and the bounding box is consistently ahead of the person
  #      then the value should be increased.
  # TIP: This offset is dynamic so you can change the value and it will update existing
  #      tracked objects, this makes it easy to tune.
  # WARNING: Fast moving objects will likely not have the bounding box align.
  annotation_offset: 0

face_recognition:
  enabled: true
  model_size: small
lpr:
  enabled: false
classification:
  bird:
    enabled: false

docker-compose file or Docker CLI command

Using Unraid and can't find this file.

Relevant Frigate log output

NA

Relevant go2rtc log output

NA

Operating system

UNRAID

Install method

Docker CLI

Network connection

Wired

Camera make and model

Amcrest

Screenshots of the Frigate UI's System metrics pages

NA

Any other information that may be helpful

No response

Answered by hawkeye217

Sep 16, 2025

Take the polygon you're drawing for the zone.

If side AB is 10 units (whether you call that "meters" or "feet", it doesn't matter) in real life but 100 pixels in the image, the scale is 0.1 units/pixel
If side CD is 10 units but appears as 50 pixels due to perspective, the scale is 0.2 units/pixel

The output units depend entirely on what units were used for the distances parameter:

If distances were in meters → speed_magnitude is in m/s
If distances were in feet → speed_magnitude is in ft/s

So, the code simply takes what you have set for unit system and:

For metric zones (distances in meters): converts m/s to km/h
For imperial zones (distances in feet): converts ft/s to mph

The system is …

View full answer

hawkeye217 · 2025-09-15T23:25:03Z

hawkeye217
Sep 15, 2025
Collaborator

This code has been tested by many users with both meters (metric) and feet (imperial), it is unlikely to be a bug.

As noted in the docs, there are a large number of factors that affect speed estimation.

Can you post a screenshot of your speed estimation zone from the zone editor?

14 replies

deweycash Sep 16, 2025
Author

I'm still working to verify if the bottom bounding box remains in the zone. It for sure does it with right to left but not for left to right.

hawkeye217 Sep 16, 2025
Collaborator

The code you pointed to was for reporting speed (converting MPH to KPH). I'm not sure I would understand the code where the distances are entered.

Right, I wrote the code so it's pretty familiar.

frigate/frigate/util/velocity.py

Line 81 in 5f34a18

def calculate_real_world_speed(

The calculation is unit_system-agnostic. The create_ground_plane() function calculates a scale factor (real-world distance per pixel) that varies across the zone to account for perspective distortion. The code I pasted before takes this distance per pixel (whether feet per pixel or meters per pixel) and multiplies it based on what you've set for unit_system.

deweycash Sep 16, 2025
Author

I don't see how it can be agnostic in that way. Where in the code are the distances implemented and where do you differentiate between metric and imperial for the distances entered in the UI?

No matter what changes I make, the speed estimation works best when I enter meters even though the UI asks for feet.

hawkeye217 Sep 16, 2025
Collaborator

Take the polygon you're drawing for the zone.

If side AB is 10 units (whether you call that "meters" or "feet", it doesn't matter) in real life but 100 pixels in the image, the scale is 0.1 units/pixel
If side CD is 10 units but appears as 50 pixels due to perspective, the scale is 0.2 units/pixel

The output units depend entirely on what units were used for the distances parameter:

If distances were in meters → speed_magnitude is in m/s
If distances were in feet → speed_magnitude is in ft/s

So, the code simply takes what you have set for unit system and:

For metric zones (distances in meters): converts m/s to km/h
For imperial zones (distances in feet): converts ft/s to mph

The system is unit-agnostic - it just uses whatever units the zone was calibrated with.

Answer selected by deweycash

hawkeye217 Sep 16, 2025
Collaborator

No matter what changes I make, the speed estimation works best when I enter meters even though the UI asks for feet.

Again, this is likely because your bounding box is changing size/shape as it moves through the zone.

You could try to make your zone even smaller, and move the bottom edge downward into the driveway so that the bottom center of car bounding boxes will always pass through the zone.

deweycash Sep 16, 2025
Author

Take the polygon you're drawing for the zone.

If side AB is 10 units (whether you call that "meters" or "feet", it doesn't matter) in real life but 100 pixels in the image, the scale is 0.1 units/pixel If side CD is 10 units but appears as 50 pixels due to perspective, the scale is 0.2 units/pixel

The output units depend entirely on what units were used for the distances parameter:

If distances were in meters → speed_magnitude is in m/s If distances were in feet → speed_magnitude is in ft/s

So, the code simply takes what you have set for unit system and:

For metric zones (distances in meters): converts m/s to km/h For imperial zones (distances in feet): converts ft/s to mph

The system is unit-agnostic - it just uses whatever units the zone was calibrated with.

That makes sense. Thanks for explaining.

I'll continue to test in the AM when I have daylight again.

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Speed zone measurements fixed in meter. #20085

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Speed zone measurements fixed in meter. #20085

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deweycash Sep 15, 2025

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Replies: 1 comment · 14 replies

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hawkeye217 Sep 15, 2025 Collaborator

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deweycash Sep 16, 2025 Author

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hawkeye217 Sep 16, 2025 Collaborator

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deweycash Sep 16, 2025 Author

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hawkeye217 Sep 16, 2025 Collaborator

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hawkeye217 Sep 16, 2025 Collaborator

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deweycash Sep 16, 2025 Author

deweycash
Sep 15, 2025

Replies: 1 comment 14 replies

hawkeye217
Sep 15, 2025
Collaborator

deweycash Sep 16, 2025
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hawkeye217 Sep 16, 2025
Collaborator

deweycash Sep 16, 2025
Author

hawkeye217 Sep 16, 2025
Collaborator

hawkeye217 Sep 16, 2025
Collaborator

deweycash Sep 16, 2025
Author