Guide: Local License Plate Recognition With Home Assistant, CodeProject.AI, and Frigate

[NickM-27]

I spent quite a while trying different ideas to get a working & fully local license plate setup and I am quite happy with this one so I am sharing a guide on how to do this.

Prerequisites

• You must have CodeProject.AI setup with the license plate plugin installed, that is outside the scope of this guide.
• Frigate+ is recommended for its built in license plate detection, however this can definitely work without Frigate+ with some modifications that are called out in the code comments

Preliminary Info

The approach in this guide requires using a python script to run some of the logic because it would be much more difficult to do in a Home Assistant automation. This script is called directly from home assistant.

Python Script

1. Create a folder to hold your scripts (if you don't already have one) inside of the Home Assistant /config directory. For this guide I will call mine /config/bashscript
2. Create a file called license_plate.py in /config/bashscript

The contents of the file should be

import datetime
import io
import json
import requests
import sys

from PIL import Image

# host for frigate instance
FRIGATE_HOST = "http:https://10.0.0.0:5000"

# host for CodeProject.AI instance
CODE_PROJECT_HOST = "http:https://10.0.0.0:4100"

event_id = sys.argv[1]
json = requests.get(f"{FRIGATE_HOST}/api/events/{event_id}").json()
camera = json["camera"]

# get clean snapshot if event has bended otherwise get snapshot without bounding boxes
if json["end_time"]:
    img_bytes = requests.get(f"{FRIGATE_HOST}/clips/{camera}-{event_id}-clean.png").content
else:
    img_bytes = requests.get(f"{FRIGATE_HOST}/api/events/{event_id}/snapshot.jpg?bbox=0").content

img = Image.open(io.BytesIO(img_bytes))

attributes = json["data"]["attributes"]

# Checks to ensure that license plate is detected
# this will need to be removed if not using a model that detects license plates (like frigate+)
if not attributes:
    with open("/config/bashscript/detected_plates.log", "a") as log:
        log.write(f"[{datetime.datetime.now()}]: {camera} - could not find license plate in {json}\n")
        sys.exit()

box = attributes[0]["box"]

detect = requests.get(f"{FRIGATE_HOST}/api/config").json()["cameras"][camera]["detect"]
known_res = (detect["width"], detect["height"])

# find coordinates of image to send to detector
x_mid = (box[0] + (box[2] / 2)) * known_res[0]
y_max = (box[1] + box[3]) * known_res[1]

cropped_image = img.crop((max(0, x_mid - 150), 0, min(known_res[0], x_mid + 150), known_res[1]))
cropped_image.save("/config/bashscript/crop.jpg")

# you will likely need to create multiple iterations for each vehicle
# 0/8/B for example are often mixed up
known_plates = {
    # Bob's Car
    "ABC128": "Bob's Car",
    "ABC12B": "Bob's Car",
    
    # Steve's Truck
    "123TR0": "Steve's Truck",
    "123TRO": "Steve's Truck",
}

with open('/config/bashscript/crop.jpg', 'rb') as fp:
    response = requests.post(
        f'{CODE_PROJECT_HOST}/v1/image/alpr',
        files=dict(upload=fp),
    )
    print(response.json())
    plates = response.json()
    plate = None

    if len(plates["predictions"]) > 0 and plates["predictions"][0].get("plate"):
        plate = str(plates["predictions"][0]["plate"]).replace(" ", "")
        score = plates["predictions"][0]["confidence"]
        print(f"Checking plate: {plate} in {known_plates.keys()}")

        with open("/config/bashscript/detected_plates.log", "a") as log:
            log.write(f"[{datetime.datetime.now()}]: {camera} - detected {plate} as {known_plates.get(plate)} with a score of {score}\n")

        if plate in known_plates.keys():
            print(f"{camera} - Found a known plate: {known_plates[plate]}")
        else:
            plate = None
    else:
        with open("/config/bashscript/detected_plates.log", "a") as log:
            log.write(f"[{datetime.datetime.now()}]: {camera} - No plates detected in run: {plates}\n")

    if plate is None:
        print(f"No valid results found: {plates['predictions']}")
        sys.exit()

vehicle_name = known_plates[plate]

# Add Sub Label To Car Event
requests.post(f"{FRIGATE_HOST}/api/events/{event_id}/sub_label", json={"subLabel": vehicle_name, "subLabelScore": round(score, 2)})

There are a few things to note here:

• CodeProject.AI port might be different depending on where and how it is setup
• Some characters are commonly mixed up like 0/O and 8/B, these variations should be added so this works more reliably.
• This will create a file detected_plates.log which will provide info on what plates are detected and if it matches a known plate
• The latest image sent to ALPR will be saved as /config/bashscript/crop.jpg

Home Assistant Configuration

The next step is adding this python script to the Home Assistant configuration.yaml file. This is done via the shell_command type:

shell_command:
  # run license plate inference for vehicle
  check_license_plate: python3 /config/bashscript/license_plate.py "{{ event_id }}"

Home Assistant Automation

The final step is setting up the Home Assistant automation, this will run on a car event only when it has a license plate attribute detected. It will then run 3 iterations of checking for the license plate and stop when a license plate is detected.

This single automation will run on all cameras that detect a car with a license plate in the zones defined, so if you have a driveway zone specified for the garage and driveway camera it will run on both.

alias: >-
  Outside - Driveway - Frigate Try To Read License Plate - License Plate
  Seen
description: ""
trigger:
  - platform: mqtt
    topic: frigate/events
    id: frigate-event
    payload: license_plate
    # will just want to check that the label == "car" if not using a model that supports license_plate
    value_template: "{{ value_json[\"after\"][\"current_attributes\"][0]['label'] }}"
    variables:
      after_zones: "{{ trigger.payload_json[\"after\"][\"entered_zones\"] }}"
      before_zones: "{{ trigger.payload_json[\"before\"][\"entered_zones\"] }}"
      current_zones: "{{ trigger.payload_json[\"after\"][\"current_zones\"] }}"
      camera: "{{ trigger.payload_json[\"after\"][\"camera\"] }}"
      id: "{{ trigger.payload_json[\"after\"][\"id\"] }}"
      label: "{{ trigger.payload_json[\"after\"][\"label\"] }}"
condition:
  - condition: and
    conditions:
      # setup zones that you want to look for cars with license plates for detection
      - condition: template
        value_template: >-
          {{ ["street", "driveway"] | select("in", after_zones) | list | length
          > 0 }}
        alias: Has been in the street
      - condition: template
        value_template: "{{ [\"driveway\"] | select(\"in\", current_zones) | list | length > 0 }}"
    alias: Was in the street and is now in the driveway
  - condition: template
    value_template: "{{ trigger.payload_json['after']['sub_label'] is none }}"
action:
  - choose:
      - conditions:
          - condition: trigger
            id: frigate-event
        sequence:
          - service: shell_command.check_license_plate
            data_template:
              event_id: "{{id}}"
              camera: "{{camera}}"
          - repeat:
              count: 2
              sequence:
                - wait_for_trigger:
                    - platform: mqtt
                      topic: frigate/events
                      payload: "{{ trigger.payload_json['after']['id'] }}"
                      value_template: "{{ value_json['after']['id'] }}"
                  timeout:
                    hours: 0
                    minutes: 10
                    seconds: 0
                    milliseconds: 0
                  continue_on_timeout: false
                - condition: template
                  value_template: >-
                    {{ wait.trigger.payload_json['after']['sub_label'] is none
                    }}
                - service: shell_command.check_license_plate
                  data_template:
                    event_id: "{{id}}"
                    camera: "{{camera}}"
    default: []
trace:
  stored_traces: 20
mode: parallel
max: 10

[weitheng]

Thank you for sharing. How do I get this to work without Frigate+?

[NickM-27]

What do you get when you run it from the cli directly?

[weitheng]

I am running Home Assistant OS, I tried running the commands on the cli interface, not sure if I am supposed to run it here but I get this

Error: unknown command "python3" for "ha"
Run 'ha --help' for usage. 
FATA[0000] Error while executing rootCMd: unknown command "python3" for "ha"

However when I tried to call the shell command service in the Developer Tools page, I get

stdout: ""
stderr: |-
  Traceback (most recent call last):
    File "/usr/local/lib/python3.11/site-packages/requests/models.py", line 971, in json
      return complexjson.loads(self.text, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/simplejson/__init__.py", line 514, in loads
      return _default_decoder.decode(s)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/simplejson/decoder.py", line 386, in decode
      obj, end = self.raw_decode(s)
                 ^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/simplejson/decoder.py", line 416, in raw_decode
      return self.scan_once(s, idx=_w(s, idx).end())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  simplejson.errors.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

  During handling of the above exception, another exception occurred:

  Traceback (most recent call last):
    File "/config/bashscript/license_plate.py", line 16, in <module>
      json = requests.get(f"{FRIGATE_HOST}/api/events/{event_id}").json()
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/requests/models.py", line 975, in json
      raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
  requests.exceptions.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
returncode: 1

Looks like python3 is not installed in the OS?

[NickM-27]

Looks like python3 is not installed in the OS?

yeah, I personally run HA container and get a shell there, so you may need to see if that is possible.

to be clear when running from within HA you are sending a valid event_id correct?

[BradFD]

@weitheng Hi I am having the same issue as you, how did you solve this? Thank you.

I am running Home Assistant OS, I tried running the commands on the cli interface, not sure if I am supposed to run it here but I get this

Error: unknown command "python3" for "ha"
Run 'ha --help' for usage. 
FATA[0000] Error while executing rootCMd: unknown command "python3" for "ha"

However when I tried to call the shell command service in the Developer Tools page, I get

stdout: ""
stderr: |-
  Traceback (most recent call last):
    File "/usr/local/lib/python3.11/site-packages/requests/models.py", line 971, in json
      return complexjson.loads(self.text, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/simplejson/__init__.py", line 514, in loads
      return _default_decoder.decode(s)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/simplejson/decoder.py", line 386, in decode
      obj, end = self.raw_decode(s)
                 ^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/simplejson/decoder.py", line 416, in raw_decode
      return self.scan_once(s, idx=_w(s, idx).end())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  simplejson.errors.JSONDecodeError: Expecting value: line 1 column 1 (char 0)

  During handling of the above exception, another exception occurred:

  Traceback (most recent call last):
    File "/config/bashscript/license_plate.py", line 16, in <module>
      json = requests.get(f"{FRIGATE_HOST}/api/events/{event_id}").json()
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "/usr/local/lib/python3.11/site-packages/requests/models.py", line 975, in json
      raise RequestsJSONDecodeError(e.msg, e.doc, e.pos)
  requests.exceptions.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
returncode: 1

Looks like python3 is not installed in the OS?

[BradFD]

Figured out this does not work in Hass OS because Pillow and Requests are not installed. You can pip install them but they don't stay installed after any update or restart so this script doesn't see workable with Hass OS.

[gavink2]

I'm doing a similar function using a local instance of ALPR Snapshot from Plate Recognizer, together with the Plate Recognizer HA integration. It's working perfectly and was pretty simple to set up. It's also possible to use their free cloud-based product for up to 2500 detections a month. Local is much faster if you have a good machine with an Nvidia GPU as well as being more private, of course.

In short, I set up a zone on a Frigate camera and when a car is detected in that zone, an automation is triggered and one frame is sent to ALPR Snapshot via the HA integration. That then triggers an HA event if a plate is detected. If the license plate is our car's and our gate is closed or closing, then open the gate.

configuration.yaml:

image_processing:
  - platform: platerecognizer
    api_token: xxxx
    server: http:https://192.168.x.x:8080/v1/plate-reader/
    regions:
      - gb
    watched_plates:
      - ab12cde
    detection_rule: strict
    region: strict
    source:
      - entity_id: camera.gate #  Supplied by Frigate integration

automation:

alias: Open gate when our car detected in driveway
description: ""
trigger:
  - platform: state
    entity_id:
      - binary_sensor.plate_car_occupancy # Provided by Frigate integration, state changes to 'on' when a car is detected in the 'plate' zone.
    to: "on"
condition: []
action:
  - service: image_processing.scan
    data: {}
    target:
      entity_id: image_processing.platerecognizer_gate
  - wait_for_trigger:
      - platform: event
        event_type: platerecognizer.vehicle_detected
        event_data:
          plate: aa12cde # Plate to check for
    timeout:
      hours: 0
      minutes: 0
      seconds: 15
      milliseconds: 0
    continue_on_timeout: false
  - condition: state
    entity_id: cover.gate
    state:
      - closed
      - closing
  - service: cover.open_cover
    data: {}
    target:
      entity_id: cover.gate
mode: single

docker-compose.yaml for ALPR Snapshot

version: "3.8"
services:
  alpr-snapshot:
    container_name: alpr-snapshot
    restart: unless-stopped
    image: platerecognizer/alpr-gpu
    environment:
      - LICENSE_KEY=xxxxx
      - TOKEN=xxxxx
      - WORKERS=8 # Adjust based on number of CPU cores. See docs.
    volumes:
      - ./alpr-snapshot/license:/license
    ports:
      - "192.168.x.x:8080:8080/tcp"
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Frigate config:

cameras:
  gate:
    ffmpeg:
      output_args:
        record: preset-record-generic-audio-copy
      inputs:
        - path: rtsp:https://127.0.0.1:8554/gate # go2rtc
          input_args: preset-rtsp-restream-low-latency
          roles:
            - record
            - detect
    motion:
      mask:
        - 1920,0,1920,406,1138,381,824,412,0,464,0,0
    zones:
      plate:
        coordinates: 1316,714,589,711,424,1080,1534,1080

[Jens-Wymeersch]

Here you go

image_processing:
  - platform: platerecognizer
    api_token: token
    regions:
      - il
    save_file_folder: /config/www/plates
    save_timestamped_file: True
    always_save_latest_file: True
    watched_plates:
      - plate1 
      - plate2 
      - plate3
      - plate4
      - plate5
    detection_rule: strict
    region: strict
    source:
      - entity_id: camera.parking_car #  Supplied by Frigate integration

and the following automation

alias: Plate Recognition
description: ""
trigger:
  - platform: state
    entity_id:
      - binary_sensor.parking_entry_zone_car_occupancy
    to: "on"
condition: []
action:
  - service: image_processing.scan
    data: {}
    target:
      entity_id: image_processing.platerecognizer_parking_car
mode: single

[NickM-27]

it's pulling from the camera directly, so it will just get the latest frame from the camera, not one related to a specific event. That also works completely differently than the automation shown in this guide

[Jens-Wymeersch]

@NickM-27 Please explain a bit better. The plate_recognizer, works based on a snapshot that's been taken. I assume that codeproject.ai uses the actual footage (meaning goes through the camera feed). Correct ?
Is there any way to take better snapshots ? Higher in quality ?
What are these values doing and what can they be ?
```
# Optional: height to resize the snapshot to (default: shown below)
height: 270
# Optional: jpeg encode quality (default: shown below)
quality: 70

[NickM-27]

I assume that codeproject.ai uses the actual footage (meaning goes through the camera feed). Correct ?

no, that is incorrect. Codeproject.ai simply uses a snapshot the same as any other service. But I have frigate choose the best snapshot and send that to codeproject ai instead of just having it pull the latest from the camera entity

What are these values doing and what can they be ?

they are doing what they say, I don't understand the question. height can be any number up to the height of the detect camera stream and quality is between 1 and 100

[pilot1981]

Hi,
I have the same setup, but I have problem to select the right frame to sent to PlateRecognizer: how do you solved this issue?

[wizu]

@NickM-27 How to get found vehicles names back in home assistant?
It would be great if eg. entity camera.parking_car or binary_sensor.parking_car_occupancy would have attribute with vehicles names found

[NickM-27]

they are in the sub_label field in the MQTT payload for frigate events. I use this to update notifications with the vehicles name or other.

Would be interesting to have some sort of entity capture that as well

[Jens-Wymeersch]

Hi Nick, so if understand it correctly, you need a way to find the right frame for the snapshot within the footage to check the plates. In the case of platerecognizer, this will be done by ALPR Snapshot @gavink2 Am I correct ?

[gavink2]

ALPR Snapshot works on whatever single frame it receives via its API. How that frame is chosen is not down to ALPR Snapshot. If you want something that's monitoring a camera stream then check out https://platerecognizer.com/stream, point it at a go2rtc stream provided by Frigate and get events back into Home Assistant via a webhook. Otherwise, I refer you back to @NickM-27 's solution at the top of this thread. As for best setup for an ALPR camera, there's plenty of guidance at https://platerecognizer.com/camera-setup-for-best-anpr, much of which is not Platerecognizer-specific.

[idontcare99999]

Hey Nick, FYI line 34 in the python script has an indentation issue; it's copying over with 2 tabs instead of 8 spaces.

[NickM-27]

thanks, updated

[wizu]

@NickM-27 since updating frigate to beta3 there is now a lot of events for plate recognition. Some times a few per socond. Even where there is no motion, only one stationary car. It looks like it has someting to do with new stationary object detection enchantments.

[2023-10-30 09:37:38.222917]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 406, 'inferenceMs': 400, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 938}
[2023-10-30 09:37:48.751307]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 548, 'inferenceMs': 541, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 582}
[2023-10-30 09:37:48.886178]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 611, 'inferenceMs': 604, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 629}
[2023-10-30 09:37:49.266554]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 521, 'inferenceMs': 515, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 984}
[2023-10-30 09:37:58.305343]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 622, 'inferenceMs': 612, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 659}
[2023-10-30 09:37:58.308090]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 603, 'inferenceMs': 597, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 621}
[2023-10-30 09:37:58.694733]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 398, 'inferenceMs': 389, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 960}
[2023-10-30 09:38:08.457358]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 630, 'inferenceMs': 619, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 665}
[2023-10-30 09:38:08.517737]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 676, 'inferenceMs': 667, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 693}
[2023-10-30 09:38:08.824515]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 374, 'inferenceMs': 367, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 845}
[2023-10-30 09:38:18.787246]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 612, 'inferenceMs': 600, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 645}
[2023-10-30 09:38:18.865125]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 608, 'inferenceMs': 600, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 634}
[2023-10-30 09:38:19.185613]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 405, 'inferenceMs': 399, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 946}
[2023-10-30 09:38:28.953807]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 582, 'inferenceMs': 577, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 642}
[2023-10-30 09:38:29.058222]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 699, 'inferenceMs': 689, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 726}
[2023-10-30 09:38:29.396703]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 472, 'inferenceMs': 456, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 954}
[2023-10-30 09:38:39.239702]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 613, 'inferenceMs': 606, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 651}
[2023-10-30 09:38:39.256281]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 545, 'inferenceMs': 539, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 565}
[2023-10-30 09:38:39.569821]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 336, 'inferenceMs': 330, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 850}
[2023-10-30 09:38:50.336016]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1091, 'inferenceMs': 1073, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1121}
[2023-10-30 09:38:50.547480]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1118, 'inferenceMs': 1088, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1184}
[2023-10-30 09:39:00.318780]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1016, 'inferenceMs': 1011, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1047}
[2023-10-30 09:39:00.603546]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1200, 'inferenceMs': 1186, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1266}
[2023-10-30 09:39:01.102464]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 789, 'inferenceMs': 783, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1738}
[2023-10-30 09:39:10.957736]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1186, 'inferenceMs': 1172, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1262}
[2023-10-30 09:39:11.090775]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1249, 'inferenceMs': 1242, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1280}
[2023-10-30 09:39:11.551026]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 608, 'inferenceMs': 601, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1571}
[2023-10-30 09:39:20.783358]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 752, 'inferenceMs': 736, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 832}
[2023-10-30 09:39:20.815185]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 806, 'inferenceMs': 798, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 888}
[2023-10-30 09:39:21.296742]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 488, 'inferenceMs': 481, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1262}
[2023-10-30 09:39:30.511360]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 620, 'inferenceMs': 607, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 666}
[2023-10-30 09:39:30.533641]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 588, 'inferenceMs': 581, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 618}
[2023-10-30 09:39:30.837765]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 338, 'inferenceMs': 328, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 901}
[2023-10-30 09:40:10.247422]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 7908, 'inferenceMs': 7902, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 19564}
[2023-10-30 09:40:10.360555]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 8024, 'inferenceMs': 8018, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 19703}
[2023-10-30 09:40:11.775900]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1560, 'inferenceMs': 1552, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 21123}
[2023-10-30 09:40:11.822003]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1443, 'inferenceMs': 1431, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 10916}
[2023-10-30 09:40:12.792672]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1000, 'inferenceMs': 994, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2667}
[2023-10-30 09:40:12.970537]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1119, 'inferenceMs': 1097, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2685}
[2023-10-30 09:40:13.922785]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1130, 'inferenceMs': 1123, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2405}
[2023-10-30 09:40:13.993006]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1042, 'inferenceMs': 1027, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2300}
[2023-10-30 09:40:22.697737]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 945, 'inferenceMs': 917, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 980}
[2023-10-30 09:40:22.797645]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 953, 'inferenceMs': 948, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1043}
[2023-10-30 09:40:23.864295]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1147, 'inferenceMs': 1141, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2061}
[2023-10-30 09:40:24.048742]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1216, 'inferenceMs': 1209, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2236}
[2023-10-30 09:40:24.755903]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 880, 'inferenceMs': 874, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2830}
[2023-10-30 09:40:24.799476]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 764, 'inferenceMs': 752, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2850}
[2023-10-30 09:40:25.280439]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 530, 'inferenceMs': 523, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 3234}
[2023-10-30 09:40:32.577818]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 904, 'inferenceMs': 894, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 964}
[2023-10-30 09:40:32.694478]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 926, 'inferenceMs': 919, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 965}
[2023-10-30 09:40:33.504430]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 914, 'inferenceMs': 909, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1568}
[2023-10-30 09:40:33.664876]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 896, 'inferenceMs': 891, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1619}
[2023-10-30 09:40:34.423788]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 922, 'inferenceMs': 916, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2298}
[2023-10-30 09:40:34.482487]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 846, 'inferenceMs': 822, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2327}
[2023-10-30 09:40:42.669445]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 854, 'inferenceMs': 848, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 879}
[2023-10-30 09:40:42.765417]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 960, 'inferenceMs': 947, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1002}
[2023-10-30 09:40:43.274206]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 513, 'inferenceMs': 507, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1475}
[2023-10-30 09:40:46.554206]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 929, 'inferenceMs': 914, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 978}
[2023-10-30 09:40:46.730532]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1019, 'inferenceMs': 1012, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1079}
[2023-10-30 09:40:47.981645]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1416, 'inferenceMs': 1410, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2239}
[2023-10-30 09:40:48.353518]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1292, 'inferenceMs': 1266, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1327}
[2023-10-30 09:40:49.467122]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 939, 'inferenceMs': 923, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 983}
[2023-10-30 09:40:49.734043]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 821, 'inferenceMs': 814, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 899}
[2023-10-30 09:40:50.255791]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 795, 'inferenceMs': 788, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1399}
[2023-10-30 09:40:50.450214]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 724, 'inferenceMs': 717, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1568}
[2023-10-30 09:40:53.621022]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1493, 'inferenceMs': 1485, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1575}
[2023-10-30 09:40:53.886894]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1499, 'inferenceMs': 1482, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1606}
[2023-10-30 09:40:54.696891]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1077, 'inferenceMs': 1061, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2358}
[2023-10-30 09:40:54.920333]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1072, 'inferenceMs': 1040, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2420}
[2023-10-30 09:40:55.196719]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 506, 'inferenceMs': 500, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2684}
[2023-10-30 09:41:05.773089]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1853, 'inferenceMs': 1842, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1953}
[2023-10-30 09:41:05.963411]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 2080, 'inferenceMs': 2073, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2195}
[2023-10-30 09:41:07.000829]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1197, 'inferenceMs': 1184, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2201}
[2023-10-30 09:41:07.271950]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1356, 'inferenceMs': 1322, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2560}
[2023-10-30 09:41:07.819019]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 833, 'inferenceMs': 815, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2970}
[2023-10-30 09:41:14.551880]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1492, 'inferenceMs': 1477, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1572}
[2023-10-30 09:41:14.697499]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1571, 'inferenceMs': 1536, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1617}
[2023-10-30 09:41:15.440695]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 887, 'inferenceMs': 879, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2169}
[2023-10-30 09:41:24.192084]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1309, 'inferenceMs': 1300, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1396}
[2023-10-30 09:41:24.406224]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1319, 'inferenceMs': 1312, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1363}
[2023-10-30 09:41:25.218874]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1045, 'inferenceMs': 1032, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1934}
[2023-10-30 09:41:34.272590]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1176, 'inferenceMs': 1171, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1237}
[2023-10-30 09:41:34.370495]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 1292, 'inferenceMs': 1276, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1337}
[2023-10-30 09:41:35.285338]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 927, 'inferenceMs': 919, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 2248}
[2023-10-30 09:41:44.084196]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 821, 'inferenceMs': 814, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 991}
[2023-10-30 09:41:44.119784]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 874, 'inferenceMs': 867, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1050}
[2023-10-30 09:41:54.294468]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 795, 'inferenceMs': 789, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 830}
[2023-10-30 09:41:54.524596]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 881, 'inferenceMs': 830, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 929}
[2023-10-30 09:41:54.940314]: parking - No plates detected in run: {'success': True, 'predictions': [], 'message': 'No plates found', 'processMs': 650, 'inferenceMs': 645, 'code': 200, 'command': 'alpr', 'moduleId': 'ALPR', 'executionProvider': 'CPU', 'canUseGPU': False, 'analysisRoundTripMs': 1324}

[NickM-27]

This doesn't have anything to do with the stationary object changes, this has to do with the change that makes license plate being detected a significant change. So now every time a better license plate is detected frigate will send an mqtt message for it.

[wizu]

Do you have any idea how to mitigate this? Run plate recognition only when motion is detected?
It's killing my cpu ;)

[NickM-27]

I don't know your setup so it's difficult to suggest, I personally have not run in to this issue

[NickM-27]

you could use the stationary tag to not run the ALPR on objects that are stationary

[idontcare99999]

Good morning @NickM-27, thanks again for sharing this solution; it's performing better than I what I could build in nodered. I'm curious about lines 42 to 45 of the script where x_mid is defined but then y_max is defined and not used. Following the same logic as x_mid, I created y_mid and then used it in the img.crop function and it seems to be working. Is there something I'm not thinking through about the design?

[NickM-27]

I used to use y_max but I found that if the license plate y coords were off it would be clipped and that it was just as accurate to include the entire vertical strip in the image sent to ALPR

[LyncTaylor]

@NickM-27 thank you for all of your hard work on Frigate. I just launched my first custom model using Frigate+ and now I want to do license plate detection. Very exciting!

My issue is that I have tuned my LPR cameras to clearly read license plates at night, which basically blacks out the entire image besides the license plate and maybe the tail lights. There is no image of a vehicle for Frigate to detect.

Does anyone know how to trigger the license plate reading process if a vehicle cannot be seen in the image?

[NickM-27]

Currently frigate requires the car to be detected. Another user has similar and was able to get things to work in a similar scenario.

[kyle4269]

So, I think Nick is referring to my situation. It's similar to yours where at night it's pitch black and all you really see is head/tail lights and the license plate. Originally all I would get at night was motion boxes driving in or out of my driveway. After some tweaking I was able to identify the front of my truck but for whatever reason the rear of my truck was never identified. I did end up running a lot of clips through frigate while playing with different settings. What helped me was increasing the motion threshold to 80.

[thundergreen]

Deara all,

I am also trying to install this stuff but it fails to install the license plate recognizer in openpjojectai home asistant addon.

this is my log:

23:27:43:ALPR: No schemas installed
23:27:43:ALPR: (No schemas means: we can't detect if you're in light or dark mode)
23:27:44:ALPR:              Installing CodeProject.AI Analysis Module                
23:27:44:ALPR: ======================================================================
23:27:44:ALPR:                    CodeProject.AI Installer                           
23:27:44:ALPR: ======================================================================
23:27:44:ALPR: 4.07 GiB available on Docker
23:27:44:ALPR: General CodeProject.AI setup                                          
23:27:44:ALPR: Setting permissions...Done
23:27:44:ALPR: GPU support                                                           
23:27:44:ALPR: CUDA Present...No
23:27:44:ALPR: ROCm Present...No
23:27:45:ALPR: Processing module ALPR 2.7.2                                          
23:27:45:ALPR: Installing Python 3.8
23:27:45:ALPR: Python 3.8 is already installed
23:27:52:ALPR: Upgrading PIP in base python install... done
23:27:52:ALPR: Virtual Environment already present
23:27:53:ALPR: Checking for Python 3.8...Found Python 3.8.18. present
23:27:54:ALPR: Installing updated setuptools in virtual environment... Done
23:27:54:ALPR: Downloading libssl...Already downloaded
23:27:59:ALPR: Ensuring PIP compatibility... Done
23:27:59:ALPR: Python packages will be specified by requirements.linux.txt
23:28:08:ALPR: Upgrading PIP in virtual environment... done
23:28:09:ALPR:   - Installing PaddelPaddle, the Deep Learning platform...Already installed
23:28:11:ALPR: Installing libssl...Done
23:28:14:ALPR: Downloading OCR models... already exists...Expanding... Done.
23:28:15:ALPR: Installing Python packages for ALPR
23:28:15:ALPR: Installing GPU-enabled libraries: No
23:28:23:ALPR: Upgrading PIP in base python install... done
23:28:23:ALPR: Virtual Environment already present
23:28:23:ALPR: Checking for Python 3.8...Found Python 3.8.18. present
23:28:25:ALPR: Installing updated setuptools in virtual environment... Done
23:28:27:ALPR: Downloading libssl...Already downloaded
23:28:28:ALPR: Searching for python3-pip...All good.
23:28:31:Preparing to install module 'ALPR'
23:28:31:Downloading module 'ALPR'
23:28:32:Installing module 'ALPR'
23:28:32:Installer script at '/app/setup.sh'
23:28:32:ALPR: Hi Docker! We will disable shared python installs for downloaded modules
23:28:32:ALPR: No schemas installed
23:28:32:ALPR: (No schemas means: we can't detect if you're in light or dark mode)
23:28:32:ALPR:              Installing CodeProject.AI Analysis Module                
23:28:32:ALPR: ======================================================================
23:28:32:ALPR:                    CodeProject.AI Installer                           
23:28:32:ALPR: ======================================================================
23:28:33:ALPR: 4.09 GiB available on Docker
23:28:33:ALPR: General CodeProject.AI setup                                          
23:28:33:ALPR: Setting permissions...Done
23:28:33:ALPR: GPU support                                                           
23:28:33:ALPR: CUDA Present...No
23:28:33:ALPR: ROCm Present...No
23:28:34:ALPR: Processing module ALPR 2.7.2                                          
23:28:34:ALPR: Installing Python 3.8
23:28:34:ALPR: Python 3.8 is already installed
23:28:34:ALPR: Upgrading PIP in virtual environment... done
23:28:43:ALPR:   - Installing PaddelPaddle, the Deep Learning platform... (✔️ checked) Done
23:28:51:ALPR: Installing libssl...Done
23:29:03:ALPR: Installing updated setuptools in virtual environment... Done
23:29:04:ALPR: Downloading libssl...Already downloaded
23:29:07:ALPR: Downloading OCR models... already exists...Expanding... Done.
23:29:08:ALPR: Installing Python packages for ALPR
23:29:08:ALPR: Installing GPU-enabled libraries: No
23:29:22:ALPR: Ensuring PIP compatibility... Done
23:29:22:ALPR: Python packages will be specified by requirements.linux.txt
23:29:32:ALPR: Installing libssl...Done
23:29:34:ALPR:   - Installing PaddelPaddle, the Deep Learning platform...Already installed
23:29:36:ALPR: Downloading OCR models... already exists...Expanding... Done.
23:29:37:ALPR: Installing Python packages for ALPR
23:29:37:ALPR: Installing GPU-enabled libraries: No
23:29:38:ALPR: Searching for python3-pip...All good.
23:29:47:ALPR: Upgrading PIP in base python install... done
23:29:47:ALPR: Virtual Environment already present
23:29:47:ALPR: Checking for Python 3.8...Found Python 3.8.18. present
23:30:00:ALPR: Searching for python3-pip...All good.
23:30:06:ALPR: Upgrading PIP in virtual environment... done
23:30:23:ALPR: Ensuring PIP compatibility... Done
23:30:23:ALPR: Python packages will be specified by requirements.linux.txt
23:30:31:ALPR: Installing updated setuptools in virtual environment... Done
23:30:32:ALPR: Downloading libssl...Already downloaded
23:30:37:ALPR:   - Installing PaddelPaddle, the Deep Learning platform...Already installed
23:30:51:ALPR: Ensuring PIP compatibility... Done
23:30:51:ALPR: Python packages will be specified by requirements.linux.txt
23:30:53:ALPR: Installing libssl...Done
23:30:58:ALPR:   - Installing PaddelPaddle, the Deep Learning platform...Already installed
23:30:58:ALPR: Downloading OCR models... already exists...Expanding... Done.
23:30:59:ALPR: Installing Python packages for ALPR
23:30:59:ALPR: Installing GPU-enabled libraries: No
23:31:32:ALPR: Searching for python3-pip...All good.
23:32:17:ALPR: Ensuring PIP compatibility... Done
23:32:17:ALPR: Python packages will be specified by requirements.linux.txt
23:32:35:ALPR:   - Installing PaddelPaddle, the Deep Learning platform...Already installed

Seems hanging somehow.. any idea? Would love getting this ruunning as local ALPR does not work for home asistant OS.

[NickM-27]

what version of CodeProject is the addon set to?

[xavan83]

Do you find a working version? I try 2.5.4 and 2.3.2 (either locally or in docker).
The installation seems to work well but the module fails to start

[JohnNeville]

Someone created a docker based project you can just tie into MQTT rather than even relying on Home Assistant and it seems pretty reliable. https://github.com/ljmerza/frigate_plate_recognizer

[DrSpaldo]

I've just installed this. It looks pretty good, took a bit of mucking around to get it working with my camera but it appears to be going well now. It is appending the plate to the Frigate log, which is great :)

[danielszilagyi]

I made a fork of it and added something quickly just to be able to run it as a HA addon... currently the config.yml needs to be manually copied into the data directory, will improve it if I have some spare time

[BliXem1]

Can you tell me how this will work with MQTT within HA? This project is working perfectly and it's also showing in Frigate. The solution I want is to open the garage when the license plate has been detected. :)

[blakdog1978]

Good morning I was looking for a solution like this for a long time, what are the instructions to install it in Assioos

[jeremyward]

I have Frigate working and detecting cars and people. I'm trying to get license_plate working. It seems like if I use Frigate+ there are already trained models for this object type? Or will I need to train my own? I don't quite understand how to stand up Frigate+ as opposed to Frigate. Is that just installing the beta? I'm happy to pay for training and development. I was just hoping not to have to train (annotate and send images) etc. Thanks in advance for any words of wisdom you might have.

[NickM-27]

All of your questions should be answered in this docs page

at this time you must annotate images yourself as the base model needs inputs from your cameras to be as accurate as possible.

[BradFD]

@NickM-27 If I have snapshot height set to 270 will that negatively affect OCR on the plates because the image is so small? Is it better to not set height or set it to full size of camera?

[NickM-27]

yes, that will reduce how well it works

[BradFD]

Codeproject.AI appears to send the images through Yolov5 to detect a license plate and then goes on to OCR it and Yolo isn't finding license plates a lot of the times even when there are good license plates that were found by Frigate.