CodeProject.AI Version 2.0

Installed 2.0.7. Had same issue with 2.0.6. BI starts up fine, AI Server is found and starts fine. Any alert on the License camera causes AI: Timeout in BI Log. Using GPU.

If I shut down CodeAi and fire Deepstack back up, plates are processed just fine. Shut Deepstack down and start CodeAI and get the AI: Timeout message in the log when it detects a plate. Any helpful suggestions are welcome.

Thank you.

Code:
21:56:47:Request 'custom' dequeued for processing (...f2d7ae)
21:56:47:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:56:47:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:56:47:Response received (...f2d7ae)
21:56:47:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...f2d7ae) took 38ms
21:57:18:Client request 'detect' in the queue (...99d08f)
21:57:18:Request 'detect' dequeued for processing (...99d08f)
21:57:18:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...99d08f) took 184ms
21:57:19:Response received (...99d08f)
21:57:19:Request 'custom' dequeued for processing (...60c5c8)
21:57:19:Client request 'custom' in the queue (...60c5c8)
21:57:19:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Detecting using actionnetv2
21:57:19:Response received (...60c5c8)
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...60c5c8) took 75ms
21:57:19:Client request 'custom' in the queue (...df4d94)
21:57:19:Request 'custom' dequeued for processing (...df4d94)
21:57:19:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Detecting using ipcam-animal
21:57:19:Response received (...df4d94)
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...df4d94) took 41ms
21:57:19:Client request 'custom' in the queue (...05f708)
21:57:19:Request 'custom' dequeued for processing (...05f708)
21:57:19:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Detecting using ipcam-combined
21:57:19:Response received (...05f708)
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...05f708) took 44ms
21:57:19:Client request 'custom' in the queue (...824980)
21:57:19:Request 'custom' dequeued for processing (...824980)
21:57:19:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Detecting using ipcam-dark
21:57:19:Response received (...824980)
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...824980) took 39ms
21:57:19:Client request 'custom' in the queue (...d99b62)
21:57:19:Request 'custom' dequeued for processing (...d99b62)
21:57:19:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Detecting using ipcam-general
21:57:19:Response received (...d99b62)
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...d99b62) took 41ms
21:57:19:Client request 'custom' in the queue (...6601b3)
21:57:19:Request 'custom' dequeued for processing (...6601b3)
21:57:19:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:19:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:57:19:Response received (...6601b3)
21:57:19:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...6601b3) took 41ms
21:57:42:Client request 'detect' in the queue (...292120)
21:57:42:Request 'detect' dequeued for processing (...292120)
21:57:42:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...292120) took 49ms
21:57:43:Response received (...292120)
21:57:43:Client request 'custom' in the queue (...b97900)
21:57:43:Request 'custom' dequeued for processing (...b97900)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Client request 'detect' in the queue (...ca2d58)
21:57:43:Request 'detect' dequeued for processing (...ca2d58)
21:57:43:Object Detection (YOLOv5 6.2): Detecting using actionnetv2
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...b97900) took 67ms
21:57:43:Response received (...b97900)
21:57:43:Client request 'custom' in the queue (...cdb581)
21:57:43:Request 'custom' dequeued for processing (...cdb581)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-animal
21:57:43:Response received (...ca2d58)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...ca2d58) took 81ms
21:57:43:Client request 'custom' in the queue (...8d0b80)
21:57:43:Request 'custom' dequeued for processing (...8d0b80)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...cdb581) took 49ms
21:57:43:Response received (...cdb581)
21:57:43:Client request 'custom' in the queue (...8efde1)
21:57:43:Request 'custom' dequeued for processing (...8efde1)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using actionnetv2
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-combined
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...8d0b80) took 81ms
21:57:43:Response received (...8d0b80)
21:57:43:Client request 'custom' in the queue (...cdce8f)
21:57:43:Request 'custom' dequeued for processing (...cdce8f)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...8efde1) took 75ms
21:57:43:Response received (...8efde1)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Client request 'custom' in the queue (...522299)
21:57:43:Request 'custom' dequeued for processing (...522299)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-animal
21:57:43:Response received (...cdce8f)
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-dark
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...cdce8f) took 47ms
21:57:43:Client request 'custom' in the queue (...602b6c)
21:57:43:Request 'custom' dequeued for processing (...602b6c)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...522299) took 51ms
21:57:43:Response received (...522299)
21:57:43:Client request 'custom' in the queue (...6f761e)
21:57:43:Request 'custom' dequeued for processing (...6f761e)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-combined
21:57:43:Response received (...602b6c)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...602b6c) took 40ms
21:57:43:Client request 'custom' in the queue (...3214a8)
21:57:43:Request 'custom' dequeued for processing (...3214a8)
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-general
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-dark
21:57:43:Response received (...6f761e)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...6f761e) took 61ms
21:57:43:Client request 'custom' in the queue (...156147)
21:57:43:Request 'custom' dequeued for processing (...156147)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...3214a8) took 43ms
21:57:43:Response received (...3214a8)
21:57:43:Request 'custom' dequeued for processing (...890264)
21:57:43:Client request 'custom' in the queue (...890264)
21:57:43:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-general
21:57:43:Response received (...156147)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...156147) took 49ms
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...890264) took 41ms
21:57:43:Response received (...890264)
21:57:43:Client request 'custom' in the queue (...63e4c0)
21:57:43:Request 'custom' dequeued for processing (...63e4c0)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:57:43:Response received (...63e4c0)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...63e4c0) took 42ms
21:57:43:Request 'detect' dequeued for processing (...4890a4)
21:57:43:Client request 'detect' in the queue (...4890a4)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Client request 'detect' in the queue (...3601ed)
21:57:43:Request 'detect' dequeued for processing (...3601ed)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...4890a4) took 87ms
21:57:43:Response received (...4890a4)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...3601ed) took 86ms
21:57:43:Response received (...3601ed)
21:57:43:Request 'custom' dequeued for processing (...0697f8)
21:57:43:Client request 'custom' in the queue (...0697f8)
21:57:43:Client request 'custom' in the queue (...2f0c6d)
21:57:43:Request 'custom' dequeued for processing (...2f0c6d)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using actionnetv2
21:57:43:Object Detection (YOLOv5 6.2): Detecting using actionnetv2
21:57:43:Response received (...0697f8)
21:57:43:Response received (...2f0c6d)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...2f0c6d) took 137ms
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...0697f8) took 137ms
21:57:43:Client request 'custom' in the queue (...7dbf6c)
21:57:43:Request 'custom' dequeued for processing (...7dbf6c)
21:57:43:Client request 'custom' in the queue (...ff0e48)
21:57:43:Request 'custom' dequeued for processing (...ff0e48)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-animal
21:57:43:Response received (...7dbf6c)
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-animal
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...7dbf6c) took 51ms
21:57:43:Client request 'custom' in the queue (...345111)
21:57:43:Request 'custom' dequeued for processing (...345111)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...ff0e48) took 52ms
21:57:43:Response received (...ff0e48)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Client request 'custom' in the queue (...7f3a0b)
21:57:43:Request 'custom' dequeued for processing (...7f3a0b)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-combined
21:57:43:Response received (...345111)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...345111) took 44ms
21:57:43:Object Detection (YOLOv5 6.2): Detecting using ipcam-combined
21:57:43:Response received (...7f3a0b)
21:57:43:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...7f3a0b) took 40ms
21:57:43:Client request 'custom' in the queue (...6fd071)
21:57:43:Request 'custom' dequeued for processing (...6fd071)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:43:Client request 'custom' in the queue (...fd58a9)
21:57:43:Request 'custom' dequeued for processing (...fd58a9)
21:57:43:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Object Detection (YOLOv5 6.2): Detecting using ipcam-dark
21:57:44:Response received (...6fd071)
21:57:44:Object Detection (YOLOv5 6.2): Detecting using ipcam-dark
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...6fd071) took 50ms
21:57:44:Client request 'custom' in the queue (...8d540f)
21:57:44:Request 'custom' dequeued for processing (...8d540f)
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...fd58a9) took 52ms
21:57:44:Response received (...fd58a9)
21:57:44:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Client request 'custom' in the queue (...c79073)
21:57:44:Request 'custom' dequeued for processing (...c79073)
21:57:44:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Object Detection (YOLOv5 6.2): Detecting using ipcam-general
21:57:44:Response received (...8d540f)
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...8d540f) took 41ms
21:57:44:Client request 'custom' in the queue (...169e4c)
21:57:44:Request 'custom' dequeued for processing (...169e4c)
21:57:44:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Object Detection (YOLOv5 6.2): Detecting using ipcam-general
21:57:44:Response received (...c79073)
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...c79073) took 42ms
21:57:44:Client request 'custom' in the queue (...444c75)
21:57:44:Request 'custom' dequeued for processing (...444c75)
21:57:44:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:57:44:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...169e4c) took 43ms
21:57:44:Response received (...169e4c)
21:57:44:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:57:44:Response received (...444c75)
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...444c75) took 40ms
21:57:44:Client request 'detect' in the queue (...0e4154)
21:57:44:Request 'detect' dequeued for processing (...0e4154)
21:57:44:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...0e4154) took 40ms
21:57:44:Response received (...0e4154)
21:57:44:Client request 'custom' in the queue (...884058)
21:57:44:Request 'custom' dequeued for processing (...884058)
21:57:44:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:57:44:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:57:44:Response received (...884058)
21:57:44:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...884058) took 27ms
21:58:15:Client request 'detect' in the queue (...898fca)
21:58:15:Request 'detect' dequeued for processing (...898fca)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'detect' (...898fca) took 176ms
21:58:15:Response received (...898fca)
21:58:15:Client request 'custom' in the queue (...0e5293)
21:58:15:Request 'custom' dequeued for processing (...0e5293)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Detecting using actionnetv2
21:58:15:Response received (...0e5293)
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...0e5293) took 118ms
21:58:15:Client request 'custom' in the queue (...2e83cb)
21:58:15:Request 'custom' dequeued for processing (...2e83cb)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Detecting using ipcam-animal
21:58:15:Response received (...2e83cb)
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...2e83cb) took 35ms
21:58:15:Client request 'custom' in the queue (...b92f48)
21:58:15:Request 'custom' dequeued for processing (...b92f48)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Detecting using ipcam-combined
21:58:15:Response received (...b92f48)
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...b92f48) took 43ms
21:58:15:Client request 'custom' in the queue (...0f7031)
21:58:15:Request 'custom' dequeued for processing (...0f7031)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Detecting using ipcam-dark
21:58:15:Response received (...0f7031)
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...0f7031) took 38ms
21:58:15:Client request 'custom' in the queue (...504c70)
21:58:15:Request 'custom' dequeued for processing (...504c70)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Detecting using ipcam-general
21:58:15:Response received (...504c70)
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...504c70) took 47ms
21:58:15:Client request 'custom' in the queue (...d37493)
21:58:15:Request 'custom' dequeued for processing (...d37493)
21:58:15:Object Detection (YOLOv5 6.2): Retrieved objectdetection_queue command
21:58:15:Object Detection (YOLOv5 6.2): Detecting using license-plate
21:58:15:Response received (...d37493)
21:58:15:Object Detection (YOLOv5 6.2): Queue and Processing Object Detection (YOLOv5 6.2) command 'custom' (...d37493) took 38ms
[/ICODE]

[CODE]Operating System: Windows (Microsoft Windows 11 version 10.0.22621)
CPUs:             1 CPU x 10 cores. 20 logical processors (x64)
GPU:              NVIDIA GeForce GTX 1080 (8 GiB) (NVidia)
                  Driver: 528.24 CUDA: 12.0 Compute: 6.1
System RAM:       128 GiB
Target:           Windows
BuildConfig:      Release
Execution Env:    Native
Runtime Env:      Production
.NET framework:   .NET 7.0.2
System GPU info:
  GPU 3D Usage       1%
  GPU RAM Usage      6.1 GiB
Video adapter info:
  NVIDIA GeForce GTX 1080:
    Adapter RAM        4 GiB
    Driver Version     31.0.15.2824
    Video Processor    NVIDIA GeForce GTX 1080
Global Environment variables:
  CPAI_APPROOTPATH = C:\Program Files\CodeProject\AI
  CPAI_PORT        = 32168
CodeAI.pngAIInstalled.png
 
To all having issues with the current version of BI and CP.AI obtaining the custom model list and AI not working. Try disabling BI from starting CP.AI and make sure CP.AI service is set to start automatically. After making the changes reboot the server. This should get your system working until BI (Ken) can fix it

1674891788172.png
1674891850212.png
 
To all having issues with the current version of BI and CP.AI obtaining the custom model list and AI not working. Try disabling BI from starting CP.AI and make sure CP.AI service is set to start automatically. After making the changes reboot the server. This should get your system working until BI (Ken) can fix it

View attachment 152242
View attachment 152243
Cheers Mike. Did you have any joy figuring out why BI isn’t using the best plate % to read the plate data?
 
Hey all, trying to get CodeProject.Ai 2.06 to work and having issues. Looking at the logs, it looks like it is compiled for a GPU but my laptop is just using internal graphics. I am just trying to get the Explorer to work. Not trying to get into BI yet. I see there is a new 2.07 maybe that will help my problem?

05:35:04: ALPR_adapter.py: W0128 05:35:04.840937 19092 init.cc:179] Compiled with WITH_GPU, but no GPU found in runtime.

Also it has errors trying to load the OCR, I get these errors.
05:35:00: OCR_adapter.py: Fatal Python error: initfsencoding: unable to load the file system codec
05:35:00: OCR_adapter.py: ModuleNotFoundError: No module named 'encodings'
05:35:00: OCR_adapter.py: Current thread 0x000066d4 (most recent call first):
 
Hey all, trying to get CodeProject.Ai 2.06 to work and having issues. Looking at the logs, it looks like it is compiled for a GPU but my laptop is just using internal graphics. I am just trying to get the Explorer to work. Not trying to get into BI yet. I see there is a new 2.07 maybe that will help my problem?

05:35:04: ALPR_adapter.py: W0128 05:35:04.840937 19092 init.cc:179] Compiled with WITH_GPU, but no GPU found in runtime.

Also it has errors trying to load the OCR, I get these errors.
05:35:00: OCR_adapter.py: Fatal Python error: initfsencoding: unable to load the file system codec
05:35:00: OCR_adapter.py: ModuleNotFoundError: No module named 'encodings'
05:35:00: OCR_adapter.py: Current thread 0x000066d4 (most recent call first):
Post the below screenshot
1674914198588.png
 
Wonder what the new colored icons represent. I get everything red-green-violet-purple-yellow. Now it also shows timings as well again :) The CPAI 2.07 and BI 5.6.9.4 are running just fine for me. But it's an almost clean W11 machine using CPU mode.

Screenshot 2023-01-28 090013.png
 
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Wonder what the new colored icons represent. I get everything red-green-violet-purple-yellow. Now it also shows timings as well again :) The CPAI 2.07 and BI 5.6.9.4 are running just fine for me. But it's an almost clean W11 machine using CPU mode.

View attachment 152283View attachment 152283
I think they are just random colors now, new versions will start color coding the lines like if green it is OK
 
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Some combination of updates with BI and CPAI broke the communication between them. With 5.6.9.4, BI can stop CPAI 2.07/2.06 but no longer start it, AI no longer logs queries, custom models don't show, etc. I've rebuilt, repaired, reinstalled, and restarted this combination with no luck.

Rollback to 5.6.9.3 and 2.06 did not solve the problem. Rollback further to 5.6.8.4 did reconnect BI and CPAI correctly. Upgrade then to CPAI 2.07 and everything still worked, so in my Neanderthal opinion it seems like an issue with a recent update to BI.
 
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Some combination of updates with BI and CPAI broke the communication between them. With 5.6.9.4, BI can stop CPAI 2.07/2.06 but no longer start it, AI no longer logs queries, custom models don't show, etc. I've rebuilt, repaired, reinstalled, and restarted this combination with no luck.

Rollback to 5.6.9.3 and 2.06 did not solve the problem. Rollback further to 5.6.8.4 did reconnect BI and CPAI correctly. Upgrade then to CPAI 2.07 and everything still worked, so in my Neanderthal opinion it seems like an issue with a recent update to BI.
See the below post, it works for me on two systems that I have.
 
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See the below post, it works for me on two systems that I have.

On my system with 2.0.7 (not beta) and BI 5.6.9.4, the cpai server was already configured with automatic startup by default. I went and unchecked startup with BI in BI settings, rebooted, and now am having a worse problem. BI is not communicating with either of the started modules, and stop now/start now gives me this error after hitting start now:

Untitled.jpg

I went back and re-enabled start with BI, hit start now... and it's working again. So the latest still seems broken.
 
On my system with 2.0.7 (not beta), the cpai server was already configured with automatic startup by default. I went and unchecked startup with BI in BI settings, rebooted, and still having the same issue. BI doesn't communicate with any modules until I stop now/start now.

Similar issue here. Tried it again with those settings and getting same errors.
 
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@MikeLud1 I mentioned in another thread about the OCR being less accurate than Plate Recognizer... just wanted to mention that was with medium. After switching to high, it's at least on par with the online service. Not sure if you still planned on improving OCR, but it's fine on my end now (night plates and all, working great).
 
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@MikeLud1 I mentioned in another thread about the OCR being less accurate than Plate Recognizer... just wanted to mention that was with medium. After switching to high, it's at least on par with the online service. Not sure if you still planned on improving OCR, but it's fine on my end now (night plates and all, working great).

Interesting. I'm still using PR because I did some comparisons on the previous version of CPAI/LPR and was underwhelmed by the accuracy. I need to try the new version set to high.

To do the side-by-side testing, I've been sending the alert images to CPAI/LPR via a Node Red flow. Does anyone know how I can get it to use the 'high' setting when submitting the image via http post? I don't remember seeing anything about that in the CPAI API docs.

Edit: just did another test (without any flag to set 'high' mode) with 100 daytime tag images... CPAI and PR disagree on 58/100. I can see from the patterns of letters and numbers that PR is doing better. Among other things, CPAI isn't dealing well with stacked letters. Lots of the tags here are in the format of either 5 numbers and then two stacked letters, or 1 number, two stacked letters, then 4 more numbers.
 
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I have the service that does not start Codeproject does not start Error 1053
The service did not
respond quickly enough to the launch or control request.

I uninstall everything reinstall
Windows 10 Pro
 

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