AOSSIE-Org / AOSSIE-Org/PictoPy

BUG: AI tagging hangs indefinitely when an image fails to load, blocking subsequent tagging jobs

Aperta
#1,444 5 commenti 0 reazioni 0 assegnatari Vedi su GitHub
Lingua principale
Python
Stelle
284
Fork
680
Merge medio
7g 5h
PR unite (30g)
4

Descrizione

### Is there an existing issue for this?

- [x] I have searched the existing issues

### What happened?

## Description
While AI tagging, if the backend encounters an image that it cannot load (cv2.imread() returns None), the entire AI-tagging job stops instead of skipping the image. The folder remains in the "processing" state indefinitely, and any following queued folder also remain pending until the ai tagging process of the folder that has problematic image, is turned off. Still we need to restart the subsequent folder's ai tagging.

### What backend logs:-

Failed to load image: -path-
Error processing untagged images: object of type 'NoneType' has no len()

problematic image:-

Image

properties: -
format: JPEG
resolution: 616 x 920
size: 79kb

### Current behavior

https://github.com/user-attachments/assets/95be7292-6dce-4393-8641-b4f79dc12696

### Expected behavior
- If an image cannot be loaded, log the error.
- Skip the problematic image.
- Continue processing the remaining images.
- Continue processing later queued folders.

### Record

- [x] I agree to follow this project's Code of Conduct

Guida per i contributori

Apri la guida per i contributori

Direzione di ricerca

Start at the backend AI-tagging path that calls cv2.imread() and produces the reported load and NoneType errors. Trace how one failed image affects the current folder's processing state and the queued folders, then use the existing AI-tagging tests or reproduce with the reported image. Done means the failure is logged, that image is skipped, remaining images are processed, and later folders continue.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Valutazione

Stack tecnologico
opencv, python
Ambito
backend, computer-vision, machine-learning
Tipo di issue
Bug
Difficoltà
4/5
Tempo stimato
3-5 giorni
Stato di attività
Tranquilla
Chiarezza
Abbastanza chiara
Idoneità per principianti
52/100

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