BUG: Unreadable images interrupt the entire AI-tagging process

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
82/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Active
Tech stack
opencv, python
Domain
backend, machine-learning

Research direction

Start with backend/app/models/ObjectClassifier.py and backend/app/utils/images.py; trace how get_classes() handles an image that OpenCV cannot load and how the tagging loop updates isTagged. Check the existing tests for image classification and batch processing. Done means unreadable images are skipped without ending the batch, while successfully processed images with no detections are marked tagged.

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Description

bug possible-duplicate
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What happened?

Description

The AI-tagging process can terminate unexpectedly when PictoPy encounters an image that OpenCV cannot load or decode(corrupt or unsupported image).

In ObjectClassifier.get_classes() (backend/app/models/ObjectClassifier.py), cv2.imread() returns None when an image cannot be loaded. However, image_util_classify_and_face_detect_images() (backend/app/utils/images.py) directly calls len(classes) without checking for None, resulting in a TypeError that interrupts the entire tagging batch.

Expected Behaviour

  • Unreadable images should be logged and skipped.
  • Their isTagged status should remain False.
  • Processing should continue with the remaining images.
  • Successfully processed images with no detections should still be marked as tagged.

Actual behaviour

  1. Application retrieves multiple untagged images from the database.
  2. YOLO processes images sequentially.
  3. One image is corrupt or unreadable.
  4. get_classes() returns None.
  5. len(None) raises a TypeError.
  6. The entire tagging batch terminates.
  7. Remaining images are not processed during that run.

Why Does This Matter?
A single unreadable image can prevent an entire batch of otherwise valid images from receiving AI-generated tags. Handling such failures individually would improve the reliability and stability of PictoPy's AI-tagging pipeline.

Proposed Solution
Handle the None return value before accessing len(classes). Skip the unreadable image and continue processing the remaining images.

Record
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Dominant language
Python
Stars
313
Forks
755
Avg merge
6d 36m
Merged PRs (30d)
8

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