optimize coordinate validation, handling both -ve and OOB error

[musaqlain]

Achieved an ~8.2x speedup (9.5 min -> 1.1 min) in dataset validation by reducing disk I/O and vectorizing coordinate checks.

Changes

• Optimized I/O: Caches image dimensions to avoid repeated file opens.
• Vectorization: Replaced iterrows with Pandas boolean masking.
• Unified Error Reporting: Merged checks for negative/OOB coordinates into a single report that lists all invalid boxes to aid debugging.
• Polygon Support: Automatically converts non-rectangular geometries to valid bounding boxes.

Fixes #1244

AI-Assisted Development

• I used AI tools (e.g., GitHub Copilot, ChatGPT, etc.) in developing this PR
• I understand all the code I'm submitting, thanks

[bw4sz]

Thanks for your contribution, lets wait for the tests to pass.

[bw4sz]

@jveitchmichaelis, my general philosophy has always been to focus DeepForest on novice users and help avoid errors before they happen. So in this case there is a raise if there are bad coordinates in the annotations. I think that is still right and not too annoying, instead of just printing or warning. I think we want to prioritize, 'hey there is a problem here' versus streamlined and betting the user understands what they are doing. Agreed?

[bw4sz]

Agreed, thanks.

[bw4sz]

have a look at test failures.

[jveitchmichaelis]

Requested some of the LLM-style comments be cleaned up.

It would be interesting to run this with a profiler. I assume 90+% of the time is spent opening images (and so caching is the real win to avoid reloading images). Hence why I don't think vectorizing is the most important aspect here, even though it's definitely an improvement.

I tested filtering a 100M row numpy array on Colab, takes a couple of seconds. So minutes of runtime is likely I/O.

[jveitchmichaelis]

@bw4sz sorry to belabor this review. If the goal is to show users where issues are, I think it's important that the output is actually useful for debugging. Seeing that some number of boxes are malformed, or only their coordinates, doesn't help me fix what's wrong with my dataset. What do you think about reporting a complete list of image name / box issues? e.g.

Errors:
image_name, box coords, error

In the existing code we print an error string for all invalid boxes, while this PR reports a number only for negative coords:

errors.append(f"Found {bad_count} annotations with negative coordinates.")

@musaqlain If you move the logic for negative coordinates into the oob_mask I think that would be better.

[musaqlain]

completely makes sense, i have updated the logic to catch both the -ve coordinates as well as out of bound coordinates to give users a detailed report. thanks cc @jveitchmichaelis

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 87.21%. Comparing base (3146b96) to head (7f018f5).
⚠️ Report is 6 commits behind head on main.

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1285      +/-   ##
==========================================
- Coverage   87.89%   87.21%   -0.69%     
==========================================
  Files          20       20              
  Lines        2776     2784       +8     
==========================================
- Hits         2440     2428      -12     
- Misses        336      356      +20     

Flag	Coverage Δ
unittests	87.21% <100.00%> (-0.69%)	⬇️

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[musaqlain]

PTAL cc @jveitchmichaelis