Add image_quality: sharpness/contrast/brightness gate before OCR#406
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OCR and template matching quietly fail on a blurry, washed-out or too-dark capture, and the caller can't tell a missing element from an unreadable one. Measure sharpness (variance of the Laplacian), contrast (grayscale stddev) and brightness (mean), and gate on them with named issues (blurry / low_contrast / too_dark / too_bright) so a script can pre-process or re-capture before OCR. Reuses visual_match's grayscale loader; cv2/numpy lazily imported.
Up to standards ✅🟢 Issues
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| Metric | Results |
|---|---|
| Complexity | 22 |
| Duplication | 0 |
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Why
OCR and template matching quietly fail on a blurry, washed-out or too-dark capture — the locate returns nothing and the caller can't tell a missing element from an unreadable one.
image_qualitymeasures the three things that wreck recognition and gates on them:image_qualityreturns the raw metrics,is_blurryis the common one-liner, andquality_gateturns them into{passed, issues}flaggingblurry/low_contrast/too_dark/too_brightso a script can pre-process or re-capture before OCR.Design
visual_match._haystack_gray— source is any ndarray / path / PIL image, or the live screen when omitted (regionapplies to the screen grab). cv2/numpy lazily imported.__all__→AC_image_quality/AC_quality_gate→ read-onlyac_*MCP tools → Script Builder (Image). Qt-free verified.Tests
test/unit_test/headless/test_image_quality_batch.py(cv2 viaimportorskip) — metric types/range, sharp-vs-blurry ordering +is_blurry, gate pass/fail with issues, dark-frame flag, tunable brightness range, the pure executor path, and 5-layer wiring. 16 passed with theimg_histogramsibling.