feat(stitch): add normalize_mosaic — averaging companion to the accumulators#11
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…ccumulators The stitch_batch_* accumulators deliberately leave normalization to the caller. normalize_mosaic completes that API: given a (canvas, counts) pair it returns (fill_value, mosaic) where covered pixels (counts >= min_overlap) become canvas/counts, under-covered pixels become NaN, and fill_value is the median of covered pixels (a neutral background for renderers that treat NaN as zero). Lifted from holoptycho/vit_inference.py MosaicWriterOp._normalise_full so holoptycho can import it instead of carrying the inline copy. Tested in tests/test_stitch.py (averaging, NaN masking, fill=median, inclusive threshold, higher-threshold exclusion, empty input, float32 output, input immutability, and an end-to-end stitch->normalize round-trip). Co-authored-by: Himanshu Goel <4122621+himanshugoel2797@users.noreply.github.com>
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Adds
normalize_mosaic(canvas, counts, min_overlap=0.5)toptychoml.stitch.Why
The
stitch_batch_*accumulators deliberately leave normalization to the caller (their docstrings say "No normalization happens here").normalize_mosaicis the missing other half of that API — it averages a(canvas, counts)pair into a display mosaic:counts >= min_overlap) →canvas / countsNaN(fill_value, mosaic)wherefill_valueis the median of covered pixels — a neutral background for renderers that treatNaNas zeroLifted from
holoptycho/vit_inference.py'sMosaicWriterOp._normalise_fullso holoptycho can import it instead of carrying the inline copy (a follow-up updates the H3 PR NSLS2/holoptycho#40 to use it).Tests
tests/test_stitch.py(+9 cases): averaging, NaN masking of thin coverage,fill_value == median(covered), inclusive threshold boundary, higher-threshold exclusion, empty input →(0.0, all-NaN), float32 output, input immutability, and an end-to-endstitch_batch_livestitch_into → normalize_mosaicround-trip that recovers the patch value.Docs
README Stitching table + usage example updated to use
normalize_mosaic; AGENTS.md note added.