125 lines
4.7 KiB
Markdown
125 lines
4.7 KiB
Markdown
# Implementation Plan
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## Milestone 0 — Source reconnaissance + repository foundation
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- verify Python package/CLI setup
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- detect both expected source files:
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- `data/raw/smk_all_da.json`
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- `data/raw/smk_all_en.json`
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- inspect actual top-level JSON structures without guessing
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- compute bytes + SHA-256
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- count/summarize records with bounded memory
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- compare DA/EN identity sets
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- write `source-manifest.json`
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- document any structural surprises
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Done when we understand the actual dumps well enough to implement the parser from evidence rather than assumptions.
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## Milestone 1 — Streaming source loading + canonical schema
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- implement bounded-memory loader for actual observed structure
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- join DA/EN by `object_number`, never by row position
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- preserve localized text where useful
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- map stable non-language fields into canonical columns
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- preserve source provenance/disagreements
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- parse physical dimensions/units defensively
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Done when a representative fixture/sample becomes deterministic canonical rows.
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Status: source loading and pre-canonical audits complete. Stable source-row identity, lossless
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logical Parquet, duplicate taxonomy, dimension vocabulary, palette/visual quality and DA/EN
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precedence recommendations are documented. Canonical entities are implemented in Milestone 2
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without collapsing duplicate `object_number` groups.
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## Milestone 2 — Canonical entities + first analyzable corpus
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- pair language rows only by SMK `id`
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- preserve `object_number` as non-unique grouping metadata
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- canonicalize fields with explicit provenance and conflicts
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- resolve image bundles atomically
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- preserve and normalize dimension observations without choosing conflicts
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- freeze `smk-public-visual-v1` with machine-readable exclusion reasons
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Status: complete. See `CANONICALIZATION.md` and `CORPUS_V1.md`. Visual/colour feature extraction
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has not started.
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## Milestone 3 — Deterministic per-artwork visual features
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Implemented for frozen `smk-public-visual-v1`:
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- perceptual centroid
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- explicitly uniform bulk-palette weights and duplicate-colour handling
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- sRGB -> OKLab -> OKLCH with achromatic hue semantics
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- palette entropy/effective size with documented uniform-weight limitation
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- palette spread
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- weighted lightness/chroma variation
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- hue concentration
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- SMK visual fields + provenance
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- physical/image geometry + logs
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Status: complete. Raw features remain corpus-independent; no corpus normalization or similarity is
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implemented. See `FEATURE_EXTRACTION_V1.md` and `FEATURE_EXAMPLES.md`.
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## Milestone 4 — Corpus statistics + normalization
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Status: complete. Profile every candidate scalar over frozen `smk-public-visual-v1`, preserve raw
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OKLab semantics and missingness, generate robust-01/midrank-percentile/robust-z variants, add log
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aspect and circular-hue diagnostics, and publish separate unweighted candidate families. See
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`NORMALIZATION_V1.md` and `FEATURE_VECTOR_V1.md`.
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## Milestone 5 — Pilot build
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Status: complete as deterministic exact similarity. Three separately versioned profiles use
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sample-calibrated component scales, explicit weights, shared-component missingness and explainable
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contributions. QA examples, stability diagnostics and performance measurements are published in
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`SIMILARITY_V1.md` and `SIMILARITY_EXAMPLES.md`.
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## Milestone 6 — Similarity preparation
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Review Milestone 4 correlations and feature families, then define separately versioned distance
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profiles and weights. Do not reinterpret normalized OKLab axes as perceptual OKLab geometry.
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## Milestone 7 — Full dataset build
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Run the complete local source snapshots.
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Definition of done:
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Every canonical artwork has valid applicable features or explicit nulls/flags explaining missing/invalid prerequisites. Every normalization result references a frozen profile and exact source manifest.
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## Milestone 8 — Similarity
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Implement separate profiles rather than one magic similarity score:
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- `color`
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- `visual`
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- `composition`
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Use fast feature-vector candidate retrieval followed optionally by weighted-palette optimal-transport reranking. Do not precompute all pairs.
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## Milestone 9 — Art Tone v1
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Implement versioned, explainable sonification mapping separate from objective features.
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Initial mapping:
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- centroid hue -> chromatic pitch class
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- physical area percentile -> register
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- chroma -> velocity/richness
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- lightness -> timbral brightness
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- palette entropy -> complexity
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- contrast -> attack
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- hue concentration -> tonal purity
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Emit exact inputs alongside every mapped result.
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## Milestone 10 — Exploration
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Only after Dataset v1 is trustworthy, start experimental interfaces/notebooks/apps:
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- nearest-neighbour browser
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- colour-space explorer
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- timelines
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- artist palette fingerprints
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- mosaics
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- sonification player
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