138 lines
4.3 KiB
Markdown
138 lines
4.3 KiB
Markdown
# Implementation Plan
|
||
|
||
## Milestone 0 — Source reconnaissance + repository foundation
|
||
|
||
- verify Python package/CLI setup
|
||
- detect both expected source files:
|
||
- `data/raw/smk_all_da.json`
|
||
- `data/raw/smk_all_en.json`
|
||
- inspect actual top-level JSON structures without guessing
|
||
- compute bytes + SHA-256
|
||
- count/summarize records with bounded memory
|
||
- compare DA/EN identity sets
|
||
- write `source-manifest.json`
|
||
- document any structural surprises
|
||
|
||
Done when we understand the actual dumps well enough to implement the parser from evidence rather than assumptions.
|
||
|
||
## Milestone 1 — Streaming source loading + canonical schema
|
||
|
||
- implement bounded-memory loader for actual observed structure
|
||
- join DA/EN by `object_number`, never by row position
|
||
- preserve localized text where useful
|
||
- map stable non-language fields into canonical columns
|
||
- preserve source provenance/disagreements
|
||
- parse physical dimensions/units defensively
|
||
|
||
Done when a representative fixture/sample becomes deterministic canonical rows.
|
||
|
||
Status: source loading and pre-canonical audits complete. Stable source-row identity, lossless
|
||
logical Parquet, duplicate taxonomy, dimension vocabulary, palette/visual quality and DA/EN
|
||
precedence recommendations are documented. Canonical artwork generation remains blocked on an
|
||
explicit duplicate-group resolution policy and is intentionally not performed in this milestone.
|
||
|
||
## Milestone 2 — Weighted palette + colour conversion
|
||
|
||
- locate ordinary/weighted palette fields in the actual snapshot/enrichment data
|
||
- parse `colors_palette_weighted_s` when present
|
||
- normalize explicit weights
|
||
- implement/verify sRGB -> OKLab -> OKLCH
|
||
- preserve malformed input as quality warnings rather than crashing the run
|
||
|
||
Done when known palette fixtures produce stable colour rows and valid weights.
|
||
|
||
## Milestone 3 — Feature extraction
|
||
|
||
Implement:
|
||
|
||
- perceptual centroid
|
||
- palette entropy
|
||
- effective palette size
|
||
- palette spread
|
||
- weighted lightness/chroma variation
|
||
- hue concentration
|
||
- SMK visual fields + provenance
|
||
- physical/image geometry + logs
|
||
|
||
Done when one artwork can produce a complete feature record independently of the corpus.
|
||
|
||
## Milestone 4 — Quality layer
|
||
|
||
Implement documented flags and source/language/aspect checks.
|
||
|
||
Produce `quality-report.json` with:
|
||
|
||
- DA/EN raw record counts
|
||
- unique IDs and language overlap
|
||
- public-domain count
|
||
- usable-image/visual count
|
||
- valid feature count
|
||
- null counts by feature
|
||
- flag counts
|
||
- parser warnings/errors
|
||
- palette-size distribution
|
||
- obvious extreme values
|
||
|
||
## Milestone 5 — Pilot build
|
||
|
||
Run 100–500 representative artworks.
|
||
|
||
Review distributions and manually inspect selected records. Fix parser/source/math errors only; do not artistically tune features.
|
||
|
||
## Milestone 6 — Corpus definition + normalization
|
||
|
||
Do not hardcode 39,508.
|
||
|
||
- define the exact inclusion predicate for `smk-public-visual-v1`
|
||
- record included/excluded counts and reasons
|
||
- compute count/null/min/max/quantiles
|
||
- median + MAD
|
||
- implement `robust_01`
|
||
- implement `percentile_01`
|
||
- implement robust z-score
|
||
- freeze profile with source-manifest reference
|
||
|
||
## Milestone 7 — Full dataset build
|
||
|
||
Run the complete local source snapshots.
|
||
|
||
Definition of done:
|
||
|
||
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.
|
||
|
||
## Milestone 8 — Similarity
|
||
|
||
Implement separate profiles rather than one magic similarity score:
|
||
|
||
- `color`
|
||
- `visual`
|
||
- `composition`
|
||
|
||
Use fast feature-vector candidate retrieval followed optionally by weighted-palette optimal-transport reranking. Do not precompute all pairs.
|
||
|
||
## Milestone 9 — Art Tone v1
|
||
|
||
Implement versioned, explainable sonification mapping separate from objective features.
|
||
|
||
Initial mapping:
|
||
|
||
- centroid hue -> chromatic pitch class
|
||
- physical area percentile -> register
|
||
- chroma -> velocity/richness
|
||
- lightness -> timbral brightness
|
||
- palette entropy -> complexity
|
||
- contrast -> attack
|
||
- hue concentration -> tonal purity
|
||
|
||
Emit exact inputs alongside every mapped result.
|
||
|
||
## Milestone 10 — Exploration
|
||
|
||
Only after Dataset v1 is trustworthy, start experimental interfaces/notebooks/apps:
|
||
|
||
- nearest-neighbour browser
|
||
- colour-space explorer
|
||
- timelines
|
||
- artist palette fingerprints
|
||
- mosaics
|
||
- sonification player
|