An alternative region-forming frontend selected by --clustering watershed
(the color_mode field is replaced by clustering: color-cluster | bw |
watershed across CLI, Rust, Python, and Node — it selects the algorithm,
not a color space).
The algorithm is the watershed hierarchy by volume on the 4-adjacency
pixel graph, implemented from the papers:
Cousty, Bertrand, Najman, Couprie, "Watershed Cuts: Minimum Spanning
Forests and the Drop of Water Principle", IEEE TPAMI 31(8), 2009.
Najman, Cousty, Perret, "Playing with Kruskal", ISMM 2013.
Edge weights are the max per-channel color difference between adjacent
pixels (no gradient image); a counting-sorted Kruskal pass builds the
binary partition tree as a flat parents array; a leaves-to-root pass
computes subtree area and volume; each merge's persistence (the volume of
the smaller side, plateau-corrected) becomes its MST edge's saliency; and
cutting the hierarchy is single-linkage over MST edges below the cut
level. Watershed cuts label every pixel — no watershed-line pixel class —
so the output is a strict, gapless partition that drops straight into
both stacked and cutout modes. Integer arithmetic and flat u32 arrays
throughout; deterministic across platforms; ~66 ms on a 1400x775 photo.
The one dial, --watershed-detail (0..=255), maps exponentially to a
target region count (each +25.5 doubles it) since the persistence
distribution is far too skewed for a linear threshold. filter_speckle
absorbs undersized basins into their most color-similar neighbour rather
than dropping them, preserving the partition. The largest region is
emitted first as a solid full-canvas background layer so stacked mode
keeps its seam-free overdraw; the mosaic flatten is unaffected.
Tests: partition invariant (disjoint masks tiling the canvas), detail
monotonicity, min-area absorption, determinism, watershed cases in the
pipeline/golden suites, stacked-vs-cutout interior equivalence, a
watershed seam test, and SegmentKey coverage for the new params.
A stateful, image-owning converter for the desktop tuning loop. The consumer
calls render / render_svg / render_with_progress with a fresh Config each
frame and never reasons about cachability: Session compares the Config's
SegmentKey (its clustering-relevant projection — color mode, color precision,
layer difference, speckle, binary threshold settings) to what it last
clustered and re-segments only when that changes. Everything else — fit mode,
curve params, compositing, palette, optimization — reuses the cached
Segmentation.
Config::segment_key is public too, so callers that hold their own state (e.g.
the wasm/JS side) can compare keys with the same source of truth.
Tests cover the key partition (finish-phase params share a key; clustering
params change it) and that Session output matches the one-shot pipeline for
both a reused-segmentation render and a re-segmented one.
Add desktop app screenshot referenced by the README
Condense the unreleased changelog notes
Documents the systematic verification that the 1.0 pipeline reproduces 0.6.x
stacked output byte-for-byte: 475 parameter configurations (full per-parameter
sweeps + randomized interactions, pixel/polygon/spline, color/bw), geometry
compared against the 0.6.x cmdapp reference at path-precision 8. Zero geometry
mismatches (worst deviation 1e-8). Records the two bugs found and fixed during
verification (stacked hole-punching; relative-writer subpath origin), the
intentional differences (compact SVG encoding, empty-path omission), and the
reproduction procedure.
Chris Tsang
Chris Tsang