d91f468d443aebecb33865b010bfdd1e831f8ed8
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Commits
| Author | SHA1 | Message | Date | |
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  dtourolleandClaude Opus 5
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f87bf6ebc0 |
Charge a colour mode for its rarity, and keep the verdict
The refinement worked and could not be controlled. Pruning modes below a share threshold made the flag's removal a *discrete* event: below the line its Mahalanobis distance was enormous and nothing rescued it, above the line it sat at zero and nothing removed it. A control over that would appear dead through most of its travel and then start eating sky. So the prune is gone. A mode is charged `−ln(share × k)` nats, floored at zero, and that cost enters both tests — doubled in the chi-square, which is a squared distance, and directly in the log density. Rarity becomes a distance rather than a threshold, and the things a photographer wants to remove separate along it. Measured on the synthetic frame the tests build: a flag holding 1.6% of the sky is more than half gone by **2.95 nats** and a cloud bank holding a third of it survives to **5.75**. The whole interval between them is somewhere a control can sit. `the_flag_goes_before_the_cloud_does` pins the ordering, which is the property that makes one slider worth offering at all. Measured against an even split rather than against one, so raising `clusters` describes a category more finely without making every colour in it look rarer. Floored at zero so a dominant mode earns no *discount* — a bonus there would let the commonest colour outvote a bad chi-square, which is the one direction this must not bend. `Refinement` holds the per-pixel verdict, quantised to a byte over ±16 nats — an eighth of a nat per step, far finer than the narrowest transition the gate can be asked for, and the same size as the coverage buffer it sits beside. `apply` is then a smoothstep, and the model is never consulted again. That is `distance.rs`'s arrangement deliberately: there a signed distance field is computed once and feather, grow and shrink become arithmetic on it, "which is what makes those live controls rather than ones that stall on every drag". Same shape, different field. The blur moved with it, from the gate to the verdict. Smoothing the evidence rather than the decision means it is paid for once in `compute` instead of on every frame of a drag, and it is the better thing to smooth in any case. `apply` at `STRICTNESS_OFF` returns the weights untouched without reading the verdict at all. A control whose off position is *very nearly* the unrefined mask cannot answer "is this helping"; one whose off position is the unrefined mask can. `strictness_zero_changes_nothing` holds it to that, and `strictness_is_monotonic` holds the rest of the travel to only ever removing more — a slider that gave weight back partway up would be one whose direction nobody could predict. The synthetic sky is smooth enough to sit on `VARIANCE_FLOOR`, where a real one has noise and therefore a real spread, which moves every crossing down together. The ordering survives that; the placement is a calibration. Which is the honest argument for a control rather than a constant, and why the default sits at half scale instead of at the flag's measured crossing. The example sweeps the whole range and writes a frame per nat, because the question a photographer asks of a slider is where to put it, and that needs the travel rather than a point on it. Verified: fmt clean, clippy -D warnings clean, 60 dr-segment tests. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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4f4abd335f |
Cut a scene category back to the pixels that agree with it
A flag in the sky came out weighted as sky, and no feather setting fixed it. The scene model's logits are `[1, 150, 80, 80]`, so one cell is eight input pixels; at the 1600px proxy the letterbox scale is 0.4 and **one cell is 20 proxy pixels**, which `rasterise`'s bilinear then spreads across one more either side. A flag is a handful of cells whose softmax is dominated by the sky around it. The information was never in the grid, so nothing downstream of the grid can recover it. Tiling is the answer for an instance and is not available here: a category has no bounding box to tile over — sky is wherever the sky is. But the photograph is at full proxy resolution even though the weights are not, and it knows exactly where the flag is. So the model says *what*, and the pixels say *which of them*, which is the division of labour arm C already draws between the instance model and the watershed. ## Seeds, and why the erosion radius is not a guess Threshold the weights high, take `signed_distance`, and keep what is more than 1.5 cells inside. One cell *is* the model's resolution and the bilinear spreads it across one more, so the band either side of the boundary is smear rather than evidence. Deriving the radius from `Scene::cell_pixels` rather than picking a pixel count means it stays right if the proxy edge or the export changes. The mirror of that set is a confident *exterior*, free from the same field. ## Dropping small modes is the step that makes it work Four k-means modes per side, not one Gaussian: sky is blue at the zenith, white where the cloud is and pale at the horizon, and one blob over all three rejects two of them. Then modes holding under 3% of a side are discarded, and without that step the whole thing fails on the case it was built for. A small flag deep in the sky has both a high weight and a large distance from the boundary, so it lands in the interior sample and teaches the model its own colour. It cannot be excluded geometrically. It can be excluded by share. Luminance is weighted at a quarter against chrominance for the same reason the watershed's gradient is. Sky's variance is dominated by luminance, so at equal weight the distribution is a long bright streak that a mid-grey flag sits comfortably inside. A flag is separated by chrominance; a cloud is separated by luminance alone. Not zero, or a dark bird against a bright sky survives. ## Two tests, because either alone is wrong Absolute — is this colour plausible under the category, as a chi-square on the Mahalanobis distance. Comparative — is it likelier inside than outside. A pixel must pass both. The absolute test is what catches the flag, whose colour is far from *both* sides and which the comparative test alone would leave at even odds. The comparative test is what stops the absolute one needing a constant tuned per category. ## What this cannot do, written down rather than left to be discovered An intruder large enough to hold its own mode is kept. By share, a flag over a fifth of the sky and a cloud bank over a fifth of the sky are the same object, and colour does not separate them either — a white cloud is as far from blue sky in chrominance as many intruders are. So `min_cluster` is not a threshold with a correct value waiting to be found; it is the trade-off itself, set where a photographic intruder falls. Both ends are pinned by tests — `a_flag_in_the_sky_is_removed` and `an_intruder_larger_than_min_cluster_survives` — so that moving the number reads as moving the trade-off rather than as fixing a bug. The case left open is a large unrecognised object in a clean category, which wants the boundary snapped to watershed basins and is a different mechanism. ## Safe to apply without a control It is subtractive: the output is the input times a factor in `0..=1`. The worst failure available to it is losing part of a real sky, never gaining a region, so a blue car below the horizon that was never in the mask cannot be pulled into it. And a factor in `0..=1` cannot raise a sum, so `scene.rs`'s partition still holds when every category is refined independently — the weight taken off the flag lands in the unlisted remainder, which is where a flag belongs, ADE20K having no class for one. Every path without the evidence to judge returns the weights untouched and says which path it took. A refinement that silently did nothing is indistinguishable from the feature being off, and an empty seed set fitted to a distribution would reject every pixel. The signature is deliberately unchanged: categories are addressed by name, not by index, so a sharper mask cannot create the stale-index hazard the signature exists to guard against. The example writes `<prefix>-<category>-refined.ppm` beside the coarse one, never instead of it — whether this is an improvement is a comparative judgement and one image cannot answer it. Verified: fmt clean, clippy -D warnings clean, 57 dr-segment tests. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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8df6000e4b |
Decode the scene model into per-category weights
The weights landed last commit with nothing to read them. This is the decoder, and the shape of it follows from one property worth stating before the code: the categories must partition the image. ## Why a partition, and not a mask per category The scene tab applies one grade to every pixel of a category — lift the sky, desaturate foliage — and both grades meet at the horizon. If each category carried an independent mask, feathering them outward would make the boundary band belong to both, so both grades would land there and every horizon would acquire a visible seam. Feathering has to *blend* there, not accumulate. So `marginalise` takes one softmax over all 150 channels and sums within each category. Grouping cannot change a total of one, so the listed categories plus the unlisted remainder sum to one at every pixel, by construction rather than by normalising afterwards. `parse_categories` refuses a descriptor that claims a class twice, because that is the one input that would quietly make the property untrue. ## The descriptor is data, and hand-written `models/scene/categories.txt` groups ADE20K's 150 classes into the eight a photographer would recognise. It is a file rather than a table in Rust for the reason `models/LICENCE.md` predicted — a vocabulary is model metadata — and it is line-oriented with comments rather than JSON like the `.classes.json` beside it, because that file is generated and this one is argued. Why `swimming pool` is water and not architecture belongs next to the line that says so. Classes are named, not indexed. An index is silently wrong after a re-export; a name is loudly wrong, and the loader refuses one the model does not have. ## Resolution, kept visible `Scene` holds the native 80×80 logit grid and resamples on demand rather than upsampling once at load. The coarseness is real — it is what the graph produces — and a type that hides it behind an early resize invites callers to expect detail that was never there. `rasterise` is where the letterbox inverse lives, once. `Letterbox` and `Window` become `pub(crate)` and `to_proto` generalises to `to_grid`, because both dense outputs this crate reads are an even fraction of the same letterboxed square and differ only in the divisor. ## Verified by looking, which is the only way this gets verified `examples/scene.rs` writes the photograph dimmed outside each category. A transposed axis or an off-by-one in the inverse produces perfectly plausible weights over slightly the wrong pixels, and no unit test catches that. On an indoor frame the person mask lands on the person, including the outstretched arm, and sky reads ~5% against a bright ceiling. It doubles as the benchmark, because every timing quoted while this model was chosen came off a laptop compiling other things and none of them belong in a document. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |