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Commits
| Author | SHA1 | Message | Date | |
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  dtourolleandClaude Opus 5
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4af3b93dfa |
Index faces from the native render, not from a preview of it
Implements the FR-CULL-8 written two commits ago. The sweep fetched the JPEG preview embedded in each RAW and used that one buffer for both detection and the crop; it now fetches the original, renders it through the same path export uses, reduces that for the detector, and warps the crop back out of the native frame. Three pieces, and each exists for a reason worth stating. dr_face::Pixels lets the warp sample 8-bit RGBA directly. A 24 MP native frame is 96 MB as RGBA and 288 MB converted to the f32 RGB align.rs was written against, and the warp reads about forty thousand pixels out of it. Converting the whole frame to sample 0.2% of it is NFR-RES-2's budget spent on a copy, per image, for a whole library. The variant costs one branch per sample and a test asserts both layouts produce identical crops. The detector gets a box-filtered reduction to 1600px, not the native frame and not a point-sampled one. Averaging rather than sampling because the detector's job is finding small faces and decimation is precisely the operation that removes them: at 4x, fifteen of every sixteen pixels are discarded and a 40px face survives or not depending on where it falls relative to the sample grid. 1600 rather than 640 leaves the letterbox a mild 2.5x rather than a 9x, and bounds the f32 buffer at 20 MB. Landmarks come back in the reduction's coordinates and are scaled to native in one place before any crop pixel is read. This is the failure mode that would not announce itself -- unscaled landmarks put every crop near the top-left corner, which yields faces of something else, cleanly embedded and confidently clustered. The sweep fetches SWEEP_LANES-wide and renders sequentially. Not a placeholder for a parallel version: there is one GPU, so concurrent renders queue on it regardless, and each materialises a native frame. Overlapping them would multiply the one allocation that threatens the memory budget while buying parallelism that does not exist. The chunk drops from 96 to 6 for the same reason -- 96 held 8 MB previews, this holds whole RAWs. The stored edit is deliberately not applied, which is where this departs from export::render_from_library. Face geometry is normalised to the frame, so indexing a cropped render would record boxes against a frame that changes whenever the user changes their mind, and every stored box would quietly become wrong. Orientation is applied: that is a fact about the file rather than an edit. examples/face_native.rs renders one file and indexes it both ways, so the claim behind all of this can be checked against photographs rather than re-read out of the catalog it came from. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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e7b526c550 |
Specify face indexing at native resolution, and say what the proxy cost
FR-CULL-8 said detection runs against the thumbnail or proxy tier and never a full decode, and faces.md §5 said the aligned crop is sampled from that same proxy. Both are wrong in the same place: they treat detection and cropping as one resolution problem when they are two, with opposite answers. Detection does not care. §4.1 fixes the graph's input at 640x640 and letterboxes whatever arrives, so a face filling 2% of the frame reaches the model at 12px whether the buffer handed over is 1024px or 6000px. Every pixel above the detector's own input is discarded before inference. The crop cares about nothing else. §5's warp produces the fixed 112x112 ArcFace sees, so source resolution converts directly into whether those 112 pixels were photographed or interpolated. Reading crop_px across the 18,671 faces the proxy-tier implementation stored: 47.3% were upsampled to reach the embedder, 314 of them by more than 2x, the smallest from 34 source pixels. An upsampled crop does not fail loudly -- it yields a confident embedding of detail that was never there, and the damage appears three stages later as clusters that will not separate. So FR-CULL-8 now specifies four stages with the resolutions named separately: render native through FR-EXP-9's pipeline, downscale for the detector, map boxes and landmarks back to native, crop and align from the native render. The affordability the old rule bought is met instead by when the pass runs -- background, preempted, resumable -- and the requirement says plainly what it now costs on a remote library: the original rather than FR-NC-3's byte range, 412 GB across the reference library's 19,107 images, so a whole-library pass is a transfer under FR-NC-6 rather than something that may start on its own. MIN_CROP_EDGE replaces the MIN_DETECT_EDGE this branch briefly had. Same number, guarding the quantity that turned out to matter. faces.md §7b records both measurements, and marks the second as unexplained rather than dressing it as a finding. Grouped by the buffer detection ran against, faces per image was 0.078 at 1024 or below and 1.82 at 2048 or better, controlled for file type and size. That gap is real and reproducible and I cannot account for it, because the letterbox above says detector input should not matter. M4 is where it gets settled. The crop measurement does not depend on it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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144d2e4e84 |
Revert the detection floor: it guards the wrong resolution
Reverts 53f7cdf and e92d22d. The floor those added sat on Detector::detect, refusing any buffer under 1025px on the reasoning that a small buffer finds no faces. That reasoning does not survive §4.1: the detector letterboxes every input to 640x640, so a face occupying 2% of the frame presents at 12px to the model whether it is handed a 1024px buffer or a 6000px one. Detector input is precisely the quantity that does not matter. Worse than merely useless, it blocks the design FR-CULL-8 now specifies, where the detector is deliberately fed a downscale and the crop is taken from the native render. A guard on detect() rejects exactly that call. What the measurement actually supports is a floor on the *crop* source, which is where resolution converts into embedding quality, and which faces.crop_px already records: 47% of the reference library's faces were upsampled to reach 112x112. That floor is a separate change against the native-resolution path and does not belong on the detector. The 23x faces-per-image gap by source_edge that motivated the original commit is kept in faces.md §7b, restated as the unexplained observation it is rather than the causal claim it was written as. V12 stands: those runs cropped at 1024 whatever detection did, and that is reason enough to look at them again. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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1d800d56b0 |
Refuse to detect faces on a proxy too small to find one
Detection was run on whatever proxy the caller happened to have. A small one does not fail -- the image is letterboxed into the detector's 640px input at any size -- so it comes back with almost nothing, and the caller then writes a face_index row saying the photograph was examined. That row is the damage. Nothing distinguishes it from "examined properly, no faces in this one", so the image is never looked at again. The measurement, on the reference library of 23,531 images. Runs against a 1024-edge proxy: 0.078 faces per image, 90% of them finding nothing at all. Runs against 2048 or better: 1.82. To rule out the obvious objection that small proxies just come from small photographs, the same comparison restricted to DNGs -- 1,592 of them averaging 21 MB against 7,724 averaging 23 MB, so the same kind of file in the same library -- gives 0.078 against 1.82 again. Twenty-three fold, on identical source material, identical weights, identical options. So the floor goes in the detector rather than in either sweep, because both of them, the example tool and any future job handler are equally entitled to get this wrong, and there is one place that sees every attempt. It is 1025, not 1024, and the odd-looking number is the point: 1024 is exactly ThumbSize::Large, the tier proxies are stored at and the tier one of the two sweeps was detecting on. A floor that admitted 1024 would admit precisely the population this exists to exclude. Written as a minimum rather than a maximum so the test at each call site is `edge < MIN_DETECT_EDGE` with no boundary left to get wrong. ProxyTooSmall is its own error variant rather than an empty result because the caller has to tell it apart from a failure: nothing is wrong with the image or the model, and the answer is to go and find better pixels, not to retry these ones. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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0407fb8d2d |
Format the two new examples
They were written after the last fmt run and CI gates on --check. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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39c34d4e44 |
Say what actually counts as a rival, now that a name anchors too
assign's denominator is the identities the user has ruled on, and it reads Cluster::person to find them. Master's "Let a name hold a group together" widened what sets that field: a confirmation, a name, or an ignore, where before it was a confirmation alone. The behaviour is right either way — a named person is exactly the identity a suggestion should be discounted against — but the module note and faces.md §9.1 both said "a confirmation", which is now too narrow. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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b2250cc460 |
Measure a regroup on the tablet, not just on the desktop
The GPU question needed a number nobody had: how a regroup divides on the hardware whose CPU is weakest. dr-face carries no weights and touches no display, and dr-catalog's example needs only a catalog file, so both run under adb shell against a copy of a real library. On the same 18,143 faces — desktop against the tablet — scan 0.96s / 2.61s, agglomerate 1.69s / 2.16s, score 0.26s / 0.40s. The scan is half the pass on the tablet and under a third on the desktop, because twenty cores of AVX2 pull ahead of NEON much further than the merge engine's single-threaded hashing does. So a GPU GEMM is worth roughly 2× a regroup on the tablet and 1.5× here, and it is the tablet that should decide whether it is built. The two architectures agree exactly: the same 1,531,969 evidence pairs, the same 2,518 groups holding the same 16,246 faces, the same reliability table. That is a better check on the NEON kernel than the unit test can be. Two instruments, both read-only: the example now prints its phases, and dr-face gains scan_bench, which needs no library at all and so can answer "how fast is this machine" on a device with nothing on it. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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a67402961d |
Let the merge engine's dot product use the machine's kernel too
Engine::cross is the one place a dot product is computed during agglomeration — when two groups become adjacent through a third and their sub-threshold pairs, never summed because they were never interesting, have to be accounted for. It was calling the portable loop while the scan beside it had AVX2 or NEON, which on the reference library was 1,753,514 dot products taking 0.54s. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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b4d39ba33a |
File each pair under its component once, not once per component
Seeding the merge heaps was 3.06s of a 5.93s regroup on the reference 18,143-face library — more than half the pass, spent before a single merge was considered. Every component scanned the whole pair list looking for the pairs that were its own: 475 components against 804,499 pairs, 382 million set lookups to place 804,499 of them. A pair can only ever join two faces of one component, since that is what a component is, so the union-find that finds the components can file the pairs at the same time and hand each agglomeration the list it needs. The membership set inside agglomerate goes with it — it existed only to run that filter — and the heap can be sized up front now that the pair count is known. Ordering is preserved deliberately: pairs are filed in the order they arrive, which is the global (i, j) order, so the seeded heap breaks its ties exactly as before and the merge order is unchanged. Same 2,518 groups holding the same 16,246 faces on the reference library, at 3.5s rather than 5.9s. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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e596eb0657 |
Give the similarity scan the machine's SIMD, and its cache
The scan is O(n²) dot products and nothing else, so its speed is the face subsystem's speed — and it was running at 0.7 flops per cycle. Two separate faults, both measured over the reference 18,143-face library on twenty cores. It walked the whole embedding array once per row, ~336 GB of traffic, where a column tile that fits in L2 is read once per tile of rows: 4.64s → 2.81s. And the workspace builds for baseline x86-64 — SSE2, no FMA — into which the portable loop was not being vectorised at all: 2.81s → 0.86s, 195 GFLOP/s. So the dot product is now chosen per machine. AVX2 + FMA where is_x86_feature_detected! finds it; NEON unconditionally on aarch64, since Advanced SIMD is in that baseline and every Android device the app builds for has it — with the explicit vfmaq, because LLVM will not fuse a multiply and an add without being told to. The portable loop stays as the definition the others are tested against, and the_fastest_kernel_agrees_with_the_portable_one is the only check the NEON path gets on a machine that is not aarch64. Faces::embeddings is one flat buffer rather than a Vec per face: the pointer chase defeated both the prefetcher and the tiling, and it is also the layout a GPU pass would want. Behaviour is unchanged and that is checked rather than asserted — the same 1,531,969 pairs from all three kernels, and on the real library the same 2,518 groups holding the same 16,246 faces with the same confidence distribution. A full regroup there goes from 10.0s to 5.9s; the rest is the agglomeration, which is a sequential heap walk and is where the next look should go. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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ebb7d3cf5c |
Score a suggestion against the people the user has named
The number beside a suggestion was the mean calibrated probability between the face and the rest of its group, which measures the wrong thing twice. It punishes coverage: a person with two hundred faces over fifteen years is *meant* to have members a given photograph is orthogonal to, so a correct suggestion onto a well-photographed person scored low for being well photographed. And it never asked who else the face might be — a face matching Anna at 0.95 and nobody else, and one matching Anna at 0.95 and her sister at 0.93, came out identical, when the second is the only one worth the user's attention. dr_face::assign answers both, and multiplies them: the mean of the best ten calibrated matches into the identity (the old mean, capped, which is what stops coverage counting against it), times that identity's share of the evidence against every *named* rival. Only named people compete, and per person rather than per group. Both halves of that had to be measured on a real 18,000-face library rather than reasoned about. Normalising across every group made the number useless — median suggestion 21%, four in five under half — because clustering leaves one person spread over many groups, so a face competed against itself; and keying rivals by group left Catherine competing with Catherine, median 39%. Per named person: median 99.5%. Rivals are gathered below the merge threshold, down to even odds: a named person matching at 0.6 will never be merged into but is exactly the competition to discount for. That would be a second similarity scan, the expensive half of regrouping a library, so cluster_scored scans once at the looser floor and hands the merge engine the subset at or above the threshold — pair for pair what it would have scanned for itself, held to that by a test. Leave-one-out over that library's 2,702 confirmations across 54 named people: 99.33% of faces placed on the right person against the old mean's 99.15%, and the number shown for the right person moves from a median of 90.4% to 99.3%. It errs low — 100% correct wherever it states 80% or more — which is the safe direction, and docs/faces.md §9.1 says plainly that the low bands are not calibrated. The example that measures it comes too: this is a claim about a library's numbers, and nobody should have to take it on faith. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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c8e05831f4 |
Show the confidence, and say which curve it came from
FR-CULL-9 was read as "no fit, no number", so every library without 200 confirmed positive pairs showed "Confidence unavailable" on every suggestion — which is every library, until enough confirmations exist to fit one. The confirmations are made on this screen, ranked by the number it was withholding, so the degraded state was also the permanent one. There has always been a curve: Calibration::default is the reference implementation's fitted MBF sigmoid, which is what clustering already operates at. It is a published operating point, not an invention, and what the requirement forbids is presenting it *as though it were measured on this library*. So the percentage is shown, and the screen says once, above the grid, where the curve came from. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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1d4c3348af |
Let a 32-pixel face count, and move the blur floor with it
64 source pixels was too strict: it threw away 70% of everything the detector
finds, and plenty of what it took were faces a person could name.
Lowering it is not a one-line change, because the two floors are coupled. A face
under 112 pixels is *upsampled* to reach the embedder and upsampling invents no
edges, so a small face scores low on sharpness however crisp the original was.
Re-measured over the reference library with `face_index --quality`:
min crop min sharp size cut blur cut kept
32 0.000 44% 0% 56%
32 0.002 44% 3% 53%
32 0.005 44% 8% 48%
32 0.010 44% 16% 40%
32 0.020 44% 27% 30%
64 0.020 70% 7% 23%
Holding the blur floor at 0.020 while dropping the size floor to 32 would have
rejected a further 27% — for being small rather than for being blurred — and
kept only 30%, barely more than the 23% the strict pair kept. Most of the point
of lowering the size floor would have gone straight back out through the other
gate.
0.005 removes 8% of what the size floor leaves, which is the same job 0.020 was
doing at 64 (7%): the large-but-soft face this gate exists for. Together they
now keep 48% of what the detector finds, against 23% before.
The box pre-filter follows down to 24, staying below what the real floor accepts
so it cannot reject a face that would have cleared 32.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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dtourolleandClaude Opus 5
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a1790c3e67 |
Stop indexing faces too small or too blurred to be anyone
The library was storing faces at 52 source pixels and embedding whatever came
back. There was a size floor, but it was 40 pixels on the *bounding box*, and
there was no blur gate at all — so a subject walking through a half-second
exposure detected confidently, aligned cleanly, and produced a perfectly
ordinary-looking 512-vector. Nothing downstream can tell that apart from a real
face, and because blurs resemble each other more than they resemble the people
they were, they cluster together and weld unrelated identities into one group.
Two floors, both measured rather than guessed. `face_index --quality` runs the
detector over real proxies with both gates disabled and prints the distribution;
over 1,503 faces in 600 images of the reference library:
percentile crop px sharpness
1% 16 0.0006
25% 23 0.0025
50% 38 0.0071
75% 76 0.0284
99% 352 0.4282
The median face in a personal library is 38 pixels. Most of what the detector
finds is background: people across a square, a face on a poster, a stranger at
the next table. They are real detections and useless identifications.
**Size, on the crop rather than the box.** "At least 64x64" has to mean the
pixels the *embedder* sees, and the box is not that — the ArcFace template
reaches past it for forehead and chin, so the aligned crop spans roughly 1.3x
the box's shorter edge. The floor is therefore `min_source_px` on the aligned
crop, applied after the warp fixes the scale, and `min_face_px` drops to 48 as
what it always really was: a cheap pre-filter set low enough that it cannot
reject a face the real floor would have kept.
**Sharpness.** Variance of the Laplacian divided by the variance of the luma it
was taken over. The division is the part that matters: raw Laplacian variance
scales with contrast, so a threshold on it would quietly discard every backlit
portrait in the library. The ratio asks how much of the crop's variation is
edges rather than broad gradients, and is invariant to exposure.
What each pair removes, cumulatively, of everything the detector finds:
min crop min sharp size cut blur cut kept
64 0.000 70% 0% 30%
64 0.010 70% 3% 27%
64 0.020 70% 7% 23%
80 0.010 76% 2% 21%
64 and 0.020. The size floor does most of the work, and the blur floor removing
only 7% on top of it is the point rather than a disappointment: at 64 pixels
most faces are already sharp, and what it takes out is the large-but-soft one —
precisely the face that would otherwise contribute a confident, wrong embedding.
The two gates are not independent and the doc comments say so: a face under 112
pixels was upsampled to reach the embedder, and upsampling invents no edges, so
small faces score low on sharpness even when the original was crisp. That is why
`--quality` prints them together.
**This will re-index.** Around 70% of what the current settings store falls below
the new floors — faces between 20 and 40 pixels that nobody could identify. The
People screen gets shorter and every group in it gets better.
66 dr-face tests pass, including that a blurred crop scores below a sharp one,
that halving the contrast does not move the score, and that an upsampled face
scores below the same face at full size.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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dtourolleandClaude Opus 5
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7275c020d7 |
Group people at the threshold the library actually supports
0.90 left a third of the reference library ungrouped: 1,213 of 1,813 faces in a
group, and the rest sitting alone in a screen that had nothing to offer for
them.
"Is 0.90 too tight" is not answerable from the number. It is a probability, and
which cosine it lands on depends on the calibration — so the first half of this
is a way to ask the question properly. `face_index --tune` runs the real
clusterer over the real embeddings at ten thresholds and prints what each one
produces. It writes nothing; comparing thresholds by applying them would have
each one pollute the next.
On the reference library:
P cosine groups grouped largest
0.95 0.449 311 62% 51
0.90 0.403 316 67% 51
0.85 0.374 318 70% 57
0.80 0.353 328 74% 69
0.75 0.335 327 77% 69
0.70 0.319 326 79% 81
0.50 0.267 303 85% 90
The count of *groups* is the signal, not the count of grouped faces. Loosening
from 0.95 makes it climb: real people are being assembled out of fragments. It
peaks at 0.80 and then falls — and a falling group count while the grouped faces
keep rising is the shape of over-merging, separate identities being welded
together. That is the FR-CULL-10 failure, and the one the user cannot undo by
hand.
So 0.80: the loosest setting still building people rather than melting them
together. A third more of the library gets grouped than at 0.90, and the largest
group grows by eighteen faces rather than by forty.
The table is one library, and the doc comment says so — `--tune` reruns it on
any other.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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dtourolleandClaude Opus 5
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c10dca984f |
Regroup the library without stopping the window
Pressing Regroup on a real library did not come back. Clustering 1,813 faces is the textbook agglomeration — compute every pairwise cosine, then repeatedly scan all live group pairs, score each with average link, and merge the best — and the scan is inside the loop. Each merge rescans every surviving pair, and each score is recomputed from scratch over every cross pair. Some 1.6 million pair scores per merge, some 700 merges to do. Three changes, none of which alter the answer. Only above-threshold pairs can ever matter. An average that reaches the threshold must have at least one term at or above it, so two groups with no qualifying pair between them can never merge — not now, and not after any sequence of merges, since merging only adds terms. The new `neighbours` module produces exactly that sparse list: 7,875 pairs rather than 1.6 million on the reference library. It also means the n^2 matrix is never materialised, so memory goes from O(n^2) to O(edges) — 2.5 GB to a few hundred KB at 25,000 faces. Merges cannot cross components, so the connected components of that graph are independent problems: four hundred small agglomerations instead of one large one. Average link is additive — sum(A u B, C) = sum(A, C) + sum(B, C) — so a merged group's scores follow by addition. Kept as running (sum, count) per adjacent pair, a score costs one division instead of a nested loop, and a heap with lazy invalidation replaces the rescan. Measured on the reference library: 0.28s, release, for all 1,813 faces. An exact ANN index was tried and removed, and neighbours.rs records why so it is not rediscovered as a good idea. IVF with a triangle-inequality bound is exact and prunes beautifully on synthetic clusters; on real embeddings it prunes *nothing* — 946 of 946 cell pairs survive. Median pair angle is 88.5 degrees and the merge threshold is 66.2, so the bound needs cells of radius under ~10 degrees, but two photographs of the same person sit 36-60 degrees apart. No ball-based partition of a 512-d near-orthogonal space can be tight enough. So the scan stayed exhaustive and got an unrolled dot product and its blocks spread across cores instead. Correctness is held by keeping the old implementation as an oracle: three tests run both engines over the same population — plain, under co-occurrence and anchor constraints, and with a size-weighted calibration — and assert the clusters are identical. Determinism is asserted at a size where the threaded path is in play. 62 tests pass. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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b846b312b8 |
Run the formatter over the face branch before it reaches CI
🐳 Android image / Build and push (push) Successful in 2s
Build and test / android-image (push) Successful in 2s
Build and test / Desktop (Linux) (push) Successful in 1h21m32s
Build and test / Layer separation (push) Successful in 37s
Traceability / Requirement traces (push) Successful in 25s
Build and test / Android (aarch64) (push) Failing after 33m58s
The merge of the SCRFD/MobileFaceNet work brought 69 rustfmt diffs across
dr-catalog, dr-face and dr-ui with it, so `cargo fmt --all -- --check` fails
on master and the Desktop job stops at its Format step — before clippy, the
tests or the release build have run at all. That makes the whole desktop
half of CI blind: a real compile error behind this would look exactly the
same from the outside. There was nothing behind it, as it turns out — with
the formatting fixed, clippy, the test suite and the release build all pass.
Every .rs hunk is `cargo fmt --all` on the pinned 1.92.0 toolchain, not a
hand edit, but it is worth being precise about what that moved, because it
is more than whitespace. Besides reflowing signatures and call chains,
rustfmt reordered the `pub mod` and `pub use` items in dr-face/src/lib.rs so
the `#[cfg(feature = "inference")]` entries sort in place, added the trailing
semicolon inside `let ... else { return }` bodies in identity_ui.rs, wrapped
a bare closure body in braces in cluster.rs, adjusted trailing commas, and
dropped a stray blank line at the end of identity_ui.rs. All of it is
semantically inert; none of it changes behaviour.
docs/traceability.md rides along because it has to. The matrix records each
TRACES tag by line number, and reflowing develop.rs, lib.rs, faces.rs,
identity.rs and identity_ui.rs moved them — FR-CAT-8, FR-CAT-9, FR-CULL-10,
FR-DEV-3, FR-DEV-3a and FR-DEV-3c all shift by a line or two. The matrix was
verified up to date on
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dtourolle
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d777f7f44d |
Merge branch 'master' into worktree-faces-scrfd-mbf
Build and test / Desktop (Linux) (push) Failing after 25s
Build and test / Layer separation (push) Successful in 22s
Traceability / Requirement traces (push) Successful in 58s
🐳 Android image / Build and push (push) Successful in 1s
Build and test / android-image (push) Successful in 1s
Build and test / Android (aarch64) (push) Failing after 33m26s
# Conflicts: # docs/traceability.md # ui/dr-ui/src/develop.rs # ui/dr-ui/src/segmentation.rs |
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dtourolleandClaude Opus 5
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f00b92a0e6 |
Turn face boxes back into sensor space before matching regions
Faces are found on the thumbnail, which is cached the right way up -- the grid would lie on its side otherwise. Segmentation runs on a proxy rendered through a neutral edit graph, which carries no orientation and is therefore in sensor order. For anything shot in portrait the two differ by a quarter turn, so a face and the person containing it were being compared in spaces 90 degrees apart: no match, or worse, a match against somebody else's region. The transform goes on the face rather than on the proxy. Instance masks are defined in the proxy's space and sampled long afterwards, so turning that space would be a far larger change than naming a region warrants. Also two things the first screenshot of the running app showed that no test would have: 110 of 23,528 displayed as "0%", which reads as the feature having done nothing. One decimal below ten percent, and a floor so real progress never shows as none. The rail picked some near-black covers, because the largest face in a group is often the nearest one in a badly lit frame and a black square beside a name identifies nobody. It now cuts the best few and takes the first legible one, falling back to the largest when a person's every photograph is dark -- which happens, and showing it beats showing nothing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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b55812812a |
Name segmented people from the faces already recognised in them
The segmenter knows it found a person; the face index knows which person. Joining them turns "person" in the mask list into "Anna", which is the difference between a vocabulary of eighty COCO classes and one that includes the user's family. Selecting a subject in a group photograph stops being a guessing game between three identical rows. Containment, not IoU. A face is a small part of the person it belongs to, so a correct pairing has an IoU near zero and anything IoU-based would reject every true match. Confirmed names only. A suggestion is the system's guess, and printing a guessed name onto a mask region would launder it into a fact. Writing the tests corrected the design once: a tight head-and-shoulders portrait, where the face fills most of the person box, is the case where naming is most certain, not least. An earlier guard rejected exactly that and has been removed, with the reasoning left as a test because it is easy to get backwards a second time. The names hang on the develop session, set when the image opens because that is the one moment the catalog and the image id are both in reach. Every segmentation run afterwards picks them up for free, and a library with no face indexing behaves exactly as it did before. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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2ac069a6b3 |
Index the library's faces, and group them into people
Wires dr-face to dr-catalog: a background sweep that reads the proxy the grid already built, detects, aligns, embeds and stores, then a clustering pass that turns those embeddings into suggested people. Detection runs on the Large thumbnail tier and nowhere else. That is what makes the feature affordable -- a browsed library has already paid for its proxies, so face indexing adds no RAW decode that was not already happening -- and it is why an image whose proxy is missing is skipped rather than fetched: requesting one here would put face indexing on the network path FR-CULL-8 keeps it off. The sweep keeps no cursor. It asks the catalog what is missing, so it resumes after process death with no repeated work beyond the in-flight image, and cancelling is dropping the receiver. recluster writes only the suggested half. Confirmed faces go in as anchors and come back untouched, and a cluster of one stays nameless -- naming every stray face would fill the People view with noise the user then has to dismiss. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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00e78dc2ac |
Cluster faces into people, and calibrate what a similarity means
FR-CULL-9 forbids thresholding a bare cosine anywhere in the subsystem, so calibrate fits P(same person) per library and reports whether the fit is trustworthy. Two details carry most of the weight. The fit runs against a 200-bin histogram rather than a pair list: a 25,000-face library has ~3e8 pairs and no gradient descent is running over that. And a fresh library has no valid calibration, because the positives have to come from user confirmations or burst siblings -- bootstrapping them from high cosine would fit the calibration to the belief it was supposed to test. Clustering defends against the over-merging FR-CULL-10 warns about with constraints rather than a better threshold: two faces in one photograph never merge, and two groups confirmed as different people never merge. Average link rather than single link, so one strong edge cannot weld two families together. Calibration is defined once, in dr-face, and dr-catalog re-exports it. Two implementations of one probability model is exactly how a number comes to mean the wrong thing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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19981c1033 |
Detect, align and embed faces with SCRFD and MobileFaceNet
Ports the pipeline from the C++ reference in ../scene-actor-extraction (MIT, same author). End to end on real portraits it separates identities the way the reference's fitted calibration says it should: 0.596 between distinct photographs of one person, 0.05 between different people, either side of MBF's 0.267 boundary. Three things are structural rather than incidental: Aligned112 can only be built by align::warp, so Embedder::embed cannot be handed an unaligned bounding-box crop. That mistake yields 512 plausible unit-norm numbers and no error, so the type system refuses it instead. Embedding carries its ModelId and cosine() returns None across models, because a cross-model similarity is the one mistake that produces plausible garbage rather than a failure. The model-free half -- alignment, embedding arithmetic, f16 storage -- sits outside the inference feature and is covered by 11 tests that need no weights on the machine. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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dtourolleandClaude Opus 5
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72410f39c6 |
Answer M1: tract loads both face graphs once their dims are pinned
Neither InsightFace export parses as shipped -- SCRFD fails at its input node, ArcFace at the first Conv -- which is the same wall dr-segment hit on YOLO's dynamic export. Both load cleanly with the input dims frozen, so the pure-Rust runtime holds for the face pipeline too. tools/fix-face-model-shapes.sh does the freezing, and exists so the artefact is reproducible rather than a binary someone once produced. It takes two forms because the two graphs need different ones: ArcFace's batch is a named dim_param, SCRFD's H and W are dynamic but unnamed. Also notes YuNet loading with no intervention, which matters for the licence question in faces.md 2.3. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |