The live session scored crops with the matcher regardless of the configured identity
backend, so a live scan used the old mechanism while the still path used the enrolled
gallery - measured, the matcher accepts look-alikes and cannot say "unknown", which is
exactly what a real shelf needs it to do. score_crop now routes to the gallery, and
the decision uses a similarity scale (accept at 0.60 with a 0.04 margin) rather than
inlier-count thresholds that mean nothing on a cosine.
The synthetic walk is gone: demo.py, demo_replay.py, the /v1/demo endpoints and the
walk benchmark are deleted, and the app's frame source is the camera on a device (the
single-image replay stays for the simulator, which has no camera).
stock-demo merges both demo shelves (27 products, 62 views, trained head) so one
server recognises either image, and run-demo-server.sh starts it bound to 0.0.0.0 and
prints the exact URL to put in Config.xcconfig.
Verified over the LAN with 1280x960 frames: 40 frames in 5.3s, 9 products recognised
on demo image 1.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The head is now part of a store rather than a script: `finetune_identity.py --stock
stock-lopia` trains on that store's enrolled views (27 views over 7 products, 12
augmented copies each) and writes `<stock>/identity_head.pt`, which ProductGallery
loads automatically.
The first attempt exposed a trap worth recording. A discriminative head has no "none
of the above": trained with an angular margin to separate seven products, it maps
everything confidently onto those seven, including cartons it has never seen.
raw features, threshold 0.55 home shelf 78% accepted foreign shelves 5% accepted
trained head, threshold 0.40 home shelf 58% accepted foreign shelves 44% accepted
Rejection - the gallery's whole point - disappears. The fix needs no retraining: the
two mechanisms fail in opposite directions, so raw features decide *whether* to answer
(they generalise) and the head decides *what* to answer (it discriminates). Measured:
47% accepted on the home shelf, 5% on foreign ones. Lower coverage than raw-only,
which is the honest trade.
The head also needs its own threshold (0.40 against the raw 0.55): a trained head's
similarity scale is its own, and reusing the raw line rejected almost everything it
said.
175 tests pass.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The external evaluation showed the matcher cannot say "unknown": on 20 shelves whose
products are absent it claimed 47.8% of facings as recognised, and its raw inlier
score cannot be thresholded (foreign shelves' median 6.0 overlaps the real shelf's
5.2 p10, and the worst offenders score 50-190). So the gallery becomes a selectable
identity backend:
--identity gallery (or DORAYAKI_IDENTITY=gallery)
Measured on the same 20 foreign shelves: false accepts fall from 47.8% to 13.8% at
the default threshold and to **5%** at 0.60, with a smooth curve to pick an operating
point from. Detection is unchanged (the gallery only names boxes).
What it does NOT fix, measured on a milk shelf where 14 of 107 facings really are
明治 おいしい牛乳 (labelled by eye): precision is 28-45%, no better than the matcher's
39%, and enrolling the shelf from itself (1-3 views per product) is flat at ~25%
top-1. Off-the-shelf DINOv2 is not discriminative enough for fine-grained SKUs - the
same conclusion the literature reaches, now measured here.
The packaging's own text is a real channel and resolution-bound: 51% recall
single-scale, 60% with the tag reader's multi-scale + red-channel trick, 85% where
the text is large (MEGMILK) and 28% where it is small vertical Japanese.
docs/EXTERNAL-EVAL.md records all of it, including the identity architecture it
implies: reject by default, name from the tag when readable, from the packaging text
when legible, from a fine-tuned embedding otherwise, and hand the rest to the
operator.
175 tests pass.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
From the deep review's hygiene list:
- The demo replay (a still recognition replayed as if it were live tracking) moves
out of app.py into demo_replay.py, and every payload it produces is marked
`synthetic: true`. It had been committed inside an unrelated feature commit, which
made a film-making device look like part of the server.
- `/v1/health` reports the OCR engine and whether it is available. Without it an
operator could not tell a broken OCR from a shelf with no readable tags.
- A failed OCR pass is counted and logged instead of swallowed: `TagService.stats()`
now carries `passes_failed` and `last_pass_error`.
- The live classification budget goes back to the measured production defaults
(16 in flight, 2 attempts); a demo walk over a nearly static shelf asks for more
through `--live-inflight` / `--live-attempts` rather than silently changing the
default for everyone.
162 tests pass.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Web client: price-tag boxes in a distinct style (teal, chip with the parsed
price), an OCR section in the product card showing the tag's price, name,
discount and the reconciliation verdict with both values, and a compliance strip
under the stage. The photo stage now draws the image into the canvas and uses the
same containment as the media, so boxes cannot drift from the product (they did:
the canvas stretched to the stage while the photo was contained).
Server fixes the browser runs forced out:
- Photo mode reconciled before identities existed, so every tag read "tag only".
The tag bundle is cached with the still and classify re-reconciles against the
identities it just produced, returning them per detection.
- The still path only ever read the first four bands (plan hands out two per
call); it now drains every band, twice at different upscales so the vote has
independent reads.
- Voting is confidence-weighted and a dead heat stays unread; the plausible
shelf-price range is 10-9999, which stops a misread from becoming a ¥26,300
price. The compliance coverage bar moved to 0.7 after 0.5 produced a false
"group without price tag" on a shelf where every facing has one.
Measured on add/image6.jpg: 8 readable tags with names and prices, 942 ms for
the whole still, 7 real price mismatches, no false violations, and hints that
say plainly when a row could not be read.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The engine owns the OCR backend, a shared OCR lock and a dedicated tag worker, so
a tag pass can never occupy a classification worker or slow a frame. A live
session plans rail bands on the frame loop (cheap), runs them on that worker, and
attaches whatever the cache already knows; a still runs one synchronous pass.
Tracks and detections now carry price_tag and reconciliation, and the summary
carries price_tags plus compliance. Config can turn tags off per session.
Compliance now fails closed on evidence quality. The first real run produced 106
"missing tag" violations on a shelf where only 8 tags were readable - our tag
*detection* was incomplete, not the shelf. A row is only judged when at least
half its facings carry tag evidence; otherwise it reports "tags detected for 14
of 91 facings in this row" as a hint. Missing-tag findings are one per row with a
count, not one per facing, and price_mismatch still reports every disagreement.
On add/image6.jpg the result is now 7 real price mismatches and no false
violations.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Detection and classification share the CPU, and leaving both uncapped made them
fight: uncapped torch plus 8 workers measured 11.1 results/s single-client and
dropped to 6.3/s each with two clients. Capping torch at 4 threads and
classifying with 5 workers gives 12.2 results/s single-client (46 ms detection)
and 8.5/s each for two clients, with zero unexplained label changes.
Detection is the part that must stay real-time; classification fills in behind
it, so the defaults now protect detection's share of the CPU. Both remain
overridable (--classify-workers, DORAYAKI_TORCH_THREADS).
docs/API.md records the shipped numbers and says which configuration produced
them, including the honest gap: detection during a live pan is ~46-48 ms, not
the 28 ms of an idle measurement.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
The client is a pure API consumer: live frames go out over /v1/stream, stills
over /v1/recognize, and it can be pointed at another server with
?server=&token=. It keeps the team's visual identity (green palette, logo, EN/JA
toggle) and adds the live surface: state-coloured boxes with name/price chips,
interpolated track boxes, a results/s + latency HUD, quality hints, and an
in-view list that highlights a SKU's boxes.
Photo mode recognises the whole shelf at once instead of one click per product:
boxes paint immediately, identities fill in, tapping a product inspects it
(crop + feature-match visualization), the algorithm selector re-classifies, and
dispense updates stock.
Fixes found by browser verification with a synthetic camera feed:
- The WebSocket handler referenced its processing loop before definition, so the
socket died with NameError and live mode never received a result. Helpers are
now defined inside the handler.
- The client never kept a reference to the video element, so frame capture
returned early and nothing was ever sent.
- /v1/images/{id}/classify passed detection dicts where the engine expected box
dicts (KeyError: 'width').
- Placeholder chrome (thumb, dashes, hidden groups) leaked on first paint.
Verified in Chrome with a fake camera: live 8-12 results/s, ~60 products in
view, product card and feature matching populated, photo 105/170 recognised on
image.jpg and 59/111 on add/image6.jpg, 0 console errors.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
app.py becomes the recognition server: /v1 REST (health, catalog + template
images, samples, recognize, classify, inspect, stock) plus /v1/stream over
WebSocket, with token auth, size limits, one consistent error shape, and the
legacy /api/sessions routes kept but now backed by the real engine instead of
the fake adapter. Default bind is 127.0.0.1; LAN and TLS are opt-in.
stream_client.py is the reference non-browser client (camera, video file, or a
pan across a still) and prints results/s, RTT, detect time and label stability.
Live quality work driven by measurements:
- Labels are remembered per place: a fresh track covering a recently recognized
product inherits the label instead of queueing for classification again. On a
continuous pan this raised recognized products in view from 9/66 to 17-21/66
and cut time-to-first-label to ~1.6 s.
- Classification priority is newest-first, because sorting by area spends the
budget on products that are already leaving the frame.
- Per-session classification concurrency raised to 12.
hybrid_fast (ORB shortlist then hybrid) is implemented but NOT the default: it
is 3.4x faster (13 ms vs 44 ms p50) yet agreed with hybrid on only 47% of
accepted crops across 553 crops, far below the 98% adoption rule. It stays
available for experiments; the numbers are in the commit and the benchmark.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
engine.py is the process-wide, thread-safe core the server is built on: one
YOLO instance behind a lock, thread-local matcher access, a classification pool
so detection is never starved, the stock catalog under its own lock, empty-slot
geometry normalized to a reference width (the detector's gap thresholds are
absolute pixels), and a small LRU of recent still images for later
classify/inspect calls. Detection output is normalized so every client sees the
same box shape.
tracking.py wraps ultralytics BYTETracker. The important part: ultralytics
mints track ids from a process-global counter and resets it when a tracker is
constructed, so with two live clients one client's ids would be re-issued while
the other still held cached labels. SessionTracker gives each tracker its own
counter and never touches the global one.
Measured defaults (bench_engine.py, M5): detection p50 26-29 ms on CPU vs
112 ms on MPS with byte-identical boxes, so device defaults to cpu; hybrid
classifies at ~51 ms/crop, 92 crops/s across 8 threads.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
