macOS Vision holds the GIL for the ~124 ms it takes to read a rail band, so the
tag pass decides the live frame rate: with 2 bands every 15 frames a walking
session fell to 1.7 results/s, and 1 band every 30 frames brings it back to 7.2
with tags still settling in a second or two. Measured, not guessed - the same
walk, same method (orb), only the OCR duty cycle changed.
The walk also gained a `pan` parameter (default 0.06, clamped 0.005-0.5) so a
demo can choose its pace: pan=0.02 sweeps a row over ~8 s, which reads like a
person rather than a time-lapse.
Demo configuration measured end to end on the walk (20 s, 1280x720, one client):
orb, pan=0.02, 1 band/30 frames -> 7.2 results/s, 178 labelled tracks, 21
distinct SKUs, first label 0.7 s, 3 readable price tags.
Co-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>
/v1/demo/plan + /v1/demo/frame give a demo the one thing a still image cannot:
motion. The virtual camera pans a shelf row left to right, steps up, and pans
back - a store employee sweeping a shelf - over a real shelf photo, ~1 ms per
frame, deterministic, and wrapping so an unattended demo loops. The client drives
only `step`, so the walk's speed follows whatever frame rate it can sustain.
Three real defects came out of measuring the walk rather than a still:
- Bands were keyed by on-screen position, so a walking camera minted a new key
every few pixels of pan and re-read the same shelf continuously. Keyed by row
now; OCR stops chasing the window.
- The classification budget was consumed by retries of products already leaving
the frame. Retries now skip anything near the edge, and a track gets 2 attempts.
- The pan advance was 0.22 of the window per step: a facing was in view for one
or two frames, so nothing could ever be classified (9 labelled tracks in 20 s).
0.06 keeps a product in view for about a second.
Measured on the walk (20 s, 1280x720 frames, one client): orb labels 296 tracks
across 21 SKUs with the first label at 0.2 s, while hybrid manages 13 tracks /
4 SKUs at 5.4 s - so the live default for a demo is the fast method, with hybrid
kept for tap-to-inspect.
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>
test_live: a product is unconfirmed, then labelled, without blocking frames; a
weak match never names a SKU and ends as needs_confirmation; a restarted id is
re-verified rather than inheriting its old label; inherited labels are flagged;
latest-frame-wins accounting; payload shape; glare hint; per-session
classification cap; a method change clears labels and drops late results.
test_api: health, catalog shape (no local paths), template-image 404s including
traversal-looking ids, stock validation, recognize over raw bytes and multipart,
415/422/400 with one error shape, classify/inspect roundtrip, IMAGE_EXPIRED,
token auth on REST and the socket, the WebSocket protocol (hello, frames,
config/reset/ping, recoverable BAD_FRAME and UNDECODABLE_IMAGE, inspect and
TRACK_NOT_IN_VIEW), the session limit, and the legacy session route.
Server fixes the tests forced out: FastAPI validation errors now use the same
{"error": {...}} envelope as every other failure, and the legacy session routes
accept a missing JSON body instead of returning 500.
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>
