tngo
aaed5d388f
Replaces the DINO gallery as the recognition path with the measured design from docs/research/2026-09-28-recognition-architecture/DECISION.md: RootSIFT over a per-SKU reference pyramid, similarity-constrained RANSAC and a hue check, fused with an OCR text lane that vetoes a match whose package text names a different catalog row and answers on its own when geometry finds nothing. - identity_lanes.py: LocalLane, TextLane, DecisionPolicy. The text lane builds each row's signature from the catalog name plus its packshot OCR, so a row with no enrolled image is still answerable. - engine.py: every backend groups scores by canonical SKU, so no view-suffixed id reaches the wire and a name and price always resolve. decide_crop fuses the lanes; score_crop_lanes feeds the live path. Both lanes are built in warm_up (non-fatally) so the first frame is not the slow one, and the text lane shares the engine's OCR lock. - live.py: decides per crop on the lane's own rank order. The lane's order comes from its rank key and its value is only the gate, so re-sorting by value named a different SKU than the lane chose; averaging in an empty observation also diluted a good confidence below the accept line. - identity.py: a `local` score scale whose gate is one absolute line, because the value is already a margin over the runner-up. - app.py, run-demo-server.sh: `--identity local` is the default; the demo server no longer starts on the leaked `stock-lopia` catalog. - eval_live.py: the replay vocabulary accepts `local`, so a recording from the shipped server can be scored. - README.md: documents the three backends and what each is for. Measured on the corrected image6 labels at live resolution: 36/61 in-catalog facings named at 95% precision, 0 of 48 unknowns accepted, 11 labels sourced from the text lane alone. Through the real server: /v1/recognize 90% precision, /v1/stream 100% precision, no view-suffixed ids on either.
71 lines
1.9 KiB
Bash
Executable file
71 lines
1.9 KiB
Bash
Executable file
#!/usr/bin/env bash
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# Start the recognition server and write ignored local app endpoints.
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#
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# ./run-demo-server.sh
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# ./run-demo-server.sh stock-milk150
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set -euo pipefail
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ROOT="$(cd "$(dirname "$0")/../.." && pwd)"
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cd "$(dirname "$0")"
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STOCK="${1:-stock-lopia-clean}"
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PORT="${PORT:-8099}"
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TOKEN_FILE=".demo-token"
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if [ ! -d "$STOCK" ]; then
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echo "no such store: $STOCK" >&2
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exit 1
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fi
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IP="$(ipconfig getifaddr en0 2>/dev/null || ipconfig getifaddr en1 2>/dev/null || true)"
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if [ -z "$IP" ]; then
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echo "this Mac has no LAN address; the phone cannot reach it" >&2
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exit 1
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fi
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if [ ! -s "$TOKEN_FILE" ]; then
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umask 077
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openssl rand -hex 32 > "$TOKEN_FILE"
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fi
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TOKEN="$(tr -d '[:space:]' < "$TOKEN_FILE")"
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umask 077
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cat > "$ROOT/iosApp/Configuration/Config.local.xcconfig" <<EOF
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APP_RECOGNITION_BASE_URL = http:/\$()/$IP:$PORT
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APP_RECOGNITION_API_TOKEN = $TOKEN
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EOF
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mkdir -p config
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cat > config/local.env <<EOF
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DORAYAKI_API_TOKEN=$TOKEN
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EOF
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PROPERTIES="$ROOT/androidApp/local.properties"
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touch "$PROPERTIES"
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TMP="$(mktemp)"
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awk -v url="app.recognitionBaseUrl=http://$IP:$PORT" -v token="app.recognitionApiToken=$TOKEN" '
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BEGIN { url_found = 0; token_found = 0 }
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$0 ~ /^app\.recognitionBaseUrl=/ { print url; url_found = 1; next }
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$0 ~ /^app\.recognitionApiToken=/ { print token; token_found = 1; next }
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{ print }
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END {
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if (!url_found) print url
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if (!token_found) print token
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}
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' "$PROPERTIES" > "$TMP"
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mv "$TMP" "$PROPERTIES"
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cat <<EOF
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Dorayaki demo server
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store $STOCK
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listening http://0.0.0.0:$PORT
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phone URL http://$IP:$PORT
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Local app configuration was updated. Rebuild both apps.
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The API token is in $TOKEN_FILE and is not printed.
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EOF
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exec env YOLO_OFFLINE=1 DORAYAKI_STOCK_DIR="$STOCK" DORAYAKI_API_TOKEN="$TOKEN" \
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.venv/bin/python -u code/app.py \
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--port "$PORT" --host 0.0.0.0 --identity local \
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--live-conf 0.15 --classify-workers 8
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